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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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 case study on a high-shear rotating photobioreactor validates the framework, demonstrating a trade-off between bioconvective mixing and thermodynamic entropy. The results indicate that a fractional order of α ≈ 0.85 and optimized nanoparticle loading can reduce entropy production by 22% compared to classical theory, offering significant potential for future bio-manufacturing applications.

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