All Academic Research: OJS
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CYBERSECURITY RISK MITIGATION IN INDUSTRIAL CONTROL SYSTEMS ANALYZING PHYSICAL HYBRID AND VIRTUAL TEST BED APPLICATIONS
Get PDFIndustrial Control Systems (ICS) play a vital role in industries such as oil, utilities, and manufacturing, forming the backbone of critical infrastructure. With the increasing integration of network capabilities in ICS, their exposure to cyber-attacks has grown significantly. However, due to the sensitivity of these systems, access to detailed technical information is limited, making cybersecurity research challenging. To address this, researchers have employed various physical, hybrid, and virtual testbeds to simulate and analyze cyber threats. This systematic review, conducted following PRISMA guidelines, aims to evaluate the effectiveness of these testbeds in mitigating cybersecurity risks in ICS, particularly within the context of a clean water supply system. The findings reveal that physical testbeds offer a comprehensive understanding of the behavior and dynamics of ICS components, such as sensors and actuators, under real-world conditions affected by external factors like pressure, temperature, and mechanical wear. However, physical testbeds' high cost and complexity limit their widespread use. While more cost-effective, hybrid testbeds fail to capture crucial physical dynamics, which may lead to incomplete assessments of cybersecurity vulnerabilities. Virtual testbeds provide the most affordable option, offering scalability and ease of implementation. However, they deliver a limited view of ICS operations that can impair the development of accurate detection and prevention mechanisms. The results underscore the trade-offs associated with each testbed type, suggesting that an integrated approach, blending physical and virtual elements, may offer the most effective framework for cybersecurity research in ICS while balancing cost and realism
IT PROJECT MANAGEMENT FRAMEWORKS: EVALUATING BEST PRACTICES AND METHODOLOGIES FOR SUCCESSFUL IT PROJECT MANAGEMENT
Get PDFThis study provides a systematic review of IT project management frameworks, examining the effectiveness, adaptability, and risk management strategies of methodologies such as Agile, Waterfall, PRINCE2, Scrum, and hybrid approaches. A total of 133 peer-reviewed articles were analyzed to gain insights into how these frameworks are being applied across various industries and project environments. The findings reveal that hybrid models, which combine the structured governance of traditional methodologies like Waterfall with the iterative flexibility of Agile, are becoming increasingly popular, especially in industries requiring both regulatory compliance and adaptability to changing requirements. Agile frameworks were shown to significantly improve project delivery speed, stakeholder satisfaction, and risk mitigation through continuous iterations and feedback loops, while traditional methodologies like Waterfall remain essential in sectors with strict documentation and control requirements. The review also highlights the critical role of risk management across all frameworks, with hybrid models offering the most comprehensive approach by integrating early-stage planning with ongoing risk assessment. Despite the success of these frameworks in IT-related industries, a notable gap was identified in their application to non-IT sectors, suggesting a need for further research to explore their broader applicability. This review underscores the continued relevance of traditional, Agile, and hybrid project management frameworks, while also pointing to future opportunities for expanding their use beyond IT
ELECTRIC VEHICLE POWERTRAIN DESIGN: INNOVATIONS IN ELECTRICAL ENGINEERING
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The electrification of vehicles is reshaping transportation, with electric vehicle (EV) powertrain design playing a key role in this shift. This review focuses on recent innovations in electrical engineering that have enhanced EV powertrains, particularly in areas like electric motors, power electronics, energy storage, and thermal management. Advances in motor technology, such as permanent magnet synchronous motors (PMSM), and the integration of silicon carbide (SiC) and gallium nitride (GaN) semiconductors have improved efficiency and reduced heat generation. Battery innovations, including solid-state technologies and advanced battery management systems, along with regenerative braking, have extended EV range and efficiency. Power electronics, including inverters and onboard chargers, now utilize wide-bandgap semiconductors to minimize energy losses and improve thermal performance. Additionally, novel cooling solutions are addressing thermal challenges in EV systems. Looking forward, modular powertrain architectures and AI-driven control strategies offer promising advancements for various vehicle types. This review provides an overview of how electrical engineering innovations are driving the future of EV powertrain design and sustainable transportation
Design and Implementation of Automatic Power Transfer and Load Shed Controller
Get PDFThe automatic power transfer and load shed controller (APTLSC) make the new levels of standby power plants. Programmable logic controller (PLC), adjustable interlocks and counters with time delay can improve switchgear, generator, and engine economics and extend power plants life. Electronic counting timers for loading, unloading or transferring are adjustable from 0.1 seconds to 132 minutes. PLC provides a wide range of logic controls (about 4000 nos. of Rungs). In this paper, PLC DVP SS series controller have used. The APTSLC combined with electronic load shed control and emergency generators or standby power plants. If there is Auto synchronizing base load mode in the Gen-set, the emergency system is started and synchronized with the utility. The APTLC is activated by an auxiliary contact of voltage monitoring relay (VMR). The output contacts in the APTLC to initiate separation and shutdown of the standby system. There are two standby power plants or generators. If utility (normal source) fails, one power plant starts to load basis (peak or non-peak hour)
POWER SYSTEM STABILITY CONSIDERING THE INFLUENCE OF DISTRIBUTED ENERGY RESOURCES ON DISTRIBUTION NETWORKS
Get PDFThe increasing penetration of distributed energy resources (DERs) into power systems has transformed the landscape of energy distribution, bringing both opportunities and significant challenges. This study examines the impact of DER integration on power system stability, focusing on voltage stability, frequency stability, and transient stability. A comprehensive review of 50 high-quality studies from peer-reviewed journals and reputable conference proceedings was conducted, utilizing advanced modeling, simulation, and empirical analysis methods. The findings reveal that the intermittent nature of renewable DERs, such as solar and wind, leads to voltage fluctuations, necessitating advanced control strategies to maintain stability. Additionally, the displacement of traditional synchronous generators by inverter-based DERs reduces system inertia, posing severe frequency stability challenges that require innovative solutions like synthetic inertia and fast frequency response mechanisms. Transient stability issues are also exacerbated by DER integration, highlighting the need for advanced inverter controls and enhanced fault ride-through capabilities. Energy storage systems (ESS) are identified as crucial for buffering the variability of renewable DERs, providing essential services such as frequency regulation and voltage support. However, high costs and scalability issues remain barriers to widespread ESS adoption. The study underscores the importance of supportive regulatory and policy frameworks in facilitating the seamless integration of DERs while maintaining grid stability. Effective policies that promote smart grid technologies and DER-friendly regulations are essential for ensuring a stable, resilient, and sustainable power grid. This research contributes to a deeper understanding of the complex dynamics introduced by DERs and offers insights into developing robust strategies to address stability challenges in modern power systems
CLOUD SECURITY POSTURE MANAGEMENT AUTOMATING RISK IDENTIFICATION AND RESPONSE IN CLOUD INFRASTRUCTURES
Get PDFCloud Security Posture Management (CSPM) tools have become essential in addressing the growing security challenges faced by organizations as they migrate to cloud environments. This study explores the effectiveness of CSPM tools in automating the identification and response to security risks within cloud infrastructures, highlighting their role in reducing misconfigurations, improving compliance, and enhancing overall security posture. Through a mixed-method approach, combining a comprehensive literature review, a survey of IT security professionals, and detailed case study analyses, this research provides a robust evaluation of CSPM tools' capabilities and the challenges associated with their implementation. The findings reveal that organizations utilizing CSPM tools experience significant reductions in security incidents and operational inefficiencies, with automation playing a crucial role in enabling real-time threat detection and response. However, the study also identifies critical barriers to CSPM adoption, including integration complexities, cost concerns, and organizational resistance to automated security solutions. These challenges suggest that while CSPM tools offer substantial benefits, their successful deployment requires careful planning, adequate resource allocation, and strategic change management to address both technical and human factors. This study contributes to the existing literature by providing detailed insights into the practical applications and limitations of CSPM tools, offering valuable guidance for organizations seeking to enhance their cloud security strategies through automation
A COMPREHENSIVE REVIEW OF MACHINE LEARNING AND DEEP LEARNING APPLICATIONS IN CYBERSECURITY: AN INTERDISCIPLINARY APPROACH
Get PDFCybersecurity is increasingly becoming a critical concern as the complexity and frequency of cyber-attacks continue to rise. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to enhance cybersecurity systems, offering dynamic capabilities in real-time threat detection, anomaly detection, and intrusion prevention. This article (45) presents a systematic review of the applications of ML and DL in cybersecurity, adhering to the PRISMA guidelines. The review covers several key domains, including network security, cloud security, and Internet of Things (IoT) security, highlighting how ML/DL models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) outperform traditional rule-based systems. It also addresses challenges such as adversarial attacks, data privacy concerns, and the computational resource demands of DL models. Current solutions like adversarial training, federated learning, and model optimization techniques are examined for their potential to mitigate these issues. The findings suggest that while ML/DL technologies hold great promise, further research and innovation are necessary to overcome the inherent challenges, ensuring that these systems can be deployed effectively and securely in real-world environments
DATA-DRIVEN TRANSFORMATION: OPTIMIZING ENTERPRISE FINANCIAL MANAGEMENT AND DECISION-MAKING WITH BIG DATA
Get PDFThis study explores the transformative impact of big data on enterprise financial management, highlighting significant improvements in decision-making efficiency, data quality, and risk management capabilities. Through a mixed-methods approach that includes meta-analysis, surveys, interviews, and case studies, the research reveals a substantial increase in decision-making efficiency, better integration of diverse data sources, and more accurate financial reporting. Practical benefits such as reduced data processing times and improved credit risk assessments are identified, alongside challenges like the skill gap in data science, cultural resistance, and technical difficulties in integrating new technologies with legacy systems. Addressing these challenges requires strategic leadership, continuous training, and investment in scalable infrastructure. Despite these hurdles, the long-term advantages of big data adoption, including enhanced financial reporting and resource allocation, underscore its value in driving organizational performance and competitiveness. This study provides empirical evidence and practical insights, offering a valuable foundation for organizations aiming to leverage big data in their financial management practices.
 
ADVANCED QUERY OPTIMIZATION IN SQL DATABASES FOR REAL-TIME BIG DATA ANALYTICS
Get PDFThis study investigates the effectiveness of advanced query optimization techniques in SQL databases, focusing on multi-level indexing, query rewriting, and dynamic query execution plans. The research employs a qualitative approach, gathering data from a variety of SQL databases characterized by large datasets typical of big data environments. Through structured interviews, focus groups, and observational methods, insights from database administrators highlight the practical benefits and challenges associated with implementing these techniques. The findings reveal significant improvements in query performance, with multi-level indexing reducing data retrieval times by approximately 40%, query rewriting decreasing execution times by 35%, and dynamic query execution plans enhancing resource utilization efficiency by 25%. These techniques were also praised for their ease of use, adaptability to different data types and query complexities, and overall reliability. This study contributes to the existing body of knowledge by providing a comprehensive analysis of the practical applications and performance enhancements offered by advanced query optimization methods in SQL databases, underscoring their value in managing large-scale, dynamic data environments
DESIGN AND DEVELOPMENT OF A SMART FACTORY USING INDUSTRY 4.0 TECHNOLOGIES
Get PDFThis systematic literature review examines the operational and organizational impacts of Industry 4.0 technologies on smart factories, drawing on insights from 120 peer-reviewed articles published between 2010 and 2024. The study follows the PRISMA guidelines to ensure a transparent and rigorous review process, focusing on the key enablers of smart manufacturing, including cyber-physical systems (CPS), the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and machine learning (ML). The findings reveal that smart factories offer significant benefits, including enhanced flexibility and customization, predictive maintenance that reduces downtime by up to 50%, and improved supply chain integration through real-time data sharing. Big data analytics plays a crucial role in optimizing operations by allowing factories to perform continuous real-time adjustments, improving efficiency and reducing resource waste. The review also highlights the evolving role of the workforce, with a growing need for technical skills and increased human-machine collaboration in smart manufacturing environments. However, challenges such as interoperability, cybersecurity, and the economic feasibility of large-scale smart factory implementations remain underexplored in the literature. Emerging technologies like blockchain and 5G offer promising solutions, but further research is required to assess their full potential. Overall, this review provides a comprehensive understanding of the current state of smart factory technologies and outlines key areas for future research, particularly in addressing gaps related to standards, workforce adaptation, and security concerns