February 2026

Decoding affective states and personal preferences from physiological responses remains a fundamental challenge in affective computing due to strong heterogeneity across neural, autonomic, and attentional signals, as well as the coupling between transient emotions and long-term preferences. Most existing methods address these factors independently and lack explicit mechanisms to preserve the intrinsic structural regularities and invariances of physiological affective responses, limiting their applicability in real-world scenarios such as music therapy. In this paper, we propose a symmetry-aware and […]

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The digital marketing is like a forest fire, with generative AI becoming viral in other industry segments as well. It is a financial revolution. The potential of the generative AI is effective and correct and can save a large amount of time not only to accelerate the end of the month closing but also detail the financial stories. But all their novelty and convenience cannot be buried under the carpet of any cobbles of ethical problems. The Temptation […]

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An analysis of 800 public companies finds that companies are struggling to turn productivity gains into profit.

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Three ways CEOs can ensure their senior leaders remain aligned.

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AI governance has a production problem. Over the past several years, regulators, standards bodies, and industry leaders have converged on a clear consensus: AI systems must be accountable. Frameworks like the EU AI Act, the NIST AI Risk Management Framework, and emerging global standards all define expectations around fairness, auditability, risk management, and oversight. But there is a fundamental disconnect. Governance exists as policy. n AI exists as infrastructure. And somewhere between the two, accountability breaks down. The […]

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This paper proposes a geometric propagation model on the plane formed by two alternating integer fields placed on parallel layers y=n. Odd layers carry an expansion field and even layers carry a collapse field. Local directions are specified through explicit gradient (tangent-slope) laws for the fields Ψ, yielding parallel corridors, trajectory merging, and event-driven switching at inter-layer boundaries. This gradient field is connected to the Riemann–P (three-singularity Fuchsian) differential equation: choosing a half-integer local exponent produces square root […]

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We develop emph{Semantic Thermodynamics}, an information-theoretic framework for analyzing hallucinations in transformer systems under finite resources. The central object is mutual information between latent facts and model outputs, together with Fano-style lower bounds on semantic error. We clarify the stochastic assumptions required for non-degenerate information measures, distinguish true data-generating uncertainty from model-implied uncertainty, and replace unsupported hard capacity formulas with explicit capacity surrogates tied to precision, context budget, and effective representational rank. Under standard identification assumptions, we derive […]

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This study investigates the dripping and leakage problem in kitchenware known as the “teapot effect” through a multidisciplinary experimental approach encompassing fluid mechanics, material science, and ergonomic design. Unlike previous studies confined to idealized geometries and single-fluid analyses, this work systematically examines 32 distinct spout geometries from commercially available teapots, coffee pots, and milk jugs under realistic operating conditions. Experiments were performed using four fluids with contrasting rheological properties water, boiling black tea, cow’s milk, and Turkish coffee […]

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