Purpose - This study examines how preparation for strict Port State Control Inspections (PSCI) by the Australian Maritime Safety Authority (AMSA), influences the welfare, living and working conditions of seafarers on board PSC vessels in New South Wales. It examines how the onus of ensuring regulatory compliance for ships – particularly in terms of safety, the environment, and labor – impacts the physical, mental, and emotional well-being of seafarers. Some elements of seafarers’ welfare were examined, revealing that compliance with work/rest hours, food quality, accommodation, leadership practices, and the length of the voyage are determining factors. Methodology – A quantitative, cross-sectional research design was employed to gather information from 300 seafarers across various ranks and vessel classes. Data revealed strong demand for improvements in leadership, standardization of living conditions, management of fatigue, and regard for seafarers’ physical well-being. Findings – The findings show extensive differences in welfare between ranks, with senior (management and operational) staff reporting higher levels of fatigue, stress and dissatisfaction than support personnel. A last-minute rush of too little food, improper accommodation and disrupted work/rest hours in expectation of an imminent inspection heightened stress and fatigue. Originality –.These findings suggest that allowing sufficient time to carry out inspections and attending to the welfare of seafarers can significantly reduce stress levels, which, in turn, improves the morale of the entire crew, leading to safer.

Construction of a multimodal knowledge graph for LNG carrier port state control inspections based on improved visual prompt tuning

The concept of competition is used in many areas from economy to health. As in all sectors, there is competition in the maritime transport industry. Although various Regional Memoranda of Understanding (MoU) have created risk assessment indexes for risk profile calculation, an index to measure competition in maritime transportation has not yet been used in the literature. Ships must successfully pass the inspections they face within the framework of international rules to maintain their commercial existence. The aim of this article is to provide a new perspective on measuring competition between ship types based on the results of inspections in maritime literature. In this context, for the first time in literature, the Ship Inspection Competition Index (SICI) is defined to examine the competition of ship types, flag states, recognized organizations, and ships subject to other inspection regimes. The SICI analysis was conducted using United States Coast Guard (USCG) Port State Control inspection results between 2020 and 2023. The results showed that tankers are more competitive than other ship types. At the same time, a competitiveness analysis of recognized organizations found that one organization was perfectly competitive (SICI = 0.0) compared to others. In the study, Cronbach's Alpha reliability test was conducted, and the result was 0.943 for the deficiency-based measure and 0.976 for the detention-based measure. Both results indicate a high level of reliability. It is considered that this study may provide valuable insights for future research.

ABSTRACT Port state control (PSC) inspections are essential for identifying substandard ships, enhancing safety, protecting the marine environment, and safeguarding crew welfare. Yet, correlations between ship features and deficiencies remain underexplored. This study develops a deficiency analysis framework using the Apriori algorithm to mine association rules between ship particulars and deficiencies, as well as among deficiencies. Using Paris MoU data, the results reveal overlooked deficiency clusters and ship type–deficiency patterns. The framework supports more targeted high-risk ship selection, improves PSC efficiency, and contributes to predictive models, maritime digitalization, and smart port development.

ABSTRACT Port State Control (PSC) serves as a crucial mechanism for enforcing maritime regulations and safeguarding against substandard shipping practices. Effective inspections by well-trained port state control officers (PSCOs) are essential to ensure compliance without causing undue delays or detentions, although some shipowners may attempt to evade inspections, complicating enforcement efforts. This review examines scholarly publications from 2017 and 2024 to identify prevailing research themes and methodological approaches in PSC studies. Four primary research themes emerged: (i) evaluation of the PSC regimes, (ii) strategies for effectively targeting substandard vessels, (iii) determinants of PSC inspection outcomes, and (iv) the impact of PSC inspections. A synthesis of these themes revealed nine critical issues, including disparities in regional enforcement practices, inconsistencies in inspector training and legal frameworks, limitations of current risk assessment models, data imbalance and generalizability challenges, integration of artificial intelligence (AI) and machine learning in inspection planning, influence of vessel characteristics and inspection history, impact of external shocks, effectiveness in improving compliance and safety, and stakeholder perceptions and enforcement gaps. The review identifies key knowledge gaps and offers directions for future research to strengthen PSC practices and enhance global maritime safety.

Machine learning approaches for identifying substandard ships in port state control inspections with imbalanced data

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and safety indicators of ships, including but not limited to flag state, ship age, past deficiencies, and detention history. By analyzing these factors in depth, this research enhances the efficiency and accuracy of PSC inspections, provides decision support for port authorities, and offers strategic guidance for shipping companies to comply with international safety standards. During the research process, I first conducted detailed data preprocessing, including data cleaning and feature selection, to ensure the effectiveness of model training. Using the Random Forest algorithm, I identified key factors influencing the detention risk of ships and established a risk prediction model accordingly. The model validation results indicated that factors such as ship age, tonnage, company performance, and flag state significantly affect whether a ship exhibits a high deficiency rate. In addition, this study explored the potential and limitations of applying the Random Forest model in predicting high deficiency risk under PSC, and proposed future research directions, including further model optimization and the development of real-time prediction systems. By achieving these goals, I hope to provide valuable experience for other global shipping hubs, promote higher international maritime safety standards, and contribute to the sustainable development of the global shipping industry. This research not only highlights the importance of machine learning in the maritime domain but also demonstrates the potential of data-driven decision-making in improving ship safety management and port inspection efficiency. It is hoped that this study will inspire more maritime practitioners and researchers to explore advanced data analytics techniques to address the increasingly complex challenges of global shipping.

Maritime transport remains a fundamental pillar of international trade, and ship inspections are essential to ensuring navigational safety and environmental protection. Port State Control (PSC) is a regulatory mechanism used to examine foreign-flagged vessels to verify their compliance with international maritime conventions. Among the various elements assessed during PSC inspections, a ship’s type and age significantly influence the likelihood of identifying deficiencies. This study focuses on the PSC inspection data collected from the ports of Kocaeli, a major maritime hub in Turkey. The objective is to analyze how vessel age and type correlate with the frequency and severity of recorded deficiencies. Using the Analysis of Variance (ANOVA) statistical method, the research compares deficiency rates across various ship categories and age brackets. Additionally, the study examines inspection frequency regarding the risk profiles of different vessels. Findings indicate that ships aged 12 years or older exhibit a higher number of deficiencies, with general cargo vessels being particularly prone to non-compliance. While the overall effectiveness of PSC inspections is evident, the study highlights the need for improved efficiency in inspection protocols. It is suggested that risk assessment models be refined to include more detailed criteria and that inspection strategies be adapted based on vessel characteristics. Furthermore, enhancing pre-inspection preparedness by ship operators may contribute to better compliance outcomes. The study aims to support safer maritime operations by offering targeted recommendations for optimizing PSC inspections at Kocaeli Port.

ABSTRACT Limited port state control (PSC) inspection resources pose an urgent need to enhance PSC inspection efficiency. While existing PSC officers (PSCOs) assignment randomly assign ships with PSCOs, this study aims to predict ship deficiency numbers under various deficiency categories (types of non-compliance) using machine learning (ML) models. Moreover, not all foreign visiting ships to a port are inspected by PSC, which leads to a large amount of unlabeled data remaining unexplored. Using the port of Singapore as a case study, this paper utilizes both labeled and unlabeled data to predict ship deficiency numbers under the six deficiency categories of individual ships. A semi-supervised multi-target regression (SSMTR) framework is developed, which innovates in using prediction performance on the validation dataset to judge the reliability of unlabeled data. The SSMTR framework is extended to various ML regression methods, such as decision tree (DT), support vector regression (SVR), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP), resulting in DT-SSMTR, SVR-SSMTR, XGBoost-SSMTR, and MLP-SSMTR. Across four experiment groups with different numbers of labeled data samples, the mean squared error improves on average by 13.65% for DT-SSMTR, 1.62% for SVR-SSMTR, 4.39% for XGBoost-SSMTR, and 2.65% for MLP-SSMTR compared to models that only use labeled data.

This literature review provides a structured quantitative analysis of existing research on the application of machine learning models (MLMs) and multi-criteria decision-making methods (MCDM) in the context of port state control (PSC). The aim of the study is to capture current research trends, identify thematic priorities, and demonstrate how these analytical tools have been used to support decision-making and risk assessment in the maritime domain. Rather than evaluating the effectiveness of individual models, the study focuses on the distribution and frequency of their use and provides insights into the development of methodological approaches in this area. Although several studies suggest that the integration of MLMs and MCDM techniques can improve the objectivity and efficiency of PSC inspections, this report does not provide a comparative assessment of their performance. Instead, it lays the groundwork for future qualitative studies that will assess the practical benefits and challenges of such integration. The findings suggest a fragmented but growing research interest in data-driven approaches to PSC and highlight the potential of advanced analytics to support maritime safety and regulatory compliance.

Port State Control (PSC) inspections play a crucial yet challenging role in detecting substandard ships. However, the process becomes more complicated when several states in large geographical regions agree to collaborate with the same rules. This study addresses this challenge by proposing an innovative hybrid methodology that combines content analysis of PSC appeal cases with the Fuzzy Analytical Hierarchy Process (F-AHP) to assess regulatory inconsistencies within the Asia–Pacific region. A total of 43 PSC appeal cases from the Tokyo MoU were analysed to identify key areas of regulatory disputes. The findings indicate that SOLAS (53%) and MARPOL (26%) regulations are the most frequently contested, followed by document validity and equipment maintenance issues. Notably, in 84% of successful appeals, PSC officers were found to have applied overly strict interpretations or detained ships without conducting sufficient additional tests. Post-2015, successful appeals favouring shipowners increased to 68%, primarily due to deficiencies related to the International Safety Management (ISM) Code. By applying F-AHP and expert reviews, the study prioritised the most problematic areas of disputes, assigning a combined weight of 0.728 to procedural non-compliance and PSC officers' omissions. Key examples include expired ship equipment certificates (0.159), disagreements on regulatory interpretation and allegations of hidden defects by the crew (0.145), and failure to perform detailed examinations or additional tests (0.131). To address these challenges, the paper recommends adopting digital tools for recording inspections and real-time verification of certificates. Furthermore, clear communication of PSC procedures to seafarers may reduce disputes in ship detentions. These findings offer practical insights for policymakers and port authorities to reduce unnecessary delays and improve compliance through inspection consistency.

This study analyzes the results of Port State Control (PSC) inspections carried out under the Paris Memorandum of Understanding between 2018 and 2022. Through a correspondence analysis, the most frequent deficiencies were identified according to the type of ship being inspected. The study sample included 186,255 inspections obtained from the THETIS platform. The results revealed significant heterogeneity in deficiency profiles across ship types, highlighting specific patterns associated with each category. Container ships, oil tankers and bulk carriers, for instance, exhibited distinctive deficiency profiles. The study emphasizes the necessity for a tailored approach to PSC inspections, with the objective of optimizing resources through the utilization of risk zone indicators for the inspector. The identification of specific risk indicators would not only facilitate the work of inspectors but also enable the earlier detection of potential problems and more effective intervention. The study provides a solid foundation for future research and decision-making on PSC inspections, with the aim of enhancing maritime safety and pollution prevention.

This study investigates the application of different neural network (NN) models in assessing the risk of the detention of offshore vessels during port state control (PSC) inspections. The focus is on the use of different NN models (“nnet”, “mlp”, “neuralnet”, “rsnns”) to identify the main risk factors based on historical data on vessels and their inspections. The main objective of this research is to improve maritime safety and the efficiency of inspection procedures by applying techniques that can more accurately predict the probability of detention of the offshore vessels. These models make it possible to analyse complex patterns in the data, such as the relationships between the country of inspection, flag, memorandum, age, tonnage and previous deficiencies, and the risk of detention. Understanding these patterns is crucial for inspection teams’ proactive action as it helps direct resources to potentially high-risk vessels. Implementing these models into PSC processes helps to optimise resource allocation, reduce unnecessary costs, and increase the reliability of decision-making processes. NN models significantly help in recognising non-linear patterns and provide high accuracy in risk prediction. The study also includes a comparative analysis of the elements that determine the accuracy, sensitivity, and other performance aspects of the models to determine the most appropriate approach for practical implementation. The results emphasise the importance of applying artificial intelligence (AI) in various aspects of modern maritime safety management. This research opens up new opportunities for the development of intelligent support systems that not only increase safety but also improve the efficiency of inspection processes on a global scale.

The Port State Control (PSC) inspection of liquefied natural gas (LNG) carriers is crucial in maritime transportation. PSC inspection requires rapid and accurate identification of defects with limited resources, necessitating professional knowledge and efficient technical methods. Knowledge distillation, as a model lightweighting approach in the field of artificial intelligence, offers the possibility of enhancing the responsiveness of LNG carrier PSC inspections. In this study, a knowledge distillation method is introduced, namely, the multilayer dynamic multi-teacher weighted knowledge distillation (MDMD) model. This model fuses multilayer soft labels from multi-teacher models by extracting intermediate feature soft labels and minimizing intermediate feature knowledge fusion. It also employs a comprehensive dynamic weight allocation scheme that combines global loss weight allocation with label weight allocation based on the inner product, enabling dynamic weight allocation across multiple teachers. The experimental results show that the MDMD model achieves a 90.6% accuracy rate in named entity recognition, which is 6.3% greater than that of the direct training method. In addition, under the same experimental conditions, the proposed model achieves a prediction speed that is approximately 64% faster than that of traditional models while reducing the number of model parameters by approximately 55%. To efficiently assist in PSC inspections, an LNG carrier PSC inspection knowledge graph is constructed on the basis of the recognition results to quickly and effectively support knowledge queries and assist PSC personnel in making decisions at inspection sites.

Arctic shipping is a significant source of greenhouse gas (GHG) emissions, including carbon dioxide and black carbon, which intensify climate risks in the region. While the International Maritime Organization (IMO) has established the International Code for Ships Operating in Polar Waters (Polar Code) to address environmental and safety concerns of polar navigation, it falls short in promoting the decarbonization of Arctic shipping. The collaboration between the IMO and the Arctic Council, along with the contributions of the Arctic Council’s task forces, is essential but requires further strengthening. In response to the climate crisis, the IMO has raised environmental standards, leading efforts to promote low-carbon growth in Arctic shipping through measures such as sulfur limits, heavy fuel oil bans, and reductions in black carbon emissions. Despite these initiatives, the governance of Arctic shipping decarbonization remains fragmented. To achieve meaningful decarbonization, the Polar Code must be strengthened and expanded into a unified regulatory framework. Additionally, enhanced collaboration between the IMO and the Arctic Council is crucial to maximize their collective impact. As a key player in Arctic shipping, China must strengthen compliance with international regulations through updated domestic legislation and Arctic policies. By actively engaging in multilateral mechanisms and developing a port state control inspection network, China can play a pivotal role in advancing Arctic shipping governance and IMO energy efficiency standards, contributing to a more coordinated and sustainable approach to the region’s environmental challenges and global maritime governance.

ABSTRACT In global maritime safety, the efficiency of Port State Control (PSC) is paramount in ensuring the safety of sea. Facing the challenge of effectively identifying high-risk vessels, this study innovatively enhances PSC inspection efficiency. This study aims to reduce maritime accidents by significantly improving the efficiency and accuracy of detained vessels prediction through multi-source data fusion technology and the enhanced AdaBoost algorithm. AdaBoost, improves model accuracy by combining multiple weak classifiers. By comprehensively analyzing ship inspection records from 2015 to 2022 across various Chinese ports, combined with additional vessel information, this research constructs a developed predictive model to forecast the likelihood of ships being detained by PSC in Chinese ports. The proposed model successfully identified numerous non-detained and detained ships and achieved highly satisfactory predictive results on the training dataset. Through in-depth analysis of crucial evaluation metrics such as precision, recall, F1 score, and ROC, this study provides strong technical support for accurately identifying high-risk vessels that play a vital role in enhancing maritime safety. Moreover, our findings offer valuable insights for port managers to optimize ship selection processes and for shipping companies to improve operational efficiency, having a profound impact on the safety and development of the maritime transportation industry.

This research investigates the legal and practical dimensions of Port State Control (PSC) inspections as mandated by the International Convention for the Prevention of Pollution from Ships (MARPOL). Utilizing a qualitative methodology, the study integrates comprehensive document analysis, semi-structured interviews with maritime stakeholders, and illustrative case studies to evaluate the efficacy of PSC inspections in ensuring regulatory compliance and environmental protection. The findings indicate a relatively stable compliance rate of 94.1% to 95.7% over a five-year period, with recurring deficiencies primarily identified in Oil Record Book discrepancies and sewage treatment systems. Significant regional variations were observed, wherein European ports demonstrated stricter enforcement mechanisms compared to those in other regions, highlighting disparities in global implementation practices. Challenges such as resource constraints, inconsistent inspector training, and limited collaboration between port states and flag states were identified as critical barriers to effective enforcement. The study emphasizes the necessity of targeted training programs, enhanced inter-regional cooperation, and the integration of advanced technologies to improve inspection processes and foster a uniform enforcement regime. These findings contribute to the discourse on maritime governance by offering evidence-based recommendations for policymakers and regulatory bodies aimed at strengthening the effectiveness of PSC inspections and advancing marine environmental protection.

Improving port state control through a transfer learning-enhanced XGBoost model

A knowledge graph-based inspection items recommendation method for port state control inspection of LNG carriers

Port state control (PSC) inspections, considered a crucial means of maritime safety supervision, are viewed by the industry as a critical line of defense ensuring the stability of the international supply chain. Due to the high level of globalization and strong regional characteristics of PSC inspections, improving the accuracy of these inspections and efficiently utilizing inspection resources have become urgent issues. The construction of a PSC inspection ontology model from top to bottom, coupled with the integration of multisource data from bottom to top, is proposed in this paper. The RoBERTa-wwm-ext model is adopted as the entity recognition model, while the XGBoost4 model serves as the knowledge fusion model to establish the PSC inspection knowledge graph. Building upon an evolutionary game model of the PSC inspection knowledge graph, this study introduces an evolutionary game method to analyze the internal evolutionary dynamics of ship populations from a microscopic perspective. Through numerical simulations and standardization diffusion evolution simulations for ship support, the evolutionary impact of each parameter on the subgraph is examined. Subsequently, based on the results of the evolutionary game analysis, recommendations for PSC inspection auxiliary decision-making and related strategic suggestions are presented. The experimental results show that the RoBERTa-wwm-ext model and the XGBoost4 model used in the PSC inspection knowledge graph achieve superior performance in both entity recognition and knowledge fusion tasks, with the model accuracies surpassing those of other compared models. In the knowledge graph-based PSC inspection evolutionary game, the reward and punishment conditions (n, f) can reduce the burden of the standardization cost for safeguarding the ship. A ship is more sensitive to changes in the detention rate β than to changes in the inspection rate α. To a certain extent, the detention cost CDC plays a role similar to that of the detention rate β. In small-scale networks, relevant parameters in the ship’s standardization game have a more pronounced effect, with detention cost CDC having a greater impact than standardization cost CS on ship strategy choice and scale-free network evolution. Based on the experimental results, PSC inspection strategies are suggested. These strategies provide port state authorities with auxiliary decision-making tools for PSC inspections, promote the informatization of maritime regulation, and offer new insights for the study of maritime traffic safety management and PSC inspections.