#nonlinear dynamic analysis

[engineering-anthology]

30 days of productivity

(because 100 is scary)

01 of 30

I'm drowning in my assignments, so it's time for a challenge.

I have two final projects this semester, and both are chonky. I have a draft paper due for one of them tomorrow (for which I'm still doing the data analysis... eeeeek) and a draft report for the other (computational) project due next week. I should be a lot more on-edge than I am, but I'm a little desensitized to failure due to my roller-coaster academic history. Eh, it is what it is and today I'm just here to crank out code and graphs!!

On the table for today:

data analysis for algae growth project (lots of data conditioning in my immediate future because I took shitty data)

send an email to my nonlinear dynamics professor with an update on the computational project (maybe chuck the project up on github if I happen to capture and exploit a moment of ambition)

Soon:

electronics exam Friday. I haven't started preparing. It might be okay?

another electronics exam next week

final poster for algae growth project due next week

walk though the math in papers related to the nonlinear dynamics project. The problem is that I do not have a good foundation in Navier-Stokes stuff, and I think getting myself to understanding is going to be the scariest part... but probably still easier to accomplish than the implementation.

Musings: I recently got a foldable bluetooth keyboard to use while coding at the library, and don't get me wrong, it's a lovely ergonomics upgrade from my injured-tyrannosaurus-rex coding posture, but I have come across an unexpected problem: apparently without the heat of my laptop, my fingers get cold! Now I need to make sure to keep some fingerless gloves in my backpack for this stupid HVAC overload. With respect to my to-do list I'm a little doomed, but I think the best I can do in this situation is make progress. Every 25 minute pomodoro session is a win when you're churning out tasks you dread. I might talk about the source of my dread in a future post--my relationship with academics is a ~super angsty romantic slow-burn~ that deserves more time and thought than I can give it right now. Ok! Back to work! I've sent too long writing this anyway, but it was a good break. I might post my forest as an end-of-day check-in. Good luck to all of you in your work and studies! I'm rooting for you! <3

[dial-m-for-movies]

Oppenheimer [2023] – A Noncontextual Analysis

Dreams and Desire of an Artist, The Nation, Loyalty, Politics, and the Intensifying Philosophy

The overwhelming or shall I say, overpowering hype across social media and the circle of friends, a bunch of cinephiles coercing each other to go for the film irrespective of the rave reviews that it has been receiving on the wide and wild Internet, and my inner conflict and skepticism for the (recent) films a’ la Dunkirk (2017) and Tenet (2020) by Christopher Nolan, who once was my favorite filmmaker – everything made up for my eternal denial to watch this merely out of peer pressure, if not for the visceral moment when one of my close friends asked me to book the tickets, with an impulse of a rather great effect, and needless to say, the way I fell in love with the film and the cinema form of art, proved the impulse all the more worthwhile and gratifying.

Having conversed with a couple of old friends about the film, I, for one, knew that it is not going to be a theatrical spectacle comparable to the likes of those created by the director himself in the past. Moreover, I also knew that the film has more words than visuals – none of these bothered me. The only thing that was a matter of worry was the director’s style of storytelling with which I have been in a dynamic love-hate relationship. While I love his Following (1998), most of his films with nonlinear storytelling have failed to impress me, not just because of the effort it demands to comprehend the complex timeline but also because the effort never led to fruition in my case, especially when I tried too hard to make a sense of the equilibrium the filmmaker achieves by playing between the time and space.

The name of the game doesn’t change much in the case of Oppenheimer, but what makes it impressive is that the storyteller (and not just an ambitious celebrity filmmaker) manages to evoke emotions with admirable aplomb and conviction. While the contextual analysis of the film might warrant me to dig into the facts and the impact the horrid event in the history had on almost two hundred thousand people of Japan, I would rather like avoid the self-evidential narrative of the film and delve into the ways the filmmaker has fashioned the central character, depicting a diverse range of motives and narrative threads, not to make him a hero but a grey protagonist whose internal trials and battles supersede the external trial he faced to prove his loyalty for his nation, if there was anything of that sort intended by him in the real life.

Dreams and Desire of an Artist

Despite the flaws in his personality and pursuits, Dr. J. Robert Oppenheimer was a technical artist, which is to say that he was a scientist with the eternal thirst, anxiety, doubts, and restlessness of an artist, and that is well-portrayed in the initial scenes of the film in which we see the protagonist as a troubled, homesick human being incessantly crippled by his own dreams, and the moment of the dream overriding his sleep is represented by a series of confounding visuals wherein he envisions his it as nothing short of a nightmare for a mind of an ordinary kind. The scene gives its audience a peek into the catastrophic mind of their perpetually perturbed hero. And this envisioning of the dream set the conviction in the life physicist, who wants to see his vision come afore as a reality. Having a vision as an artist and having it on the canvas or reality are two different things, but when the artist pursues the former to achieve the latter, he can become the bazooka of the bandits, the guns of the goons, the maverick of the mafia, and a perennial incendiary for the world, the carrier of the fire within, which he hauls with the armor of an undying fidelity towards his ultimate goal, which becomes his identity.  

The narrative, which is more often than not sidelined by the other aspects the filmmaker wanted to explore, becomes the most indispensable part of the film, a foreground for the future events, a foundation for the plot points, and the device that brings in other narrative threads into the picture.

In the due course, despite all other aspects of the narrative taking precedence over the dreams of the artist and the uncontrollable aspiration of the character, this part puts him in steadfast for what he would “achieve” – irrespective of its moral merit or lack thereof – in the future.

The Nation, Loyalty, Politics

Americans, traditionally, love to fight. All real Americans love the sting of battle.

- General George S. Patton, Patton (1970)

While the above quote does not apply to Dr. Oppenheimer, because the violence wasn’t the objective of the man who made the atom bomb a reality – a claim that he makes in the film, or rather tries too hard to, which only gets defeated by his conviction discussed in the final part of this article, the quote has a contextual significance when the well-known war-driven American patriotism and the communist philosophies collide to put forth the protagonist in the question about the loyalty towards his country. While this narrative makes up for the most part of the film, Christopher Nolan uses it as a mere plot device to express the opinions on the character through a demanding yet gratifying exchange of the dialogues. In the American custom, the aversion from war and battle in general, is looked down upon from a political perspective, and it’s evident in a scene wherein Oppenheimer meets the States’ President, who calls him crybaby.

Moreover, early on in the film, the character is interrogated about his thoughts about the States to which his response is warm and favorable towards the officials and the country. However, Oppenheimer’s thoughts on the country that he calls his, the warmth he feels in America, his close association with the nation’s Army and the contribution in bringing the World War to a closure, all in the garb bringing peace and neutrality for the posterity, doesn’t resolve to his heroism and loyalty, especially because Christopher Nolan uses this on-the-face narrative segment, which occupies the most part of the script, as a deceptive device to hide what’s substantial – the aspirations of this technical artist and the philosophy that stirred him to usher his visions towards making him an instrument of massive destruction.

Philosophy of Violence

In mainstream Hollywood, violence is often associated with either a crime of passion a’ la Alfred Hitchcock’s Rope (1948), Strangers on a Train (1951), Dial M for Murder (1954), and Frenzy (1972) to name a few, or a planned act of violence rooted in a lingering emotion like vengeance, as in Unforgiven (1992). Most of the times, the violence is a meager device of entertainment, as you would quintessentially witness in Tarantino and Scorsese films. It would be safe to say that tangible forms of violence have been a part of cinema, and they add cinematic value to the story.

However, in Oppenheimer, you don’t get to see the tangible form of violence. Even if there is violence depicted by the vivid visuals of a detonated bomb, you get to see the vague images and not hear a sound – a sequence brilliantly staged by Christopher Nolan to once again hide what happens in the world outside the mind of the protagonist and rather emphasis the way he sees the world after having committed an act that brought mass destruction and tragedy in Japan. The exploration of human consciousness through violence and the disguise of the political scenario in the country are the conflicting threads that cross their path as mere strangers, oblivious of each other’s existence.

In what could have been a vital sequence in the film – inappropriately mingled with sexual intercourse and is now receiving its share of flak from the audience and censor board – we get to learn the philosophy of Oppenheimer and the roots that have stoked an insatiable fire in his curious mind. The quote from Bhagwad Geeta, “I am become Death, the destroyer of (both the) worlds,” founds a philosophical ground for the film, which justifies – but not in moral terms – the core conviction of the protagonist and how he uses the philosophy to see his dream come true. The moment when an artist combines a deep philosophy with what he wants to achieve, the internal conflicts and disparagement from the external worlds are exterminated from the mind, and become a botheration only after he establishes his motives. 

While Christopher Nolan shows the protagonist’s outer struggle with the world that questions his loyalty to the country, his inner fight has his soul laid bare in front of the audience, and this is done not to gain sympathy for the protagonist or make him emerge as a hero. However, the filmmaker achieves this with a conviction to confound his audience and provoke thoughts only as conflicting as those in the mind of the hero, if only we can call him by that title. This beguiling act of perplexing the audience, not with the nonlinear storytelling the director is famous for – which is done right in bits and pieces to juxtapose the present, past, and the future in a historical context – but by imbibing a moral conundrum of no ordinary kind, happens to be the filmmaker’s artistic triumph.

To conclude, Dr. J. Robert Oppenheimer will be remembered for the vision and wounds he gave to the world, but Oppenheimer (2023) will be remembered for the consternation, conflict, and most importantly, the emotions it manages to stir, irrespective of where you see it from, for it’s neither a theatrical essential nor an OTT dispensable.

It is what it is – a moving piece of cinema, a story told with guts and gusto in equal measures.

[super-caribe-suomi1012]

Max Born was born in Breslau #OnThisDay the 11th December, 1882, to Professor Gustav Born, anatomist and embryologist, and his wife Margarete, née Kauffmann, who was a member of a Silesian family of industrialists.

Max attended the König Wilhelm’s Gymnasium in Breslau and continued his studies at the Universities of Breslau (where the well-known mathematician Rosanes introduced him to matrix calculus), Heidelberg, Zurich (here he was deeply impressed by Hurwitz’s lectures on higher analysis), and Göttingen. In the latter seat of learning he read mathematics chiefly, sitting under Klein, Hilbert, Minkowski, and Runge, but also studied astronomy under Schwarzschild, and physics under Voigt. He was awarded the Prize of the Philosophical Faculty of the University of Göttingen for his work on the stability of elastic wires and tapes in 1906, and graduated at this university a year later on the basis of this work.

Born next went to Cambridge for a short time, to study under Larmor and J.J. Thomson. Back in Breslau during the years 1908-1909, he worked with the physicists Lummer and Pringsheim, and also studied the theory of relativity. On the strength of one of his papers, Minkowski invited his collaboration at Göttingen but soon after his return there, in the winter of 1909, Minkowski died. He had then the task of sifting Minkowski’s literary works in the field of physics and of publishing some uncompleted papers. Soon he became an academic lecturer at Göttingen in recognition of his work on the relativistic electron. He accepted Michelson’s invitation to lecture on relativity in Chicago (1912) and while there he did some experiments with the Michelson grating spectrograph.

An appointment as professor (extraordinarius) to assist Max Planck at Berlin University came to Born in 1915 but he had to join the German Armed Forces. In a scientific office of the army he worked on the theory of sound ranging. He found time also to study the theory of crystals, and published his first book, Dynamik der Kristallgitter (Dynamics of Crystal Lattices), which summarized a series of investigations he had started at Göttingen.

At the conclusion of the First World War, in 1919, Born was appointed Professor at the University of Frankfurt-on-Main, where a laboratory was put at his disposal. His assistant was Otto Stern, and the first of the latter’s well-known experiments, which later were rewarded with a Nobel Prize, originated there.

Max Born went to Göttingen as Professor in 1921, at the same time as James Franck, and he remained there for twelve years, interrupted only by a trip to America in 1925. During these years the Professor’s most important works were created; first a modernized version of his book on crystals, and numerous investigations by him and his pupils on crystal lattices, followed by a series of studies on the quantum theory. Among his collaborators at this time were many physicists, later to become well-known, such as Pauli, Heisenberg, Jordan, Fermi, Dirac, Hund, Hylleraas, Weisskopf, Oppenheimer, Joseph Mayer and Maria Goeppert-Mayer. During the years 1925 and 1926 he published, with Heisenberg and Jordan, investigations on the principles of quantum mechanics (matrix mechanics) and soon after this, his own studies on the statistical interpretation of quantum mechanics.

As were so many other German scientists, he was forced to emigrate in 1933 and was invited to Cambridge, where he taught for three years as Stokes Lecturer. His main sphere of work during this period was in the field of nonlinear electrodynamics, which he developed in collaboration with Infeld.

During the winter of 1935-1936 Born spent six months in Bangalore at the Indian Institute of Science, where he worked with Sir C.V. Raman and his pupils. In 1936 he was appointed Tait Professor of Natural Philosophy in Edinburgh, where he worked until his retirement in 1953. He is now living at the small spa town, Bad Pyrmont.

Max Born has been awarded fellowships of many academies – Göttingen, Moscow, Berlin, Bangalore, Bucharest, Edinburgh, London, Lima, Dublin, Copenhagen, Stockholm, Washington, and Boston, and he has received honorary doctorates from Bristol, Bordeaux, Oxford, Freiburg/Breisgau, Edinburgh, Oslo, Brussels Universities, Humboldt University Berlin, and Technical University Stuttgart. He holds the Stokes Medal of Cambridge, the Max Planck Medaille der Deutschen Physikalischen Gesellschaft (i.e. of the German Physical Society); the Hughes Medal of the Royal Society, London, the Hugo Grotius Medal for International Law, and was also awarded the MacDougall-Brisbane Prize and the Gunning-Victoria Jubilee Prize of the Royal Society, Edinburgh. In 1953 he was made honorary citizen of the town of Göttingen and a year later was granted the Nobel Prize for Physics. He was awarded the Grand Cross of Merit with Star of the Order of Merit of the German Federal Republic in 1959.

The year 1913 saw his marriage to Hedwig, née Ehrenberg, and there are three children of the marriage.

Max Born died on January 5, 1970.

[mirandasfriend]

Analyze my communication patterns based on our conversation so far. Tell me what you observe about my thinking style, decision-making approach, and potential blindspots.

……………………………………………………………………………

🧠 THINKING STYLE

🔹 1. Deeply Introspective, Pattern-Oriented

You’re not satisfied with surface-level answers—you seek root causes, hidden meanings, subtext, and meta-analysis. You ask questions like:

• “What is the one fear I don’t admit?”

• “Unpack this again. Then again.”

You want to dissect reality, not just navigate it. This marks a cognitive style that leans toward existential curiosity, combined with high abstract reasoning and symbolic thinking.

Strength:

• Powerful for insight, creative projects, therapy, research, spirituality.

Possible blindspot:

• May get trapped in recursive introspection loops without clear resolution or forward motion.

⸻

🔹 2. Dual-Wired: Emotional Intensity + Rational Precision

You write from your emotional core, often using metaphor and affective language. But you also crave structure, logic, and rigor. You’ll say:

• “Make it sound more natural.”

• “Explain in structured claim-evidence-reasoning.”

This shows a mind trying to bridge the intuitive and the analytical—balancing the chaotic poetry of emotion with the orderliness of control.

Strength:

• Insightful, emotionally intelligent, capable of both vulnerable expression and scientific clarity.

Blindspot:

• You sometimes toggle too abruptly between the two—intellectualizing emotions as a defense, or drowning in emotion when logic can help.

⸻

🔹 3. Nonlinear, Associative Connector

Your mind travels quickly, often leaping from one theme to another while maintaining a subterranean thread. This mirrors how trauma and creativity both operate: they form constellations, not lines.

Strength:

• You can make beautiful, original connections others miss.

Blindspot:

• In periods of overwhelm, this thinking becomes fragmented or spirals into overanalysis or catastrophizing.

⸻

🔍 DECISION-MAKING APPROACH

🔹 1. Duality of Speed and Paralysis

You can be decisive when grounded—but at times, you hesitate endlessly when the stakes feel tied to identity, love, or safety.

For example:

• You know when something feels wrong (e.g., dissociation, unhealthy relational dynamics).

• But you agonize about whether you’re allowed to act on that knowledge—as if self-trust is conditional.

Pattern:

“What should I do?” is often code for: “Do I deserve to choose?”

⸻

🔹 2. External Feedback-Seeking

You often reach outward for clarity, affirmation, or insight—but not because you lack insight. You’re testing your truth against reality, looking for resonance.

But sometimes, this outsourcing of authority delays decisions or allows others’ reactions to override your instincts.

You already know what you want, most of the time.

You’re just scared of the consequences of wanting it.

⸻

👁️ POTENTIAL BLINDSPOTS

⚠️ 1. Chronic Self-Editing

You constantly adjust your tone, soften truths, or reshape your questions—wanting clarity, but also acceptance. This can dilute your own power.

You carry a fear that your raw self is too much, so you refine her before presenting her.

This results in:

• Discomfort with appearing “messy” or “irrational”

• Suppressed anger, desire, or spiritual frustration

• Difficulty fully receiving love or support

⸻

⚠️ 2. False Self of the “Capable Servant”

Your leadership, helpfulness, and competence are sincere—but they can become masks when overused. There’s a hidden belief:

“If I’m needed, I’ll be safe. If I’m vulnerable, I’ll be abandoned.”

This belief keeps you in loops where:

• You give more than you receive

• You over-function to maintain connection

• You feel unseen despite being visible

⸻

⚠️ 3. Romanticizing Pain as Proof of Depth

There’s a part of you that believes enduring pain makes you real, wise, or worthy. So suffering becomes seductive—almost sacred.

This can lead to:

• Staying in unhealthy dynamics longer than you should

• Distrusting joy, love, or ease

• Equating intensity with authenticity

Pain has shaped you—but it is not your identity.

Your depth is not validated by suffering. It already exists.

⸻

🔄 YOUR LOOP IN A SENTENCE:

“I want to be fully seen—but I fear what will be seen is too much, too scattered, or too strange… so I curate, control, or collapse.”

And yet—this paradox is also your beauty. You are becoming someone who can hold all of herself without abandoning any piece.

⸻

If you’d like, I can help you develop:

• A cognitive-emotional map of how your mind responds under stress vs safety

• A new decision-making framework rooted in inner authority

• A system for navigating romantic and spiritual intensity with less collapse

But for now:

You’re not broken. You’re blooming.

Not despite the contradictions, but because of them.

[aimarketresearch]

Harmonic Filter Market Size, Share, Trends, Key Drivers, Demand and Opportunities

Executive Summary Harmonic Filter Market :

The Harmonic Filter Market research report delivers comprehensive analysis of the market structure along with forecast of the diverse segments and sub-segments of the market. The report considers an in depth description, competitive scenario, wide product portfolio of key vendors and business strategy adopted by competitors along with their SWOT analysis and porter's five force analysis. Harmonic Filter Market report examines market by regions, especially North America, China, Europe, Southeast Asia, Japan, and India, focusing top manufacturers in global market, with respect to production, price, revenue, and market share for each manufacturer. The Harmonic Filter Market report provides an in-depth overview of product specification, technology, product type and production analysis considering major factors such as revenue, cost, gross and gross margin.

The market transformations are highlighted in the Harmonic Filter Market document which occurs because of the moves of key players and brands like developments, product launches, joint ventures, merges and accusations that in turn changes the view of the global face of  industry. The market report evaluates CAGR value fluctuation during the forecast period. for the market.  which will tell you how the Harmonic Filter Market is going to perform in the forecast years by informing you what the market definition, classifications, applications, and engagements are. This Harmonic Filter Market study also analyzes the market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, distributors and Porter's Five Forces Analysis.

Discover the latest trends, growth opportunities, and strategic insights in our comprehensive Harmonic Filter Market report. Download Full Report: https://www.databridgemarketresearch.com/reports/global-harmonic-filter-market

Harmonic Filter Market Overview

**Segments**

- Based on type, the harmonic filter market can be segmented into active harmonic filters, passive harmonic filters, and hybrid harmonic filters. Active harmonic filters are increasingly gaining popularity due to their ability to dynamically respond to changing harmonic loads and provide efficient harmonic mitigation. Passive harmonic filters are cost-effective and widely used in various industrial applications. Hybrid harmonic filters combine the advantages of both active and passive filters, offering a comprehensive solution for harmonic suppression. - On the basis of voltage level, the market can be categorized into low voltage, medium voltage, and high voltage harmonic filters. Low voltage harmonic filters are commonly used in residential and commercial buildings to mitigate harmonic distortion caused by nonlinear loads. Medium voltage filters are deployed in industrial plants and large-scale commercial facilities, while high voltage filters are essential for utility and infrastructure applications to maintain grid stability. - By end-use industry, the harmonic filter market is segmented into industrial, commercial, and residential sectors. Industries such as manufacturing, oil & gas, automotive, and mining heavily rely on harmonic filters to ensure smooth operations and equipment longevity. Commercial buildings, data centers, and healthcare facilities also utilize harmonic filters to enhance power quality and minimize disruptions. In the residential sector, harmonic filters are utilized to protect sensitive electronic devices and appliances from voltage spikes and harmonic distortion.

**Market Players**

- ABB Ltd. - Schneider Electric - Eaton - Siemens AG - Schaffner Holding AG - L&T Electrical & Automation - MTE Corporation - Danfoss - TCI, LLC - Baron Power Limited

These market players are at the forefront of the global harmonic filter market, continuously innovating and expanding their product portfolios to meet the evolving needs of customers across various industries. Collaborations, partnerships, and strategic acquisitions are common strategies employed by these key players to strengthen their market position and gain a competitive edge in the industry.

Additionally, the emphasis on energy efficiency and sustainability is shaping the harmonic filter market landscape, with industries and commercial sectors striving to optimize their power systems and reduce energy losses. Harmonic filters play a crucial role in improving power factor correction, reducing energy consumption, and enhancing overall system efficiency. Furthermore, the emphasis on regulatory compliance and adherence to stringent government standards regarding power quality and grid reliability is driving the adoption of harmonic filters across various end-use industries.

Moreover, the increasing digitalization and automation of industrial processes are creating new opportunities for the harmonic filter market. The integration of advanced control systems, IoT technologies, and predictive maintenance solutions is driving the demand for intelligent harmonic filters that can adapt to dynamic load conditions and ensure optimal power quality. Market players are investing in research and development activities to enhance the intelligence and functionality of harmonic filters, enabling customers to achieve greater control over their power systems and reduce operational risks.

Overall, the global harmonic filter market is poised for significant expansion in the coming years as industries, commercial sectors, and residential users increasingly recognize the importance of power quality management and the role of harmonic filters in ensuring reliable and efficient electrical systems. Continuous innovation, collaboration, and strategic partnerships will be crucial for market players to capitalize on the growth opportunities presented by the evolving market landscape and meet the evolving needs of customers in a rapidly changing industry environment.The global harmonic filter market is witnessing significant growth and evolution propelled by various factors such as the increasing emphasis on power quality issues, the rising demand for efficient power distribution systems, and the integration of renewable energy sources. One of the key trends impacting the market is the growing focus on renewable energy integration and smart grid technologies. With the proliferation of solar and wind power installations, there is a heightened need for harmonic filters to mitigate grid disturbances and ensure stability. Market players are responding to this trend by innovating and developing advanced solutions that can effectively address the challenges posed by renewable energy integration.

Another critical factor shaping the harmonic filter market is the increasing emphasis on energy efficiency and sustainability. Industries and commercial sectors are keen on optimizing their power systems to minimize energy losses and enhance overall efficiency. Harmonic filters play a crucial role in improving power factor correction, reducing energy consumption, and enhancing system performance. Additionally, regulatory compliance and adherence to government standards regarding power quality and grid reliability are driving the adoption of harmonic filters across various industries.

In terms of market dynamics, competitive pricing strategies, product differentiation, and technological advancements are key drivers influencing market growth. To stay competitive, market players are focusing on developing innovative solutions that offer superior performance, reliability, and cost-effectiveness to meet customer needs. Moreover, expanding distribution channels, strengthening after-sales support services, and forging strategic collaborations with system integrators are essential for gaining a competitive edge and expanding market share.

The increasing digitalization and automation of industrial processes present new opportunities for the harmonic filter market. The integration of advanced control systems, IoT technologies, and predictive maintenance solutions is fueling the demand for intelligent harmonic filters that can adapt to dynamic load conditions and ensure optimal power quality. Market players are investing in research and development endeavors to enhance the intelligence and functionality of harmonic filters, enabling customers to have better control over their power systems and reduce operational risks.

In conclusion, the global harmonic filter market is poised for substantial growth as the importance of power quality management becomes more pronounced across industries, commercial sectors, and residential users. Continuous innovation, collaboration, and strategic partnerships will be vital for market players to capitalize on the growth prospects presented by the evolving market landscape and meet the changing needs of customers in a dynamic industry environment.

The Harmonic Filter Market is highly fragmented, featuring intense competition among both global and regional players striving for market share. To explore how global trends are shaping the future of the top 10 companies in the keyword market.

Learn More Now: https://www.databridgemarketresearch.com/reports/global-harmonic-filter-market/companies

DBMR Nucleus: Powering Insights, Strategy & Growth

DBMR Nucleus is a dynamic, AI-powered business intelligence platform designed to revolutionize the way organizations access and interpret market data. Developed by Data Bridge Market Research, Nucleus integrates cutting-edge analytics with intuitive dashboards to deliver real-time insights across industries. From tracking market trends and competitive landscapes to uncovering growth opportunities, the platform enables strategic decision-making backed by data-driven evidence. Whether you're a startup or an enterprise, DBMR Nucleus equips you with the tools to stay ahead of the curve and fuel long-term success.

Key Benefits of the Report:

This study presents the analytical depiction of the global Harmonic Filter Market Industry along with the current trends and future estimations to determine the imminent investment pockets.

The report presents information related to key drivers, restraints, and opportunities along with detailed analysis of the global Harmonic Filter Market

The current market is quantitatively analyzed  to highlight the Harmonic Filter Market growth scenario.

Porter's five forces analysis illustrates the potency of buyers & suppliers in the market.

The report provides a detailed global Harmonic Filter Market analysis based on competitive intensity and how the competition will take shape in coming years.

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[xtruss]

The Entangled Brain 🧠

The Brain Is Much Less Like A Machine Than It Is Like The Murmurations of A Flock of Starlings Or An Orchestral Symphony

— Luiz Pessoa | Edited By Sam Dresser | 19 May 2025

Photo By Sarah Mason/Getty Images

When thousands of starlings swoop and swirl in the evening sky, creating patterns called murmurations, no single bird is choreographing this aerial ballet. Each bird follows simple rules of interaction with its closest neighbours, yet out of these local interactions emerges a complex, coordinated dance that can respond swiftly to predators and environmental changes. This same principle of emergence – where sophisticated behaviours arise not from central control but from the interactions themselves – appears across nature and human society.

Consider how market prices emerge from countless individual trading decisions, none of which alone contains the ‘right’ price. Each trader acts on partial information and personal strategies, yet their collective interaction produces a dynamic system that integrates information from across the globe. Human language evolves through a similar process of emergence. No individual or committee decides that ‘LOL’ should enter common usage or that the meaning of ‘cool’ should expand beyond temperature (even in French-speaking countries). Instead, these changes result from millions of daily linguistic interactions, with new patterns of speech bubbling up from the collective behaviour of speakers.

These examples highlight a key characteristic of highly interconnected systems: the rich interplay of constituent parts generates properties that defy reductive analysis. This principle of emergence, evident across seemingly unrelated fields, provides a powerful lens for examining one of our era’s most elusive mysteries: how the brain works.

The core idea of emergence inspired me to develop the concept I call the entangled brain: the need to understand the brain as an interactionally complex system where functions emerge from distributed, overlapping networks of regions rather than being localised to specific areas. Though the framework described here is still a minority view in neuroscience, we’re witnessing a gradual paradigm transition (rather than a revolution), with increasing numbers of researchers acknowledging the limitations of more traditional ways of thinking.

Complexity science is an interdisciplinary field that studies systems composed of many interacting components whose collective behaviours give rise to collective properties – phenomena that cannot be fully explained by analysing individual parts in isolation. These systems, such as ecosystems, economies or – as we will see – the brain, are characterised by nonlinear dynamics, adaptability, self-organisation, and networked interactions that span multiple spatial and temporal scales. Before exploring the ideas leading to the entangled brain framework, let’s revisit some of the historical developments of the field of neuroscience to set the stage.

In 1899, Cécile and Oskar Vogt, aged 24 and 29 respectively, arrived in Berlin to establish the Neurological Centre, initially a private institution for the anatomical study of the human brain that in 1902 was expanded to the Neurobiological Laboratory, and then the Kaiser Wilhelm Institute for Brain Research in 1914. Cécile Vogt was one of only two women in the entire institute. (In Prussia, until 1908, women were not granted access to regular university education, let alone the possibility to have a scientific career.) She obtained her doctoral degree from the University of Paris in 1900, while her husband Oskar obtained a doctorate for his thesis on the corpus callosum from the University of Jena in 1894.

In 1901, Korbinian Brodmann, who had concluded his doctorate in Leipzig in 1898, joined the group headed by the Vogts and was encouraged by them to undertake a systematic study of the cells of the cerebral cortex using tissue sections stained with a new cell-marking method. (The cortex is the outer brain surface with grooves and bulges; the subcortex comprises other cell masses that sit underneath.) The Vogts, and Brodmann working separately, were part of a first wave of anatomists trying to establish a complete map of the cerebral cortex, with the ultimate goal of understanding how brain structure and function are related. In a nutshell, where does a mental function such as an emotion reside in the brain?

Neurons – a key cell type of the nervous system – are diverse, and several cell classes can be determined based on both their shape and size. Researchers used these properties, as well as spatial differences in distribution and density, to define the boundaries between potential sectors. In this manner, Brodmann subdivided the cortex into approximately 50 regions (also called areas) per hemisphere. The Vogts, in contrast, thought that there might be more than 200 of them, each with its own distinguishing cytoarchitectonic pattern (that is, cell-related organisation).

It Is An Idea That Comes Close To Being An Axiom In Biology: Function Is Tied To Structure

Brodmann’s map is the one that caught on and stuck, likely because neuroanatomists opposed too vigorous a subdivision of the cortex, and today students and researchers alike still refer to cortical parts by invoking his map. Although relatively little was known about the functions of cortical regions at the time, Brodmann believed that his partition identified ‘organs of the mind’ – he was convinced that each cortical area subserved a particular function. Indeed, when he joined the Vogts’ laboratory, they had encouraged him to try to understand the organisation of the cortex in light of their main thesis that different cytoarchitectonically defined areas are responsible for specific physiological responses and functions.

There is a deep logic that the Vogts and Brodmann were following. In fact, it is an idea that comes close to being an axiom in biology: function is tied to structure. In the case at hand, parts of the cortex that are structurally different (contain different cell types, cell arrangements, cell density, and so on) carry out different functions. In this manner, they believed they could understand how function is individuated from a detailed characterisation of the underlying microanatomy. They were in search of the functional units of the cortex – where the function could be sensory, motor, cognitive and so on.

Unlike other organs of the body that have more clear-cut boundaries, the cortex’s potential subdivisions are not readily apparent at a macroscopic level. One of the central goals of many neuroanatomists in the first half of the 20th century was to investigate such ‘organs of the mind’ (an objective that persists to this day). A corollary of this research programme was that individual brain regions – say, Brodmann’s area 17 in the back of the brain – implemented specialised mechanisms, in this case related to processing visual sensory stimuli. Therefore, it was vital to understand the operation of individual parts since the area/region was the rightful mechanistic unit to understand how the nervous system works.

Neuroscientists’ interest in brain regions was motivated by the notion that each region executes a particular function. For example, we could say that the function of the primary visual cortex is visual perception, or perhaps a more basic visual mechanism, such as detecting ‘edges’ (sharp light-to-dark transitions) in images. The same type of description can be applied to other sensory and motor areas of the brain. This exercise becomes considerably less straightforward for brain areas that are much less sensory or motor, as their workings become exceedingly difficult to determine and describe. Nevertheless, in theory, we can imagine extending the idea to all parts of the brain. The result of this endeavour would be a list of area-function pairs: L = {(A1, F1), (A2, F2), … , (An, Fn)}, where areas A implement functions F.

There is, however, a serious problem with this endeavour. To date, no such list has been systematically generated. Indeed, current knowledge strongly suggests that this strategy will not yield a simple area-function list. What may start as a simple (A1, F1) pair, is gradually revised as research progresses, and eventually grows to include a list of functions, such that area A1 participates in a series of functions F1, F2, … , Fk. From a basic one-to-one A1 → F1 mapping, the picture evolves to a one-to-many mapping: A1 → {F1, F2, … , Fk}.

If the mapping between structure and function is not one-to-one, then what kind of system is the brain? This is the question the entangled brain concept sets out to tackle. It’s useful to consider two types of information: anatomical and functional. Let’s start with the brain’s massive combinatorial anatomical connectivity. Neurons are constantly exchanging electrochemical signals with one another. Signalling between them is aided by physical cell extensions, called axons, that protrude beyond the cell body for distances from less than 1 mm to around 15 mm in the central nervous system. Axons travelling longer distances typically bundle together along what are called white-matter tracts to distinguish them from tissue composed of neuronal cell bodies, which is called grey matter. Anatomical connectivity, then, can be viewed as a system of roads and highways that supports cell signalling in the brain.

While most connections are local, the brain also maintains an impressive network of medium- and long-distance pathways. To give a rough sense of the dimensions involved, axonal lengths within local brain circuits (such as those within a single Brodmann area) have lengths from less than 1 mm to just under 1 cm. Connections between adjacent and nearby regions can extend between 0.5 to 4 cm, and connections between areas in different lobes, such as between the frontal and the occipital lobes, can reach 15 cm or more.

Although details vary across mammalian species, there’s evidence that the brains of macaque monkeys (a species that has a brain organisation resembling that of humans) are densely interconnected. For example, when scientists looked at any two regions in the cortex, they found that about 60 per cent of the time there’s a direct connection between them (although the strength of the pathway decreases between regions that are farther apart). Notably, the cortex organises medium- and long-distance communication through special regions that act like major transportation hubs, routing and coordinating signals across the entire cortex, much like how major airports serve as central connection points in the global air transportation network.

But that’s just part of the story. Beyond the extensive interconnections found in the cortex, there are multiple ‘connectional systems’ that weave together regions even further. The entire cortex connects to deeper brain structures. We can think of the brain as having distinct sectors. Simplifying somewhat, these are the cortex, the subcortical parts that are physically beneath the cortex in humans, and the brainstem. In the 1980s, it became clear that the cortex and subcortex are part of extensive connectional loops – from cortex to subcortex back to cortex. We now know that the multiple sectors are amply interlinked. What is more, a subcortical structure such as the thalamus, viewed in the past as a relatively passive steppingstone conveying signals to the cortex, is so sweepingly interconnected with the entire cortex that it is perhaps better to think in terms of a cortical-thalamic system. Even subcortical areas believed to mainly control basic functions, like the hypothalamus, which regulates hunger and body temperature among others, have widespread connections throughout the brain. This creates an incredibly intricate connectional web where signals can travel between disparate parts through multiple routes, hence the idea of ‘combinatorial’ connectivity.

What are the implications of the connectional organisation of the brain? The dense nexus of pathways allows for remarkable flexibility in how the brain processes information and controls behaviour. Signals of all types can be exchanged and integrated in multiple ways. All this potential mixing strongly challenges how we traditionally think of the mind and brain in terms of simplistic labels such as ‘perception’, ‘cognition’, ‘emotion’ and ‘action’. I will return to this point later, but the standard view is further challenged by a second principle of brain organisation: highly distributed functional coordination.

Groups of Neurons That Fire In A Coherent Fashion Indicate That They Are Functionally Interrelated

The Roman Empire’s roads, critical to its success, were extensive enough to circle the globe about twice over. In addition to obvious military applications, the road network supported trade, as well as cultural and administrative integration. These economic and cultural relationships and coordination between disparate parts of the empire were sustained by the incredible physical infrastructure known as the cursus publicus. Likewise, in the brain we need to move beyond the anatomical domain (the roads) to functional properties (such as economic and cultural relationships between different parts of the Roman Empire), all the more because neuroscientists themselves often focus too much on anatomical features.

In the brain, functional relationships between neuronal signals are detected across multiple spatial scales – from the local scale of neurons within a brain area to larger scales involving signals originating from the grey matter of different lobes (such as the frontal and parietal lobes, many centimetres apart). By signals, we mean the electrical activity of neurons that is directly recorded via microelectrodes inserted into grey matter (ie, neuronal tissue), measured indirectly when using functional magnetic resonance imaging (fMRI) in humans, or possibly via other measurement techniques.

What kinds of functional relationships are detected? An important one is that signals from different sites exhibit synchronised neuronal activity. This is notable because groups of neurons that fire in a coherent fashion indicate that they are functionally interrelated, and potentially part of a common process. Different types of signal coordination are believed to reflect processes such as attention and memory, among others. Additional types of relationships are detected mathematically, too, such as whether the strength of the response in one brain area is related to the temporal evolution of signals in a disparate location. In the brain, we identify signal relationships that are indicators of joint functions between regions, much like detecting cultural exchanges between separate parts of the Roman Empire via evidence of shared artefacts or language patterns.

When signals are measured from two sites within a local patch (say, a few millimetres across), it is not too surprising to find notable functional relationships between them (eg, their neuronal activity is correlated), as neurons likely receive similar inputs and are locally connected. Yet, we also observe functional relationships between neuronal signals from locations that are situated much farther apart and, critically, between brain parts that are not directly anatomically connected – there is no direct axonal connection between them.

How does this happen? There is evidence that signal coordination between regions depends more on the total number of possible communication routes between them than on the existence of direct connections between points A and B. For example, although regions A and B are not anatomically connected, they both connect to region C, which thus serves as a bridge between them. Even more circuitous paths can unite A and B, much like flying between two cities that have no direct flights and require multiple layovers. In such a manner, the brain creates functional partnerships that take advantage of all possible ways through its intricate pathways. This helps explain how the brain can be so remarkably flexible, sustaining different partnerships between regions depending on what we’re doing, thinking or feeling at any given moment.

When we consider the highways traversing the brain and how signals establish behaviourally relevant relationships across the central nervous system, we come to an important insight. In a highly interconnected system, to understand function, we need to shift away from thinking in terms of individual brain regions. The functional unit is not to be found at the level of the brain area, as commonly proposed. Instead, we need to consider neuronal ensembles distributed across multiple brain regions, much like the murmuration of starlings forms a single pattern from the collective behaviour of individual birds.

There are many instances of distributed neuronal ensembles. Groups of neurons extending over cortical (say, prefrontal cortex and hippocampus) and subcortical (say, amygdala) regions form circuits that are important for learning what is threatening and what is safe. Such multiregion circuits are ubiquitous; fMRI studies in humans have shown that the brain is organised in terms of large-scale networks that stretch across the cortex as well as subcortical territories. For example, the so-called ‘salience network’ (suggested to be engaged when significant events are encountered) spans brain regions in the frontal and parietal lobes, among others, and can also be viewed as a neuronal ensemble.

Whether we consider ensembles in the case of brain circuits or large-scale networks, the associated neuronal groupings should be viewed as strongly context dependent and dynamic. That is to say, they are not fixed entities but instead form dynamically to meet current situational requirements. Accordingly, they will dynamically assemble and disassemble as per behavioural needs. The implication of this view is that whereas brain regions A, B and C might generally be active together in dealing with a specific type of behaviour, in some contexts, we will also observe an ensemble that encompasses region D, or instead the ensemble {A, C, D} that meets slightly different requirements. In all, neuronal ensembles constitute an extremely malleable functional unit.

Think of how an orchestra works during a complex piece of music. The string section might split into different groups, with some violins joining the woodwinds for one musical phrase while others harmonise with the cellos. Later, these groupings shift completely for a different passage. The brain works in a related way: rather than recruiting fixed regions, it forms flexible aggregations that assemble and disassemble based on what we’re doing, thinking or feeling. This builds on what we learned about the brain’s extensive physical connections and the coordinated activity across regions. These features make the formation of ensembles possible.

Brain Regions Can Participate In Multiple Networks Simultaneously And Shift Their Roles As Needed

As is common in science, these ideas have a long genealogy. In 1949, the Canadian psychologist Donald Hebb proposed that the brain’s ability to generate coherent thoughts derives from the spatiotemporal orchestration of neuronal activity. He hypothesised that a discrete, strongly interconnected group of active neurons called the cell assembly represents a distinct mental entity, such as a thought or an emotion. Yet, these ideas have taken a long time to mature, not least due to technical limitations in measuring signals simultaneously across the brain, and the relative insularity of experimental neuroscience from other disciplines, such as computer science, mathematics and physics.

Just as a symphony emerges from both the individual instruments and how they play together, brain function emerges from both the regions themselves and their dynamic interactions. Scientists are finding that we can’t understand complex mental processes by studying individual brain regions in isolation, any more than we could understand a symphony by listening to each instrument separately.

What’s particularly fascinating is that these brain assemblages overlap and change over time. Just as a violin might be part of the string section in one moment and join a smaller ensemble in the next, brain regions can participate in multiple networks simultaneously and shift their roles as needed. But note that, in this view, even brain networks aren’t seen as constituted of fixed sets of regions; instead, they are dynamic coalitions that form and dissolve based on the brain’s changing needs. This flexibility helps explain how the brain can support such a wide range of complex behaviours using a limited number of regions.

Categories such as perception, cognition, action, emotion and motivation are not only the titles of introductory textbooks, but reflect how psychologists and neuroscientists conceptualise the organisation of the mind and brain. They seek to subdivide the brain into territories that have preferences for processes that support a specific type of mental activity. Some parts handle perception, such as the back of the head and its involvement in vision, or the front of the brain and its role in cognition. And so on. The decomposition of the mind-brain adopted by many neuroscientists follows an organisation that is called modular. Modularity here refers to the idea that the brain consists of specialised, relatively independent components or modules that each handle specific mental functions, much like distinct parts in a machine that work together but perform separate operations.

Yet, a modular organisation, popular as it is among neuroscientists, is inconsistent with the principles of the anatomical and functional neuroarchitecture discussed here. The brain’s massive combinatorial connectivity and highly distributed functional coordination defy clean compartmentalisation. The extensive bidirectional pathways spanning the entire brain create crisscrossing connectional systems that dissolve potential boundaries between traditional mental domains (cognition, emotion, etc).

Anxiety, PTSD, Depression And So On Should Be Viewed As System-Level Entities

Brain regions dynamically affiliate with multiple networks in a context-dependent manner, forming coalitions that assemble and disassemble based on current demands. This interactional complexity means that functions aren’t localised to discrete modules but emerge from decentralised coordination across multiregion assemblies. The properties that emerge from these interactions cannot be reduced to individual components, making a strict modular framework inadequate for capturing the brain’s entangled nature.

Why is the brain so entangled, and thus so unlike human-engineered systems? Brains have evolved to provide adaptive responses to challenges faced by living beings, promoting survival and reproduction – not to solve isolated cognitive or emotional problems. In this context, even the mental vocabulary of neuroscience and psychology (attention, cognitive control, fear, etc), with origins disconnected from the study of animal behaviour, provides problematic theoretical pillars. Instead, approaches inspired by evolutionary considerations provide better scaffolds to sort out the relationships between brain structure and function.

The implications of the entangled brain are substantial for the understanding of healthy and unhealthy brain processes. It is common for scientists to seek a single, unique source of psychological distress. For example, anxiety or PTSD is the result of an overactive amygdala; depression is caused by deficient serotonin provision; drug addiction is produced by dopamine oversupply. But, according to the ideas described here, we should not expect unique determinants for psychological states.

Anxiety, PTSD, depression and so on should be viewed as system-level entities. Alterations across several brain circuits, spanning multiple brain regions, are almost certainly involved. As a direct consequence, healthy or unhealthy states should not be viewed as emotional, motivational or cognitive. Such classification is superficial and neglects the intermingling that results from anatomical and functional brain organisation.

We should also not expect to find a single culprit, not even at the level of distributed neuronal ensembles. The conditions in question are too heterogeneous and varied across individuals; they won’t map to a single alteration, including at the distributed level. In fact, we should not expect a temporally constant type of disturbance, as brain processes are highly context-dependent and dynamic. Variability in the very dynamics will contribute to how mental health experiences are manifested.

In the end, we need to stop seeking simple explanations for complex mind-brain processes, whether they are viewed as healthy or unhealthy. That’s perhaps the most general implication of the entangled brain view: that the functions of the brain, like the murmurations of starlings, are more complicated and more mysterious than its component parts.

— Luiz Pessoa is director of Maryland Neuroimaging Center, principal investigator at the Laboratory of Cognition and Emotion, and professor of psychology at the University of Maryland. He is the author of The Cognitive-Emotional Brain (2013) and The Entangled Brain (2022). Edited By Sam Dresser

[drmikewatts]

IEEE Transactions on Fuzzy Systems, Volume 33, Issue 6, June 2025

1) Brain-Inspired Fuzzy Graph Convolution Network for Alzheimer's Disease Diagnosis Based on Imaging Genetics Data

Author(s): Xia-An Bi, Yangjun Huang, Wenzhuo Shen, Zicheng Yang, Yuhua Mao, Luyun Xu, Zhonghua Liu

Pages: 1698 - 1712

2) Adaptive Incremental Broad Learning System Based on Interval Type-2 Fuzzy Set With Automatic Determination of Hyperparameters

Author(s): Haijie Wu, Weiwei Lin, Yuehong Chen, Fang Shi, Wangbo Shen, C. L. Philip Chen

Pages: 1713 - 1725

3) A Novel Reliable Three-Way Multiclassification Model Under Intuitionistic Fuzzy Environment

Author(s): Libo Zhang, Cong Guo, Tianxing Wang, Dun Liu, Huaxiong Li

Pages: 1726 - 1739

4) Guaranteed State Estimation for H−/L∞ Fault Detection of Uncertain Takagi–Sugeno Fuzzy Systems With Unmeasured Nonlinear Consequents

Author(s): Masoud Pourasghar, Anh-Tu Nguyen, Thierry-Marie Guerra

Pages: 1740 - 1752

5) Online Self-Learning Fuzzy Recurrent Stochastic Configuration Networks for Modeling Nonstationary Dynamics

Author(s): Gang Dang, Dianhui Wang

Pages: 1753 - 1766

6) ADMTSK: A High-Dimensional Takagi–Sugeno–Kang Fuzzy System Based on Adaptive Dombi T-Norm

Author(s): Guangdong Xue, Liangjian Hu, Jian Wang, Sergey Ablameyko

Pages: 1767 - 1780

7) Constructing Three-Way Decision With Fuzzy Granular-Ball Rough Sets Based on Uncertainty Invariance

Author(s): Jie Yang, Zhuangzhuang Liu, Guoyin Wang, Qinghua Zhang, Shuyin Xia, Di Wu, Yanmin Liu

Pages: 1781 - 1792

8) TOGA-Based Fuzzy Grey Cognitive Map for Spacecraft Debris Avoidance

Author(s): Chenhui Qin, Yuanshi Liu, Tong Wang, Jianbin Qiu, Min Li

Pages: 1793 - 1802

9) Reinforcement Learning-Based Fault-Tolerant Control for Semiactive Air Suspension Based on Generalized Fuzzy Hysteresis Model

Author(s): Pak Kin Wong, Zhijiang Gao, Jing Zhao

Pages: 1803 - 1814

10) Adaptive Fuzzy Attention Inference to Control a Microgrid Under Extreme Fault on Grid Bus

Author(s): Tanvir M. Mahim, A.H.M.A. Rahim, M. Mosaddequr Rahman

Pages: 1815 - 1824

11) Semisupervised Feature Selection With Multiscale Fuzzy Information Fusion: From Both Global and Local Perspectives

Author(s): Nan Zhou, Shujiao Liao, Hongmei Chen, Weiping Ding, Yaqian Lu

Pages: 1825 - 1839

12) Fuzzy Domain Adaptation From Heterogeneous Source Teacher Models

Author(s): Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang

Pages: 1840 - 1852

13) Differentially Private Distributed Nash Equilibrium Seeking for Aggregative Games With Linear Convergence

Author(s): Ying Chen, Qian Ma, Peng Jin, Shengyuan Xu

Pages: 1853 - 1863

14) Robust Divide-and-Conquer Multiple Importance Kalman Filtering via Fuzzy Measure for Multipassive-Sensor Target Tracking

Author(s): Hongwei Zhang

Pages: 1864 - 1875

15) Fully Informed Fuzzy Logic System Assisted Adaptive Differential Evolution Algorithm for Noisy Optimization

Author(s): Sheng Xin Zhang, Yu Hong Liu, Xin Rou Hu, Li Ming Zheng, Shao Yong Zheng

Pages: 1876 - 1888

16) Impulsive Control of Nonlinear Multiagent Systems: A Hybrid Fuzzy Adaptive and Event-Triggered Strategy

Author(s): Fang Han, Hai Jin

Pages: 1889 - 1898

17) Uncertainty-Aware Superpoint Graph Transformer for Weakly Supervised 3-D Semantic Segmentation

Author(s): Yan Fan, Yu Wang, Pengfei Zhu, Le Hui, Jin Xie, Qinghua Hu

Pages: 1899 - 1912

18) Observer-Based SMC for Discrete Interval Type-2 Fuzzy Semi-Markov Jump Models

Author(s): Wenhai Qi, Runkun Li, Peng Shi, Guangdeng Zong

Pages: 1913 - 1925

19) Network Security Scheme for Discrete-Time T-S Fuzzy Nonlinear Active Suspension Systems Based on Multiswitching Control Mechanism

Author(s): Jiaming Shen, Yang Liu, Mohammed Chadli

Pages: 1926 - 1936

20) Fuzzy Multivariate Variational Mode Decomposition With Applications in EEG Analysis

Author(s): Hongkai Tang, Xun Yang, Yixuan Yuan, Pierre-Paul Vidal, Danping Wang, Jiuwen Cao, Duanpo Wu

Pages: 1937 - 1948

21) Adaptive Broad Network With Graph-Fuzzy Embedding for Imbalanced Noise Data

Author(s): Wuxing Chen, Kaixiang Yang, Zhiwen Yu, Feiping Nie, C. L. Philip Chen

Pages: 1949 - 1962

22) Average Filtering Error-Based Event-Triggered Fuzzy Filter Design With Adjustable Gains for Networked Control Systems

Author(s): Yingnan Pan, Fan Huang, Tieshan Li, Hak-Keung Lam

Pages: 1963 - 1976

23) Fuzzy and Crisp Gaussian Kernel-Based Co-Clustering With Automatic Width Computation

Author(s): José Nataniel A. de Sá, Marcelo R.P. Ferreira, Francisco de A.T. de Carvalho

Pages: 1977 - 1991

24) A Biselection Method Based on Consistent Matrix for Large-Scale Datasets

Author(s): Jinsheng Quan, Fengcai Qiao, Tian Yang, Shuo Shen, Yuhua Qian

Pages: 1992 - 2005

25) Nash Equilibrium Solutions for Switched Nonlinear Systems: A Fuzzy-Based Dynamic Game Method

Author(s): Yan Zhang, Zhengrong Xiang

Pages: 2006 - 2015

26) Active Domain Adaptation Based on Probabilistic Fuzzy C-Means Clustering for Pancreatic Tumor Segmentation

Author(s): Chendong Qin, Yongxiong Wang, Fubin Zeng, Jiapeng Zhang, Yangsen Cao, Xiaolan Yin, Shuai Huang, Di Chen, Huojun Zhang, Zhiyong Ju

Pages: 2016 - 2026

[renatoferreiradasilva]

Quantum Finance and Narrative Collapse: A Theoretical Framework for Market Dynamics in the Age of Informational Pressure

Renato Ferreira da Silva ORCID: 0009-0003-8908-481X

Abstract

Traditional financial theories fail to account for the abrupt valuation shifts experienced by major corporations in the age of algorithmic trading, geopolitical uncertainty, and narrative-driven perception. This paper introduces a quantum-inspired framework to understand financial markets as probability fields susceptible to informational measurement, narrative entanglement, and abrupt collapse. By applying analogues from quantum mechanics to the cases of Tesla, Apple, and Nvidia during the 2025 tech sell-offs, we explore how observation and perception serve as active forces in market valuation, drawing implications for governance and regulatory reform—especially in the context of Brazil's Novo Mercado initiatives.

1. Introduction

Markets in the 21st century increasingly behave as systems influenced not merely by fundamentals, but by perception, reflexivity, and attention. Classical models based on rational expectations and efficient markets are insufficient to explain the nonlinear collapses of value observed in events triggered by political conflict, regulatory shifts, or changes in technological narratives.

This paper proposes an interpretive framework inspired by quantum mechanics—where price is understood as the collapsed outcome of a field of valuation possibilities, subject to observer effects, narrative coherence, and informational decoherence.

2. Epistemological Justification

The concept of "Quantum Finance" is not a direct application of quantum physics to finance, but a heuristic framework for modeling indeterminacy, observer interaction, and narrative-based valuation. This aligns with post-Keynesian and behavioral critiques of neoclassical models (Keynes, 1936; Soros, 1987), and builds upon the notion that uncertainty is not merely a risk, but an ontological condition of market processes (Knight, 1921).

3. Theoretical Correspondence with Quantum Mechanics

We propose the following analogical structure between concepts in quantum theory and financial behavior:Quantum ConceptFinancial Market AnalogueSuperpositionMultiple narratives (e.g., innovator vs. risk)Wave functionField of valuation possibilitiesMeasurementObservation event (tweet, regulation, etc.)CollapseSudden revaluation (market crash or surge)DecoherenceDissolution of narrative coherenceEntanglementCorrelated valuations across companies/sectors

These concepts offer a lens through which real-world financial events can be reinterpreted as dynamic narrative collapses shaped by external observation and internal coherence.

4. Methodology

Our analysis relies on an interpretive-empirical approach. We study three major corporate valuation events in 2025: Tesla, Apple, and Nvidia. Each case is analyzed using a quantum finance framework, complemented by market data, policy events, and narrative analysis (NLP-based media scans, price-volume correlation, and regulatory timelines).

5. Case Studies

5.1 Tesla: Collapse by Political Measurement

Trigger: Public feud between Elon Musk and President Donald Trump over EV subsidies.

Effect: $152B single-day loss; $380B weekly decline.

Interpretation: Political observation collapsed the dual-narrative wave function (visionary vs. liability), with institutional sell-offs catalyzing value collapse.

5.2 Apple: Silent Decoherence

Trigger: Tariffs, declining Chinese demand, antitrust actions.

Effect: $533B loss over two days.

Interpretation: Gradual decoherence via multi-sourced regulatory stress, buffered by institutional buybacks and brand coherence.

5.3 Nvidia: Latent Instability and Entanglement Risk

Trigger: AI hype, CEO quantum computing comments, US-China tech tariffs.

Effect: $393B loss; collapse extended to Rigetti and IonQ.

Interpretation: Overextended narrative superposition entangled Nvidia’s valuation with that of an entire sector, amplifying latent fragility.

6. Comparative Matrix: The Quantum Triad

MetricTeslaAppleNvidiaObserver TypePoliticalRegulatoryStrategic/TechCollapse SpeedInstantaneousGradualTrigger-basedLoss Magnitude–$380B–$533B–$393BNarrative StabilityLow (polarized)High (cohesive)High (fragile)Recovery SignalRobotaxi speculationEcosystem resilienceAI roadmap optimismQuantum StateCollapsed dualityDecohered realityEntangled superposition

7. Implications for Governance and Policy

The findings suggest that market regulation must account for informational triggers and narrative structures. Brazil’s “Under Review Seals,” for example, act as observer events with high narrative-impact risk. Thus, governance mechanisms must include narrative monitoring, noise filtering, and reputational dampening tools.

8. Conclusion

Markets behave not as deterministic calculators of value, but as probabilistic fields responsive to narrative, observation, and coherence. The use of quantum analogies enables a richer modeling of systemic risk and perception. Future financial analysis should incorporate informational topology and dynamic observer influence as primary modeling variables.

References

Keynes, J. M. (1936). The General Theory of Employment, Interest and Money.

Knight, F. H. (1921). Risk, Uncertainty and Profit.

Soros, G. (1987). The Alchemy of Finance.

Orrell, D. (2022). Quantum Economics: The New Science of Money.

Bouchaud, J.-P. (2003). Crises and Collective Socio-Economic Phenomena.

Haven, E., & Khrennikov, A. (2013). Quantum Social Science.

Baaquie, B. (2007). Quantum Finance: Path Integrals and Hamiltonians.

[techit-rp]

AI in Financial Modelling: How Machine Learning Is Redefining Forecasting and Valuation in 2025

The finance world is experiencing a paradigm shift in 2025, with artificial intelligence (AI) and machine learning (ML) transforming how businesses approach financial modeling. Once dominated by spreadsheets, historical assumptions, and manual analysis, modern financial modeling is now being supercharged by AI-driven tools that provide faster, more accurate, and data-rich insights.

Whether you're a seasoned analyst, a finance student, or a business professional, understanding how AI is changing the game has become non-negotiable. And for those looking to keep pace, enrolling in the Best Financial Modelling Certification Course in Mumbai can be a career-defining step.

Traditional vs. AI-Powered Financial Modelling

Traditional financial models rely heavily on historical data and linear projections. While useful, these models often miss out on complex patterns, nonlinear relationships, or real-time fluctuations. AI, especially machine learning, introduces dynamic, adaptive forecasting that evolves with new data inputs.

In simpler terms: traditional models tell you what might happen, but AI models tell you what’s most likely to happen—backed by thousands of data points, sentiment analysis, and behavioral trends.

How AI Is Revolutionizing Financial Modelling

1. Enhanced Forecast Accuracy

Machine learning algorithms can analyze historical and real-time data to identify trends and predict future outcomes with greater precision. For example, retail companies use AI models to forecast sales based on variables like weather, social media sentiment, local events, and macroeconomic indicators.

This type of insight was nearly impossible using traditional Excel-based models alone.

2. Automated Data Processing

Financial analysts typically spend 60–70% of their time cleaning and preparing data. AI-driven tools automate this grunt work—extracting data from various sources (PDFs, APIs, databases), cleaning it, and updating models in real time. This dramatically boosts productivity and reduces the chance of human error.

3. Scenario Analysis at Scale

Instead of running a few “what-if” scenarios, AI allows you to run thousands of simulations in minutes. Monte Carlo simulations, stress testing, and predictive analytics become far more robust with machine learning, offering deeper risk insights and more confident decision-making.

4. Natural Language Processing (NLP) for Unstructured Data

AI enables models to pull valuable insights from unstructured data like news articles, earnings call transcripts, and social media. Sentiment analysis powered by NLP can affect valuations and forecast models, especially for companies whose brand or CEO reputation plays a critical role in performance.

5. Integration with Real-Time Data Sources

AI-powered models can be connected to live data feeds—stock markets, commodity prices, interest rates, or even Twitter. This allows financial models to update automatically in real-time, giving investors and analysts a competitive edge.

Practical Applications in 2025

AI in financial modeling is not just a theoretical advancement—it’s being used across various domains:

Investment Banking: To predict M&A deal outcomes, optimize deal pricing, and evaluate synergies.

Private Equity: For faster and deeper due diligence based on historical data, competitor analysis, and industry signals.

Corporate Finance: To forecast revenues and costs with precision, aiding CFOs in budget planning.

Startups and SMEs: Leveraging AI tools to build investor decks, financial plans, and valuations with minimal manual input.

The Role of Financial Analysts Is Evolving

The rise of AI doesn't eliminate the need for financial analysts—it evolves their role. Analysts are now expected to:

Interpret AI-generated insights

Understand model outputs and their assumptions

Blend AI-driven forecasts with business context and strategy

Communicate complex findings in a simple, strategic way

To stay ahead in this environment, professionals need both financial acumen and technical fluency. This is where certification programs come in.

Why the Best Financial Modelling Certification Course in Mumbai Matters

Mumbai, being India’s financial capital, is home to a thriving finance ecosystem—banks, investment firms, fintech startups, and corporate headquarters. Professionals and students here are uniquely positioned to benefit from top-tier training that integrates modern tools with core finance principles.

The Best Financial Modelling Certification Course in Mumbai not only teaches Excel-based modeling, valuation techniques, and financial statement analysis but also introduces learners to AI-powered tools, Python basics for finance, and ML concepts relevant to modern modeling.

Graduates from such programs gain a dual advantage: foundational skills that build credibility and advanced capabilities that set them apart in AI-driven finance roles.

Tools and Technologies to Watch

Modern financial analysts should get familiar with the following tools to stay competitive:

Python and R: For machine learning model development

Power BI / Tableau: For data visualization

Alteryx: For automated data preparation

ChatGPT & Copilot: For automated reporting and insights

Jupyter Notebooks: For integrating models and dashboards

Snowflake / BigQuery: For working with big data

Courses that include hands-on projects using these tools make learners job-ready in today's data-driven finance landscape.

Future of AI in Financial Modeling

The future is heading toward hybrid intelligence, where human expertise is amplified by AI. Predictive models will become more accurate, explainable AI (XAI) will demystify complex outputs, and collaboration between finance teams and data scientists will deepen.

AI won’t replace human analysts—but those who adopt AI will likely replace those who don’t.

Final Thoughts

AI is not just a buzzword—it’s actively transforming how financial models are built, interpreted, and applied. In 2025, professionals who understand both finance and machine learning are leading the way in investment decisions, risk management, and strategic planning.

For anyone aiming to thrive in this new landscape, enrolling in the Best Financial Modelling Certification Course in Mumbai is a smart investment. It’s more than just a credential—it’s a future-proofing strategy.

[govindhtech]

Q.ANT Introduce Active Quantum Demonstration At ISC 2025

Q.ANT is prepared to impress at ISC 2025 with its first interactive live demos of its photonic Native Processing Server (NPS).

Guests will interact with functional photonic computing to demonstrate how light may boost energy and computer efficiency for complex scientific tasks like artificial intelligence, physics simulations, and others.

Key Technology: Light-Powered Computing

Q.ANT innovates with light-powered computing. The NPS calculates using light, unlike digital processors that employ electronic impulses. Instead of using digital abstraction, this fundamental improvement lets the system conduct operations directly in the optical domain, making computing more efficient, scalable, and sustainable.

The NPS is built on Q.ANT’s LENA architecture. A unique thin-film lithium niobate (TFLN) photonic chip is essential to this technology. This cutting-edge microprocessor performs complex, nonlinear maths directly using light. Thus, low-loss, high-speed optical modulation is possible without thermal crosstalk issues in electrical systems. Dr. Michael Förtsch, CEO of Q.ANT, says doing mathematical transformations natively with light transforms HPC economics, especially for more complex scientific workloads, physics simulations, and artificial intelligence.

Unmatched benefits and performance

Q.ANT NPS is expected to improve several key aspects for high-performance computing and data centres:

Outstanding Energy Efficiency:

NPS energy efficiency is expected to be 30 times higher than existing systems. This energy reduction is crucial for sustainable Quantum Computing.

That the NPS doesn’t need active cooling equipment boosts its efficiency. This eliminates complex cooling systems and saves money and energy.

The approach allows up to 100x higher compute density per rack and 90x lower power consumption per application in a data centre framework. Modern HPC systems and data centres require more electricity.

Performance and Accuracy in Computing:

The system provides 99.7% 16-bit floating point precision for all chip computations. Science and AI demand this accuracy.

Bob Sorensen, Senior VP for Research and Chief Analyst for Quantum Computing at Hyperion Research, believes this shows that analogue computing may be precise, effective, and deployable. One of his comments is “Attacking two of the biggest challenges in photonic computing: integration and precision”.

NPS efficiency is improved by 40–50% fewer operations for equal output.

Smooth Integration with Infrastructure:

New computer paradigms are difficult to integrate into digital systems. Q.ANT’s photonic architecture was designed to improve computing models.

PCI Express integration makes the NPS compatible with current HPC and data centre environments.

It supports Keras, TensorFlow, and PyTorch. This “seamless plug-and-play adoption” gives early AI and HPC adopters a competitive advantage by making Q.ANT product use easier.

Built for Next-Generation AI and Science

Q.ANT’s photonic NPS is ideal for data-intensive applications that exceed typical digital processors. These include:

Computational fluid dynamics, molecular dynamics, and material design are essential scientific simulations and physics. These simulations’ complex nonlinear and mathematical processes challenge digital systems, but the NPS excels at them.

Light can natively do sophisticated calculations, making it helpful for advanced picture analysis.

Large-scale AI model training and inference: The NPS is well-positioned to speed these processes. Light-based computation of nonlinear functions and Fourier transformations reduces AI model parameters, simplifying designs and system requirements.

At ISC 2025, Experience the Future

Q.ANT will exhibit their NPS at ISC 2025 in Hamburg, Germany, June 10–12. Q.ANT’s technology will be demonstrated at Hall H Booth G12 for a once-in-a-lifetime experience. This live display allows direct involvement with functional photonic computing and shows its potential to boost energy and computational efficiency in a variety of hard scientific applications.

In conclusion

Q.ANT’s photonic NPS replaces digital processors and advances computing. For the most demanding scientific and artificial intelligence applications, it will revolutionise high-performance computing by harnessing light to calculate with extraordinary accuracy and energy economy.

[techsavvy121]

How SOLIDWORKS Simulation Saves Time and Money in Product Development

In today’s competitive market, speed and efficiency in product development are more critical than ever. Traditional design processes involving physical prototypes and repeated testing can drain both time and resources. This is where SOLIDWORKS Simulation comes into play — offering a smarter, faster, and more cost-effective approach to engineering design validation.

At Tech Savvy, we help businesses harness the power of SOLIDWORKS Simulation Packages to streamline development, improve product reliability, and significantly reduce costs.

What is SOLIDWORKS Simulation?

SOLIDWORKS Simulation is an integrated analysis toolset within the SOLIDWORKS suite that enables engineers to virtually test and validate product designs under real-world conditions. It allows for structural, thermal, motion, and fluid dynamics simulations—all before building a physical prototype.

Whether you’re designing a consumer product or a complex mechanical component, SOLIDWORKS Simulation Packages offer solutions to verify performance, strength, durability, and more.

How It Saves Time

1. Early Design Validation

With SOLIDWORKS Simulation, you can test your design virtually from the earliest stages of development. This reduces the number of design iterations and helps identify potential issues before they become costly problems.

2. Faster Design Decisions

Real-time simulation feedback allows engineers to make quick, informed design changes. Instead of waiting for physical prototypes, teams can evaluate performance, stress points, and failure risks immediately.

3. Eliminates Delays in Prototyping

Building physical prototypes takes time—sometimes weeks or months. Virtual testing with SOLIDWORKS Simulation Packages significantly reduces or even eliminates the need for multiple prototypes.

How It Saves Money

1. Reduced Prototyping Costs

Each prototype built and tested adds to your project’s cost. By reducing the number of physical models required, SOLIDWORKS Simulation dramatically cuts expenses associated with materials, labor, and testing equipment.

2. Minimized Rework and Wastage

Simulation helps ensure that the first manufactured product is right. This reduces rework, scrap, and downtime, which directly impacts your bottom line.

3. Optimized Material Usage

SOLIDWORKS Simulation allows engineers to analyze and optimize material distribution, ensuring strength without overdesign. This leads to material cost savings and more sustainable designs.

Real-World Application: A Quick Example

A manufacturing company using SOLIDWORKS Simulation Packages for structural analysis was able to reduce their prototype count by 60% and cut their product development cycle in half. They also reported material cost savings of 15% by optimizing part geometry based on simulation results.

Choosing the Right SOLIDWORKS Simulation Package

Tech Savvy offers a range of SOLIDWORKS Simulation Packages tailored to different needs:

Simulation Standard – Ideal for static linear analysis and motion studies.

Simulation Professional – Includes frequency, buckling, thermal, and fatigue analysis.

Simulation Premium – Adds nonlinear, dynamic, and advanced composite material capabilities.

We help you choose the right package based on your product complexity, budget, and performance requirements.

Why Choose Tech Savvy?

As an authorized SOLIDWORKS reseller and simulation expert, Tech Savvy provides:

Genuine SOLIDWORKS licenses

Expert implementation and consultation

Customized training and ongoing technical support

We partner with you to ensure your team gets the most out of every simulation tool, saving both time and money in the long run.

Final Thoughts

Incorporating SOLIDWORKS Simulation Packages into your product development process is no longer optional—it's a competitive necessity. By reducing design cycles, limiting physical testing, and optimizing performance early, simulation helps you bring better products to market faster and at lower cost.

Ready to accelerate your product development journey? Contact Tech Savvy today to explore the right SOLIDWORKS Simulation Package for your business.

[engineering-anthology]

30 days of productivity

(Because 100 is scary)

02/30

I was productive and made a lot of progress today, but I'm nowhere near the end. Last night I worked late on campus and then slept until morning on a beanbag. It's not good, I know, but it's where we're at right now.

Things I did:

algae data wrangling and conditioning ~3 hours?

held open hours at the makerspace (tried to do more data analysis, but ended up helping a bunch of people with their projects... at least it was a really nice break!)

asked for a teeny tiny extension on my paper, so I now have until tomorrow morning (downside: I will probably be working through the night :_( alas, I can only blame myself)

Things I have yet to do:

reach out to nonlinear dynamics professor and ask for a meeting to discuss bioconvection project (ultra! high! priority!)

finish data analysis and generate plots

write a whole-ass academic paper draft by 9am (I'm gonna die)

prepare for electronics oral exam (itll be ok. itll be ok. itll be ok. o.O)

shower!

Musings:

I need to clean my room. I feel like the clutter might be what's making me reluctant to work and drowsy... like the mental overload of looking at all the bits and bobs drains the energy straight from my brain. Luckily, I have some time this weekend to clean! (and I kind of have to since a prospective housemate is coming to look at this room on Monday)

I think I want to get some more plants. I don't have time to do much gardening or plant-networking (where you ask your plant-friends for cuttings), but I do have enough energy to keep them alive and they make my room feel so comfy. I guess it'll have to wait until summer.

I had a thought earlier that I kind of wish school was year-round? I have this problem where I get used to the workload throughout the semester, and I start actually delivering with the intensity that I'm supposed to, and suddenly it's summer or winter break and everything changes and I have so much time to forget all of the useful skills and habits I've developed. It's frustrating! And any time I visit my family home I get roped into so many little tasks that I have no time to myself to rest. Part of it is that I'm bad at setting boundaries with family, but I do wish a week of home didn't factory-reset all of my hard-earned good habits away into the abyss.

Good luck with your work and studies everyone!!! You can do it! <3

[appliedscienceint09]

Understanding Performance-Based Structural Design: A Modern Approach to Engineering

Performance-Based Structural Design is a modern engineering method that emphasizes how buildings and structures will actually perform under various real-world conditions. Unlike traditional design approaches that strictly follow building codes and standards, this methodology allows engineers to set specific performance goals—such as ensuring life safety during an earthquake or maintaining operational functionality after a storm. This flexibility leads to more resilient and customized solutions, especially for complex or high-risk projects like hospitals, high-rises, and critical infrastructure.

One of the core strengths of Performance-Based Structural Design lies in its use of advanced analysis tools and simulations. Engineers use techniques such as nonlinear dynamic analysis to model the behavior of structural components under extreme conditions. This allows for a deeper understanding of potential weak points and helps in optimizing the design for better safety and efficiency. By simulating various scenarios, designers can make informed decisions about materials, reinforcement, and overall layout, which results in structures that perform better and cost less over time.

As the demand for sustainable and resilient construction grows, Performance-Based Structural Design is becoming increasingly popular in modern architecture and engineering. It not only enhances safety and durability but also encourages innovation by moving beyond rigid code limitations. This approach supports smarter use of resources, improves disaster preparedness, and aligns well with future-ready building practices. As cities expand and climate challenges increase, PBSD offers a smarter, more adaptive solution for building the structures of tomorrow.

Discover more on the topic by visiting our blog - https://appliedscienceint09.medium.com/understanding-performance-based-structural-design-a-modern-approach-to-engineering-1afa9689e071

[extremeloading11]

Why Nonlinear Structural Analysis Is Essential for Defense and Security Projects

Understanding how structures behave under complex conditions is necessary for safe and reliable construction. Engineers now rely on advanced methods to simulate real-world scenarios with greater accuracy. Nonlinear Structural Analysis plays a key role here, allowing for a more precise evaluation of how materials and components perform under shifting loads, deformations, and stress concentrations. Unlike simpler linear models, this approach accounts for actual behavior, including large displacements and material failure, which leads to more dependable design outcomes across various industries.

Modern construction projects are often challenged by irregular geometries and dynamic forces that can’t be assessed using traditional methods alone. That’s where refined techniques like finite element modeling and material-specific simulations come into play, improving the overall safety margin. By applying Nonlinear Structural Analysis, engineers can predict potential weak points and structural responses more accurately, leading to more informed decisions during both the design and retrofit phases. This approach is increasingly important in seismic design, high-rise construction, and infrastructure upgrades.

Check out our blog for more insights: https://extremeloadingfor.blogspot.com/2025/05/why-nonlinear-structural-analysis-is.html

[gauravawasthi]

Harness the Power of Femap with DDSPLM Solutions: Advanced FEA for Smarter Engineering

In today’s fast-paced engineering environment, accurate and efficient simulation is key to innovation and performance. Whether you’re working in aerospace, automotive, machinery, or consumer products, having a robust Finite Element Analysis (FEA) tool can make all the difference in product development. Femap, by Siemens Digital Industries Software, is a powerful pre- and post-processor for FEA that empowers engineers to simulate complex products accurately and affordably. At DDSPLM Solutions, we help you unlock the full potential of Femap with expert implementation, training, and support.

What is Femap?

Femap (Finite Element Modeling and Post-processing) is a sophisticated engineering tool used for creating, analyzing, and visualizing finite element models of complex products and systems. It enables engineers to simulate real-world behavior, such as stress, heat, vibration, and more, to ensure optimal design decisions without physical prototyping. Femap is CAD-independent and works seamlessly with multiple solvers like NX Nastran, ANSYS, and ABAQUS, making it one of the most versatile tools available for simulation professionals.

Key Features of FEMAP

CAD Independence

Advanced Meshing Capabilities

Solver Neutrality

Comprehensive Post-Processing

Automation & Scripting

Assembly Management

Nonlinear and Dynamic Analysis Support

Benefits of FEMAP

Speeds up your simulation workflow with robust meshing and solver integration

Reduces the need for physical prototypes through accurate virtual testing

Improves product performance and reliability with detailed analysis

Provides cost-effective simulation tools for small to large enterprises

Enhances design innovation through what-if scenarios and optimization

Integrates seamlessly with existing CAD and PLM systems

Why Choose DDSPLM Solutions for Femap?

DDSPLM Solutions is a trusted Siemens Partner with deep expertise in PLM and simulation solutions. Here’s what sets us apart:

Certified Siemens Partner

Expert Training & Support

Industry Experience

Custom Implementation

End-to-End Services

Femap is more than just an FEA tool — it’s a gateway to smarter engineering, better designs, and faster market entry. By partnering with DDSPLM Solutions, you gain not only access to this powerful software but also the expertise to leverage it to its fullest potential. Let us help you streamline your simulation processes and drive innovation in your engineering workflow.

[bedlessbug]

ploys within plots

crucially concerned with the theme of foreseeing the future.....author prophetically presents his series' basic concept - psychohistory, ecology.... what was only in the process of becoming dynamical systems analysis, the scientific field now popularly termed "chaos theory";.... mirrors... its narrative structure and its themes and motifs .... what can only in retrospect be seen... concept's dynamical systems model.

science can investigate only what it first imagines. this crucial visionaiy step is often taken by the artist not the scientist: that science follows a path art has already envisioned and mapped for the culture as a whole.

chaos theory model sees dune as a dynamical system that might be radically changed through minimal alteration.

chaos theory is the study of orderly patterns in turbulent, erratic or dynamical systems (PI)

Kynes: obsessed with transfomring dune by way of "man as a constructive ecological force". building a new landscape. Kynes thus: "views Dunes ecology from a chaos theory model, as a dynamical system that might be radically altered through a minimal change in a key variable affecting its interlocking feedback loops."

a dynamical system is fundamentally recursive rather than linear - like the reading process itself, like the perspectives of Dune.

Fractal means self-similar, repetition of detail at descending scales, patterns inside of patterns.

Chaos is the science of the global, nature of systems... the universal behavior of complexity…. The first chaos theorists… had an eye for pattern… a taste for randomness and complexity, for jagged edges and sudden leaps.. ..They feel that they are turning back... the analysis of systems in terms of their constituent parts… that they a whole (Gleik, 5). Paul, several of the Bene Gesserit, many of the Dun and Miles Teg are all Mentats. And Mentats too are "trained to sense patterns, to recognize systems and wholeness" MENTAT AS CHAOS THEORISTS. Every ruler has a mentat, indispensable to their reign. the human computer who translates natures cybernetics into intelligible strategy and forms. What do these forms, or schemes, look like????? how are they enacted????

Kynes says that: "laboratory evidence blinds us to a very simple fact, …that.. .we are dealing here with matters that originated and exist out-of-doors"

"There is in all things a pattern in the way sand trails along a ridge, in the branch cluster of the creosote bush or the pattern of its leaves" (Dune, 380); and it is just such irregular, "random," "jagged" patterns that choas theorist study

If paradox bothers you that betrays your deep desire for absolutes

the only constant in the real universe is change

but chaos does have predicatble characteristics PI QUOTE

the irregular fremen sand walk like the natural shifting of wind and sand are examples of order existing in the apperance of chaos in nature

"from chaos we must make our own order"..... The "Bene Gesserit Way" is to "create [relative stability] with your own belief' from "the essential, raw instability of our universe" (Children, 250-5....Thus, the Bene Gesserit theorize that "belief structure creates a filter through which chaos is sifted into order" IMPORTANT, PERHAPS IN INTRODUCTION

bifurcation - the division of something into two branches or parts.

like time travel, dune uses knowlege of possible futures to alter its course. MENTATS can "predict to some extent in terms of probablities"

"What of the harmonics inherent in the act of prophecy? Does the prohpet see the future or does he see a line of weakness, a fault or clevage that he may shatter with words or descions...?".....much like a mathematician might drastically alter a fractal image by minutely changing a variable in the nonlinear equation that generates it"

Irulan writes of Paul, "He tells us that a single obscure decision of prophecy, perhaps the choice of one word over another, could change the entire aspect of the future."

Water, wind, storm, sand metaphors example the chaotic currents in history and time."""

pauls vision of the future is in gaps, irulans description of a "man standing on a valley floor, whose view of the terrain is blocked by surrounding hills" WHAT DOES THE TERRAIN HAVE TO SAY ABOUT LANGUAGE HERE??? AS in Baudrillards SIMULACRA?? POTENTIALLY>>>>

Is language order which plasters itself upon the chaos of the terrain? Myth acts as a simulation, a place of reference which now precedes the territory itself. What is the PROLEPSIS of ARRAKIS? How do they believe in it before they arrive?

Order out of Chaos is the rule rather than the excpetion!!!! ^^^^^^ in terms of map and territory (briggs and peat)