Vol. 6 No. 01 (2024): Volume 06 Issue 01

The problem of urban transport infrastructure is considered in terms of ensuring the rational organization of vehicle maintenance and repair based on the principle of optimal placement of car service enterprises in urban areas and the list of services offered. Factors have been identified that make it possible to solve the problem of the chaotic location of such enterprises.

This study explores the advancement of optical recognition for plastic bottles through the implementation of sophisticated lighting systems. The research focuses on enhancing the accuracy and efficiency of identification processes, critical for recycling and waste management. Through a systematic examination of various lighting conditions, the study aims to optimize optical recognition algorithms for improved performance in diverse environmental settings. The results offer valuable insights into the potential of advanced lighting systems in refining the optical identification of plastic bottles, contributing to the advancement of sustainable waste management practices.

This case study delves into the intricate details of fatigue failure observed in the drill pipe SS105 thread. Through meticulous investigation and analysis, the study aims to unravel the complexities surrounding the failure mechanism, exploring contributing factors and potential preventive measures. The findings provide valuable insights for the oil and gas industry, enhancing understanding and mitigating risks associated with drill pipe thread fatigue failure.

This study explores innovative advancements in material science by incorporating culinary chemistry techniques in the fabrication of composite copper/fly ash foam. Utilizing egg yolk as a foaming agent, the resulting material exhibits enhanced mechanical strength, thermal conductivity, and a unique foam structure. The interdisciplinary approach merges the strengths of copper and fly ash with the emulsifying properties of egg yolk, introducing a novel dimension to material synthesis. The study's findings offer promising insights into the potential applications of this egg-citing composite foam in fields requiring lightweight, insulating, and structurally sound materials.

This study delves into the transformation of waste into a valuable resource by exploring the impact of adhesive paper waste weight percentage in biobriquettes derived from cassava skin waste. Through a systematic investigation, the research assesses the influence of varying proportions of adhesive paper waste on the physical and combustion characteristics of the biobriquettes. The results reveal insights into the feasibility of incorporating adhesive paper waste as a binding agent in cassava skin biobriquettes, providing a sustainable and economically viable approach to waste utilization.

Usage-Based Insurance (UBI) is revolutionizing how insurers calculate premiums based on observed driving habits, with telematics and connected vehicles providing growing potential for more responsive and fairer insurance. The traditional way of calculating the premium is based on the static models that curate the premium for an individual based on the past driving history, and neglecting the driving habits. This old method has both advantages and disadvantages, but it doesn’t provide a premium based on the risks of the drivers’ driving habits. Insureds were asked to pay the premium based on the algorithm, which focuses on the static rating tables rather than using the real-time user driving habits data. However, these system creates complex interactions between the insurer and insured, specifically for privacy, data manipulation, and self-interested driving behavior. This article highlights the role of artificial intelligence (AI) in enhancing Universal Basic Income (UBI) by analyzing data, refining risk modeling, and enabling dynamic pricing in real-time. Additionally, we model these interactions using dynamic game theory under incomplete information. For this, we define an insurer as a leader who sets pricing schemes and monitors strategies, and an insured as the follower who reacts to the incentives and possibly changes behavior. We propose a ready-for-action AI platform with individualized driver feedback, fraud detection, and dynamic pricing mechanisms, and derive equilibrium strategies for both insured and insurer, and propose a robust pricing method for strategic manipulation. The simulation-based synthetic driving data highlights how game-theoretic pricing can perform better than traditional pricing methods in all aspects. The study also elaborates on key regulatory and moral implications and charts the way forward with future evolution and research gaps in this new area of driving, where technology drives the future.