International Journal of Science and Engineering

MACHINE LEARNING-GUIDED DESIGN OF NANOLUBRICANTS FOR MINIMIZING ENERGY LOSS IN MECHANICAL SYSTEMS

Kollol Sarker Jogesh — 2024-07-04

This study explores the significant potential of machine learning-guided design in optimizing nanolubricants, focusing on their application in reducing friction and wear in mechanical systems. Utilizing neural networks and genetic algorithms, the research demonstrates how advanced computational techniques can accurately predict and enhance the tribological properties of nanolubricants. The findings reveal that nanolubricants, particularly those containing graphene and carbon nanotubes, exhibit marked improvements in reducing friction coefficients and wear rates compared to traditional mineral oil-based lubricants. Additionally, the enhanced thermal stability and load-carrying capacity of these nanolubricants contribute to substantial energy savings and increased operational efficiency. The study underscores the economic and environmental benefits of adopting nanolubricants, highlighting their potential to transform lubrication technology and support sustainable industrial practices.

SUSTAINABLE MATERIALS SELECTION IN BUILDING DESIGN AND CONSTRUCTION

Abul Kashem Mohammad Yahia — 2024-09-12

This systematic review explores the selection and utilization of sustainable materials in building design and construction, emphasizing their environmental, economic, and social impacts. The review follows the PRISMA guidelines, identifying 50 relevant studies published between 2010 and 2023. The findings highlight that sustainable materials, including recycled steel, bamboo, and low-carbon concrete, significantly reduce greenhouse gas emissions, energy consumption, and resource depletion compared to traditional materials. Life Cycle Assessment (LCA) proved crucial in evaluating these environmental benefits. Economically, although the initial costs of sustainable materials are often higher, their long-term financial advantages—such as reduced operational costs, energy savings, and lower maintenance expenses—make them viable investments. Market trends indicate that growing demand is gradually lowering the costs of these materials. Socially, sustainable materials improve indoor air quality, reduce the health risks associated with volatile organic compounds (VOCs), and enhance occupant well-being, promoting community engagement by supporting local economies. Despite these benefits, challenges remain, particularly regarding the availability and cost of sustainable materials in developing regions. The review concludes that overcoming these barriers requires continued technological advancements, government incentives, and more robust regulatory frameworks to accelerate the adoption of sustainable building practices. Overall, the review emphasizes the critical role of sustainable materials in addressing climate change, promoting economic sustainability, and fostering social inclusivity in construction while underscoring the need for global efforts to support the transition towards eco-friendly and resilient built environments.

A FEASIBILITY STUDY ON UNDERGROUND INFRASTRUCTURE IMPLEMENTATION TO ENHANCE DHAKA’S ELECTRICAL GRID RELIABILITY

SM Deen Amin — 2024-08-10

This study systematically reviews the feasibility of implementing underground power infrastructure to enhance the reliability of Dhaka's electrical grid. Given the city's frequent power outages due to adverse weather and aging overhead power lines, this review synthesizes findings from peer-reviewed journal articles, technical reports, conference papers, and case studies published between 2010 and 2023. The review highlights the significant benefits of underground systems, including improved grid reliability and resilience, reduced outages, and enhanced urban aesthetics and safety. However, it also identifies substantial economic and technical challenges, such as high initial installation costs and complex maintenance requirements. Recent technological advancements, such as improved cable materials and installation techniques, have made underground power lines more feasible and cost-effective. Case studies from cities like Amsterdam, London, New York, and Toronto provide valuable insights into successful implementation strategies, emphasizing the importance of integrated urban planning and stakeholder collaboration. These findings offer a robust foundation for policymakers, utility companies, and urban planners to consider transitioning Dhaka's power infrastructure to an underground system, aiming to mitigate the impacts of severe weather and enhance overall grid reliability.

FUTURE TRENDS IN SQL DATABASES AND BIG DATA ANALYTICS: IMPACT OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Siful Islam — 2024-08-06

This study systematically reviews the integration of machine learning (ML) and artificial intelligence (AI) into SQL databases and big data analytics, highlighting significant advancements and emerging trends. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review of 60 selected articles published between 2010 and 2023 was conducted. The findings reveal substantial improvements in query optimization through ML algorithms, which adapt dynamically to changing data patterns, reducing processing times and enhancing performance. Additionally, embedding ML models within SQL databases facilitates real-time predictive analytics, streamlining workflows, and improving the accuracy and speed of predictions. AI-driven security systems provide proactive and real-time threat detection, significantly enhancing data protection. The development of hybrid systems that combine relational and non-relational databases offers versatile and efficient data management solutions, addressing the limitations of traditional systems. This study confirms the evolving role of AI and ML in transforming data management practices and aligns with and extends previous research findings.

A FRAMEWORK FOR LEAN MANUFACTURING IMPLEMENTATION IN THE TEXTILE INDUSTRY: A RESEARCH STUDY

Shirin Begum — 2024-07-15

This study investigates the implementation of lean manufacturing in the textile industry, focusing on its potential to enhance efficiency, reduce waste, and improve product quality. Lean manufacturing principles, including Just-In-Time (JIT) production, Kanban systems, value stream mapping, 5S workplace organization, and continuous improvement (Kaizen), have been widely recognized for their effectiveness in various manufacturing sectors. However, their application in the textile industry remains underexplored. Through a systematic literature review of 60 review papers and empirical analysis involving case studies, interviews, and surveys with industry experts, managers, and workers, this research identifies the significant benefits of lean practices in textile manufacturing. The findings reveal substantial efficiency gains, waste reduction, and quality improvements among companies adopting lean principles. Nevertheless, the study also highlights several industry-specific challenges, such as high variability in raw materials, complex production processes, and market pressures from fast fashion. These challenges necessitate tailored approaches and effective change management strategies for successful lean integration. Additionally, the research emphasizes the need for investment in advanced technologies and flexible manufacturing systems to enhance responsiveness to market changes. The study concludes that despite the challenges, lean manufacturing offers valuable strategies for textile companies aiming to improve their competitiveness and sustainability in the global market. This research contributes to the growing body of knowledge on lean manufacturing and provides practical insights for textile manufacturers seeking to adopt and optimize lean practices.

ADVANCED MACHINE LEARNING TECHNIQUES FOR CYBERSECURITY: OPPORTUNITIES AND EMERGING CHALLENGES

Ms Roopesh — 2024-09-11

This study investigates applying advanced machine learning techniques in enhancing cybersecurity systems, particularly in phishing detection, network intrusion detection, and malware and ransomware classification. Supervised learning algorithms such as random forests and support vector machines (SVM), deep learning models like convolutional neural networks (CNN) and recurrent neural networks (RNN), and ensemble methods were employed to improve detection accuracy and reduce false positives. The study also addresses key challenges, including adversarial attacks, data imbalance, and the need for continuous learning to adapt to evolving threats. Results indicated that machine learning models, especially deep learning techniques, demonstrated high accuracy in detecting complex threats, with phishing detection models achieving over 96% accuracy and network intrusion detection models reaching 98.2%. The study also explored the use of transfer learning and continuous learning systems, which showed promise in adapting to new threats while minimising the need for extensive retraining. However, adversarial vulnerabilities and the challenge of catastrophic forgetting in continuous learning models remain significant obstacles. Recommendations include integrating adversarial training, improving data augmentation techniques, and optimising continuous learning systems for real-time threat adaptation. This research contributes to the growing body of knowledge on machine learning applications in cybersecurity, highlighting both its potential and the need for ongoing refinement to address emerging cyber threats.

SUSTAINABLE SUPPLIER SELECTION IN INDIRECT PROCUREMENT: BEST PRACTICES AND CASE STUDIES IN ENGINEERING

Shaikh Shofiullah — 2024-08-06

This systematic review examines sustainable supplier selection in indirect procurement within the engineering sector, focusing on best practices, criteria, and impacts on organizational performance. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review synthesizes findings from 35 articles published between 2000 and 2023. Key best practices identified include the integration of sustainability criteria into supplier evaluations, the use of lifecycle assessments (LCAs) to gauge environmental impact, and the implementation of carbon footprint analyses. Additionally, the emphasis on fair labor practices and the requirement for suppliers to obtain certifications like SA8000 enhance social sustainability and corporate social responsibility (CSR). The evolution of supplier selection criteria to encompass environmental, social, and economic factors reflects a more holistic approach, addressing complex sustainability challenges and driving long-term value and resilience in supply chains. The review also highlights the positive impacts of sustainable procurement practices, such as cost savings, risk mitigation, enhanced brand reputation, and improved operational efficiency. These findings underscore the strategic importance of sustainable procurement in achieving organizational sustainability goals and fostering sustainable development in the engineering sector.

AI-POWERED PREDICTIVE ANALYTICS FOR INTELLECTUAL PROPERTY RISK MANAGEMENT IN SUPPLY CHAIN OPERATIONS: A BIG DATA APPROACH

Md Abdur Rauf — 2024-07-16

The rapid advancement of technology and the increasing complexity of global supply chains have heightened the need for robust intellectual property (IP) risk management strategies. This study explores the application of artificial intelligence (AI) and big data analytics in enhancing IP risk management within supply chains. A comprehensive literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, identifying 578 records through database searches and an additional 90 records through other sources. After removing duplicates, 568 records were screened, with 196 full-text articles assessed for eligibility. Ultimately, 135 articles were included in the final synthesis. The findings reveal that AI-driven predictive analytics significantly enhance the detection and mitigation of IP risks by analyzing large volumes of data from various sources, such as patent filings, market trends, and social media. Big data analytics tools like Hadoop and Spark facilitate real-time monitoring and early identification of potential IP threats, providing a comprehensive view of the supply chain landscape. Several successful case studies across different industries, including pharmaceuticals, electronics, and fashion, demonstrate the practical applications of these technologies in addressing IP risks. However, the review also highlights several challenges, including data quality, scalability, model interpretability, data privacy, and integration with legacy systems. Despite these challenges, the benefits of AI and big data analytics in IP risk management are substantial, enabling organizations to protect their intellectual assets more effectively. The study underscores the need for future research to address these challenges and explore innovative solutions to maximize the potential of AI and big data analytics in IP risk management. By investing in the necessary infrastructure and expertise, organizations can enhance their resilience and maintain a competitive edge in the global market.