Search Results (68)

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. Full article

With the increasing digitalization of maritime transportation, the demand for structured and interoperable data has grown. While the S-100 framework developed by the International Hydrographic Organization (IHO) provides a foundation for standardizing maritime information, a data model for representing marine casualties has not yet been developed. As a result, past incident data—such as collisions or groundings—remain fragmented in unstructured formats and are excluded from electronic navigational systems, limiting their use in safety analysis and route planning. To address this gap, this paper proposes a data model for structuring and visualizing marine casualty information within the S-100 standard. The model was designed by defining an application schema, constructing a machine-readable feature catalogue, and developing a portrayal catalogue and custom symbology for integration into Electronic Navigational Charts (ENCs). A case study using actual casualty records was conducted to examine whether the model satisfies the structural and portrayal requirements of the S-100 framework. The proposed model enables previously unstructured casualty data to be standardized and spatially integrated into digital chart systems. This approach allows accident information to be used alongside other S-100-based data models, contributing to risk-aware route planning and future applications in smart ship operations and maritime safety services. Full article

The eyeglass lens manufacturing industry has become increasingly vulnerable to supply chain risks due to overlapping global disruptions, including the COVID-19 pandemic, the Suez Canal blockage, the Russia–Ukraine conflict, Red Sea shipping insecurity, and recent U.S. import tariffs. These events have challenged inventory planning, supplier coordination, and cost control across the industry. This study aims to evaluate how five operational constructs—stock system, inventory optimization, standardized methodology, production capability, and logistics performance—influence inventory resilience during global crises. Using an empirical case study, data were collected from 215 supply chain professionals at a multinational lens manufacturer in Southeast Asia and analyzed via Structural Equation Modeling (SEM). The results show that inventory optimization (β = 0.93) is the most influential factor in mitigating supply–demand imbalances, followed by logistics performance and production capability. This study offers practical recommendations, including real-time demand tracking, modular production systems, and scalable logistics strategies, to enhance inventory resilience. These findings contribute to both theory and practice by providing a validated framework tailored to high-precision manufacturing under persistent global risk. Full article

Enhancing the safety standards of autonomous ships is a shared objective of all stakeholders involved in the maritime industry. Since the existing hazard analysis work for autonomous ships often exhibits a degree of subjectivity, in the absence of data support, the verification of hazard analysis results has become increasingly challenging. In this study, a formal verification method in a risk-based assessment framework is proposed to verify the hazard analysis results for autonomous ships. To satisfy the characteristics of high time sensitivity, time automata are adopted as a formal language while model checking based on the formal verification tool UPPAAL is used to complete the automatic verification of the liveness of system modeling and correctness of hazard analysis results derived from extended System-Theoretic Process Analysis (STPA) by traversing the finite state space of the system. The effectiveness of the proposed method is demonstrated through a case study involving a remotely controlled ship. The results indicate that the timed automata network model for remotely controlled ships, based on the control structure, has no deadlocks and operates correctly, which demonstrates its practicability and effectiveness. By leveraging the verification of risk analysis results based on model checking, the framework enhances the precision and traceability of these inputs into RBAT. The results disclose the significance of the collaborative work between safety and system engineering in the development of autonomous systems under the definition of human–computer interaction mode transformation. These findings also hold reference value for other intelligent systems with potential hazards. Full article

Despite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that govern vessel conduct to avoid collisions. Borderline encounter situations—such as those between head-on and crossing, or overtaking and crossing—pose particular challenges, often resulting in inconsistent or ambiguous interpretations. This study models navigator awareness as a probabilistic function of encounter angle, aiming to identify interpretive transition zones and cognitive uncertainty in rule application. A structured survey was conducted with 101 licensed navigators, each evaluating simulated ship encounter scenarios with varying relative bearings. Responses were collected using a Likert scale and analyzed in angular sectors known for interpretational ambiguity: 006–012° for head on to crossing (HC) and 100–160° for overtaking to crossing (OC). Gaussian curve fitting was applied to the response distributions, with the awareness center (μ) and standard deviation (σ) serving as indicators of consensus and ambiguity. The results reveal sharp shifts in awareness near 008° and 160°, suggesting cognitively unstable zones. Risk-averse interpretation patterns were also observed, where navigators tended to classify borderline situations more conservatively under uncertainty. These findings suggest that navigator awareness is not deterministic but probabilistically structured and context sensitive. The proposed awareness modeling framework helps bridge the gap between regulatory prescriptions and real world navigator behavior, offering practical implications for MASS algorithm design and COLREGs refinement. Full article

Amid the dual imperatives of global trade expansion and low-carbon transition, intelligent maritime shipping has emerged as a central driver for the innovation of international logistics systems, now entering a critical window period for the deep integration of Internet technologies and automated port infrastructure. While existing research predominantly focuses on isolated applications of intelligent technologies, systematic evaluations of the synergistic effects of technological integration on maritime ecosystems, policy compatibility, and contributions to global carbon emission governance remain under-explored. Leveraging bibliometric analysis, this study systematically examines 488 publications from the Web of Science (WoS) Core Collection (2000–2024), yielding three pivotal findings: firstly, China dominates the research landscape, with a 38.5% contribution share, where Artificial Intelligence (AI), the Internet of Things (IoT), and port automation constitute the technological pillars. However, critical gaps persist in cross-system protocol standardization and climate-adaptive modeling, accounting for only 2.7% and 4.2% of the literature, respectively. Secondly, international collaboration networks exhibit pronounced “Islamization”, characterized by an inter-team collaboration rate of 17.3%, while the misalignment between rapid technological iteration and existing maritime regulations exacerbates industry risks. Thirdly, a dual-track pathway integrating Cyber–Physical System (CPS)-based digital twin ports and open-source vertical domain-specific large language models is proposed. Empirical evidence demonstrates its efficacy in reducing cargo-handling energy consumption by 15% and decision-making latency by 40%. This research proposes a novel tripartite framework, encompassing technological, institutional, and data sovereignty dimensions, to resolve critical challenges in integrating multi-source maritime data and managing cross-border governance. The model provides academically validated and industry-compatible strategies for advancing sustainable maritime intelligence. Subsequent investigations should expand data sources to include regional repositories and integrate interdisciplinary approaches, ensuring the adaptability of both technical systems and international policy coordination mechanisms across diverse maritime ecosystems. Full article

The autonomy of transport systems presents a transformative opportunity to enhance logistics efficiency, improve safety, and support decarbonization. In the maritime sector, the International Maritime Organization (IMO) has been working since 2016 to develop a mandatory regulatory framework for Maritime Autonomous Surface Ships (MASSs), aiming to finalize a comprehensive code. Simultaneously, pilot projects are underway in national waters under the oversight of national administrations. Naval applications of autonomous ships demonstrate their potential, as emerging doctrines highlight their strategic and operational advantages. Although the military sector is not governed at the international level, safely managing interactions between military and commercial MASSs is crucial for ensuring safe navigation. Classification societies play a vital role in achieving high safety standards and ensuring regulatory compliance. This study aims to propose a framework for certifying maritime autonomous vessels. Through a thorough analysis of the existing literature and by identifying gaps, this study outlines a structured pathway to facilitate the certification and operation of MASSs, addressing key technical, operational, and safety considerations. This research contributes to designing a risk-informed approach for the development of autonomous surface vehicles. Full article

Fishing vessels are known to have a fatality rate from accidents nearly 100 times higher than that of merchant ships. However, since most cases are resolved internally without maritime tribunals, obtaining accurate statistics on accidents is challenging, making quantitative analysis and evaluation of accident risks difficult. Studies using inconsistent analytical methods often fail to converge on unified results or preventive measures, which contributes to the persistently high occurrence of fishing vessel accidents. Therefore, this study aimed to establish a standardized method for analyzing marine accidents on fishing vessels by applying the Formal Safety Assessment (FSA) technique, originally developed for merchant ships. The research focuses on the globally prevalent trap boat fishery, specifically examining common injuries occurring on fishing vessels. Quantitative data were collected from insurance approval records, while qualitative data were obtained through surveys. The research followed the five stages of the FSA framework: hazard identification, risk estimation, identification of risk control options, cost-benefit assessment, and recommendations for decision-making. The findings suggest that the FSA framework can be effectively applied to analyze fishing vessel accidents based on insurance data, leading to selective and effective preventive measures. Full article

The rapid development of mining activities, the shipping industry, and public transportation across the land and water areas of Boedingi Village can have both positive and negative impacts on the water quality of this area. This study aimed to determine the heavy metal content in yellowtail fish (Caesio cuning), a type of fish that is generally consumed by Boedingi villagers in North Konawe, Indonesia. A descriptive method with an environmental health risk assessment (EHRA) was employed to measure metal profiles (Pb, Cd, and Hg). The results showed that the metal levels in the water exceeded the thresholds, indicating significant heavy metal pollution. In addition, the Cd levels of the fish surpassed the food safety standard according to the USA Food and Drug Administration (FDA). Since Indonesia began its large-scale shift from conventional to electrical transportation, global and government collaboration have become critical in managing the environmental and health impacts of the related industries. This is especially important because this area is one of the largest nickel producers (the raw material of electrical transportation) both nationally and globally. Full article

Physical Trading Card Games (TCGs) face inherent challenges, including vulnerability to theft, damage, and counterfeiting. Trading systems primarily depend on third-party marketplaces that collect fees from each trade, without benefiting the game developers. Players also deal with problems associated with shipping, such as high prices, long shipping times, and the risk of counterfeit goods. This paper introduces a blockchain-based solution that decouples card ownership from the physical media, which represents ownership with secure and verifiable digital tokens. The system leverages Ethereum Virtual Machine (EVM), the ERC-1155 standard, and InterPlanetary File System (IPFS) storage, ensuring flexibility, scalability, and cost-efficiency. The adoption of the lazy minting technique minimizes upfront costs for game developers by creating tokens only when acquired by end users. Physical representations of such digital goods can be printed on demand as they remain only a game accessory. The system also provides low-cost exchanges, significantly reducing the financial and logistical burdens associated with the trading of physical assets. Finally, the protocol empowers developers to monetize secondary markets through transaction fees. This approach addresses the limitations of physical card systems and also unlocks new opportunities for innovation and revenue in the TCG ecosystem. Full article

Container transportation has the advantages of standardization, high efficiency, and high safety, which are essential for promoting the development of the world economy and trade. Emergencies such as severe weather, public health incidents, and social security incidents can negatively affect the operational reliability of the container shipping network. To ensure the network security and high-quality operation of container shipping, a double-layer coupled container transportation network is first described to analyze the evolution of the container shipping network and the risk propagation dynamics of operation participants. On this basis, a cascade failure model of the container shipping network considering risk level is constructed. To evaluate the vulnerability of the container shipping network, the transmission mechanism of cascade failure effects of the container shipping network under different emergency development trends and the evolution law and influence path of the container shipping network structure are both analyzed. Finally, we empirically studied the container shipping network in China and the United States, and characteristic parameters of the China–U.S. container shipping network are calculated and analyzed. The model’s validity is verified through practical cases and model simulation results, and the cascading failure process of the container shipping network in China and the United States under three types of attacks is simulated. Suggestions are provided for effective improvement in the vulnerability of the container shipping network under every kind of contingency. Full article

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä’s Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations. Full article

Environmental and occupational noise has the potential to result in health risks. The presence of high noise levels aboard ships can cause substantial hazards that affect the well-being of those employed in the maritime industry. The study and implementation of occupational noise reduction aboard ships are of the highest priority for ensuring the well-being of marine workers, compliance with regulatory standards, protection of the environment, and improvement of overall operational efficiency and safety within the maritime sector. A scoping study was conducted to collect and summarize the existing scientific literature about approaches to preventing occupational noise in vessel operations. We searched electronic databases for papers published up to June 2024. Initially, 94 articles were identified for screening, and the present research produced 16 studies, which were finally analyzed. Resultantly, noise control may begin with elimination, substitution, engineering, administrative, and hearing protection (ear plugs or muffs). Noise control innovation would be started with engineering techniques. Hearing protection devices (HPDs) could be used to reduce noise and as an instrument of communication between sailors. More research needs to be carried out in order to find the best ways for maritime vessels to reduce noise at work and to see how well they work in lowering the risks that come with noise for workers on board. Full article

Liquefied natural gas (LNG), recognized as the fossil fuel with the lowest carbon emission intensity, is a crucial transitional energy source in the global shift towards low-carbon energy. As the natural gas industry undergoes rapid expansion, the complexity of investment business models has increased significantly. Optimizing the combination of various segments within the value chain has become standard practice, making it essential to control risks and enhance economic benefits in these multifaceted scenarios. This paper introduces an integrated economic model encompassing upstream, liquefaction, shipping, regasification, and consumption, suitable for both upstream and downstream integration. The model offers a comprehensive analysis of the primary business models and key factors across each segment of the value chain. By constructing a robust economic evaluation framework, the study aims to provide a holistic approach to understanding the economic feasibility of LNG projects. Two detailed case studies are conducted to demonstrate the application and effectiveness of the proposed model, highlighting its capability to guide investment decisions, support risk management, and optimize asset portfolios. The integrated economic model developed in this study serves as a valuable tool for stakeholders in the LNG industry. It not only facilitates informed investment decision-making but also enhances the strategic management of risks and resources. By leveraging this model, investors and managers can better navigate the complexities of the LNG business, ensuring sustainable and economically viable operations. Full article

Negative effects on the port environment can originate from ports’ hinterland, ports’ activities and operations, and from ships. According to the available literature and long-lasting experience of numerous ports presented in different sources, pollution from ships belongs to the group of environmental priorities in ports. After a theoretical introduction where the importance of ports, their development, and the challenges/risks faced by ports (with special attention to environmental risks) are analyzed, an overview of the literature from the domain of prevention of pollution from ships is presented. Based on the standard structure of the PDCA (Plan, Do, Check, Act) management cycle, in this paper, a process model of the management (sub)system of prevention of pollution from ships in a port is proposed. Key demands related to the modeled (sub)system and bases which directly determine those demands are made concrete through an analysis of a case study: the Port of Bar (Montenegro). A categorization of domains of risks of pollution from ships in the analyzed port is conducted using the Analytic Hierarchy Process (AHP) method: considerations are based on a three-level hierarchy model: “0”—goal; level “1”—criterion; and level “2”—choice. The results of the analyses show that a domain with the highest rank of risk of pollution from ships is variant (alternative) M1: unloading oil/oil derivatives from ships to tanks (and vice versa), with a composite weight of 0.5365 (53.65%). The results of considerations presented in this paper can be used in a process of optimization of the (sub)system of prevention of pollution from ships in a multipurpose port as well as a reliable base for further research in this domain. Full article