A data-driven conceptual framework for understanding the nature of hazards in railway accidents

Hazards threaten railway safety by their potential to trigger railway accidents. Whilst there are a considerable number of prior works investigating railway hazards, few offer a holistic view of hazards across jurisdictions and time and demonstrate policy implementation due to the inability to analyse a large amount of safety-related textual data. The conceptual framework HazardMap is developed to overcome this gap, employing open-sourced Natural Language Processing topic model BERTopic for the automated analysis of textual data from Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB) railway accident reports. The topic modelling depicts the relationships between hazards, railway accidents and investigator recommendations and is further extended and integrated with the existing risk theory and epidemiological accident models. Results show that each hazard in the railway system has different aspects and could trigger a railway accident when combined with other hazards. Each aspect can be partially or fully addressed by implementing hazard mitigation policies such as introducing new technologies or regulations. A case study of the application to the risk at level crossings is provided to illustrate how HazardMap works with real-world data. This demonstrates a high degree of coverage within the existing risk management system, indicating the capability of helping policymaking for managing risks with adequate accuracy. The primary contributions of the framework proposed are to enable a huge amount of knowledge accumulated for an intuitive policymaking process to be summarised, and to allow other railway investigators to leverage lessons learnt across jurisdictions and time with limited human intervention. Future research could incorporate data from road, aviation or maritime accidents

A Reinforcement Learning-based Framework for Proactive Supply Chain Risk Identification

Over the past few decades, global supply chains (GSCs) have seen a significant increase with the widespread adoption of digital technologies and improved trade policies. GSCs are a network of organisations or individuals across the world involved in producing and delivering goods and services to customers. While this globalisation and the use of global technologies have increased the efficiency of supply chain operations, it has also exposed them to various additional uncertainties and risk types that can negatively impact their operations. Thus, for GSCs to function properly, such uncertainties must be managed. Hence, supply chain risk management is critical in the smooth operation of GSCs. The first task in supply chain risk management is risk identification, where risk managers identify the risk events that may negatively impact their operations for further analysis. It is crucial that risk identification is undertaken in a timely manner so that risk managers can be proactive in managing the possible impacts of the identified risks on their operations. This task can be done manually which is tedious and time-consuming, however, with the increased sophistication and capability of artificial intelligence (AI), there is a potential for AI algorithms to be used to enhance the efficacy and efficiency of this task. A review of the existing literature detailed in this thesis highlights that while AI has been widely employed in different disciplines, it has shortcomings which are specific to the area of risk identification in supply chains. In other words, the majority of the existing risk identification techniques in supply chain risk management are either reactive or predictive in their working nature. This means that such techniques either identify the risk events after they occur or predict future occurrences of the known risk events based on their past pattern of occurrences. However, as emphasised in this thesis, for the supply chain risk identification process to be effective and comprehensive, it has to be proactive in its working nature rather than reactive or predictive. By being proactive, the risk identification techniques aim to identify beforehand known or unknown events of risks that have the potential to occur and negatively impact an activity. The analysis obtained assists the risk manager to perform the steps of risk analysis and risk evaluation on the identified risks before developing plans to manage them. Existing literature on supply chain risk identification lacks techniques to achieve this aim. To address this gap in the literature, this thesis develops a framework, namely Reinforcement Learning-based Supply Chain Risk Identification, which assists risk managers in automatedly and accurately identifying the risk events that may have the potential to impact their operations and bring them to his/her attention for further follow up. The proposed framework adopts the science and engineering research approach and four different frameworks are developed that identify the risk events of interest to the risk manager, extract related news articles on these risk events and analyse them, before recommending the most important news articles to the risk manager for follow-up actions. The functionality and viability of these prototypes are validated by experiments and systematised by a supply chain case study to highlight their effectiveness

MODELLING AND SYSTEMATIC ASSESSMENT OF MARITIME CONTAINER SUPPLY CHAIN RISKS

Maritime container supply chains (MCSCs) is exposed to various risks arising from both internal operations and the external environment, and the increasing complexity of the modern global logistics system makes the situation even worse, thus causing a significant challenge to the effective risk management of MCSCs. However, systematic studies on this topic are relatively few. In view of this, this study aims to explore and analyse various MCSC risks, develop suitable risk assessment methods, and evaluate the overall performance of MCSCs from a systematic perspective, so as to ensure the safety, reliability, and resilience of MCSCs. This research starts with the identification and classification of all possible risk factors that may be involved in an MCSC based on a comprehensive literature review, and the research results are further validated through a Delphi expert survey. The identified risk factors are then analysed, screened, and assessed in detail. The novelty of this study lies not only on the risk assessment of MCSCs under an uncertain environment from a supply chain level but also on the consideration of the impact of risk condition of each individual MCSC on the overall performance of the entire container supply network. The research results will provide useful insights and valuable information for both researchers and practitioners on the risk analysis and assessment of MCSCs, which is beneficial to different types of stakeholders involved in the maritime shipping industry. The work is also able to provide a theoretical foundation for risk-based decision making and shipping route optimisation in further work. Although the risk assessment methods are presented on the basis of the specific context in MCSCs, it is believed that, with domain-specific knowledge and data, they can also be tailored for a wide range of applications to evaluate the reliability and performance of other supply chain systems, especially where a high level of uncertainty is involved

Simulation combined model-based testing method for train control systems

A Train Control System (TCS) is utilised to guard the operational safety of the trains in railway systems. Therefore, functional testing is applied to verify consistency between the TCS and specification requirements. Traditional functional testing in TCSs is mainly based on manually designed test cases, which is becoming unsuitable for testing increasingly complex TCSs. Therefore, Model-Based Testing (MBT) methods have been introduced into TCS functional testing, to improve the efficiency and coverage of TCS testing, with application difficulties. To overcome the difficulties of applying MBT methods to test TCSs, the author introduces simulation combined MBT which combines an MBT method with simulation. Modelling method and implementation method for the proposed approach were explained in detail. Two case studies were undertaken to explore the effectiveness of the testing platform developed. The testing results obtained prove that the testing platform can be utilised to implement the functional testing of TCSs. To prove that the MBT platform is effective in detecting errors in the SUT, validation and verification was undertaken, which include validation of specification requirements and verification of the MBT platform. The testing performance is proven to be better than existing MBT methods in terms of coverage and efficiency

The 2nd International Electronic Conference on Applied Sciences

This book is focused on the works presented at the 2nd International Electronic Conference on Applied Sciences, organized by Applied Sciences from 15 to 31 October 2021 on the MDPI Sciforum platform. Two decades have passed since the start of the 21st century. The development of sciences and technologies is growing ever faster today than in the previous century. The field of science is expanding, and the structure of science is becoming ever richer. Because of this expansion and fine structure growth, researchers may lose themselves in the deep forest of the ever-increasing frontiers and sub-fields being created. This international conference on the Applied Sciences was started to help scientists conduct their own research into the growth of these frontiers by breaking down barriers and connecting the many sub-fields to cut through this vast forest. These functions will allow researchers to see these frontiers and their surrounding (or quite distant) fields and sub-fields, and give them the opportunity to incubate and develop their knowledge even further with the aid of this multi-dimensional network