https://hspublishing.org/JRECS <p><em><strong>Journal of Research in Engineering and Computer Sciences (JRECS) </strong></em>ISSN-3049-7590 is a peer-reviewed academic journal published on bi-monthly bases that publishes high-quality research in the fields of engineering and computer sciences. The journal provides a platform for researchers, engineers, and scientists from around the world to share their latest research findings, ideas, and innovations.</p> <p>Engineering and computer sciences are two fields that are constantly evolving and pushing the boundaries of what is possible. They are integral to the development of new technologies and innovations that have transformed the way we live and work. Research in these fields seeks to understand the underlying principles that govern complex systems, as well as to develop new tools and techniques for solving complex problems. From artificial intelligence and machine learning to robotics and biotechnology, engineering and computer science research are at the forefront of many cutting-edge fields. As the demand for new technologies and innovative solutions continues to grow, the importance of research in these fields cannot be overstated.</p> Headstart Publishing - United Kingdom en-US Journal of Research in Engineering and Computer Sciences 3049-7590 https://hspublishing.org/JRECS/article/view/1583 <p>Nonlinear operator differential equations have been studied using various approaches and have found numerous applications in the mathematical sciences. During 1980--1981, generalized nonlinear systems based on Exponential Type Operators were developed and applied to automatic control systems for spacecraft navigation. In this paper, we introduce a new concept of causality for moving objects whose velocity (w) satisfies (w&lt;c), where (c) denotes the speed of light. Unlike the classical notion of causal operators, the proposed causality is derived from the finite propagation speed of information between the moving object and the observer. This formulation leads naturally to a nonlinear causal operator differential equation. We use the (k)-norm of an operator in a Banach space and investigate Lipschitzian operators associated with the proposed equation. Finally, by applying the contraction mapping principle and the Banach Fixed Point Theorem, we establish the existence and uniqueness of solutions.<br>Keywords: Causal operators, Lipschitzian operators, nonlinear operator differential equations, (k)-norm, complete metric space, Banach space.</p> Reza Ahangar Copyright (c) 2026 Journal of Research in Engineering and Computer Sciences 2026-07-13 2026-07-13 4 04 01 24 10.63002/jrecs.404.1583 https://hspublishing.org/JRECS/article/view/1603 <p>This study presents a novel, AI-assisted approach to industrial reliability analysis that integrates Failure Modes, Effects and Criticality Analysis (FMECA) and the Failure Reporting, Analysis, and Corrective Action System (FRACAS) with Natural Language Processing (NLP). We developed an algorithm that automates and streamlines the analysis of equipment field-failure reports and other unstructured maintenance records. The proposed framework combines unsupervised clustering to identify recurring equipment failure modes with a supervised Support Vector Machine (SVM) classifier with a Radial Basis Function (RBF) kernel to categorize equipment field reliability reports by failure mode and underlying mechanism at scale. Using an&nbsp;&nbsp;train–test split, the proposed model achieved&nbsp;&nbsp;accuracy on the test dataset, indicating effective generalization to unseen maintenance reports. The <em>Confusion Matrix</em> metrics across all classes showed true positive rate (TPR) (or sensitivity) of 0.91, indicating the model’s strong ability to correctly identify positive samples. The false positive rate (FPR) averaged 0.03 across all classes, demonstrating excellent specificity (true negative rate, TNR of 0.97). Operationally, the methodology reduces the resource-intensive manual work required to prepare, interpret, and process FRACAS reports, thus enabling timelier, data-driven equipment reliability analysis. Overall, the study demonstrates the feasibility and benefits of using AI-assisted reliability tools that balance automation with human expertise through a human-in-the-loop approach.</p> Esther Yu Guangjiang Cao Yehia F Khalil Jian Zou Tharindu P De Alwis Copyright (c) 2026 Journal of Research in Engineering and Computer Sciences 2026-07-21 2026-07-21 4 04 25 47 10.63002/jrecs.404.1603