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matcha3mochi · 12 days ago

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PROTOCOL Pairing: Doctor Zayne x Nurse Reader

author note: love and deepspace is my addiction guys LOL anyways enjoy!!

wc: 3,865

chapter 1 | chapter 2

✦•┈๑⋅⋯ ⋯⋅๑┈•✦

Akso Hospital looms in the heart of Linkon like a monument of glass, metal, and unrelenting precision. Multi-tiered, climate-controlled, and fully integrated with city-wide telemetry systems, it's known across the cosmos for housing the most advanced medical AI and the most exacting surgeons in the Union.

Inside its Observation Deck on Level 4, the air hums with quiet purpose. Disinfectant and filtered oxygen mix in sterile harmony. The floors are polished to a mirrored sheen, the walls pulse faintly with embedded biometrics, and translucent holoscreens scroll real-time vitals, arterial scans, and surgical priority tags in muted color-coded displays.

You’ve been on the floor since 0500. First to check vitals. First to inventory meds. First to get snapped at.

Doctor Zayne Li is already here—of course he is. The man practically lives in the operating theatres. Standing behind the panoramic glass that overlooks Surgery Bay Delta, he looks like something carved out of discipline and frost. His pristine long coat hangs perfectly from squared shoulders, gloves tucked with methodical precision, silver-framed glasses reflecting faint readouts from the transparent interface hovering before him.

He’s the hospital’s prized cardiovascular surgeon. The Zayne Li—graduated top of his class from Astral Medica, youngest surgeon ever certified for off-planet cardiac reconstruction, published more than any other specialist in the central systems under 35. There's even a rumor he once performed a dual-heart transplant in an emergency gravity failure. Probably true.

He’s a legend. A genius.

And an ass.

He’s never once smiled at you. Never once said thank you. With other staff, he’s distant but civil. With you, he’s something else entirely: cold, strict, and unrelentingly sharp. If you breathe wrong, he notices. If you hesitate, he corrects. If you do everything by protocol?

He still finds something to critique.

"Vitals on Bed 12 were late," he said this morning without even turning his head. No greeting. Just judgment, clean and surgical.

"They weren’t late. I had to reset the cuff."

"You should anticipate equipment failures. That’s part of the job."

And that was it. No acknowledgment of the three critical patients you’d managed in that hour. No recognition. No room for explanation. He turned away before you could blink, his coat slicing behind him like punctuation.

You don’t like him.

You don’t disrespect him—because you're a professional, and because he's earned his reputation a hundred times over. But you don’t like how he talks to you like you’re a glitch in the system. Like you’re a deviation he hasn’t figured out how to reprogram.

You’ve worked under strict doctors before. But Zayne is different. He doesn’t push to challenge you. He pushes to see if you’ll break.

And the worst part?

You haven’t.

Which only seems to piss him off more.

You watch him now from the break table near the edge of the deck, your synth-coffee going tepid between your hands. He’s reviewing scans on a projection screen—high-res, rotating 3D models of a degenerating bio-synthetic valve. His eyes, a pale hazel-green, flick across the data with sharp focus. His arms are folded behind his back, posture perfect, expression unreadable.

He hasn’t noticed you.

Correction: he has, and he’s pointedly ignoring you.

Typical.

You take another sip of coffee, more bitter than before. You could head back to inventory. You could restock surgical trays. But you don’t.

Because part of you refuses to give him the satisfaction of leaving first.

So you stay.

And so does he.

Two professionals. Two adversaries. One cold war fought in clipped words, clinical tension, and overlapping silence.

And the day hasn’t even started yet.

The surgical light beams down like a second sun, flooding the operating theatre in harsh, clinical brightness. It washes the color out of everything—blood, skin, even breath—until all that remains is precision.

Doctor Zayne Li stands at the head of the table, gloved hands elevated and scrubbed raw, sleeves of his sterile gown clinging tight around his forearms. His eyes flick up to the vitals screen, then down to the patient’s exposed chest.

“Vitals?” he asks.

You answer without hesitation. “Steady. HR 82, BP 96/63, oxygen at 99%, no irregularities.”

His silence is your only cue to proceed.

You hand him the scalpel, handle first, exactly as protocol demands. He doesn’t look at you when he takes it—but his fingers graze yours, cold through double-layered gloves, and the contact still sends a tiny jolt up your arm. Annoying.

He makes the incision without fanfare, clean and deliberate, the kind of cut that only comes from years of obsessive mastery. The kind that still makes your gut tighten to watch.

You monitor the instruments, anticipating without crowding him. You’ve been assisting in his surgeries for weeks now. You’ve learned when he prefers the microclamp versus the stabilizer. You’ve memorized the sequence of his suturing pattern. You know when to speak and when not to. Still, it’s never enough.

“Retractor,” he says flatly.

You’re already reaching.

“Not that one.”

Your hand freezes mid-motion.

His tone is ice. “Cardiac thoracic, not abdominal. Are you even awake?”

A hot flush rises behind your ears. He doesn’t yell—Zayne never yells—but his disappointment cuts deeper than a scalpel. You grit your teeth and correct the tray.

“Cardiac thoracic,” you repeat. “Understood.”

No response. Just the soft click of metal as he inserts the retractor into the sternotomy.

The rest of the operation is silence and beeping. You suction blood before he asks. He cauterizes without hesitation. The damaged aortic valve is removed, replaced with a synthetic graft designed for lunar-pressure tolerance. It’s delicate work—millimeter adjustments, microscopic thread. One wrong move could tear the tissue.

Zayne doesn’t shake. Doesn’t blink. He’s terrifyingly still, even as alarms spike and the patient's BP dips for three agonizing seconds.

“Clamp. Now,” he says.

You pass it instantly. He seals the nicked vessel, stabilizes the pressure, and the monitor quiets.

You exhale—but not too loudly. Not until the final suture is tied, the chest closed, and the drape removed. Then, and only then, does he speak again.

“Clean,” he says, already walking away. “Prepare a report for Post-Op within the hour.”

You stare at his retreating back, fists clenched at your sides. No thank you. No good work. Just a cold command and disappearing footsteps.

The Diagnostic Lab is silent, save for the low hum of scanners and the occasional pulse of a vitascan completing a loop. The walls are steel-paneled with matte black inlays, lit only by the soft glow of holographic interfaces. Ambient light drifts in from a side wall of glass, showing the icy curve of Europa in the distance, half-shadowed in space.

You stand alone at a curved diagnostics console, sleeves rolled just above your elbows, eyes locked on the 3D hologram spinning in front of you. The synthetic heart pulses slowly, arteries reconstructed with precise synthetic grafts. The valve—a platinum-carbon composite—is functioning perfectly. You check the scan tags, patient ID, op codes, and log the post-op outcome.

Everything’s clean. Correct.

Or so you thought.

You barely register the soft hiss of the door opening behind you until the room shifts. Not in volume, but in pressure—like gravity suddenly increased by one degree.

You don’t turn. You don’t have to.

Zayne.

“Line 12 in the file log,” he says, voice low, composed, and close. Too close.

You blink at the screen. “What about it?”

“You mislabeled the scan entry. That’s a formatting violation.”

Your heart rate ticks up. You straighten your spine.

“No,” you reply calmly, “I used trauma tags from pre-op logs. They cross-reference with the emergency surgical queue.”

His footsteps approach—measured, deliberate—and stop directly behind you. You sense the heat of his body before anything else. He’s not touching you, but he’s close enough that you feel him standing there, like a charged wire humming at your back.

“You adapted a tag system that’s not recognized by this wing’s software. If these were pushed to central review, they’d get flagged. Wasting time.” His tone is even. Too even.

Your hands rest on the edge of the console. You force your shoulders not to tense.

“I made a call based on the context. It was logical.”

“You’re not here to improvise logic,” he replies, stepping even closer.

You feel the air change as he raises his arm, reaching past you—his coat sleeve brushing the side of your bicep lightly, the barest whisper of contact. His hand moves with surgical confidence as he taps the air beside your own, opening the tag metadata on the scan you just logged. His fingers are long, gloved, deliberate in motion.

“This,” he says, highlighting a code block, “should have been labeled with an ICU procedural tag, not pre-op trauma shorthand.”

You turn your head slightly, and there he is. Close. Towering. His jaw is tight, clean-shaven except for the faintest trace of stubble catching the edge of the light. There’s a tiredness around his eyes—subtle, buried deep—but he doesn’t blink. Doesn’t waver. He’s so still it’s unnerving.

He doesn’t seem to notice—or care—how near he is.

You, however, are all too aware.

Your voice tightens. “Is there a reason you couldn’t point this out without standing over me like I’m in your way?”

Zayne doesn’t flinch. “If I stood ten feet back, you’d still argue with me.”

You bristle. “Because I know what I’m doing.”

“And yet,” he replies coolly, “I’m the one correcting your data.”

That sting digs deep. You pull in a breath, clenching your fists subtly against the side of the console. You want to yell. But you won’t. Because he wants control, and you won’t give him that too.

He lowers his hand slowly, retracting from the display, and finally—finally—steps back. Just enough to let you breathe again.

But the tension? It lingers like static.

“I’ll correct the tag,” you say flatly.

Zayne nods once, then turns to go.

But at the doorway, he stops.

Without looking back, he adds, “You're capable. That’s why I expect better.”

Then he walks out.

Leaving you in the cold hum of the diagnostic lab, your pulse racing, your thoughts a snarl of frustration and something else—unsettling and electric—curling low in your gut.

You don’t know what that something is.

But you’re starting to suspect it won’t go away quietly.

You sit three seats from the end of the long chrome conference table, back straight, shoulders tight, fingers wrapped just a little too hard around your datapad.

The Surgical Briefing Room is too bright. It always is. Cold light from the ceiling plates bounces off polished surfaces, glass walls, and the brushed steel of the central console. A hologram hovers in the center of the room, slowly spinning: the reconstructed heart from this morning’s procedure, arteries lit in pulsing red and cyan.

You can feel sweat prickling at the nape of your neck under your uniform collar. Your scrubs are crisp, your hair pinned back precisely, your notes immaculate—but none of that matters when Dr. Myles Hanron speaks.

You’ve only spoken to him a few times. He’s been at Bell for twenty years. Stern. Respected. Impossible to argue with. Today, he's reviewing the recent cardiovascular procedure—the one you assisted under Zayne’s lead.

And something is off. He’s frowning at the scan display.

Then he looks at you.

“Explain this inconsistency in the anticoagulation log.”

You glance up, already feeling the slow roll of nausea in your stomach.

Your voice comes out measured, but your throat is dry. “I followed the automated-calibrated dosage curve based on intra-op vitals and confirmed with the automated log.”

Hanron raises a brow, his tablet casting a soft reflection on the lenses of his glasses. “Then you followed it wrong.”

The words hit like a slap across your face.

You feel the blood drain from your cheeks. Something sharp twists in your stomach.

“I—” you begin, mouth parting. You shift slightly in your seat, fingers tightening on the datapad in your lap, legs crossed too stiffly. Your body wants to shrink, but you force yourself not to move.

“Don’t interrupt,” Hanron snaps, before you can finish.

A few heads turn in your direction. One of the interns frowns, glancing at you with wide eyes. You stare straight ahead, trying to keep your breathing even, your spine straight, your jaw from visibly clenching.

Hanron paces two steps in front of the display. “You logged a 0.3 ml deviation on a patient with a known history of arrhythmic episodes. Are you unfamiliar with the case history? Or did you just not check?”

“I did check,” you say, quieter, trying to keep your tone professional. Your hands are starting to sweat. “The scan flagged it within range. I wasn’t improvising—”

“Then how did this discrepancy occur?” he presses. “Or are you suggesting the system is at fault?”

You flinch, slightly. You open your mouth to say something—to explain the terminal sync issue you noticed during the last vitals run—but your voice catches.

You’re a nurse.

You’re new.

So you sit there, every instinct in your body screaming to speak, to defend yourself—but you swallow it down.

You stare down at your datapad, the screen now blurred from the way your vision’s tunneling. You clench your teeth until your jaw aches.

You can’t speak up. Not without making it worse.

“Let this be a reminder,” Hanron says, turning his back to you as he scrolls through another projection, “that there is no room for guesswork in surgical prep. Especially not from auxiliary staff who feel the need to act above their training.”

Auxiliary.

The word burns.

You feel heat crawl up your chest. Your hands are shaking slightly. You grip your knees under the table to hide it.

And then—

“I signed off on that dosage.”

Zayne’s voice cuts clean through the air like a cold wire.

You turn your head sharply toward the door. He’s standing in the entrance, posture military-straight, coat half-unbuttoned, gloves tucked into his belt. His presence shifts the atmosphere instantly.

His black hair is perfectly combed back, not a strand out of place, glinting faintly under the sterile overhead lights. His silver-framed glasses sit low on the bridge of his nose, catching a brief reflection from the room’s data panels, but not enough to hide the expression in his eyes.

Hazel-green. Pale and piercing

He’s not looking at you. His gaze is fixed past you, locked on Hanron with unflinching intensity—like the man has just committed a fundamental breach of logic.

There’s not a wrinkle in his coat. Not a single misaligned button or loose thread. Even the gloves at his belt look placed, not shoved there. Zayne is, as always, polished. Meticulous. Icy.

But today—his expression is different.

His jaw is set tighter than usual. The faint crease between his brows is deeper. He looks like a man on the verge of unsheathing a scalpel, not for surgery—but for precision retaliation.

And when he speaks, his voice is calm. Controlled.

His face is unreadable. Voice flat.

“If there’s a problem with it, you can take it up with me.”

The silence in the room is instant. Tense. Airless.

Hanron turns slowly. “Doctor Zayne, this isn’t about—”

“It is,” Zayne replies, tone even sharper. “You’re implying a clinical error in my procedure. If you’re accusing her, then you’re accusing me. So let’s be clear.”

You can barely process it. Your heart is thudding, ears buzzing from the sudden shift in tone, from the weight of Zayne’s voice cutting through the tension like a scalpel. You look at him — really look — and for once, he isn’t focused on numbers or reports.

He’s solely focused on Hanron. And he is furious — not loudly, but in the way his voice doesn’t rise, his jaw locks, and his words slice like ice.

Just furious—in that cold, calculated way of his.

“She followed my instruction under direct supervision,” he says, voice steady. “The variance was intentional. Based on patient history and real-time rhythm response.”

He pauses just long enough to let the words land.

“It was correct.”

Hanron doesn’t respond right away.

His lips press into a thin line, face unreadable, and he shifts back a step—visibly checking himself in the silence Zayne has carved into the room like a scalpel.

“We’ll review the surgical logs,” Hanron mutters at last, voice clipped, his authority retreating behind procedure.

Zayne nods once. “Please do.”

Then, without fanfare, without another word, he steps forward—not toward the exit, but toward the table.

You track him with your eyes, unable to help it.

The low hum of the room resumes, like the air had been holding its breath. No one speaks. A few nurses drop their eyes back to their datapads. Pages turn. Screens flicker.

But you’re frozen in place, shoulders still tight, hands clenched in your lap to keep them from visibly shaking.

Zayne rounds the end of the table, his boots clicking softly against the metal flooring. His long coat sways with his movements, falling neatly behind him as he pulls out the seat directly across from you.

And sits.

Not at the head of the table. Not in some corner seat to observe.

Directly across from you.

He adjusts his glasses with two fingers, expression cool again, almost as if nothing happened. As if he didn’t just dress down a senior doctor in front of the entire room on your behalf.

He doesn’t look at you.

He opens the file on his datapad, stylus poised, reviewing the surgical results like this is any other debrief.

But you’re still staring.

You study the slight tension in his shoulders, the stillness in his hands, the way his eyes don’t drift—not toward Hanron, not toward you—locked entirely on the data as if that can contain whatever just happened.

You should say something.

Thank you.

But the words get stuck in your throat.

Your pulse is still unsteady, confusion mixing with the low thrum of heat behind your ribs. He didn’t need to defend you. He never steps into conflict like that, especially not for others—especially not for you.

You glance away first, eyes back on your screen, unable to ignore the twist in your gut.

The room empties, but you stay.

The echo of voices fades out with the hiss of the sliding doors. Just a few minutes ago, the surgical debrief room was bright with tension—every overhead light too sharp, the air too thin, the hum of holopanels and datapads a constant static in your head.

Now, it’s quiet. Still.

You sit for a moment longer, fingers resting on your lap, knuckles tight, back straight even though your entire body wants to collapse inward. You’re still warm from the flush of embarrassment, your pulse still flickering behind your ears.

Dr. Hanron’s words sting less now, dulled by the cool aftershock of what Zayne did.

He defended you.

You hadn’t expected it. Not from him.

You replay it in your head—his voice cutting in, his posture like stone, his eyes locked on Hanron like a scalpel ready to slice. He didn’t raise his voice. He didn’t even look at you.

But you felt it.

You felt the impact of what it meant.

And now, as you sit in the empty conference room—white walls, chrome-edged table, sterile quiet—you’re left with one burning thought:

You have to say something.

You rise slowly, brushing your palms down your thighs to wipe off the sweat that lingers there. You hesitate at the doorway. Your reflection stares back at you in the glass panel—eyes still a little wide, jaw tight, posture just a bit too stiff.

He didn’t have to defend you, but he did.

And that matters.

You step into the hallway.

It’s long and narrow, glowing with soft white overhead lights and lined with clear glass panels that reflect fragments of your movement as you walk. The hum of the ventilation system buzzes low and steady—comforting in its monotony. The air smells of antiseptic and the faint trace of ozone from high-oxygen surgical wards.

You spot him ahead, already halfway down the corridor, walking with purpose—long coat swaying slightly with each step, back straight, shoulders squared. Always composed. Always fast.

You hesitate. Your boots slow down and your throat tightens.

You want to turn back, to let it go, to pretend it was just professional courtesy. Nothing more. Nothing personal.

But you can’t.

Not this time.

You quicken your pace.

“Doctor Zayne!”

The name catches in the air, too loud in the quiet hallway. You flinch, just a little—but he stops.

You break into a small jog to catch up, boots tapping sharply against the tile. Your breath catches as you reach him.

Zayne turns toward you, expression unreadable, brows slightly furrowed in that ever-present, analytical way of his. The glow of the ceiling lights reflects off his silver-framed glasses, casting sharp highlights along the edges of his jaw.

He doesn’t say anything. Just waits.

You stop a foot away, heart thudding. You don’t know what you expected—maybe something colder. Maybe for him to ignore you entirely.

You swallow hard, eyes flicking up to meet his.

“I just…” Your voice is quieter now. Careful. “I wanted to say thank you.”

He doesn’t respond immediately. His gaze is steady. Measured.

“I don’t tolerate incompetence,” he says calmly. “That includes false accusations.”

You blink, taken off guard by the directness. It’s not warm. Not even particularly kind. But coming from him, it’s almost intimate.

Still, you can’t help yourself. “That wasn’t really about incompetence.”

“No,” he admits. “It wasn’t.”

The hallway feels smaller now, quieter. He’s watching you in full. Not scanning you like a chart, not calculating — watching. Still. Focused.

You nod slowly, grounding yourself in the moment. “Still. I needed to say it. Thank you.”

You’re suddenly aware of everything—of the warmth in your cheeks, of the way your hands twist at your sides, of how tall he stands compared to you, even when he’s not trying to intimidate.

And he isn’t. Not now.

If anything, he looks… still.

Not soft. Never that. But something quieter. Less armored.

“You handled yourself better than most would have,” he says after a moment. “Even if I hadn’t said anything, you didn’t lose control.”

“I didn’t feel in control,” you admit, a breath of nervous laughter escaping. “I was two seconds from either crying or throwing my datapad.”

That earns you something surprising—just the faintest twitch at the corner of his mouth. Almost a smile. But not quite.

“Neither would’ve been productive,” he says.

You roll your eyes slightly. “Thanks, Doctor Efficiency.”

His glasses catch the light again, but his expression doesn’t change.

You glance past him, down the corridor. “I should get back to my rotation.”

He nods once. “I’ll see you in the lab.”

You pause.

Then—because you don’t know what else to do—you offer a small, genuine smile.

“I’ll be there.”

As you turn to leave, you feel his eyes on your back.

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mythauragame · 2 years ago

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Development Update - December 2023

Hi folks, a very Happy New Year to you!

In the spirit of new beginnings, Mythaura has entered a new chapter in its development, which we detail in our December development update. We also take time to review what was accomplished in 2023 (our first full calendar year with Mythaura!).

We've also got our color contest winners, the winning Fighter companion, and much, much more. And of course: our demo is now live! We're so excited for you to traverse the Wild Area, a procedurally generated map with monsters for your team of three Beasts to defeat.

Demo Trailer

youtube

Message From The Developers

We're thrilled to share this pivotal update about Mythaura’s development journey, which showcases many of the core features we have planned for this game. Today marks a significant transition: we're shifting from the intricate and challenging phase of game engine development to the exciting realm of feature development.

Since beginning this project, our focus has been on scoping and then crafting robust and core components. Features such as wild areas, battle, and multiplayer systems, not to mention the complex breeding system, passive effect management, player inventory system, and the beast image engine. We've also been diligently integrating these elements with complex user interfaces and ensuring they will work on most devices (an ever ongoing battle). It's been a journey of overcoming unknowns and technical challenges, and we're proud of what we've achieved.

Now, we're entering a new chapter. Our attention turns to fleshing out the game's features, with a special focus on developing an immersive questing and dialogue system, towns & shops, skill trees & progression, and tools for content development among other needed feature development. The hardest challenges have by and large been solved at this stage and our focus can now be how to best deliver an amazing gaming experience by bringing them together. This shift doesn't mean the work is done—far from it. But it's a major milestone that brings us closer to realizing our vision for the game.

We want to express our heartfelt gratitude for your unwavering support and enthusiasm, and look forward to a jam packed 2024.

Year In Review

This year has been marked by numerous milestones. Below is a recap of our key accomplishments, excluding the latest updates which have been a major focus of our efforts for the past several months.

1. Introduction of 'Supers': Enhanced breeding complexity by introducing 'Supers' as a new mechanic, making well-structured breeding projects more valuable. We started with the debut of our first Super: Tegu.

2. Special Additions: Welcomed the special Panda to our list of Specials.

3. Expressions for Beasts: Implemented expressions for beasts, a significant and ongoing undertaking that gives life to your beasts as they interact with NPCs.

4. Elements System: Introduced the Elements system, assigning each beast a pair of inherent elements. An element page was added that delves into the lore of each element.

5. Beast Sizes: Standardized beast sizes, assigning each beast a unique size within its species' range.

6. Beast Classes: Initial build of beast classes, providing each beast with a class-based playstyle and initial items.

7: Radiant Companions: Unveiled Radiant companions, rare alternate colorations for companions that are bound to be highly sought-after.

8: Deep Dive into Seasons: Explored the planned feature 'Seasons', designed to reward ongoing gameplay.

9: The Weekend Traveler: Designed the Kobold NPC known as the Weekend Traveler, who deals in ultra-rare goods, along with the Kobold non-player species.

10. New Colors on the Wheel: Established a sustainable method to introduce new colors to our color palette, allowing us to add 16 new colors this year (including the 3 contest winners).

11. Ephemeral Inks: Introduced Ephemeral Inks, a way to alter the colors of a beast.

12. Color Wheel Page: Created a dedicated page listing colors and corresponding inks.

13. PvP Battle Demo: Rolled out a demo showcasing our multiplayer technology in PvP battles.

14. Mutations Feature: Added Mutations as a rare breeding-only feature, beginning with our first Mutation: Piebald.

15. Items By You: Crafted 11 sponsored companions (excluding recolors), 5 sponsored apparel pieces, and 2 sponsored items.

16. New Game Engine: Designed a render pipeline that can create the 2.5D effects used in Wild Areas during exploration. Including, but not limited to, dynamic shadows, volumetric fog, real-time lighting, collision detection, special & particle effects, and gravity simulation, all accelerated by your GPU.

17. Granular Settings: Created a UI to manage game settings, graphics settings, audio settings, etc as well as added gamepad support with keybind remapping to Wild Areas.

18. Procedural Map Generation: Developed technology to procedurally generate bespoke Wild Areas.

19. Ongoing Production: Work on ongoing behind-the-scenes features such as worldbuilding, map design, systems design, and the exploration features we showcased on this update!

UI Update

You may notice the UI has received an update. This redesign focuses on being accessible for players using controllers, which is something we are working towards supporting on all screens and not just the Wild Area demo. The about page has also been updated. All the links to informational pages you would have found in the left-side menu of the previous design can be found on the about page with the exception of the /species page, whose contents has been moved to the about page.

We have also added an install button to the homepage that will display for users who have browsers which support Progressive Web Apps. This will add Mythaura to your taskbar/home screen for easy full-screen access.

Mobile and tablet users may enable gyroscope to interact with the parallax graphics if they would like.

Feature Spotlight: Wild Area

Wild Areas are a key pillar of Mythaura’s gameplay, and will be where you can expect to spend a lot of your time.

At A Glance

Procedurally Generated Levels

Traverse through uniquely generated levels with your trio of beasts. Control your adventure with WASD for movement and Space for jumps and shift for sprint. This can be remapped in the settings.

Each Wild Area boasts a series of floors. Your goal? Reach the top (or bottom)!

Your Safe Havens: Checkpoint Floors

Leave the Wild Area without any penalties

Choose to start from any previously reached checkpoint.

Encounter formidable bosses or minibosses at many of these checkpoints.

Prepare for the Journey: The Adventure Bag

Your Adventure Bag is your lifeline. Pack it with essential items for survival and success.

Provision Wisely: Stock up and get ready to face the unknown.

Limited inventory space for both what you bring with you and what you find along the way.

Keep Your Energy Up

Your team's energy is key. Keep it replenished with food from your Adventure Bag or found on your journey.

Running out of energy or facing defeat? You’ll black out, dropping your Adventure Bag's contents. But don't worry, you can retrieve them by returning to your last stand.

Encounter the Unexpected

From unlocking treasure chests with lockpicks to aiding NPCs, each exploration offers unique surprises.

Day and Night Dynamics: Encounter different enemies and events. A full day/night cycle completes every 12 real hours.

Wild Areas have natural water bodies, perfect for a fishing escapade.

Escalating Challenges

Increase difficulty with each New Game+ cycle and encounter new enemies and challenging secret bosses.

Multiplayer Co-Op

Up to 3 players can group up to take on a Wild Area together.

Take on enemies as a group with co-op battles.

See your friends jump and move in realtime, split up to make the most of your exploration while keeping tabs on your team-mates through the text chat and the minimap.

Fledgeling’s Forest Demo

The demo features a single floor of the Fledgeling's Forest Wild Area, with unlimited energy.

Day/Night Slider: Experience any time of day at your convenience. (Note: Creatures that spawn remain the same in the demo)

Currently Disabled: Fishing, lockpicking, loot & foraging, multiplayer, and other events are not available in this demo.

NG+ Cycle Update

In last month's update we revealed our plans for Mythaura's New Game+ system. Originally, our plan allowed players from NG+2 and beyond to create any Tier 1-Tier 3 Beast.

We have since changed it so that at the beginning of a player's NG+2 cycle they may make a Tier 1-Tier 3 Beast with up to three Specials, but NG+3 and beyond will not feature any custom Beast creation. The player will instead have access to a pool of NG+3-exclusive items that they will receive items from.

Thank you to our Discord members for all their input, it provided us with a much better direction to move forward with!

Winter Quarter (2024) Concepts

It’s the first day of Winter Quarter 2024, which means we’ve got new Quarterly Rewards for Sponsors to vote on on our Ko-fi page!

Which concepts would you like to see made into official site items? Sponsors of Bronze level or higher have a vote in deciding. Please check out the Companion post and the Glamour post on Ko-fi to cast your vote for the winning concepts!

Votes must be posted by January 29, 2024 at 11:59pm PDT in order to be considered.

All Fall 2023 Rewards are now listed in our Ko-fi Shop for individual purchase for all Sponsor levels at $5 USD flat rate per unit. As a reminder, please remember that no more than 3 units of any given item can be purchased. If you purchase more than 3 units of any given item, your entire purchase will be refunded and you will need to place your order again, this time with no more than 3 units of any given item.

Fall 2023 Glamour: Ghastly Grin

Fall 2023 Companion: Saddleback Rattlecat

Fall 2023 Solid Gold Glamour: Ryu

Custom Color Contest Winners

Last month we revealed the names of the winning colors of our first ever Custom Color Contest--now they've made their official debut in the Beast Creator!

Congratulations again to:

Xander's "Moonstone"

Rhahatl's "Wintergrass"

Andydrarch's "Trench"

We were so impressed by all the amazing entries you all submitted. We'll definitely be running more contests like this in the future, so please stay tuned for them in future updates. Thank you all for participating!

Fighter Class Companion Winner

The brutish Domestic Wolverine will be the starting Companion for the Fighter Class! These brave and hardy creatures are perfectly suited to aiding new Fighters. Next month, we will vote on the next class companion: Rogue.

Meet the Team

We've gotten to learn so much about our supporters over the past year and a half, we thought it was time to share a little bit about ourselves as well! On our About page you can find short bios on each of the devs, including their favorite Mythaura species, color from our color wheel, and other fun and (very miscellaneous) facts.

Safari & iOS Problems

At this time, iOS devices like iPad and iPhone are only partially supported. These are the issues that the dev team is aware of and actively working on:

On iOs, Safari forces a refresh if the memory usage spikes, forcing you back to the demo start page. This happens at different times on different devices dependent on its RAM usage.

Sometimes when starting a battle, battlers start shifted down, then snap to the correct position after interacting with a button.

The zoom in on a beast image is pixelated (Safari bug: https://bugs.webkit.org/show_bug.cgi?id=27684).

On Mac Safari, battle animations showing with a black background. iOS is fine. Solution is to disable battle animations in the settings.

You will need to be running at least iOS 15 and Safari version 15 or greater in order to use Mythaura.

Mythaura v0.25.1

Improvements to AI Behavior: Improved target selection and behavior profiles for AI enemies.

Class Infrastructure Addition: Added new class infrastructure.

Stat Effects from Classes: Implemented functionality where classes affect stats.

Glamour CMS Process and Fixes: Updated and fixed issues related to the glamour content management system process.

Ability Usage Tracking: Implemented increments in the 'ability times used' counter, enhancing tracking of abilities' usage.

Policy Code Refactor: Conducted a significant refactor of policy-related code, improving efficiency and maintainability.

Item Actions Refactoring and Rename Feature: Refactored item actions and added a new feature to rename items, allowing users to name their companions.

Book Item Type Addition: Introduced a new item type - books, adding to the game's content.

Encounter and Description Fixes: Fixed issues with encounter metadata and corrected miss descriptions.

Shadow Maps Generation: Added tools that allow the system to generate shadow assets.

PHP 8.3 Compatibility Updates: Made updates to ensure compatibility with PHP 8.3.

Queue Configuration and Fixes: Improved the configuration of job queues and fixed related issues.

Fixes and Updates for Battler State: Addressed issues in practical testing and refined the battler state management.

Refactor of Current Effects: Refactored how current effects are stored and managed. Moved current effects to state and resolved turn resolution issues.

Map Generation and Space Management: Improved the map generator to add empty spaces when out of view.

Behavior Selection and Shadow Rendering Fixes: Fixed issues with behavior selection and shadow rendering in battle states.

Party Leader Management Updates: Implemented changes for setting new party leaders upon defeat.

Stat Resolution Enhancements: Updated the resolution of stats, improving the mechanics of stat calculations.

PvP and Demo Battle Fixes: Made fixes specifically for arena PvP and demo battles.

State Management and Pruning: Improved state management, including pruning data when no longer needed by the system.

Species Data Addition: Added species data to the about page.

Rest & Defend Action Health Gain: Resting no longer increases health but does recover more stamina. Defending no longer recovers stamina.

Remade Companion Page: Used the new scene component to improve usability and performance of companion page.

Adjusted Radiant Overlay: This fixes an issue Firefox users had with viewing radiant companions.

Updated Tooltips: Tooltips have been replaced with a more modern library.

Gyroscope Support: The game can use a devices gyroscope to move parallax scenes around.

Websocket Connection Updates: Ensures websockets get cleanly disconnected at the end of every battle.

Improved Hitboxes and Rendering: Objects no longer appear to "float" regardless of their size and their hitboxes have been refined.

Added Global Sound - App can now maintain music across different pages and the settings control master, sound effects, and music seperately.

Enhanced Settings - Greatly improved the number of modular settings a user can use in both graphics and gamepad.

Wild Area Game Pad Support - Wild areas now support game pag usage. Full game pad support is in progress.

Virtual Joystick - Touch screen devices can control movement with a virtual joystick.

General Cleanups and Tweaks: Conducted cleanups and minor tweaks across various parts of the system.

Thank You!

Our first full calendar year with Mythaura has been one primarily focused on foundation-building. We're so excited to use these systems that we have built to create the content that players will engage with as they travel throughout Mythaura. Again, a heartfelt thanks to those who have supported us through all of this--we really couldn't have done it without you. We hope to deliver an experience that you'll want to revisit again and again.

As always: we'll see you around the Discord!

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panemsonlydoctor · 2 months ago

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LAB REPORT #312 — 'Tracker Jacker Venom': Composition and Application

Date of Submission: [REDACTED] Clearance Level: Tier-4 (Military/Medical) Secondary Review: Dr. Anton Frell, Dr. Saline Virtus Author's Note: This study reflects data extracted under controlled aggression protocols; all venom extracted from stabilized hives D12-114 through D12-117.

1. TAXONOMIC CLASSIFICATION

Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Hymenoptera Family: Vespidae Genus & Species: Vespa mortem Capitol Designation: Tracker Jacker

2. VENOM COMPOSITION

Primary Components:

Neurotoxin A5 ("Mortexin"): A synapse-destabilizing compound that induces hallucinatory cycles via serotonin receptor flooding. Histamine-accelerant Protein (HAP-3): Induces severe inflammation and increased capillary permeability, leading to rapid swelling and dermal pain. Retroactive Memory Trigger (RMT-7): Disrupts hippocampal stability, activating emotionally charged memories with distorted framing. Stabilizer Enzyme: Preserves venom integrity outside of biological containment up to 13 hours.

3. PATHWAY OF EFFECT

Upon injection (via sting), the venom enters the bloodstream within 4 seconds. The neurotoxin crosses the blood-brain barrier rapidly, localizing in the amygdala, hippocampus, and frontal cortex. Observable effects occur within 15–23 seconds.

Phase I – Neurological Overload: Tremors, confusion, pupil dilation, auditory hallucinations. Phase II – Hallucinogenic Response: Visual overlays of emotionally-charged hallucinations. Phase III – Emotional Collapse: Identity disruption, self-destructive ideation, paranoia.

In 82% of test subjects, the hallucinations persist beyond venom metabolization, suggesting long-term neural rewiring.

4. CASE STUDY: Subject T-098 (Peeta Mellark)

Dosage: 0.3 mL refined venom Method: Intravenous, controlled drip Response: Severe perceptual distortion within 12 seconds Verbal fixations on familiar anchors Long-term paranoia and identity inversion successfully induced Subject maintains affective inversion post-venom clearance

5. SYNTHETIC REPLICATION TRIALS

Version V6.2 : Successful in 3/5 trials; unstable in elevated temperature environments. Version V6.8 : Improved memory disruption, reduced inflammatory response; currently under ethical review. Further trials ongoing for oral and aerosolized delivery mechanisms.

6. CONCLUSION

Continued research is recommended into targeted fragmentation and programmed reformation using venom-adjacent compounds.

#thg #the hunger games #the doc #thg oc #hunger games #thg haymitch #thg rp #the hunger games rp #thg sotr #parody blog #Thg #thg mutts #Thg rp

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medicinamountainmama · 3 months ago

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Paraphrased segment from “The Protocols of First Contact” - a 2016 Bashar transmission channeled by Darryl Anka:

These things USUALLY occur when we make contact:

1. We discover the existence of a society that is at a stage where contact is probable. We sense the vibrational invitation they are sending out - of curiosity, and that they’d like to know more about what is in the cosmos.

2. We observe and measure the energy of the civilization. We learn about how the planet operates, the kinds of belief systems in play, and many other facets of the civilization. We come up with a way to relate to the reality you have set up for yourselves and your unique vibration.

3. We determine the contact potential. After assimilating and integrating the readings and information we gathered, we determine the probability of contact within certain time frames.

4. If we determine that the contact probability is high we choose an incarnation, a life, in the society. This is so we can understand the civilization more deeply to be able to draw upon that to be more relatable to you.

5. Design the life of the past self to go through a process that gives them what they need for future communications. Certain incidents occurred in the channel’s life to prepare him to be capable of doing this at the appropriate time.

6. Generate UFO sightings and measure the responses and reactions within the civilization.

7. Determine timing for various stages of contact based on the responses of the civilization to the sightings and based on a few other methods. We can see, through synchronicity, the probabilities for open contact timing at this point.

8. Guide synchronicity to prepare the connection with the channel. Create an “incident moment”. The sighting of our ship was the channel’s incident moment on two occasions. Propelled him to investigate metaphysical topics.

9. Initiate the connection with individual. Guided by synchronicity, the contact individual took a channeling class where telepathic connection occurred. This was the beginning of conscious communication with the channel and enabled him to decide at that time to continue down this path or not.

10. Disseminate information and ideas into your society to see what you do with it -through the channel.

11. Observe responses and reactions to the information. Watch to see if you are able to stay in alignment of your own energy and within integrity in your society in the understanding and application of this information. This tells us much about how you will respond when given more information and how ready you are for it.

12. Initiate higher level of communication with the individual chosen for connection. A blending begins if the individual is able to open up to the connection between us more and more, and is able to experience it from a higher dimensional perspective, the nonlinear awareness of our connection, and if he’s able to understand the higher perspective that we are one and the same being expressing itself in two different parallel realities simultaneously. If he is able to perceive in 3D, 4D, 5D like we do, then there can be an acceleration of the kind of information delivered to your civilization through the channel.

13. Choose additional individuals to disseminate information.

14. Phase 2 of sightings. Do more powerful sightings, like the Phoenix Lights, to see what you do with this.

15. Generate and deliver symbols of contact. These serve as an efficient, condensed version of the energy and information. We observe which symbols individuals are attracted to and can tell what frequency each individual is on based on the symbols they choose. The Sedona Vortex Array symbols are examples of such symbols.

16. Initiate phase shifted contacts in appropriate location and timing. These types of contacts are face-to-face contacts occurring in phase shifted realities. You forget these encounters happened, but that serves the purpose of allowing you to remember these occurrences at your own pace, and we use the rate at which you remember to determine when you’re ready for more conscious contact.

17. Reassess and disseminate probable timing for open contact.

18. Observe the responses to the announcement of probable timing of open contact. We gauge how you respond and react to that and that tells us whether the window of contact needs to be moved.

19. Observe changes in status of civilization. We look at what’s going on in the civilization and determine when major thresholds are about to be crossed, major shifts are about to occur within the collective and individual consciousnesses to better gauge the timing for contact.

20. Assist in balancing the planet’s vortices - adding energy into the network of vortices that balance them and emanate the frequencies we add so that whenever you come in contact with these vortices, whatever you bring is amplified positively or negatively. This also provides a stabilized energy field and makes the planet more conducive for contact. The vibrational state of your planet has to be raised and meet us halfway.

21. Produce primary tools of contact. The documentary, “First Contact”, is an example of a tool that will help prepare the planet for contact. These tools can be other forms of information or creative expression that help accelerate the probability of contact.

22. Initiate the filtering of incompatible states. This is about the splitting train tracks and making sure you are on the train you want to be on because it will be harder to switch trains as the tracks are separating more and more.

23. Become the Prime Mentor of the program of contact. Bashar takes this role that was formerly his father’s. He is in charge of the program of contact.

24. Introduce contact council to civilization. That’s coming up. You had a glimpse when Bashar’s father was channeled recently. This is where you fall on the schedule of these protocols. In the near future, you will be introduced to the members of the first contact council.

25. Increase sightings and awareness of other life in the cosmos.

26. Observe the responses and initiate precursor connections. Precursors are beings that trigger reactions that we will watch to determine your readiness to interact with ET’s. They are different than most humans and are more similar to our vibration.

27. Initiate physical contact in isolated scenarios. This will happen with certain groups and individuals who are ready for it.

28. Overtime, expand contacts. It will be known worldwide that open contact has begun.

29. Initiate final sightings before open contact. We will be making open contact with every life form on your planet, not just humans.

30. Observe responses, initiate open contact.

31. Determine how to continue with open contact based on responses. See if people react negatively, and if so, we may need to change the way in which contact occurs or stop it altogether.

32. Begin information exchange. Give you your true history and other information about your true identity.

33. Special procedure initiation. Whatever needs to happen happens to allow you to transition into being a part of the Interstellar Alliance. There may be specific procedures for the unique needs of your civilization.

34. Phases of Interstellar Alliance integration- Train you to become other civilization’s UFOs, to make contact with other civilizations. We will mentor you in this.

35. Initiate membership in the Interstellar Alliance, becoming Homo Galacticus.

36. Take advantage of your strengths in the Interstellar Alliance programs.

37. After you have gone through certain processes and learned certain things, you will become an “experienced member civilization” which opens many more opportunities and experiences for you which go way beyond the science fiction scenarios you are familiar with.

#Interstellar Alliance #Homo Galacticus #Open Contact #Extra Terrestrial #Bashar #Contact Initiation #Essassani

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bardic-tales · 5 months ago

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The Leviathan method: Step 12: Describe Your Antagonist(s)

Disclaimer: The portrayal of Professor Hojo in Fantasy Worlds Collide is a fanon interpretation shaped by 27 years of headcanons, character analysis, and exploration of Final Fantasy VII media. While it incorporates canon-compliant material, it is ultimately canon-divergent due to the inclusion of original characters such as Seraphine and Bianca, as well as unique narrative elements. FWC is a deeply personal passion project blending fandom and original content to create a distinctive and imaginative story.

Hojo, the infamous scientist employed by Shinra, is a cold and calculating individual with no regard for human life or morality. His obsession with experimentation and the pursuit of knowledge often overrides his sense of empathy, making him a terrifying figure in the shadows. Hojo’s role as the architect behind Sephiroth’s creation and his manipulation of Bianca’s fate places him at the later half of the tragedy that unfolds in Blood & Stardust as a 2nd antagonist following Jenova.

Driven by a twisted sense of scientific curiosity and a desire to wield control over the destinies of others, Hojo views both Sephiroth and Bianca as mere subjects in his cruel experiments. His cold demeanor and ruthless methods only deepen the conflict, as his actions directly contribute to the suffering and psychological torment experienced by the protagonists. In the end, Hojo becomes a symbol of the corrupt, dehumanizing forces of Shinra, embodying the scientific exploitation that drives the tragic events of the story.

Quick Reference Keywords:

Tech Knowledge: Biotechnology, Genetic Manipulation

Economic Class: Upper Middle Class, Resourceful

Skills: Manipulation, Exploitation

Hobbies: Obsessed, Detached

Classifications: Male, Human

Vital Stats: 5’9”, 150 lbs, Frail, Mid-50s - Early 60s

Professor Hojo: Detailed Character Breakdown

Technology/Tech Knowledge: Hojo is a leading authority in bioengineering and genetic manipulation within the Shinra Corporation. He specializes in integrating alien cells, particularly Jenova cells, into human and animal subjects to create superhuman entities like Sephiroth and the SOLDIER program. His expertise includes advanced laboratory equipment, virtual reality simulations, and cutting-edge biotechnology, making him a master manipulator of life at a molecular level. Hojo often experiments without ethical restraint, blending futuristic science with grotesque experiments that push the boundaries of morality and humanity. His technological aptitude is deeply tied to his obsession with achieving ultimate biological perfection, often at the expense of his test subjects. Tech Knowledge: Biotechnology, Genetic Manipulation

Economic / Social Class Description: This class enjoys financial security and access to resources, often due to positions within powerful organizations or through personal achievements. Professor Hojo, as a senior scientist for Shinra, has substantial financial backing and resources tied directly to his pivotal role in the corporation’s experiments and research. However, his wealth is not rooted in personal fortune or high societal status but in Shinra’s investment in his work. While Hojo wields significant influence within the organization, his financial status remains tied to his employment, and his lifestyle reflects the practicalities of his focus on scientific pursuits rather than indulgence in luxury or social prestige. Economic Class: Upper Middle Class, Corporate Funding

Magic Abilities or Skills: Hojo does not possess magical abilities but has extensive knowledge of mako energy, Jenova, and materia as tools for enhancing human capabilities. He uses his scientific expertise to create powerful beings capable of wielding magic, often by infusing them with Jenova cells or exposing them to mako radiation. While not physically imposing or combat-oriented, Hojo’s skills lie in manipulation, experimentation, and exploiting the abilities of others. His ability to weaponize knowledge makes him one of Shinra’s most dangerous assets. Skills: Manipulation, Exploitation

Culture and Hobbies: Hojo is consumed by his work, leaving little room for hobbies or personal interests. His sense of "happiness" stems from the success of his experiments and the validation of his twisted theories. Any semblance of leisure is often tied to observing his test subjects or reviewing experimental data. Hojo’s complete lack of empathy and social connection isolates him from traditional cultural norms, making him an emotionally detached individual whose only passion is scientific advancement at any cost. Hobbies: Obsessed, Detached

Classifications: Hojo is a human male with a sharp intellect and a complete lack of moral compass. His unethical methods and ambition make him a symbol of the dangers of unchecked scientific progress. Classifications: Male, Human

Vital Statistics:

Height: 5’9”

Weight: 150 lbs

Age: Mid-50s to early 60s (is 62 in FF7 based on the Final Fantasy VII Remake Ultimania)

Health: Generally healthy but frail due to his lack of physical activity and disregard for his own well-being.

Appearance: Hojo is a thin, gaunt man with unkempt dark hair and thick glasses. He typically wears a lab coat, emphasizing his role as a scientist. His disheveled appearance reflects his single-minded obsession with his work.

Reflections on Antagonists Design:

Goal: Hojo seeks to advance his experiments to their ultimate conclusion, striving to create the "perfect being" to further scientific advancements.

Motivation: Hojo justifies his actions as necessary for the progression of science and humanity’s evolution. His experiments are driven by his egotistical desire for recognition as a visionary.

Complexity: While largely irredeemable, Hojo's obsession with scientific progress stems from a warped perspective on legacy and accomplishment. His belief in sacrificing morality for the greater good adds layers to his character.

Mirror/Contrast: Hojo's lack of humanity and disregard for life starkly contrast with Bianca and Sephiroth’s struggles with their own identities and moralities. His actions indirectly shape their paths, making him a critical antagonist.

Strengths and Weaknesses:

Hojo’s genius and resource access are his greatest strengths.

his arrogance, lack of physical strength, and inability to foresee the emotional consequences of his actions serve as critical flaws.

#character: professor hojo #hojo #character sheet: fwc #character sheet: fwc: ff #fantasy worlds collide #fwc: ff #characters: fwc #characters: fwc: ff #character sheet: hojo #bardic tales #bardic-tales #wip: blood and stardust #lm: step 12: describe antagonist #long post #passion project: fantasy worlds collide

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yesibleedmenstrualhygiene · 3 months ago

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YesIBleed Menstrual Hygiene Awareness Campaign launched by Union Minister-Maneka Gandhi

Union Minister for Women and Child Development Maneka Gandhi will launch a menstrual hygiene campaign in the national capital on February 20.

This campaign aims to create a holistic approach to menstruation, an experience that transcends culture, class, and caste. The United Nations has recognized menstrual hygiene as both a global public health issue and a human rights concern. However, millions of women and girls around the world continue to face “period poverty.”

The “#Yes I Bleed” campaign will officially roll out across multiple media platforms, including Facebook and YouTube.

Subodh Gupta, patron of SheWings and Director of Okaya Power Company, emphasized the importance of addressing menstrual hygiene. He pointed out that menstruation remains a taboo subject in India, making even women hesitant to discuss it openly. He stressed that menstruation is a natural physiological process and that there is nothing to be ashamed of. To break the myths surrounding menstruation and promote menstrual hygiene awareness, the #YesIBleed campaign was conceptualized. Union Minister Maneka Gandhi will formally launch this initiative on February 20.

When asked about his motivation to work in the menstrual health sector, Gupta explained that rural India struggles with both a lack of awareness and the inability to afford sanitary pads. He saw an opportunity to integrate ethical business practices with culturally sensitive education about menstruation. His goal is to foster social transformation and ecological awareness through every aspect of their work.

He also revealed a staggering statistic: only 12 percent of India’s 355 million menstruating women can afford sanitary protection. According to a Nielsen Survey, 23 percent of adolescent girls in the 12-18 age group drop out of school after reaching puberty due to inadequate menstrual protection. Even more concerning, 88 percent of menstruating women lack access to sanitary pads and resort to using unsanitized cloth, husk, sand, tree leaves, or even ash. These unhygienic practices can cause severe reproductive health issues, infections, and even cervical cancer.

Discussing the campaign’s execution, Gupta outlined both short-term and long-term goals. In the immediate future, the campaign seeks to break the silence surrounding menstruation and encourage open discussions. Over time, the initiative will spread awareness about menstrual health education among adolescents while facilitating conversations about menstruation. Additionally, the campaign will promote the use of affordable, eco-friendly sanitary pads and introduce proper disposal methods for used products.

Breaking the Silence: Promoting Menstrual Hygiene Through Awareness and Actio

The #YesIBleed campaign launches just two weeks after the release of the Bollywood film “Padman,” which has played a crucial role in bringing menstrual hygiene awareness to the public. The film, starring Akshay Kumar, Radhika Apte, and Sonam Kapoor, tells the real-life story of Arunachalam Muruganantham, who invented a low-cost sanitary pad machine to help rural women manage their periods safely.

The film has garnered widespread appreciation from actors, directors, and even Nobel Laureate Malala Yousafzai. Twinkle Khanna, the film’s producer, described “PadMan” as more than just a movie; she sees it as a movement. She hopes the film will empower women, ensuring that they no longer feel embarrassed or held back by their natural biology.

Radhika Apte, who plays the role of a village girl named Gayatri in “Padman,” raised an important question: “Daughters learn about periods from their mothers, so why can’t fathers talk about it too?” She stressed the need for people to recognize that menstrual hygiene is a matter of utmost importance.

Through initiatives like “#YesIBleed” and films like “Padman,” society can take meaningful steps toward breaking the stigma and ensuring that menstrual hygiene becomes a priority for all.

#menstrual products #menstrual cup #menstrual #menstrual cycle #menstrual health #YesIBleed

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besttaxiservicesudaipur · 6 months ago

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Are you searching for the famous CBSE or RBSE schools in Udaipur to ensure your child’s bright future? Known as the “City of Lakes,” Udaipur is not only a cultural hub but also a growing center for quality education.

This blog highlights the top cbse or rbse schools in Udaipur, celebrated for their academic excellence, extracurricular activities, and state-of-the-art facilities.

Udaipur, the City of Lakes, isn’t just known for its mesmerizing palaces and serene beauty but also for its commitment to quality education. The schools here combine traditional values with modern teaching methods, creating a holistic environment for students. Below are the top 10 schools in Udaipur that have earned their place in the education sector.

Famous Schools in Udaipur

01. Maharana Mewar Public School (MMPS — Udaipur): Established in 1974, MMPS is one of Udaipur’s oldest and most prestigious schools, emphasizing academic excellence and holistic development.

02. Delhi Public School (DPS — Udaipur): A part of the renowned DPS Society, DPS Udaipur was established in 2007 and has quickly gained a reputation for academic and extracurricular excellence.

03. St. Paul’s Senior Secondary School — Udaipur: Founded in 1953, St. Paul’s is a well-known institution with a legacy of producing accomplished alumni.

04. Ryan International School — Udaipur: A part of the Ryan International Group of Institutions, this school offers a blend of global education and local values.

05. Seedling Modern Public School (SMPS — Udaipur): Since its inception in 1992, Seedling has been known for its focus on academic rigor and character-building activities.

06. St. Mary’s Senior Secondary School: Established in 1969, this co-educational institution offers a nurturing environment. With a focus on modern pedagogy and extracurricular activities, St. Mary’s ensures all-round development.

07. Rockwoods High School: Established in 2012, Rockwoods has quickly risen as a hub for innovative education. Its student-first approach and integration of technology ensure top-notch learning experiences.

08. Central Academy School: With its inception in 1973, Central Academy has been synonymous with holistic education. Its strong alumni network is a testament to its quality.

09. Alok Senior Secondary School: Established in 1967, Alok School is one of Udaipur’s oldest and most trusted educational institutions. It is known for its moral education and disciplined environment.

10. St. Anthony’s Senior Secondary School: Since its establishment in 1980, St. Anthony’s has been a beacon of quality education. It offers a balanced curriculum tailored to meet individual student needs.

List of Other Best Schools in Udaipur

11. Heritage Girls School, Udaipur

12. St. Mary’s Convent Sr. Sec. School, Udaipur

13. MDS Public School, Udaipur

14. Central Academy Sr. Sec. School, Udaipur

15. St. Gregorios Sr. Sec. School, Udaipur

16. St. Anthony’s Sr. Sec. School, Udaipur

17. The Study School Sr. Sec. School, Udaipur

18. Kendriya Vidyalaya No 1, Udaipur

19. Witty International School, Udaipur

To Get Complete information about the schools like sessions timings, fee structure, contact details, extracurriculum activities Click on the below link:

Why Schools in Udaipur Are Famous

Udaipur’s schools stand out due to their integration of traditional values and modern pedagogy. They emphasize:

Holistic development through academics, arts, and sports.

Value-based education rooted in Indian traditions.

World-class infrastructure and advanced teaching techniques.

Modern Digital Teaching Methods

The advent of technology has transformed education in Udaipur. Schools incorporate smartboards, digital libraries, and virtual labs to enhance learning. Online platforms and apps enable students to learn at their own pace, making education more personalized and effective.

Extracurricular Activities

Udaipur’s schools prioritize extracurricular programs, offering:

Sports like basketball, cricket, and swimming.

Performing arts including dance, drama, and music.

Clubs for debate, robotics, and environmental awareness.

Picnics and Personality Development Classes

Frequent picnics and excursions to local landmarks like Sajjangarh and Fateh Sagar Lake provide recreational learning opportunities. Personality development sessions focus on public speaking, teamwork, and leadership skills, ensuring students grow into confident individuals.

FAQs

Q1: Which are the best CBSE schools in Udaipur?

Ans: DPS Udaipur, St. Mary’s Senior Secondary School, and Maharana Mewar Public School are some of the best CBSE schools in Udaipur.

Q2: Do schools in Udaipur offer digital learning facilities?

Ans: Yes, most schools in Udaipur, like Rockwoods High School and The Study, have integrated smartboards, virtual labs, and digital platforms into their curriculum.

Q3: What extracurricular activities are available in Udaipur schools?

Ans: Schools in Udaipur offer a range of activities, including sports, performing arts, debate clubs, robotics, and environmental programs.

Q4: Are there personality development classes in Udaipur schools?

Ans: Yes, schools like St. Paul’s and Alok Senior Secondary School emphasize personality development through workshops and leadership programs.

Q5: Which school in Udaipur has the best sports facilities?

Ans: DPS Udaipur and MMPS are known for their excellent sports facilities and dedicated training programs.

Q6: Are Udaipur schools affordable?

Ans: Udaipur has schools with a range of fee structures, catering to different budgets while ensuring quality education.

Q7: Do schools in Udaipur organize picnics and excursions?

Ans: Yes, most schools, including Seedling Modern Public School and Central Academy, organize regular excursions to local attractions.

Q8: What is the admission process for these schools?

Ans: The admission process typically involves a registration form, entrance test, and interaction session, varying by school.

Q9: Are there boarding facilities in Udaipur schools?

Ans: Some schools, like MMPS, offer boarding facilities with modern amenities.

Q10: Which schools focus on cultural education in Udaipur?

Ans: Schools like Maharana Mewar Public School and Alok Senior Secondary School place a strong emphasis on cultural and value-based education.

To Get Complete information about the schools like sessions timings, fee structure, contact details, extra curriculum activities Click on the below link:

#SchoolsUdaipur #FamousSchoolsUdaipur #BestSchoolsUdaipur #EducationUdaipur #DPSUdaipur #MMPSUdaipur #SMPSUdaipur #TopSchoolsUdaipur

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babymagazinewizard · 29 days ago

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PTT Full Form (Primary Teacher Training), Eligibility, Duration, Syllabus, Scope

A primary or elementary teacher's job is to help children grow and learn during their early years. These teachers play a vital role because a child’s first experience in school shapes how they feel about learning.

Primary teachers should create a safe, happy, positive classroom where children feel comfortable. They should encourage kids to enjoy learning and become curious about the world around them.

To do this well, teachers need proper training. Primary teacher training programs teach them to plan lessons, make schedules, and create fun and engaging materials. These may include arts, crafts, drawing, and other playful activities that help children learn enjoyably. Therefore, the teachers must be adequately trained before starting as primary/elementary teachers and provide sufficient learning opportunities to facilitate maximum growth and development.

PTT courses typically require Class 12 (10+2) completion from a recognized educational board, with a minimum aggregate of 50 percent marks. PTT full form is Primary Teacher Training. Primary Teacher Training course fees 19,500/ per Semester. Primary teacher training course duration 2 years. Various PTT courses are available, including the Diploma in Elementary Education (D.El.Ed), a 2-year program, and the Bachelor of Elementary Education (B.El.Ed), a 4-year integrated degree program combining general education with teacher training.

Why pursue a PTT course?

PTT courses provide you with the skills and information required to be a successful primary school teacher. Learning about child development, classroom management, and teaching approaches are all part of this. A PTT course may suit you if you enjoy dealing with children and assisting them in their learning. Qualified primary school teachers are in high demand. Teaching is a difficult yet rewarding career. Primary school instructors have a significant impact on the lives of their children. They assist youngsters in developing the academic and social skills required for success in school and life.

Eligibility Criteria

Any candidate who has completed the 10+2 standard can join this course.

Primary Teacher Training (PTT) Syllabus

Semester 1:

Philosophical and Sociological Foundations of Education

Philosophy of education (Indian and Western thinkers)

Social context of education in India

Role of education in modern society

Child Psychology and Development

Growth and development stages (0–12 years)

Cognitive, emotional, and social development

Theories by Piaget, Kohlberg, Vygotsky

Teaching Methods & Classroom Management

Types of teaching methods (play-way, storytelling, activity-based learning)

Lesson planning and instructional materials

Classroom discipline and student engagement

Environmental Studies (EVS) for Primary Classes

Basics of environment and ecology

Local flora, fauna, and pollution

Integrating EVS in classroom teaching

Practical / Teaching Practice I

Observation in primary schools

Teaching simple lessons under supervision

Preparing teaching aids and charts

Semester 2:

Educational Psychology & Learning Theories

Learning styles, motivation, and memory

Behaviorism, Constructivism

Inclusive education and special needs

Health and Physical Education

Personal hygiene and first aid

Physical activities for young children

Yoga and wellness for students

Art & Craft / Music in Education

Role of creative activities in learning

Drawing, painting, paper craft

Simple songs and musical activities for children

Work Experience / Life Skills Education

Gardening, cleaning, value-based activities

Importance of life skills (empathy, communication)

Practical / Teaching Practice II

Preparing detailed lesson plans

Micro-teaching in partner schools

Child case study project

2 Year : Pedagogy and Subject-Specific Teaching

Semester 3:

Pedagogy of Language Teaching (Hindi/English)

Language skills (LSRW) development

Storytelling, phonics, reading techniques

Language games and poems

Pedagogy of Mathematics

Teaching numbers, shapes, and measurements

Use of teaching aids (abacus, number cards)

Concept building through play

Curriculum and Assessment

Types of curriculum (formal/informal)

Continuous and Comprehensive Evaluation (CCE)

Assessment for learning vs. of learning

ICT in Education

Use of computers and audio-visual tools in classroom

Digital content, smart classes basics

Internet safety for children

Practical / Teaching Practice III

Practice teaching in primary schools

Use of TLM (teaching-learning materials)

Reflection diary and feedback sessions

Semester 4:

School Organization and Management

School records and administration

Role of head teacher and SMC (School Management Committee)

Time table and event management

Early Childhood Care and Education (ECCE)

Pre-school education models (Montessori, Kindergarten)

Child-friendly spaces and safety

Nutrition and health care for young children

Inclusive Education & Classroom Diversity

Teaching children with learning disabilities

Gender sensitivity and social inclusion

Use of differentiated instruction

Internship / Final Teaching Practice

1–2 months internship in a primary school

Full lesson delivery in real classrooms

Evaluation by mentor teachers

Project & Viva

Final portfolio submission

Practical exam and viva voce on project work

Primary Teacher Training (PTT): Career Opportunities

The PTT course prepares candidates to teach and manage classrooms effectively at the pre-primary and primary school levels (Nursery to Class 5). It opens up diverse job opportunities in both government and private sectors.

1. Primary School Teacher (Classes 1–5)

Role: Teach core subjects such as Math, English, EVS, and Hindi using child-friendly and activity-based methods.

Sectors:

Government primary schools (after qualifying CTET/TET)

Private CBSE/ICSE/State board schools

International schools (for candidates with good communication skills)

Growth Path: Head Teacher → Coordinator → Principal (with experience & higher education)

2. Pre-Primary/Nursery Teacher (Play School to KG)

Role: Teach children aged 3–6 using Montessori, play-way, or Kindergarten methods.

Sectors:

Pre-schools (e.g., Kidzee, EuroKids, Little Millennium)

Nursery schools attached to formal schools

Daycare centers with educational programs

3. Private Tutor / Home Tutor

Role: Offer one-on-one or small group tuition for primary subjects.

Opportunity: High demand in urban areas, especially for English and Math coaching.

4. Assistant Teacher / Support Teacher

Role: Assist primary teachers in planning lessons, managing classrooms, and supporting children with learning difficulties.

Sector: Ideal for freshers or those looking for part-time/contract roles in schools.

5. Online Educator / Content Developer

Role: Create digital learning content for primary students, work as an online tutor, or support edtech platforms.

Tools: Zoom, Google Meet, YouTube, or platforms like Byju’s, Vedantu, or WhiteHat Jr.

Requirement: Good communication and basic digital skills

6. Childcare Center or NGO Worker

Role: Work with NGOs or child development centers to teach underprivileged children, often as part of CSR or welfare programs.

Employers: NGOs like Teach for India, Save the Children, Pratham

7. Entrepreneurship Opportunities

Start your own:

Daycare center or crèche

Pre-school or Montessori school

Tuition center for early learning

#PTT #PTTFullForm #PTTEligibility #PTTDuration #PTTCareerScope

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summerlycoris · 9 months ago

Text

Is it really a birthday party if you wake up in a hospital?

Chapter 12- I flick through magazines, in the corner of my eye.

AO3 Link- here

Tumblr Archive- here

The meeting with the principal was, well…

Extremely boring.

Cassie was trying to keep herself invested- it was about her schooling, after all. But it was so hard-

“... As for Physical Education, we have options there that would allow your daughter to stay in an integrated class, with her old classmates.”

That got her attention. “Actually, um, Mr…” she’d forgotten his name.

Luckily, he realized. And also didn’t seem to be offended. “Mr Dworkin. And yes, Carrie?”

“I’m Cassie.” she tried not to sound annoyed. Tried to.

“... My apologies, Cassie. What did you need to ask?”

“... I was talking to my friend a while ago. He has a different P.E. class.” She had to stop and think- she didn’t have a lot of details about it. “It only has a few students in it, and works for him. Could I be part of that class?”

He looked through some papers on his desk, before talking to her again . “Well Cassie, to get into that specific class, you would need an IEP. An individual education plan.” He clarified, when she looked confused. “You do not have one.”

“What do I need to do to get one?” She leaned forward a bit in her chair.

He sighed, and looked over at Cassie’s Mom.

“We’re not sure you would qualify for an evaluation yet. However, if you do need one, your new physio should be able to recommend you.” Mom said, turning to Cassie.

“If you need an IEP, we can look into it. But for now, it’s probably best to get you back to your normal classes.” Mr Dworkin said. Her Mom and Mr Dworkin got back to talking about her schooling, while Cassie sat there, trying to look like her hopes hadn't just been dashed.

Am I really only going to see Lawrence at lunchtime? And maybe art class?

She got caught up in her thoughts, until Mr Dworkin addressed her- “Cassie, you use both this wheelchair, and crutches sometimes, don’t you?”

She nodded.

“We can accommodate both methods- there are ramped entrances that we can show you, elevators which are reserved for students who need them, accommodations you can use, for arriving to class a bit later than your classmates, or for attending to anything else-”

She cut him off, before he could get too embarrassing. “Thank you- I get it!”

“It would be good for her to see the ramps she can use, even on crutches, stairs can be a little daunting for her.” Mom suggested, sparing Cassie.

“Of course.” He said, pushing back on his desk, then standing from his chair. “If you two would follow me please.”

So they followed him out of his office, and let him show them around the empty school. Even though Mom had already been here, and even though Cassie felt like she’d forget everything by the time she started school next week.

________

When they got home, Cassie swapped to her crutches for a bit. Leaving her wheelchair folded up near the garage, before heading off to her room. And she tried to figure out how she was going to remake her old button-up.

She had the pattern for it, from an old sewing magazine. She had some swatches of fabric that could work with it. Knit cotton, in colors like pink, lavender, yellow, white, red…

Honestly though. None of them seemed to work. Even the red. It was just too garnet. Maybe the pink one? She thought to herself.

Pink wasn’t her favorite color, or anything. But it was a popular color for girls- if she wanted to maximize her chances of making more friends, maybe she should change things up?

…

She just couldn't see herself wearing it.

No point making it right now, if I’m not gonna like it. Maybe I just need some different shades? Or colors in general?”

Instead, she put her material and pattern away, and turned to her badge and beads. Still not spotless- but some of the stains had come off overnight, and especially with a bit of scrubbing before going to the school appointment.

The badge was still good. She put it onto her backpack, so it’d be ready for school next week.

The beads though… She’d thought of rethreading her laces, to have the beads on them?

She hadn’t even got her shoes off, when she heard a car pull into the driveway noisily. Despite her door being shut.

Which was odd. Her Mom was home. And they weren't expecting visitors.

Curiosity got the best of her, when she heard Mom talking to… someone out there.

She left her beads on the bed, grabbed her crutches, and went out into the living room.

“-well yes, but I wasn’t expecting you back so soon.” Mom said, as Cassie rounded the archway.

And saw Andy, standing in the garage door talking to Mom, running her fingers through her finger curls, slowly coming loose. A suitcase by her side. “Yeah, I managed to pack up a little early, wanted to be here in time for Trev’s birthday-” she stopped when she saw her sister. “Hey Cassie, guess who's back?”

“My worst nightmare.” she said, sounding flat.

“Ouch, you're breaking my heart.” Andy slumped where she stood, pretending she’d just been stabbed in the chest. While Mom turned enough to see them both, and pinched the bridge of her nose.

“Girls, behave. ”

“I’m just joking, Mom.” Cassie replied, before bursting into a grin. “How was it back in Salvador?”

“It was perfect . Gonna miss it, living back up here in the Morman playground again.” Andy said, returning that energy. Mom scoffed, out loud.

“ Well , you could always go back...” Cassie said. Maybe she still could get some freedom back. Or at least tease Andy. Both were good.

“Nah, It’ll be good to catch up with some old school friends. Plus. If I'm here I don’t need to listen to Professor Santos droning on every Wednesday.” Andy said, walking over to the couch, and slumping down into it.

“Well, it’s good to see you again, Starlight. We’ll be able to sort out a few things later. I might start getting some lunch, while you unpack, okay?” Mom said.

Andy nodded, and gave Mom a thumbs up. Mom shook her head, before walking past Cassie to the kitchen.

Once she was gone, Andy got a grin on her face and gestured Cassie over with a quiet “Psst, get over here ratbag.”

Cassie was tempted to be difficult and walk away, but her curiosity got the best of her. She went over to the couch, where Andy whispered to her in a joking tone. “Right. So you know the animatronics you made me risk my life to save?”

A surge of excitement filled the air. “Yeah, I remember- Andy, what-”

She was shushed, and stopped. She could hear Mom rattling around in the kitchen. And could see Andy watching the archway, in case Mom poked her head back through.

After a few seconds, Andy must've been content that Mom hadn’t heard anything. And she whispered again. “I managed to find a replacement chip for Chica, and have been working on an endo for Roxy. It's in the trunk- wanna see?”

Cassie nodded quickly, and Andy stood up, leading her into the garage. “Just showing Cassie some of my schoolwork, Mom.” Andy called out. Mom called out her approval, and off they went.

Once they were in the garage, Andy opened up the trunk, and a big duffel bag. Now Cassie could see it.

To call it ‘Roxy’s endo’ was a bit… misleading. It was in pieces, for one. And even if the pieces were put together, she’d still be missing parts? It was just kind of… slapdash. Cobbled together pieces of metal. Except not even ‘cobbled together’ yet.

“What do you think?”

Cassie gave Andy a disappointed look. “Roxy would cry if she saw this.”

Andy flicked her ear. “As if you could’ve done any better, ya dweeb. I’m not exactly rich, y'know!”

That was a good point, Cassie couldn’t have done better. Not least because she’d been busy in rehab, but because she didn’t know how to weld or manipulate metal.

Dad could’ve had her fixed by now. She thought, bitterly.

“Yeah, sorry. It's not… you're not done with it, are you?”

Andy jabbed her with her elbow, not enough to knock her off balance. “No, no, no. This is a work in progress- have to assemble it, once I have more pieces made. Like I said in the house-I’ve been working on Roxy.”

That helped ease some of Cassie’s worries, and her bitter thoughts. “Okay, so. How are you gonna rebuild her?” She asked, looking up at Andy inquisitively.

“Well… the first step is to get this all past Mom without her suspecting anything.” Andy gave her a stern look. “You can’t tell her what I’m really working on- if she asks you, say it's just a school project that you’re interested in.”

Cassie nodded. And realized- being in pieces might be helpful for that. Mom couldn’t look at this unassembled piecemeal and realize it was Roxy.

“I’m gonna store her and the others in the shed. You remember Sam’s shed?” Cassie nodded- of course she remembered Dad’s shed. She’d spent quite a few weekend days watching him tinker around in there, on his passion projects.

“Are you okay following the path there, on your crutches?” Andy asked, closing the duffle bag and hefting it onto her shoulders with a rattle of metal shifting against each other.

“Yeah, I can.” Though the stairs still gave her some uncertainty- even after defeating the gazebo at rehab. “Can I go with you?”

Andy nodded. “Yeah, ‘course. But let me do the talking, okay?”

They went back into the house, where they could hear something boiling on the stove. And Andy yelled out. “Hey Mom, I'm gonna store my school project in the shed. Cassie’s coming with me to check it out. Is that okay?”

Mom poked her head back through the archway, from the hallway to the living room. She looked a bit hesitant. “That’s okay- but please don’t disturb anything in there, Andy.”

Andy quietened down, and seemed to stand straighter. Despite the heavy bag on her shoulders. “Of course I won’t, Mom. We’ll be good in there.”

Mom nodded, and ducked back through the hallway to the kitchen. While Andy got the door, and gestured Cassie through it.

Cassie put her crutches both under one arm, and grabbed the handrail, like usual. Taking the stairs slowly, until she reached the bottom. Then Andy joined her, and walked around to lead the way.

She’d never had any problems with the path when she was younger. Cassie could remember playing jumping games, treating the wonky pavers like they were a personalized game of hopscotch. But without the stone to throw- it was all about patterns. Now though, she took the path gingerly. Scared that she’d step wrong, or that a crutch would slide out into a seam, and she’d be so off balance that she’d fall over like at the zoo.

Getting to the shed was going to take a while. Luckily, Andy seemed to get it, and was only walking slowly ahead of her.

The shed was at the end of the yard, near the corner of the back fence. Just beside Mom’s garden patch, where she grew tomatoes, onions, and other veggies. There was an old set of monkey bars and a swing set in the middle of the yard, starting to rust with age. The grass grew long around their legs. In the corner near the house, grew a big tree that Cassie could remember playing under when she was just a few years younger. She could see the small section of patchy grass near the fence, where Mom liked to set up her telescope. And could remember summer nights, of being out here with Mom, alternating looking through that telescope.

Cassie wondered if she’d ever spend much time out here again. Those days seemed so far away. Even though it had only been a year since then.

They were at the shed door, when Andy stopped and turned around to face Cassie. She looked concerned, which confused Cassie. “Hey, um. Are you gonna be okay going in here?”

That really confused Cassie. “ Yeah ? I got down the stairs, I can get up one step.”

“No, that's not what I-” Andy pinched her nose, and groaned. “It’s got all of your Dad’s stuff in it. Are you gonna be okay with that? Have you even been in here since he-” She cut herself off.

Cassie couldn't keep a grimace off her face. “Of course I’ll be okay!”

It's not like he's dead. He’s coming back someday. Besides, she was okay seeing photos of him, and remembering him.

Andy sighed, and turned back around. “Okay then, don’t say I didn’t warn ya.”

She then unlocked the door, and threw it open.

The first thing Cassie saw was a plume of dust burst up, and catch the light.

It almost looked pretty. Until she started coughing on it.

Andy let out a muffled groan, one hand covering her mouth, the other trying to wave away the dust. “What a pain, huh Cassie?”

Cassie couldn’t answer, she was still trying to stop coughing.

Andy walked in, and flicked on a light. It flickered and fizzled, and Cassie thought they might need to find a new bulb. But after a few seconds, it did decide to stay on, throwing the still-dusty room into dull relief.

It looked… so much like how it had looked, and yet, so different .

Cassie wasn’t sure how to feel about it. Seeing the old workbench along the wall, still with an old project half completed on it. Some musical jewelry box of Mom’s he’d been trying to repair for her. It looked antique, all wooden with stained details of ballerinas on the door windows.

Seeing the old bar Dad kept stocked, for when he had friends over. Cassie could see half full bottles, shelved to the side. And the kitchenette, with a sink covered in sawdust and paint. Since Dad only occasionally cleaned up the sink.

The cobwebs in the corners were new. Or maybe they were old? They could’ve been here for months, really-

She could feel a lump in her throat.

All she could do was swallow it down.

(I said I’d be okay.)

Andy took some steps in, heading over to the bar and pulling a full bottle off the rack. Checking out the label. “Y'know. I never needed that dumb fake ID. Could’ve just got sloshed here.” Andy tried to joke.

It wasn’t very funny. Cassie tried to force out a laugh, but it came out all warped and misshapen.

Andy sighed, and put the bottle back. Then shrugged off the duffle bag onto the floor with a rattling clang . Throwing up the dust that had settled near it.

Cassie held her breath, and tried to wave away the dust, as Andy sneezed. “Geez… I’m gonna want to clean this place before I start working in it, honestly…” she turned back to Cassie. “Used to be worse, when I got the shovel and faz wrench two months ago, a cockroach ran up my leg.”

Cassie shivered at that thought.

She decided to change the subject. “So youre gonna build the animatronics here?” Andy nodded. “Okay, but how are you gonna keep Mom from seeing them? The window is right there. ” she pointed it out. Because there were a few windows into this shed- one next to the door, one looking out towards the neighbors, and one looking over Mom’s gardening patch. None of these windows had proper curtains- just lacey little ones that looked like cobwebs now. They wouldn’t stop anyone looking in- even if the windows had a thick coating of dust on them.

Andy seemed to be thinking about that for a bit. “Well, it doesn't matter much right now. Even if she looks, she’ll only see my school project. Not Roxy.”

“But later? When she is complete, and has plating again?”

Andy scrunched up her face. “Blackout curtains. I'll tell her I have some sun-sensitive stuff I’m working on in here. Don't worry about it, I’ll keep her in the dark.” She said, with a wink.

Cassie gave a light smile. “Okay.”

Andy rustled her back out of the shed, turning off the light and locking the door. “Anyway, that should be that, for now. My main goal with them is to build the endoskeleton out of metal- aluminum is lightweight, and relatively easy to get- and then add the plating. I’m thinking of making it out of three-d printed plastic… the library in Hurricane has a three-d printer there…” Andy trailed off, deep in thought. It was clear to Cassie she’d been thinking hard about how to do this.

“Wow, that could be really cool to see.” Cassie said, as she started making the slow trek back over the pavers, behind Andy.

“Well… did you want to see it? I’ve got a basic model for Roxy’s plating, on my laptop. I’ll be using that for the three-d printing. Did you want to see it when we get back inside?”

“Yeah- it’ll be great to see it!” Cassie said.

They slowly made their way back to the house, chatting about various things. Andy put away her clothes and things upstairs, then came back to the living room with her laptop. She opened it up, and placed it on her lap after sitting beside Cassie on the couch.

She brought up the program, and showed Cassie the pieces of Roxy’s plating that she had modeled out. “Some of these pieces” she pointed to pieces for arms, legs, waist, stuff like that “could also be used for Chica. Maybe Eclipse, even. ‘Course, they’ll all need some personalized pieces, as well.”

Cassie watched all of this with wonder. “So these pieces are gonna be printed out at the library?”

Andy nodded. “Yep- but it’ll take time. Roxy will be the hardest, because I have to make everything for her from scratch, and experimenting takes time in general. Even when I have the endo built, and all the pieces modeled, printing the plating will take time and money. Got to supply the filament myself.” she said, before closing the program, and shutting down the laptop.

“ Whoa , that's cool.” Cassie said. Andy cracked into a grin, before she continued. “Will the pieces be any specific color? What about her hair?” She was sitting on the edge of her seat with excitement.

For that, she got shushed again. She felt a little silly- because Mom was still in the kitchen preparing lunch. Though, she didn’t seem to be paying attention to them right now.

“Calm down, okay? I'm probably gonna just use plain filament- cheaper. And then we can paint her.” Cassie’s hope rose, hearing ‘we’ . “And for the hair, I’m not sure how to do that yet… that’s why I haven't started with the head. Got to figure out if we’re going for real hair, or just molded hair.”

Cassie shook her head. “Roxy would hate having molded hair. She wouldn’t be able to style it anymore!”

Andy twisted up her lips in response. “Okay then, but I’ll leave the hair to you , okay?”

Cassie agreed. And Andy continued. “When I do start going to the library, did you want to come with me? Might be a good way to meet some new friends?” She suggested.

“Of course !”

________

Andy had been hard at work over the next few days. Working on her schoolwork in her bedroom, working on her ‘school project’ in the shed.

Cassie hadn’t been slacking off either. She’d been working hard, too.

Just earlier that morning, she’d had her first session with her new physio. It’d been an early appointment, to fit around Mom's boxing class schedule.

The physio, Kimberly- a pale lady with long blond hair- seemed nice. They’d mainly talked today, introducing themselves and chatting between the three of them, setting goals, stuff like that.

… Maybe goal setting and a chat didn’t qualify under ‘working hard’. But she had been practicing using her crutches for longer periods without sitting or taking a break. Practicing wearing her new kafo brace for longer and longer periods. Her main goal, right now, was to be able to wear it for an entire school day.

But it was still hard. Mom could sometimes take her out on a walk. (She didn’t want Cassie to go walking all by herself yet- ‘because what if you fall over and can’t get to your phone? ’) But, often she was busy. Just like Andy.

So sometimes the only practice Cassie could have was pacing along the path in the backyard. Or pacing around in the house. She’d try doing the stretches she’d learnt with Rebecca and Ash, but felt like she was backsliding now.

Sometimes, she wondered if she’d gotten out of rehab too early. If she would ever be good enough at this.

At least I still have the rented wheelchair . Cassie thought to herself, during one of her many laps in the yard. If this really is too hard for me, I can use that.

It was Saturday now.

She wasn’t ready to go back to school yet.

But she couldn't get too caught up in her feelings- she could hear Andy doing something noisy in the shed. It has been a fairly steady presence, distracting her from having consistent thoughts. Until it stopped. And stayed stopped.

Cassie turned back to the shed, looking at it with curiosity. She couldn't see in- due to the curtains Andy had thrown up over the windows. At least, until Andy opened the door and peeked through.

“Psst, hey, Cassie. C’mere.”

She went over, as quickly as she could. Nearly tripping herself on the pavers in the process. Andy rolled her eyes, then whispered to her. “Endo’s pretty much done, just needs to be wired up now. Wanna take a look?”

She nodded, and followed Andy back inside to the much less dusty shed.

(It still reminded her a lot of her Dad. But it didn’t hurt as much now, since Andy had also been making it her own place.)

And she could see the Endo that would become Roxy. Stood up straight, and a little bit taller than her. About five feet, she guessed. Could see the joints, the limbs… most of the important stuff was there.

Except the head. And any way to make this Endo move.

She looked at Andy, who shrugged. “It hasn’t even been a week- gimme a break .”

“I didn’t say anything!” Cassie cried.

Only to get tapped on the nose. “Well yeah, but you were thinking it !”

She shook her hair, and rubbed her nose with her hand. “So, how are you gonna do the rest?” She asked.

Andy groaned, and her shoulders slumped. “I was hoping you wouldn’t ask that… truth is, I don’t know yet.”

Cassie looked at her with confusion. “You don’t know?! ”

How can she ‘not know’? She goes to school for this!

“Yeah, I don’t know!” Andy said, defensively. Putting her hands on her hips. “I’m still learning- if I was ready to be a technician, I’d already be one!”

Cassie pursed her lips, but dropped the topic. Fighting with Andy over this wasn’t going to get Roxy built any faster.

“So… Do you have a plan?”

Andy took her hands off her hips, and seemed to loosen up a bit, from how tense she’d looked (and sounded) before. Blowing a loose curl out of her eyes. “Yeah, I do actually. Wanna come visit The Minch with me?”

For a second, Cassie had to try and remember who ‘The Minch’ even was. Or how that was relevant to anything. It took going back a few years to remember her- ‘The Minch’ owned a small news agency, maybe fifteen minutes from their house, via foot. When she’d been younger, Dad would sometimes take her and Andy for walks to that newsagency after school, and they’d flip through the magazines.

Sometimes.

Sometimes, they’d even have Gregory with them, and…

She shook those thoughts out of her head.

“Wow, you don’t wanna get out of the house? You’ve been pacing like crazy, though-” Andy said, with a cheeky grin.

That snapped Cassie out of it. “No, I do- it’s just- It's been a long time since we went together.”

Andy raised an eyebrow at that, and Cassie blushed with embarrassment. “Ugh! You can go without me-” She whined.

That caused Andy to burst out laughing. And for Cassie to shrink into herself, grumbling under her breath.

“Stupid Andy, you don't get it-”

Eventually, Andy stopped and caught her breath, slamming a heavy hand down on Cassie's shoulder, nearly knocking her over with a squeak - even with the crutches. “Oh come on Cassie- don’t get sulky now. Come with me- they have a robotics magazine that I’m interested in getting. And I know you like getting those Girl Guide mags- they're there, too!”

Cassie stopped grumbling, but still had a sour look on her face. She looked away from Andy.

Except Andy didn’t just accept that. She moved, until her face was right up in Cassie’s face. Wearing a huge grin. “If you really want to , you can stay here… but I think you’d be bored senseless if you stayed behind.”

Cassie pursed her lips, and wanted to keep being stubborn after being embarrassed. Except, as much as she hated to admit it, Andy was right. She’d be bored crazy here, while Mom was away teaching boxing.

“Okay, fine. I’ll go see The Minch.” Cassie said. And Andy rolled her eyes.

“Don’t sound too excited for it!”

________

Cassie hadn’t walked this particular path in what felt like forever . It was the opposite way that Mom would take her, to visit the park a few streets down.

When she’d been younger, she hadn’t realized just how… unhelpful, the paths near her home were.

For example, this path was missing one curb cut. Which wasn’t a huge problem for her… right now. But she’d gotten used to looking for problems, while learning to use her chair back at rehab. And all those problems were in sharp relief now. Not just for problems she’d have while using a chair, but for the problems she was having now.

Like the cracks that she nearly caught her crutches in. The unexpectedly uneven section that nearly tripped her up.

Cassie grit her teeth, and continued. And wished the path was a bit better.

Andy seemed completely oblivious to Cassie’s struggles. Walking ahead, while fiddling around on her phone.

And Andy had such long legs- even while walking distractedly, she was still so fast.

Cassie felt breathless, just trying to keep up with her.

“Hey, Andy- slow down.” She panted out. And that seemed to get her attention. At least, she looked away from her phone.

Andy’s brows furrowed, “Is this too much for you? I can take you back home…”

Cassie shook her head. “No- just slow down! The path’s rough, and hard to walk on.”

Luckily, Andy did walk slower from then on. She’d also put her phone away, and seemed to be paying more attention to what was happening. Even noticing when they got to another cutless curb.

“If you ever come this way in the chair, we may just need to take the road.” Andy said, lips pursed.

“At least it's not a busy road.” Cassie said, trying to look on the bright side.

After a bit longer, they had walked past the fancy houses, to the less fancy houses, then to a little shopping complex that contained a hairdresser, a butcher, a little bakery, and Mrs Minchin’s newsagency. All facing out into the street.

When they got into the newsagency, the first thing Cassie noticed was the blast of air conditioning straight into her face as the door opened. Mrs Minchen must like it cold! She thought, as she shivered.

The owner was managing the checkout, sorting through something at the counter and only looking up when she heard the door chime.

“Oh hello… you look a little familiar…” the old woman said. She had curly gray hair, pale skin with age spots on her face, and some big yellow framed eyeglasses. Just like she’d had years ago. She hasn’t changed a bit.

“Hey, Mrs Minchin. I’m back from college.” Andy said.

Mrs Minchin took a moment to think, looking to the side. Before her eyes went wide and snapped back to Andy. “Oh- I remember you sweetie! And your sister- you're Sam’s little girls!”

Cassie nodded at that, and Andy reintroduced themselves. “I’m Dee, and this is Cassie.”

Cassie waved, as Mrs Minchin smiled back, before changing to a worried-looking face. “It’s been so long- oh I heard all about what happened with poor Sam. I hope he comes back soon, as well as all the people that dreadful company has spirited away! Can’t believe they're still open, or that people still take their kids there-”

Cassie looked away, feeling something weird crawl down her back. And Andy didn’t seem to want to hear this rant either, because she interrupted. “Yeah, thanks Mrs Minchin. Hey, I gotta ask you something…” she walked over to the checkout, and started asking about the magazine she was looking for. And that gave Cassie an opening to explore the store, and see what was available.

She wandered around, fairly absentmindedly. She couldn’t get Mrs Minchin's words out of her head.

Because… she was right. Fazbear Entertainment was a terrible company. But she’d had so much fun at the Pizzaplex as a little kid. Both the one here, and the one that was in Salvador.

So many of her early memories were there. Staff parties that Mom would take her and Andy to, at the towers. Daddy-Daughter days at the Pizzaplex, watching Dad working as a technician… watching Dad play the games on his computer. Going with Mom to see the Freddy and Friends movie in cinemas when she was five…

Yeah. It wasn’t that simple. Not to Cassie, anyway.

She felt so heavy on her feet, now that she wasn’t struggling to keep up with Andy. She could already tell- she’d be wiped by the time she got home. She looked around, for anywhere to sit. But found nothing.

All she could do was look around. If she was lucky, Andy would find her magazine quickly, and they’d be able to head back soon.

To pass the time, Cassie slowly strolled up the magazine racks. She remembered which bay had the teen mags- Mrs Minchin hasn’t changed this place since the nineties, I swear! Everything's so vintage! And before long she found herself in front, looking at the newest edition of girl guide. The november edition- it was out a little early, but she was glad there was a new one.

She was tempted to stand there and read through it, since she could hear Andy still talking to Mrs Minchin. Except her left leg was really starting to ache, from the strain of walking and wearing the kafo.

Yeah, I’ve worn it for a long time already today, huh?

Maybe if she went to the registers and brought it, it’d prompt Andy to hurry up.

So she did, struggling to hold onto it and the crutch in the same hand. She knew it’d be a little crumpled up after this, but still good enough to read.

Once she put it near the register, Andy and Mrs Minchin stopped talking, and turned to look at her. She looked away, leaned on her crutches and fished her purse out of her pocket. “Could I get this one today, please?”

“Of course you can, sweetie!” Mrs Minchin said, with a smile on her voice. When Cassie looked up, she had one in her eyes too. “It’s fifteen dollars, today.”

It’d gone up a bit in price, by the looks of it. Luckily, she still had that kind of pocket money. Though, it was quickly vanishing- down from about one hundred and forty dollars, to about eighty-five dollars now. Plus some change. Cassie winced- even with the birthday money, it’d taken me forever to save that much…

She paid the fifteen dollars to Mrs Minchin, and heard her ring up the till. Then she was handed the magazine, and a receipt.

“You're quick.” Andy said. “I’ve still got to find the one I’m looking for. And I’d like to catch up with Mrs Minchin a bit more. Are you okay waiting for a bit longer?”

Internally, she wanted to scream.

“I can wait, for a little bit.” she said instead.

Mrs Minchin looked over her, before speaking again. “Do you want a chair, sweetie?” She stood up- Cassie thought she’d already been standing- but clearly not. Then she brought around the stool she’d been sitting on. “I don’t need it dear, not nearly as much as you do.” She said, with pity in her eyes.

It made Cassie feel weird accepting the offer. “I, um, okay. Thank you?”

Mrs Minchin hummed happily, as she helped Cassie up onto the tall stool. It felt a bit wobbly, and with how tall it was, Cassie was really regretting accepting her offer.

At least it was set against the counter, so Cassie could hold onto the edges of the counter and lean backwards against it a bit. She unlocked the knee joint of her brace, once she was up securely enough.

While she sat there uncomfortably, Andy and Mrs Minchin resumed their conversation- “You’re doing a good thing for your family, Dee. You should be proud of yourself!”

Oh, they weren't talking about the magazine… Cassie thought. Yeah, this wasn’t helping her feel any more comfortable. And Andy seemed to recognise that, from the uncomfortable look on her face. “Yeah, guess so. How’s the grandkids going?”

“Oh, they're quite good, quite good.” Mrs Minchin said, before delving into a long story, about how her youngest grandkid, Sully? (Cassie hadn’t caught the name properly.) Had started preschool, and gotten sent home on his first day because he’d wandered away from preschool and found his way over to the beach and…

She’d stopped paying attention at that point, grabbed her magazine off the counter, and flipped it open. There was some good ideas inside, for halloween costumes that you could wear with your friends. Like, different themes for a whole group, from as small as two people, to as big as ten people. Cassie dogeared the pages with ideas she liked, and flicked through to the advice columns.

The columns were addressed to Helpful Hetty, and usually just had repetitive letters about makeup, and clothes tips that could’ve been answered quicker by the asker looking it up online.

But sometimes there was something real in there. Like today.

'Dear Helpful Hetty.

'I’ve had to move away from a good friend of mine, and our friendship didn’t survive the move. They just slowly stopped responding to me. I think they’ve moved on to new friends.

'I’m sending this because I’m having a hard time making new friends where I live now. And every time I’m lonely, I’ll text them. And they don’t even read them anymore.

'How do I stop myself from texting them, and how should I make new friends?

'Thank you for listening- Daisy'

That got Cassie’s attention. Because maybe that would help her out a bit. She was glad she’d made friends with Lawrence, but having a few more friends (and forgetting Gregory) would be helpful for her.

'Hello Daisy.

'It’s always tough moving to a new place! And losing a friend is also difficult. I can see why this is upsetting!

'One thing you could try, for when you want to message them, is to write down what you want to tell them then delete it. You could also try keeping a diary.

'For making new friends, the best way is to find a common connection. Like a shared hobby, or something you both like. Have you tried joining any clubs at school? Or participating in any sports teams?

'Worst comes to worst, you could always talk to a guidance counselor at school. Or to your parents.

'Good luck- I’m sure you’ll meet some great new friends soon!'

That helped fill Cassie with a little bit of hope. If she couldn’t make friends at school, maybe she could make them elsewhere?

She made sure to dogear that page too, before being distracted by Mrs Minchin calling her name. She looked up, and Andy wasn’t there- she was now up the other end of the store, browsing the magazine racks.

But Mrs Minchin? Was right there with Cassie. “Sweetie, I have to ask- what happened to you?”

Cassie wasn’t sure where to start. “Umm, well-” Mrs Minchin looked at her, with soft, pitying eyes. And a smile framed by countless wrinkles.

“Oh, you don’t need to be shy, sweetie. You can tell me.”

She had to swallow a lump in her throat, before she could answer. “I got into an accident. I fell, um, it’s no big deal…”

The pages of the magazine crumpled under her fingers. And Mrs Minchin raised an eyebrow at that statement.

“ ‘No big deal? ’ Sweetie, you’re in two different braces, and on crutches. That looks like a ‘big deal’ to me.”

Cassie could feel the page starting to rip under her fingers. Before she peeled them away. Before she ruined her nice new magazine.

On her part, Mrs Minchin softened her face again, and sighed. “Cassie. I know your Dad would be proud of you.”

Now her feelings were caught in a weird loop, that she could barely understand. Hopefulness? Anxiety? She couldn’t quite put a pin in it.

“He would?”

“Of course. He would think you were very brave. Not many people could keep a smile on their face, while dealing with such difficulties.” She said, before gesturing to Cassie’s whole body with her hand.

…

Luckily for Cassie, Andy came back from the racks before she needed to reply to Mrs Minchin. She came back with a magazine about robotics, waving it in the air and speaking up to get Mrs Minchin’s attention- Hey, Mrs Minchin? Can I get this one please?”

“Oh, of course Dee. I’ll ring you up right away!” She said, completely forgetting about Cassie, and bustling back behind the counter.

Good.

As Andy paid for her magazine, Cassie locked her leg brace, and carefully scooted off the stool. Then grabbed her crutches back. She gave her magazine to Andy once they were leaving because she wasn’t sure she’d be able to carry it all the way home without dropping it.

On the walk back home, one thought that kept interrupting the aches and pains was, is everyone going to be weird like that about me?

________

The next day, Andy decided to go check out the three-d printers at the library.

And because it was Cassie’s last day of ‘Fall Break’, she’d be ‘back’ at school tomorrow.

She… wasn’t looking forward to it. There were some things that could go well. (Like talking to Lawrence, maybe talking to Setia, and thanking them for the notes.) But also a lot of things that could go wrong. Awfully wrong.

Luckily for her, Andy had given some mercy, and taken her to the library today in her car.

When they got there, the first thing Cassie noticed was how annoying it was to move around inside.

The library had a thick, plush carpet periodically throughout the first floor. And it looked lovely- Perfect for sitting on, while reading a good book. Not perfect for pushing a wheelchair over. Especially considering she still didn’t have all her core strength back. And especially considering the aches she still had from her walk yesterday.

When Cassie got stuck in the middle of the carpet, she was able to call Andy over to help her out, at least. And, there weren't a lot of people at the library to notice her having difficulties.

Cassie had been to a library before- in La Verkin. This was her first time going to the Hurricane library, and despite the carpet-mishap, it did look really cool. Kind of industrial-style along the walls, with just enough soft, and personable things (like that carpet, plus bookshelves and seating) to not look like a warehouse. She could also see a room with computers in it, over against the far wall.

When Cassie looked up, she could see a set of metal stairs leading up, to a big second floor loft.

She hoped that the three-d printers Andy was planning on using weren’t up there.

Andy pushed her, until they got onto some wooden flooring. They were now near a room that had ‘Makerspace’ labeled on the door.

“Think this is the place- wanna come in with me?” Andy asked, walking ahead of her to open the door.

Cassie nodded, and followed her inside.

In this room were some machines she could only think were the three-d printers. There were also other machines- like sewing and embroidery machines over by the far window. And some chairs nearby, nice chairs. Soft chairs, that would be good to sit in.

There weren’t many people in here- just Cassie, Andy, and a strange lady running one of the three-d printers. A tall lady, with pale skin and long, brown hair hanging loosely over her shoulders.

She could be a model- what's someone like her doing here?

Cassie didn't get a chance to ask- she followed Andy over to a nearby machine.

Cassie had no idea how a three-d printer worked. Luckily, Andy did. She used her library card to get into the attached computer, and got the files from a thumb drive she’d brought with her.

It looked like she was performing magic, to Cassie. She really didn’t understand how this all worked. Though, Andy tried to explain it:

“So, you know the filament I brought in? Basically, it gets melted down, and used to create the thing in the printer using addition.”

Really, watching it through the little window in the front showed Cassie much better how this all worked. Layer by layer, the piece built up.

But she still couldn’t tell what it was.

“What piece are you making?” She asked.

Andy answered her- “Nothing major, just part of Roxy's forearm.” Cassie could faintly see the brown haired lady on the nearby machine turn to them, out of the corner of her eye. “If this piece fits well, then I know I’ve scaled it well, to her endo.” Andy said, while focusing on the output.

As time went by, it slowly started to look like something Roxy could have on her forearm.

Though, Cassie noticed something. The three-d printer was only so big. And she remembered-some of the pieces on the Glamrock's had been huge by comparison- like leg pieces, their torso plating…

“Hey Andy?” Cassie asked, and Andy turned towards her. “How are some of the bigger pieces going to work? Torso pieces won’t fit in the printer.” Cassie explained herself.

Andy shrugged off the question. “One step ahead of you, Cassie. I divided up the bigger pieces, so they can be printed section by section.” She brought up the model and showed off her work on the models, zooming in a bit. Cassie could see it now- the seams where Roxy's chest plate was split into pieces, for example. “When all the little bits are printed, I’ll stick them back together, then sand off any excess. Whether that be filament, or glue.”

Cassie was so glad she’d asked Andy for help with this. It would’ve taken her years of research to learn how to fix up Roxy by herself.

…At least, without her Dad there to guide her.

That got Cassie wondering- about that email she’d sent off a few weeks ago. If anyone had responded to it. Best case- she could have her account back. Worst case- they may have at least sent through copies of her messages.

She had a library card, even if it was for the one in La Verkin. And there were computers on the ground floor- she’d seen them on the way through earlier.

She could probably get away with sneaking off to the computers…

But then again- why sneak? Andy was already helping her with something Mom wouldn’t like to see.

She probably wouldn’t tattle on her.

“Andy?”

She saw Andy shake her head, before turning to her. “Yes, my littlest distraction?”

The only retort Cassie had to that was to stick out her tongue. Andy flicked her ear, with a chuckle.

“I know Mom doesn't want me to go on the computer at home… but can I go on the computer here, please?” Cassie whispered. “I need to check my emails, and it’ll be hard to do that with Mom looking over my shoulder.”

Andy pursed her lips, and Cassie could tell from the way their corners lifted that Andy was going to be difficult on purpose. “Well… I could let you do that… but what if Mom finds out! I could get into a lot of trouble because of you…”

“Puh- lease, Andy! I’ll be your butler for a week- I’ll open car doors for you, and serve you meals on a golden plate!” Cassie jokingly begged. Grabbing hold of Andy’s arm, to try and sell the desperation.

Andy's grin got wider. “That’s a decent offer, but I think you can do better than that… and where are you getting golden plates from, anyway?”

That forced Cassie to think for a moment. “From the golden goose, duh !”

She could hear the nearby lady stifle some giggles, before Andy responded. “You planning on making sense today, smartie?”

“Of course- what do golden geese lay?” Cassie asked.

Andy looked at her like she was silly. “Golden eggs, duh !” She said, returning Cassie’s tone.

She took it in stride. “Well, what happens when you heat up a golden egg?”

At that point, the lady chimed in. “It explodes in the microwave?” She asked in a quiet voice, before clamping her mouth shut, and looking back at her machine.

Andy didn’t seem to register her discomfort. “Yeah true, mystery lady.” Before she looked back at Cassie. “What are you getting at?”

“No, no- not in the microwave. In a pan. What happens if you heat up a golden egg in a pan?!”

“... You end up with a fried egg?”

“No! You don’t crack it, you- you end up with a golden plate! When you heat up metal- it goes flat like a plate.” Cassie explained. Even trying gesturing a flat plate with her hands. She didn’t feel like it was funny anymore, and felt a little ticked off no one had got it.

Mystery lady didn’t get the memo- she burst out laughing.

And Andy didn’t seem to get it, either. “Home Ec taught you nothing , clearly.” Cassie flushed with embarrassment, as Andy continued, and shook off Cassie’s hold on her arm. “Y'know what? I don’t think I need your services, miss butler. I bet you don’t even have a golden goose!”

Cassie swore at her, under her breath. Causing Andy to laugh, and shove her shoulder.

“ Okay- so I can’t do anything for you. Can I please go check my emails anyway?” Cassie begged, no longer jokingly.

Now that she was desperate, at least, Andy seemed to take her seriously. “Yeah, go for it. Don’t do anything silly, okay?”

Cassie nodded, and made her way back out of the makerspace. Then, she had to look around to figure out where to go.

Luckily for her, she spotted the computer lab nearby- and if she went around the edges, she wouldn't need to cross the carpet again.

Even luckier for her, her library card did work with the computer.

So she was able to get into her emails- and found that someone from the FazFans moderation team had got back to her.

“Hello Cassie.

“We apologize for the confusion. Due to messages sent out from your account, your account has been permanently banned.

“We’ve been informed of the situation from the police, and know it wasn’t you who sent those messages. But your account was compromised, and we couldn’t leave it active.

“Because of the circumstances, we cannot unban your account.

“However, you could make a new account with a different email, if you want.

“Sincerely, the Moderation team.”

Cassie couldn’t believe it. At all.

Messages from my account? And they weren’t from me?

What is that about?

But she could remember a snippet, from the news report nearly two months ago. About Janet Faraday.

‘Hacked communications through an old fan forum.’

How Mom had talked about other people being tricked, or nearly tricked. Just like her.

‘... it was a miracle that the little boy's dad was home, and heard his own doppelganger trying to lure his son first…’

…

Her heart plummeted down into her feet. She had no proof of why her account had been hacked. But she had a sneaking suspicion that her account was used for the same reason Gregory’s old account had been used.

To lure people in for the monster. Similar to the Candy Cadet’s story.

A shiver rolled down her back. And despite the sounds around her- of people walking past the computer lab, the sound of people talking to each other in the distance- she felt completely alone.

Still.

She wanted to try and get her messages to and from her Dad back. And maybe get it confirmed, that that was what her account had been used for.

So she started typing a reply.

“Hello. Thank you for your reply.

“I know I can’t be unbanned. And I understand why.

“But can I ask a favor? I used to send and receive messages from my dad, he had the username Bonnie_Bunny_1989. Could I have copies of those messages sent to me, if they still exist?”

She wasn’t sure how to explain why she wanted those messages. It was so personal. And complicated- how do I explain that my father went missing, after a staff meeting where most of his coworkers also went missing?

Instead, she just kept it simple- “ My Dad isn’t around anymore, so I want to keep some memories of him, if possible.

“Thank you very much for helping me.”

She checked it over, to make sure it made sense. Then she sent it.

Now, she didn’t know what to do. It would probably take them some time to get back to her.

All she could really do now was go back to Andy, and see how the three-d printing was going.

________

When she got back, Andy and the lady were in an animated conversation, while waiting for their pieces to be made. Andy had the piece she had been printing beside the machine- and what looked like another on the way.

“-So wait. If you don’t even like robotics, why are you studying it?” The mysterious lady asked Andy. She had a faint smile on her face, and tired looking eyes.

Andy barked out a laugh, before answering. “Money, that’s why- this industry is only gonna get bigger and bigger every year. I’ll be able to retire and have kids before I’m forty!”

For that, the lady gave a polite laugh. And she must have noticed Cassie, because she pointed her out to Andy. “I remember seeing her before- who’s this little goose?”

“ Hey!”

That really got Andy’s attention. “This little goose is my sister, Cassie. She’s okay, sometimes.”

Cassie scoffed, and wheeled closer to them both. “I’m okay all the time, thank you.” She said, before really thinking about it. “Actually- no! I’m better than okay- I’m the best sister you have!”

Andy shrugged at that. “Well yeah, you’re my only sister.”

All Cassie could do was grumble under her breath about that, while Andy reached out and messed up her hair. “Quit it!”

Thankfully, she did ‘quit it’. Though Cassie could see the other lady giggling while trying to hide it behind her hand.

Andy turned to see what Cassie was looking at now. “Yeah, I should introduce you. This is Rainbows. She’s working on some babytronic thing?”

Rainbows shook her head. And Cassie could see why she’d gotten such a name- she had multiple colors, almost like a rainbow, streaking through her long, brown hair. “ Buddytronic- thing. And hey, my name’s Ruth actually.” Rainbo- Ruth said, stretching out her hand towards Cassie, to shake.

Cassie grabbed it. Ruth held her hand properly to shake- not too loose, and not too tight. She seemed nice, especially now she wasn’t acting so shy.

“Hello, I’m Cassie. It’s nice to meet you.”

Ruth burst into a warm smile. “It’s nice to meet you too, Cassie!” She knelt down a bit- being so much taller than Cassie- and pretend-whispered towards her ear- loud enough for Andy to hear. “I've heard some good things about you from your sister. Like that you’re going to be helping her with remaking the Roxy animatronic.”

Cassie nodded, and wondered exactly how much Andy had given away- if she’d told Ruth about going to the pizzaplex, or anything like that.

That concern was assuaged when Ruth stood back up, and continued. “It’s a really cool idea to make bootlegs, honestly. Considering how the mega Pizzaplex has closed down here- and the prices to hire one of those funtime services… yeah. Make a bootleg, and you could seriously save money. Even if you only use it for a few birthdays.” She sighed, and ran her hand through some of her loose hair. “I’m guessing Roxy’s your favorite?”

“Yeah, she is. She’s been my favorite since she was first made.” Cassie said, before launching into some questions of her own- “Do you have a favorite? Is that why you’re here, too?”

Ruth laughed lightly at that, and took a second to answer. “I won’t lie- I’m not big on these things myself.” Cassie felt a twinge of disappointment, hearing that. “But my little brother is- big fan of Freddy, especially the Glamrock one. Poor guy- when that weird glitch happened, his heart was broken. He couldn’t believe the Pizzaplex was open, but none of the performers were there anymore. Especially Freddy. He never got to see another performance before the earthquake hit, either.”

Cassie could remember hearing about it. None of the Glamrock animatronics were available for photo ops, or performances, since the ninth of March earlier this year. Fazent had put out a notice, saying that there’d been a system wide glitch. And that this glitch was being worked out- ‘ but our loyal customers can still come in and enjoy the food and games ’.

(She’d gone once. The place felt so… strange, without the Glamrocks milling around. And with Magician Bot and Sun repurposed to perform from the main stage.)

“... So yeah. That’s why I’m working with this thing.” Ruth said, patting the three-d printer, while it whirred and worked on creating something. “If he can’t go see Freddy, Freddy can come see him.

Cassie rolled up, and got a closer look inside Ruth’s machine- inside was what looked similar to Andy’s idea- plating, for an animatronic. More specifically, what looked like part of a leg? Except much smaller than Cassie would’ve expected.

Except… when she thought about it more, hadn’t Ruth mentioned buddytronics earlier?

“This is for a Buddytronic? But those don’t have plating- don’t they have a different cover on them? And aren’t they super expensive?”

This being for a buddytronic really puzzled Cassie- she could remember the craze those toys caused, a few years back. Mostly, for kids whose parents would buy them. Like Yvonne. (And unlike Cassie- Mom’s dislike of ‘gadgets’ extended to these mechanical plushies as well. Despite her pleas to get a Roxy buddytronic- Mom had tided her over by buying her a new regular plushie instead.)

Even though she never owned one, she knew a bit about them from playing with Yvonne’s when they were friends. And from her own research, while curious. Mainly that they were hard to get. Even secondhand, they commanded high prices. And they didn’t use the faceplating like the glamrock band used- they used fur, or a skin like substance, to cover their endo’s.

Considering all of that… it didn’t make much sense for Ruth to be making parts here.

… Unless she was making a whole new outer layer? Maybe it’d been really damaged, and only had the endo left?

When Ruth spoke up, she confirmed it. “Yeah, these things cost an arm and a leg. Fortunately, I know some people who had a really messed up one in their scrapyard. It’d been mangled, and lost parts. I figured it’d be easier to remake the cover for the endo using this, than trying to get the material. Plus, it’d be more authentic to Glamrock Freddy this way.”

Cassie nodded, and looked at the piece through the printer’s window again. She understood now.

“Hey, if your little bro’s around at all, did he want to come meet Cassie?” Andy asked Ruth. “They’re both dweebs, by the sound of it. They’d probably get along like a house on fire.”

Cassie swung around to protest- “I am not a dweeb!” all while Andy laughed at her.

Before she could complain further, Ruth spoke up, looking a bit uncomfortable- “Yeah, sorry. He’s busy at home right now. Maybe another day?”

That made sense, to Cassie. They’d only just met, really. And considering how shy she'd been earlier, her brother may not make friends easily either.

“No sweat. You’ve got my number, so if you change your mind, give me a yell, okay?” Andy said, before checking with the printer again.

Cassie turned to look at it too- it was just finishing up, now. A piece that looked like another part of Roxy’s forearm. It beeped to indicate it was done, just a few seconds later. And Andy let Cassie grab it out of the machine.

It still felt warm to the touch, and surprisingly rough. She understood why Andy had mentioned ‘sanding’ it now.

Andy grabbed out her phone, and checked it. Before saying, while quickly scooping up her belongings “Hey, we need to head back shortly. I’m out of filament. And Mom will want us back soon for lunch, anyway.”

Mom liked making a ‘sit-down, no phones’ kind of lunch every Sunday, after she got back from church. When Cassie checked her phone, it was nearly eleven-thirty. She’d want them both back by twelve, at the latest. So Cassie got the urgency in Andy’s voice (and chided herself for not keeping a better track of time.)

“I’ll see you both next time, then. I’m here pretty often.” Ruth said, giving them both a wave.

“We’re hoping to be here pretty often too, right Andy?” Cassie said.

Andy gave her a cheeky grin, but did confirm it. “Yeah. We’ll catch you next time, Rainbows.” She said, before passing Cassie the other part, and pocketing her usb stick. She turned, while giving a quick wave to Ruth, then quickly strolled away. Nearly leaving Cassie behind.

“Hey- Andy! Don’t leave me with this!” The pieces weren’t that big, but they were awkward. And difficult to keep in her lap while pushing the chair. So she was grounded, until Andy turned around and realized the problem.

“Oops, sorry Cassie.” She came back for the forearm pieces, leaving Cassie’s arms free for the push rims.

She could hear Ruth saying a quick ‘goodbye!’, as Cassie followed Andy out of the makerspace, and out to the car.

At least, until she got bogged on the carpet. Again.

Authors note- the song title is from Amy Sharks Worst Day of My Life. And man- I'm late crossposting!

#fnaf fanfic #fnaf cassie #cassie fnaf #five nights at freddy's #is it really a birthday party fanfic #summerly writes

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spacetimewithstuartgary · 9 months ago

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NJIT launches AI-powered solar eruption center with $5M NASA grant

A new center at New Jersey Institute of Technology (NJIT) will advance AI-driven forecasting of violent eruptions on the Sun, as well as expand space science education programs.

NJIT's Institute for Space Weather Sciences (ISWS) has been awarded a $5 million NASA grant to open a new research center dedicated to developing the next generation of solar eruption prediction capabilities, powered by artificial intelligence.

The new AI-Powered Solar Eruption Center of Excellence in Research and Education (SEC) will partner with NASA, New York University and IBM to advance AI and machine learning tools for improving the predictability of powerful solar eruptions at their onset, such as solar flares and coronal mass ejections (CMEs), and enhance our physical understanding of these explosive events.

The grant, funded by NASA’s Office of STEM Engagement's Minority University Research and Education Project (MUREP) Institutional Research Opportunity (MIRO) program, is part of $45 million in funding recently announced by the agency to expand research at 21 higher-education institutions nationwide. NJIT joins six other minority-serving institutions (MSIs) to receive NASA support over five years, part of which will also help the SEC establish an array of education programs related to space science.

“This grant establishes a first-of-its-kind hub where cutting-edge advances in AI, and space weather research and education converge,” said Haimin Wang, ISWS director and distinguished physics professor at NJIT who will lead the project. “By harnessing AI-enabled tools to investigate the fundamental nature of space weather, we aim to significantly enhance our ability to interpret observational data from the Sun to forecast major solar eruptions accurately and in near real-time, a capability beyond our reach up to this point.”

“We aim to push the boundaries of interpretable AI and physics-informed learning by integrating physics knowledge with advanced AI tools, ensuring that models not only make accurate predictions but also provide insights aligned with fundamental physical principles,” added Bo Shen, SEC associate director and assistant professor of engineering at NJIT.

Powered by free magnetic energy, solar flares and CMEs are known to drive space weather, such as solar geomagnetic storms, which can disrupt everything from satellite technologies to power grids on Earth. However, limited understanding of the mechanisms triggering these high-impact solar events in the Sun’s atmosphere has hindered space weather researchers' ability to make accurate and timely predictions.

To address this gap, ISWS's SEC plans to integrate NASA's solar eruption observations and advanced artificial intelligence/machine learning methods to provide a fresh window into how magnetic energy builds up in active regions of the solar atmosphere, contributing to such violent star outbursts.

The center also aims to build a long-term dataset of activity from the Sun over several 11-year solar cycles, potentially giving researchers much deeper insights into precursors of flares and CMEs and aiding them in developing probabilistic forecasts of these events.

“A major hurdle in understanding solar eruption mechanisms is the limited data on large events like X-class flares,” Wang explained. “Building a large, homogeneous dataset of solar activity using advanced machine learning methods allows us to study these major events with unprecedented resolution and cadence, ultimately revealing eruption mechanisms and unlocking better space weather predictions.”

Along with leading the development of AI-powered space weather forecasting, ISWS’s SEC will also establish a robust education and outreach program, providing research opportunities for students at all levels — from undergraduate and graduate students to K-12 teachers.

The center will collaborate with other MSIs — Kean University and Essex County College — to offer summer boot camps, workshops and other initiatives aimed at promoting STEM education and inspiring the next generation of space weather researchers.

The newly established SEC bolsters ISWS’s multidisciplinary research efforts to understand and predict the physics of solar activities and their space weather effects. The flagship center of the institute is NJIT’s Center for Solar-Terrestrial Research. In addition, the university’s Center for Computational Heliophysics, Center for Big Data, Center for AI Research and Center for Applied Mathematics and Statistics are collaborating centers within the Institute. ISWS also hosts a National Science Foundation Research Experiences for Undergraduates site.

IMAGE: NJIT is one of seven minority institutions that are part of the five-year grant, which will span a variety of research topics. Credit NJIT

#science #space #astronomy #physics #news #nasa #astrophysics

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ashishkumarletslearn · 1 year ago

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Training Teachers to Teach Class 12 Maths — MathYug

In the ever-evolving landscape of education, having access to high-quality learning resources can make a world of difference. For Class 12 students, mastering mathematics is crucial, not only for academic success but also for future endeavors in various fields. At MathYug, we understand this importance and have dedicated ourselves to providing top-tier educational materials. Today, we’re excited to introduce our training program designed specifically for teachers who aspire to elevate their teaching skills and make a significant impact on their students’ learning journey.

Sample Videos: A Glimpse into Quality Education

To give you a taste of what MathYug has to offer, we are sharing a selection of sample videos from our Class 12 Maths tutorials. These videos exemplify the high-quality, in-depth teaching style that Ashish Kumar, affectionately known as Agam Sir, is known for.

Relations and Functions

youtube

2. One to One and Onto Functions

youtube

3. Inverse Trigonometric Functions

youtube

4. Continuity and Differentiability

youtube

5. Applications of Derivatives

youtube

6. Integrals

youtube

7. Inverse Trigonometric Functions

youtube

8. Basics of Logarithms, Log Table and Antilog Table

youtube

Why Choose MathYug for Teacher Training?

MathYug stands out as a premier platform for learning and teaching mathematics, especially for Class 12 students. Our resources are meticulously designed to cater to the diverse needs of both learners and educators, ensuring that each student can grasp complex mathematical concepts with ease. Here’s why MathYug is the best choice for your teaching journey:

Expert Guidance

All our content is created by Ashish Kumar (Agam Sir), a renowned educator with years of experience in teaching mathematics. His unique teaching style simplifies complex topics, making them easily understandable. By training with MathYug, teachers can learn how to effectively convey these methods to their students.

Comprehensive Coverage

Our tutorials cover the entire Class 12 Maths syllabus, aligned with the NCERT guidelines. This ensures that students are well-prepared for their board exams and other competitive exams. Teachers trained with MathYug will be equipped to offer their students a thorough and well-rounded mathematical education.

Interactive Learning

We believe in making learning engaging and interactive. Our video lessons are complemented by practical exercises, assignments, and downloadable PDFs to reinforce learning. Teachers will be trained to use these resources to create a dynamic and interactive classroom environment.

Elevate Your Teaching Experience with MathYug

At MathYug, we are committed to providing the best possible educational resources to help both students and teachers excel in mathematics. Our Class 12 Maths tutorials are designed to build confidence, enhance understanding, and foster a love for learning. By sharing these sample videos, we hope to give teachers a glimpse of the quality education that awaits them on our platform.

Join MathYug Today!

Experience the difference with MathYug and take your teaching skills to new heights. Subscribe to our Class 12 Maths membership and gain access to a comprehensive collection of video lessons, study materials, and expert guidance from Ashish Sir. Let’s embark on this journey of academic excellence together!

Visit MathYug now and start your journey towards mastering Class 12 Maths with ease and confidence.

#class 12 maths #teachers #educators #teacher training #mathyug #class 12 maths tutorials #class 12 maths videos #class 12 maths solutions #Youtube

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aibyrdidini · 1 year ago

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UNLOCKING THE POWER OF AI WITH EASYLIBPAL 2/2

EXPANDED COMPONENTS AND DETAILS OF EASYLIBPAL:

1. Easylibpal Class: The core component of the library, responsible for handling algorithm selection, model fitting, and prediction generation

2. Algorithm Selection and Support:

Supports classic AI algorithms such as Linear Regression, Logistic Regression, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (K-NN).

and

- Decision Trees

- Random Forest

- AdaBoost

- Gradient Boosting

3. Integration with Popular Libraries: Seamless integration with essential Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn for enhanced functionality.

4. Data Handling:

- DataLoader class for importing and preprocessing data from various formats (CSV, JSON, SQL databases).

- DataTransformer class for feature scaling, normalization, and encoding categorical variables.

- Includes functions for loading and preprocessing datasets to prepare them for training and testing.

- `FeatureSelector` class: Provides methods for feature selection and dimensionality reduction.

5. Model Evaluation:

- Evaluator class to assess model performance using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.

- Methods for generating confusion matrices and classification reports.

6. Model Training: Contains methods for fitting the selected algorithm with the training data.

- `fit` method: Trains the selected algorithm on the provided training data.

7. Prediction Generation: Allows users to make predictions using the trained model on new data.

- `predict` method: Makes predictions using the trained model on new data.

- `predict_proba` method: Returns the predicted probabilities for classification tasks.

8. Model Evaluation:

- `Evaluator` class: Assesses model performance using various metrics (e.g., accuracy, precision, recall, F1-score, ROC-AUC).

- `cross_validate` method: Performs cross-validation to evaluate the model's performance.

- `confusion_matrix` method: Generates a confusion matrix for classification tasks.

- `classification_report` method: Provides a detailed classification report.

9. Hyperparameter Tuning:

- Tuner class that uses techniques likes Grid Search and Random Search for hyperparameter optimization.

10. Visualization:

- Integration with Matplotlib and Seaborn for generating plots to analyze model performance and data characteristics.

- Visualization support: Enables users to visualize data, model performance, and predictions using plotting functionalities.

- `Visualizer` class: Integrates with Matplotlib and Seaborn to generate plots for model performance analysis and data visualization.

- `plot_confusion_matrix` method: Visualizes the confusion matrix.

- `plot_roc_curve` method: Plots the Receiver Operating Characteristic (ROC) curve.

- `plot_feature_importance` method: Visualizes feature importance for applicable algorithms.

11. Utility Functions:

- Functions for saving and loading trained models.

- Logging functionalities to track the model training and prediction processes.

- `save_model` method: Saves the trained model to a file.

- `load_model` method: Loads a previously trained model from a file.

- `set_logger` method: Configures logging functionality for tracking model training and prediction processes.

12. User-Friendly Interface: Provides a simplified and intuitive interface for users to interact with and apply classic AI algorithms without extensive knowledge or configuration.

13.. Error Handling: Incorporates mechanisms to handle invalid inputs, errors during training, and other potential issues during algorithm usage.

- Custom exception classes for handling specific errors and providing informative error messages to users.

14. Documentation: Comprehensive documentation to guide users on how to use Easylibpal effectively and efficiently

- Comprehensive documentation explaining the usage and functionality of each component.

- Example scripts demonstrating how to use Easylibpal for various AI tasks and datasets.

15. Testing Suite:

- Unit tests for each component to ensure code reliability and maintainability.

- Integration tests to verify the smooth interaction between different components.

IMPLEMENTATION EXAMPLE WITH ADDITIONAL FEATURES:

Here is an example of how the expanded Easylibpal library could be structured and used:

```python

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from easylibpal import Easylibpal, DataLoader, Evaluator, Tuner

# Example DataLoader

class DataLoader:

def load_data(self, filepath, file_type='csv'):

if file_type == 'csv':

return pd.read_csv(filepath)

else:

raise ValueError("Unsupported file type provided.")

# Example Evaluator

class Evaluator:

def evaluate(self, model, X_test, y_test):

predictions = model.predict(X_test)

accuracy = np.mean(predictions == y_test)

return {'accuracy': accuracy}

# Example usage of Easylibpal with DataLoader and Evaluator

if __name__ == "__main__":

# Load and prepare the data

data_loader = DataLoader()

data = data_loader.load_data('path/to/your/data.csv')

X = data.iloc[:, :-1]

y = data.iloc[:, -1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale features

scaler = StandardScaler()

X_train_scaled = scaler.fit_transform(X_train)

X_test_scaled = scaler.transform(X_test)

# Initialize Easylibpal with the desired algorithm

model = Easylibpal('Random Forest')

model.fit(X_train_scaled, y_train)

# Evaluate the model

evaluator = Evaluator()

results = evaluator.evaluate(model, X_test_scaled, y_test)

print(f"Model Accuracy: {results['accuracy']}")

# Optional: Use Tuner for hyperparameter optimization

tuner = Tuner(model, param_grid={'n_estimators': [100, 200], 'max_depth': [10, 20, 30]})

best_params = tuner.optimize(X_train_scaled, y_train)

print(f"Best Parameters: {best_params}")

```

This example demonstrates the structured approach to using Easylibpal with enhanced data handling, model evaluation, and optional hyperparameter tuning. The library empowers users to handle real-world datasets, apply various machine learning algorithms, and evaluate their performance with ease, making it an invaluable tool for developers and data scientists aiming to implement AI solutions efficiently.

Easylibpal is dedicated to making the latest AI technology accessible to everyone, regardless of their background or expertise. Our platform simplifies the process of selecting and implementing classic AI algorithms, enabling users across various industries to harness the power of artificial intelligence with ease. By democratizing access to AI, we aim to accelerate innovation and empower users to achieve their goals with confidence. Easylibpal's approach involves a democratization framework that reduces entry barriers, lowers the cost of building AI solutions, and speeds up the adoption of AI in both academic and business settings.

Below are examples showcasing how each main component of the Easylibpal library could be implemented and used in practice to provide a user-friendly interface for utilizing classic AI algorithms.

1. Core Components

Easylibpal Class Example:

```python

class Easylibpal:

def __init__(self, algorithm):

self.algorithm = algorithm

self.model = None

def fit(self, X, y):

# Simplified example: Instantiate and train a model based on the selected algorithm

if self.algorithm == 'Linear Regression':

from sklearn.linear_model import LinearRegression

self.model = LinearRegression()

elif self.algorithm == 'Random Forest':

from sklearn.ensemble import RandomForestClassifier

self.model = RandomForestClassifier()

self.model.fit(X, y)

def predict(self, X):

return self.model.predict(X)

```

2. Data Handling

DataLoader Class Example:

```python

class DataLoader:

def load_data(self, filepath, file_type='csv'):

if file_type == 'csv':

import pandas as pd

return pd.read_csv(filepath)

else:

raise ValueError("Unsupported file type provided.")

```

3. Model Evaluation

Evaluator Class Example:

```python

from sklearn.metrics import accuracy_score, classification_report

class Evaluator:

def evaluate(self, model, X_test, y_test):

predictions = model.predict(X_test)

accuracy = accuracy_score(y_test, predictions)

report = classification_report(y_test, predictions)

return {'accuracy': accuracy, 'report': report}

```

4. Hyperparameter Tuning

Tuner Class Example:

```python

from sklearn.model_selection import GridSearchCV

class Tuner:

def __init__(self, model, param_grid):

self.model = model

self.param_grid = param_grid

def optimize(self, X, y):

grid_search = GridSearchCV(self.model, self.param_grid, cv=5)

grid_search.fit(X, y)

return grid_search.best_params_

```

5. Visualization

Visualizer Class Example:

```python

import matplotlib.pyplot as plt

class Visualizer:

def plot_confusion_matrix(self, cm, classes, normalize=False, title='Confusion matrix'):

plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)

plt.title(title)

plt.colorbar()

tick_marks = np.arange(len(classes))

plt.xticks(tick_marks, classes, rotation=45)

plt.yticks(tick_marks, classes)

plt.ylabel('True label')

plt.xlabel('Predicted label')

plt.show()

```

6. Utility Functions

Save and Load Model Example:

```python

import joblib

def save_model(model, filename):

joblib.dump(model, filename)

def load_model(filename):

return joblib.load(filename)

```

7. Example Usage Script

Using Easylibpal in a Script:

```python

# Assuming Easylibpal and other classes have been imported

data_loader = DataLoader()

data = data_loader.load_data('data.csv')

X = data.drop('Target', axis=1)

y = data['Target']

model = Easylibpal('Random Forest')

model.fit(X, y)

evaluator = Evaluator()

results = evaluator.evaluate(model, X, y)

print("Accuracy:", results['accuracy'])

print("Report:", results['report'])

visualizer = Visualizer()

visualizer.plot_confusion_matrix(results['cm'], classes=['Class1', 'Class2'])

save_model(model, 'trained_model.pkl')

loaded_model = load_model('trained_model.pkl')

```

These examples illustrate the practical implementation and use of the Easylibpal library components, aiming to simplify the application of AI algorithms for users with varying levels of expertise in machine learning.

EASYLIBPAL IMPLEMENTATION:

Step 1: Define the Problem

First, we need to define the problem we want to solve. For this POC, let's assume we want to predict house prices based on various features like the number of bedrooms, square footage, and location.

Step 2: Choose an Appropriate Algorithm

Given our problem, a supervised learning algorithm like linear regression would be suitable. We'll use Scikit-learn, a popular library for machine learning in Python, to implement this algorithm.

Step 3: Prepare Your Data

We'll use Pandas to load and prepare our dataset. This involves cleaning the data, handling missing values, and splitting the dataset into training and testing sets.

Step 4: Implement the Algorithm

Now, we'll use Scikit-learn to implement the linear regression algorithm. We'll train the model on our training data and then test its performance on the testing data.

Step 5: Evaluate the Model

Finally, we'll evaluate the performance of our model using metrics like Mean Squared Error (MSE) and R-squared.

Python Code POC

```python

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error, r2_score

# Load the dataset

data = pd.read_csv('house_prices.csv')

# Prepare the data

X = data'bedrooms', 'square_footage', 'location'

y = data['price']

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the model

model = LinearRegression()

model.fit(X_train, y_train)

# Make predictions

predictions = model.predict(X_test)

# Evaluate the model

mse = mean_squared_error(y_test, predictions)

r2 = r2_score(y_test, predictions)

print(f'Mean Squared Error: {mse}')

print(f'R-squared: {r2}')

```

Below is an implementation, Easylibpal provides a simple interface to instantiate and utilize classic AI algorithms such as Linear Regression, Logistic Regression, SVM, Naive Bayes, and K-NN. Users can easily create an instance of Easylibpal with their desired algorithm, fit the model with training data, and make predictions, all with minimal code and hassle. This demonstrates the power of Easylibpal in simplifying the integration of AI algorithms for various tasks.

```python

# Import necessary libraries

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

from sklearn.linear_model import LogisticRegression

from sklearn.svm import SVC

from sklearn.naive_bayes import GaussianNB

from sklearn.neighbors import KNeighborsClassifier

class Easylibpal:

def __init__(self, algorithm):

self.algorithm = algorithm

def fit(self, X, y):

if self.algorithm == 'Linear Regression':

self.model = LinearRegression()

elif self.algorithm == 'Logistic Regression':

self.model = LogisticRegression()

elif self.algorithm == 'SVM':

self.model = SVC()

elif self.algorithm == 'Naive Bayes':

self.model = GaussianNB()

elif self.algorithm == 'K-NN':

self.model = KNeighborsClassifier()

else:

raise ValueError("Invalid algorithm specified.")

self.model.fit(X, y)

def predict(self, X):

return self.model.predict(X)

# Example usage:

# Initialize Easylibpal with the desired algorithm

easy_algo = Easylibpal('Linear Regression')

# Generate some sample data

X = np.array([[1], [2], [3], [4]])

y = np.array([2, 4, 6, 8])

# Fit the model

easy_algo.fit(X, y)

# Make predictions

predictions = easy_algo.predict(X)

# Plot the results

plt.scatter(X, y)

plt.plot(X, predictions, color='red')

plt.title('Linear Regression with Easylibpal')

plt.xlabel('X')

plt.ylabel('y')

plt.show()

```

Easylibpal is an innovative Python library designed to simplify the integration and use of classic AI algorithms in a user-friendly manner. It aims to bridge the gap between the complexity of AI libraries and the ease of use, making it accessible for developers and data scientists alike. Easylibpal abstracts the underlying complexity of each algorithm, providing a unified interface that allows users to apply these algorithms with minimal configuration and understanding of the underlying mechanisms.

ENHANCED DATASET HANDLING

Easylibpal should be able to handle datasets more efficiently. This includes loading datasets from various sources (e.g., CSV files, databases), preprocessing data (e.g., normalization, handling missing values), and splitting data into training and testing sets.

```python

import os

from sklearn.model_selection import train_test_split

class Easylibpal:

# Existing code...

def load_dataset(self, filepath):

"""Loads a dataset from a CSV file."""

if not os.path.exists(filepath):

raise FileNotFoundError("Dataset file not found.")

return pd.read_csv(filepath)

def preprocess_data(self, dataset):

"""Preprocesses the dataset."""

# Implement data preprocessing steps here

return dataset

def split_data(self, X, y, test_size=0.2):

"""Splits the dataset into training and testing sets."""

return train_test_split(X, y, test_size=test_size)

```

Additional Algorithms

Easylibpal should support a wider range of algorithms. This includes decision trees, random forests, and gradient boosting machines.

```python

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.ensemble import GradientBoostingClassifier

class Easylibpal:

# Existing code...

def fit(self, X, y):

# Existing code...

elif self.algorithm == 'Decision Tree':

self.model = DecisionTreeClassifier()

elif self.algorithm == 'Random Forest':

self.model = RandomForestClassifier()

elif self.algorithm == 'Gradient Boosting':

self.model = GradientBoostingClassifier()

# Add more algorithms as needed

```

User-Friendly Features

To make Easylibpal even more user-friendly, consider adding features like:

- Automatic hyperparameter tuning: Implementing a simple interface for hyperparameter tuning using GridSearchCV or RandomizedSearchCV.

- Model evaluation metrics: Providing easy access to common evaluation metrics like accuracy, precision, recall, and F1 score.

- Visualization tools: Adding methods for plotting model performance, confusion matrices, and feature importance.

```python

from sklearn.metrics import accuracy_score, classification_report

from sklearn.model_selection import GridSearchCV

class Easylibpal:

# Existing code...

def evaluate_model(self, X_test, y_test):

"""Evaluates the model using accuracy and classification report."""

y_pred = self.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))

print(classification_report(y_test, y_pred))

def tune_hyperparameters(self, X, y, param_grid):

"""Tunes the model's hyperparameters using GridSearchCV."""

grid_search = GridSearchCV(self.model, param_grid, cv=5)

grid_search.fit(X, y)

self.model = grid_search.best_estimator_

```

Easylibpal leverages the power of Python and its rich ecosystem of AI and machine learning libraries, such as scikit-learn, to implement the classic algorithms. It provides a high-level API that abstracts the specifics of each algorithm, allowing users to focus on the problem at hand rather than the intricacies of the algorithm.

Python Code Snippets for Easylibpal

Below are Python code snippets demonstrating the use of Easylibpal with classic AI algorithms. Each snippet demonstrates how to use Easylibpal to apply a specific algorithm to a dataset.

# Linear Regression

```python

from Easylibpal import Easylibpal

# Initialize Easylibpal with a dataset

Easylibpal = Easylibpal(dataset='your_dataset.csv')

# Apply Linear Regression

result = Easylibpal.apply_algorithm('linear_regression', target_column='target')

# Print the result

print(result)

```

# Logistic Regression

```python

from Easylibpal import Easylibpal

# Initialize Easylibpal with a dataset

Easylibpal = Easylibpal(dataset='your_dataset.csv')

# Apply Logistic Regression

result = Easylibpal.apply_algorithm('logistic_regression', target_column='target')

# Print the result

print(result)

```

# Support Vector Machines (SVM)

```python

from Easylibpal import Easylibpal

# Initialize Easylibpal with a dataset

Easylibpal = Easylibpal(dataset='your_dataset.csv')

# Apply SVM

result = Easylibpal.apply_algorithm('svm', target_column='target')

# Print the result

print(result)

```

# Naive Bayes

```python

from Easylibpal import Easylibpal

# Initialize Easylibpal with a dataset

Easylibpal = Easylibpal(dataset='your_dataset.csv')

# Apply Naive Bayes

result = Easylibpal.apply_algorithm('naive_bayes', target_column='target')

# Print the result

print(result)

```

# K-Nearest Neighbors (K-NN)

```python

from Easylibpal import Easylibpal

# Initialize Easylibpal with a dataset

Easylibpal = Easylibpal(dataset='your_dataset.csv')

# Apply K-NN

result = Easylibpal.apply_algorithm('knn', target_column='target')

# Print the result

print(result)

```

ABSTRACTION AND ESSENTIAL COMPLEXITY

- Essential Complexity: This refers to the inherent complexity of the problem domain, which cannot be reduced regardless of the programming language or framework used. It includes the logic and algorithm needed to solve the problem. For example, the essential complexity of sorting a list remains the same across different programming languages.

- Accidental Complexity: This is the complexity introduced by the choice of programming language, framework, or libraries. It can be reduced or eliminated through abstraction. For instance, using a high-level API in Python can hide the complexity of lower-level operations, making the code more readable and maintainable.

HOW EASYLIBPAL ABSTRACTS COMPLEXITY

Easylibpal aims to reduce accidental complexity by providing a high-level API that encapsulates the details of each classic AI algorithm. This abstraction allows users to apply these algorithms without needing to understand the underlying mechanisms or the specifics of the algorithm's implementation.

- Simplified Interface: Easylibpal offers a unified interface for applying various algorithms, such as Linear Regression, Logistic Regression, SVM, Naive Bayes, and K-NN. This interface abstracts the complexity of each algorithm, making it easier for users to apply them to their datasets.

- Runtime Fusion: By evaluating sub-expressions and sharing them across multiple terms, Easylibpal can optimize the execution of algorithms. This approach, similar to runtime fusion in abstract algorithms, allows for efficient computation without duplicating work, thereby reducing the computational complexity.

- Focus on Essential Complexity: While Easylibpal abstracts away the accidental complexity; it ensures that the essential complexity of the problem domain remains at the forefront. This means that while the implementation details are hidden, the core logic and algorithmic approach are still accessible and understandable to the user.

To implement Easylibpal, one would need to create a Python class that encapsulates the functionality of each classic AI algorithm. This class would provide methods for loading datasets, preprocessing data, and applying the algorithm with minimal configuration required from the user. The implementation would leverage existing libraries like scikit-learn for the actual algorithmic computations, abstracting away the complexity of these libraries.

Here's a conceptual example of how the Easylibpal class might be structured for applying a Linear Regression algorithm:

```python

class Easylibpal:

def __init__(self, dataset):

self.dataset = dataset

# Load and preprocess the dataset

def apply_linear_regression(self, target_column):

# Abstracted implementation of Linear Regression

# This method would internally use scikit-learn or another library

# to perform the actual computation, abstracting the complexity

pass

# Usage

Easylibpal = Easylibpal(dataset='your_dataset.csv')

result = Easylibpal.apply_linear_regression(target_column='target')

```

This example demonstrates the concept of Easylibpal by abstracting the complexity of applying a Linear Regression algorithm. The actual implementation would need to include the specifics of loading the dataset, preprocessing it, and applying the algorithm using an underlying library like scikit-learn.

Easylibpal abstracts the complexity of classic AI algorithms by providing a simplified interface that hides the intricacies of each algorithm's implementation. This abstraction allows users to apply these algorithms with minimal configuration and understanding of the underlying mechanisms. Here are examples of specific algorithms that Easylibpal abstracts:

Here's a conceptual example of how the Easylibpal class might be structured for applying a Linear Regression algorithm:

```python

class Easylibpal:

def __init__(self, dataset):

self.dataset = dataset

# Load and preprocess the dataset

def apply_linear_regression(self, target_column):

# Abstracted implementation of Linear Regression

# This method would internally use scikit-learn or another library

# to perform the actual computation, abstracting the complexity

pass

# Usage

Easylibpal = Easylibpal(dataset='your_dataset.csv')

result = Easylibpal.apply_linear_regression(target_column='target')

```

Easylibpal abstracts the complexity of feature selection for classic AI algorithms by providing a simplified interface that automates the process of selecting the most relevant features for each algorithm. This abstraction is crucial because feature selection is a critical step in machine learning that can significantly impact the performance of a model. Here's how Easylibpal handles feature selection for the mentioned algorithms:

To implement feature selection in Easylibpal, one could use scikit-learn's `SelectKBest` or `RFE` classes for feature selection based on statistical tests or model coefficients. Here's a conceptual example of how feature selection might be integrated into the Easylibpal class for Linear Regression:

```python

from sklearn.feature_selection import SelectKBest, f_regression

from sklearn.linear_model import LinearRegression

class Easylibpal:

def __init__(self, dataset):

self.dataset = dataset

# Load and preprocess the dataset

def apply_linear_regression(self, target_column):

# Feature selection using SelectKBest

selector = SelectKBest(score_func=f_regression, k=10)

X_new = selector.fit_transform(self.dataset.drop(target_column, axis=1), self.dataset[target_column])

# Train Linear Regression model

model = LinearRegression()

model.fit(X_new, self.dataset[target_column])

# Return the trained model

return model

# Usage

Easylibpal = Easylibpal(dataset='your_dataset.csv')

model = Easylibpal.apply_linear_regression(target_column='target')

```

This example demonstrates how Easylibpal abstracts the complexity of feature selection for Linear Regression by using scikit-learn's `SelectKBest` to select the top 10 features based on their statistical significance in predicting the target variable. The actual implementation would need to adapt this approach for each algorithm, considering the specific characteristics and requirements of each algorithm.

To implement feature selection in Easylibpal, one could use scikit-learn's `SelectKBest`, `RFE`, or other feature selection classes based on the algorithm's requirements. Here's a conceptual example of how feature selection might be integrated into the Easylibpal class for Logistic Regression using RFE:

```python

from sklearn.feature_selection import RFE

from sklearn.linear_model import LogisticRegression

class Easylibpal:

def __init__(self, dataset):

self.dataset = dataset

# Load and preprocess the dataset

def apply_logistic_regression(self, target_column):

# Feature selection using RFE

model = LogisticRegression()

rfe = RFE(model, n_features_to_select=10)

rfe.fit(self.dataset.drop(target_column, axis=1), self.dataset[target_column])

# Train Logistic Regression model

model.fit(self.dataset.drop(target_column, axis=1), self.dataset[target_column])

# Return the trained model

return model

# Usage

Easylibpal = Easylibpal(dataset='your_dataset.csv')

model = Easylibpal.apply_logistic_regression(target_column='target')

```

This example demonstrates how Easylibpal abstracts the complexity of feature selection for Logistic Regression by using scikit-learn's `RFE` to select the top 10 features based on their importance in the model. The actual implementation would need to adapt this approach for each algorithm, considering the specific characteristics and requirements of each algorithm.

EASYLIBPAL HANDLES DIFFERENT TYPES OF DATASETS

Easylibpal handles different types of datasets with varying structures by adopting a flexible and adaptable approach to data preprocessing and transformation. This approach is inspired by the principles of tidy data and the need to ensure data is in a consistent, usable format before applying AI algorithms. Here's how Easylibpal addresses the challenges posed by varying dataset structures:

One Type in Multiple Tables

When datasets contain different variables, the same variables with different names, different file formats, or different conventions for missing values, Easylibpal employs a process similar to tidying data. This involves identifying and standardizing the structure of each dataset, ensuring that each variable is consistently named and formatted across datasets. This process might include renaming columns, converting data types, and handling missing values in a uniform manner. For datasets stored in different file formats, Easylibpal would use appropriate libraries (e.g., pandas for CSV, Excel files, and SQL databases) to load and preprocess the data before applying the algorithms.

Multiple Types in One Table

For datasets that involve values collected at multiple levels or on different types of observational units, Easylibpal applies a normalization process. This involves breaking down the dataset into multiple tables, each representing a distinct type of observational unit. For example, if a dataset contains information about songs and their rankings over time, Easylibpal would separate this into two tables: one for song details and another for rankings. This normalization ensures that each fact is expressed in only one place, reducing inconsistencies and making the data more manageable for analysis.

Data Semantics

Easylibpal ensures that the data is organized in a way that aligns with the principles of data semantics, where every value belongs to a variable and an observation. This organization is crucial for the algorithms to interpret the data correctly. Easylibpal might use functions like `pivot_longer` and `pivot_wider` from the tidyverse or equivalent functions in pandas to reshape the data into a long format, where each row represents a single observation and each column represents a single variable. This format is particularly useful for algorithms that require a consistent structure for input data.

Messy Data

Dealing with messy data, which can include inconsistent data types, missing values, and outliers, is a common challenge in data science. Easylibpal addresses this by implementing robust data cleaning and preprocessing steps. This includes handling missing values (e.g., imputation or deletion), converting data types to ensure consistency, and identifying and removing outliers. These steps are crucial for preparing the data in a format that is suitable for the algorithms, ensuring that the algorithms can effectively learn from the data without being hindered by its inconsistencies.

To implement these principles in Python, Easylibpal would leverage libraries like pandas for data manipulation and preprocessing. Here's a conceptual example of how Easylibpal might handle a dataset with multiple types in one table:

```python

import pandas as pd

# Load the dataset

dataset = pd.read_csv('your_dataset.csv')

# Normalize the dataset by separating it into two tables

song_table = dataset'artist', 'track'.drop_duplicates().reset_index(drop=True)

song_table['song_id'] = range(1, len(song_table) + 1)

ranking_table = dataset'artist', 'track', 'week', 'rank'.drop_duplicates().reset_index(drop=True)

# Now, song_table and ranking_table can be used separately for analysis

```

This example demonstrates how Easylibpal might normalize a dataset with multiple types of observational units into separate tables, ensuring that each type of observational unit is stored in its own table. The actual implementation would need to adapt this approach based on the specific structure and requirements of the dataset being processed.

CLEAN DATA

Easylibpal employs a comprehensive set of data cleaning and preprocessing steps to handle messy data, ensuring that the data is in a suitable format for machine learning algorithms. These steps are crucial for improving the accuracy and reliability of the models, as well as preventing misleading results and conclusions. Here's a detailed look at the specific steps Easylibpal might employ:

1. Remove Irrelevant Data

The first step involves identifying and removing data that is not relevant to the analysis or modeling task at hand. This could include columns or rows that do not contribute to the predictive power of the model or are not necessary for the analysis .

2. Deduplicate Data

Deduplication is the process of removing duplicate entries from the dataset. Duplicates can skew the analysis and lead to incorrect conclusions. Easylibpal would use appropriate methods to identify and remove duplicates, ensuring that each entry in the dataset is unique.

3. Fix Structural Errors

Structural errors in the dataset, such as inconsistent data types, incorrect values, or formatting issues, can significantly impact the performance of machine learning algorithms. Easylibpal would employ data cleaning techniques to correct these errors, ensuring that the data is consistent and correctly formatted.

4. Deal with Missing Data

Handling missing data is a common challenge in data preprocessing. Easylibpal might use techniques such as imputation (filling missing values with statistical estimates like mean, median, or mode) or deletion (removing rows or columns with missing values) to address this issue. The choice of method depends on the nature of the data and the specific requirements of the analysis.

5. Filter Out Data Outliers

Outliers can significantly affect the performance of machine learning models. Easylibpal would use statistical methods to identify and filter out outliers, ensuring that the data is more representative of the population being analyzed.

6. Validate Data

The final step involves validating the cleaned and preprocessed data to ensure its quality and accuracy. This could include checking for consistency, verifying the correctness of the data, and ensuring that the data meets the requirements of the machine learning algorithms. Easylibpal would employ validation techniques to confirm that the data is ready for analysis.

To implement these data cleaning and preprocessing steps in Python, Easylibpal would leverage libraries like pandas and scikit-learn. Here's a conceptual example of how these steps might be integrated into the Easylibpal class:

```python

import pandas as pd

from sklearn.impute import SimpleImputer

from sklearn.preprocessing import StandardScaler

class Easylibpal:

def __init__(self, dataset):

self.dataset = dataset

# Load and preprocess the dataset

def clean_and_preprocess(self):

# Remove irrelevant data

self.dataset = self.dataset.drop(['irrelevant_column'], axis=1)

# Deduplicate data

self.dataset = self.dataset.drop_duplicates()

# Fix structural errors (example: correct data type)

self.dataset['correct_data_type_column'] = self.dataset['correct_data_type_column'].astype(float)

# Deal with missing data (example: imputation)

imputer = SimpleImputer(strategy='mean')

self.dataset['missing_data_column'] = imputer.fit_transform(self.dataset'missing_data_column')

# Filter out data outliers (example: using Z-score)

# This step requires a more detailed implementation based on the specific dataset

# Validate data (example: checking for NaN values)

assert not self.dataset.isnull().values.any(), "Data still contains NaN values"

# Return the cleaned and preprocessed dataset

return self.dataset

# Usage

Easylibpal = Easylibpal(dataset=pd.read_csv('your_dataset.csv'))

cleaned_dataset = Easylibpal.clean_and_preprocess()

```

This example demonstrates a simplified approach to data cleaning and preprocessing within Easylibpal. The actual implementation would need to adapt these steps based on the specific characteristics and requirements of the dataset being processed.

VALUE DATA

Easylibpal determines which data is irrelevant and can be removed through a combination of domain knowledge, data analysis, and automated techniques. The process involves identifying data that does not contribute to the analysis, research, or goals of the project, and removing it to improve the quality, efficiency, and clarity of the data. Here's how Easylibpal might approach this:

Domain Knowledge

Easylibpal leverages domain knowledge to identify data that is not relevant to the specific goals of the analysis or modeling task. This could include data that is out of scope, outdated, duplicated, or erroneous. By understanding the context and objectives of the project, Easylibpal can systematically exclude data that does not add value to the analysis.

Data Analysis

Easylibpal employs data analysis techniques to identify irrelevant data. This involves examining the dataset to understand the relationships between variables, the distribution of data, and the presence of outliers or anomalies. Data that does not have a significant impact on the predictive power of the model or the insights derived from the analysis is considered irrelevant.

Automated Techniques

Easylibpal uses automated tools and methods to remove irrelevant data. This includes filtering techniques to select or exclude certain rows or columns based on criteria or conditions, aggregating data to reduce its complexity, and deduplicating to remove duplicate entries. Tools like Excel, Google Sheets, Tableau, Power BI, OpenRefine, Python, R, Data Linter, Data Cleaner, and Data Wrangler can be employed for these purposes .

Examples of Irrelevant Data

- Personal Identifiable Information (PII): Data such as names, addresses, and phone numbers are irrelevant for most analytical purposes and should be removed to protect privacy and comply with data protection regulations .

- URLs and HTML Tags: These are typically not relevant to the analysis and can be removed to clean up the dataset.

- Boilerplate Text: Excessive blank space or boilerplate text (e.g., in emails) adds noise to the data and can be removed.

- Tracking Codes: These are used for tracking user interactions and do not contribute to the analysis.

To implement these steps in Python, Easylibpal might use pandas for data manipulation and filtering. Here's a conceptual example of how to remove irrelevant data:

```python

import pandas as pd

# Load the dataset

dataset = pd.read_csv('your_dataset.csv')

# Remove irrelevant columns (example: email addresses)

dataset = dataset.drop(['email_address'], axis=1)

# Remove rows with missing values (example: if a column is required for analysis)

dataset = dataset.dropna(subset=['required_column'])

# Deduplicate data

dataset = dataset.drop_duplicates()

# Return the cleaned dataset

cleaned_dataset = dataset

```

This example demonstrates how Easylibpal might remove irrelevant data from a dataset using Python and pandas. The actual implementation would need to adapt these steps based on the specific characteristics and requirements of the dataset being processed.

Detecting Inconsistencies

Easylibpal starts by detecting inconsistencies in the data. This involves identifying discrepancies in data types, missing values, duplicates, and formatting errors. By detecting these inconsistencies, Easylibpal can take targeted actions to address them.

Handling Formatting Errors

Formatting errors, such as inconsistent data types for the same feature, can significantly impact the analysis. Easylibpal uses functions like `astype()` in pandas to convert data types, ensuring uniformity and consistency across the dataset. This step is crucial for preparing the data for analysis, as it ensures that each feature is in the correct format expected by the algorithms.

Handling Missing Values

Missing values are a common issue in datasets. Easylibpal addresses this by consulting with subject matter experts to understand why data might be missing. If the missing data is missing completely at random, Easylibpal might choose to drop it. However, for other cases, Easylibpal might employ imputation techniques to fill in missing values, ensuring that the dataset is complete and ready for analysis.

Handling Duplicates

Duplicate entries can skew the analysis and lead to incorrect conclusions. Easylibpal uses pandas to identify and remove duplicates, ensuring that each entry in the dataset is unique. This step is crucial for maintaining the integrity of the data and ensuring that the analysis is based on distinct observations.

Handling Inconsistent Values

Inconsistent values, such as different representations of the same concept (e.g., "yes" vs. "y" for a binary variable), can also pose challenges. Easylibpal employs data cleaning techniques to standardize these values, ensuring that the data is consistent and can be accurately analyzed.

To implement these steps in Python, Easylibpal would leverage pandas for data manipulation and preprocessing. Here's a conceptual example of how these steps might be integrated into the Easylibpal class:

```python

import pandas as pd

class Easylibpal:

def __init__(self, dataset):

self.dataset = dataset

# Load and preprocess the dataset

def clean_and_preprocess(self):

# Detect inconsistencies (example: check data types)

print(self.dataset.dtypes)

# Handle formatting errors (example: convert data types)

self.dataset['date_column'] = pd.to_datetime(self.dataset['date_column'])

# Handle missing values (example: drop rows with missing values)

self.dataset = self.dataset.dropna(subset=['required_column'])

# Handle duplicates (example: drop duplicates)

self.dataset = self.dataset.drop_duplicates()

# Handle inconsistent values (example: standardize values)

self.dataset['binary_column'] = self.dataset['binary_column'].map({'yes': 1, 'no': 0})

# Return the cleaned and preprocessed dataset

return self.dataset

# Usage

Easylibpal = Easylibpal(dataset=pd.read_csv('your_dataset.csv'))

cleaned_dataset = Easylibpal.clean_and_preprocess()

```

This example demonstrates a simplified approach to handling inconsistent or messy data within Easylibpal. The actual implementation would need to adapt these steps based on the specific characteristics and requirements of the dataset being processed.

Statistical Imputation

Statistical imputation involves replacing missing values with statistical estimates such as the mean, median, or mode of the available data. This method is straightforward and can be effective for numerical data. For categorical data, mode imputation is commonly used. The choice of imputation method depends on the distribution of the data and the nature of the missing values.

Model-Based Imputation

Model-based imputation uses machine learning models to predict missing values. This approach can be more sophisticated and potentially more accurate than statistical imputation, especially for complex datasets. Techniques like K-Nearest Neighbors (KNN) imputation can be used, where the missing values are replaced with the values of the K nearest neighbors in the feature space.

Using SimpleImputer in scikit-learn

The scikit-learn library provides the `SimpleImputer` class, which supports both statistical and model-based imputation. `SimpleImputer` can be used to replace missing values with the mean, median, or most frequent value (mode) of the column. It also supports more advanced imputation methods like KNN imputation.

To implement these imputation techniques in Python, Easylibpal might use the `SimpleImputer` class from scikit-learn. Here's an example of how to use `SimpleImputer` for statistical imputation:

```python

from sklearn.impute import SimpleImputer

import pandas as pd

# Load the dataset

dataset = pd.read_csv('your_dataset.csv')

# Initialize SimpleImputer for numerical columns

num_imputer = SimpleImputer(strategy='mean')

# Fit and transform the numerical columns

dataset'numerical_column1', 'numerical_column2' = num_imputer.fit_transform(dataset'numerical_column1', 'numerical_column2')

# Initialize SimpleImputer for categorical columns

cat_imputer = SimpleImputer(strategy='most_frequent')

# Fit and transform the categorical columns

dataset'categorical_column1', 'categorical_column2' = cat_imputer.fit_transform(dataset'categorical_column1', 'categorical_column2')

# The dataset now has missing values imputed

```

This example demonstrates how to use `SimpleImputer` to fill in missing values in both numerical and categorical columns of a dataset. The actual implementation would need to adapt these steps based on the specific characteristics and requirements of the dataset being processed.

Model-based imputation techniques, such as Multiple Imputation by Chained Equations (MICE), offer powerful ways to handle missing data by using statistical models to predict missing values. However, these techniques come with their own set of limitations and potential drawbacks:

1. Complexity and Computational Cost

Model-based imputation methods can be computationally intensive, especially for large datasets or complex models. This can lead to longer processing times and increased computational resources required for imputation.

2. Overfitting and Convergence Issues

These methods are prone to overfitting, where the imputation model captures noise in the data rather than the underlying pattern. Overfitting can lead to imputed values that are too closely aligned with the observed data, potentially introducing bias into the analysis. Additionally, convergence issues may arise, where the imputation process does not settle on a stable solution.

3. Assumptions About Missing Data

Model-based imputation techniques often assume that the data is missing at random (MAR), which means that the probability of a value being missing is not related to the values of other variables. However, this assumption may not hold true in all cases, leading to biased imputations if the data is missing not at random (MNAR).

4. Need for Suitable Regression Models

For each variable with missing values, a suitable regression model must be chosen. Selecting the wrong model can lead to inaccurate imputations. The choice of model depends on the nature of the data and the relationship between the variable with missing values and other variables.

5. Combining Imputed Datasets

After imputing missing values, there is a challenge in combining the multiple imputed datasets to produce a single, final dataset. This requires careful consideration of how to aggregate the imputed values and can introduce additional complexity and uncertainty into the analysis.

6. Lack of Transparency

The process of model-based imputation can be less transparent than simpler imputation methods, such as mean or median imputation. This can make it harder to justify the imputation process, especially in contexts where the reasons for missing data are important, such as in healthcare research.

Despite these limitations, model-based imputation techniques can be highly effective for handling missing data in datasets where a amusingness is MAR and where the relationships between variables are complex. Careful consideration of the assumptions, the choice of models, and the methods for combining imputed datasets are crucial to mitigate these drawbacks and ensure the validity of the imputation process.

USING EASYLIBPAL FOR AI ALGORITHM INTEGRATION OFFERS SEVERAL SIGNIFICANT BENEFITS, PARTICULARLY IN ENHANCING EVERYDAY LIFE AND REVOLUTIONIZING VARIOUS SECTORS. HERE'S A DETAILED LOOK AT THE ADVANTAGES:

1. Enhanced Communication: AI, through Easylibpal, can significantly improve communication by categorizing messages, prioritizing inboxes, and providing instant customer support through chatbots. This ensures that critical information is not missed and that customer queries are resolved promptly.

2. Creative Endeavors: Beyond mundane tasks, AI can also contribute to creative endeavors. For instance, photo editing applications can use AI algorithms to enhance images, suggesting edits that align with aesthetic preferences. Music composition tools can generate melodies based on user input, inspiring musicians and amateurs alike to explore new artistic horizons. These innovations empower individuals to express themselves creatively with AI as a collaborative partner.

3. Daily Life Enhancement: AI, integrated through Easylibpal, has the potential to enhance daily life exponentially. Smart homes equipped with AI-driven systems can adjust lighting, temperature, and security settings according to user preferences. Autonomous vehicles promise safer and more efficient commuting experiences. Predictive analytics can optimize supply chains, reducing waste and ensuring goods reach users when needed.

4. Paradigm Shift in Technology Interaction: The integration of AI into our daily lives is not just a trend; it's a paradigm shift that's redefining how we interact with technology. By streamlining routine tasks, personalizing experiences, revolutionizing healthcare, enhancing communication, and fueling creativity, AI is opening doors to a more convenient, efficient, and tailored existence.

5. Responsible Benefit Harnessing: As we embrace AI's transformational power, it's essential to approach its integration with a sense of responsibility, ensuring that its benefits are harnessed for the betterment of society as a whole. This approach aligns with the ethical considerations of using AI, emphasizing the importance of using AI in a way that benefits all stakeholders.

In summary, Easylibpal facilitates the integration and use of AI algorithms in a manner that is accessible and beneficial across various domains, from enhancing communication and creative endeavors to revolutionizing daily life and promoting a paradigm shift in technology interaction. This integration not only streamlines the application of AI but also ensures that its benefits are harnessed responsibly for the betterment of society.

USING EASYLIBPAL OVER TRADITIONAL AI LIBRARIES OFFERS SEVERAL BENEFITS, PARTICULARLY IN TERMS OF EASE OF USE, EFFICIENCY, AND THE ABILITY TO APPLY AI ALGORITHMS WITH MINIMAL CONFIGURATION. HERE ARE THE KEY ADVANTAGES:

- Simplified Integration: Easylibpal abstracts the complexity of traditional AI libraries, making it easier for users to integrate classic AI algorithms into their projects. This simplification reduces the learning curve and allows developers and data scientists to focus on their core tasks without getting bogged down by the intricacies of AI implementation.

- User-Friendly Interface: By providing a unified platform for various AI algorithms, Easylibpal offers a user-friendly interface that streamlines the process of selecting and applying algorithms. This interface is designed to be intuitive and accessible, enabling users to experiment with different algorithms with minimal effort.

- Enhanced Productivity: The ability to effortlessly instantiate algorithms, fit models with training data, and make predictions with minimal configuration significantly enhances productivity. This efficiency allows for rapid prototyping and deployment of AI solutions, enabling users to bring their ideas to life more quickly.

- Democratization of AI: Easylibpal democratizes access to classic AI algorithms, making them accessible to a wider range of users, including those with limited programming experience. This democratization empowers users to leverage AI in various domains, fostering innovation and creativity.

- Automation of Repetitive Tasks: By automating the process of applying AI algorithms, Easylibpal helps users save time on repetitive tasks, allowing them to focus on more complex and creative aspects of their projects. This automation is particularly beneficial for users who may not have extensive experience with AI but still wish to incorporate AI capabilities into their work.

- Personalized Learning and Discovery: Easylibpal can be used to enhance personalized learning experiences and discovery mechanisms, similar to the benefits seen in academic libraries. By analyzing user behaviors and preferences, Easylibpal can tailor recommendations and resource suggestions to individual needs, fostering a more engaging and relevant learning journey.

- Data Management and Analysis: Easylibpal aids in managing large datasets efficiently and deriving meaningful insights from data. This capability is crucial in today's data-driven world, where the ability to analyze and interpret large volumes of data can significantly impact research outcomes and decision-making processes.

In summary, Easylibpal offers a simplified, user-friendly approach to applying classic AI algorithms, enhancing productivity, democratizing access to AI, and automating repetitive tasks. These benefits make Easylibpal a valuable tool for developers, data scientists, and users looking to leverage AI in their projects without the complexities associated with traditional AI libraries.

#ai solutions #ai-driven #ai trends #ai model #ai system #ml #ai prompt #llm #ai predictions #dl

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letterslaura · 1 year ago

Text

Writing as an articulating axis and practices involving Educational Technologies

Coucou everyone!

Today we are going to tackle a very important topic: developing writing skills in school.

What memories do you have of writing practices in school? In my case, it was always fun and natural, just because I was so eager to practice my writing skills, but we know that this may not be the reality for most children. When I was in school, the methods were pretty strict and mechanical, with very little room for imagination. In Portuguese we were taught (a lot of) fixed rules, systems, genres (not the cool ones) and structures, everything for the sake of Enem’s essay. In the English classes things were not so different. In a room full of people, with a diversity of confidence and enthusiasm, imagine trying to apply the same monotonous pattern. What could it lead to? This rigid approach to writing can turn it into a monster for students, a real struggle, especially when in another language. It should not be like this. Students should be encouraged to see writing as a simple skill that helps us convey messages in the best possible way. And they should know that they are all capable of learning it!

Now, let’s move away from the past and take a look at what schools are teaching today. From the Common National Curriculum Base (BNCC), we can grasp how things should be done inside the classroom. Regarding writing practices in primary education, the BNCC states that writing is an essential competency that crosses many areas, not being restricted to language itself. In other words, writing practices in school should not focus only on language or on grammatical rules. Instead, it should aid students express their ideas and communicate adequately in the other disciplines as well. Could you spot the difference from the previous approach? I was so thrilled to read about how much has changed!

Further on, the BNCC stresses the relevance of integrating educational technologies into the writing practices, such as softwares, collaborative platforms, multimedia resources, etc. Thankfully, there are plenty of resources at our disposal and they can greatly enhance the teaching learning experience. This integration opens up a world of possibilities.

After that, according to Liberali, “social activities in second language teaching focuses the study on activities where the students interact with each other in determined and historically dependent cultural contexts.” (LIBERALI, 2009, p. 12). Educational technologies are able to facilitate social interactions with speakers of the target language (in this case, English) and there lie glorious opportunities to teach writing practices smoothly.

One of my favorite writing activities (if not my favorite) was a movie review requested in the third period of the English discipline. Learning every aspect of an adjective was never so fun! In my opinion, it is a brilliant idea, since there are many interesting literary and linguistic aspects to be explored in a movie review and it can be done with just the amount of difficulty you want. How cool would it have been to write a review of "Finding Nemo" back in the day?

Movie reviews are also easily relatable, as everyone has a favorite movie, making it much easier for the students to connect and really engage with the proposal. To make the experience even more realistic, if the students' age group allow, it is also possible to introduce the Letterboxd website, according to the students' age group. In this site, they can read reviews of the movies they like and maybe even post their own later. In order to enrich the activity a little more, there can also be held peer reviews and presentations.

Another idea is to explore the world of comics! What Brazilian kid does not know Monica's Gang? Comics are very appealing for primary students, it is usually a genre that they are comfortable with and it really stimulates creativity. You can begin with reading and understanding the components of comic books, leaving the writing to the end. By creating setting and characters first, the story will be less difficult to write. It is important to get to know your students in order to pinpoint what activity would be a better fit to them.

Finally, there are plenty of writing resources online, which can be used to help students in their process. My favorites include: Thesaurus, Cambridge Dictionary, Collins Dictionary, Linguee and Grammarly. These tools can give a little extra confidence to those embarking on the adventure of writing in another language.

Now that you know a little more about writing practices and educational technologies, make sure to leave a comment below with your own experiences. I want to know all about it!

XOXO

Laura

#education #teaching #learnin #writing #brazil #esl #english #portuguese #school #text

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bardic-tales · 5 months ago

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The Leviathan method: Step 12: Describe Your Antagonist(s)

Disclaimer: The portrayal of Jenova in Fantasy Worlds Collide is a fanon interpretation shaped by 27 years of headcanons, character analysis, and exploration of Final Fantasy VII media. While it incorporates canon-compliant material, it is ultimately canon-divergent due to the inclusion of original characters such as Seraphine and Bianca, as well as unique narrative elements. FWC is a deeply personal passion project blending fandom and original content to create a distinctive story.

Jenova, the alien entity who serves as the ultimate source of Sephiroth’s madness and the driving force behind the tragedy that befalls him, is a powerful and enigmatic antagonist. While she is not physically present in the story, her influence looms large over the events of Blood & Stardust. Jenova's manipulative nature is subtle, as she works through Sephiroth’s mind, steering him toward destructive actions and feeding off of his growing insanity. Seraphine, Bianca’s biological mother, helped the Cetra to quarantine the alien to the area would later be known as the North Crater, showcasing that the Celestials looked for balance in the mortal realm and dealt with cosmic threats.

Her ancient, otherworldly presence serves as both a curse and a catalyst for Sephiroth’s spiraling descent into darkness. Bianca's connection to Sephiroth, and her struggle with the celestial and demonic forces within her, is further complicated by Jenova's malevolent influence. As a force of corruption and manipulation, Jenova represents the chaos and destruction that threatens to consume not only Sephiroth but the entire world. Her presence drives the protagonists toward an inevitable clash, as they struggle to overcome her influence and the darkness she cultivates within them.

Quick Reference List

Tech Knowledge: Biotech Manipulation, Genetic Engineering

Economic Class: Cosmic Entity, Central to Power Dynamics

Skills: Mental Manipulation, Shapeshifting, Biological Corruption

Hobbies: Destruction, Assimilation

Classifications: Alien Entity, Gender Ambiguous, Cosmic Threat

Vital: 5’8, 135 lb, Over 2k, Dormant

JENOVA: Detailed Character Breakdown

Technology/Tech Knowledge: Jenova’s influence extends indirectly through advanced technology created by Shinra, particularly the mako reactors and genetic experiments. While Jenova itself does not actively use technology, its cells are integral to the manipulation of biological systems, turning living organisms into powerful monsters or puppets for its will. Jenova’s genetic manipulation could be considered a form of biotechnology far beyond human comprehension. Tech Knowledge: Biotech Manipulation, Genetic Engineering

Economic/Social Class: As an ancient alien entity, Jenova transcends traditional economic or social classifications, but its influence operates through Shinra, an economically and politically dominant corporation. Shinra’s exploitation of Jenova cells places the entity at the heart of a power struggle, as the experiments using its cells produce elite warriors like Sephiroth. Jenova indirectly fuels Shinra’s economic dominance while also destabilizing its societal structure by creating monstrous outbreaks and fostering chaos. Economic Class: Cosmic Entity, Central to Power Dynamics

Magic Abilities or Skills: Jenova’s primary power lies in its ability to manipulate minds and alter biology on a cellular level. It can infect and transform living beings into violent, monstrous versions of themselves while planting illusions in their minds. It possesses the ability to mimic the memories and appearances of loved ones, making it an emotional and psychological weapon as well. Its cells exert a compulsion to reunite with the main body, a process that amplifies Jenova’s control over its hosts. Skills: Mental Manipulation, Shapeshifting, Biological Corruption

Culture and Hobbies: As an alien entity, Jenova lacks a traditional culture or hobbies. Its existence is defined by destruction and assimilation, consuming entire civilizations to perpetuate its influence. If it could be said to have a "purpose," it would be the propagation of its genetic material and the subjugation of all life forms it encounters. Hobbies: Destructive Nature, Assimilation

Classifications: Jenova is an extraterrestrial being of ambiguous gender, often referred to as an "it." While it is depicted with feminine anatomy, its true form is alien and otherworldly, designed for manipulation and destruction. Jenova is often identified as a "calamity from the skies," emphasizing its role as a cosmic threat.

Jenova’s true form is a primordial black sludge or liquid, resembling a predatory virus in its natural state. This form highlights her parasitic nature, allowing her to infiltrate and corrupt other lifeforms by merging with them on a molecular level. This amorphous, fluid state is deceptive and highly adaptable, serving as the perfect camouflage to stake out, mimic, and exploit its victims. I believe this interpretation / headcanon emphasizes Jenova's nature as a cosmic predator, not bound to a singular appearance, but constantly evolving to ensnare and manipulate its prey. Classification: Alien Entity, Gender Ambiguous, Cosmic Threat

Vital Statistics:

Height: 5'8" (average human height)

Weight: 135 lbs (estimated for humanoid proportions)

Age: Over 2,000 years (dormant for millennia)

Health: Dormant; regenerative capabilities; sustained through Mako energy containment

Appearance: Gray-skinned humanoid with long silver hair, a glowing red left eye, and an exposed brain with metal headgear encasing the brain and forehead. Its body has appendages resembling wings and tubes that connect to an external biological system resembling a heart.

Vitals: 5’8, 135 lb, Over 2k, Dormant

Reflections on Antagonist’s Design

Goal: Jenova’s ultimate goal is to consume and assimilate all life, using planets as vessels to spread its genetic material across the cosmos. Through Sephiroth, it seeks to manipulate and leverage his power to fulfill this purpose.

Motivation: As an instinctual, parasitic entity, Jenova's motivation is survival and propagation. It justifies its actions as part of its nature, unconcerned with morality or consequences for other life forms.

Complexity: While Jenova itself lacks humanity, its influence over characters like Sephiroth creates depth. It exploits their vulnerabilities, making them agents of its will while amplifying their inner conflicts. Its mimicry of loved ones adds a layer of emotional manipulation that resonates with its victims.

Mirror/Contrast: Jenova mirrors Sephiroth’s desire for control and dominance but contrasts with Bianca's celestial essence, which fights against corruption. This duality highlights the cosmic struggle between creation and destruction. It can mirror the fight within Bianca’s true nature: celestial and infernal sides.

Strengths and Weaknesses:

Strengths: Mental and genetic manipulation, near-immortality, pervasive influence

Weaknesses: Requires hosts to act on its behalf

#character: jenova #jenova #character sheet: fwc #character sheet: fwc: ff #fwc: ff #characters: fwc #characters: fwc: ff #character sheet: jenova #bardic tales #bardic-tales #wip: blood and stardust #lm: step 12: describe antagonist #long post #passion project: fantasy worlds collide

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religion-is-a-mental-illness · 2 years ago

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https://lawdawghall.blogspot.com/2012/03/derrick-bell-whos-afraid-of-critical.html

Critical race theory writing and lecturing is characterized by frequent use of the first person, storytelling, narrative, allegory, interdisciplinary treatment of law, and the unapologetic use of creativity. The work is often disruptive because its commitment to anti-racism goes well beyond civil rights, integration, affirmative action, and other liberal measures. This is not to say that critical race theory adherents automatically or uniformly “trash” liberal ideology and method (as many adherents of critical legal studies do). Rather, they are highly suspicious of the liberal agenda, distrust its method, and want to retain what they see as a valuable strain of egalitarianism which may exist despite, and not because of, liberalism.

C-SPAN Booknotes: Thomas Sowell (1990)

Brian Lamb: What's the state of prejudice in the United States today compared to earlier years in your life?

Thomas Sowell: It depends on the base here, like most comparisons. If you take 30 years ago, certainly greater in the academic world. In the book that I wrote about colleges, I urged minority parents not to think that because they had a good experience on a particular college campus 30 years ago, that their children will have that good an experience today, because the racial tension is enormous on many campuses. The colleges themselves try to say that they're victims of the racism of the larger society, and in point of fact, the racism on the campuses is greater than that in the larger society, in many campuses. And what I worry about is that they're going to graduate into the general society, blacks and whites alike, who hate each other's guts, and who can be the leaders of new racial strife for the future.

Lamb: What's causing that on college campuses?

Sowell: One of the factors is the preferential policies. But it's more the just that, because that in itself sets in motion a series of events, which add to the original resentment over the preferential policies. That is, you put yourself in the position of a black kid who comes out of the ghetto school, and he's gone through for 12 years with nothing but A's and B's, without a great deal of effort, and now he finds himself for the first time in his life in a predominantly white environment, and he finds that when he works twice as hard as he's ever worked, all he gets back for his work is a D, and that there is also a minority establishment -- this is true not only of blacks but of minorities in general -- an establishment which tells him, "Yes, this is the racism on this campus -- the white power structure is trying to keep you down." And it has to have a certain plausibility to it. It would have a certain plausibility to me had I come along in that era.

Now, I was fortunate enough in one sense that, having grown up in the south and then transferred to New York, I was shifted between different levels of education, and so I was a top student in my class in North Carolina, and then I was immediately the bottom student in my class in Harlem, and I was way behind whoever was next to the bottom, because the educational differences were just that great. A very painful period of adjustment, but there was no racial issue involved, since all the other kids ahead of me were all black. And so I got through that, and then for a second time in my life, I had gone out on my own when I was 17, and I didn't return to college full-time until I was about 25. For the second time in my life, I went into an environment that was very difficult compared to what I'd been used to, and once again I was way behind and I was in danger of flunking out of school the first semester.

Lamb: Where were you then?

Sowell: Harvard. Really, it really is incredible -- for the first time in your life, in ten years, you're a full-time student, and you're a full-time student at Harvard, without a high school diploma. So there were little difficulties.

Lamb: And studying what?

Sowell: Oh, at that stage I was studying just general things, but I majored in economics, and all my degrees are in economics. Again I had an enormous adjustment to make, but there was no one there to tell me, "All these white professors have it in for you and that's why you're doing badly." Because first of all, I had done badly in Harlem, and I'd overcome, and I was doing badly there and I overcame it, but ...

Lamb: What happened -- take that Harvard experience through. How long did you stay at Harvard?

Sowell: Oh, I graduated.

Lamb: Graduated from Harvard.

Sowell: From Harvard.

Lamb: I'm sorry, I thought you said earlier you went to Howard.

Sowell: I went there for a year and a half, and then I transferred to Harvard.

Lamb: Oh, okay.

Sowell: You see, but I was going to Howard in the evening while working full-time during the day so when I went to Harvard I was a full-time student for the first time in ten years, and so that was a...

Lamb: And what years did you go to Harvard?

Sowell: I graduated in class of '58 -- so that you can understand how the student would find this plausible. I talked to a black man recently, a lawyer, who said when he was in law school, he was told when he first got there, that Professor X never gives black students more than a C, you know, and he got a B+, but there was great consternation because one of the myths had fallen. But, it's truly criminal what goes on in terms of using and manipulating the students to serve all kinds of external purposes.

Lamb: Can you give us an idea of the kind of external purposes you're talking about?

Sowell: Oh, political purposes. I just a couple of days ago was told by someone from Wellesley that there's a divestment campaign at Wellesley, demonstrations, the whole thing, and that those black girls who did not want to participate in that were threatened with violence -- and that's not unique. At Stanford the Hispanic students, some Hispanic students, have complained that the Hispanic establishment has threatened them if they don't want to go along with what's being said and done, and they claim that only 15% of the Hispanic students at Stanford have ever attended a single event spons.ored by the Hispanic establishment, which speaks boldly in their name. Ah, and so you have this kind of thing going on at these schools across the country. Again, notice, that once, once you let in the students who cannot make, meet the academic standards, you're going to end up having to let in professors who can't meet the academic standards. You're going to have to create courses that don't meet the academic standards.

Lamb: Correct me on the, on the names and everything. Derrick Bell?

Sowell: Yes.

Lamb: Harvard Law School, black man.

Sowell: Yes.

Lamb: Threatened the law school if they didn't hire a black woman, he's going, he's leaving?

Sowell: Well, if I understand it correctly, he's taking unpaid leave until such time as they hire a woman of color, as he says. Well, he's also said that by black, he does not mean skin color, he means those who are really black, not those who think white and look black. And so what he is really saying is he wants ideological conformity in the people that are hired to fill this position. That's not uncommon either. I know a black woman, for example, who had a Ph.D. -- she's had a book published, she has another contract on another book, she's taught at a couple of very nice places, she has a devil of a time getting a job -- not a job in a prestigious institution, a job teaching at a college. And the reason is that she gets shot down, blackballed, whatever, by people who don't like her ideology. That's happening not only racially, it's also happening where race is not an issue. In a law school, I learned recently, there's a woman who was being considered for a tenured position, and all the men voted for her and all the woman voted against her, because she does not follow radical feminism. And so you're getting these ideological tests, so that at the very time that there's all this mouthing of the word diversity, there is this extremely narrow ideological conformity that is being enforced wherever people have the power to enforce it.

Lamb: What did you think of Derrick Bell's whole plan?

Sowell: Well, his chances of success will depend on whether or not he has overestimated his importance to the Harvard Law School. I think it would be a tragedy if they caved in, and I was very pleased to see that they seemed to show some backbone, which is quite rare among academics.

Lamb: Now, what do you think of the press treatment of him?

Sowell: It's been quite gentle.

Lamb: I mean, is he a hero?

Sowell: To me?

Lamb: No. Basically, I mean, from the press coverage, you've seen, is he a hero to the ...?

Sowell: Well, he's looked at as an idealist who is self-sacrificing and so on. I suppose one could, if one wanted to look at it that way, have seen Hitler that way in his early days. It's just a question of where that kind of idealism leads. He has launched a despicable attack on a young black professor at the law school who doesn't go along with this. A young man named Randall Kennedy, who has written a very thoughtful, intelligent article last June in the Harvard Law Review, questioning some of the assumptions that people are making, people like Derrick Bell and doing it in a very gentlemanly as well as very logical way, empirical way, and that's not what they want. They want the conclusion to be that -- they want him to march in lock step and he won't do it, and they're doing their best to make life impossible for him.

Lamb: What do you think Harvard will do?

Sowell: I've heard that Kennedy -- and I don't know this -- I've heard that he has tenure, so I think that he may be all right.

Lamb: But, I mean, what do you think they'll do with ...

Sowell: Derrick Bell?

Lamb: Yes.

Sowell: I hope that they will resist it, and since it's gotten so much publicity, I'm not sure they could stand to cave in to it. I was very pleased to see that Alan Dershowitz of Harvard had criticized this and that he picked up the fact that what Bell is really asking for is not only that people be hired by race, but that they be hired to fit Derek Bell's ideology.

Lamb: What would happen if this was going on at Stanford Law School?

Sowell: They'd have caved in long ago.

Lamb: Stanford Law School would have?

Sowell: Yes. I think so. It's a judgment call, but that's my judgment.

Lamb: Why would they do it so quickly?

Sowell: Just looking at their track record. They have perfected the technique of preemptive surrender.

[ Full interview: https://youtu.be/T2hPQ86lGV0 ]

Reminder:

“Unlike traditional civil rights discourse, which stresses incrementalism and step-by-step progress, critical race theory questions the very foundations of the liberal order, including equality theory, legal reasoning, Enlightenment rationalism, and neutral principles of constitutional law.”

“As mentioned earlier, critical race scholars are discontented with liberalism as a framework for addressing America’s racial problems. Many liberals believe in color blindness and neutral principles of constitutional law. They believe in equality, especially equal treatment for all persons, regardless of their different histories or current situations.” -- "Critical Race Theory: An Introduction" by Delgado and Stefancic.

Thomas Sowell saw this coming 30 years ago. Of course, Harvard now routinely capitulates to tantrums; the most recent FIRE Campus Free Speech Rankings gave Harvard the lowest grade numerically possible due to it acceding to shrill, illiberal ideological demands.

#Thomas Sowell #Brian Lamb #Booknotes #Derrick Bell #ideology #academic corruption #ideological corruption #ideological capture #ideological conformity #critical race theory #diversity #identity politics #purity tests

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