Article
Environmental Sciences
Christos Bountzouklis, Dennis M. Fox, Elena Di Bernardino
Summary: The percentage of wildfires with an undetermined origin is significant in Europe and Mediterranean France. Fire experts recognize the importance of documenting and researching fire causes for effective fire policies and prevention strategies. Machine learning is being used to classify fire ignitions based on environmental and anthropogenic features in Southern France, with factors such as spatiotemporal properties and topographic characteristics being considered important.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Engineering, Geological
Tengyuan Zhao, Chao Song, Shifeng Lu, Ling Xu
Summary: In rock engineering, determining the UCS is a crucial task which can be estimated from available index properties, but linear assumptions in model development often lead to inaccurate predictions of UCS.
ROCK MECHANICS AND ROCK ENGINEERING
(2022)
Article
Ergonomics
Dimitrios I. Tselentis, Eleonora Papadimitriou, Pieter van Gelder
Summary: Recent research in transport safety focuses on using intelligent systems to process large amounts of data and reduce accidents in transportation. Machine Learning (ML) and Artificial Intelligence (AI) applications have been developed to address safety problems and improve transportation efficiency. This paper reviews the use of ML and AI methods in different transport modes to identify good practices and experiences that can be shared. The most popular methods include ANN, SVM, Hidden Markov Models, and Bayesian models. Road transport mode has a wider variety and more studies compared to other modes.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Review
Chemistry, Multidisciplinary
Ady Suwardi, FuKe Wang, Kun Xue, Ming-Yong Han, Peili Teo, Pei Wang, Shijie Wang, Ye Liu, Enyi Ye, Zibiao Li, Xian Jun Loh
Summary: Biomaterials research has historically been hindered by long development periods, but the application of machine learning in materials science has greatly accelerated progress. The combination of machine learning with high-throughput theoretical predictions and experiments has shifted the traditional trial and error paradigm to a data-driven paradigm, which is driving the discovery and application of biomaterials.
ADVANCED MATERIALS
(2022)
Article
Computer Science, Artificial Intelligence
Su Nguyen, Binh Tran
Summary: This paper proposes a new approach called XMAP for developing AI systems that can provide accuracy and explanations. XMAP is highly modularised and provides interpretability for each step, achieving competitive predictive performance in classification tasks.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Yousaf Ayub, Yusha Hu, Jingzheng Ren
Summary: In this research, artificial intelligence algorithms were used to predict high quality syngas with better moles fractions of hydrogen and methane using hydrothermal gasification. Comparative analysis of different algorithms showed that XGB had the best prediction result. The study also found that temperature and resident time were the most important factors affecting the mole fractions of hydrogen and methane in the syngas, and higher hydrogen and oxygen contents in the biomass significantly contributed to the production of quality syngas.
Article
Energy & Fuels
Jennifer P. Spinti, Philip J. Smith, Sean T. Smith, Oscar H. Diaz-Ibarra
Summary: This paper describes the integration of Bayesian decision theory into a digital twin framework for optimizing the operation of a biomass boiler. The study focuses on the Atikokan Generating Station in Ontario, Canada. By using prior information, plant data, and science-based models, the researchers aim to determine an optimal operational setpoint for the boiler while accounting for uncertainties. The decision-making process involves defining the decision space, calculating outcome probabilities, creating a decision/cost model, identifying utility, and maximizing expected utility.
Review
Engineering, Marine
Rajakannu Amuthakkannan, K. Vijayalakshmi, Saleh Al Araimi, Maamar Ali Saud Al Tobi
Summary: Fishing wealth is a significant resource for Oman and plays a crucial role in its economy. The application of AI in boat automation technology is essential for addressing the challenges faced by fishermen and ensuring a safe and secure fishing experience.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Review
Food Science & Technology
Yuandong Lin, Ji Ma, Qijun Wang, Da-Wen Sun
Summary: There is an increasing interest in the role of nondestructive and rapid detection technologies in the food industry. Machine learning techniques have shown great potential in handling complex data and building calibration models. Novel nondestructive technologies, such as acoustic analysis, machine vision, electronic nose, and spectral imaging, combined with machine learning, have wide applications in food quality assessment.
CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION
(2023)
Article
Computer Science, Artificial Intelligence
Valerio La Gatta, Vincenzo Moscato, Marco Postiglione, Giancarlo Sperli
Summary: In this paper, a novel model-agnostic Explainable AI technique named CASTLE is proposed to provide rule-based explanations based on both the local and global model's workings. The framework has been evaluated on six datasets in terms of temporal efficiency, cluster quality and model significance, showing a 6% increase in interpretability compared to another state-of-the-art technique, Anchors.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Review
Medicine, General & Internal
Charlotte J. J. Haug, Jeffrey M. M. Drazen
Summary: This article introduces the history of artificial intelligence in medicine, its applications in image analysis, disease outbreak identification, and diagnosis, as well as the use of chatbots.
NEW ENGLAND JOURNAL OF MEDICINE
(2023)
Review
Computer Science, Information Systems
Seunghee Lee, Seonyoung Kim, Jieun Lee, Jong-Yeup Kim, Mi-Hwa Song, Suehyun Lee
Summary: Explainable AI (XAI) is a methodology that provides an explanation for artificial intelligence, and there is growing recognition of its importance in various fields. This study aims to identify the use of XAI in pharmacovigilance research. Among 781 papers, only 25 met the selection criteria. The study provides an intuitive review of the potential of XAI in pharmacovigilance and identifies key challenges in its implementation.
Article
Medicine, Research & Experimental
Harini Narayanan, Fabian Dingfelder, Itzel Condado Morales, Bhargav Patel, Kristine Enemaerke Heding, Jais Rose Bjelke, Thomas Egebjerg, Alessandro Butte, Michael Sokolov, Nikolai Lorenzen, Paolo Arosio
Summary: The article introduces a Bayesian optimization algorithm to accelerate the design of biopharmaceutical formulations, optimizing drug formulations in a small number of experiments. It also demonstrates the advantages of this method over traditional approaches, efficiently utilizing historical information and simultaneously optimizing multiple biophysical properties.
MOLECULAR PHARMACEUTICS
(2021)
Article
Engineering, Industrial
Bonnie Johnson
Summary: Advancements in computational thinking and data science have enabled artificial intelligence systems to adapt to complex situations and generate actionable knowledge. However, the increasing volume and variability of data pose challenges in terms of system development and implementation. Safety is a crucial concern for artificial systems supporting critical decisions, and methods are needed to prevent failures and ensure desired behavior.
Article
Engineering, Environmental
Kinga Szatmari, Sandor Nemeth, Alex Kummer
Summary: In this article, a resilience-based reinforcement learning approach is proposed to address the potential thermal runaway issue in batch reactors. By calculating the resilience metric for reactors and utilizing Deep Q-learning to decide when to intervene in the system, resilient-based mitigation systems can be effectively developed.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)
Article
Engineering, Chemical
Fereshteh Sattari, Dereje Tefera, Kaushik Sivaramakrishnan, Samir H. Mushrif, Vinay Prasad
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2020)
Article
Engineering, Chemical
Yewei Ni, Fereshteh Sattari, Lianne Lefsrud, Modusser Tufail
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2020)
Article
Engineering, Industrial
Daniel Kurian, Fereshteh Sattari, Lianne Lefsrud, Yongsheng Ma
Article
Engineering, Chemical
Fereshteh Sattari, Vinay Prasad
Summary: The study analyzed the hydrous pyrolysis of a physical mixture representing cellulose and lignin using FTIR and H-1-NMR spectroscopy, and developed a reaction network based on data-driven approaches. The data-driven reaction network was consistent with known chemistry of cellulose and lignin pyrolysis, incorporating elements of both reaction mechanisms.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2022)
Article
Engineering, Chemical
Aadil Khan, Fereshteh Sattari, Lianne Lefsrud, Modusser Tufail
Summary: The article aims to propose practical recommendations at the regional level using PSM principles to enhance safety and risk assessment for SCES. Suggestions include developing more detailed facility-specific licensing procedures and updating probability data sources used in the quantitative risk assessment processes.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2021)
Article
Engineering, Chemical
Hadiseh Ebrahimi, Fereshteh Sattari, Lianne Lefsrud, Renato Macciotta
Summary: This study classified and analyzed the causes of railway loss incidents in the Canadian railway industry using the Safety Management System (SMS) framework and Human Factors Analysis and Classification System (HFACS) approach. The research identified the importance of supervisory and organizational factors in preventing railway loss incidents and provided corresponding recommendations.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2021)
Article
Engineering, Chemical
Fereshteh Sattari, Lianne Lefsrud, Daniel Kurian, Renato Macciotta
Summary: The study aims to control and minimize the total number of incidents that occur within an oil and gas operation by applying a multidisciplinary approach to explore and develop Asset Integrity Management (AIM). This systematic approach can improve AIM to better understand Process Safety Management (PSM) as a whole and the underlying dynamics ever-present in the system.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2022)
Article
Engineering, Civil
Nafiseh Esmaeeli, Fereshteh Sattari, Lianne Lefsrud, Renato Macciotta
Summary: This study identifies the main causes and consequences of rail transportation accidents involving dangerous goods through detailed analysis, and highlights the gaps in safety management systems. Recommendations are provided for improving each element of the system.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Engineering, Chemical
Hadiseh Ebrahimi, Fereshteh Sattari, Lianne Lefsrud, Renato Macciotta
Summary: This study developed a procedure to estimate the risk of hazmat railway incidents and integrated population vulnerability in risk assessment. By simulating threat zones and preparing hazard maps, and creating vulnerability maps with the characteristics of the affected population, risk maps were generated. The procedure was tested in a city in Canada and confirmed the importance of considering population vulnerability in risk assessment.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2022)
Article
Engineering, Chemical
Fereshteh Sattari, Karthik Srinivasan, Anjana Puliyanda, Vinay Prasad
Summary: This work presents a methodology for generating reaction networks from spectroscopic data using data-driven methods. It is applied to the hydrothermal liquefaction (HTL) of Monterrey pine biomass and its constituents. The generated reaction network includes pathways representing decomposition of the biomass components, hydrolysis, and reformation of produced molecules, and is consistent with the literature. The data-driven approach provides a diagnostic tool for identifying the most probable reaction chemistry and can be used for process understanding, design, and control.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Public, Environmental & Occupational Health
Ariel Couture, Rose Marie Charuvil Elizabeth, Lianne Lefsrud, Fereshteh Sattari
Summary: This study investigated occupational exposure to respirable crystalline silica (RCS) in the road construction industry in Alberta. The highest exposure levels were found in the sand and gravel industry, and the lowest in concrete truck operators. Seasonal variability was also found to affect RCS exposure levels.
TOXICOLOGY AND INDUSTRIAL HEALTH
(2023)
Article
Engineering, Civil
Nafiseh Esmaeeli, Fereshteh Sattari, Lianne Lefsrud, Renato Macciotta
Summary: Canada's national rail network is crucial for transporting goods and passengers, with an annual transfer of over $320 billion worth of goods and 100 million passengers. Recent severe incidents have highlighted the need for increased awareness and risk assessment. This study focused on risk assessment using the Safety Risk Model (SRM) on the Canadian railway system, specifically main-track derailments and collisions with fatalities and injuries. The study found that the risk of main-track derailments was higher than collisions, and the risk to members of the public and employees formed the majority of individual risk. The introduction of Enhanced Train Control (ETC) showed potential in reducing the risk of derailments and collisions and improving railway safety in Canada.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Education, Scientific Disciplines
Marnie V. Jamieson, Lianne M. Lefsrud, Fereshteh Sattari, John R. Donald
Summary: Societal goals have shifted towards global sustainability concerns, diversity, and equity, leading to new demands on engineers and organizations. Sustainable engineering leadership and management focus on developing and operating complex designs in a sustainable manner, integrating safety and risk management, to better prepare students for future roles in a world demanding sustainable solutions.
EDUCATION FOR CHEMICAL ENGINEERS
(2021)
Article
Engineering, Civil
Fereshteh Sattari, Renato Macciotta, Lianne Lefsrud
Summary: The amount of dangerous goods transported by rail in Canada is increasing steadily, emphasizing the need for sustainable safety levels. This study provides insight into key occurrence types and causes for dangerous goods transport by rail, indicating adequate performance against reported lagging indicators.
TRANSPORTATION RESEARCH RECORD
(2021)
Article
Engineering, Industrial
Hao Sun, Haiqing Wang, Ming Yang, Genserik Reniers
Summary: To maintain continuous production, chemical plant operators may choose to ignore or handle faults online rather than shutting down process systems. However, the interaction and interdependence between components in a digitalized process system are significant, and faults can propagate to downstream nodes, potentially leading to risk accumulation and major accidents. This study proposes a dynamic risk assessment method that integrates the system-theoretic accident model and process approach (STAMP) with the cascading failure propagation model (CFPM) to model the risk accumulation process. The proposed method is applied to a Chevron refinery crude unit and demonstrates its effectiveness in quantifying the process of risk accumulation and providing real-time dynamic risk profiles for decision-making.
Article
Engineering, Industrial
M. Rempel
Summary: This article examines a major maritime disaster scenario and explores the evacuation process in such a situation. The study finds that there are various factors that affect the number of lives saved, including the uncertainty of individuals' medical condition, the arrival time of maritime and air assets, and the decision policies used. The authors formulate the multi-domain operation as a sequential decision problem using a modeling framework and provide decision support through a hypothetical case study.
Review
Engineering, Industrial
D. Scorgie, Z. Feng, D. Paes, F. Parisi, T. W. Yiu, R. Lovreglio
Summary: This study investigates the application and effectiveness of VR safety training solutions in various industries such as construction, fire, aviation, and mining. The findings suggest a need for more studies that adopt theories and measure long-term retention. Two meta-analyses demonstrate that VR safety training outperforms traditional training in terms of knowledge acquisition and retention.
Article
Engineering, Industrial
Shital Thekdi, Terje Aven
Summary: This paper examines biases in risk studies and investigates how to identify and address them to ensure high-quality risk analysis. By considering biases related to systematic error, event inclusion, models, and cognitive factors, the paper explores their influence on risk characterization. The insights gained from this exploration can be valuable to risk analysts, policymakers, and other stakeholders involved in risk study applications.
Article
Engineering, Industrial
Maryam Lari
Summary: Occupational health and safety (OHS) are crucial for employee well-being and productivity. This study examines the impact of OHS practices on employee productivity in a UAE Fire and Security company, finding that OHS interventions can enhance workplace ambiance and significantly boost employee productivity.
Review
Engineering, Industrial
Elleke Ketelaars, Cyrille Gaudin, Simon Flandin, Germain Poizat
Summary: This systematic literature review examines the literature on resilience training (RT), specifically focusing on the effectiveness of RT interventions in preparing professionals to effectively respond to critical situations. The review identifies five types of RT and suggests the need for conceptual advancements, vocational education and training perspectives, and a cross-disciplinary approach in future research to enhance resilience in safety-related domains.
Article
Engineering, Industrial
Meng Shi, Zhichao Zhang, Wenke Zhang, Yi Ma, Hanbo Li, Eric Wai Ming Lee
Summary: This study investigates pedestrian behaviours and evacuation processes in both fire and non-fire conditions using Minecraft. The results demonstrate the potential of Minecraft for realistically simulating evacuation processes, as the behaviours and flow patterns of pedestrians in virtual experiments fit well with real-life experiments. The study also shows that pedestrians exhibit fire avoidance behaviours and orderly queuing during a fire emergency, resulting in faster evacuation.
Article
Engineering, Industrial
Andrea Bikfalvi, Esperanza Villar Hoz, Gerusa Gimenez Leal, Monica Gonzalez-Carrasco, Nuria Mancebo
Summary: This paper proposes a solution for integrating occupational safety and health (OSH) into education, combining theoretical foundations and empirical evidence. The findings include analysis of teachers as stakeholders, barriers and facilitators of OSH integration, and the development of an ICT tool for interaction and sharing in this field. The main contribution lies in envisioning, orchestrating, and validating a solution to integrate OSH into schools and ultimately contribute to sustainable development goals.
Article
Engineering, Industrial
David Rehak, Alena Splichalova, Martin Hromada, Neil Walker, Heidi Janeckova, Josef Ristvej
Summary: This article discusses the adoption of a new directive on the resilience of critical entities and emphasizes the importance of assessing their level of resilience in relation to current security threats. The authors have developed a tool, known as the CERFI Tool, which uses a probabilistic algorithm to predict the failure point of critical entity resilience based on the relationship between threat intensity and protection. The tool is important for increasing the safety of technically oriented infrastructures, particularly in the energy and transport sectors.
Review
Engineering, Industrial
Leonardo Leoni, Ahmad Bahootoroody, Mohammad Mahdi Abaei, Alessandra Cantini, Farshad Bahootoroody, Filippo De Carlo
Summary: This paper presents a systematic bibliometric analysis (SBA) on the research of machine learning and deep learning in the field of safety. The main research areas, application fields, relevant authors and studies, and temporal evolution are investigated. It is found that rotating equipment, structural health monitoring, batteries, aeroengines, and turbines are popular fields, and there is an increase in popularity of deep learning and new approaches such as deep reinforcement learning.
Article
Engineering, Industrial
Tom Becker, Peter Ayton
Summary: By analyzing global civil aviation data, we found that there is a significant increase in the number of accidents and safety critical incidents, as well as the fatalities, when the Pilot-in-Command acts as the Pilot Flying instead of the Pilot Monitoring. Most of these events occurred in technically airworthy aircraft without any emergencies, and the flight crew assessed them as preventable. These findings align with the crew assignment effect, suggesting that role-dependent status hierarchy and cognitive overload contribute to ineffective flight crew teamwork. The measures implemented to enhance flight crew teamwork, such as Crew Resource Management training, have not been successful in preventing these issues.
Article
Engineering, Industrial
Ben Hutchinson, Sidney Dekker, Andrew Rae
Summary: This study found that health and safety audits often fail to identify critical deficiencies, with corrective actions mainly focusing on superficial fixes rather than addressing significant operational risks.
Review
Engineering, Industrial
Sina Rasouli, Yaghoub Alipouri, Shahin Chamanzad
Summary: Construction projects are risky environments, but the development of Personal Protective Equipment (PPE) and comprehensive safety management can effectively control the number of accidents.
Article
Engineering, Industrial
Laura Mills, Verity Truelove
Summary: This study investigated the use of police location communities (PLCs) for obtaining information about roadside drug testing (RDT) among drivers, and found that drivers who used PLCs were more concerned about being caught for drug driving, had a better understanding of the related penalties and procedures, and knew others who also used PLCs to avoid detection for drug driving. Furthermore, the study found that the use of PLCs was associated with choosing back roads for driving, which may reduce the risk of detection for drug driving.
Review
Engineering, Industrial
Chao Wu, Xi Huang, Bing Wang
Summary: After decades of development, the safety discipline in China has made remarkable progress. The project led by the Safety & Security Theory Innovation and Promotion Center of Central South University has filled the gaps in safety science education by creating textbooks and courses for postgraduate students. These achievements have played an important role in the development of safety science and can serve as a reference for basic research and talent training in safety science globally.