The aim of this study was to evaluate the antifungal spectrum of activity, synergy, and mode of action of carboxy-terminally amidated antimicrobial peptides (AMPs) derived from tachyplesin-I (T-I) from the horseshoe crab Tachypleus tridentatus and a lysine-rich analogue of magainin-2 (MSI-94) from the clawed frog Xenopus laevis. In vitro antimicrobial tests against 17 fungal strains demonstrated that the modified AMPs exhibited broad antifungal activity, particularly against filamentous fungi and yeasts relevant to aquaculture and agriculture. Additive antimicrobial activity was observed with the combination of T-I and MSI-94 against Candida albicans and Rhodotorula mucilaginosa, indicating an enhancement of their antiyeast properties. Furthermore, we found that both peptides target the fungal cell surface, increasing membrane permeability and leading to cell death. Overall, our findings highlight the biotechnological potential of aquatic AMPs in developing novel antifungal therapeutics applicable across various fields.

Given the pace of port digitalisation, this study provides a mapping of the smart port cybersecurity literature to clarify its intellectual structure and emerging research directions. Bibliographic records period 2010–2025 were retrieved from the Scopus and analysed Bibliometrix/Biblioshiny. The dataset comprises 460 publications from 344 sources, with an annual growth rate of 11.02% and an average of 9.97 citations per article, indicating a expanding research domain. The analysis examines publication trends, co-authorship and citation networks, and conceptual structures through bibliometric and text-mining techniques. Results show a sharp increase in publications after 2017, driven by the integration of the Internet of Things (IoT), artificial intelligence and automation in port systems. International collaboration is prominent, with research leadership concentrated in the USA, China, India and the UK. Conceptual analysis highlights network defence, intrusion detection and AI-based security, while revealing gaps at the intersection of governance, cyber–physical resilience and operational security in smart ports.

Adapting Barker’s ((2019). The Journal of Navigation, 72(3), 539–554) taxonomy of wayfinding behaviours – originally developed for man-made environments, paper and screen – we examined which behaviours are also found in the outdoors. In the analysis of the collected data from a questionnaire (n=401), we find that participants employ every category in Barker’s framework of social, semantic and spatial behaviours. Our respondents report the use of digital maps on a mobile phone as the most common behaviour, with following directional signs as the second most used. Furthermore, social wayfinding behaviours figure prominently and the participants express preferences for various information sources. We demonstrate similarities of behaviours across the different types of environments and we confirm the applicability of Barker’s taxonomy of wayfinding behaviours also in nature. Our study generates knowledge that potentially can make navigation simpler and more efficient through wayfinding design, and lead to heightened feeling of safety in the outdoors. Wayfinding behaviour studies, like this one, can serve as a bridge between human psychology and practical design.

Safe navigation of maritime autonomous surface ships (MASS) relies on two capabilities: path planning and collision avoidance. This review surveys classical algorithms and modern AI techniques for embedding the International Regulations for Preventing Collisions at Sea (COLREGs) into autonomous navigation. We organise prior work into three families—classical search/optimisation, real-time reactive methods, and learning-based approaches—and discuss their strengths and limitations with respect to rules compliance, computational cost, and onboard constraints. Building on these insights, we outline a large-language-model framework, Navigation-GPT, which couples reasoning-and-acting (ReAct) prompting with low-rank adaptation (LoRA). We further propose a three-phase deployment roadmap for MASS: core model integration, domain fine-tuning, and integrated operations. The paper concludes with open challenges and research directions toward reliable, explainable, and fully compliant MASS navigation.

Ship path planning represents a fundamental challenge in intelligent navigation, requiring careful balance between route optimality, safety in complex marine environments. To address the limitations of conventional A* algorithms, this paper proposes an improved multi-factor and multi-scale A* algorithm. The methodology begins with processing ENC data, where canny edge detection combined with adaptive thresholding constructs obstacle maps. A novel dual-layer multi-scale grid framework is established: They are used to rapid global path searching, and precise collision avoidance. The algorithm innovatively integrates a multi-factor function that simultaneously considers obstacle distribution, environment effects, navigation rules, and ship dynamic constraints, with adaptive weight adjustment optimizing the search process. Path refinement employs smoothing algorithms to significantly reduce waypoint numbers. Simulation experiments conducted in Dalian port demonstrate the algorithm’s superior performance: maintaining safe clearance even in obstacle-dense areas and using the shorter length. Experimental results confirm that generated paths better satisfy practical navigation requirements.

Maritime safety faces growing challenges due to an expanding global fleet, tighter schedules, and increasingly complex stakeholder interactions. This study integrates multiple data sources to determine a more accurate representation of major marine accident causative factors in the United Kingdom. Logistic regression and data modelling are applied to Automatic Identification System data (2011–2017) and reported accidents from the Marine Accident Investigation Branch (2013–2019). Results show that larger vessels, daytime transits, service ships, winter conditions, and confined high-density areas such as ports impact accident likelihood. Interviews validate the data and emphasize the influence of port geometry and channel complexity. Among major UK ports, London, Plymouth and Milford Haven exhibit the highest accident-to-traffic densities. While maritime regulations and safety management systems in ports and vessels are seen as adequate by industry professionals, human factors require the greatest attention to improve maritime safety.

Swelling soils, particularly those rich in smectite, present significant challenges to civil engineering due to their shrinking–swelling behaviour. Lime stabilization is a commonly used practice to address this, but the reactivity of smectite minerals in an alkaline limestone environment differs widely. This study investigates the reactivity of two Moroccan smectite-rich clays – montmorillonite-dominated bentonite and stevensite/saponite-rich bentonite – when treated with aerial lime. Through mineralogical, microstructural and mechanical analyses, this study highlights the distinct behaviour of montmorillonite, which reacts with lime to form calcium silicate hydrate gels, compared to the inert response of stevensite/saponite. Despite its low pozzolanic activity, stevensite-bentonite demonstrates greater mechanical strength, reaching 2.5 MPa in the S3 mixture (90% stevensite-bentonite and 10% lime). This strength is attributed to the formation of calcite through the de-dolomitization of dolomite. The findings reveal different stabilization mechanisms between dioctahedral and trioctahedral smectites, offering new insights for soil stabilization strategies involving these smectite types.

Maritime transport plays a vital role in global logistics and trade; however, its environmental impact, particularly CO₂ emissions, has become a growing concern. Current estimation methodologies are divided into top-down and bottom-up approaches. Top-down methods rely on macro-statistical data but often lack specificity regarding individual ship characteristics, leading to high uncertainty. Bottom-up methods, increasingly prevalent due to advancements in ship equipment and big data technology, estimate CO₂ emissions based on detailed ship activity trajectories, offering greater precision. This study integrates data from multiple vessel-position transmitting devices — AIS, V-Pass, and LTE-Maritime — to estimate CO₂ emissions from maritime activities in the coastal regions of South Korea. By combining these data sources, the study develops a comprehensive and accurate emissions assessment, improving reliability and supporting more informed decision-making in maritime environmental management and policy development.

Accurate vessel traffic prediction is significant for efficient waterway management and lock scheduling. This paper presents a deep-learning framework that integrates a multi-graph convolutional network with a gated recurrent unit network, considering spatio-temporal patterns appropriately, for vessel traffic flow prediction. Three unstructured graphs are constructed to represent spatio-temporal relationships among traffic flows at different locations. Subsequently, multi-graph convolution is employed to quantitatively extract such patterns among adjacent nodes in the graphs. Those extracted patterns are then passed to a gated recurrent unit layer for further temporal features extraction in sequential data. The model is believed to improve prediction accuracy and reliability. To prove this, extensive experiments on regional and station-based predictions are conducted using two real-world datasets to evaluate the model’s capability. The jointly trained model demonstrates superior performance and outperforms conventional methods. The strong forecasting ability enables managers to adjust schedules promptly, enhancing efficiency and intelligence of waterway operations.