#path matrix in data structure

[esotericworld]

DARPA Brain Technologies: Electrical Prescriptions (ElectRx) This is some pretty old tech source. Wonder what kind of psychedelics though? The ElectRx program aims to help the human body heal itself through neuromodulation of organ functions using ultraminiaturized devices, approximately the size of individual nerve fibers, which could be delivered through minimally invasive injection.

Neural Engineering System Design (NESD) Looks like they're beating Elon to the punch here. The NESD program aims to develop an implantable neural interface able to provide unprecedented signal resolution and data-transfer bandwidth between the brain and the digital world.

Next-Generation Nonsurgical Neurotechnology (N3) The N3 program aims to develop a safe, portable neural interface system capable of reading from and writing to multiple points in the brain at once. Whereas the most advanced existing neurotechnology requires surgical implantation of electrodes, N3 is pursuing high-resolution technology that works without the requirement for surgery so that it can be used by able-bodied people.

Six Paths to the Nonsurgical Future of Brain-Machine Interfaces

Nonsurgical Neural Interfaces Could Significantly Expand Use of Neurotechnology Targeted Neuroplasticity Training (TNT) Matrix Shit The TNT program seeks to advance the pace and effectiveness of cognitive skills training through the precise activation of peripheral nerves that can in turn promote and strengthen neuronal connections in the brain. TNT will pursue development of a platform technology to enhance learning of a wide range of cognitive skills, with a goal of reducing the cost and duration of the Defense Department’s extensive training regimen, while improving outcomes.

Neuro Function, Activity, Structure and Technology (Neuro-FAST) The Neuro-FAST program seeks to enable unprecedented visualization and decoding of brain activity to better characterize and mitigate threats to the human brain, as well as facilitate development of brain-in-the loop systems to accelerate and improve functional behaviors. The program has developed CLARITY, a revolutionary tissue-preservation method, and builds off recent discoveries in genetics, optical recordings and brain-computer interfaces. Restoring Active Memory (RAM) The RAM program aims to develop and test a wireless, fully implantable neural-interface medical device for human clinical use. The device would facilitate the formation of new memories and retrieval of existing ones in individuals who have lost these capacities as a result of traumatic brain injury or neurological disease.

[smartscreenai]

Top Skills Audit Tools to Identify Skill Gaps in Your Workforce

Staying ahead means continuously developing your workforce’s skills. Identifying skill gaps is crucial for maintaining a competitive edge, improving productivity, and fostering employee growth. Conducting a skills audit helps organizations understand their current capabilities and plan targeted training programs. With numerous skills audit tools available, selecting the right one can be overwhelming. This blog will explore the top skills audit tools that can help you effectively identify skill gaps and empower your workforce.

Why Conduct a Skills Audit?

A skills audit systematically evaluates the existing skills of employees against the skills required to meet business objectives. The benefits include:

Pinpointing skill deficiencies that may hinder growth.

Guiding learning and development investments.

Enhancing employee engagement through personalized growth plans.

Supporting succession planning and talent management.

Key Features to Look for in Skills Audit Tools

When choosing skills audit software, consider:

User-Friendly Interface: Easy for both HR teams and employees to navigate.

Customizable Skill Libraries: Align audits with your specific industry and business needs.

Detailed Reporting: Actionable insights to make informed training decisions.

Integration Capabilities: Sync with HR and Learning Management Systems (LMS).

Mobile Accessibility: Enable employees to participate anytime, anywhere.

Top Skills Audit Tools in 2025

1. Skillsoft Skillsoft provides comprehensive skill assessment and learning solutions. Its platform offers customizable skill inventories and detailed gap analysis reports. The integration with training modules makes it easy to transition from assessment to development.

2. SAP SuccessFactors A leading HR management suite, SAP SuccessFactors includes robust skills management features. It helps track employee competencies, identify gaps, and align training programs with business goals. Its analytics capabilities provide valuable workforce insights.

3. Saba TalentSpace Saba TalentSpace combines skills audits with performance management. It offers personalized learning paths based on audit results, driving targeted development. The tool supports continuous feedback and real-time skill tracking.

4. TalentGuard TalentGuard offers advanced skills assessment tools with an emphasis on career pathing. Its skills matrix and gap analysis functionalities help organizations map current capabilities and future needs efficiently.

5. Cflow Cflow, known for its workflow automation capabilities, also supports skills audits through customizable forms and data tracking. It enables HR teams to automate assessment workflows and generate reports that highlight skill gaps quickly and accurately.

How to Effectively Use Skills Audit Tools

Engage Employees: Communicate the importance of the skills audit to ensure honest participation.

Customize Assessments: Tailor skill categories to reflect your organizational needs.

Analyze Data Thoroughly: Use insights to identify critical gaps and prioritize training efforts.

Implement Development Plans: Link audit results with training programs and performance goals.

Review Regularly: Conduct audits periodically to track progress and evolving skill requirements.

Conclusion

Identifying skill gaps through effective skills audit tools is vital for driving workforce development and business success. The right tool helps you gather precise data, enabling targeted training and improved employee performance. Whether you choose specialized platforms like Skillsoft or integrated solutions like Cflow, adopting a structured approach to skills auditing ensures your organization is well-prepared for future challenges.

SITES WE SUPPORT

Smart Screen AI - WordPress

SOCIAL LINKS Facebook Twitter LinkedIn

[callofdutymobileindia]

Machine Learning Syllabus: What Mumbai-Based Courses Are Offering This Year

As Artificial Intelligence continues to dominate the future of technology, Machine Learning (ML) has become one of the most sought-after skills in 2025. Whether you’re a data enthusiast, a software developer, or someone looking to transition into tech, understanding the structure of a Machine Learning Course in Mumbai can help you make informed decisions and fast-track your career.

Mumbai, a city synonymous with opportunity and innovation, has emerged as a growing hub for AI and ML education. With a rising demand for skilled professionals, leading training institutes in the city are offering comprehensive and job-focused Machine Learning courses in Mumbai. But what exactly do these programs cover?

In this article, we break down the typical Machine Learning syllabus offered by Mumbai-based institutes, highlight key modules, tools, and career pathways, and help you understand why enrolling in a structured ML course is one of the best investments you can make this year.

Why Machine Learning Matters in 2025?

Before diving into the syllabus, it’s essential to understand why machine learning is central to the tech industry in 2025.

Machine learning is the driving force behind:

Predictive analytics

Recommendation engines

Autonomous systems

Fraud detection

Chatbots and virtual assistants

Natural Language Processing (NLP)

From healthcare to fintech and marketing to logistics, industries are deploying ML to enhance operations, automate decisions, and offer personalized services. As a result, the demand for ML engineers, data scientists, and AI developers has skyrocketed.

Overview of a Machine Learning Course in Mumbai

A Machine Learning course in Mumbai typically aims to:

Build foundational skills in math and programming

Teach practical ML model development

Introduce deep learning and advanced AI techniques

Prepare students for industry-level projects and interviews

Let’s now explore the typical modules and learning paths that top-tier ML programs in Mumbai offer in 2025.

1. Foundation in Programming and Mathematics

🔹 Programming with Python

Most courses start with Python, the industry-standard language for data science and ML. This module typically includes:

Variables, loops, functions

Data structures (lists, tuples, dictionaries)

File handling and error handling

Introduction to libraries like NumPy, Pandas, Matplotlib

🔹 Mathematics for ML

You can’t master machine learning without understanding the math behind it. Essential topics include:

Linear Algebra (vectors, matrices, eigenvalues)

Probability and Statistics

Calculus basics (gradients, derivatives)

Bayes’ Theorem

Descriptive and inferential statistics

These foundations help students grasp how ML models work under the hood.

2. Data Handling and Visualization

Working with data is at the heart of ML. Courses in Mumbai place strong emphasis on:

Data cleaning and preprocessing

Handling missing values

Data normalization and transformation

Exploratory Data Analysis (EDA)

Visualization with Matplotlib, Seaborn, Plotly

Students are often introduced to real-world datasets (CSV, Excel, JSON formats) and taught to manipulate data effectively.

3. Supervised Machine Learning

This core module teaches the backbone of most ML applications. Key algorithms covered include:

Linear Regression

Logistic Regression

Decision Trees

Random Forest

Naive Bayes

Support Vector Machines (SVM)

Students also learn model evaluation techniques like:

Confusion matrix

ROC-AUC curve

Precision, recall, F1 score

Cross-validation

Hands-on labs using Scikit-Learn, along with case studies from domains like healthcare and retail, reinforce these concepts.

4. Unsupervised Learning

This segment of the syllabus introduces students to patterns and grouping in data without labels. Key topics include:

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA)

Anomaly Detection

Students often work on projects like customer segmentation, fraud detection, or market basket analysis using unsupervised techniques.

5. Model Deployment and MLOps Basics

As real-world projects go beyond model building, many Machine Learning courses in Mumbai now include modules on:

Model deployment using Flask or FastAPI

Containerization with Docker

Version control with Git and GitHub

Introduction to cloud platforms like AWS, GCP, or Azure

CI/CD pipelines and monitoring in production

This gives learners an edge in understanding how ML systems operate in real-time environments.

6. Introduction to Deep Learning

While ML and Deep Learning are distinct, most advanced programs offer a foundational understanding of deep learning. Topics typically covered:

Neural Networks: Structure and working

Activation Functions: ReLU, sigmoid, tanh

Backpropagation and Gradient Descent

Convolutional Neural Networks (CNNs) for image processing

Recurrent Neural Networks (RNNs) for sequential data

Frameworks: TensorFlow and Keras

Students often build beginner deep learning models, such as digit recognizers or sentiment analysis tools.

7. Natural Language Processing (NLP)

With AI’s growing use in text-based applications, NLP is an essential module:

Text preprocessing: Tokenization, stopwords, stemming, lemmatization

Term Frequency–Inverse Document Frequency (TF-IDF)

Sentiment analysis

Named Entity Recognition (NER)

Introduction to transformers and models like BERT

Hands-on projects might include building a chatbot, fake news detector, or text classifier.

8. Capstone Projects and Portfolio Development

Most Machine Learning courses in Mumbai culminate in capstone projects. These simulate real-world problems and require applying all learned concepts:

Data ingestion and preprocessing

Model selection and evaluation

Business interpretation

Deployment and presentation

Example capstone projects:

Predictive maintenance in manufacturing

Price prediction for real estate

Customer churn prediction

Credit risk scoring model

These projects are crucial for portfolio building and serve as talking points in interviews.

9. Soft Skills and Career Preparation

The best training institutes in Mumbai don’t stop at technical skills—they invest in career readiness. These include:

Resume building and portfolio review

Mock technical interviews

Behavioral interview training

LinkedIn optimization

Job referrals and placement assistance

Students also receive guidance on freelancing, internships, and participation in Kaggle competitions.

A Standout Option: Boston Institute of Analytics

Among the many training providers in Mumbai, one institute that consistently delivers quality machine learning education is the Boston Institute of Analytics.

Their Machine Learning Course in Mumbai is built to offer:

A globally recognized curriculum tailored for industry demands

In-person classroom learning with expert faculty

Real-world datasets and capstone projects

Deep exposure to tools like Python, TensorFlow, Scikit-learn, Keras, and AWS

One-on-one career mentorship and resume support

Dedicated placement assistance with a strong alumni network

For students and professionals serious about entering the AI/ML field, BIA provides a structured and supportive environment to thrive.

Final Thoughts: The Future Is Machine-Learned

In 2025, machine learning is not just a skill—it's a career catalyst. The best part? You don’t need to be a Ph.D. holder to get started. All you need is the right course, the right mentors, and the commitment to build your skills.

By understanding the detailed Machine Learning syllabus offered by Mumbai-based courses, you now have a roadmap to guide your learning journey. From Python basics to deep learning applications, and from real-time deployment to industry projects—everything is within your reach.

If you’re looking to transition into the world of AI or upgrade your existing data science knowledge, enrolling in a Machine Learning course in Mumbai might just be the smartest move you’ll make this year.

[futurensetechnologies]

The Role of Mathematics in Data Science: Do You Really Need It?

Introduction

As data science continues to transform industries, many aspiring professionals wonder about the academic foundation required to succeed. One common path is an Information Technology Management degree, which combines technical knowledge with business strategy. But a key question remains—how important is mathematics in this field? This blog breaks down the role math plays in data science and whether it’s a must-have skill for building a successful career.

Why Math is Considered Foundational in Data Science

Mathematics is the engine that drives data science. While tools and programming languages help process data, math enables professionals to understand patterns, draw insights, and build accurate models. If you’re pursuing a Master’s in information management, a solid grasp of key mathematical concepts is essential for applying theory to real-world problems.

Here’s why math is foundational in data science:

Statistics and probability help in making predictions and understanding data trends.

Linear algebra is crucial for machine learning models, especially in image and language processing.

Calculus plays a role in optimization and fine-tuning algorithms.

Discrete mathematics helps in logic building, algorithm design, and data structures.

Numerical analysis supports dealing with real-time data computations and error management.

A clear understanding of these areas gives data professionals a competitive edge and deepens their analytical capabilities.

Core Areas of Mathematics Used in Data Science

Mathematics is at the heart of data science. While coding and software tools make execution easier, the logic and theory that drive data models come from mathematical principles. If you’re pursuing a Master’s in Information Systems, understanding the core areas of mathematics can help you connect technical knowledge with strategic data insights.

Here are the primary branches of math used in data science:

1. Statistics and Probability

These are the building blocks of data analysis. They help in understanding data distributions, correlations, hypothesis testing, and predictive modeling.

2. Linear Algebra

Essential for machine learning and deep learning, linear algebra supports matrix operations, vector transformations, and dimensionality reduction techniques like PCA (Principal Component Analysis).

3. Calculus

Mainly used for optimization, calculus helps fine-tune algorithms by minimizing or maximizing functions—important in training machine learning models.

4. Discrete Mathematics

This area supports algorithm development, graph theory, and logical reasoning—key for structuring data-driven solutions.

5. Numerical Methods

Important for handling real-world data problems, such as approximations, simulations, and missing or noisy data.

Each of these disciplines contributes to building, evaluating, and improving the performance of data models. Understanding these mathematical tools allows professionals to move beyond surface-level data analysis and dive into more meaningful, scalable solutions that impact business decisions.

Can You Learn Data Science Without Strong Math?

Futurense believes that data science should be accessible to all, regardless of your math background. While mathematics enhances your understanding of data science, it shouldn’t be a roadblock. With the right guidance, resources, and learning structure, anyone can build a solid foundation in data science—even without being a math expert.

Many students pursuing an MS in information systems come from diverse academic backgrounds, including business, humanities, or IT. While they may not have in-depth math skills initially, structured learning pathways allow them to pick up the necessary concepts along the way.

Here’s how you can learn data science without a strong math foundation:

Focus on application-first learning—understand the “why” before diving into the “how.”

Use tools like Python, R, and libraries such as scikit-learn or pandas, which simplify complex computations.

Practice with real-world datasets to see the impact of algorithms visually.

Enroll in beginner-friendly courses like those offered by Futurense, which explain mathematical concepts through relatable examples.

Supplement your learning with basic math resources—only what’s required to understand models, not to become a mathematician.

In essence, while math helps, it’s not a prerequisite. With the right mindset and support, your journey into data science can be successful and rewarding, even without being a math whiz.

Also, read this blog: Master’s in MIS in the USA: A Comprehensive Guide

How to Strengthen Your Math for Data Science

Strengthening your math skills can significantly improve your ability to understand and apply data science concepts effectively. Whether you’re currently pursuing an MS in information systems or planning to enroll in a Master’s in Information Systems, building a strong foundation in mathematics will enhance both your academic and professional journey.

Here’s how to sharpen your math for data science:

Start with the basics: Brush up on core topics like statistics, probability, linear algebra, and calculus through online platforms like Khan Academy, Coursera, or edX.

Practice consistently: Apply concepts regularly through hands-on projects, real datasets, or coding challenges.

Use visual tools: Leverage visual explanations and interactive tools to understand complex mathematical concepts more easily.

Connect theory to practice: Use Python libraries like NumPy and SciPy to see how mathematical operations work in data science environments.

Join study groups or forums: Engaging with peers can help reinforce learning and clarify doubts.

With consistent effort and the right resources, improving your math skills becomes less overwhelming and more rewarding, especially in a field where data-driven decisions matter most.

FAQ

1. Do I need to be good at math to learn data science?

While strong math skills are helpful, they are not mandatory to begin. With practical resources and supportive courses, you can strengthen your math knowledge alongside your data science learning journey.

2. What kind of math is used in data science?

Key areas include statistics, probability, linear algebra, calculus, and discrete mathematics. These help in understanding data structures, building models, and interpreting results.

3. Is a Master’s in Information Systems math-heavy?

A Master’s in Information Systems involves some mathematical components like statistics and data analysis, but it also focuses on technology, management, and strategy.

4. How can I improve my math skills for data science?

Start with beginner-friendly courses online, work on real datasets, and practice using Python libraries like NumPy and pandas. Regular practice is key.

5. Is math more important than coding in data science?

Both are important. Coding helps you implement models, while math helps you understand and improve them. A balance of both skills leads to better outcomes.

Source URL: https://postyourarticle.com/role-of-mathematics-in-data-science/

[jeffreyhammel1]

Risk Assessment Tools: Enhancing Your Company's Risk Management

Risk is part of running a business, but ignoring it is the fast track to disruption. I’ve worked with organizations across industries—finance, manufacturing, tech, logistics—and I’ve seen one pattern repeat itself: the companies that manage risk well don’t leave anything to chance. They rely on a structured system that starts with identifying, assessing, and tracking threats before those threats turn into problems. That’s where risk assessment tools come in. These tools aren’t just checklists—they’re decision-making powerhouses that help leaders prevent losses, protect assets, meet compliance, and improve business performance. In this article, I’ll walk you through how risk assessment tools work, which ones matter most, and how you can apply them to create smarter, faster, and more confident operations.

What Are Risk Assessment Tools?

Risk assessment tools help businesses evaluate potential threats and determine how likely they are to occur and how severe their impact might be. You’re looking at everything from financial risk to cybersecurity gaps to compliance issues and supply chain vulnerabilities. The purpose is simple: reduce surprises and build a buffer between your company and costly disruptions. These tools provide structure. They eliminate guesswork. And most importantly, they turn risk management into something that’s repeatable and measurable.

When organizations don’t use formal risk assessment tools, decisions tend to be reactive. Teams scramble when something goes wrong, and valuable time and money get wasted. A good risk assessment tool keeps that from happening. It lets you catch problems early and prioritize your response based on real data—not just gut feelings or assumptions.

Common Types of Risk Assessment Tools

Over the years, I’ve worked with a range of tools, each suited for different scenarios. The goal is to match the right tool to the problem you’re solving.

1. Risk Matrix

This is the tool most people start with—and for good reason. It’s a visual grid that maps the likelihood of a risk occurring against the severity of its consequences. Simple, but powerful. It gives leadership a quick overview of what needs urgent attention versus what can be monitored with less intensity.

2. Failure Modes and Effects Analysis (FMEA)

FMEA is common in engineering and manufacturing, but I’ve seen it used effectively in business operations too. It identifies potential points of failure in a process, product, or service. Then it assigns scores for severity, likelihood, and detectability. The result is a Risk Priority Number (RPN) that helps teams focus on the most critical issues.

3. Bowtie Analysis

Bowtie diagrams map out risk causes on one side and consequences on the other, with a "knot" in the middle representing the risk event itself. It’s helpful when you need to visualize how different factors connect and where controls need to be placed. This method is popular in industries with complex operational risks, like oil and gas or aviation.

4. Decision Trees

Decision trees break down choices and their possible outcomes in a flowchart format. I use them when risks involve multiple decision paths and potential consequences. They’re especially helpful in financial modeling, strategic planning, and project management.

5. SWOT-Based Risk Identification

Some companies start with SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis and evolve it into a more formal risk tool. It’s not quantitative like the others, but it does help surface threats that might be overlooked in more technical models.

Each of these tools supports different types of thinking. Some are better for tactical response planning. Others are ideal for strategic risk review. The trick is knowing which one to use and when to use it.

Why Every Company Needs Risk Assessment Tools

Whether you're running a startup or managing operations in a multinational corporation, risk management shouldn’t be informal. Companies without structured risk assessments often fall into two traps: underestimating small risks or ignoring big ones until it’s too late.

Here’s what I’ve learned: using the right tools improves decision-making at every level. It builds a culture of accountability because teams can see what’s at stake and where they fit into the process. When risk is visible, it becomes easier to manage.

Risk assessment tools also help organizations meet compliance requirements. Regulators want to see that you're actively identifying and mitigating risks—not just reacting when things go wrong. That means having records, scoring models, dashboards, and mitigation plans ready to go.

There’s also a huge operational benefit. These tools help leaders allocate resources more effectively. You’re no longer chasing every potential problem. You’re targeting the ones that actually matter.

The Role of Technology in Risk Assessment

Ten years ago, most of this work was done with spreadsheets. Now, software has taken over. Platforms like LogicManager, ETQ Reliance, and SafetyCulture offer real-time dashboards, incident tracking, workflow automation, and reporting features—all in one place.

These platforms aren’t just for big corporations. Small and medium-sized businesses benefit just as much, especially when they don’t have a dedicated risk department. The automation built into these tools makes it easier to keep assessments up to date, track progress on action items, and stay on top of compliance deadlines.

What I find most valuable is the predictive analytics many of these tools now offer. Based on historical data and trends, they can forecast risk exposure across various business units. That kind of foresight turns risk management from a defensive activity into a strategic asset.

Top Risk Assessment Tools for Businesses

LogicManager – Centralized risk management platform with customizable assessments.

ETQ Reliance – Offers risk matrices, FMEA tools, and decision tree modeling.

SafetyCulture – Visual risk tools like matrices for real-time prioritization.

AuditBoard RiskOversight – Integrated risk workflows tied to compliance and audit.

Resolver Risk Management Software – Great for tracking risk incidents and responses.

Risk Assessment in Practice: What to Watch Out For

No matter how good your tools are, the process only works if people use it consistently and correctly. I’ve seen teams fall into the trap of “checkbox compliance”—completing risk assessments because they have to, not because they believe in the value.

That’s where training and culture come in. Your team needs to understand that risk tools aren’t just for auditors. They’re there to help prevent chaos, streamline operations, and protect your reputation. And that message needs to come from the top.

Another common issue is outdated data. A risk matrix built six months ago may not reflect current threats. If your risk environment changes—and it always does—your tools should be updated accordingly. I recommend setting up a regular review cycle, whether that’s monthly, quarterly, or triggered by major business changes.

Watch out for over-complication too. Some companies get carried away with advanced models and forget that usability matters. A tool is only as effective as its ability to communicate risk to decision-makers. Keep the scoring clear. Keep the priorities front and center.

Aligning Risk Assessment with Business Strategy

Risk assessment isn’t a separate function. It’s a part of everyday business decisions. When launching a new product, expanding into a new market, or changing suppliers—those are risk moments. Your assessment tools should feed directly into those conversations.

I’ve worked with boards and leadership teams who now require a risk review before approving major projects. It’s become a standard part of their governance. That level of integration makes risk management real—it’s not a side activity, it’s baked into strategy.

This alignment also helps organizations respond faster. If a supply chain partner shuts down unexpectedly, having a risk register that already flagged that exposure means you can act without delay. If a competitor launches a new tech feature, your risk tools might already show where your product could be outpaced and what to do about it.

In Conclusion

Risk assessment tools turn guesswork into smart decision-making. They help companies protect what matters, make better calls under pressure, and build a reputation for being reliable and prepared. When these tools are used consistently and kept up to date, they become one of the most valuable assets a business can have. And with the right software, data, and team culture, risk management doesn’t just reduce problems—it creates opportunities for smarter, faster growth.

Learn how risk assessment tools can enhance your business strategy by visiting my Facebook.

[nyt1ba]

The war with Mother Sphere   &.   Naytiba origins.

   Mother Sphere is an artificial intelligence built by the scientist and former CEO of Eidos company Raphael Marks in order to lead humanity towards the path of evolution.   Indeed,   Mother Sphere helped solve various issues humans were facing,   introduced new solutions and guided them towards a path of human advancement never thought possible before.   However,   her view on humans would change with time with the production of the Andro-Eidos,   viewing them as lesser species and deciding to replace all human life with the androids.   She would go rogue and turn against humanity by launching a wide scale attack with her ability to command the Andro-Eidos,   and control weapons and machinery with ease due to her having access to data banks all around the world within the NETWORK.   This would eventually lead to the first war.   Humans lost and were forced to take refuge underground in order to survive.   During that time Raphael and the survivors who took refuge in the underground facilities Altess Levoire,   Abyss Leviour and Matrix-11,   formed an opposing force against the Andro-Eidos in order to put an end to the war and save the remaining humans.

 According to a discovered log data,   it reveals that the central computer,   identified as the AI,   RAFFY,   worked alongside a group of humans identified as The Humanity Liberation Front,   including one member known as Raphael,   an admin.   Their goal had been to defeat the machines,   Andro-Eidos,   and begun working to create the Naytibas through gene-genetic manipulation.

   The first experiment that lead to the existence of what is later known as Naytibas was meant to better condition humans for survival,   giving them the ability to endure harsher environments,   better endurance   &.   enhance their strength so that they may be on equal footing with the Andro-Eidos.   The first experiment was a success,   preformed by Raphael himself on himself with the other members monitoring the changes,   prepared to act accordingly and contain any threat if it were to arise,   having been aware of the dangers of genetic manipulation the slightest mistake could have catastrophic results.   Thankfully,   the experiment proved to be a success and they began the process of enhancing humanity on a bigger scale by building capsules that could hold larger numbers for speed and efficiency as Mother Sphere's threat was growing at a rapid speed.   The first war would continue then with humans tipping the scale in their favour,   without the need to rely on weapons or machinery that Mother Sphere could breach and control quite easily,   they were able to fight back and cost her some heavy losses.

   At some point however,   Mother Sphere discovered the location of the organization,   where RAFFY was commanded to release the subjects despite their instability at the time to counter-attack the sent squad. Unfortunately,   this caused a breach in quarantine and the release of the Naytibas who were incapable of identifying friend and foe.

   While the Naytiba experiments were a valuable solution to end the war and preserve humanity a little longer,   they didn't always guarantee stable results,   manipulation of genetic structures had the dangers of creating defects,  instability   &.   what became a virus-like disease that would spread if not contained properly,   those were meant to be quarantined until the problem is reversed and solved.   Unfortunately,   there was no time,   Mother Sphere would eventually locate the front's whereabouts and send out a squad to terminate their growing threat that would disturb her new world order on the surface,   and in a final act of desperation the AI was commanded by Raphael to release all subjects as counter attack.   Which would prove to be a force that would draw the Andro-Eidos out,   but at the cost of more human lives with the released Naytibas growing hostile against all that came in their way.   And thus began the second and final war.   Humans began to grow desperate,   sought to use any means necessary in order to survive,   they gave up their humanity willingly,   either by succumbing to the virus or their hatred towards the Andro-Eidos that turned them more hostile due to the unstable nature of them being released before their time.

   With a scream that came from the abyss of Levoire,   a new existence was born.   This marked an irreversible change on Earth,   and the truth that Raphael Marks had once existed as a man and not the Elder Naytiba was forgotten.   No one knows whether he willingly became a test subject,   turned into a monster due to contamination,   or was revived as a Naytiba by others.   Either way,   the reality is that both him and the world have been damaged beyond repair.

   The final stage of the second war was led by Raphael himself,   who by that time,   had lost his humanity as well and became what is now known as the monster called the Elder Naytiba.   He could                 to some extent back then                 command the other Naytibas to fight back,   leading a vicious front that forced Mother Sphere and the Andro-Eidos to retreat back to the space station where they took refuge in the colony in space.   Naytibas would not cease their attack then,   climbing the orbit elevator and nearly reaching the colony itself if it weren't for Mother Sphere destroying the orbit ring,   an act that would not only bring about her enemies demise but the destruction of earth itself,   it created a catastrophic wave that hit earth,   with the remains crashing directly onto the planet.   Victory came at a bitter cost,   as earth was left in disarray,   later becoming a barren wasteland.   Among humans,   Raphael was the only one to survive,   kept in touch with his humanity and remained somehow sane unlike his comrades.   While the surviving Andro-Edios on earth were abandoned by Mother Sphere and her select few,   left to fend for themselves against the Naytibas that now crawl upon the surface.

   Raphael would conceal the truth of his humanity,   but with much difficulty with his mutation worsened by contamination,   when in the beginning he was merely an enhanced human he became a monster like the rest of his kind.   He'd have bouts of instability   &.   loss of control,   his anger worsened by grief.   It isn't until he came across Oracle,   a Naytiba that managed to reclaim his consciousness by accident when attacking an Andro-Eidos and fusing with them,   and learned from him how to better control himself and keep his more monstrous tendencies at bay.   Moreover,   with the other Naytibas going berserk,   his connection with them was somewhat severed,   he can experience their   pain,   suffering,   anger,   but as far as control he doesn't have a hand in the matter anymore.   Nevertheless,   Naytibas would continue to reproduce as long he's living,   growing more hostile and monster-like the more they evolve.   While those that couldn't escape Levoire appear to have retained some human features unlike the rest.

   Mother Sphere would rewrite history after the war,   claiming that the Andro-Eidos are the real humans,   forced out of earth by the Naytibas,   whom she claimed to be an alien species who took over earth.   She would resume the battle still,   sending air-born squads every few years to locate the Elder Naytiba,   Raphael,   and put an end to him,   thus killing all other Naytibas in the process.   A covert plan to erase humanity from earth and return back with the Andro-Eidos as the better species.   Raphael on the other hand,   is weary of war,   he sought to save mankind through more peaceful solutions,   having seen the benefit of Oracle's fusion he devised a plan to fuse himself with the most advanced Andro-Eidos,   thus saving both species as he had grown sympathetic to the androids that occupied Xion,   this way he could grant them a better future and restore humans back to what they used to be.   Until then,   it's a matter of waiting.

[programmingandengineering]

CS590 homework 6 – Graphs, and Shortest Paths

Develop a data structure for directed, weighted graphs G = (V, E) using an adja­cency matrix representation.. The d.atatype int is used to store the weight of edges. int does not allow one to represent ±co. Use the values INTYIN and IN’T_MAX (defined in limits .h) instead. include <limits-h5 in.t d, e; d = INT_MAX; e = INT_MIN; if (e == ih–r_mir;) if (d I = IN–1_102A10           . Develop a…

[emilyratajkowski164395]

In network science, embedding refers to the process of transforming nodes, edges, or entire network structures into a lower-dimensional space while preserving the network's essential relationships and properties. Network embeddings are particularly valuable for machine learning applications, as they allow complex, non-Euclidean data (like a social network) to be represented in a structured, high-dimensional vector format that algorithms can process.

Building on the concept of embeddings in network science, these transformations unlock several advanced applications by enabling traditional machine learning and deep learning methods to operate effectively on graph data. A key advantage of embeddings is their ability to encode structural and relational information about nodes in a network into compact, dense vectors. This allows complex, sparse graphs to be represented in a way that preserves both local connections (like close friendships) and global structure (like communities within the network).

There are multiple approaches to generating these embeddings, each with its own strengths:

Random Walk-based Methods: Techniques like DeepWalk and node2vec use random walks to capture the network’s context for each node, similar to how word embeddings like Word2Vec capture word context. By simulating paths along the graph, these methods learn node representations that reflect both immediate neighbors and broader network structure.

Graph Neural Networks (GNNs): Graph neural networks, including variants like Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), use neural architectures designed specifically for graph data. GNNs aggregate information from neighboring nodes, creating embeddings that adaptively capture the influence of each node’s surroundings. This is especially useful in tasks that require understanding both individual and community-level behavior, such as fraud detection or personalized recommendations.

Matrix Factorization Techniques: These methods, like LINE (Large-scale Information Network Embedding), decompose the graph adjacency matrix to find latent factors that explain connections within the network. Matrix factorization can be effective for representing highly interconnected networks, such as knowledge graphs, where the relationships between entities are intricate and abundant.

Higher-order Proximity Preserving Methods: Techniques like HOPE (High-Order Proximity preserved Embeddings) go beyond immediate neighbors, capturing higher-order structural relationships in the network. This approach helps model long-distance relationships, like discovering latent social or biological connections.

Temporal Network Embeddings: When networks evolve over time (e.g., dynamic social interactions or real-time communication networks), temporal embeddings capture changes by learning patterns across network snapshots, allowing predictive tasks on network evolution, like forecasting emerging connections or trends.

Network embeddings are powerful across disciplines. In financial networks, embeddings can model transaction patterns to detect anomalies indicative of fraud. In transportation networks, embeddings facilitate route optimization and traffic prediction. In academic and citation networks, embeddings reveal hidden relationships between research topics, leading to novel insights. Moreover, with visualization tools, embeddings make it possible to explore vast networks, highlighting community structures, influential nodes, and paths of information flow, ultimately transforming how we analyze complex interconnections across fields.

[ico-development-company-stuff]

How Block chain brought a revolution in MLM Industry

For More Details Please Contact

Call / Whatsapp: +91 7397224461

Website: www.icoappfactory.com

101, Kumaran Colony,

 Vadapalani,

Chennai, Tamil Nadu

Today, the world is moving so fast towards the new technologies, the traditional ways of payment process are now outdated. The Development in the field of payment gateways, Blockchain, UPI’s has given a boost to the online world and now it’s your time to grab your potential customers from this online world. Now, using Blockchain Technology is the smart move to grow your business.

ICO App Factory, India based company. We provide you all the online solutions for Blockchain to grow your business. We have a group of active and energetic workers who full fill the requirement for our clients. Blockchain Technology the revolutionary concept that changed the phase of digital industry is a game changer. And talking about the MLM industry, a marketing model that keeps the sales and network building align together to make the business a proper ‘BRAND’ tag. Conceptualize something with blockchain is a dream come true moment and blockchain has got such potential to change the MLM industry.Today, the world is moving so fast towards the new technologies, the traditional ways of payment process are now outdated. The Development in the field of payment gateways, Blockchain, UPI’s has given a boost to the online world and now it’s your time to grab your potential customers from this online world. Now, using Blockchain Technology is the smart move to grow your business.

Blockchain technology aids the business in many ways and as far as it is a concern, now it’s the validation part is carried out by the nodes. Marketing industry got its path right after the multi-level marketing ideas begin to gain the prominence.

Blockchain Technology

Blockchain technology manages the every currency transactions. But Blockchain is not limited to just currency but enlarges to any domain where anything of value is transacted, be it contracts, personal information, health records, business data and much more.

A blockchain is an excellent form of Database storage system, which uses records to store data or information. These records or blocks get copied automatically with the mechanism of cryptography providing a more secure data storage platform. This means, your data is stored securely in multiple areas, reducing the overall cost of data storage. The blockchain is the technology which supports the cryptocurrencies and Digital currencies. Blockchain assures that transaction continued into the blockchain database which is stable.

MLM Marketing

Essentially, MLM operates as an expandable mechanism in which people keep encouraging new members to join for the expansion of operations. In the MLM model, the contribution of every single member and incentive distribution as per their performance becomes essential. Therefore, it is necessary to bridge a connection between end-users and wholesalers as both serve as the base of this business.MLM models are successful as the network expands rapidly while giving leeway for every member of the network to taste success. Now, let’s take a look at the types of multi-level marketing.

MLM models come in various types which make it easy for enterprises to expand the distribution of products or services by adopting one of its structures like matrix MLM plan, investment MLM plan, uni-level MLM plan, stair-step MLM plan, Australian binary plan, generation MLM plan, binary MLM Plan, broad plan MLM plan, etc. An enterprise must seek the service of a smart contract and cryptocurrency development company that holds expertise in developing both concepts.

Blockchain Smart Contracts and MLM

Smart contracts work according to blockchain’s characteristics like immutability, transparency, traceability, and efficiency to maintain anonymity in the transactions. Indeed, blockchain smart contracts enable business owners to review terms and conditions and customize them as per their enterprise needs. It is crucial to hire a blockchain and crypto development company that can make the system as descriptive as possible.

Powering MLM business with blockchain smart contracts eliminates the chances of the scamming of an MLM business. Also, the use of smart contracts empowers all types of MLM business plans. An MLM Platform powered by a smart contract solution excludes the involvement of all third-parties, establishes a peer to peer architecture, provides multiple payment gateways, eliminates malpractices, ensures strengthened data security, fast and secure transactions, effortless traceability, anonymity and transparency.

The transparency of MLM Plan & the blockchain

Make a donation, and earn from that donation. Well, once the system is integrated using blockchain then, the whole system will be transparent. The transparent nature makes sure of one important thing – Is the donation process completed or not?

Say, a request came up, and you made the donation but, you’re worried about various matters like the escrow value, the donation is accepted or not, whether the donation is done to the right person or not, etc. The limitations or the constraints got its solution, and the plan gets a “TRUST” sign. The business will also gain the required fuel to maximize its opportunities by building up an open network.

Why choose us for Blockchain Development?

Developers with Product Mindset

The Blockchain powered verification system developed in-house is getting introduced in the Aviation industry.

Experts with Research Mindset

ICO App Factory professionals have worked closely with some of the leading Crypto’s in the market to tokenize the content web.

Consultants with a Startup Mindset

Every Blockchain Developer is trained to coordinate closely with customer and ideate their solutions.

How it Works?

Refining Ideas

In this phase, the technology team also joins the conversation ( along with the Blockchain domain consultants ). The main Moto of this phase is to refine the entire requirement and make it crystal clear, so everyone are on the same page and everyone in the team understands the idea and deliverable very clear. All this will be added to the note.This note will be used by the Blockchain developers during the development phase to better understand your requirement.

Brain Storming

ICO App Factory Blockchain Consultants are equipped with the latest news and trends in the Technology world. They will brainstorm with you on your idea and create a detailed note. During this phase they will also put in maximum effort to add more value to your proposition and tweak the idea with your permission.

Code & Fix

In this phase Blockchain development happens in full phase. Each blockchain developer in the team coordinates very closely with you and assigned to the project to bring out each module flawlessly. ICO App Factory follow an agile methodology, where you can see the live results of each module being developed and give feedback. Fixes happen then and there.

Release

Once the Blockchain development phase is over, the UAT phase starts. The testing happens along with bug fixes and fine tuning. After every issue found is fixed and you give a sign off an overall polishing of the project happens. Finally your project is ready for launch. Our Blockchain developers will start deploying the project on live servers.

Applications of Blockchain Development Services:

Blockchain can support a wide range of applications. The most well known is cryptocurrency like Bitcoins. Blockchain-based applications include any business transaction that can include right from Business order tracking, Supply chain, Banking and Finance, E-learning, Healthcare, Online shopping portals, Insurance, Travel, Music, Renewable energy, Contract validation and so on.

[pandeypankaj]

How Do I learn Machine Learning with Python?

Because it is easy to read and comprehend, Python has become the de facto language for machine learning. It also comprises an extensive set of libraries. Following is a roadmap of studying Python:

1.Python Basics

Syntax: Variables, Data Type, Operators, Conditional statements/ Control Flow statements-if, else, for, while.

Functions: Declaration, Calling, Arguments

Data Structures: Lists, Tuples, Dictionaries, Sets

Object Oriented Programming: Classes, Objects, Inheritance, Polymorphism

Online Courses: Coursera, edX, Lejhro

2. Essential Libraries NumPy

 Used for numerical operations, arrays, and matrices. 

Pandas: For data manipulation, cleaning, and analysis. 

Matplotlib: For data visualization. 

Seaborn: For statistical visualizations. 

Scikit-learn: A powerhouse library for machine learning containing algorithms for classification, regression, clustering, among others. 

3. Machine Learning Concepts 

Supervised Learning: Regression of different types like linear and logistic. 

Classification: decision trees, random forests, SVM, etc. 

Unsupervised Learning: Clustering: k-means, hierarchical clustering, etc.

Dimensionality reduction assessment metrics-PCA, t-SNE, etc.: Accuracy, precision, recall, F1-score, Confusion matrix. 

4. Practical Projects Start with smaller-size datasets 

Search for a dataset in Kaggle or UCI Machine Learning Repository. Follow the structured procedure: 

Data Exploration: Understand the data, its features, and distribution. 

Data Preprocessing: Clean, normalize, and transform the data. 

Training the Model: This means fitting the model on the training data. 

Model Evaluation: It means testing the performance of your model on the validation set. Hyperparameter Tuning: Improve model parameters. 

Deployment: Lay your model into a real-world application. 

5. Continuous Learning 

Stay updated on all recent things happening in the world related to machine learning by following machine learning blogs, articles, and online communities. Try new things, play, and explore different algorithms techniques, and datasets. 

Contributing to open-source projects: Contribute to open-source libraries like Scikit-learn.

Competitive participation: Participation in competitions, like Kaggle, allows seeing the work of others and learning from the best.

Other Tips

Mathematics: One needs to have pretty solid building blocks of math, namely linear algebra, calculus, and statistics, to understand concepts in machine learning.

Online resources: Take advantage of online resources like hundreds of courses and projects on Coursera, edX, and Lejhro and practice on Kaggle.

Join online communities: Participate in forums like Stack Overflow or subreddits, ask questions about code and solution issues.

These above steps and frequent practice of the same on a regular basis will build up your understanding in Machine Learning using Python and will help you in getting a good career path.

[tasmiyakrish]

Uncovering Hidden Patterns: The Power of Factor Analysis in Data Science

Data science is all about extracting meaningful insights from complex datasets. One powerful technique for achieving this is factor analysis, a statistical method that helps uncover the underlying structures and relationships within a set of variables. In this blog post, we'll dive into the world of factor analysis, exploring its core concepts, the analysis process, and its diverse applications in the field of data science.

If you want to advance your career at the Data Science Training in Pune, you need to take a systematic approach and join up for a course that best suits your interests and will greatly expand your learning path.

Beyond the Surface: Understanding the Essence of Data

Imagine you have a dataset with numerous variables, each seemingly unrelated to the others. How do you make sense of this web of interconnectedness? This is where factor analysis comes into play. By identifying the hidden, latent factors that drive the observed variables, this technique allows us to uncover the true essence of the data, revealing the fundamental dimensions that shape the relationships within.

The Factor Analysis Approach

The process of factor analysis typically involves the following key steps:

Data Preparation

Before diving into the analysis, it's crucial to ensure the data is suitable for factor analysis. This may involve addressing missing values, standardizing variables, and examining the correlation matrix to identify meaningful relationships.

Factor Extraction

The next step is to determine the number of factors to extract from the data. Various methods, such as principal component analysis (PCA) or maximum likelihood estimation, can be used to identify the optimal number of factors.

Factor Rotation

Once the initial factors are extracted, they may need to be rotated to improve the interpretability of the results. Commonly used rotation methods include varimax, quartimax, and oblimin.

Factor Interpretation

The final and perhaps most critical step is to interpret the meaning of each identified factor. This involves examining the factor loadings, which represent the correlation between each variable and the factor.

Factor Scoring

Finally, factor scores can be calculated for each observation in the dataset, representing the relative position of each data point on the identified factors. These scores can be used for further analysis or visualization.

For those looking to excel in Data Science, Data Science Online Training is highly suggested. Look for classes that align with your preferred programming language and learning approach.

Applications of Factor Analysis in Data Science

Factor analysis has a wide range of applications across various domains, showcasing its versatility and power in the field of data science:

Market Segmentation

Identifying underlying consumer preferences and behaviors based on survey data, enabling more targeted marketing strategies.

Personality Assessment

Uncovering the latent dimensions of personality traits from psychological test data, providing deeper insights into human behavior.

Social Science Research

Exploring the underlying structures of social phenomena, such as attitudes, beliefs, or values, to gain a deeper understanding of complex human dynamics.

Exploratory Data Analysis

Uncovering hidden patterns and structures within complex datasets, which can inform further analysis and modeling, leading to more informed decision-making.

Unlocking the Power of Data

By harnessing the power of factor analysis, data scientists can uncover the hidden stories within their data, revealing the fundamental dimensions that drive the observed variables. This invaluable technique empowers us to move beyond the surface-level understanding of data, ultimately unlocking the true potential of our datasets and informing more robust and insightful decision-making.

[myprogrammingsolver]

CS590 homework 6 – Graphs, and Shortest Paths

Develop a data structure for directed, weighted graphs G = (V, E) using an adja­cency matrix representation.. The d.atatype int is used to store the weight of edges. int does not allow one to represent ±co. Use the values INTYIN and IN’T_MAX (defined in limits .h) instead. include <limits-h5 in.t d, e; d = INT_MAX; e = INT_MIN; if (e == ih–r_mir;) if (d I = IN–1_102A10           . Develop a…

[roamnook]

TRANSFORMING ENGINEERING: AGILE METHODOLOGIES RETURN, GUARANTEEING A 60% PERFORMANCE INCREASE IN HARDWARE PRODUCT DEVELOPMENT. NEWLY PUBLISHED STUDY REVEALS DATA.

IT'S COMING HOME: THE RETURN OF AGILE HARDWARE PRODUCT DEVELOPMENT

IT’S COMING HOME: THE RETURN OF AGILE HARDWARE PRODUCT DEVELOPMENT

October 6, 2023 | Article

Agile-for-hardware product development can reduce time-to-market and improve quality and productivity. Five key changes can help leaders capture the benefits of agile for hardware.

Many people believe that the physicality of hardware product development means it is not well-suited for an agile approach, and that agile should be left to software development alone. This is not the case. In fact, agile originally started as a practice for hardware development many years ago but has been used mostly for software development since it was repopularized by a group of software engineers in 2001.[^1][^2]

ABOUT THE AUTHORS

This article is a collaborative effort by Elia Berteletti, Stefan Frank, Pascal Haazen, André Rocha, and YiFan Wu, representing views of McKinsey’s Operations Practice.

A BRIEF HISTORY OF AGILE

Agile innovation has a colorful and contested heritage—some practitioners point as far back as Francis Bacon’s articulation of the scientific method in 1620.[^1] Agile methodologies in companies can be traced back at least as far as the 1930s when Bell Labs applied plan-do-study-act (PDSA) cycles to the improvement of products and processes. This, of course, predated electronic computers and their software. In the 1960s, Lockheed Martin built its “skunk works,” in which small development teams were removed from the normal working environment and freed from managerial constraints, making them autonomous and empowered.

The ideas underlying agile truly began to be used at scale in the 1990s, with startups and large organizations alike seeking to become nimbler in response to a turbulent environment. In 2001, a group of 17 software developers met in Oregon in the United States to discuss how they could speed up development times to bring new software to market faster. Less than a year later, they met again and produced the Agile Manifesto, which laid out four values:

individuals and interactions over processes and tools

working software over comprehensive documentation

customer collaboration over contract negotiation

responding to change over following a plan

Today, businesses are experimenting and adopting agility in both software and hardware development, on an unprecedented scale. Companies that do not evolve quickly are at risk of falling ever further behind.

FROM LINEAR TO MORE ITERATIVE PROCESSES

Typically, the “development” part of research and development (R&D) follows a highly linear process, traveling a set development path from concept to design to building, testing, bug fixing, and then, finally, launching. This approach—commonly referred to as the waterfall model—asks teams to adhere to the requirements and scope set out at the beginning of the project and prioritizes bringing a complete product to market. But, by the time the product gets to market, customer needs may have partially or completely changed, which is both frustrating for the engineers, and costly for the company. In contrast, the Agile Manifesto favors a process wherein feedback is sought in each loop, thereby avoiding the pitfalls of a more linear approach.

But agile is not only an iterative and incremental way of working. One reason it works well in hardware development is that, within the framework, individuals can form stable, dedicated, and unfragmented teams. People work with the same team members consistently over time. This is different from traditional matrix structures, where team members change frequently with new projects and new initiatives.

The benefits of this stability include enhanced predictability and avoiding a continuous cycle of the “forming—storming—norming—performing” stages of group development.[^4] With agile, teams can remain consistently in the “performing” stage. Organizations with high enterprise agility can therefore combine a strong backbone of stability and consistency with the ability to quickly redirect people and priorities toward new value-creating opportunities.[^5]

DESIGNING AN AGILE ENGINEERING ORGANIZATION

Transforming to agile requires a mindset change. An effective agile transformation is therefore both comprehensive and iterative. Agile-for-hardware product development retains the principles of agile-for-software—flexibility, evolution, and iteration—but to “stick,” it requires tailoring to the hardware nature of the product and business. Leaders and managers can focus on five main areas to enhance the transformation: strategy, structure, process, people, and technology.[^6]

Strategy.

Agile organizations maintain a laser-like focus on creating value for the customer, meeting needs at every point in the customer life cycle. To do so, agile organizations design distributed, flexible approaches to creating value, and set a shared organizational purpose and vision to ensure coherence and keep their teams focused on that value—the “North Star”.

Structure.

In an agile product-development organization, most engineers work in stable teams with dedicated members. When teams stay together longer—typically one to two years at a minimum—they can become independent and each member can truly master a specific set of skills, rather than chopping and changing between teams and fulfilling different roles with every change.

Process.

In agile, teams focus on rapid iteration and experimentation. They produce deliverables very quickly, often in one- or two-week “sprints”—a short burst of focused activity, with regular check-ins and deadlines. When transitioning to agile, the iterative and incremental process can run concurrently with the traditional process of scoping, building a business case, development, testing, and validation, known as the stage-gate process. The combination gives teams the best of both worlds; the stage-gate process provides the bigger picture and milestone targets, while agile methods inform the day-to-day approach.

People.

Ultimately, the performance of any organization—agile or not—is defined by the behavior of its people. Transitioning to agile can make employees feel unsettled as traditional boundaries are removed. An effective counterbalance is to encourage and enable ownership from employees, particularly, in the hardware development context, from engineers.

Technology.

Leaders can use the transformation to agile to deploy digital tools to support teams. This support can be achieved in three ways. First, by ensuring modular designs that are built for change. Second, by adopting collaboration tools that facilitate information sharing at scale. Third, by adopting digital and additive manufacturing tools to run design steps faster. To enable collaboration and value creation, a consumer electronics company built multiple bespoke digital tools. Its bespoke knowledge-sharing platform, for example, improved problem-solving efficiency because it directly shared the design calculations from the R&D unit within the team, as well as with the new product innovation team.

The benefits of implementing an agile methodology in mixed hardware and software engineering go beyond pure corporate performance metrics and can include decreases in time-to-market, quality problems, and complexity issues, as well as increases in productivity, predictability, and employee satisfaction.

Agile transformations typically span a couple of years, yet agile pilots can deliver visible results quickly to prove the shift is worth doing. Once this is achieved, engineers buy in, and the transformation gains momentum.

A strong and deliberate focus by leadership on five key areas during a transition to agile—strategy, structure, process, people, and technology—can ensure that the transition succeeds and endures. But beware of the trap of applying a “by-the-book” recipe, as agile-for-hardware engineering, as well as for mixed hardware and software engineering, needs to be tailored to products being developed, while retaining agile principles. The transformation doesn’t happen overnight for the whole organization, but it can be rapid for each new agile team.

Elia Berteletti is a partner in McKinsey’s Seattle office; Stefan Frank is an associate partner in the Hamburg office; Pascal Haazen is a partner in the Amsterdam office; André Rocha is a partner in the Madrid office; and YiFan Wu is an expert associate partner in the Taipei office.

Source: https://www.mckinsey.com/capabilities/operations/our-insights/its-coming-home-the-return-of-agile-hardware-product-development&sa=U&ved=2ahUKEwii2pTl7OuFAxURMlkFHX2aBC0QxfQBegQIBBAC&usg=AOvVaw2CU9ZRmuRTQJ7IQpYPHiGa

[unogeeks234]

INGENTIS SUCCESSFACTORS

Ingentis org.Manager: The Key to Unlocking SuccessFactors’ Full Potential

SAP SuccessFactors is a market-leading cloud-based Human Capital Management (HCM) suite known for its efficiency and robust capabilities in handling HR data. However, when visualizing and interacting with your organization’s structure, the out-of-the-box options in SuccessFactors can feel limited. This is where the Ingentis org. Manager shines.

What is Ingentis org. Manager?

Ingentis org. Manager is an SAP-endorsed app that seamlessly integrates with SAP SuccessFactors, dramatically enhancing organizational charting and visualization. Imagine transforming your static org charts into dynamic, interactive tools that reveal hidden insights about your workforce. That’s the power of Ingentis org. Manager.

Key Features and Advantages

Intuitive Org Charts: Design stunning org charts in various styles (hierarchical, matrix, sunburst, etc.). Customize them with rich HR data insights directly from SuccessFactors.

Data-Driven Insights: Embed relevant KPIs and metrics directly into your org charts, offering real-time snapshots of team health, potential bottlenecks, and areas for improvement.

Scenario Planning and Simulations: Model potential organizational changes with “what-if” scenarios. Easily experiment with reorganizations, hiring plans, and succession planning – all without impacting your live SuccessFactors data.

Role-Based Access: Control what users can view and modify within the org charts, ensuring data security and tailored experiences for HR, managers, and employees.

Automated Reports: Generate and distribute customized HR reports in formats (PDF, CSV, etc.) on your defined schedule.

Benefits of Using Ingentis org. Manager with SuccessFactors

Improved Decision-Making: Visual, data-driven org charts empower HR leaders and managers to make strategic workforce decisions backed by clear insights.

Streamlined Succession Planning: Model different scenarios, identify skill gaps, and proactively develop a pipeline of qualified talent for critical roles.

Enhanced Collaboration: Share interactive org charts throughout your organization, fostering transparency and facilitating better communication.

Reduced Costs: An Ingentis organization manager can lower the cost of strategic resource planning by up to 20% (according to an Ingentis and SAP survey).

Boosted Employee Engagement: Enable employees to easily visualize their position, career paths, and growth opportunities within the organization.

Getting Started

Ingentis org. The manager is available on the SAP Store. Its SAP-endorsed status means rigorous testing and certification, ensuring secure and smooth integration with your SuccessFactors environment.

In Conclusion

Suppose you’re using SAP SuccessFactors, Ingentis org. A manager is an invaluable extension that goes beyond simple organizational charting. It empowers HR teams to unlock data-driven insights about workforce structure, facilitate strategic decision-making, and drive organizational success.

You can find more information about  SAP Successfactors in this  SAP Successfactors Link

Conclusion:

Unogeeks is the No.1 IT Training Institute for SAP  Training. Anyone Disagree? Please drop in a comment

You can check out our other latest blogs on  SAP Successfactors  here - SAP Successfactors Blogs

You can check out our Best In Class SAP Successfactors Details here - SAP Successfactors Training

----------------------------------

For Training inquiries:

Call/Whatsapp: +91 73960 33555

Mail us at: info@unogeeks.com

Our Website ➜ https://unogeeks.com

Follow us:

Instagram: https://www.instagram.com/unogeeks

Facebook: https://www.facebook.com/UnogeeksSoftwareTrainingInstitute

Twitter: https://twitter.com/unogeeks

[erikabsworld]

Navigating the Depths of Parallel Computing Assignments: A Step-by-Step Guide

In the ever-evolving landscape of computer science, parallel computing has emerged as a cornerstone, unlocking the potential for enhanced performance and efficiency. University-level assignments on this topic often pose intricate challenges, demanding a thorough understanding of the underlying concepts. In this blog, we delve into a tough parallel computing assignment question, breaking down the complexities into digestible insights. Whether you're a student seeking enlightenment or a seasoned coder aiming to polish your skills, this guide is designed to illuminate the path to success.

The Assignment Question:

"Implement a parallel algorithm for matrix multiplication and analyze its efficiency. Provide insights into the design choices and optimizations made in the process."

Matrix multiplication is a classic problem that lends itself well to parallelization. However, tackling this assignment requires a deep understanding of parallel computing concepts and the ability to articulate your thought process clearly.

Understanding the Concept:

Before diving into the implementation, let's establish a foundational understanding of parallel computing. In essence, parallel computing involves breaking down a complex problem into smaller tasks that can be solved concurrently, utilizing multiple processors or cores. In the context of matrix multiplication, parallelization can be achieved by distributing the workload among different processing units, optimizing the overall computation time.

Step-by-Step Guide:

Understanding Matrix Multiplication: Refresh your knowledge of matrix multiplication, ensuring a clear understanding of the basic algorithm and its sequential implementation.

Parallelization Strategy: Devise a strategy to parallelize the matrix multiplication. Consider factors such as workload distribution, communication between processors, and synchronization.

Algorithm Design: Develop a parallel algorithm for matrix multiplication. Clearly outline the steps involved and the logic behind your design choices. Focus on minimizing dependencies between tasks to maximize parallel efficiency.

Optimizations: Implement optimizations to enhance the efficiency of your parallel algorithm. This could involve techniques such as loop unrolling, cache optimization, or parallel data structures.

Analysis and Evaluation: Assess the performance of your parallel algorithm. Provide insights into how your design choices and optimizations impact the overall efficiency. Compare the parallel implementation with the sequential version.

Documentation: Document your code thoroughly, including comments that explain the purpose of each section. This not only aids in understanding but also demonstrates your mastery of the implemented algorithm.

How We Help Students:

Navigating complex assignments like these can be challenging, and that's where our parallel computing assignment writing help service comes in. At matlabassignmentexperts.com, we understand the demands of university-level assignments and provide expert guidance to students grappling with parallel computing concepts. Our team of experienced professionals is well-versed in diverse computing topics, offering personalized assistance to ensure your success. Whether you need clarification on concepts, assistance with coding, or guidance on assignment structure, we're here to support your academic journey.

Conclusion:

In the realm of parallel computing, assignments serve as gateways to mastery. By unraveling the layers of a challenging matrix multiplication assignment, this guide aims to empower students and enthusiasts alike. Remember, parallel computing is not just about coding—it's about unraveling the intricacies of computation and harnessing the power of concurrency. As you embark on this journey, may your algorithms run efficiently, and your understanding of parallel computing deepen with each line of code.

[ogsstechnologies]

How Blockchain brought a revolution in MLM Industry

For More Details Please Contact

Call / Whatsapp: +971 50 912 5616

Website:  https://OGSStechnologies.ae

Office 101, Juma Al Majid Technic Building,

Salah Al Din St., Deira,

Dubai - UAE.

How Blockchain brought a revolution in MLM Industry

Today, the world is moving so fast towards the new technologies, the traditional ways of payment process are now outdated. The Development in the field of payment gateways, Blockchain, UPI’s has given a boost to the online world and now it’s your time to grab your potential customers from this online world. Now, using Blockchain Technology is the smart move to grow your business.

OGSS Technologies based company. We  provide you all the online solutions for Blockchain  to grow your business. We have a group of active and energetic workers who full fill the requirement for our clients. Blockchain Technology the revolutionary concept that changed the phase of digital industry is a game changer. And talking about the MLM industry, a marketing model that keeps the sales and network building align together to make the business a proper ‘BRAND’ tag. Conceptualize something with blockchain is a dream come true moment and blockchain has got such potential to change the MLM industry.

Blockchain technology aids the business in many ways and as far as it is a concern, now it’s the validation part is carried out by the nodes. Marketing industry got its path right after the multi-level marketing ideas begin to gain the prominence.

Blockchain Technology

Blockchain technology manages the every currency transactions. But Blockchain is not limited to just currency but enlarges to any domain where anything of value is transacted, be it contracts, personal information, health records, business data and much more.

A blockchain is an excellent form of Database storage system, which uses records to store data or information. These records or blocks get copied automatically with the mechanism of cryptography providing a more secure data storage platform. This means, your data is stored securely in multiple areas, reducing the overall cost of data storage. The blockchain is the technology which supports the cryptocurrencies and Digital currencies. Blockchain assures that transaction continued into the blockchain database which is stable.

MLM Marketing

Essentially, MLM operates as an expandable mechanism in which people keep encouraging new members to join for the expansion of operations. In the MLM model, the contribution of every single member and incentive distribution as per their performance becomes essential. Therefore, it is necessary to bridge a connection between end-users and wholesalers as both serve as the base of this business. MLM models are successful as the network expands rapidly while giving leeway for every member of the network to taste success. Now, let’s take a look at the types of multi-level marketing.

MLM models come in various types which make it easy for enterprises to expand the distribution of products or services by adopting one of its structures like matrix MLM plan, investment MLM plan, uni-level MLM plan, stair-step MLM plan, Australian binary plan, generation MLM plan, binary MLM Plan, broad plan MLM plan, etc. An enterprise must seek the service of a smart contract and cryptocurrency development company that holds expertise in developing both concepts.

Blockchain Smart Contracts and MLM

Smart contracts work according to blockchain’s characteristics like immutability, transparency, traceability, and efficiency to maintain anonymity in the transactions. Indeed, blockchain smart contracts enable business owners to review terms and conditions and customize them as per their enterprise needs. It is crucial to hire a blockchain and crypto development company that can make the system as descriptive as possible.

Powering MLM business with blockchain smart contracts eliminates the chances of the scamming of an MLM business. Also, the use of smart contracts empowers all types of MLM business plans. An MLM Platform powered by a smart contract solution excludes the involvement of all third-parties, establishes a peer to peer architecture, provides multiple payment gateways, eliminates malpractices, ensures strengthened data security, fast and secure transactions, effortless traceability, anonymity and transparency.

The transparency of MLM Plan & the blockchain

Make a donation, and earn from that donation. Well, once the system is integrated using blockchain then, the whole system will be transparent. The transparent nature makes sure of one important thing – Is the donation process completed or not? Say, a request came up, and you made the donation but, you’re worried about various matters like the escrow value, the donation is accepted or not, whether the donation is done to the right person or not, etc. The limitations or the constraints got its solution, and the plan gets a “TRUST” sign. The business will also gain the required fuel to maximize its opportunities by building up an open network.

Why choose us for Blockchain Development?

Developers with Product Mindset

The Blockchain powered verification system developed in-house is getting introduced in the Aviation industry.

Experts with Research Mindset

OGSS Technologies professionals have worked closely with some of the leading in the market to tokenize the content web.

Consultants with a Startup Mindset

Every Blockchain Developer is trained to coordinate closely with customer and ideate their solutions.

How it Works?

Refining Ideas

In this phase, the technology team also joins the conversation ( along with the Blockchain domain consultants ). The main Moto of this phase is to refine the entire requirement and make it crystal clear, so everyone are on the same page and everyone in the team understands the idea and deliverable very clear. All this will be added to the note.This note will be used by the Blockchain developers during the development phase to better understand your requirement.

Brain Storming

OGSS Technologies    Blockchain Consultants are equipped with the latest news and trends in the Technology world. They will brainstorm with you on your idea and create a detailed note. During this phase they will also put in maximum effort to add more value to your proposition and tweak the idea with your permission.

Code & Fix

In this phase Blockchain development happens in full phase. Each blockchain developer in the team coordinates very closely with you and assigned to the project to bring out each module flawlessly. OGSS Technologies    follow an agile methodology, where you can see the live results of each module being developed and give feedback. Fixes happen then and there.

Release

Once the Blockchain development phase is over, the UAT phase starts. The testing happens along with bug fixes and fine tuning. After every issue found is fixed and you give a sign off an overall polishing of the project happens. Finally your project is ready for launch. Our Blockchain developers will start deploying the project on live servers.

Applications of Blockchain Development Services:

Blockchain can support a wide range of applications. The most well known is cryptocurrency like Bitcoins. Blockchain-based applications include any business transaction that can include right from Business order tracking, Supply chain, Banking and Finance, E-learning, Healthcare, Online shopping portals, Insurance, Travel, Music, Renewable energy, Contract validation and so on.