How do navy escorts influence piracy risk in East Africa? A Bayesian network approach

Navy escorts are considered crucial in countering illegal piracy attacks. In this paper, a novel approach is developed to investigate the effect of navy escorts on piracy incidents by models based on two enhanced Tree-Augmented Naïve (TAN) Bayesian networks. This approach offers a systematic investigation into the various factors that influence pirate activities, and helps to identify changes in piracy attack behaviors when confronted by navy escorts and assess the effectiveness of anti-piracy measures. An empirical study is conducted utilizing a unique data set compiled from multiple sources from 2000 to 2019. The empirical evidence shows that there was a gradual reduction in the incidence of piracy attacks in East Africa following the implementation of navy escorts in 2009, but with a surge in 2010 and 2011. The data set is, thus, divided into two time periods at the point of 2009 to facilitate a robust and comprehensive analysis, resulting in the development of two TAN models. Meanwhile, the geographical distribution of pirate attacks has shifted from international waters to port areas and territorial waters. We argue that the surge and geographical shift could be attributed to the calculating behavior of pirates when they encounter external pressures. Finally, a Shapely approach is introduced to evaluate the potential effectiveness of the implemented risk management strategies from a Game Theory perspective. This study offers new insights into the promotion of navy escorts and contributes to the development of a framework for assessing piracy risks in uncertain and dynamic anti-piracy environments.

Deep Learning-Assisted Triboelectric Smart Mats for Personnel Comprehensive Monitoring toward Maritime Safety

Monitoring the crew of a ship can be performed by combining sensors and artificial intelligence methods to process sensing data. In this study, we developed a deep learning (DL)-assisted minimalist structure triboelectric smart mat system for obtaining abundant crew information without the privacy concerns of taking video. The smart mat system is fabricated using a conductive sponge with different filling rates and a fluorinated ethylene propylene membrane. The proposed dual-channel measurement method improves the stability of the generated signal. Comprehensive crew and cargo monitoring, including personnel and status identification, as well as positioning and counting functions are realized by the DL-assisted triboelectric smart mat system according to the analysis of instant sensory data. Real-time monitoring of crews through fixed and mobile devices improves the ability and efficiency of handling emergencies. The smart mat system provides privacy concerns and an effective way to build ship Internet of Things and ensure personnel safety.

Can Survey Measures Predict Key Performance Indicators of Safety? Confirmatory and Exploratory Analyses of the Association Between Self-Report and Safety Outcomes in the Maritime Industry

Safety management may be improved if managers implement measures based on reliable empirical knowledge about how psychological factors cause or prevent accidents. While such factors are often investigated with self-report measures, few studies in the maritime industry have investigated whether self-report measures predict objectively registered accidents. The current pre-registered study used structural equation modelling to test whether “Safety attitude,” “Situation awareness,” “Reporting attitude” and “Safe behaviour” predicted “Number of reports” and “Number of safety events” in the following year. The study was conducted among crew on chemical tanker vessels operating in Arctic and Baltic waters. The pre-registered model of expected associations between self-reported safety factors and recorded safety outcomes was not supported. However, an exploratory model based on the pre-registered hypotheses supported an association between self-reported “Safe behaviour” and the overall number of recorded safety outcomes. While much safety research in the maritime industry builds on the assumption that self-reported behaviour, attitude or cognitions are causally related to actual accidents, the current study shows that such a relationship can be difficult to confirm. Until more conclusive studies are performed, the assumed causal relationship between self-reported psychological factors and safety outcomes should be treated with caution.

Exploring the Underlying Causes of Chinese Eastern Star, Korean Sewol, and Thai Phoenix Ferry Accidents by Employing the HFACS-MA

Maritime safety is a significant topic in the maritime industry since the numerous dangers at sea could lead to loss of property, environmental pollution, and even casualties. Existing research illustrates that human factors are the primary reasons of maritime accidents. Indeed, numerous maritime accidents can be classified into different types of human factors. In this context, the Human Factors Analysis and Classification System for Maritime Accidents (HFACS-MA) model is introduced in this paper. The HFACS-MA framework consists of five levels, complying with the core concepts of HFACS and the guiding principles of the International Maritime Organization (IMO). Based on the five levels of the framework, this research explores the underlying causes of Chinese Eastern Star, Korean Sewol, and Thai Phoenix accidents, and a comparative analysis is conducted. The analysis demonstrates the utility of applying the HFACS-MA model to the maritime industry, and the results emphasize the importance of the following categories: legislation gaps, organizational process, inadequate supervision, communication (ships and VTS), decision errors, and so on. Consequently, the research enables increased support for HFACS-MA and its application and provides valuable information for safety management and policy development in the maritime industry at different levels.