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Showing posts from July, 2026

Topology-Driven Fault Detection in Smart Cities

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Topology-Driven Fault Detection in Smart Cities Topology-Driven Fault Detection in Smart Cities: Utilizing Dynamic Graph Filtrations and Persistent Homology for IoT Connectivity Patterns Shrishti Rastogi / Research Article Abstract: The rapid proliferation of IoT devices in smart cities necessitates robust, real-time monitoring systems to detect infrastructure faults. Traditional graph-theoretic methods often fail to capture multi-scale structural changes under fluctuating environmental conditions. This paper presents a topology-driven framework utilizing Topological Data Analysis (TDA), specifically persistent homology, to analyze time-varying graph filtrations of IoT connectivity patterns. We integrate Distance-to-Measure (DTM) filtrations to mitigate noise, coupled with machine learning classifiers applied to stable topological summaries. Empirical evaluation on simulated smart city datasets demonstrates superior F1-s...
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Subgroup Verification in Complex Numbers

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Subgroup Verification in Complex Numbers Subgroup Verification in Complex Numbers Testing subgroup properties of H = {a + bi ∈ ℂ ∣ ab ≥ 0} Mathematical Solution Define H = {a + bi ∈ ℂ ∣ a, b ∈ ℝ, ab ≥ 0} . That is, the real and imaginary parts must have the same sign (or one of them is zero). 1. Identity The additive identity in ℂ is 0 + 0i. Since 0·0 = 0 ≥ 0, we have 0 ∈ H. ✅ 2. Closure Take z₁ = 2 + i and z₂ = −1 − 2i. Both satisfy ab ≥ 0. Their sum is 1 − i, and 1×(−1) = −1 3. Inverse For z = a + bi ∈ H, we have ab ≥ 0. Its inverse is −z = −a − bi. Then (−a)(−b) = ab ≥ 0, so −z ∈ H. ✅ Conclusion ✔ Identity exists ✔ Inverses exist ✘ Closure fails Therefore, H is not a subgroup of (ℂ, +). Python Verification A Python program can test many examples to provide evidence ...
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Algorithmic Verification of Algebraic Structures

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Algorithmic Verification of Algebraic Structures Algorithmic Verification of Algebraic Structures Subgroups of Complex Numbers and Centralizers in Dihedral Groups Abstract The formal verification of algebraic properties within continuous and discrete mathematical frameworks represents a critical intersection of abstract algebra and computational theory. This paper investigates the structural integrity of specific algebraic sets, focusing on a constrained subset of complex numbers and the symmetric properties of dihedral groups. Through rigorous analytical deduction, we evaluate the subgroup criteria for the set H = {a + bi | a, b ∈ ℝ, ab ≥ 0} under addition, demonstrating its failure to satisfy the closure property. Furthermore, we systematically classify the centralizers for reflection and rotation elements within the dihedral group D n , delineating the divergent algebraic behaviors based on the parity of n. Introductio...
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Maths Blog: Subgroups and Centralizers

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Disproving a Subgroup Property Algorithmic Verification of Algebraic Structures Exploring Subgroups of Complex Numbers and Centralizers in Dihedral Groups Abstract This blog investigates two algebraic problems: verifying whether a constrained subset of complex numbers forms a subgroup under addition, and classifying centralizers in dihedral groups. We show that the set H = {a + bi | a, b ∈ ℝ, ab ≥ 0} fails closure and is not a subgroup of ℂ. We also provide a parity-dependent classification of centralizers in D n , highlighting differences between odd and even n. Problem 72: Subgroup of Complex Numbers Consider H = {a + bi | a, b ∈ ℝ, ab ≥ 0} under addition. To be a subgroup, H must satisfy identity, closure, and inverse properties. Identity 0 = 0 + 0i ∈ H since 0·0 = 0 ≥ 0. ✅ Closure Let x = 2 + 0i ∈ H and y = 0 - 3i ∈ H. Then x+y = 2 - 3i. Here ab = 2·(-3) = -6 ...
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Disproving a Subgroup Property

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Disproving a Subgroup Property Disproving the Property for Subgroups Suppose H is a nonempty subset of a group G with the property: If a, b ∈ H , then a -1 b -1 ∈ H . Is this enough to guarantee H is a subgroup? The answer is no . While every subgroup satisfies this property, a subset can satisfy it without being a subgroup, typically by failing to include the identity element. Subgroup Criteria Non-empty Closed under the group operation Contains the identity element Closed under inverses First Counterexample Consider the cyclic group Z 3 = {0,1,2} under addition mod 3. Let H = {1} . Non-empty: Yes, H contains 1. Property check: For a = b = 1, we compute (-1) + (-1) = -2 ≡ 1 (mod 3). Since 1 ∈ H, the property holds. Subgroup check: H does not contain the identity 0. Also, 1+1 = 2 ∉ H, and the inverse of 1 is 2 ∉ H. Therefore, H is not a subgroup. ...
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Real Analysis & Calculus Revision Guide

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Real Analysis Complete Real Analysis & Calculus Revision Guide Continuity • Uniform Continuity • Differentiability • Monotone Functions • Sequences • Limit Points • Topology & Theorems 1. Boundedness Theorem If a function f is continuous on a closed interval [a,b], then it is bounded. There exist real numbers M and m such that: m ≤ f(x) ≤ M for all x ∈ [a,b] Example f(x)=x² on [-2,2] Minimum value = 0 Maximum value = 4 Hence f(x) is bounded. Continuous functions on closed intervals never "blow up" to infinity. 2. Extreme Value Theorem If f is continuous on [a,b], then f attains both: Absolute Maximum Absolute Minimum Example f(x)=x² on [-1,2] Minimum = 0 at x=0 Maximum = 4 at x=2 3. Intermediate Value Theorem (IVT) If f is continuous on [a,b] and k lies between f(a) and f(b), then there exists c∈(a,b) such that: f(c)=k Example f(x)=x³ f(1)=1 and f(2)=8 Since 5 lies between 1 and 8, ...
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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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