Enhancing Operations Quality Improvement through Advanced Data Analytics

Accurate prediction of accident risks plays a crucial role in proactively implementing safety measures and allocating resources effectively. This paper introduces an innovative approach aimed at improving accident risk prediction by harnessing unique data sources and extracting insights from diverse yet sparse datasets. Traditional models often face limitations due to a lack of diversity and scope in the available data, which hinders their predictive capabilities. In response to this challenge, our study integrates a broad spectrum of heterogeneous data, encompassing traffic flow, weather conditions, road infrastructure details, and historical accident records. To overcome the difficulties associated with sparse data, we employ advanced data science techniques such as feature engineering, imputation, and machine learning. The paper introduces a novel dataset that amalgamates various data types, establishing a robust foundation for our predictive model. Through meticulous analysis, we derive valuable insights from these diverse sources, significantly enhancing our ability to assess accident risks. The proposed approach offers numerous advantages, including the capacity to predict accidents in areas that were previously underrepresented and under varying conditions. We rigorously evaluate the model's performance through extensive experimentation and validate its accuracy using real-world accident data. Our results indicate substantial improvements in prediction accuracy compared to conventional models. This research contributes significantly to the field of accident risk prediction by highlighting the potential benefits of integrating heterogeneous sparse data and leveraging advanced data science techniques. The study underscores the importance of tapping into novel data sources and extracting concealed patterns and insights to promote safety and optimize resource allocation in accident-prone regions, fostering more secure environments.

Section: Data Processing and Cleaning Preparationmentioning

confidence: 99%

Advanced analytics for predicting traffic collision severity assessment

Mohammad Fokhrul Islam Buian,

Ramisha Anan Arde,

Md Masum Billah

et al. 2024

“…Noman et al (2020) and Hoque et al (2024) have undertaken a commendable and noteworthy project, showcasing a robust data retrieval approach coupled with an advanced framework for predicting data accuracy. This project stands as a valuable augmentation to the data generation work for electricity generation [18,19,28,24,29]. Biswas et al (2024) describes gently in her 3 different papers industrial sustainability and mechanical characterization works in different industry [20,21,22,23,25].The performance of wind energy conversion systems (WECs) relies on various subsystems such as the aerodynamic wind turbine, mechanical gears, and electrical generator [9].…”

Section: Introductionmentioning

confidence: 99%

Investigating the effectiveness of a mobile wind turbine generating electricity from vehicle air movement

Joyeshree Biswas,

Suman Das

2024

This study presents an evaluation of the effectiveness of a portable wind generator designed to harness wind flow generated by moving vehicles for electricity production. With a growing emphasis on renewable energy sources and the need for sustainable power solutions, innovative approaches such as utilizing vehicle-induced wind currents have gained attention. The portable wind generator under examination is engineered to capture airflow from passing vehicles and convert it into electrical energy. Through a combination of field experiments and theoretical analysis, the performance and efficiency of the device were assessed under various environmental and traffic conditions. Factors such as wind speed, direction, and vehicle velocity were considered in the evaluation process. The results indicate promising potential for the portable wind generator as a supplementary power source, particularly in environments with high vehicular traffic. However, challenges such as variability in wind patterns and optimal positioning of the generator remain areas for further investigation and refinement. Overall, this study contributes to the understanding of utilizing unconventional wind resources and provides insights into the practical implementation of mobile wind energy technology for sustainable electricity generation.

“…This initiative is a valuable addition to ongoing endeavors in the realm of mechanical characterization of materials, presenting a set of supplementary features poised to enhance the research experiences of aspiring students, particularly those engaged in studies related to materials science and mechanical engineering. This project contributes significantly to the evolving landscape of virtual lab research within the field, offering innovative tools and methodologies tailored to advance knowledge and understanding in mechanical material characterization [36,37,38,39,40].This synergy between experimental observations and theoretical predictions underpins the continuous evolution of materials science and mechanical engineering, driving innovations across diverse sectors ranging from aerospace and automotive to biomedical and renewable energy. In this introduction, we explore the transformative impact of advanced microscopy on mechanical engineering, underscoring its pivotal role in propelling the frontiers of materials science and technology (Uddin et al, 2024 [16,45]).…”

Section: Introductionmentioning

confidence: 99%

Mechanical characterization of materials using advanced microscopy techniques

Suman Das,

Joyeshree Biswas,

Iqtiar Md Siddique

2024

This review explores the synergistic relationship between advanced microscopy techniques and mechanical engineering, outlining their profound impact on materials science and mechanical system design. We delve into the multifaceted applications of electron microscopy, X-ray diffraction, and spectroscopic methods in understanding microstructural dynamics, mechanical properties, and failure mechanisms of materials integral to mechanical engineering. Through a comprehensive synthesis of recent research, we emphasize the pivotal role these techniques play in optimizing material performance, bolstering structural integrity, and driving innovation in mechanical design. By elucidating the intricate details of material behavior at the microscale, advanced microscopy contributes to informed decision-making in material selection and design processes. Furthermore, we address emerging trends and prospects, underscoring the continued synergy between advanced microscopy and mechanical engineering. This collaboration remains at the forefront of materials science and technology, promising ongoing advancements that will shape the future landscape of mechanical design and materials innovation.