Software plays a vital role in modern society's application systems. Typically, it is assumed that there are finite faults in the software during software development. However, the development of new functions, such as user requests or other sources, can cause the size of test-case pools to increase, necessitating the reduction of the test suite due to time and resource constraints. To address this issue, this study proposes a Fuzzy Expert System (FES)-based method to improve the fault detection effectiveness of reduced test suites. Specifically, the FES approach is integrated into the traditional test suite reduction technique, the GE algorithm. The performance of our proposed modified Fuzzy-GE algorithm is evaluated based on real programs by using objective criteria. The results show that our approach can effectively reduce the sizes of test suites while also improving the effectiveness of fault detection.

Virtual reality (VR) has been among the most disruptive technology in recent years. One can see the invisible and hear the inaudible with the help of the mixed technology. VR is totally an immersive environment. Visual senses, aural, and proprioceptive senses are under the control of the system. The utility of VR is growing in the fields of entertainment, education, sports, military, and more. Virtual reality videos have an impact on human memory. All technology comes with its advantages and disadvantages. VR headsets can cause cybersickness and shut down a significant number of neurons in the hippocampus that are responsible for retaining, recalling, and remapping. An empirical study is carried out to investigate the effects of virtual reality videos on declarative memory (associative memory) of human beings.

Building rules on ontology is the main task of mining the logical layer of the Semantic Web. A major effort has been made to develop algorithms capable of efficiently processing relational data and complex background knowledge. One of the promising technologies used in this effort is inductive logic programming (ILP). Steam boilers are an important equipment in power plants, and boiler trips can lead to a complete shutdown of the plant. Thus, it is essential to detect possible boiler trips in critical times to maintain normal and safe operating conditions. To address this challenge, the automatic rule-learning approach is used in this study to diagnose rule extraction. The learning examples are event sequences obtained by simulating an industrial steam boiler model. The ontology is considered as prior conceptual knowledge in ILP to induce supervision rules. The latter are eventually introduced into a scenario recognition system capable of continuously analyzing the event flow arriving at the supervisory center and alerting the operator when a fault situation is detected.

The upsurge growth of quantum computers poses many threats to existing classical cryptographic algorithms. The polynomials and root-converging methods are found to be suitable for developing a new generation of cryptographic algorithms. Among the many root-convergence methods, the Newton-Raphson is a promising approach according to the literature. It is an approximation method of finding the real root using linear approximation iteratively. Research advancements of the N/R method have improved the performance and space complexities. This research work proposes a new encryption and decryption algorithm for mitigating key size-based attacks using polynomial interpolations and gives a detailed account of various root convergence methods that are being used in the algorithms along with their merits and demerits.

The demand for compressed air systems to power pneumatic tools has steadily increased across various industries, such as chemicals, construction, mining, and oil and gas. However, due to the high complexity of air compressor technology and the need for higher availability, standard maintenance activities are insufficient. As a result, compressed air systems require a proper high-level maintenance strategy and effective monitoring. This article addresses the imperfect preventive maintenance (PM) optimization problem by implementing preventive replacements and minimal repairs to maximize the average availability of the air compressor subject to continuous degradation while satisfying minimal maintenance cost rate requirements. Each maintenance action is associated with a cost and duration, and the imperfect PM and corrective maintenance results are modeled on the maintenance cost rate and availability. The proposed imperfect PM optimization model aims to determine the optimal number of imperfect repairs and the replacement time. A mathematical model is introduced as nonlinear programming, and an algorithm is proposed to solve the optimization problem. Overall, this article provides a comprehensive and formal analysis of the maintenance strategy for air compressors, offering valuable insights into the optimization of imperfect preventive maintenance.

The rapid changes in the educational sector driven by the daily growth in technology breakthroughs have produced a very effective learning environment. Assessment is crucial to ascertain how well students learn and the amount of relevant knowledge and skills they have mastered. Current systems have limitations concerning volume, manpower, and variety in assessment methodologies. A physical paper evaluation is very repetitive, difficult, and complex and entails numerous logistical operations. Such a handwritten paper grading technique steadily increases the length of time needed to examine the answers and does not guarantee correctness in scoring the answers. Online evaluation cannot guarantee the correctness of the solutions supplied by other test takers. The solution is to make the examiner’s job easier while reviewing papers and judging how creatively pupils responded to the questions. This inspired the development of an online automatic grading system that grades students' handwritten papers. Natural Language Processing methods like TF-IDF and BERT can be used to determine the count of important keyword frequencies in students' responses, and how closely the text matches the original answer. An inference system that uses fuzzy logic can later be used to grade the responses. Therefore, this paper proposes an online grading system that combines NLP and Fuzzy Logic to score and evaluate fellow students’ handwritten papers.

Imperfect repair (IR) models are strongly recommended in reliability modeling because of the limitations presented by homogeneous (HPP) and non-homogeneous Poisson processes (NHPP). Among the imperfect repair (IR) models, the generalized proportional intensity model (GPIM) highlights the effect of preventive (PM) and corrective (CM) actions of maintenance and certain covariates on the future performance of complex repairable systems (CRS). In this work, the GPIM with log-linear and power laws was adapted for the reliability and maintenance of two gas turbines that had operated for nearly 6 years on one of the production divisions of SONATRACH (Algeria, Hassi Messaud). The objective was to evaluate the effect of preventive (PM) and corrective (CM) maintenance actions on the reliability of the system and to show the influence of the covariates representing the number of shutdowns, the time elapsed since the last maintenance action (TSLMA), gas leakage, and vibration failures on the future gas turbine performance. A comparison study between the basic generalized proportional intensity model (basic GPIM), extended GPIM, intensity reduction model (IRM), and non-homogeneous Poisson process (NHHP) made it possible to propose GPIM as the best-fitting model. This judgment was based on the maximum likelihood approach using the MATLAB programming language to estimate the parameters of the models and perform a likelihood ratio test (LR).