Search Results (172)

The swift advancement of fifth-generation (5G) wireless technology brings forth a range of enhancements to address the increasing demand for data, the proliferation of smart devices, and the growth of the Internet of Things (IoT). This highly interconnected communication environment necessitates using multiple-input multiple-output (MIMO) systems to achieve adequate channel capacity. In this article, a 2-port MIMO system using two flipped parallel 1 × 2 arrays and a 2-port MIMO system using two opposite 1 × 4 arrays designed and fabricated antennas for 5G wireless communication in the sub-6 GHz band, are presented, overcoming the limitations of previous designs in gain, radiation efficiency and MIMO performance. The designed and fabricated single-element antenna features a circular microstrip patch design based on ROGER 5880 (RT5880) substrate, which has a thickness of 1.57 mm, a permittivity of 2.2, and a tangential loss of 0.0009. The 2-port MIMO of two 1 × 2 arrays and the 2-port MIMO of two 1 × 4 arrays have overall dimensions of 132 × 66 × 1.57 mm³ and 140 × 132 × 1.57 mm³, respectively. The MIMO of two 1 × 2 arrays and MIMO of two 1 × 4 arrays encompass maximum gains of 8.3 dBi and 10.9 dBi, respectively, with maximum radiation efficiency reaching 95% and 97.46%. High MIMO performance outcomes are observed for both the MIMO of two 1 × 2 arrays and the MIMO of two 1 × 4 arrays, with the channel capacity loss (CCL) ˂ 0.4 bit/s/Hz and ˂0.3 bit/s/Hz, respectively, an envelope correlation coefficient (ECC) ˂ 0.006 and ˂0.003, respectively, directivity gain (DG) about 10 dB, and a total active reflection coefficient (TARC) under −10 dB, ensuring impedance matching and effective mutual coupling among neighboring parameters, which confirms their effectiveness for 5G applications. The three fabricated antennas were experimentally tested and implemented using the MIMO Application Framework version 19.5 for 5G systems, demonstrating operational effectiveness in 5G applications. 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

As global trade expands, the pressure on container terminals to improve efficiency and capacity grows. Several inspections are performed during the loading and unloading process to minimize delays. In this paper, we explore corrosion as it poses a persistent threat that compromises the durability of containers and leads to costly repairs. However, identifying this threat is no simple task. Corrosion can take many forms, progress unpredictably, and be influenced by various environmental conditions and container types. In collaboration with the Port of Sines, Portugal, this work explores a potential solution for a real-time computer-vision system, with the aim to improve container inspections using deep-learning algorithms. We propose a system based on the semantic segmentation model, DeepLabv3+, for precise corrosion detection using images provided from the terminal. After preparing the data and annotations, we explored two approaches. First, we leveraged a pre-trained model originally designed for bridge corrosion detection. Second, we fine-tuned a version specifically for cargo container assessment. With a corrosion detection performance of 49%, this work showcases the potential of deep learning to automate inspection processes. It also highlights the importance of generalization and training in real-world scenarios and explores innovative solutions for smart gates and terminals. Full article

The global port and maritime industry is experiencing a new paradigm shift known as the artificial intelligence transformation (AX). Thus, domestic container-terminal companies should focus beyond mere automation to a paradigm shift in AI that encompasses operational strategy, organizational structure, system, and human resource management. This study proposes a resilience-based AX strategy and implementation system that allows domestic container-terminal companies to proactively respond to the upcoming changes in the global supply chain, thus securing sustainable competitiveness. In particular, we aim to design an AI-based governance model to establish a trust-based logistics supply chain (trust value chain). As a research method, the core risk factors of AX processes were scientifically identified via text-mining and fault-tree analysis, and a step-by-step execution strategy was established by applying a backcasting technique based on scenario planning. Additionally, by integrating social control theory with new governance theory, we designed a flexible, adaptable, and resilience-oriented AI governance system. The results of this study suggest that the AI paradigm shift should be promoted by enhancing the risk resilience, trust, and recovery of organizations. By suggesting AX strategies and policy as well as institutional improvement directions that embed resilience to secure the sustainable competitiveness of AI-based smart ports in Korea, this study serves as a basis for establishing strategies for the domestic container-terminal industry and for constructing a global leading model. Full article

The maritime domain is experiencing significant transformation, driven by the integration of autonomous technologies. Autonomous ships and smart maritime systems depend on the sophisticated interplay of artificial intelligence, sensor infrastructures, and communication protocols to achieve safe, reliable, and efficient operations. Central to this evolution is the imperative for seamless interoperability among agents operating within heterogeneous maritime environments. Semantic interoperability, which ensures that information is interpreted and exchanged consistently and meaningfully across systems, emerges as a critical enabler of coordinated multi-agent cooperation. This paper explores the role of semantic interoperability in the coordination of multi-agent systems, the challenges involved, and the technological frameworks that facilitate its implementation. 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 scoping review investigates the digital transition of critical energy infrastructure (CEI) in maritime ports, which are increasingly vital as energy hubs amid global decarbonisation efforts. Recognising the growing role of ports in integrating offshore renewables, hydrogen, and LNG systems, the study examines how digital technologies (such as automation, IoT, and AI) support the resilience, efficiency, and sustainability of port-based CEI. A multifaceted search strategy was implemented to identify relevant academic and grey literature. The search was performed between January 2025 and 30 April 2025. The strategy focused on databases such as Scopus. Due to limitations encountered in retrieving sufficient, directly relevant academic papers from databases alone, the search strategy was systematically expanded to include grey literature such as reports, policy documents, and technical papers from authoritative industry, governmental, and international organisations. Employing Arksey and O’Malley’s framework and PRISMA-ScR (scoping review) guidelines, the review synthesises insights from 62 academic and grey literature sources to address five core research questions relating to the current state, challenges, importance, and future directions of digital CEI in ports. Literature distribution of articles varies across continents, with Europe contributing the highest number of publications (53%), Asia (24%) and North America (11%), while Africa and Oceania account for only 3% of the publications. Findings reveal significant regional disparities in digital maturity, fragmented governance structures, and underutilisation of digital systems. While smart port technologies offer operational gains and support predictive maintenance, their effectiveness is constrained by siloed strategies, resistance to collaboration, and skill gaps. The study highlights a need for holistic digital transformation frameworks, cross-border cooperation, and tailored approaches to address these challenges. The review provides a foundation for future empirical work and policy development aimed at securing and optimising maritime port energy infrastructure in line with global sustainability targets. Full article

Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score. Full article

The evaluation of port service quality (PSQ) is critical for enhancing the competitiveness of container terminals. As technological innovation continues to reshape port operations, PSQ has shifted beyond operational efficiency to deliver smart, reliable, and sustainable services. However, few studies have addressed PSQ in the context of smart port evolution, especially with a focus on container terminals. This study employs a five-dimensional framework, comprising resources, outcomes, process, management, image, and social responsibility, to analyze how PSQ influences customer satisfaction and how customer satisfaction, in turn, affects customer loyalty and referral intention. The data was collected through a survey targeting users of container terminals in five major ports in Korea that undergoing smart port transformation, resulting in a final sample of 324 respondents. The findings reveal that resource-related, process-related, and image- & social responsibility-related PSQ dimensions significantly enhance customer satisfaction, which in turn has a positive effect on customer loyalty and referral intention. In contrast, the outcome-related and management-related dimensions did not have a significant impact on customer satisfaction. The analysis results and various implications discussed in this study are expected to provide helpful information and insights for establishing strategies to enhance the competitiveness of smart ports in the future. Full article

Digital Shipping Corridors (DSCs) are gaining traction as integrated models for increasing transparency, efficiency, and sustainability in maritime logistics. Yet, the enabling conditions for their effective implementation remain insufficiently explored. This study employs a qualitative thematic review approach, analyzing the academic literature, global policy documents, and selected case studies to identify and synthesize the critical success factors for DSC development. The analysis reveals seven interdependent factors: technological infrastructure, economic feasibility, regulatory frameworks, logistical efficiency, logistical security, stakeholder collaboration, and environmental sustainability. These factors are not independent but interact dynamically, requiring coordinated development across technical, institutional, and environmental domains. This study proposes a dynamic interaction framework that illustrates how progress in one area (e.g., digital infrastructure) depends on readiness in others (e.g., governance and cross-sector collaboration). The outcomes contribute both conceptually and practically. The framework offers a system-level understanding of DSC implementation and identifies key leverage points for intervention. The findings provide strategic guidance for policymakers, port authorities, and supply chain stakeholders pursuing digitally enabled sustainable maritime corridors. This study also highlights areas for future empirical validation, particularly in relation to governance integration and cross-border alignment. Full article

Self-sovereign identity (SSI) is an emerging model for digital identity management that empowers individuals to control their credentials without reliance on centralized authorities. This work presents YouGovern, a blockchain-based SSI system deployed on Binance Smart Chain (BSC) and compliant with W3C Decentralized Identifier (DID) standards. The architecture includes smart contracts for access control, decentralized storage using the Inter Planetary File System (IPFS), and long-term persistence via Web3.Storage. YouGovern enables users to register, share, and revoke identities while preserving privacy and auditability. The system supports role-based permissions, verifiable claims, and cryptographic key rotation. Performance was evaluated using Ganache and Hardhat under controlled stress tests, measuring transaction latency, throughput, and gas efficiency. Results indicate an average DID registration latency of 0.94 s and a peak throughput of 12.5 transactions per second. Compared to existing SSI systems like Sovrin and uPort, YouGovern offers improved revocation handling, lower operational costs, and seamless integration with decentralized storage. The system is designed for portability and real-world deployment in academic, municipal, or governmental settings. Full article

Hydrogen compression is a critical process in hydrogen storage and distribution, particularly for energy infrastructure and transportation. As hydrogen technologies expand beyond limited industrial applications, they are increasingly supporting the green economy, including offshore energy systems, smart ports, and sustainable marine industries. Efficient compression technologies are essential for ensuring reliable hydrogen storage and distribution across these sectors. This study focuses on optimizing hydrogen compression using a Liquid Piston Hydrogen Compressor through numerical simulations and scaling analysis. The research examines the influence of compression chamber geometry, including variations in radius and height, on thermal behavior and energy efficiency. A computational model was developed using COMSOL Multiphysics^® 6.0, incorporating Computational Fluid Dynamics (CFD) and heat transfer modules to analyze thermodynamic processes. The results highlight temperature distribution in hydrogen, working fluid, and chamber walls at different initial pressures (3.0 MPa and 20.0 MPa) and compression stroke durations. Larger chamber volumes lead to higher temperature increases but reach thermal stabilization. Increasing the chamber volume allows for a significant increase in the performance of the hydraulic compression system with a moderate increase in the temperature of hydrogen. These findings provide insights into optimizing hydrogen compression for enhanced production and broader applications. Full article

This study presents a structured multi-agent system (MAS) architecture aimed at optimizing operational efficiency in roll-on/roll-off (Ro-Ro) terminal management through intelligent coordination and decentralized decision-making. The proposed framework enhances space allocation, route planning, traffic control, and boarding coordination, enabling real-time decision-making and adaptive operational strategies. Through structured MAS architecture, agents interact dynamically to optimize vehicle flow, reducing congestion and improving overall efficiency. The study evaluates the system’s potential benefits compared to traditional port management models, highlighting improvements in transit time reduction, resource utilization, and operational resilience. The findings suggest that MAS-based automation can enhance decision-making, sustainability, and integration with Industry 4.0 paradigms, driving the transition toward intelligent, efficient, and scalable port logistics. Full article

Ports play a crucial role in Indonesia’s economy, yet many, particularly smaller ports, lack standardized port performance indicators (PPIs) to assess and improve operational efficiency. Existing studies primarily focus on financial and operational performance, often employing either the balanced scorecard (BSC) or PESTLE analysis in isolation, with limited integration of sustainability concepts, such as smart port and green port frameworks. This study bridges this gap, aiming to develop and validate a comprehensive PPI framework that combines BSC, PESTLE, and circular economy smart and green port principles to create holistic performance assessment tools for ports. The research used a three-round Delphi method, incorporating expert evaluations and consensus from academics, consultants, port authorities, and customers to validate key performance indicators. A total of 127 PPIs were initially identified through a literature review and expert consultations, using strict selection criteria—standard deviation ≤ 1.5, interquartile range (Q3–Q1) ≤ 2.5, and ≥51% expert agreement (ratings 8–10). The final validated framework includes 114 indicators covering financial, operational, environmental, and strategic dimensions. This study provides valuable insights for port authorities to optimize performance and align with global best practices by integrating internal and external factors into a comprehensive model. Full article

The damage to shipping containers during port handling operations continues to pose a significant challenge that adversely affects operational efficiency, equipment integrity, and supply chain accountability. This study utilises real-world measurement data gathered through accelerometers to examine the occurrence and dynamics of physical impacts, particularly side and rear collisions, during the handling of containers at Klaipėda City Port. The research prioritises two critical scenarios: side impacts during stacking operations with reach stackers and rear impacts during trailer loading procedures. Impact events are meticulously recorded and analysed to ascertain the magnitudes of acceleration across multiple axes. This reveals that side impacts produce significantly greater forces, particularly in the lateral direction, than rear impacts. This study employs sensor-based monitoring, advanced data visualisation techniques, and structured scenario analysis to delineate the variability and intensity of mechanical interactions during these operations. The findings emphasise the structural stress that containers experience and underscore the importance of embedded monitoring technologies for real-time event detection and damage prevention. The results contribute to the expanding body of knowledge that supports the digital transformation of container terminals and furnish actionable insights for enhancing handling protocols, informing insurance assessments, and improving safety measures within both automated and conventional port environments. Full article