Purpose of the study: The purpose of this study is to tackle the growing issue of antimicrobial resistance and frequent Maximum Residue Limit violations in food systems. It aims to examine MediTrack as a transparent and predictive digital platform that improves antimicrobial usage tracking and supports better regulation and decision-making in livestock management. Methodology: This study follows a practical, review-based approach to examine the MediTrack system. It uses blockchain technology to ensure secure and tamper-proof data storage, along with XGBoost-based machine learning models for predicting residue risks. Relevant regulatory developments, technological tools, and One Health perspectives are reviewed. Main Findings: The study shows that MediTrack helps simplify antimicrobial usage records, improves data reliability, and sends automatic alerts for withdrawal periods. It also enables early prediction of residue violations, reducing dependence on handwritten logs and fragmented reporting, while improving traceability and accountability across livestock and food supply chains. Applications of the study: This study can be useful in livestock management, dairy and meat supply chains, veterinary practices, food safety monitoring, and public health regulation. It supports antimicrobial stewardship programs and One Health initiatives by enabling better tracking, compliance monitoring, and early detection of potential residue risks. Novelty/Originality of the study: This study is original in its integration of blockchain technology with machine learning-based prediction for antimicrobial monitoring. Unlike traditional systems, it focuses on predicting residue risks before violations occur, offering a proactive and practical approach that improves transparency, strengthens governance, and advances existing antimicrobial usage monitoring practices.
Purpose of the study: Muscular dystrophies (MDs) are uncommon, progressive genetic conditions that result in muscle degeneration. This study aims to apply an interpretable machine learning approach using XGBoost to classify muscular dystrophy subtypes from synthetic clinical data. Methodology: We implemented an XGBoost model with engineered features such as interaction terms, trained on 1,000 synthetic records containing demographic, clinical, and genomic-like variables. SHAP (SHapley Additive exPlanations) was used for post-hoc interpretability of the predictions. Main Findings: The proposed model achieved high accuracy (92.4%) and macro F1-score (91.3%) across muscular dystrophy subtypes. SHAP analysis confirmed that features such as inheritance pattern, primary muscle group, creatine kinase levels, and severity score were the most influential. Applications of the study: The framework may serve as a diagnostic support system in clinics, telemedicine, and trial triaging for neuromuscular disorders. Its explainability ensures usability in medical decision-making. Novelty/Originality of the study: This work combines XGBoost with SHAP explainability specifically for muscular dystrophy classification, providing interpretable AI insights using synthetic data for privacy and scalability.
Aim of the Study: This study examines the convergence of Operations Research, Artificial Intelligence, and simulation, highlighting how classical OR techniques enhance AI models across domains, while emphasizing simulations essential role in optimizing, validating, and modelling complex real-world intelligent systeāms. Methodology: The study employs a comprehensive analytical, literature-driven methodology integrating foundational OR, simulation principles, and contemporary AI applications, synthesizing interdisciplinary insights with domain-specific cases, and presenting a demonstration algorithm to model automated gear-control for AI-based operational decision-making. Findings: Findings indicate that OR optimization markedly improves AI efficiency in tuning, allocation, and adaptive decisions, while simulation provides scalable, safe training environments, and despite challenges like reality gaps, computational costs, and bias, their integration accelerates development and reliability. Application: The integrated OR–AI–simulation framework spans domains such as autonomous navigation, robotics, healthcare, logistics, and smart cities, enhancing forecasting, safety, and cost efficiency, while enabling high-fidelity testing, reinforcement learning, and large-scale scenario analysis for intelligent system optimization. Novelty: This study’s novelty lies in unifying classical OR optimization with AI through simulation-based experimentation, integrating interdisciplinary insights and a practical gear-control algorithm to demonstrate how OR-driven simulation operationalizes intelligent, data-driven decision systems across diverse applications.
Purpose of the Study: The study aims to explore the structure of the dual space corresponding to a subspace of a reflexive Banach space equipped with a strictly convex norm. It further seeks to analyze properties of linear continuous operators, the separation of convex subsets, and the existence of invariant subspaces. Methodology: The research is based on theoretical and functional analysis techniques. It constructs the dual space under the given conditions and uses analytical methods to examine operator properties and convex set separability. Fixed-point and existence theorems are formulated using general mapping principles. Main Findings: A dual space corresponding to a subspace of a reflexive Banach space with a strictly convex norm is developed. Several properties of linear continuous operators and convex subset separation are identified. Fixed-point and existence theorems for general maps are established. Applications of this Study: The results can be applied in advanced functional analysis, particularly in operator theory, optimization problems, and mathematical modeling where the behavior of linear operators in Banach spaces is critical. These findings are also relevant to areas involving fixed-point theory and convex analysis. Novelty/Originality of this Study: This study offers a novel construction of the dual space in a specific setting of Banach spaces—reflexive with strictly convex norms—where such structures are not commonly analyzed in depth.
Purpose of the study: Penalty method plays an important role in handling constraints of a Non-Linear Programming Problem (NLPP). There exist varieties in penalty applying procedures. Quadratic and exact penalty methods belong to that class. The present paper computationally compares the performance of quadratic and exact penalty methods when they are implemented on NLPP with inequality constraints. Methodology: We have used some benchmark functions. The constraints are applied arbitrarily to the test functions. An improved version of the metaheuristic Particle Swarm Optimization (PSO) is used to handle the unconstrained NLPP obtained from the constrained NLPP. Main Findings: The results obtained are compared under similar conditions when quadratic and exact penalty methods are used as constraints handling techniques. The computational results are also reported under different ways to use the inertia weight in improved PSO. The paper discusses the computational convergence of these two methods. The condition for the same is also discussed. Applications of this study: The research is applied on NLPP problem. Constrained NLPPs are applicable in industry. Novelty/Originality of this study: The research gives a way to handle constraint between the exact penalty and quadratic penalty methods while solving NLPP using an improved PSO method.
Purpose: This study aims to present a novel technological approach for non-invasive glucose monitoring, facilitating early diagnosis and management of diabetes. Methodology: We conducted a comprehensive review of current trends and advancements in material science and biosensor technology, focusing on their applications in non-invasive glucose monitoring systems. Main Findings: Our review highlights state-of-the-art glucose monitoring technologies, emphasizing the design of soft materials, the development of non-invasive sensors, and the utilization of sweat as an alternative analyte for glucose detection. Implications: The findings of this study are instrumental in guiding the design of innovative non-invasive glucose monitoring systems, ultimately improving patient compliance and quality of life in diabetes management. Novelty: This study introduces foundational aspects of non-invasive glucose monitoring technology, including materials, electrochemical sensors, and sweat secretion methods, along with specific approaches that enhance their effectiveness
Purpose of the Study: This study examines the computational complexity of simulating four-wheeled vehicle dynamics using C++, focusing on components such as suspension, steering, traction, and stability systems that increase algorithmic complexity. Methodology: The study analyzes the impact of data structures, numerical methods, and real-time computational constraints on simulation performance. It also evaluates the integration of physics engines and parallel processing techniques within a C++ based simulation framework. Results: Despite high computational demands, C++ demonstrates strong performance due to its high execution speed, efficient memory management, and compatibility with advanced simulation tools. These features enable accurate and efficient modeling of complex vehicle dynamics. Implications / Applications of the Study: The results indicate that C++ is well-suited for developing reliable vehicle simulations used in automotive design optimization, safety analysis, and performance evaluation, supporting advanced engineering applications. Novelty of the Study: The study provides a comprehensive assessment of both theoretical and practical aspects of simulation complexity in C++, uniquely linking computational techniques with real-world automotive modeling requirements.
Purpose of the Study: The purpose of this study is to review and analyze the intersection of Internet of Things (IoT) and Machine Learning (ML) technologies in remote healthcare monitoring. It aims to highlight how these technologies are transforming patient care by improving early detection of health issues, enhancing personalized healthcare, and fostering patient independence, particularly in the context of an aging population. Methodology: This study employs a comprehensive state-of-the-art review methodology, synthesizing existing literature on the application of IoT and ML in healthcare. It explores various use cases and technological advancements that facilitate the continuous collection and analysis of patient data through wearable sensors and remote monitoring devices. Results: The study finds that the convergence of IoT and ML has significantly advanced remote healthcare monitoring by enabling real-time data collection and intelligent data analysis. This integration allows for early detection of health issues, improved personalized healthcare services, and increased autonomy for patients, especially the elderly. Implication of the Study: The implications of this study are substantial for both healthcare providers and technology developers. It provides insights into the potential of IoT and ML to revolutionize patient monitoring systems, reduce the strain on healthcare facilities, and improve patient outcomes. The study also highlights the importance of adopting these technologies for addressing the growing demand for personalized, data-driven healthcare. Novelty: This study uniquely synthesizes the state-of-the-art developments at the intersection of IoT and ML within the healthcare sector. It contributes to the field by offering a comprehensive overview of the ways these technologies are collectively enhancing remote healthcare monitoring and shaping future trends in healthcare innovation.
Purpose of Study: The study's objective is to investigate the function of entropy in black holes, with a particular emphasis on the ways in which entropy aids in the comprehension of the properties of various varieties of black holes, such as Schwarzschild, Kerr, and charged black holes (Reissner-Nordström and Kerr-Newman). The objective of the investigation is to examine the unique entropy characteristics that are associated with each form of black hole within the context of black hole thermodynamics. Methodology: The entropy of black holes is examined through a theoretical approach that utilizes the principles of thermodynamics and information theory. The analysis entails a comparison of the entropy properties of Schwarzschild, Kerr, and charged black holes, taking into account their distinctive characteristics and the implications for black hole thermodynamics. Results: The analysis demonstrates that the inherent properties of each form of black hole are directly correlated with their distinctive entropy characteristics. Schwarzschild black holes, Kerr black holes, and charged black holes exhibit unique entropy patterns, which contribute to a more exhaustive comprehension of black hole thermodynamics and provide more profound insights into their thermodynamic behavior. Applications: The results have substantial implications for the advancement of theoretical physics, particularly in the field of black hole thermodynamics. The development of more precise models and predictions regarding black hole behavior can be facilitated by an understanding of the entropy characteristics of various varieties of black holes. This knowledge has the potential to inform future research in quantum gravity and cosmology.
Purpose of Study: The increasing focus on Artificial Intelligence (AI) worldwide has brought about potential benefits, but it also poses significant risks, especially in network security. Methodology: This study adopts a detailed comparative approach to evaluate the effectiveness of various Neural Network (NN) techniques in the context of Intrusion Detection Systems (IDS). The research focuses on four specific NN architectures: Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Each of these techniques is applied to different intrusion detection scenarios, using real-time network data. Result: The results indicate that different Neural Network techniques have varying levels of effectiveness in intrusion detection. Deep Neural Networks (DNN) demonstrated the highest accuracy in detecting complex threats, while Convolutional Neural Networks (CNN) were highly effective in pattern recognition within network traffic. Recurrent Neural Networks (RNN) performed well in temporal analysis of data, making them suitable for detecting persistent threats over time. Application of Study: The findings of this study are significant for the development of more sophisticated Intrusion Detection Systems (IDS) that can be applied in real-time network security environments. Novelty: The novelty of this study lies in its comprehensive analysis of multiple Neural Network techniques within the context of Intrusion Detection Systems (IDS). By comparing ANN, DNN, CNN, and RNN techniques, the study provides valuable insights into how AI can be leveraged to improve real-time network security.
Purpose of Study: The study aims to develop an AI-driven decision support system tailored to the specific needs of higher education institutions. This system is designed to assist administrators, educators, and stakeholders in making informed decisions related to student enrollment, course planning, resource allocation, academic performance, and institutional effectiveness. Methodology: The study employs a comprehensive framework that integrates machine learning algorithms, data analytics, and predictive modeling to process vast amounts of data from diverse sources within higher education. The system incorporates adaptive learning algorithms to personalize student experiences, optimize curricula, and identify at-risk students for intervention. Ethical considerations are embedded in the system design, ensuring fairness, accountability, and transparency. Results: The proposed decision support system demonstrated its potential to enhance decision-making processes by providing actionable insights into student performance, faculty effectiveness, and resource utilization. The system proved effective in supporting both administrative and academic decisions, while prioritizing ethical standards such as fairness and transparency. Implication of Study: The implementation of this AI-based decision support system has significant implications for higher education institutions. It offers the potential to transform decision-making processes, improve institutional effectiveness, and personalize student experiences. Furthermore, it highlights the importance of collaboration between data scientists, educators, and administrators to maintain ethical standards and ensure data integrity and security. Novelty: The novelty of this study lies in its tailored approach to higher education, incorporating adaptive learning and ethical decision-making principles within an AI-based system. This comprehensive model not only supports traditional decision-making processes but also addresses emerging challenges in higher education by leveraging data-driven insights and advanced machine learning techniques.
Purpose: This study focuses on the effect of consumer-base demographics, seller characteristics, and climatic factors on the sale of Ice Creams. Methodology: Through a 6375-row dataset sourced from 15 carts and four major brands during peak Ice Cream season, the study analyses the contingencies involved in the sale of small ticket-size, seasonal retail goods. Applications: The results can help sellers and companies better position their shops and market their products to appeal to the best-paying consumers. Findings: We find that Women tend to buy more expensive Ice Creams than Men, high-income area residents buy more expensive Ice Creams than low-income area residents, older consumers buy more expensive Ice Creams than younger consumers, but tend to buy less frequently, and that an Ice Cream cart is better placed on a main- road than near a market. The best-paying consumer is a Woman aged 60 or more, living in a high-income area, buying from a cart located near a Main Road. Novelty: Pre-existing literature on this topic accounts for only one or two of the several variables that we’ve chosen to select, and usually account for long time scales, causing a seasonal effect on Ice Cream sales. Our research is isolated in its approach to the comprehensiveness of consumer demographics.
Purpose of the study: As organizations face increasingly sophisticated and persistent cyber threats, the need for robust Security Information and Event Management (SIEM) solutions becomes paramount. This paper presents "SecureLog," an open-source SIEM solution designed to enhance threat detection and incident response capabilities. Methodology: This paper explores the architecture and components of SecureLog, detailing its data collection and log management capabilities. It examines the threat detection algorithms employed, emphasizing real-time event correlation and alerting mechanisms. The paper addresses the scalability and performance considerations associated with deploying SecureLog in large-scale environments. Main Findings: The findings highlight the benefits of using fuzzy logic in cyber threat intelligence and pave the way for further research and development in this promising field. The future prospects and challenges of integrating fuzzy logic with other advanced technologies such as machine learning and artificial intelligence. Applications of this study: SecureLog emerges as a valuable open-source SIEM solution, empowering organizations with enhanced threat detection and incident response capabilities. With its feature-rich architecture and active community support, SecureLog proves to be a reliable choice for organizations seeking to fortify their cybersecurity defences. Novelty/Originality of this study: The paper also includes practical use cases and case studies to demonstrate the effectiveness of SecureLog in enhancing threat detection and incident response. Security and compliance considerations, including data privacy and regulatory compliance, are examined, along with recommendations for securing the SecureLog deployment.
Purpose of the study: Fuzzy logic is a mathematical concept that allows for the handling of uncertain or imprecise data. In cyber security, fuzzy logic can be used to improve the accuracy and efficiency of security systems. Cyber threat intelligence plays a crucial role in modern cybersecurity by enabling organizations to proactively identify, analyse, and respond to potential cyber threats. With the increasing complexity and sophistication of cyber threats, effective cyber threat intelligence has become a critical component of modern cybersecurity. Traditional approaches often struggle to handle the inherent uncertainties and complexities in threat intelligence data. Methodology: Through a comprehensive literature review, examines the current state of cyber threat intelligence and the principles of fuzzy logic. The paper presents a detailed analysis of how fuzzy logic can be applied to various aspects of cyber threat intelligence, including threat modelling, detection and classification, risk assessment, and decision support systems. Main Findings: The findings highlight the benefits of using fuzzy logic in cyber threat intelligence and pave the way for further research and development in this promising field. The future prospects and challenges of integrating fuzzy logic with other advanced technologies such as machine learning and artificial intelligence. Applications of this study: The proposed approach has the potential to significantly improve the analysis and decision-making processes in agriculture, helping farmers to make more informed decisions about crop treatments and ultimately increasing crop yields. Novelty/Originality of this study: The outcomes of this research contribute to advancing the field of cyber threat intelligence and provide valuable insights into the practical implementation of fuzzy logic for better threat analysis and mitigation strategies.
Purpose: Heart failure is a widespread health concern. A person with a heart failure has 5 years shorter life expectancy compared to a person who has a cancer. Specifically, myocardial disease is usually involved with a treatment accompanied by an electrical conduction system. To alleviate the physical burden to heart due to ventricular pacing, epicardial electronic system made of soft and elastic materials is needed. Methodology: In this review, we discuss candidate materials for novel epicardial sensing/stimulation system that matches similar mechanical properties of heart. Materials are categorized as soft conductive materials consist of elastomer and conductive filler and tissue-like low modulus materials. Like hydrogel and its conductive composites. Main Findings: The soft nanocomposites integrated with nanomaterials as filler and elastomer/hydrogel as matrix show potential to open a new pathway in high-performance epicardial electronic system that improve accuracy, stability, and long-term usability in diagnosis and treatment of heart diseases. Implications: Multifunctional epicardial system that monitors electrical conduction of epicardium surface and stimulate epicardium simultaneously could be a powerful tool to diagnose and treat myocardial disease. Novelty: This review study is focused and written in simple terms for readers.
Purpose of the study: The proposed model shows a deterministic approach of the inventory management in which the rate of the deterioration of the inventory commodities is proportional to time, demand rate is a function of selling price and inventory holding cost both, ordering cost and deterioration rate are all function of time. Here shortages are allowed during the lead time and completely backlogged. The optimum replenishment policy is to be determined which minimizes the total cost. The rate of deterioration has been considered as non-instantaneous and that follows the two parameters Weibull distribution. In the proposed model we aim to find optimal value of the inventory level to minimize the total effective cost. Methodology: The optimal solution will have to be obtained by using Mathematica Software and has been illustrated using a numerical example. Main Findings: Since lowering the selling price would result in increase in purchase of items by the customers, hence the selling price is the major criteria to determine their buying capacity. Applications of this study: If researchers go on taking several kinds of variable costs etc., then hopefully they may find a new area of study in their research.
Purpose: Wetlands are assets in the country, and they help make cities lockable and attractive. The study aims to assess the spread of alien invasive plants that affect the wetlands and construct a policy framework that could be used to preserve and conserve groundwater resources (Wetlands) in urban areas of Mpumalanga Province, specifically White River. This research will answer why alien invasives are a threat to wetlands. Methodology: The study was conducted in White River on Longtom Street. A single observer conducted fixed-width line transect surveys to investigate species richness and abundance for all the untreated species detected next to the wetland. Three transects per habitat type were visited twice through twice per week. Main Findings: During the survey, seven different alien invasive species were observed. The leading species was Solanum mauritianum, followed by Chromolaena odorata species; the third most dominant species was Lantana camara and Tecoma stans. Most species observed were illegally dumped by the residents that stay closer to the wetland, and no awareness, information, and training about the importance of wetlands were provided to the residents. Implications: The framework can do more to help local communities manage urban wetlands, identifying opportunities for mapping and functional assessment to improve restoration and protection efforts. It can facilitate research and peer-to-peer exchange on innovative funding and financing methods for nature-based projects. Novelty: Findings and recommendations resulting from this study will be summarized into a strategic guide by the end of 2023. It is recommended that all alien invasive plants in the wetland be eradicated. Wetlands must be protected to improve human health and well-being.
Purpose of the study: The transportation problem has a huge application in logistics. Therefore, dealing a transportation problem with permissible constraints is always an interesting area. In the present paper, we have tried a version of transportation problem which is constrained in nature. We have explored strategies for dealing the subset constrained transportation problem. Methodology: An updated version of matrix minima method is proposed in this paper. This version is used for subset constrained transportation problem only. We have also applied the mathematical model for solving the same subset constrained transportation problem. Main Findings: The mathematical model gives an optimal solution of the constrained transportation problem with the help of software. It is found that the proposed updated matrix minima method does not guarantee the optimality. However, we use the matrix minima approach for having an idea of the closer feasible solution. It is found that the mathematical model is always a superior way to find an optimal solution. Applications of this study: The study has a huge application in logistics. Novelty/Originality of this study: An updated version of matrix minima method is proposed. The method is applied on constrained transportation problem and the results are compared with the optimal solution.
Purpose of the study: This research paper proposes the use of soft mapping techniques to model the relationship between crop treatment and crop yield, with the goal of analyzing and recommending the best treatment options for crops. Soft mapping combines fuzzy logic and neural networks to create a more accurate and robust model that considers uncertain or ambiguous inputs. Methodology: The model can be trained using data on past crop yields, treatment options, and other relevant factors such as climate and soil quality. By taking into account the inherent uncertainty and ambiguity in the input data, the soft mapping model can provide more accurate predictions and recommendations for the best treatment options for a given crop and environmental conditions. Main Findings: The findings of this research could have important implications for the agricultural industry, particularly in the context of sustainable agriculture and food security. Applications of this study: The proposed approach has the potential to significantly improve the analysis and decision-making processes in agriculture, helping farmers to make more informed decisions about crop treatments and ultimately increasing crop yields.
