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Traditionally, vessel air emissions are monitored onboard vessels or at fixed points at sea. These methods are not cost-effective for implementing emission control laws that address air pollution monitoring of vessels travelling over a large body of water. Unmanned aerial vehicles (UAVs) equipped with pollution monitoring sensors are becoming a research focus. However, due to battery capacity constraints, the monitoring scope of UAVs is still not optimal. Thus, using a ship (such as a patrol ship) as a UAV mobile supply base can overcome battery limitations and increase monitoring coverage. This paper investigates the joint routing and scheduling problem of ship-deployed multiple UAVs (SDMUs) for the monitoring of pollution from vessels. The artificial bee colony (ABC) algorithm based on simulated annealing is employed to minimize the total monitoring time. The model and solution algorithm are verified by real-time dynamic vessel data from Tianjin Port. [ABSTRACT FROM AUTHOR]

• New data training method is applied to improve BN in detention risk analysis in PSC. • Interrelationships among major factors in SRP systems are revealed comprehensively. • Insightful policy implications are proposed for both different stakeholders. • The rules of PSC inspection system are improved based on the obtained findings. Given to the increasing traffic volume in ports in recent years, ship selection and inspection procedure in the port state control (PSC) should be improved to reduce any unnecessary delay caused by the inefficient inspections. This study aims to newly use a data training technique and the newest PSC data to improve the usage of Bayesian Network (BN) to assess detention risk to a point where risk factors are identified, interrelationships among the factors are analysed and prior probability training based on big data is obtained more easily. To construct the BN model, a Bayesian theorem-based machine learning approach is adopted to ensure the obtained model is objective and reliable. The model is developed based on 1880 inspection records in the Paris Memorandum of Understanding (MoU) regime between 1st January 2017 and 31st March 2020. The obtained model not only present the probability distribution of each factor but also explore interrelationships among them. Compared to the Ship Risk Profiles (SRP) model, the used data-driven structure learning algorithm is more convenient and useful. The analysis results provide insights for ship owners to manage ship detention risk while support port authorities to prioritize the ship checklist and utilise more efficient ship inspection. [ABSTRACT FROM AUTHOR]

• The influence of heeling on individual's walking speed is greater than trim. • It is the best condition for evacuation when the inclination angle is less than 20°. • A small inclination angle speeds up the evacuation process. • Evacuation time increases as the number of pedestrians increases. Passenger vessels often present different heeling and/or trim angles during and after accidents, while recognising it as the main factor affecting pedestrian movement during an emergency evacuation process, there is difficulty to reproduce the evacuation activities on ships due to cost constrains and safety concerns in relevant studies. To fill the research gap, an improved social force model (SFM) incorporating both inclining and self-adjusting forces of pedestrians into the basic SFM model was constructed to simulate the pedestrian dynamics under different ship trim and heeling circumstances. The improved SFM also includes a reduction law of pedestrian speed at different heeling and/or trim angles and adds a calculation of the reduction factor in each time step. It enables the simulation of the pedestrian movement process on inclined vessels accurately. The simulation results show that when the inclination angle is less than 20°, the impact of both heeling and/or trim on an individual's walking speed and evacuation time are weaker than the one with an angle exceeding 20°. When passengers walk along the keel line on an inclined ship, the impact of heeling on speed attenuation is more significant than the one of trim. The overall evacuation time is extended with the increasing number of evacuees. The flow rate at the exit reaches the maximum when the number of evacuees is 100, and the average evacuation rate is 2.01 persons/s. The findings provide useful insights on crowd management in the process of passenger vessel evacuation under an inclined state. [ABSTRACT FROM AUTHOR]

• Use of blockchain in an MASS network to prevent the cyber threat of spoofing or tampering. • Delegated proof-of-stake mechanism in the blockchain for the consensus of the MASS nodes. • New two-layered trust model for trustworthiness assurance by global belief-of-trust informing. • Use of credits to indicate the capability of a node for reciprocal trust evaluation in MASS. Maritime Autonomous Surface Ship (MASS) is widely deemed as the future of global shipping. The cyber vulnerability has however been identified as an emerging problem and a potential barrier influencing MASS development. This paper, through the investigation of the fundamental trust problem with regards to the cyber security of MASS systems, aims to develop a blockchain-based scheme for the trust management of MASS. The innovative idea of using blockchain within the MASS context is that the mobile entities in the MASS operational environment constitute a decentralized opportunity network, which makes blockchain an appealing tool to provide a solution to evaluating and maximizing the trust over the dynamics of the entities. This paper elaborates the mechanism by which the MASS entities participate in keeping the main chain. Firstly, the paper illustrates how the Belief of Trust (BoT) among the entities is encoded and assembled into the chain, to allow MASS entities to have an initial judgement towards another entity before they get acquainted. Secondly, at the consensus layer of blockchain technique, it addresses how the witness, who has a temporary right of producing a block and append it to the chain, can be elected among the nodes and how to incent the nodes to maintain the blockchain from a proof-of-stake perspective. Finally, this paper describes how the MASS entities can use the certificate dependence information to evaluate the trust transition in the MASS operating environment. Typical scenarios are delineated to show the procedure of certificate inquiry, handover of controls between maritime supervision centers and shore-side remote control centers in case of the occurrence of unexpected events. The findings provide any entity in an MASS network with an effective solution to evaluating the degree of trust he can have for any targeted node/participant. They can therefore help choose better (more trustable) nodes to maintain the MASS network's knowledge of evidence to judge the trust on an unknown member. [ABSTRACT FROM AUTHOR]

• A passenger ship evacuation model has been established by using FDS + EVAC. • The effects of the passenger population composition in an emergency evacuation were analysed. • The effects of the ship familiarity in an emergency evacuation were analysed. • The ship's staircase layout was optimised to facilitate the evacuation process. In this research, the effects of the passenger population composition and ship familiarity in an emergency evacuation are analysed. The results identified that the effects of different population compositions on the Ro-Ro evacuation process vary significantly. It is therefore recommended that a targeted survey of the population on a specific ship should be conducted before the evacuation analysis to improve the analysis accuracy of the evacuation process. It is not always the case that a higher familiarity with the ship staircase layout necessarily results in less time to complete the evacuation, and the issue of balanced exits has to be considered due to its significant impact. The results obtained in this research can be used to aid the ship's staircase layout optimisation to facilitate the evacuation process. Given the type of Ro-Ro vessel in this analysis, it is suggested that adding a staircase towards the bow of the ship can reduce the evacuation time by 13.6%, when considering 95% of the passengers to complete an evacuation. Similarly, adding one staircase at the stern can reduce the time by approximately 10% for all passengers to complete the evacuation. It is not recommended that the size of staircases towards the middle of the ship should be adjusted. [ABSTRACT FROM AUTHOR]

During the emergency evacuation on passenger vessels, an individual's walking speed will be affected by the ship's rolling motions. It is necessary and beneficial to evaluate the effect of such motions on individuals' walking speeds in order to facilitate the evacuation and reduce the consequence of a maritime accident. In this study, we collected primary data from a series of walking experiments on-board a real ship to quantitatively evaluate the effect of different ship roll angular magnitudes on an individual's walking speed in two scenarios of flat terrains and staircases, respectively. It was found that on flat terrains, the ship's rolling motion results in the reduction of an individual's walking speed at a rate of 7%–16%. On a staircase, an individual's speed of walking up the staircases was reduced by approximately 5% due to the ship's rolling motion. The findings will contribute to the development of optimal evacuation routes on passenger ships. The used method can also be tailored to model the effect of the other dynamic environments on walking speeds at sea (e.g., on offshore platforms) and onshore (e.g., during earthquakes) to improve evacuation efficiency for disaster prevention. • The primary data from a series of walking experiments onboard a real ship was collected. • The effect of different ship rolling angles on an individual's walking speed was evaluated. • An individual's walking speed was reduced at a rate of 7%–16% on flat terrains. • An individual's speed of walking up stairs was reduced by 5% on a staircase. • The differences of individuals' walking speeds between male and female were observed and analysed. [ABSTRACT FROM AUTHOR]

• Propose a novel data-driven geometrical model to assess ship and offshore installations (SOI) collision risk. • Incorporate key dynamic risk factors into a rule-based Bayesian Network approach to model SOI collision risk. • Analyse a real SIO case near the Liverpool Burbo Bank offshore wind farm to support offshore wind farm. • Generate quantitative criteria for guiding ships anti-collision operations when passing offshore installations. Increasing human installations and vessel traffic in offshore waters render a collision risk between ships and offshore installations (SOI). Past decades have witnessed many accidents occurred in the offshore waters involving complex traffic networks. To safeguard offshore installations and improve water-bound transport safety, this paper proposes a novel Bayesian-based model to assess the SOI collision risk involving passing ships. It first identifies the relevant risk factors with the aid of a geometrical analysis concerning SOI collisions. The causal relationships between the risk factors are numerically defined by causal rules with a degree of belief structure, while a Bayesian network (BN) is constructed to aggregate the evaluated value of each risk factor and to assess the collision risks involving different navigational environments. To illustrate the new model, a real case on SOI collision risk in the Liverpool Burbo Bank offshore wind farm is investigated. The results provide empirical evidence for SOI collision risk analysis under complex water conditions and uncertain navigational environments and hence useful insights on SOI collision avoidance. [ABSTRACT FROM AUTHOR]

• Identify the hazards in maritime autonomous surface ships (MASS) development and operations. • Introduce a BN model to assess the identified hazards in maritime autonomous surface ships. • Analyse and rank the impacts of hazards on maritime autonomous surface ships operations. • Propose solutions for dealing with hazards in maritime autonomous surface ships. Maritime Autonomous Surface Ships (MASS) are attracting increasing attention in the maritime industry. Despite the expected benefits in reducing human error and significantly increasing the overall safety level, the development of autonomous ships would undoubtedly introduce new risks. The overall goal of this work is to develop an approach to evaluate the risk level of major hazards associated with MASS. To that extent, a Failure Modes and Effects Analysis (FMEA) method is used in conjunction with Evidential Reasoning (ER) and Rule-based Bayesian Network (RBN) to quantify the risk levels of the identified hazards. The results show that 'interaction with manned vessels and detection of objects' contributes the most to the overall risk of MASS operations, followed by 'cyber-attacks', 'human error' and 'equipment failure'. The findings provide useful insights on the major hazards and can aid the overall safety assurance of MASS. [ABSTRACT FROM AUTHOR]

• Incorporate ship deficiency data into port state control analysis (PSC). • Develop a bi-directional risk analysis tool to predict ships' detention likelihood and diagnose the most likely reason for the occurrence of ship deficiency. • Use a BN-based dynamic model to prioritize the impact of factors influencing PSC inspection. • Analyze the dependency and interdependency among the factors influencing PSC inspection. • Conduct an empirical study in the Tokyo MoU region to provide useful insights for rational risk based port state control. Port State Control (PSC) inspection aids to control substandard ships and ensure safety at sea. Current risk-based PSC research and practice fail to incorporate ship deficiency records into detention probability analysis, because of the difficulty introduced by the involved big deficiency data. In this paper, a new Bayesian Network (BN) based PSC risk probabilistic model is developed to analyze the dependency and interdependency among the risk factors influencing PSC inspections based on big data derived from the inspection database of Tokyo MoU for the period between 2014 and 2017. The results reveal that ship's safety condition related deficiencies as well as technical features of the inspected vessel itself are among the most influential factors concerning PSC inspections and ship detention. New Bayesian learning methods are used to improve the model efficiency in ship detention prediction. As a result, the newly developed model has shown a reliable performance on dynamic prediction and cause-effect diagnosis of ship detention probabilities by pioneering the incorporation of ship deficiency records in the analysis. The findings provide important insights on how to facilitate risk-based PSC inspections for both ship owners and port states. They provide support for port state authorities to implement rational inspection policies. [ABSTRACT FROM AUTHOR]

Ship motion is an important influencing factor in passenger ship evacuation that affects the entire evacuation process by reducing an individual's walking speed. This study aims to quantify such effects in ship berthing and sailing operations by collecting experimental data to support passenger ship evacuation decisions. The study utilized Dalian Maritime University's training ship to conduct human walking experiments to study the influence of ship motion on passengers' normal and fast walking speeds. It was found that during the berthing period, an individual's normal walking speed was 1.28–1.68 m/s, and the fast walking speed was 1.50–2.14 m/s. During the sailing, the ship's rolling motion reduced the normal walking speed by 3.8%–10.3% and the fast walking speed by 3.7%–14.0%. Due to the influence of ship rolling, the higher the deck and the farther away the rolling centre is, the smaller the athwartship and fore-aft walking speeds. Athwartship walking was slightly faster than fore-aft walking. Similarly, in the normal walking mode, the athwartship walking speed was 1.6%–3.7% faster than fore-aft walking, while in the fast walking mode, the athwartship walking speed was 0.8%–4.9% faster than fore-aft walking. Furthermore, during the berthing period, the average walking speed of the younger group was 24.1% higher than that of the older group. Finally, during the sailing, the speed reduction ratio of an individual's walking speed was 86.0%–96.2%, and the value decreased as the deck height increased. • The impact of ship motion on human walking speed was experimentally investigated. • The ship's rolling motion reduced the normal walking speed by 3.8%–10.3%. • The farther away the rolling centre is, the smaller the human walking speed. • The athwartship fast walking speed was 0.8%–4.9% faster than fore-aft fast walking. • During voyage, the reduction ratio of the human walking speed was 86.0%–96.2%. [ABSTRACT FROM AUTHOR]

• Incorporate economy and efficiency factors into safety based cargo pre-stowage planning of general cargo ships. • Develop a new preconditioning evidential reasoning to aid rational cargo pre-stowage plans and decision. • Propose a new model for dynamic decision criterion weights using entropy and AHP. • Conduct an empirical study to provide useful insights for rational cargo pre-stowage of general cargo ships. The current approach to the cargo stowage plans (CSP) of general cargo ships (GCS) is safety-driven, which means that any CSP satisfying minimum safety requirements can be used in practice. Such an approach taking into account no economic and environmental concerns cannot help sustain GCS growth in today's competitive freight transportation market. This paper introduces a revised evidential reasoning (ER) approach to cope with the complex decision-making problem associated with the CSP of GCS. The complexity mainly results from the dynamic interdependency between the decision criteria and alternatives. The revised ER can determine the functions of the safety-related criteria in the decision making process by considering the extent to which each decision alternative meets the minimum safety requirements. The model is tested in multiple forms by an empirical study using a national GSC loading laboratory, and a real-life application by a shipping company in practice. The results reveals that the new model can aid general ship owners to make sustainable CSPs from a multiple-dimensional perspective and select an optimal CSP based on specific voyage scenarios. [ABSTRACT FROM AUTHOR]

• Synergistic path planning of multi-UAVs among sailing vessels for the air pollution inspection. • Develop a dynamic multi-objective TSP model. • A heuristic algorithm based on improved PSO is proposed to determine the path reprogramming time in dynamic environment by judging and cutting the "minimal loop". • Experimental results show that our proposed method can efficiently solve the problem. The phenomena of the COVID-19 outbreak and the Arctic Iceberg melting over the past two years make us reconsider the impact our way of life has on the environment and the responsibility of business toward minimizing and potentially eliminating emissions. Increasing ship traffic in ports leads to the growing emission of air pollutants, which influences the air quality and public health in the surrounding areas. The International Maritime Organization (IMO) has adopted relevant regulations (e.g., Annex VI of IMO's pollution prevention treaty (MARPOL) and mandatory energy-efficiency measures) to address ship emissions. To ensure the effective implementation of such regulations and measures, air emission detection and monitoring has become crucial. In this paper, a dynamic multitarget path planning model is developed to realize multi-UAVs (Unmanned Aerial Vehicles) performing synergistic detection of ship emissions in ports. A path planning algorithm under a dynamic environment is developed to establish the model. This algorithm incorporates a Tabu table into particle swarm optimization (PSO) to improve its optimization ability, and it obtains the initial detection route of each UAV based on a "minimum ring" method. This paper describes a multi-UAVs synergistic algorithm to formulate the path reprogramming time in a dynamic environment by judging and cutting the "minimum ring". This finding proves the improved efficiency of air pollution detection by UAVs. It provides useful insights for maritime and port authorities to detect ship emissions in practice and to ensure ship emission reduction for better air quality in the postpandemic era. [ABSTRACT FROM AUTHOR]

• Propose a novel hybrid approach to develop a multi-data-driven BN risk model based on AIS data and subjective judgements. • Introduce a target-free data learning approach to train data-driven BNs using AIS data. • Apply the hybrid approach in an emerging research topic of vessels and offshore wind farm collision. • Establish an effective risk model to support offshore wind farm collision avoidance and decision-making. • Demonstrate advantages of the new risk model through real case analysis. Offshore wind farms (OWFs) are relatively new installations at sea. Accident records related to vessel collisions with OWFs are insufficient to support a full quantitative risk analysis using traditional probabilistic approaches. This paper aims to develop a semi-qualitative risk model to assess the vessel-turbine collision risks by incorporating Bayesian networks (BN) with evidential reasoning (ER) approaches. First, a BN is trained based on Automatic Identification Systems (AIS) data to characterise real vessel traffic flows, including the detailed information and relationships between traffic flow parameters. Secondly, through synthesising expert judgements by ER, five risk factors influencing the probability and consequence of vessel-turbine collisions are identified (incl. the associated conditional probabilities) in the established BN. Finally, the updated BN with ER input is tested through ten real scenarios and validated by processing a validity framework. This paper pioneers the use of multi-data-driven BNs to characterise traffic flows and assess vessel-turbine collision risk for navigational safety assurance near OWFs. The research findings provide empirical evidence of using ER to supplement BN subjective data to advance its applications in risk analysis. [ABSTRACT FROM AUTHOR]

Maritime terrorist accidents have a significant low-frequency-high-consequence feature and, thus, require new research to address the associated inherent uncertainty and the scarce literature in the field. This article aims to develop a novel method for maritime security risk analysis. It employs real accident data from maritime terrorist attacks over the past two decades to train a data-driven Bayesian network (DDBN) model. The findings help pinpoint key contributing factors, scrutinize their interdependencies, ascertain the probability of different terrorist scenarios, and describe their impact on different manifestations of maritime terrorism. The established DDBN model undergoes a thorough verification and validation process employing various techniques, such as sensitivity, metrics, and comparative analyses. Additionally, it is tested against recent real-world cases to demonstrate its effectiveness in both retrospective and prospective risk propagation, encompassing both diagnostic and predictive capabilities. These findings provide valuable insights for the various stakeholders, including companies and government bodies, fostering comprehension of maritime terrorism and potentially fortifying preventive measures and emergency management., (© 2024 The Author(s). Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)

Hepatitis C virus (HCV) is a major cause of chronic liver disease, infecting approximately 170 million people worldwide. HCV assembly is tightly associated with the lipoprotein pathway. Exchangeable apolipoprotein E (apoE) is incorporated on infectious HCV virions and is important for infectious HCV virion morphogenesis and entry. Moreover, the virion apoE level is positively correlated with its ability to escape E2 antibody neutralization. However, the role of apoE exchange in the HCV life cycle is unclear. In this study, the relationship between apoE expression and cell permissiveness to HCV infection was assessed by infecting apoE knockdown and derived apoE rescue cell lines with HCV. Exchange of apoE between lipoproteins and HCV lipoviral particles (LVPs) was evaluated by immunoprecipitation, infectivity testing, and viral genome quantification. Cell and heparin column binding assays were applied to determine the attachment efficiency of LVPs with different levels of incorporated apoE. The results showed that cell permissiveness for HCV infection was determined by exogenous apoE-associated lipoproteins. Furthermore, apoE exchange did occur between HCV LVPs and lipoproteins, which was important to maintain a high apoE level on LVPs. Lipid-free apoE was capable of enhancing HCV infectivity for apoE knockdown cells but not apoE rescue cells. A higher apoE level on LVPs conferred more efficient LVP attachment to both the cell surface and heparin beads. This study revealed that exogenous apoE-incorporating lipoproteins from uninfected hepatocytes safeguarded the apoE level of LVPs for more efficient attachment during HCV infection., Importance: In this study, a neglected but important role of apoE exchange in HCV LVP infectivity after virus assembly and release was identified. The data indicated that apoE expression level in uninfected cells is important for high permissiveness to HCV infection. Secreted apoE-associated lipoprotein specifically enhances infection of HCV LVPs. apoE exchange between HCV LVP and lipoproteins is important to maintain an adequate apoE level on LVPs for their efficient attachment to cell surface. These data defined for the first time an extracellular role of exchangeable apoE in HCV infection and suggested that exchangeable apolipoproteins reach a natural equilibrium between HCV LVPs and lipoprotein particles, which provides a new perspective to the understanding of the heterogeneity of HCV LVPs in composition., (Copyright © 2016, American Society for Microbiology. All Rights Reserved.)