Search Results (87)

Background: Current international regulations for cargo securing rely on simplified assumptions that may not accurately represent the behavior of oversized cargo, particularly regarding rotational inertia and ship motion effects, which are critical under tipping conditions. Methods: This study presents a methodology for the evaluation, modeling and optimization of lashing systems for large cargo units. A comparative analysis between the CSS Code and DNV guidelines is performed to assess differences in the estimation of ship-induced accelerations and the resulting tipping moments. Simulations are performed considering different stowage positions, environmental conditions, and lashing configurations. Predictive mathematical models are developed using Design of Experiments (DOE) and Response Surface Methodology (RSM). Results: The results reveal that the CSS Code systematically leads to more conservative requirements, significantly increasing the number of required lashing devices compared to DNV guidelines. The influence of lashing angles is quantified, identifying optimal configurations that minimize securing effort while ensuring tipping equilibrium. The developed models accurately estimate the number of lashing devices required for both longitudinal and transverse tipping. Conclusions: The methodology is validated through a real case study involving heavy offshore cargo and provides practical decision-support tools. The proposed approach enables more efficient and reliable securing design in maritime cargo transport. Full article

Calcium alginate is an anionic polysaccharide that forms an ionically crosslinked hydrogel network with encapsulation properties to nucleic acids and nutrients for the amelioration of osmotic stress, ion toxicity and nutrient imbalance in saline–alkali soils. Traditional soil reclamation methods, including salt leaching, incorporation of organic matter, and gypsum application, are water-intensive under a changing climate, ultimately necessitating transformative bio-based solutions for food security. Calcium alginate-based biohydrogel represents a versatile platform with a tunable macromolecular architecture, ionic crosslinking via an “egg box” mechanism and potentially promising to deliver engineered co-encapsulated nutrients and genetically modified cargoes. The mannuronic (M) and guluronic (G) acid (M/G) ratios govern ion exchange capacity, rheological behavior and release kinetics in saline- and alkali-stressed environments. Recent studies on alginate-based nutrient encapsulation showed reduced oxidative damage and a 15–50% increase in plant-available water. The irrigation intervals extended from 7 to 14 days and yield gains by 24% in wheat, with comparable improvements in maize, tomato, rice and cotton. Calcium alginate hydrogels encapsulated salt tolerance genes (HKT1, SOS1, AVP1) encoding proteins mainly involved in Na⁺ retrieval from xylem, Na⁺ extrusion from root cells and vacuolar Na⁺ sequestration, which have achieved yield gains of 40 to 75% across wheat, rice and maize. Future research should focus on optimizing mechanical strength, crosslinking chemistry and smart bioencapsulation strategies for sustainable development so that crops are capable of withstanding variable climate stresses. Full article

While drones play important roles in areas such as communication and logistics delivery, they have certain limitations in emergency rescue scenarios due to their inability to carry passengers. Building on mature drone technologies such as autonomous flight and environmental perception, unmanned passenger Electric Vertical Take-off and Landing (eVTOL) aircraft are designed with a manned cabin, enabling them to operate without an onboard pilot while rapidly transporting injured people. Consequently, eVTOLs can play a significant role in emergency rescue that cargo-only drones cannot fulfill, as they are capable of rapidly reaching emergency scenes, effectively overcoming the delays caused by traditional ground traffic congestion. Despite their potential, eVTOLs still face several critical obstacles, including signal disruption, limited coverage of dispatching centers, mutual authentication among entities, and concerns related to security and privacy preservation. As a remedy, this paper presents a lightweight authentication protocol leveraging drone assistance to overcome these challenges for unmanned eVTOL emergency rescue. In scenarios where an unmanned eVTOL experiences signal blockage due to dense urban high-rise structures, neighboring drones can serve as a transmission relay to assist the unmanned eVTOL and the dispatch center (DC) in completing mutual authentication and session key negotiation, thereby enabling the unmanned eVTOL to safely complete its mission. To enhance security, physical unclonable functions (PUFs) are integrated into unmanned eVTOLs, drones, and the DC, safeguarding sensitive data against side-channel and physical capture attacks while preserving the confidentiality of unmanned eVTOL identities to mitigate privacy risks. Our protocol achieves provable security in the random oracle model while exhibiting strong resistance to various well-known attacks. Comparative analysis with the existing drone authentication and drone-assisted emergency rescue authentication protocols reveals that our protocol not only provides stronger security guarantees but also maintains a low computational overhead. Full article

LoRaWAN’s role in global maritime logistics has allowed for efficient monitoring of ships and cargo, but it also comes with critical cybersecurity vulnerabilities. Experimental validation of three attack vectors—replay attacks, narrowband jamming and metadata inference—is conducted using a reproducible digital-twin LoRaWAN dataset reflecting Rotterdam port-like operational patterns (N = 20,000 baseline transmissions). Using controlled simulations and Kolmogorov–Smirnov statistical analysis, we show that: (1) replay attacks are feasible under Activation by Personalization (ABP) configurations lacking enforced frame-counter validation and exhibit no univariate separation from legitimate traffic under Kolmogorov–Smirnov analysis (p > 0.46 for all evaluated radio features); (2) narrowband jamming leads to significant SNR degradation (p = 2.36 × 10⁻⁵) on targeted channels without inducing broad distributional anomalies across other radio features; and (3) metadata-only analysis supports elevated metadata-based re-identification susceptibility (median $R_d=0.834$), indicating high predictability under passive observation which can reveal operationally relevant signals even when AES-128 is employed. Our proposed layered mitigation framework consists of mandatory Over-the-Air Activation (OTAA), cryptographic key rotation, channel diversity incorporating Adaptive Data Rate (ADR), gateway hardening, and protocol-level enforcement considerations, customized for maritime LPWAN scenarios. We provide experiment-backed evidence and actionable recommendations to connect academic LPWAN security research to that of industrial maritime practice. Full article

The global maritime industry, a critical pillar of international trade, continues to face persistent challenges in ensuring the integrity, security, and transparency of containerized cargo data, particularly during ocean transport. Traditional container tracking systems at sea often lack the reliability and resilience required to prevent data tampering, cyber threats, and operational inefficiencies. As supply chains become more complex and interconnected, the demand for robust, end-to-end data security solutions becomes more pressing. A promising technological advancement in this area is the convergence of smart containers, equipped with Internet of Things (IoT) sensors for real-time condition monitoring, and blockchain technology (BCT) for secure data validation. These IoT devices facilitate continuous tracking of critical parameters such as location, temperature, humidity, tilt, and the like. However, the data they generate remains vulnerable to cyberattacks, signal disruptions, and unauthorized alterations. Blockchain’s decentralized and tamper-evident architecture addresses these vulnerabilities by enabling secure data immutability, transparent audit trails, and enhanced stakeholder trust. Despite its potential, the practical integration of blockchain with smart container systems in maritime logistics remains largely underexplored. To bridge this gap, this paper proposes a blockchain-enabled smart container monitoring system that combines container real-time data with secure physical tracking. Furthermore, to ensure scalability and efficient in data storage, hybrid on/off-chain architecture is introduced, balancing blockchain integrity with performance and resource optimization. Full article

Ensuring worker safety compliance and secure cargo transportation in complex port environments is critical for modern logistics hubs. However, conventional supervision methods, including manual inspection and passive video monitoring, suffer from limited coverage, poor real-time responsiveness, and low robustness under frequent occlusion, scale variation, and cross-camera transitions, leading to unstable target association and missed risk events. To address these challenges, this paper proposes CSPC-BRS, a real-time multi-object detection and tracking framework for open-channel port scenarios. CSPC (Coordinated Spatial Perception Cascade) enhances the YOLOv8 backbone by integrating CASAM, SPPELAN-DW, and CACC modules to improve feature representation under cluttered backgrounds and degraded visual conditions. Meanwhile, BRS (Bounding Box Reduction Strategy) mitigates scale distortion during tracking, and a Multi-Dimensional Re-identification Scoring (MDRS) mechanism fuses six perceptual features—color, texture, shape, motion, size, and time—to achieve stable cross-camera identity consistency. Experimental results demonstrate that CSPC-BRS outperforms the YOLOv8-n baseline by improving the mAP@0.5:0.95 by 9.6% while achieving a real-time speed of 132.63 FPS. Furthermore, in practical deployment, it reduces the false capture rate by an average of 59.7% compared to the YOLOv8 + Bot-SORT tracker. These results confirm that CSPC-BRS effectively balances detection accuracy and computational efficiency, providing a practical and deployable solution for intelligent safety monitoring in complex industrial logistics environments. Full article

The tracking of assets or cargo is one of the main objectives of global logistics and transportation systems, ensuring operational efficiency, security, and timeliness. Currently, battery-operated GPS (Global Positioning System)-based tracking devices are used for this purpose. The main shortcoming of these devices is the lifetime of the batteries because they cannot be replaced or recharged, or because this is simply not economically feasible. Therefore, efficient methods are needed to prolong battery life as much as possible. Various existing energy-saving techniques can be applied to solve this problem. However, none of these consider situations in which multiple tracking devices are transported together and can cooperate to further increase their energy efficiency. In this study, we propose and evaluate the novel lightweight peer-to-peer energy-saving method for nearby wireless battery-powered trackers based on their cooperation. The proposed method is based on the short-range BLE (Bluetooth Low Energy) device discovery mechanism and the dynamic election of the leader tracker (with the highest battery capacity) to report the location of its own and other neighboring trackers to the central server. The experimental evaluation of the proposed method shows that, compared to the traditional approach, where each tracker sends its location individually, the proposed method allows a reduction in the average battery charge required for one position report from 19% to 240% per each cooperating tracker. The average energy consumption for one location report per node decreased from 4.68 mWh using the traditional approach to 3.93 mWh for 2 cooperating devices and 1.92 mWh for 15 cooperating devices. Full article

Ports face critical security threats from hazardous cargo misdeclaration, which poses unique challenges due to its high concealment and catastrophic potential, as exemplified by the Beirut Port explosion. Traditional resilience assessment approaches relying on hazard state transition probabilities require abundant historical data or extensive domain expertise for probability elicitation, and static indicator-based assessment frameworks fail to capture the spatiotemporal evolution characteristics of disasters. To address these challenges, this study proposes a hybrid framework that leverages the Large Language Model (LLM)’s generalizable world knowledge for data augmentation while developing a Spatiotemporal Graph Neural Network (STGNN) to predict dynamic disaster propagation. Specifically, a multimodal LLM is employed to extract structured port state descriptions from temporally aligned disaster data and infer the states at undocumented time steps. With more disaster scenarios adapted from the real cases using the LLM, a STGNN is trained to learn the disaster evolution dynamics and make efficient real-time inference for resilience assessment and intervention strategy evaluation. Validation on Tianjin and Beirut Port incidents demonstrates that the framework accurately predicts disaster propagation pathways and identifies critical intervention priorities. It also reveals that topology-based intervention strategies substantially accelerate recovery, while adverse environmental conditions significantly amplify cumulative functional loss. This study represents an advancement toward AI-driven resilience modeling, offering port operators and regulators an adaptable, scalable decision support tool for intelligent safety governance. Full article

This study examines the characteristics of stretch film used to secure palletized cargo, with the aim of rationalizing its use. Growing consumption of packaging materials requires scientifically substantiated film selection that accounts for the forces ensuring cargo stability during transportation. This study used a patented mobile device to measure the static and dynamic forces generated by different types of stretch film. Experimental data revealed a linear relationship between the number of turns, the degree of pre-stretching, and the stabilizing forces, enabling optimization of wrapping parameters and a reduction in material costs. The results contribute to improved transportation safety, reduced energy consumption and carbon footprint, and lower polymer waste. This study is relevant because it develops tools for objectively assessing the effectiveness of packaging materials and for the rational selection of stretch film, thereby supporting sustainable logistics and transportation systems. Full article

Background: The Kingdom of Saudi Arabia is adjacent to two vital marine ecosystems; the semi-enclosed Arabian Gulf and the largely landlocked Red Sea. Dinoflagellates are repeatedly found in these bodies of marine water, which serve as significant routes for cargo ships. Through these ships and ballast water, invasive dinoflagellate species and their cysts are introduced. They compete with indigenous species for nutrients and space, cause massive fish kill-off and disturb the ecological balance and biodiversity. To address these threats, the International Convention for the Control and Management of Ships’ Ballast Water and Sediments (BWM Convention) set forth guidelines intended to curtail the dissemination of such detrimental organisms. The Kingdom of Saudi Arabia was one of the co-signatory countries to this Convention. Methods of detection and monitoring include microscopy, molecular characterization and remote sensing, which are employed for the detection and monitoring of these harmful algae, in order to avert disasters such as fish die-offs. The results of several reports confirmed the presence of number of dinoflagellates in both the Arabian Gulf and the Red Sea, some of which are toxin producers, with certain species being highlighted as invasive species whose presence requires a high level of alert. Discussion: The monitoring, the change in engineering of cargo ships and the introduction of advanced surveillance methods, together with the proper treatments of ballast water, are all important security elements that ensure the safe disposal of ballast water without introducing harmful species. Full article

Ports serve as key nodes in eco-friendly and digital logistics networks, and the volume of cargo handled continues to increase in response to growing international trade. However, the increased workload within limited spaces heightens the risk of safety accidents, and the number of casualties in port stevedoring operations has continued to rise. As the era of transition toward eco-friendly and digital paradigms unfolds, the adoption of new technologies in ports presents a strategic opportunity to enhance safety management. As of 13 May 2025, the study conducted a text-mining analysis based on research abstracts related to the keyword “New technology and port safety,” in the context of internal and external environmental changes. Specifically, a total of 639 research abstracts were collected, but 138 abstracts, which were unrelated to port safety, were excluded, and 501 abstracts from the Clarivate Web of Science database were analyzed, focusing on 2676 words that appeared at least twice. The study applied Term Frequency (TF) analysis, TF–Inverse Document Frequency analysis, Semantic Network Analysis, and Topic Modeling. The results indicate that Internet of Things emerged as a core solution for strengthening port safety management. However, challenges remain, including the prevention of security breaches, high infrastructure implementation costs, and limitations in battery life. Full article

Over the past decade, digitalization in the logistics sector has heightened the significance of autonomous systems and AI-based applications, while the integration of advanced deep learning technologies with air cargo carriers has ushered in a new era in the logistics industry. This study systematically addresses the current applications of these technological advances in logistics planning, the challenges faced, and perspectives for the future. These developments are transforming the role of UAVs and autonomous systems in logistics operations by improving last-mile efficiency and reducing costs. Key functions of autonomous vehicles, including environmental perception, decision-making, and route optimization, have shown notable progress through deep learning algorithms. However, major obstacles remain to their widespread adoption, particularly in terms of energy efficiency, data security, and the absence of a mature regulatory framework. Accordingly, this paper discusses these issues in detail and highlights areas for further research. This systematic literature review reveals the disruptive potential of AACV for the logistics industry and presents findings that can guide both academic inquiry and industrial practice. The results underscore that establishing a sustainable and efficient logistics ecosystem is essential in the context of these emerging technologies. Full article

Efficient threat detection in X-ray cargo inspection is critical for the security of the global supply chain. This study evaluates YOLO-based object-detection models from YOLOv5 to the latest, YOLOv11, which is enhanced with modified loss functions and Soft-NMS to improve accuracy. The YOLO model comparison also includes DETR (Detection Transformer) and Faster R-CNN (Region-based Convolution Neural Network). Standard loss functions struggle with overlapping items, low contrast, and small objects in X-ray imagery. To overcome these weaknesses, IoU-based loss functions—CIoU, DIoU, GIoU, and WIoU—are integrated into the YOLO frameworks. Experiments on a dedicated cargo X-ray dataset assess precision, recall, F1-score, mAP@50, mAP@50–95, GFLOPs, and inference speed. The enhanced model, YOLOv11 with WIoU and Soft-NMS, achieves superior localization, reaching 98.44% mAP@50. This work highlights effective enhancements for YOLO models to support intelligent logistics in transportation services and automated threat detection in cargo security systems. Full article

Rust’s growing popularity in high-integrity systems requires automated vulnerability detection in order to maintain its strong safety guarantees. Although Rust’s ownership model and compile-time checks prevent many errors, sometimes unexpected bugs may occasionally pass analysis, underlining the necessity for automated safe and unsafe code detection. This paper presents Rust-IR-BERT, a machine learning approach to detect security vulnerabilities in Rust code by analyzing its compiled LLVM intermediate representation (IR) instead of the raw source code. This approach offers novelty by employing LLVM IR’s language-neutral, semantically rich representation of the program, facilitating robust detection by capturing core data and control-flow semantics and reducing language-specific syntactic noise. Our method leverages a graph-based transformer model, GraphCodeBERT, which is a transformer architecture pretrained model to encode structural code semantics via data-flow information, followed by a gradient boosting classifier, CatBoost, that is capable of handling complex feature interactions—to classify code as vulnerable or safe. The model was evaluated using a carefully curated dataset of over 2300 real-world Rust code samples (vulnerable and non-vulnerable Rust code snippets) from RustSec and OSV advisory databases, compiled to LLVM IR and labeled with corresponding Common Vulnerabilities and Exposures (CVEs) identifiers to ensure comprehensive and realistic coverage. Rust-IR-BERT achieved an overall accuracy of 98.11%, with a recall of 99.31% for safe code and 93.67% for vulnerable code. Despite these promising results, this study acknowledges potential limitations such as focusing primarily on known CVEs. Built on a representative dataset spanning over 2300 real-world Rust samples from diverse crates, Rust-IR-BERT delivers consistently strong performance. Looking ahead, practical deployment could take the form of a Cargo plugin or pre-commit hook that automatically generates and scans LLVM IR artifacts during the development cycle, enabling developers to catch vulnerabilities at an early stage in the development cycle. Full article

In the context of globalization and the Belt and Road Initiative, maintaining the stability and security of the China Railway Express network (CRN) is critical for international logistics operations. However, unexpected events can lead to node and edge failures within the CRN, potentially triggering cascading failures that critically compromise network performance. This study introduces a Coupled Map Lattice model that incorporates cargo flow dynamics, distributing cargo based on distance and the residual capacity of neighboring nodes. We analyze cascading failures in the CRN under three scenarios, isolated node failure, isolated edge disruption, and simultaneous node and edge failure, to assess the network’s vulnerability during emergencies. Our findings show that deliberate attacks targeting cities with high node strength result in more significant damage than attacks on cities with a high node degree or betweenness. Additionally, when edges are disrupted by unexpected events, the impact of edge removals on cascading failures depends on their strategic position and connections within the network, not just their betweenness and weight. The study further reveals that removing collinear edges can effectively slow the propagation of cascading failures in response to deliberate attacks. Furthermore, a single-factor cargo flow allocation method significantly enhances the network’s resilience against edge failures compared to node failures. These insights provide practical guidance and strategic support for the CR Express in mitigating the effects of both unforeseen events and intentional attacks. Full article