Author (s) :

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1). Mukesh V. Vekariya, Darshan Institute of Engineering and Technology for Diploma Studies, Darshan University, Rajkot, Gujarat, India
2). Jinesh Tank, Darshan Institute of Engineering and Technology for Diploma Studies, Darshan University, Rajkot, Gujarat, India
3). Vasim Chaudhary, Darshan Institute of Engineering and Technology for Diploma Studies, Darshan University, Rajkot, Gujarat, India

Abstract :

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This study presents the design, fabrication, and performance evaluation of a Savonius-type Vertical Axis Wind Turbine (VAWT) optimized for low-cost renewable energy generation. The transition from a Darrieus to a Savonius design aimed to enhance torque and RPM using lightweight blades and wind deflectors. Key parameters such as blade geometry, deflector angle, and wind speed were analyzed through Computational Fluid Dynamics (CFD) and practical testing. Results demonstrated a 22% increase in energy extraction efficiency compared to conventional models, with a fabrication cost under INR 15,000. The study highlights the potential of VAWTs for decentralized energy solutions in urban and rural settings.

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Author (s) : DOI : 10.32692/IJDI-ERET/14.2.2025.2501

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1). Attia Hussien Gomaa, Faculty of Engineering, Benha University, Cairo, Egypt

Abstract :

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Failure Mode and Effects Analysis (FMEA) is a foundational technique for identifying, prioritizing, and mitigating potential failure modes in manufacturing systems. However, traditional FMEA methods—being manual, static, and retrospective—are increasingly inadequate in today’s complex, data-driven industrial landscape. This paper introduces FMEA 4.0, a digital framework that integrates core Industry 4.0 technologies—including the Internet of Things (IoT), artificial intelligence (AI), digital twins, big data analytics, and cloud computing—to transform FMEA into a real-time, predictive, and adaptive risk management system. The study critically examines the limitations of conventional FMEA and outlines the evolution toward a more intelligent, automated, and continuous approach. FMEA 4.0 facilitates dynamic risk assessment, early failure detection, optimized maintenance planning, improved asset utilization, and enhanced overall equipment effectiveness (OEE). A structured implementation methodology is proposed, based on the DMAIC (Define, Measure, Analyze, Improve, Control) framework, to ensure systematic integration with existing quality management systems. The framework also incorporates key performance indicators (KPIs) aligned with strategic organizational goals, enabling continuous monitoring, data-driven decision-making, and sustained improvement in reliability, safety, and operational performance. By unifying digital technologies with proven quality principles, FMEA 4.0 emerges as a strategic enabler of resilience, agility, and competitiveness in smart manufacturing. The paper concludes with practical implementation guidance and insights for researchers and industry professionals advancing digital transformation in reliability and risk management.

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Author (s) : DOI : 10.32692/IJDI-ERET/14.2.2025.2502

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1). Abraham Danlami, Federal University, Wukari, Taraba State, Nigeria
2). E. J. Garba, Modibbo Adama University, Yola, Adamawa State, Nigeria
3). Y. M. Malgwi, Modibbo Adama University, Yola, Adamawa State, Nigeria
4). S. E. Dogo, Taraba State University, Jalingo, Taraba State, Nigeria

Abstract :

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A bank network is an interconnected system designed to facilitate financial transactions, minimize customer waiting times, and reduce the risk of errors caused by automated systems or bots. However, over the past three decades, cyberattacks have emerged as a significant threat to the security of these networks. The tools like penetration testing, machine learning classifiers, multi-factor authentication, network traffic analysis, and bot detection systems are reactive rather than proactive. As a result, these measures may fail to detect or prevent sophisticated attacks in real time. Such vulnerabilities can be exploited by attackers to gain unauthorized access to banking networks, posing serious risks to data integrity and customer privacy. In this study, we aim to enhance the performance of an anomaly-based Intrusion Detection System (IDS) within a banking environment by developing a Stacked Hybrid Classifier. To achieve this, the study employed the CICIDS2017 dataset, which contains realistic traffic data simulating various types of attacks and benign behaviors. The dataset's inherent class imbalance common in intrusion detection scenarios was addressed using the Synthetic Minority Oversampling Technique (SMOTE), ensuring more balanced training and improved sensitivity to minority attack classes. The model was implemented using Python, stacking multiple base classifiers including Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbors. The predictions from these models were combined to form a meta-model, resulting in a more robust and generalized detection capability. The study was evaluated using stratified k-fold cross-validation to ensure consistent and unbiased performance assessment across different data partitions. The results show that the stacked Hybrid classifier achieved the best accuracy of 99.76%, along with superior precision, recall, and F1 scores when compared to the individual classifiers. This demonstrates the effectiveness of Hybrid learning, data balancing, and rigorous validation in improving IDS performance in banking networks.

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Author (s) :

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1). Gayathri Jeevanandham, Ariel University, Ariel, ISRAEL

Abstract :

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The global rise in diabetes prevalence has intensified the demand for glucose monitoring technologies that are accurate, cost-effective, and stable. Although enzyme-based glucose sensors are widely used, they face significant limitations, including poor stability, temperature sensitivity, and high manufacturing costs. As a promising alternative, enzyme-free glucose sensors based on transition metal oxide (TMO) nanostructures such as NiO, Co3O4, CuO and ZnO offer intrinsic electrocatalytic activity, chemical robustness, and tunable physicochemical properties. This review examines recent advances in the design, synthesis, and application of TMO based nanomaterials for non-enzymatic glucose detection. We highlight how nanoengineering strategies including morphology control, doping, and composite formation enhance sensor performance. The sensors discussed demonstrate high sensitivity, low detection limits, rapid response times, and excellent selectivity in complex biological matrices. These advancements underscore the potential of TMO nanostructures to enable reliable, scalable, and wearable glucose biosensors for real-time diabetes monitoring.

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Author (s) :

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1). Lee Moyo, Harare Institute of Technology, Harare, Zimbabwe
2). Ngonidzashe A. Musiwedzingo, Harare Institute of Technology, Harare, Zimbabwe
3). Elizabeth Ticharwa, Harare Institute of Technology, Harare, Zimbabwe

Abstract :

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Marula trees are native to the southern Africa and are known for their edible fruits and nuts. Marula nutshells which are the hard marula fruit seed, a result of sclerocarya birrea (marula) fruit processing are an abundant underused biomass in Rutenga, Zimbabwe. They have a high carbon content and porous nature, making them suitable for activated carbon synthesis. This review investigates the potential of value adding sclerocarya birrea (marula) nutshells through activated carbon production, for use in the marula oil refining. The use of sclerocarya birrea biomass/nutshells for synthesis of activated carbon addresses the issue of circular economy by using waste generated at the oil manufacturing factory to produce the oil refining agent and also addresses the issues of sustainability by using waste as a raw material. The review explored physical and chemical activated carbon synthesis methods and identified pyrolysis temperature and chemical type as the key critical parameters. The review further examines the potential of marula-derived activated carbon in oil refining, specifically in the removal of contaminants, pigments and oxidation products.

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Author (s) :

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1). Lee Moyo, Harare Institute of Technology, Harare, Zimbabwe
2). F.M Saziya, Harare Institute of Technology, Belvedere, Harare, Zimbabwe
3). Elizabeth Ticharwa, Harare Institute of Technology, Harare, Zimbabwe

Abstract :

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This review provides a clear overview of fluorine, its introduction into the soil, and its cascading effects on the food chain and ecosystem. It focuses on fluorine, the most electronegative element, which has both beneficial and detrimental effects on plants and animals and further focuses on the impact of fluorine from single super phosphate fertilizers on agricultural landscapes, ecology, and interconnected systems. The literature shows that fluorine has adverse effects on plants, such as leaf necrosis and fluorosis, and on animals, such as infertility, dental and skeletal fluorosis, mitochondrial toxicity, and enzyme inhibition. The literature also shows that in low pH soils, fluoride solubility is high, leading to its leaching into the top soil, and in high pH soils, fluoride is precipitated by calcium ions in the form of calcium fluoride compounds. The review highlights a cascading effect of fluorine through the food web. Natural and human-related activities introduce fluorine into the soil, where it is absorbed by plants. The element then bioaccumulates as it moves up the food chain, from plants to herbivores and then to carnivores. Mitigation strategies for fluoride in soils include phytoremediation, fluoride-tolerant plants, fluorine reduction in the SSP manufacturing line, and fluoride sorption using amendments.

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Author (s) :

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1). Dr. G Thippanna, Dr. K. V. Subba Reddy Institute of Technology, Kurnool, Andhra Pradesh, India
2). Dr. G Ravi Kumar, Rayalaseema University College of Engineering, Rayalaseema University, Kurnool, Andhra Pradesh, India

Abstract :

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This research investigates the application of Logistic Regression for multiclass classification in dermatology, focusing on the task of identifying and categorizing six distinct dermatological disorders. The study compares two widely used multiclass classification strategies—One-vs-One (OvO) and One-vs-Rest (OvR)—to evaluate their performance in the context of dermatological disorder classification. The dataset used in this study consists of feature-rich data corresponding to various skin diseases, making the classification problem both complex and challenging. Logistic Regression, known for its simplicity and interpretability, is applied to both strategies, and the outcomes are assessed based on key metrics including classification accuracy, precision, recall, and F1-score. Through a detailed comparative analysis, the paper highlights the advantages and limitations of each approach in terms of classification effectiveness, computational efficiency, and robustness against class imbalance. The results demonstrate that both OvO and OvR strategies provide high accuracy and reliable classification performance, although differences in their predictive strengths are observed for specific dermatological conditions. The study concludes that Logistic Regression, when paired with either strategy, holds substantial promise for enhancing automated dermatological diagnosis and could serve as a valuable tool for clinicians in real-world applications. Furthermore, the findings offer insight into the optimal selection of classification approaches for various machine learning tasks within the healthcare domain.

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