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Quantum Cognition and Quantum Brain Dynamics: Modeling Room-Temperature Coherence in Neural and Retinal Systems

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Abstract Macroscopic biological entities, such as the retina and the human brain, as well as the whole organism, are required to be continuously coherent at room temperature, which is a huge challenge for modern quantum mechanics. Biological tissue is warm, wet and noisy, and this is theoretically expected to result in rapid decoherence, but growing paradigms in quantum cognition and quantum biology indicate that the neural systems somehow avoid immediate collapse induced by environment. One of the unsolved problems in this area is the exact mechanism of how cellular structures are able to remain decoherence-free and support potentially functional quantum states. The aim of this paper is to answer this salient research gap by proposing a theoretical framework for modelling the phenomenon of quantum entanglement and non locality in the brain cell microtubules and retina photoreceptors. Combining the elements of the resource theory of quantum coherence and open quantum dynamics, we propo...
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Museum of Education is a socio-pedagogical biography of Khan Sir, who went through a fight for struggle to solidarity.

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Khan Sir: A Socio-Pedagogical Biography Khan Sir: A Socio-Pedagogical Biography It is common knowledge that teaching is a noble profession. Each generation has a few people who have an impact that goes beyond their profession. They represent hope, strength, and societal change. In India today, where millions of students live in poverty and injustice, Khan Sir has become much more than just a teacher. He is a guide, a motivator, and a reminder for so many students preparing for competitive exams that dreams can conquer the toughest challenges. His classrooms are filled not only with lessons on geography, history, science, or current affairs, but also with encouragement for students who often doubt themselves. He believes education should be offered to all who desire it, not just the privileged few. His story is a testament to empathy, affordability, and culturally relevant communication, showing how digital technology is transforming education in 21st c...
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Fractional-Order Bioconvection in Trihybrid Nanofluids Flowing Over a Rotating Disk: A Hybrid Neural Network With Genetic Algorithm Method for Entropy Generation Minimization

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<p>Fractional-Order Bioconvection in Trihybrid Nanofluids Flowing Over a Rotating Disk: A Hybrid Neural Network With Genetic Algorithm Method for Entropy Generation Minimization</p> : Minimizing entropy generation in complex fluid systems is a primary concern for improving thermodynamic efficiency. This paper investigates bioconvection in a Carreau-Yasuda trihybrid nanofluid over a spinning disk, where fluid memory is modeled using fractional-order derivatives. We provide an analytical energy-based stability framework for the proposed model. Given the high computational cost associated with solving fractional partial differential equations, we propose a Hybrid Neural Network surrogate model combined with a Genetic Algorithm. The Hybrid Neural Network, trained on data obtained via the Finite Difference Method, accurately predicts Nusselt numbers and entropy generation, while the Genetic Algorithm navigates the response surface to identify Pareto-optimal solutions. A deep cas...
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Heuristic Computation and the Discovery of Mersenne Primes

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Heuristic Computation and the Discovery of Mersenne Primes Heuristic Computation and the Discovery of Mersenne Primes “Where Strategy Meets Infinity: The Quest for Mersenne Primes” Introduction: The Dance of Numbers and Heuristics Mersenne primes are not just numbers—they are milestones in the vast landscape of mathematics. Defined by the formula: \[ M_p = 2^p - 1 \] where \( p \) is itself prime, these giants challenge our computational limits and inspire new methods of discovery. But why are these primes so elusive? As \( p \) grows, the numbers become astronomically large, making brute-force testing impossible. This is where heuristic computation steps in—guiding us with smart, experience-driven strategies. “In the infinite sea of numbers, heuristics are our compass.” Let’s explore how heuristics and algorithms intertwine to unveil these mathematical treasures. 1. Mersenne Primes — Giants of Number Theory Definition: Numbers of the form \( M_p = 2^p - 1 \...
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Understanding the Laplacian of 1/r and the Dirac Delta Function Mathematical Foundations & SageMath Insights

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Unmasking the Laplacian: How Mathematics, Physics & AI Use This Powerful Operator Unveiling the Laplacian's Secrets: A Look at 1/r with SageMath Engage & Explore! Before we dive into the math, ask yourself: What does it mean when a function explodes to infinity at a point? Leave your thoughts in the comments below. We'll revisit this after exploring the Dirac delta function! Why Is the Laplacian of \( \frac{1}{r} \) Important? The function \( \frac{1}{r} \) frequently appears in physics to describe potentials like gravity and electrostatics, which depend inversely on distance. Understanding its Laplacian reveals the nature of sources concentrated at singular points. Mathematical Foundation: What Is the Laplacian and a Harmonic Function? The Laplacian operat...
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Neural Network Generalization in the Over-Parameterization Regime: Mechanisms, Benefits, and Limitations

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Neural Network Generalization in the Over-Parameterization Regime: Mechanisms, Benefits, and Limitations Neural Network Generalization in the Over-Parameterization Regime: Mechanisms, Benefits, and Limitations Introduction Over the past decade, deep neural networks (DNNs) have risen to prominence across a range of machine learning applications, achieving remarkable performance in domains such as computer vision, natural language processing, and reinforcement learning. A striking and counter-intuitive feature of modern DNNs is their propensity for over-parameterization: models often contain many more parameters than training samples, far exceeding the classical regime where statistical learning theory would predict rampant overfitting and poor generalization. Yet, these highly over-parameterized models not only fit the training data perfectly but also display outstanding generalization to unseen test data—often improving as the number of paramete...
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