This paper introduces a mathematical and computational model elucidating human decision-making processes within complex action spaces. It explores how individuals navigate such spaces under constraints of limited time and cognitive resources, resulting in choices that may be formally sub-optimal yet resource-rational. Drawing inspiration from thermodynamics, we propose a model of the human-like mind for decisionmaking capabilities. Unlike robots, humans face challenges such as emotions, fatigue, and cognitive biases, which can be integrated into cognitive systems, enabling them to add human emotion in decision-making. A key innovation of this research is the incorporation of the monotony of life into the model, influencing decision system behavior over time. Additionally, we derive a time-dependent characteristic equation for mental states based on the second law of thermodynamics, aiming to infuse systems with more human-like behavior by introducing fluctuation dynamics and chaotic oscillations into decision-making processes. We propose that a prolonged depressive phase inherently prompts the model to seek knowledge from society, media, and other sources. This knowledge builds up mental activity, leading to decisionmaking. This computational model can emulate a collection of interacting conscious entities, offering significant potential for applications in consumer research, recommendation systems, mass opinion prediction, wayfinding, and various multimedia domains.

Numerical methods are essential for simulating transient physical phenomena, providing crucial support to engineers and researchers in fields such as defense, space, and the natural sciences. By modeling these phenomena with partial differential equations (PDEs), deeper insights can be gained. Among the various PDE-solving methods, the Crank-Nicolson technique stands out for its efficiency in handling time-dependent PDEs, offering precise and stable solutions across diverse applications. In this work, we present a 3D GPU-accelerated code optimized for parallel execution in three-dimensional space-time domains. The PDE solver is applied to quantum wave function information processing, specifically in the context of the circular double-slit experiment. Additionally, we utilize the solver to simulate a dual Mach-Zehnder interferometer to model the Fenna-Matthews-Olson (FMO) complex, developing a natureinspired algorithm based on its quantum dynamics. This simulation demonstrates near-perfect intelligence in a photosynthetic system without any neural network. This work may help in the design of solar cells, potentially increasing their efficiency to a greater extent.

Current technology utilizes a sophisticated standard room measurement system to analyze the average roughness of stainless steel bearing surface. However, this process relies on random sampling at infrequent intervals, resulting in inefficiency and inadequate analysis of a large number of samples. To address this issue, we propose a solution using computer vision via laser scanning. The scanning process produces an amplified speckle pattern of the ring surface. This pattern is then analyzed to verify the number of edges that cross a predefined threshold and exhibit symmetrical patterns, particularly on smoothed ring surfaces. Remarkably, this system operates with a turnaround time of less than a second. Moreover, unlike the standard room setup, this technology can be seamlessly integrated directly into production/manufacturing units.