Article
Engineering, Chemical
Esmaeil Zarei, Kamran Gholamizadeh, Faisal Khan, Nima Khakzad
Summary: Rail transport of hazardous material is crucial in the supply chain, but poses severe risks, with the domino effect being a major concern. This study introduces a dynamic risk analysis model based on Dynamic Bayesian Network to analyze domino effects in RTHM, demonstrating its effectiveness through real-world testing with gasoline transportation.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2022)
Review
Management
Seyed Sina Mohri, Mehrdad Mohammadi, Michel Gendreau, Amir Pirayesh, Ali Ghasemaghaei, Vahid Salehi
Summary: This paper provides a comprehensive review of hazardous material transportation from an Operational Research perspective, with a focus on hazmat routing, routing-scheduling, and network design problems. The paper reviews the assumptions, objectives, constraints, and solutions of the models, along with case studies. It also highlights the challenges and features of designing models for different transportation modes, and identifies research gaps and future directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Hao Hu, Jiaoman Du, Xiang Li, Changjing Shang, Qiang Shen
Summary: Reasonable risk models in hazardous material transportation are crucial for protecting lives, properties, and the environment. This article introduces two novel risk models using different aggregation methods, one supported by an ordered weighted averaging operator and the other by a state variable weight vector. These models effectively balance overall risk with local risks, depending on the completeness of weighting information.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Engineering, Civil
Onur Derse, Murat Oturakci, Cansu Dagsuyu
Summary: This study aims to evaluate the hazards and risks associated with different modes of hazardous material transportation using integrated approaches such as fault tree analysis, DEMATEL, TOPSIS, and considering factors like speed, cost, and capacity. Ultimately, highway, marine and inland, airway, and railway transportation modes are ranked based on calculated risk scores and influencing parameters.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Environmental Sciences
Ertugrul Ayyildiz, Alev Taskin Gumus
Summary: The distribution, diffusion, and conversion processes of chemicals in hazardous materials can pose risks to the environment and social life. Various precautions should be taken during the transportation of hazardous materials to identify and minimize risks. This study focuses on identifying critical risk factors and their weights in hazardous material transportation operations.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Engineering, Industrial
Jinxian Weng, Xiafan Gan, Zheyu Zhang
Summary: This study develops a quantitative risk assessment (QRA) model for evaluating hazmat transportation accident risk, combining the frequency and consequence of possible accident scenarios. The model utilizes an event tree to identify various factors contributing to accidents, and presents a mathematical model to evaluate the quantitative consequence in terms of fatalities. A case study using Shanghai's hazmat transportation data confirms the model's capability in assessing hazmat transportation accident risk.
Article
Engineering, Industrial
Guoqi Li, Gang Pu, Jiaxin Yang, Xinguo Jiang
Summary: This study develops a multidimensional quantitative risk assessment framework for evaluating the risk of hazardous materials (HazMat) vehicles in dense urban areas. Through a case study and the use of simulation and GIS technology, the spatial distribution of different risk levels is visualized.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Management
Nishit Bhavsar, Manish Verma
Summary: This study investigates the use of subsidies as a risk mitigation tool for hazardous materials transportation by rail. By developing an optimization model and a customized solution technique, the study shows the significance of subsidies in reducing risk and ensuring fair distribution of risk.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Lukai Zhang, Yingping Wang, Peng Zhang
Summary: This research proposes a design approach by hybrid optimization, combining qualitative assessment and quantitative planning. It uses fuzzy calibration to determine the ranking index of each candidate station and integrates them into the optimization model. A numerical algorithm is proposed to verify the effectiveness of the model and a heuristic algorithm is used for large-scale cases. A case study in the railway freight network in north China is conducted. This research provides a theoretical basis and operating reference for railway transportation of hazardous materials.
APPLIED MATHEMATICAL MODELLING
(2022)
Article
Engineering, Environmental
Jinkun Men, Guohua Chen, Lixing Zhou, Peizhu Chen
Summary: This study addresses the issue of considering multiple simultaneous HazMat shipments and proposes a multi-objective transportation network design model. The proposed algorithm is competitive in solving large-scale instances and effectively coordinates multiple HazMat transportation processes.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2022)
Article
Green & Sustainable Science & Technology
Ming Sun, Ronggui Zhou
Summary: This study analyzes ten years of hazardous material (HAZMAT) truck-involved crash data to identify patterns and associations between risk factors. The study finds distinguishable characteristics in terms of collision types, roadway geometry, driver behavior, lighting conditions, and adverse weather. The findings will assist HAZMAT carriers, transportation management authorities, and policymakers in developing targeted countermeasures to reduce HAZMAT-truck-involved crashes and improve safety.
Article
Engineering, Chemical
Jinhua Cheng, Bing Wang, Chenxi Cao, Ziqiang Lang
Summary: In recent years, there has been increased attention on transportation accidents involving hazardous materials. Previous studies mainly focused on single-vehicle accidents. However, when vehicles carrying hazardous materials gather on a road, potential domino accidents can occur and lead to severe incidents. This paper proposes a quantitative risk assessment (QRA) model that uses a dynamic Bayesian network (DBN) to calculate the probability of leakage and explosion of hazardous chemicals. The model also uses event trees to analyze different scenarios and the probability of domino accidents caused by each scenario. The FN-curve and potential loss of life (PLL) are utilized as metrics to evaluate social risk. A case study in the JinShan District, Shanghai, involving multiple vehicles is analyzed, and the results show that the driver's state, road type, weather factors, and vehicle distance have significant impacts on the societal risk associated with hazardous materials transportation accidents.
Article
Engineering, Industrial
Jian Guo, Cheng Luo, Kaijiang Ma
Summary: This paper studies the risk coupling problems in hazardous material transportation safety in coastal areas by analyzing 362 accidents. The results show that the risk of hazardous materials transportation has a gradually increasing trend with the increase of risk coupling factors. The risk coupling of driver factors and road environmental factors leads to a high probability of accidents.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Chemical
Wanke Han, Tijun Fan, Shuxia Li, Liping Liu
Summary: This study proposes a new multimodal network model that considers a detour strategy and uncertainty in hazmat transportation. The study demonstrates through a case study using simulated data that changes in demand uncertainty and transit discount factor affect the total cost and risk of the multimodal hub network, thus influencing carrier's location and route decisions. The new model can effectively control and mitigate risk, making it more suitable for hazmat transportation.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2023)
Article
Engineering, Industrial
Yuntao Li, Yumeng Wang, Yuying Lai, Jian Shuai, Laibin Zhang
Summary: During the road transportation of hazardous chemicals, both random and centralized parking strategies have severe consequences. This study proposes a novel zone parking strategy that separates vehicles based on their cargo, significantly reducing the risk associated with hazardous chemicals.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Information Systems
K. Lakshmi Narayanan, R. Santhana Krishnan, Le Hoang Son, Nguyen Thanh Tung, E. Golden Julie, Y. Harold Robinson, Raghvendra Kumar, Vassilis C. Gerogiannis
Summary: Robotics is a rapidly evolving technology that has wide applications, especially in smart hospital environments. Key challenges for Autonomous Nursing Robots include path planning, patient body parameter measurement using sensors, and patient interaction. This paper presents an approach for a comprehensive Autonomous Nursing Robot system that addresses these challenges effectively.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Gayathri Soman, M. V. Vivek, M. V. Judy, Elpiniki Papageorgiou, Vassilis C. Gerogiannis
Summary: This paper focuses on emotion recognition and proposes a distributed ensemble model for emotion classification. The results show that the proposed ensemble model achieves high accuracy in emotion classification.
Article
Mathematics
Jayashree Piri, Puspanjali Mohapatra, Biswaranjan Acharya, Farhad Soleimanian Gharehchopogh, Vassilis C. Gerogiannis, Andreas Kanavos, Stella Manika
Summary: This article introduces a novel discrete artificial gorilla troop optimization (DAGTO) technique for feature selection tasks in the healthcare sector. The presented method is proven to be superior in identifying relevant features and is demonstrated to be effective in real-world applications through a case study with COVID-19 samples.
Article
Chemistry, Multidisciplinary
Efthymios N. Lallas, Ilias Santouridis, Georgios Mountzouris, Vassilis C. Gerogiannis, Anthony Karageorgos
Summary: This paper proposes a method that utilizes semantic web technologies to represent pharmaceutical manufacturing data in a unified manner and evaluate their ALCOA compliance systematically. The method uses semantic annotations and reasoning to assess ALCOA compliance.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Debabrata Swain, Santosh Satapathy, Biswaranjan Acharya, Madhu Shukla, Vassilis C. Gerogiannis, Andreas Kanavos, Dimitris Giakovis
Summary: This study proposes an approach for recognizing yoga poses using deep learning algorithms, combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The system can provide real-time feedback on yoga poses through monitoring videos.
Article
Computer Science, Artificial Intelligence
K. Haritha, M. Judy, Konstantinos Papageorgiou, Vassilis C. Georgiannis, Elpiniki Papageorgiou
Summary: The features of a dataset are crucial in constructing a machine learning model. By removing irrelevant features, the classification process can be expedited. The proposed distributed fuzzy cognitive map method exhibits high accuracy in feature selection.
Article
Computer Science, Information Systems
Debabrata Swain, Utsav Mehta, Ayush Bhatt, Hardeep Patel, Kevin Patel, Devanshu Mehta, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos, Stella Manika
Summary: Clinical support systems are affected by the issue of high variance in chronic disorder prognosis, leading to the demise of populations suffering from fatal diseases like chronic kidney disease. Machine learning can reduce randomness in clinical decision making. This study develops a machine-learning model using publicly available data to forecast chronic kidney disease occurrence.
Article
Chemistry, Analytical
Cheena Mohanty, Sakuntala Mahapatra, Biswaranjan Acharya, Fotis Kokkoras, Vassilis C. Gerogiannis, Ioannis Karamitsos, Andreas Kanavos
Summary: Diabetic retinopathy (DR) is a common complication of long-term diabetes that can cause permanent blindness. This study proposes two deep learning architectures, a hybrid network and DenseNet 121 network, for the detection and classification of DR. The DenseNet 121 model achieved superior performance and has potential for early detection and classification of DR.
Article
Computer Science, Information Systems
Satyananda Champati Rai, Samaleswari Pr Nayak, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos, Theodor Panagiotakopoulos
Summary: In recent years, there has been a significant increase in the number of cars in urban areas, particularly in India. This has led to congested traffic systems during peak hours due to challenges such as overloaded vehicles, reckless driving, and disregard for road safety rules. To address this issue, this paper proposes an IoT-based Intelligent Traffic Signal System (ITSS) that uses inductive loops and a programmable micro-controller to determine traffic density. This system synchronizes the traffic light timer with real-time traffic density for smoother vehicle flow and also prioritizes emergency vehicles using infrared sensors. The presented solution improves traffic flow and reduces delays for travelers compared to fixed systems.
Review
Computer Science, Artificial Intelligence
Jayashree Piri, Puspanjali Mohapatra, Raghunath Dey, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos
Summary: This study critically evaluates existing hybrid feature selection approaches and provides a comprehensive literature review on the hybridization of different metaheuristic/evolutionary strategies used for supporting feature selection. The study also identifies new research issues and challenges to pinpoint areas that need further exploration.
Article
Engineering, Electrical & Electronic
Abhishek Kadalagere Lingaraju, Mudligiriyappa Niranjanamurthy, Priyanka Bose, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos, Stella Manika
Summary: The large human population and rapid urbanization lead to a significant amount of garbage that needs daily collection. Improper disposal of garbage in urban areas creates an unsanitary environment and degrades the environment. To address this issue, a Smart Waste Management System is proposed, using machine learning techniques to classify air quality levels and enhance waste management productivity. This system not only proves cost-effective but also improves the well-being of individuals residing near dustbins by providing constant monitoring and reporting of air quality to city authorities.
Article
Computer Science, Artificial Intelligence
Shubham Mathesul, Debabrata Swain, Santosh Kumar Satapathy, Ayush Rambhad, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos
Summary: In this research paper, a deep learning approach based on Convolutional Neural Networks (CNNs) is proposed to enhance the detection of COVID-19 from chest X-ray images. By extracting the most significant features from the X-ray scans, the model achieved a promising accuracy of up to 97% in detecting COVID-19 cases, aiding physicians in effectively screening and identifying probable patients.
Article
Computer Science, Information Systems
Manav Garg, Pranshav Gajjar, Pooja Shah, Madhu Shukla, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos
Summary: This paper presents a comprehensive comparison between deep learning architectures and vision transformers in the field of musical key estimation. The results show that DenseNet achieves remarkable accuracy, but vision transformers demonstrate superior performance in temporal metrics. The findings contribute to accurate and efficient algorithms for music recommendation systems and automatic music transcription, providing valuable insights for practical implementations.
Proceedings Paper
Computer Science, Software Engineering
Dimitrios Chloros, Vassilis C. Gerogiannis, George Kakarontzas
Summary: This study examines the reasons and purposes of using software and project management metrics in agile software development methodologies. The findings show that metrics are used to improve agile processes, comply with protocols, enhance software and source code quality, optimize estimation and planning, and increase productivity.
PROCEEDINGS OF 2022 THE 3RD EUROPEAN SYMPOSIUM ON SOFTWARE ENGINEERING, ESSE 2022
(2022)
Article
Mathematics, Applied
Do Ngoc Tuyen, Tran Manh Tuan, Xuan-Hien Le, Nguyen Thanh Tung, Tran Kim Chau, Pham Van Hai, Vassilis C. Gerogiannis, Le Hoang Son
Summary: This paper proposes a novel approach for precipitation nowcasting using a combination of the UNet segmentation model and the PredRNN_v2 deep learning model. The proposed model significantly reduces the number of calculated operations and processing time, while maintaining reasonable errors in the predicted images.
Article
Engineering, Chemical
Chao Chen, Hang Chen, Li Mo, Shenbin Xiao, Changjun Li, Ming Yang, Genserik Reniers
Summary: Fire-induced domino effect is a major threat to hazardous material storage tanks. Previous research neglected the impact of wind load on the thermal buckling behavior of tanks exposed to fire. This paper conducts a numerical simulation to analyze the synergistic effects of fire and wind loads on the thermal post-buckling behavior of storage tanks, considering wind parameters and heat radiation parameters.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2024)
Article
Engineering, Chemical
Zhang Chu, Liu Lili, Li Wei, Li Beibei, Liu Mingxing
Summary: This study predicts the fatigue life of blasting discs using a combined simulation method and measures the deformation of conventional slotted blasting discs subjected to repeated loading using a high-precision laser scanning method. A relationship model is established between deformation, loading pressure, cycle number, and change in blasting pressure. The results show that the deformation varies linearly with the cycle number and follows a power function relationship with the change in blasting pressure. The deformation can be used to accurately predict the fatigue life of blasting discs.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2024)
Article
Engineering, Chemical
Shin-Mei Ouyang, Jia-Chi Ye, Wei-Chun Chen, Yu-Jen Chen, Chin-Feng Chen
Summary: This study employed the Taguchi method and analysis of variance to investigate the factors that influence the deterioration of pyrotechnics when stored under different humidity and temperature conditions. Various experimental methods were used to study the microstructure, heat flow, and safety of the pyrotechnics. The results showed that humidity had a significant impact on the pyrotechnics, and controlling humidity could reduce the hazards associated with storing pyrotechnics.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2024)
Article
Engineering, Chemical
Bei Li, Huiqiang Liu, Heng Gao, Chi-Min Shu, Mingshu Bi
Summary: The moisture content sprayed into the dust layer in industrial sites is a main factor affecting reignition. The study found that moisture has a certain influence on the thermophysical properties of the dust layer, playing an important role in heat transfer and heat absorption processes. The results showed that as the moisture content increased, the thermal conductivity and heat sink density of the sample increased, indicating the need to have a heat sink density higher than the heat increase value to suppress ignition.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2024)
Article
Engineering, Chemical
Yue Jing, Yong Pan, Fan Yang, Dan Wei, Wenhe Wang
Summary: A comprehensive risk assessment method for the esterification process was proposed in this study. It established a comprehensive evaluation index system and utilized improved Dempster-Shafer evidence theory and cloud model for assessment. The method was validated through case studies and showed consistency with established indexes.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2024)
