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Surface modification with noble metal is an effective strategy to improve gas sensitive properties of semiconductive materials. Herein, hollow MoS 2 /C nanoreactors assembled with nanosheets were synthesized with the incorporation of water-oil two-phase microemulsion method and following heating treatment. Through adjusting the polarity of hydrothermal reaction solvent, Au nanoparticles (NPs) of different size were loaded on MoS 2 /C nanoreactors, whose average size could be efficiently tailored from 5.73 to 42.6 nm. The results of gas sensing measurement show that Au modification can remarkably increase triethylamine (TEA) sensing properties of MoS 2 /C–based sensor at lower temperature. Especially, for Au@MoS 2 /C-2 with Au NPs size of about 9.9 nm, the response value to 50 ppm TEA at 160 °C is high up to 240.5, which is 20 times higher than that of MoS 2 /C, and the response-recovery times are highly reduced from 80 s/500 s–13 s/198 s. Besides, Au@MoS 2 /C-2 sensitive element has excellent selectivity, humidity resistance, and long-term stability for TEA. This enhancement in TEA response is properly attributed to the modified interfacial interaction between Au NPs and MoS 2 /C, which leads to the formation of Schottky contact and boosts the charge transfer. So, hollow Au@MoS 2 /C nanoreactors will be a potential candidate for TEA detection. • A group of hollow Au@MoS2/C nanoreactors were synthesized. • The sizes of Au NPs were tailored by adjusting the polarity of reaction solvent. • Au@MoS2/C-2 sensor exhibited the best sensing performance to TEA at low temperature. • The enhanced gas sensing mechanism for TEA was revealed by DFT results. [ABSTRACT FROM AUTHOR]

This study provides an empirical analysis of the injury severity outcomes of maritime accidents by exploring the influential factors for two underlying injury severity states: the injury-free state and the injury-prone state. The former may reflect the generation mechanism of accidents with limited potential to result in injury outcomes, whereas, the latter may represent injury severity when the accident falls into the injury-prone category. To account for the possible presence of these two underlying regimes, a zero-inflated ordered probit (ZIOP) model is employed using injury-severity data extracted from 1,128 maritime accident investigation reports between 2000 and 2019. The results indicate that on one hand, capsizing/sinking, hull/machinery damage and other accident type, adverse sea state, poor education background and short period of holding the present rank are more likely to be injury-prone. On the other hand, gross tonnage and water depth, distance from the coast, flag state of convenience, and accident type impact the likelihood of severe injuries if the accident is in the injury-prone category. The marginal effect analysis highlights some interesting effects caused by sea state, ship manning and gross tonnage, as well as accident type and location. The results of Akaike's information criteria (AIC), Bayesian information criteria (BIC) and Vuong's test show that the ZIOP model outperforms the traditional ordered probit model and can serve as an alternative to study the injury severity of maritime accidents. • This study investigated the relationship between the influencing factors and the injury severity of maritime accidents. • The zero-inflated ordered probit (ZIOP) regression was used for model development. • The proposed approach consists of a binary probit and an ordered probit component. • Probabilities of injury-prone were linked with variables of accident, ship, seafarer, and environment factors. [ABSTRACT FROM AUTHOR]

Based on the global maritime accident data from 2010 to 2019, density analysis and clustering analysis have been used to analyse the spatial patterns of maritime accidents in terms of accident frequency and severity. The North Sea, the Baltic Sea and the Mediterranean Sea form low severity accident clustering. More than 60% accidents are found within the sea areas less than 30 nm to the coastline. As to the spatial characteristics of maritime accident severity, the coastal waters surrounding China, Japan, South Korea, Vietnam and the Philippines, the Singapore-Malacca Strait and the Bay of Biscay form high severity accident clustering. The North Sea, the Baltic Sea and the Mediterranean Sea form low severity accident clustering in the clustering analysis although they have medium and high densities of accident severity in the density analysis. Almost 60% of serious accidents and very serious accidents are found within 30 nm to the coastline. The comparison of the results of density analysis and clustering analysis indicate that the latter can provide more abundant spatial characteristic information, while the former is superior in terms of simplicity and computational efficiency. This study provides useful information to assist the relevant maritime authorities in improving maritime traffic management. • Spatial analysis of 2,513 worldwide maritime accidents from 2010 to 2019 based on the GISIS database. • Thorough literature review on the application of GIS in maritime fields. • Identification of hot spots of maritime accidents with considering and without considering ship traffic density. • Analysis and comparison of maritime accident severity spatial patterns using density analysis and clustering analysis. • Assessment on the navigational safety of coastal waters by calculating the proportion of maritime accidents of different severity levels within specific buffer zones. [ABSTRACT FROM AUTHOR]

• A statistical analysis of 1207 marine accidents worldwide from 2010 to 2019. • A critical literature review on previous studies on marine accident severity. • A study on the relationship between the influencing factors and accident severity. • Provision of recommendations for the improvement of maritime safety. This study aims to explore the relationship between the severity of marine accidents and influencing factors. An ordered logistic regression model is used to reflect the relationship between these factors and the severity of marine accidents using the worldwide accident investigation reports in the period of 2010–2019. The obtained results show that the marine accident severity is positively associated with sinking accidents, far away from port, strong wind, heavy sea, strong current and/or good visibility. With respect to ship types, fishing vessels, yachts and sailing vessels, and other ship types are the ship types most involved in accidents of higher severity. The severity level is higher for ships having incomplete or invalid seafarers' certificates, inadequate ship manning, incomplete or invalid ship certificates and/or over 30 years of age. Seafarers with poor theoretical knowledge and less sea experience are more likely to be involved in accidents of serious consequences. Small water depth and ship types such as chemical tankers, oil tankers, container ships and/or bulk carriers are negatively related to the accident severity. The results of this study can be used to assist the relevant maritime authorities in taking effective measures of preventing the occurrence of serious marine accidents. [ABSTRACT FROM AUTHOR]

To manage and control ship collision risk, this paper aims to develop a quantitative risk analysis approach based on complex networks and Decision-making Trial and Evaluation Laboratory (DEMATEL), to study the evolution of ship collision accidents. Firstly, through the search, selection and analysis of accident investigation reports of ship collisions in the world, 46 Risk Influential Factors (RIFs) were identified, and a network model for the evolution of ship collision was established. Secondly, a risk analysis of the evolution model for ship collision accidents was conducted by utilizing the topology characteristics of the complex network theory. Thirdly, a robustness analysis was used to identify 10 key RIFs involved in ship collisions from a global network perspective, and the grey relation analysis technique was employed to verify the key RIFs. Finally, the DEMATEL method was utilized to identify 11 key RIFs from a perspective of causality, and corresponding risk control measures were proposed to mitigate those key RIFs. The research results provide theoretical support for identifying the key RIFs and controlling the risk transmission of ship collision, and have practical significance for preventing the occurrence of ship collision accidents and ensuring the safe navigation of ships. • A complex network model for the evolution of ship collision was established. • A risk analysis of the evolution model for ship collision accidents was conducted. • A robustness analysis was used to identify 10 key RIFs from a global network perspective. • DEMATEL method was utilized to identify 11 key RIFs from a perspective of causality. • The corresponding risk control measures were proposed to mitigate those key RIFs. [ABSTRACT FROM AUTHOR]

In order to analyse the research evolution and knowledge frontier in the research of marine accidents, 491 literatures on marine accidents in the Web of Science database from 2000 to 2022 are taken as data sources. Integrated with literature analysis of traditional method, CiteSpace and VOSviewer are then jointly used for the development of the knowledge network map and cluster analysis, and the knowledge of network map, research hotpots, research evolution and knowledge frontiers is obtained. It is found that there is a close cooperative relationship among journals, researchers, research institutions and countries or regions. According to the subjects and methods, the study of marine accidents can be divided into two parts: the analysis of the influential factors and accident consequences, as well as the methodology development of traditional and emerging technology. In this context, the analysis of human factors in remote-controlled ships, the prevention of accidents in Arctic waters have become research hotspots, while emerging accident analysis methods such as machine learning and big data mining also have shown powerful insights in the analysis of marine accidents. In terms of innovation, the bibliometric approach enhances the ability to handle large literature databases and conduct network analysis. Moreover, this study visualises collaborative networks, analyses evolution trends, reveals the research hotpots, and conducts a comparison and discussion of mainstream approaches in marine accident research. As a result, this study provides a theoretical basis and implementation direction for the development of maritime safety. • A systematic review and outlook of marine accident research is conducted. • A more comprehensive literature database of marine accident research is established. • The research hotpots and knowledge frontiers of marine accident research are identified. • The contributions, innovations and the future directions of marine accident research are discussed. [ABSTRACT FROM AUTHOR]

A data-driven Bayesian network model (BN) is used to analyse the relationship between the severity of marine accidents and relevant Accident Influential Factors (AIFs). Firstly, based on the marine accident investigation reports involving 1,294 ships from 2000 to 2019, the severity grades of marine accidents are classified, and a database of factors affecting the severity of marine accidents is established. Secondly, a Tree Augmented Naive Bayesian algorithm (TAN) is used to establish a data-driven BN model, and the established database of AIFs is analysed by data training and machine learning to reveal the influence of related factors on the severity of the accident and the mechanism of action. Finally, the sensitivity analysis and verification of the model are conducted. Through the analysis of the Most Probable Explanation (MPE), it explains the possible configurations in different scenarios and identifies the related potential risks. This study finds that "accident type", "engine power", "gross tonnage", "ship type" and "location" are the five most important AIFs of three accident severity grades. "Capsizing/sinking", "hull/machinery damage" and "collision" that are most likely to lead to "very serious accidents". Further, the possibility of fishing boats or other small ships leading to "very serious accidents" is also higher than that of other types of ships. The results of this study can help to analyse and predict marine accidents and ensure the safe navigation of ships and hence benefit such maritime stakeholders as safety authorities and ship owners. • A new method to analyse the relationship between the severity of marine accidents and AIFs is developed. • A database of factors affecting the severity of marine accidents is established. • A data-driven model integrated BN and TAN algorithm is proposed. • The sensitivity analysis and verification of the model are conducted. [ABSTRACT FROM AUTHOR]

Abstract: One-dimensional semiconductor nanostructures have demonstrated significant advantages for electrochemical electrode due to their remarkable size-dependent and structure-related properties. In this work, large-scale single-crystalline SnO2 nanowires have been successfully synthesized by thermal evaporation of a SnO2 and C powder under atmospheric conditions without any carrier gas or noble metal catalysts-coated substrate. The as-prepared samples exhibit uniform morphology with a thin diameter of 100–200 nm and length up to several tens of micrometers. The SnO2 nanowires directly grown on the initially deposition layer are found to grow along [301] direction, which is possibly dominated by a vapor–solid (VS) mechanism. The SnO2 nanowires synthesized by this simple and cheap method deliver a very high lithium storage capacity with good cycle stability and high rate capability, allowing for the achievement of high energy density and long cycle life for the next-generation energy conversion and storage devices. [Copyright &y& Elsevier]

Abstract: Perovskite strontium stannate (SrSnO3) nanorods were prepared by annealing the precursor SnSr(OH)6 nanorods at 600°C for 3h. The precursor nanorods were hydrothermally synthesized at 160°C for 16h using Sr(NO3)2 and SnCl4·5H2O as starting materials in the presence of surfactant cetyltrimethyl ammonium bromide (CTAB). As-prepared samples were characterized by X-ray diffraction (XRD), thermogravimetric-differential thermal analysis (TG-DTA), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and infrared ray spectroscopy (IR). The results show that the as-synthesized powders are made of SrSnO3 one-dimensional nanorods of about 0.2–1μm length and 100–150nm diameter. Possible formation mechanism of SrSnO3 with nanorod structure under certain conditions was preliminarily analyzed, in which it was thought that CTAB played an important role in the formation process of the nanorod structure. Electrochemical performance of the samples versus Li metal was also evaluated for possible use in lithium-ion batteries. [Copyright &y& Elsevier]

Abstract: A novel CuO-nanotubes/SnO2 composite was prepared by a facile solution method and its electrochemical properties were investigated as the anode material for Li-ion battery. The as-prepared composite consisted of monoclinic-phase CuO-nanotubes and cassiterite structure SnO2 nanoparticles, in which SnO2 nanoparticles were dramatically decorated on the CuO-nanotubes. The composite showed higher reversible capacity, better durability and high rate performance than the pure SnO2. The better electrochemical performance could be attributed to the introducing of the CuO-nanotubes. It was found that the CuO-nanotubes were reduced to metallic Cu in the first discharge cycle, which can retain tube structure of the CuO-nanotubes as a tube buffer to alleviate the volume expansion of SnO2 during cycling and act as a good conductor to improve the electrical conductivity of the electrodes. [Copyright &y& Elsevier]

• Survey of 1380 passengers on their likely behaviours in a Ro-Ro passenger ship. • Population's composition on this route is different from IMO recommendations. • Passengers on board are more alert and be proactive, cooperative and follow PA. • A series of multinomial logistic regression models applied. • Demographic differences in likely behaviours were observed. By examining the characteristics of passengers on a ship route between the Shandong and Liaodong Peninsula, through a questionnaire survey, this paper aims to address the likely behaviours of passengers during emergency evacuation and the demographic differences among these behaviours. A questionnaire survey of 1380 passengers shows that passengers on board are more alert and are more likely to proactively respond to evacuation alarms (62.5%), observe others' actions (59.1%), follow evacuation instructions (67.9%), obey the crew (66.2%), queue patiently (63%), return to the cabin when their families are left behind (65.1%), and be cooperative (59%) rather than competitive (44%). The multinomial logistic regression results show that passengers who are older, with limited mobility, that have more experience aboard ships and are part of a larger group, will be more likely to proactively confirm the authenticity of evacuation events. Men, elderly individuals, people who are part of a larger group and with less experience in evacuation education are more likely to follow others. When the family is left behind, elderly individuals and people who are part of a larger group are much more likely to choose to return to their cabins. Similarly, elderly passengers with larger groups are much more likely to choose to help others. Although questionnaire research has some limitations, such as a hypothetical response and closed questions, the research results are of great significance for helping passenger ship managers to develop appropriate management rules, and conduct effective evacuation education activities. [ABSTRACT FROM AUTHOR]