Volume 10, Issue 5
This issue highlights six studies that show how advanced methods are being turned into practical, real-world solutions across engineering, industry, and planning. Together, the papers focus on realism, efficiency, and integration rather than theory alone. They cover safer evaluation of autonomous vehicles under complex hazards, low-cost tools to support human–robot collaboration, smarter and more sustainable asset replacement decisions, and improved investment planning under uncertainty. The issue also presents lightweight approaches for secure and efficient edge computing, as well as robust methods to reduce material waste in manufacturing layouts. Overall, these contributions demonstrate how careful model design and practical frameworks can deliver measurable benefits in safety, performance, and sustainability across diverse application areas.
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Editorial Board
Editorial
Table of Contents
Implementation and Simulation of Sequential Adverse Condition Scenarios for Autonomous Driving
Establishing an environment that allows for the quantitative evaluation of the ability of autonomous driving systems to respond to real-world adverse conditions is crucial to ensuring their safety and reliability. This study proposes a dynamic scenario-based simulation framework that simulates complex and sequential hazardous scenarios frequently encountered in actual road environments. The proposed scenarios are…
Read More 3D Facial Feature Tracking with Multimodal Depth Fusion
As models based in artificial intelligence increase in sophistication, there is a higher demand for the integration of hardware components to heighten real-world implementations. Both facial feature tracking and shape-from-focus are known techniques in computer vision. However, the combination of these two elements, particularly in a cost-effective configuration, has not been extensively explored. In this…
Read More Economic Replacement of Plants and Equipment: A Decision-Making Framework in Engineering
While prior research has focused on siloed approaches to equipment replacement, this study introduces an integrated decision-making framework that synergizes predictive maintenance (IoT/M), dynamic multi-criteria analysis (MCDM), and sustainability-driven material selection. By validating this model through cross-sector case studies and strategic operational planning across various industrial sectors. We demonstrate a 30% improvement in replacement timing…
Read More Optimization of Investment in Decision – Making in Engineering Economy
Investment decision-making plays a pivotal role in shaping both individual and institutional economic outcomes. Given the increasing complexity and uncertainty in global markets, optimizing investment decisions has become essential for maximizing returns while managing risks. This work explores modern optimization approaches in investment decision-making, focusing on mathematical modeling techniques such as linear programming (LP), mixed-integer…
Read More CIRB-Edge for Secure, Energy-Efficient, and Real-Time Edge Computing
In this work, we present CIRB-Edge, a novel integer compression method designed specifically to overcome the limitations of traditional techniques such as Huffman coding, Delta encoding, and dictionary-based algorithms. These legacy methods often fall short in meeting the stringent requirements of secure, energy-efficient, and real-time edge computing due to their high computational overhead, memory demands,…
Read More Optimization of Sheet Material Layout in Industrial Production Using Genetic Algorithms
We address irregular polygon nesting on sheet materials with a lightweight evolutionary framework that operates directly in the layout space. The method formalizes multi-term fitness combining utilization, overlap penalties, spacing regularity, and local alignment, with all components normalized before aggregation. Feasibility is enforced by an AABB– SAT pipeline and validated via analytic ground-truth cases, degenerate…
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