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• The framework is proposed using security-driven and safety-oriented methods. • SSCA is constructed through both top-down and bottom-up perspectives. • The regulatory development of SSCA is analysed among different transport modes. • It strikes new research directions for digitalised and autonomous transport vehicles. Transportation is sensitive to risk. Given the fast development of digitalisation and automation of transport systems in the past decade, new types of security risks (e.g. cyberattacks) emerge within the context of transport safety research. To enable the integrated analysis of emerging security and classical safety-related risks in a holistic manner, safety and security co-analysis (SSCA) is highly demanded for accident prevention. SSCA in transport systems will benefit the risk analysis of complex cyber physical transport systems facing challenges from both hazards and threats. However, the nature of hazard and threat-based risks is fundamentally different, which leads to the various difficulties of analysing them on the same plane. They include the use of different risk parameters, the uncertainty levels of the risk input and the methodologies of risk inference. To address such concerns, this study firstly reviews the literature on SSCA and compares the employed methodologies and their applications within the context of transport systems. Taking into account the advantages of both security-driven and safety-oriented methods, a conceptual framework is proposed to imply the insights on SSCA for transportation through both top-down and bottom-up perspectives, followed by a quantitative illustrative case study. Then, the regulatory development and evolution of SSCA in transport in practice is analysed across different transport modes, which configures initiatives' interrelations for a cross-fertilisation purpose. As a result, the findings reveal new research directions for the safety of digitalised and/or autonomous transport vehicles and aid in the formation of future transport safety study agendas. [ABSTRACT FROM AUTHOR]

Maritime autonomous surface ships (MASS) promise enhanced efficiency, reduced human errors, and to improve maritime traffic safety. However, MASS navigation in complex maritime traffic presents challenges, especially in collision avoidance strategy optimization (CASO). This paper proposes a novel risk-based CASO approach based on safe reinforcement learning (SRL) with a reliability and risk hierarchical critic network (SRL-R2HCN) approach. Key steps in developing the approach start with the formulation of collision risk assessment. This is followed by the construction of a hierarchical network structure, supplemented by the supporting reward function, multi-objective function, and reliability measurement to realize the SRL-R2HCN. Finally, simulation experiments are conducted in mixed obstacle scenarios, and the results are compared with traditional algorithms to showcase the advancement and fidelity of the new SRL-R2HCN method. The results demonstrate that the proposed method can accurately assess collision risks in mixed obstacle scenarios and generate safe, efficient, and reliable collision avoidance strategies. The outcomes of this research provide a sound theoretical basis for the future development of MASS navigation safety and significant potential to improve the safe and efficient operations of MASS. Furthermore, the methodology could also benefit maritime transportation and shipping management. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly affected the cruise shipping industry, disrupting ports and shipping. However, current research predominantly focuses on the impact on individual ports or vessels, leaving a gap in understanding how these disruptions propagate across cruise shipping networks. To address this gap, a novel vulnerability assessment methodology that offers a comprehensive perspective on the broader impact of COVID-19 on cruise shipping networks is developed. It first uses a new weight social network analysis approach to quantify the vulnerability of each cruise port in a shipping network and then combines the curie port local pandemic risk to generate a new index to reveal the COVID-19 impact on the whole cruise shipping network systematically. The new methodology is applied to analyse the ASEAN cruise shipping network. This real-world COVID-19 pandemic case study yields valuable insights that bridge theoretical and practical domains. Integrating local port-level vulnerabilities with shipping network-level vulnerabilities creates a unique index. This index quantifies the individual and collective influence of COVID-19 risks at different cruise ports on the entire regional cruise shipping network. The results directly impact cruise lines seeking to enhance their operations' resilience in the face of COVID-19 challenges. The vulnerability index explains how risk exposure at various ports shapes the network's dynamics. This insight empowers cruise lines to optimise ship deployment schedules, lowering the network's overall COVID-19 pandemic risk. The research method and outcomes offer a pioneering perspective on the vulnerability of cruise shipping networks to COVID-19 disruptions, and other possible disruptions (e.g., climate change) in a broad sense. By elucidating interconnected vulnerabilities, cruise lines are equipped with actionable insights to navigate the complexities of global challenges. [ABSTRACT FROM AUTHOR]

As an administrative measure to ensure maritime safety, Port State Control inspections are implemented and regarded as an important line of defence in coping with potential maritime accidents. To reinforce its role, a New Inspection Regime (NIR) was developed and put into practice by Paris MoU in 2011. It is widely recognized that the implementation of NIR has transformed and modernized the PSC inspection system in the Paris MoU region, stimulated the vessel qualit, and further improved vessel quality. In this paper, the influence of the implementation of NIR on the PSC inspection system and vessel quality is revealed for the first time. Based on inspection data and records collected from the Paris MoU online database, a comparative analysis between the 'Pre-NIR' (time before the implementation of NIR) and 'Post-NIR' (time after the implementation of NIR) periods is conducted from two perspectives. A macroscopic approach is first adopted to characterise the overall changes in inspection results and inspected vessels' quality through a statistical analysis of extrinsic Key Performance Indicators (KPIs) such as detention rate and deficiency rate. Then, a microscopic analysis is conducted to assess the influence of the NIR on intrinsic attributes of the vessels and of the inspection results based on Bayesian Network models derived from Pre-NIR and Post-NIR periods. The findings of this research systematically reveal the aspects from which the NIR improves the PSC inspection system, vessel quality and maritime safety. It will generate significant impact on and contribution to the promotion and stimulation of the adoption of NIR in more ports and regions to improve safety at sea in the whole world. • Conduct a comparative analysis of the impact of New Inspection Regime on port state control inspection. • Characterise the changes in PSC inspections and inspected vessels' quality through a statistical analysis of extrinsic KPIs. • Assess the influence of the NIR on intrinsic attributes of the vessels and the inspections based on Bayesian Network models. • Reveal the aspects from which the NIR improves the PSC inspection system, vessel quality and maritime safety. [ABSTRACT FROM AUTHOR]

• Utilisation of historical records and machine learning to generate a human fatigue model. • Identification of significant factors from an objective accident/incident occurrence perspective. • Combination of LASSO and BN to propose a data-driven model to investigate human fatigue. • Providing solution to fatigue research with limited psychological data. In maritime transport, fatigue conditions can impair seafarer performance, pose a high risk of maritime incidents, and affect safety at sea. However, investigating human fatigue and its impact on maritime safety is challenging due to limited objective measures and little interaction with other risk influential factors (RIFs). This study aims to develop a novel model enabling accident data-driven fatigue investigation and RIF analysis using machine learning. It makes new methodological contributions, such as 1) the development of a human fatigue investigation model to identify significant RIFs leading to human fatigue based on historical accident and incident data; 2) the combination of least absolute shrinkage and selection operator (LASSO) and bayesian network (BN) to formulate a new machine learning model to rationalise the investigation of human fatigue in maritime accidents and incidents; 3) provision of insightful implications to guide the survey of fatigue's contribution to maritime accidents and incidents without the support of psychological data. The results show the importance of RIFs and their interdependencies for human fatigue in maritime accidents. It takes advantage of available knowledge and machine learning to open a new direction for fatigue management, which will benefit the maritime fatigue investigation and provide insights into other high-risk sectors suffering from human fatigue (e.g. nuclear and offshore). [ABSTRACT FROM AUTHOR]

Abstract This study proposes a measurement instrument for port performance in the context of container transport logistics (CTLs) by taking perspectives from different port stakeholders. An importance-performance analysis (IPA) is used to develop an analytical tool for investigating the importance and performance (IP) of major container ports in South Korea against individual CTLs criterion. The main originality of this study is the development of a measurement instrument to provide managerial and operational insights to both port managers (i.e. terminal operating companies) and policy makers (i.e. port authorities and government) for stakeholder management in CTLs. The analysis helps port managers and policy makers to converge the different objectives and concerns for better management. Highlights • Importance-performance analysis for port performance in the context of container transport logistics. • Taking different port stakeholders' perspective from port managers and policy makers. • Introduces both quantitative and qualitative port performance indicators. • Container ports from South Korea as examples. [ABSTRACT FROM AUTHOR]

Highlights • Analysis of accident severity in waterborne transportation. • Collection and statistical analysis of maritime accident data over 30 years. • Identification of key factors influencing maritime accident severity using data-driven BN and expert verification. • Dynamic prediction of maritime accident severity under high uncertainties. Abstract The rapid development of the shipping industry requires the use of large vessels carrying high-volume cargoes. Accidents incurred by these vessels can lead to a heavy loss of life and damage to the environment and property. As a leading country in international trade, China has developed its waterway transport systems, including inland waterways and coastal shipping, in the past decades. A few catastrophic shipping accidents have occurred during this period. This paper aims to develop a new risk analysis approach based on Bayesian networks (BNs) to enable the analysis of accident severity in waterborne transportation. Although the risk data are derived from accidents that occurred in China's waters, the risk factors influencing accident severity and the risk modelling methodology are generic and capable of generating useful insights on waterway risk analysis in a broad sense. To develop the BN-based risk model, waterway accident data are first collected from all accident investigation reports by China's Maritime Safety Administration (MSA) from 1979 to 2015. Based on the derived quantitative data, we identify the factors related to the severity of waterway accidents and use them as nodes of the risk model. Second, based on a receiver operating characteristic (ROC) curve, an augmented naïve BN (ABN) model is selected through a comparative study with a naïve BN (NBN) model to analyse the key risk factors influencing waterway accident severity. The results show that the key factors influencing waterway safety include the type and location of the accident and the type and age of the ship. Moreover, a novel scenario analysis is conducted to predict accident severity in various situations by combining different states (e.g., high risk) of the key factors to generate useful insights for accident prevention. More specifically, the findings can aid transport authorities, ship owners and other stakeholders in improving waterborne transportation safety under uncertainty. [ABSTRACT FROM AUTHOR]

• A novel database of maritime piracy incidents capturing the characteristics of risk factors. • A holistic framework of spatio-temporal pattern mining and analysis of maritime accidents. • A novel time-series measurement method for time-based and spatio-temporal pattern analysis. • Advanced data-driven clustering and classification analysis of maritime piracy incidents. • Piracy spatial pattern analysis, findings, and implications against influential risk factors. Since the new century, we have witnessed the fast evolution of pirate attack modes in terms of locations, time, used weapons, and targeted ships. It reveals that the current understanding of pirate attack spatio-temporal patterns is fading, requiring new technologies of big data analysis to master the hidden rules of piracy-related risk spatio-temporal patterns and rationalize the development of relevant anti-piracy measures and policies. This paper aims to develop a new framework of spatio-temporal pattern mining to realize the visualization and analysis of maritime piracy incidents from different standpoints using a new piracy incident database generated from three datasets. Time-based, space-based, and spatial-temporal pattern mining of piracy incidents are systematically investigated to dissect the influence of different risk factors and mine the characteristics of the incidents. Moreover, a novel Fast Adaptive Dynamic Time Warping (FADTW) method is proposed to uncover the hidden temporal and spatial-temporal patterns of piracy incidents. Furthermore, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied to extract the spatial distribution patterns and discover the high-risk areas. Finally, risk factors-based classification exploration has uncovered different spatial patterns. The findings, showing the global and local features of piracy incidents, have made significant contributions to rationalizing anti-pirate measures for safe navigation. [ABSTRACT FROM AUTHOR]

Seafarers are required to make quick decisions to avoid accidents in case of emergencies. However, officers with anxiety generally have a high probability of making wrong decisions that threaten safety and security during the voyage. With the help of a shiphandling simulator, this study aims to investigate the emotional changes of seafarers under simulated scenarios of emergencies. The State-Trait Anxiety Inventory (S-TAI) scale and electrocardiograph (ECG) signal are adopted to evaluate the emotions of the participant seafarers. To classify the anxiety state of the participants, a support vector machine-based method is applied to establish an anxiety recognition model. Classification results reveal that this proposed model can effectively identify different emotions of participants based on ECG features (cross-validation accuracy: 86.0%; test accuracy: 92.3%). The experimental results show that poor visibility could cause the greatest impact on the anxiety of seafarers. In addition, navigational officers and marine pilots react differently in case of emergencies. Seafarers tend to experience more anxiety when dealing with emergency situations, while marine pilots experience more anxiety during multi-ship encounter periods. Consequently, the findings of this study aid to effectively identify the scenarios that cause anxiety emotion of different professional seafarers, providing the corresponding reference for the training of seafarers. This could help prevent catastrophic accidents that pose a threat to oceans and coasts caused by human error. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS) are deemed as the future of maritime transport. Although showing attractiveness in terms of the solutions to emerging challenges such as carbon emission and insufficient labor caused by black swan events such as COVID-19, the applications of MASS have revealed problems in practice, among which MASS navigation safety presents a prioritized concern. To ensure safety, rational route planning for MASS is evident as the most critical step to avoiding any relevant collision accidents. This paper aims to develop a holistic framework for the unsupervised route planning of MASS using machine learning methods based on Automatic Identification System (AIS) data, including the coherent steps of new feature measurement, pattern extraction, and route planning algorithms. Historical AIS data from manned ships are trained to extract and generate movement patterns. The route planning for MASS is derived from the movement patterns according to a dynamic optimization method and a feature extraction algorithm. Numerical experiments are constructed on real AIS data to demonstrate the effectiveness of the proposed method in solving the route planning for different types of MASS. [ABSTRACT FROM AUTHOR]

Highlights • Analyse the impact of company performance in NIR on port state control policy. • Develop a risk prediction tool to reduce ships' detention likelihood of their ships. • Propose a dynamic risk-based game model for rationalise port state control policy. • Conduct an empirical study to provide useful insights for rational risk based port state control. Abstract This paper analyses the game relationship between port authorities and ship owners under the new inspection regime (NIR). Based on 49328 inspection reports from Paris Memorandum of Understanding (MoU) (2015-2017), we present a Bayesian Network (BN) model to determine vessel detention rates after adding company performance as a new indicator in PSC inspection. A strategic game model is formulated by incorporating the BN model outcomes. The optimal inspection rate from the game model can help improve port authority performance in PSC. An empirical study is conducted to illustrate the insights of the results and provide suggestions for port authorities. [ABSTRACT FROM AUTHOR]

The long term impact posed by climate change risk remains unclear and is subject to diverse interpretations from different maritime stakeholders. The inter-dynamics between climate change and ports can also significantly diversify in different geographical regions. Consequently, risk and cost data used to support climate adaptation is of high uncertainty and in many occasions, real data is often unavailable and incomplete. This paper presents a risk and cost evaluation methodology that can be applied to the analysis of port climate change adaptation measures in situations where data uncertainty is high. Risk and cost criteria are used in a decision-making model for the selection of climate adaptation measures. Information produced using a fuzzy-Bayesian risk analysis approach is utilized to evaluate risk reduction outcomes from the use of adaptation measures in ports. An evidential reasoning approach is then employed to synthesize the risk reduction data as inputs to the decision-making model. The results can assist policymakers in developing efficient adaptation measures that take into account the reduction in the likelihood of risks, their possible consequences, their timeframe, and costs incurred. A technical study across 14 major container ports in Greater China is presented to demonstrate the interaction between cost and risk analysis, and to highlight the applicability of the stated methodology in practice. The paper offers a useful analytical tool for assessing climate change risks to ports and selecting the most cost-effective adaptation measures in uncertain conditions. It can also be used to compare the practitioners’ perceptions of climate risks across different geographical regions, and to evaluate improvements after implementation of the selected adaptation measures with potential budgetary constraints. The methodology, together with the illustrative cases, provides important insights on how to develop efficient climate change adaptation measures in a supply chain context to improve the sustainability of development and enhance adaptation measures for ports, port cities, intermodal transport, supply chains, and urban and regional planning in general. [ABSTRACT FROM AUTHOR]

In the past decades, maritime transportation not only contributes to economic prosperity, but also renders many threats to the industry, causing huge casualties and losses. As a result, various maritime safety measures have been developed, including Port State Control (PSC) inspections. In this paper, we propose a data-driven Bayesian Network (BN) based approach to analyse risk factors influencing PSC inspections, and predict the probability of vessel detention. To do so, inspection data of bulk carriers in seven major European countries from 2005 to 2008 1 In 2008, New Inspection Regime (NIR) was first introduced in Paris MoU port state control. Two sets of data, before and after 2008 are being collected for analysis of the effect of NIR. This paper, as the first phase study, analyses the detention probability before the implementation of NIR. 1 in Paris MoU is collected to identify the relevant risk factors. Meanwhile, the network structure is constructed via TAN learning and subsequently validated by sensitivity analysis. The results reveal two conclusions: first, the key risk factors influencing PSC inspections include number of deficiencies, type of inspection, Recognised Organisation (RO) and vessel age. Second, the model exploits a novel way to predict the detention probabilities under different situations, which effectively help port authorities to rationalise their inspection regulations as well as allocation of the resources. Further effort will be made to conduct contrastive analysis between ‘Pre-NIR’ period and ‘Post-NIR’ period to test the impact of NIR started in 2008. [ABSTRACT FROM AUTHOR]

Human factors have been deemed to affect a variety of unsafe acts and hazardous conditions, with no exceptions in the maritime sector. With increasing applications of automation techniques in shipping, seafarers' roles are changing, and their competencies require to be assessed and assured for safety at sea accordingly. The studies on seafarer competencies have therefore been tightly bound with a human-machine system which consists of the interaction of seafarers and ship operational systems and sub-systems. To evaluate the seafarer competencies that fit automation systems in shipping, this paper aims to develop a new dynamic human-machine model in shipping that can be used to analyse human factors in a closed-loop system. Based on Crew Resource Management and the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers, it reflects the input, process, and output phases of the human system and its interactions with machine sub-systems. A new tool to analyse seafarer competencies is proposed to rationalise human factor evaluation in the maritime closed-loop system and reflect the dynamic human-machine cooperation process. Two case studies have been conducted to illustrate the feasibility of the new model and in the meantime to investigate seafarer competencies in the dynamic human-machine system. It produces a new human factor analysis tool to investigate maritime accidents. The results and policy implications help explore the adjustment of maritime training to support ship automation and provide guidance on risk management for traditional and autonomous ships. [ABSTRACT FROM AUTHOR]

• Holistic use of fNIRS and maritime simulation to conduct HPM objectively. • Development of a hybrid assessment model using haemoglobin data and ANN. • Pioneering psychophysiological data-driven machine learning for seafarers' HPM. • Real case analysis for classifying seafarers of different qualifications. Human errors significantly contribute to transport accidents. Human performance measurement (HPM) is crucial to ensure human reliability and reduce human errors. However, how to address and reduce the subjective bias introduced by assessors in HPM and seafarer certification remains a key research challenge. This paper aims to develop a new psychophysiological data-driven machine learning method to realize the effective HPM in the maritime sector. It conducts experiments using a functional Near-Infrared Spectroscopy (fNIRS) technology and compares the performance of two groups in a maritime case (i.e. experienced and inexperienced seafarers in terms of different qualifications by certificates), via an Artificial Neural Network (ANN) model. The results have generated insightful implications and new contributions, including (1) the introduction of an objective criterion for assessors to monitor, assess, and support seafarer training and certification for maritime authorities; (2) the quantification of human response under specific missions, which serves as an index for a shipping company to evaluate seafarer reliability; (3) a supportive tool to evaluate human performance in complex emerging systems (e.g. Maritime Autonomous Surface Ship (MASS)) design for ship manufactures and shipbuilders. [ABSTRACT FROM AUTHOR]

Port performance measurement (PPM) and comparison research, presenting a multiple criteria decision making (MCDM) issue in nature, has been intensively conducted by researchers from both decision science on modelling and port studies from empirical perspectives. Assigning an appropriate weight to each defined port performance indicator (PPI) is essential for rational decision and precise performance measurement. However, PPIs are often presented in a hierarchy, having the interdependency among them ignored. It causes concerns on the accuracy of PPIs’ weight allocation and arguments on the performance measurement results, revealing a significant research gap to be addressed. As far as MCDM modelling is concerned, the importance of criteria has been studied utilising either absolute or relative comparisons, while the calculation of their importance also takes into account both independency and interdependency factors. However, there is lack of empirical studies in the literature to provide supporting evidence to distinguish the different impacts of the two factors. This study aims to compare the analysis of PPIs importance when taking into account their independent relationship using an analytic hierarchy process (AHP) and their interdependent relationship using a decision making trial and evaluation laboratory (DEMATEL) incorporating an analytic network process (ANP), respectively. The same domain experts are invited to evaluate the importance of the defined PPIs based on both approaches. The results demonstrate that a similar variance of relative importance across the PPIs but a clear difference on their importance scores and ranking. As a result, the results make contributions to fulfil the research gap on consideration of interdependency among PPIs in PPM and on the provision of convincing empirical evidence to highlight the impact of interdependency of criteria on MCDM modelling. Another practical significance draw from this study is that use of DEMATEL can aid port stakeholders to make more rational decision as to whether the interdependency among PPIs should be taken into account in PPM and/or port choice. [ABSTRACT FROM AUTHOR]

This study develops a new port performance measurement model by taking the perspectives from different port stakeholders. The novelty lies in the modelling of interdependencies among port performance measures, and the combination of weights of interdependent measures with both qualitative and quantitative evaluations of the measures from multiple stakeholders for quantitative port performance measurement. It represents an effective performance measurement tool and offers a diagnostic instrument for performance evaluation and/or monitoring of ports and terminals so as to satisfy different requirements of various port stakeholders in a flexible manner. [ABSTRACT FROM AUTHOR]

Psychological factors have been a critical cause of human errors in sectors such as health and aviation. However, there is little relevant research in the maritime industry, even though human errors significantly contribute to shipping accidents. It becomes even more worrisome given that seafarers are changing their roles onboard ships due to the growth of automation techniques in the sector. This research pioneers a conceptual framework for assessing seafarer psychological factors using neurophysiological analysis. It quantitatively enables the psychological factor assessment and hence can be used to test, verify, and train seafarers' behaviours for ship safety at sea and along coasts. A case study on ship collision avoidance in coastal waters demonstrates its feasibility using ship bridge simulation. An experimental framework incorporating neurophysiological data can be utilised to effectively evaluate the contribution of psychological factors to human behaviours and operational risks. Hence, it opens a new paradigm for human reliability analysis in a maritime setting. This framework provides insights for reforming and evaluating operators' behaviours on traditionally crewed ships and in remote-controlled centres within the context of autonomous ships. As a result, it will significantly improve maritime safety and prevention of catastrophic accidents that endanger oceans and coasts. [ABSTRACT FROM AUTHOR]

Cybersecurity risks present a growing concern in the maritime industry, especially due to the fast development of digitalised technologies, also vis-à-vis autonomous shipping. Research on maritime cybersecurity is receiving increased attention. This paper aims to assess the cybersecurity risks in the maritime sector and improve safety at sea and in coastal areas. First, we identify all the concerned cyber threats in the sector based on literature review and expert opinion. A novel risk assessment framework of maritime cyber threats, which combines Failure Mode and Effects Analysis (FMEA) with a Rule-based Bayesian Network (RBN), is proposed and used to evaluate the risk levels of the identified threats and to better understand the threats that contribute the most to the overall maritime cybersecurity risk. The results can inform stakeholders about the most vulnerable parts in their cyber operations and stimulate the development of risk-based control measures. More specifically, the next step in managing cyber threats is to tackle the threats that are associated with unacceptable risk levels and identify cost-effective measures to manage them. To that extent, our findings provide a list of top threats – that is the areas where efforts should be focused on. As a result, this work can help the whole community to grow its resilience to cyber-attacks and improve the security of shipping operations. • Maritime cyber threats are identified and verified. • A novel maritime cybersecurity risk analysis method under high uncertainty is proposed. • Failure Mode and Effects Analysis (FMEA) with a Rule-based Bayesian Network (RBN) and a sensitivity analysis are utilised. • Quantitative analysis and prioritisation of the risk levels of maritime cyber threats are presented. • New solutions to enhancing maritime cybersecurity are discussed. [ABSTRACT FROM AUTHOR]

• Propose a new conflict detection method to calibrate the conflict criticality of ships. • Apply a traffic complexity matrix to reveal the topological and evolutionary features. • Introduce an FCI-based approach to support traffic complexity pattern assessment. • Demonstrate the new model performance through scenario analysis and validation. Maritime traffic situational awareness plays a vital role in the development of intelligent transportation-support systems. The state-of-the-art study focuses on near-miss collision risk between/among ships but reveals challenges in estimating large-scale traffic situations associated with dynamic and uncertain ship motions at a regional level. This study develops a systematic methodology to evaluate ship traffic complexity to comprehend the traffic situation in complex waters. In the new methodology, the topological and evolutionary characteristics of ship traffic networks and the uncertainty in ship movements are considered simultaneously to realise probabilistic collision detection. The methodology, through the effective integration of probabilistic conflict estimation and traffic complexity modelling and assessment, enables the evaluation of traffic complexity in a fine-grained hierarchical manner. With the AIS-based trajectory data collected from the world's largest port (i.e. Ningbo-Zhoushan Port), a thorough validation of the evaluation performance is conducted and demonstrated through scenario analysis and model robustness. Moreover, some critical research results are obtained in terms of traffic network heterogeneity analysis; statistics including occurrence frequency, temporal distribution, life cycle, and transition probability of traffic complexity patterns; and correlation examination between the number of ships and traffic complexity patterns. These findings offer new insights into improving maritime traffic awareness capabilities and promoting maritime traffic safety management. [ABSTRACT FROM AUTHOR]

It is necessary to accurately calculate ship carbon emissions for shipping suitability. The state-of-the-art approaches could arguably not be able to estimate ship carbon emissions accurately due to the uncertainties of Ship Technical Specification Database (STSD) and the geographical and temporal breakpoints in Automatic Identification System (AIS) data, hence requiring a new methodology to be developed to address such defects and further improve the accuracy of emission estimation. Firstly, a novel STSD iterative repair model is proposed based on the random forest algorithm by the incorporation of13 ship technical parameters. The repair model is scalable and can substantially improve the quality of STSD. Secondly, a new ship AIS trajectory segmentation algorithm based on ST-DBSCAN is developed, which effectively eliminates the impact of geographical and temporal AIS breakpoints on emission estimation. It can accurately identify the ships' berthing and anchoring trajectories and reasonably segment the trajectories. Finally, based on this proposed framework, the ship carbon dioxide emissions within the scope of domestic emission control areas (DECA) along the coast of China are estimated. The experiment results indicate that the proposed STSD repair model is highly credible due to the significant connections between ship technical parameters. In addition, the emission analysis shows that, within the scope of China's DECA, the berthing period of ships is longer owing to the joint effects of coastal operation features and the strict quarantine measures under the COVID-19 pandemic, which highlights the emissions produced by ship auxiliary engines and boilers. The carbon intensity of most coastal provinces in China is relatively high, reflecting the urgent demand for the transformation and updates of the economic development models. Based on the theoretical models and results, this study recommends a five-stage decarbonization scheme for China's DECA to advance its decarbonization process. • An iteratively ship technical specifications database repair model based on the random forest algorithm is proposed. • A ship AIS trajectory segmentation algorithm based on DBSCAN is proposed. • The carbon emissions from maritime transport in 2019 and 2020 within the scope of domestic emission control areas along the coast of China are estimated. • Results indicate that carbon emissions did not show a significant decrease before and after the COVID-19 pandemic and the carbon intensity of most coastal provinces in China is relatively high. [ABSTRACT FROM AUTHOR]

Maritime piracy and robbery can not only cause logistics chain disruption leading to economic consequences but also result in loss of lives, and short- and long-term health problems of seafarers and passengers. There is a justified need for further investigation in this area of paramount importance. This study analyses maritime piracy and robbery related incidents in terms of the major influencing factors such as ship characteristics and geographical locations. An analytical model incorporating Bayesian reasoning is proposed to estimate the likelihood of a ship being hijacked in the Western Indian or Eastern African region. The proposed model takes into account the characteristics of the ship, environment conditions and the maritime security measures in place in an integrated manner. Available data collected from the Global Integrated Shipping Information System (GISIS) together with expert judgement is used to develop and demonstrate the proposed model. This model can be used by maritime stakeholders to make cost-effective anti-piracy decisions in their operations under uncertainties. Discussions are given on industrial response to maritime piracy in order to minimize the risk to ships exposed to attacks from pirates. Further recommendations on how maritime security and piracy may be best addressed in terms of maritime security measures are outlined. [ABSTRACT FROM AUTHOR]

This work aims to apply a novel approach to assess the risks of maritime supply chains (MSCs) within the context of the Maritime Silk Road (MSR) by employing fuzzy logic and evidential reasoning. Compared to traditional risk analysis methods, the novel approach has its superiorities in dealing with incomplete and vague data, synthesizing multiple data formats, and preventing the loss of important risk information. A case of the risk factors influencing MSCs along the MSR is analysed, and the assessment results reveal that the fuel price is the most significant risk factor. Sensitive analysis is applied to validate and illustrate the rationality and practicality of the proposed approach. The findings can provide the MSR stakeholders with important insights for the safety management of MSCs along MSR. • Use of a hybrid of the fuzzy-ER approach for effective risk assessment of MSRs within the context of the Maritime Silk Road. • A new risk model based fuzzy logic and the ER shows its superiorities in dealing with uncertainty in data. • Define risk parameters of risk events from multi-perspective to facilitate the modelling and understanding of risks in MSCs. • Investigate the MSC to analyse the risk and priorities risk events under uncertainties for decision-making. [ABSTRACT FROM AUTHOR]

A hostile microenvironment in tumor tissues disrupts endoplasmic reticulum homeostasis and induces the unfolded protein response (UPR). A chronic UPR in both cancer cells and tumor-infiltrating leukocytes could facilitate the evasion of immune surveillance. However, how the UPR in cancer cells cripples the anti-tumor immune response is unclear. Here, we demonstrate that, in cancer cells, the UPR component X-box binding protein 1 (XBP1) favors the synthesis and secretion of cholesterol, which activates myeloid-derived suppressor cells (MDSCs) and causes immunosuppression. Cholesterol is delivered in the form of small extracellular vesicles and internalized by MDSCs through macropinocytosis. Genetic or pharmacological depletion of XBP1 or reducing the tumor cholesterol content remarkably decreases MDSC abundance and triggers robust anti-tumor responses. Thus, our data unravel the cell-non-autonomous role of XBP1/cholesterol signaling in the regulation of tumor growth and suggest its inhibition as a useful strategy for improving the efficacy of cancer immunotherapy. [Display omitted] • XBP1 deficiency in tumor cells limits tumor growth by inducing anti-tumor immunity • XBP1 drives the production of cholesterol, which is secreted via tumor-derived sEVs • Cholesterol elicits immunosuppression by activating myeloid-derived suppressor cells • Pharmacologic or genetic inhibition of XBP1/cholesterol signaling reduces tumor growth Yang et al. demonstrate that tumor-intrinsic XBP1 dictates the production of cholesterol, which is secreted via small extracellular vesicles to drive immunosuppressive reprogramming by activating myeloid-derived suppressor cells. Blocking the IRE1α-XBP1-cholesterol axis reduces tumor growth. [ABSTRACT FROM AUTHOR]

Global shipping flows through strategically important restricted waters such as Panama and Suez Canals, and hence accidents occurring in the Canals will cause serious disruptions to global supply chains. In this paper, a new data-driven Bayesian network (BN) based risk model is developed to investigate how risk factors jointly generate impact on different types of maritime accidents within restricted waters. A new risk database involving 25 factors has been developed by manual analysis of all the recorded accidents from 2005 to 2021 that occurred in the world's important restricted waters including key maritime canals, channels and straits. A data-driven BN model is constructed to analyse the key risk influential factors (RIFs) contributing to such accidents. The model is verified by sensitivity analysis and real accident cases. Further, it is tested by the already known information of the 2021 Suez Canal blockage case to generate useful insights and draw the lessons to learn. In a retrospective analysis using the currently available limited information on the Suez Canal case, the implication of the case study shows a plausible explanation for the observed findings by scenario analysis. The findings can be applied to backward risk cause diagnosis for accident investigation and forward risk prediction for accident prevention in restricted waters to avoid the reoccurrence of similar accident to the Suze Canal blockage. • A new data-driven risk model enables the systematic analysis of the risk factors of maritime accidents in restricted waters. • Analysis of the dependencies between RIFs for maritime accidents in restricted waters. • Forward risk prediction for accident analysis and prevention. • Backward risk diagnosis for root cause analysis of maritime accidents. • Analysis of the Suez Canal blockage case study to provide insights for accident investigation and prevention. [ABSTRACT FROM AUTHOR]

Due to the development of ship sizes and the traffic increase in port, ships having long turnaround time in port often result in port congestion, which seriously affects the efficiency of the ship navigation and environmental sustainability of port, it has been evident that effective ship scheduling presents a solution of the fundamental and strategic importance to port congestion. In this paper, a mixed-integer linear programming mathematical model is proposed to realize the optimization of the ship scheduling in port to minimize the total time spent by ships in port. Its methodological novelty is gained by an innovative adaptive genetic simulated annealing algorithm based on a reinforcement learning algorithm (GSAA-RL) to support the developed mathematical model, in which the genetic algorithm is considered as the basic optimization algorithm, and Q-learning with a unique property of selecting suitable parameters dynamically is developed to adjust the parameters of crossover and mutation to improve the search ability of the algorithm. Meanwhile, the dynamic parameter turning process is formulated into a Markov decision process (MDP) model with well defining the state, action, and reward function in GSAA-RL. Specifically, the state sets are proposed by analyzing the key factors affecting the scheduling efficiency and a new reward mechanism that can reduce the objective value significantly based on the quality of selected parameters is designed. The annealing operation is performed on some excellent individuals to further expand the search scope. Simulation experiments demonstrate that the proposed GSAA-RL algorithm can significantly shorten the total time spent by ships in port compared to existing approaches. This study hence helps port operators/planners to improve operational efficiency and reduce port congestion, reduce ship fuel consumption, and deliver goods to cargo owners in a timely manner, which has important practical significance for achieving the "dual carbon" goal. [ABSTRACT FROM AUTHOR]

Over the years, many efforts have been focused on developing methods to design seaport systems, yet disruption still occur because of various human, technical and random natural events. Much of the available data to design these systems are highly uncertain and difficult to obtain due to the number of events with vague and imprecise parameters that need to be modelled. A systematic approach that handles both quantitative and qualitative data, as well as means of updating existing information when new knowledge becomes available is required. Resilience, which is the ability of complex systems to recover quickly after severe disruptions, has been recognised as an important characteristic of maritime operations. This paper presents a modelling approach that employs Bayesian belief networks to model various influencing variables in a seaport system. The use of Bayesian belief networks allows the influencing variables to be represented in a hierarchical structure for collaborative design and modelling of the system. Fuzzy Analytical Hierarchy Process ( FAHP ) is utilised to evaluate the relative influence of each influencing variable. It is envisaged that the proposed methodology could provide safety analysts with a flexible tool to implement strategies that would contribute to the resilience of maritime systems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel framework for analysing and synthesising engineering system risks on the basis of a generic Fuzzy Evidential Reasoning ( FER ) approach. The approach is developed to simplify the inference process and overcome the problems of traditional fuzzy rule based methods in risk analysis and decision making. The framework, together with the FER approach has been applied to model the safety of an offshore engineering system. The benchmarking between the new model and a well-established Rule based Inference Methodology using the Evidential Reasoning ( RIMER ) is conducted to demonstrate its reliability and unique characteristics. It will facilitate subjective risk assessment in different engineering systems where historical failure data is not available in their safety practice. [ABSTRACT FROM AUTHOR]

In a 2019 European Commission report, the Blue Economy (BE) within the United Kingdom (UK) represented 22% of the European Union's (EU) BE Gross Value Added (GVA) at approximately €39 billion. Coupled with the clear value of the BE to the UK, there is an urgent need to innovate and develop technologies to decarbonise and advance the sector. A deeper understanding of the current position for multiple stakeholders must be considered before any major governmental or long-term strategy decisions can be made. This paper presents the perspective of academic, industrial and governmental stakeholders analysis of how the UK can move forward with developing innovations within the BE. Utilising a questionnaire and round table discussions, specialists from all stakeholders gave their opinions on industry-academia-governmental working relationships and technology transfer readiness. Reasonably high satisfaction was found with key aspects that enable a successful collaborative project between academia and industry towards technology commercialisation; however, there is still room for improvement. This paper offers an analysis of how to further enhance and foster technology development within the UK BE. A collaborative approach is proposed to ensure best practices, and a 'triple helix' (TH) collaboration strategy to be used as a tool for those engaging in these types of working relationships. Future directions on enhancing technology transfer innovation within the UK BE are also suggested. [ABSTRACT FROM AUTHOR]

Highlights: [•] Develop a quantitative security assessment model using fuzzy evidential reasoning. [•] Identify the major generic key security performance indicators (KSPIs) in port. [•] Provide a transparent decision making tool for security control measures selection. [•] Rationalise resource allocation based on different risk levels of port facilities. [•] Use a real case study to demonstrate the developed methodology. [ABSTRACT FROM AUTHOR]

Abstract: The risks of hazards of liquefied natural gas (LNG) carrier operations and their fundamental causes are investigated in this study. A new framework is developed through the combination of a risk matrix approach and a fuzzy evidential reasoning (FER) method. The risk matrix approach is employed to identify the major hazards associated with the LNG carrier operations. In the risk matrix methodology, linguistic terms are used to estimate how likely hazards may occur in LNG carrier operations at sea, and the consequences of the hazards. The failures of an LNG spill from a transfer arm and an LNG containment system are identified as the hazards of “very high risk”. The fault tree analysis (FTA) diagrams of “very high risk” hazards (top events) are used to identify their causes (basic events) and failure logics. Due to the unavailability of historical failure data, the safety/risk level of each cause is investigated using the FER approach. In the FER approach, the safety levels of the basic events of the top event are assessed using three safety parameters (i.e. occurrence likelihood, consequence severity and failure consequence probability) and combined using evidential reasoning (ER) in fuzzy environment in order to reveal the top events’ safety/risk levels. [Copyright &y& Elsevier]

Compared to cargo terminals, performance evaluation of cruise terminals sits in a backseat in the current port performance studies, particularly in the Northeast Asia. This paper aims to evaluate the performance of major cruise terminals in Asia, taking into account the important dimensions and criteria influencing cruise terminal development, and using primary data by experts' responses to a questionnaire survey. Complementary data are also acquired by field trips and interviews to find the hidden information that is not revealed from the set questions in the questionnaire survey. To tackle the multiplicity and uncertainty in the collected data, the hybrid of Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Consistent Fuzzy Preference Relation (CFPR) is employed to evaluate cruise terminal performance and highlight visual illustration of the test results. The findings provide useful insights for guiding cruise lines' terminal selection and for aiding cruise terminals to benchmark their performance to effectively improve their service and maximize their overall performance. • This paper evaluates performance of major cruise terminals in Asia, taking into account important dimensions and criteria influencing cruise terminal operation and development. • To tackle the multiplicity and uncertainty in the collected data, this paper applies the hybrid of PROMETHEE and Consistent Fuzzy Preference Relation. • This paper provides cruise terminal performance and highlights visual illustration of the test results. • The findings provide useful insights and managerial implications for guiding cruise lines' terminal selection and for impoving their terminal performance. [ABSTRACT FROM AUTHOR]

Abstract: Being nodal points along supply chains, ports affected by climate change would create substantial costs to the global economy and welfare, and thus it is extremely important to ensure that ports can develop effective adaptation strategies. However, there are many uncertainties, as the dynamics between climate change and ports can diversify between different regions. Against this background, through exploratory case studies of four ports in Australia, this paper investigates climate change and the adaptation strategies of ports, with a special focus on port infrastructures and the day-to-day operational impacts. Research findings indicate that, while port managers recognize climate change as an issue which requires closer attention, adaptation strategies have remained segregated and piecemeal. This highlights the fact that effective adaptation solutions are not just about physical layouts and engineering projects, but the need to fundamentally transform the current management and planning practices of ports. It is a timely reminder to port policymakers and managers on the need to refine how effective decisions should be made for the challenges posed by climate changes in the future. In this regard, further research on this topic is urgently required. [Copyright &y& Elsevier]

• Elaborate all the indicators influencing airport climate resilience. • Develop a climate resilience evaluation method using evidential reasoning. • Use big data to quantify the airport climate resilience. • Conduct an empirical study to analyse the climate resilience of the UK airports. Due to increased extreme weather events, climate adaptation has become an essential issue to be addressed by all transport infrastructures, including airports. This paper aims to develop a Climate Resilience Indicator (CRI) framework for assessing airport climate resilience, which for the first time, considers: climate exposure, vulnerability and adaptive capacity simultaneously and advances the development of climate risk analysis of airports to a point where their adaptation and resilience can be quantified under uncertainty in data. Climate-related data was collected from multiple sources to evaluate an airport's performance against each indicator. An evidential reasoning (ER) approach is used to evaluate each airport by integrating all the indicators to derive its final CRI score. The findings provide valuable insights into how urgently an airport needs to deal with climate change and reveal information to help with resource allocation for different airports nationally through proactive adaptation planning. [ABSTRACT FROM AUTHOR]

Rapid development of maritime transportation networks meets international trade demands while rendering them in high risk and disruption concerns particularly at ports being the bottlenecks of the whole flows. Port operations calls for an effective approach to assess ports vulnerability and to ensure the resilience of their associated maritime supply chains (MSC). However, traditional quantitative risk analysis reveals challenges due to data incompleteness and ambiguity, and operational and environmental uncertainty when being applied in ports vulnerability analysis. This paper aims to develop a novel port vulnerability assessment (PVA) framework, which can guide and realise a standardised vulnerability analysis process for the ports from different geographies involving in the same MSC and hence the resources can be better managed from a global network level for optimal resilience of the chain. It is especially important for the shipping and port industries which are in nature international and desires strong international uniform standardization. The fuzzy theory, evidential reasoning (ER) approach, and expected utility theory are combined in a holistic way to form the proposed PVA framework. The new framework is validated and demonstrated by using a case study in which five key ports along an established MSC in China are investigated. The findings can be used as a stand along method to compare the vulnerability levels of the ports in an MSC and/or integrated with decision optimisation methods for rational safety resource distribution from a supply chain perspective. • Use of a hybrid of the fuzzy theory and ER methods for port vulnerability assessment from a supply chain perspective. • Provide a standardised process and a novel PVA framework incorporating a centrality estimate for the first time. • Define different types of vulnerability measures considering the internal risk and external safety impact in the framework. • Investigate five key ports to analyse the vulnerability and prioritise their vulnerable levels for resource rationalisation. [ABSTRACT FROM AUTHOR]