Search Results (322)

This study proposes a novel Deep Reinforcement Learning (DRL) policy network structure for portfolio management called PortRSMs. PortRSMs employs stacked State-Space Models (SSMs) for the modeling of multi-scale continuous regime shifts in financial time series, striking a balance between exploring consistent distribution properties over short periods and maintaining sensitivity to sudden shocks in price sequences. PortRSMs also performs cross-asset regime fusion through hypergraph attention mechanisms, providing a more comprehensive state space for describing changes in asset correlations and co-integration. Experiments conducted on two different trading frequencies in the stock markets of the United States and Hong Kong show the superiority of PortRSMs compared to other approaches in terms of profitability, risk–return balancing, robustness, and the ability to handle sudden market shocks. Specifically, PortRSMs achieves up to a 0.03 improvement in the annual Sharpe ratio in the U.S. market, and up to a 0.12 improvement for the Hong Kong market compared to baseline methods. Full article

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

The maritime sector’s transition to sustainable energy is critical for achieving global carbon neutrality, with container terminals representing a key focus due to their high energy consumption and emissions. This study explores the potential of hydrogen energy as a decarbonization solution for port operations, using the Chuanshan Port Area of Ningbo Zhoushan Port (CPANZP) as a case study. Through a comprehensive analysis of hydrogen production, storage, refueling, and consumption technologies, we demonstrate the feasibility and benefits of integrating hydrogen systems into port infrastructure. Our findings highlight the successful deployment of a hybrid “wind-solar-hydrogen-storage” energy system at CPANZP, which achieves 49.67% renewable energy contribution and an annual reduction of 22,000 tons in carbon emissions. Key advancements include alkaline water electrolysis with 64.48% efficiency, multi-tier hydrogen storage systems, and fuel cell applications for vehicles and power generation. Despite these achievements, challenges such as high production costs, infrastructure scalability, and data integration gaps persist. The study underscores the importance of policy support, technological innovation, and international collaboration to overcome these barriers and accelerate the adoption of hydrogen energy in ports worldwide. This research provides actionable insights for port operators and policymakers aiming to balance operational efficiency with sustainability goals. Full article

The presented research examines the performance characteristics of Brazed Plate Heat Exchangers through computational fluid dynamics (CFD), focusing on pressure drop calculations for single-phase flow within full channels of plates featuring dimpled corrugation. This work aims to bridge gaps in the literature, particularly regarding the underexplored behavior near the ports for the studied technology and establishing a framework for future conjugate heat transfer studies. A methodology for the domain generation was developed, integrating a preliminary forming simulation to reproduce the complex plate geometry. Comprehensive sensitivity analyses were conducted to evaluate the influence of different parameters and identify the optimal settings for obtaining reliable results. The findings indicate that the $k-\epsilon$ realizable turbulence model with enhanced wall treatment offers superior accuracy in predicting pressure drops, with errors within ±4.4%. Additionally, leveraging the information derived from CFD, a strategy to estimate contributions from different channel sections without a direct reliance on those simulations was developed, offering practical implications for plate design. Full article

Accurate ship berthing behavior prediction (BBP) is essential for enabling collision warnings and support decision-making. Existing methods based on Automatic Identification System (AIS) data perform well in the task of ship trajectory prediction over long time-series and large scales, but struggle with addressing the fine-grained and highly dynamic changes in berthing scenarios. Therefore, the accuracy of BBP remains a crucial challenge. In this paper, a novel BBP method based on Light Detection and Ranging (LiDAR) data is proposed. To test its feasibility, a comprehensive dataset is established by conducting on-site collection of berthing data at Dalian Port (China) using a shore-based LiDAR system. This dataset comprises equal-interval data from 77 berthing activities involving three large ships. In order to find a straightforward architecture to provide good performance on our dataset, a cascading network model combining convolutional neural network (CNN), a bi-directional gated recurrent unit (BiGRU) and bi-directional long short-term memory (BiLSTM) are developed to serve as the baseline. Experimental results demonstrate that the baseline outperformed other commonly used prediction models and their combinations in terms of prediction accuracy. In summary, our research findings help overcome the limitations of AIS data in berthing scenarios and provide a foundation for predicting complete berthing status, therefore offering practical insights for safer, more efficient, and automated management in smart port systems. Full article

Maritime transportation is the backbone of global trade, with ports acting as pivotal nodes for the efficient and resilient movement of goods in international supply chains. However, most existing studies lack a systematic and integrated framework for assessing port connectivity. To address this gap, this study develops an integrated Bayesian Network (BN) modeling approach that, for the first time, simultaneously incorporates international connectivity, port competitiveness, and hinterland connectivity within a unified probabilistic framework. Drawing on empirical data from 26 major coastal countries in Asia, the model quantifies the multi-layered and interdependent determinants of port connectivity. The results demonstrate that port competitiveness and hinterland connectivity are the dominant drivers, while the impact of international shipping links is comparatively limited in the current Asian context. Sensitivity analysis further highlights the critical roles of rail transport development and trade facilitation in enhancing port connectivity. The proposed BN framework supports comprehensive scenario analysis under uncertainty and offers targeted, practical policy recommendations for port authorities and regional planners. By systematically capturing the interactions among maritime, port, and inland factors, this study advances both the theoretical understanding and practical management of port connectivity. Full article

Background/Objectives: Single-port laparoscopy represents a significant advancement in minimally invasive surgical techniques and is designed to reduce surgical trauma and enhance cosmetic outcomes. However, ongoing debate surrounds its relative benefits and limitations as compared with conventional multi-port laparoscopy. This study systematically reviewed and analyzed comparative outcomes between these two approaches. Methods: We conducted a comprehensive systematic search of major electronic databases from January 2000 to October 2023, following PRISMA guidelines. Only randomized controlled trials comparing single-port laparoscopy with conventional laparoscopy were included. We analyzed operative outcomes, postoperative recovery parameters, complications, and patient-reported measures using random-effects models, with heterogeneity explored through subgroup analyses. Results: Forty-three randomized controlled trials involving 5807 patients were analyzed. Single-port laparoscopy demonstrated longer operative times (weighted mean difference: +10.5 min; 95% CI: 7.83–13.18; p < 0.001), superior cosmetic satisfaction (standardized mean difference: +0.61; 95% CI: 0.39–0.83; p < 0.001), and reduced postoperative pain within 24 h (standardized mean difference: −0.58; 95% CI: −0.95 to −0.21; p = 0.002). The overall complication rates showed no significant differences (risk ratio: 0.94; 95% CI: 0.78–1.14; p = 0.31), though incisional hernia risk increased with single-port laparoscopy (odds ratio: 2.26; 95% CI: 1.23–4.15; p = 0.009). Conclusions: Single-port laparoscopy offers meaningful improvements in cosmetic outcomes and early pain relief, balanced against longer operative times and increased hernia risk. The substantial heterogeneity observed underscores the importance of surgeon experience, appropriate patient selection, and optimal technique selection in determining outcomes. Full article

This study presents a comprehensive analysis of carbon emission trends and their driving factors at Shanghai Port, with a particular focus on the decoupling relationship between port economic development and carbon emissions, as well as forecasting the timeline for achieving the port’s carbon peak. The findings reveal distinct temporal patterns in emission growth: from 2009 to 2012, Shanghai Port experienced steady increases in carbon emissions, while from 2020 to 2023, it witnessed accelerated growth, primarily driven by fuel oil consumption. Using the Logarithmic Mean Divisia Index (LMDI) decomposition model, the study identifies operational revenue as the most significant contributor to carbon emission growth, while economic intensity emerges as the strongest inhibiting factor. Notably, the carbon-promoting effects of energy structure and efficiency improvements substantially outweigh the emission reductions achieved through enhanced economic intensity. The Tapio decoupling analysis indicates that during 2010–2023, neither operational revenue nor port cargo throughput capacity achieved stable decoupling from carbon emissions at Shanghai Port. Operational revenue exhibited alternating patterns of strong and weak decoupling, while cargo throughput showed more pronounced fluctuations, cycling through phases of decoupling and negative decoupling. Scenario-based predictions using the GRU-LSTM hybrid model provide critical insights: under the baseline scenario, Shanghai Port is projected to fail to achieve a carbon peak by 2035. However, both the low-carbon and enhanced mitigation scenarios project a carbon peak around 2026, with the enhanced scenario enabling earlier attainment of the target. These findings offer valuable theoretical foundations for formulating Shanghai Port’s carbon peak strategy and provide practical guidance for emission management and policy development at ports. The methodological framework and empirical results presented in this study may serve as a reference for other major ports pursuing similar decarbonization goals. Full article

Based on a comprehensive evaluation index system for regional economic resilience and port logistics development, this study employs multiple methodologies including coupling coordination degree model, kernel density estimation, gravity center model, spatial autocorrelation model, and geographic detector model to explore the spatial–temporal evolution patterns and driving factors of coupling coordination between regional economic resilience and port logistics in the Guangxi Beibu Gulf Economic Zone from 2012 to 2022. The results indicate that: (1) The coupling coordination degree between the two systems showed an upward trend during the study period, although with stage-specific bipolar differentiation that weakened in the later stages. (2) The spatial distribution pattern of coupling coordination evolved from a “single-core” driven by Nanning to a “dual-core” led by Nanning and Yulin, forming a distinct concentric layer structure; the gravity center of coupling coordination exhibited a “southeast–northwest” dynamic migration pattern. (3) Spatial autocorrelation analysis revealed significant positive spatial dependence of coupling coordination within the study area, with spatial agglomeration values showing a “core–transition–depression” differentiation pattern. (4) Information technology level emerged as the dominant driving factor, forming a “technology–finance–infrastructure” ternary collaborative driving model with financial development level and logistics infrastructure level, which became the main force promoting the coordinated development of the coupled systems. Full article

Ports are vital nodes in global trade networks but are also significant contributors to greenhouse gas emissions. Their transition toward sustainable, nearly zero-energy operations require comprehensive and structured strategies. This study proposes a practical and scalable framework to support the energy decarbonization of ports through the phased integration of hybrid renewable energy systems (HRES) and energy storage systems (ESS). Emphasizing a systems-level approach, the framework addresses key aspects such as energy demand assessment, resource potential evaluation, HRES configuration, and ESS sizing, while incorporating load characterization protocols and decision-making thresholds for technology deployment. Special consideration is given to economic performance, particularly the minimization of the Levelized Cost of Energy (LCOE), alongside efforts to meet energy autonomy and operational resilience targets. In parallel, the framework integrates digital tools, including smart grid infrastructure and digital shadow technologies, to enable real-time system monitoring, simulation, and long-term optimization. It also embeds mechanisms for regulatory compliance and continuous adaptation to evolving standards. To validate its applicability, the framework is demonstrated using a representative case study based on a generic port profile. The example illustrates the transition process from conventional energy models to a sustainable port ecosystem, confirming the framework’s potential as a decision-making tool for port authorities, engineers, and policymakers aiming to achieve effective, compliant, and future-proof energy transitions in maritime infrastructure. Full article

This research discusses the application of Sentinel satellite data for monitoring air pollution in port areas. The Scopus and Web of Science databases were comprehensively analysed to identify relevant peer-reviewed literature and assess research publications. The systematic literature review was conducted using the PRISMA methodology for inclusion and exclusion criteria. A total of 519 articles were identified from which 70 relevant articles were finally selected and discussed in detail for their relevancy to the maritime environment. Sentinel-5P was found to have several use cases in the literature that are useful for measuring maritime air pollution, while Sentinel 1 and 2 were mainly used for other applications like oil spills and water quality, respectively. Although aerial surveys, like those conducted using unmanned aerial vehicles (UAVs), offer more precise estimates of greenhouse gases (GHGs), they are only useful for certain applications because the technology is costly and impractical for daily monitoring. Satellite-based sensors are the state of the art for obtaining remote observations of emissions in open sea. Sentinel-5P measurements offer daily data for air quality monitoring, which supports ground surveys to identify and penalize major emission sources and consequently support environmental management in accordance with contemporary policies. Pollutant concentration levels for the maritime sector can be analysed both spatially and temporally using Sentinel-5P data. In the future, addressing the limitations of the Sentinel-5P data, such as underestimation and source separation, could improve air pollution assessments. Full article

Navigation in offshore wind farm (OWF) areas is essential for construction, maintenance, safety, and traditional activities like fishing. However, the presence of OWFs extends to sea routes, negatively impacting maritime transport economics. This paper examines navigational risk indicators in the vertical and horizontal planes of the ship domain for three representative vessels navigating under different hydrometeorological conditions within the location of a proposed offshore wind farm in the Polish sector of the Baltic Sea. The study compares three types of domain parameters defined by the PIANC guidelines, Coldwell’s two-dimensional model, and Rutkowski’s three-dimensional model. The analysis includes navigational hazards located ahead of the ship’s bow and astern from the aft, as well as keeping under-keel and over-head clearance. Besides the main numerical indicators of navigational risk estimated for obstacles on the port and starboard sides, the study emphasizes the importance of such additional factors. The primary objective of this paper is to identify the ship types that can navigate and fish safely in proximity to and within the OWF area. The analysis employs hydrometeorological data, mathematical models, and operational data derived from maritime navigation and maneuvering simulators. This comprehensive approach aims to enhance maritime safety in OWF areas. Full article

Free trade zone (FTZ) policies and port integration play critical roles in advancing international shipping and port development. While Free trade zones (FTZs) promote trade liberalization and attract investment to support port infrastructure, port integration helps alleviate excessive competition, reduce redundant labor, and minimize resource inefficiencies. Given these dynamics, it is important to examine how FTZs and port integration differentially shape shipping capacity and port competitiveness across China’s coastal provinces. To this end, this study introduces a comprehensive evaluation framework for port competitiveness, which considers both port operation–related factors and the external environment. The framework employs a combination of principal component analysis and the entropy weight method to assess port competitiveness in coastal regions. The findings reveal that comprehensive port service capacity and management efficiency capacity have the most significant influence on port competitiveness, outweighing the impact of other evaluated indicators. It also reveals that the development of China’s coastal ports is regionally unbalanced, with strong competitiveness in the Yangtze River Delta, Pearl River Delta, and Bohai Rim clusters, moderate development in the southeastern cluster, and relatively weak performance in the Beibu Gulf cluster. Both FTZ and port integration policies can promote port competitiveness to some extent, especially for professional technical support and services, digital management, and overall management efficiency. The dynamics of port competitiveness under the FTZs are higher than those under port integration. The research results deepen the understanding of the roles of FTZ and port integration policies in promoting the competitiveness of ports in various regions and provide insights for ports to seize opportunities and enhance development. The reinforcement of industrial synergies with neighboring regions and the formation of complementary development patterns enhance their overall competitiveness. Exploring new modes aligned with the advancement of FTZs and port integration can further stimulate regional economic development and support national opening-up strategies. Full article

Seaports, as major transportation hubs, generate significant air pollution due to intensive ship traffic, directly affecting local air quality. While emission inventories are commonly used to manage ship-based air pollution, they reflect only the emission-related aspect of a specified period and area, limiting the broader interpretability and comparability of the results. To overcome the mentioned challenges, this research presents the PrE-PARE model, which enables the prediction, analysis, and risk evaluation of ship-sourced air pollution in port areas. The model comprises three interconnected modules. The first is applied for quantifying emissions using detailed technical and movement datasets, which are combined into individual voyage trajectories to enable a high-resolution analysis of ship-based air pollutants. In the second module, the Multivariate Adaptive Regression Splines (MARS) machine learning method is adapted to predict emissions in varying operational scenarios. In the third module, novel metric methods are introduced, enabling a standardised efficiency comparison between ships. These methods are supported by a unique classification system to determine the emission risk in different periods, evaluate the intensity of various ship types, and rank individual ships based on their operational efficiency and emission optimisation potential. By combining new methods with technical and operational shipping data, the model provides a transparent, comparable, and adaptable system for emissions monitoring. The results demonstrate that the PrE-PARE model has the potential to improve strategic planning and air quality management in ports while remaining flexible enough to be applied in different contexts and future scenarios. Full article

Dredging is essential for the maintenance of ports, waterways, lakes, and lagoons to ensure their operability and economic value. Over the last few decades, scientists have focused on the significant environmental challenges associated with dredging, including habitat destruction, loss of biodiversity, sediment suspension, and contamination with heavy metals and organic pollutants. The huge loss of sediment in coastal areas and the associated erosion processes are now forcing stakeholders to look ahead and turn potential problems into an opportunity to develop new sediment management strategies, beyond environmental protection, toward ecosystem restoration and coastal resilience. Moreover, the European and Italian strategies, such as the European Green Deal (EGD) and the Italian Ecological Transition Plan (PTE), highlight the need to reuse dredge sediment in circular economy strategies, transforming them into valuable resources for construction, agriculture, and environmental restoration projects. European legislation on dredging is fundamental to the issue of management and priorities of dredged materials, but the implementation rules are deferred to individual member states. In Italy, the Ministerial Decree 173/2016 covers the main aspects of dredge activities and dredge sediment management. Moreover, it encourages the remediation and reuse of the dredge sediment. This study starts with a comprehensive analysis of the innovative remediation techniques that minimize impacts and promote sustainable, beneficial sediment management. Different remediation methods, such as electrochemical treatments, chemical stabilization, emerging nanotechnologies, bioremediation, and phytoremediation, will be evaluated for their effectiveness in reducing pollution. Finally, we highlight new perspectives, integrated strategies, and multidisciplinary approaches that combine various technological innovations, including artificial intelligence, to enhance sediment reuse with the aim of promoting economic growth and environmental protection. Full article