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Conference of the Parties 30, the 2025 annual United Nations Framework Convention on Climate Change conference, took place in Belém, Brazil, located right on the edge of the Amazon forest. It was championed by Brazilian authorities as a symbolic centring of the Global South. Nonetheless, as previous studies in this series have shown, long-haul air travel often dominates total emissions, and this was the case for COP30. However, Belém presented a new logistical challenge. Both its location and general lack of adequate accommodation prompted the authorities to charter two large cruise ships to house thousands of delegates over the two weeks of the conference. The expected operational emission from such ships makes them several times more carbon-intensive than standard hotels. Yet the challenge was compounded by the affordability problem for delegates from the Global South, while the complete lack of affordable and accessible accommodation for civil society organisations directly contradicted the symbolic inclusion echoed by the organisers. This paper reports on travel emissions to Belém, highlights the paradox of housing delegates on carbon-intensive cruise ships, as well as the contradictions of not catering to civil society organisations in a supposedly inclusive COP. This paper concludes with a recommendation that subsequent conferences should endeavour to also account for accommodation emissions from participants.

The study on the greenhouse gas (GHG) emission characteristics and emission reduction approaches is critically important for driving the green and sustainable development across the shipping industry. However, comprehensive investigations into the distribution characteristics of GHG emissions and the emission-reduction pathways of various ship types remain insufficient, making it difficult to develop effective control measures for ship GHG emissions. Therefore, this paper systematically analyzes the GHG emission characteristics of ships under different conditions and summarizes the pathways for GHG reduction from ships. Firstly, the origins and driving factors of ship GHG emissions are analyzed. Subsequently, the characteristics of ship GHG emissions under different conditions are elaborated in detail, and the methodology for constructing the ship emission inventory is presented. Finally, the GHG emission reduction approaches for ships are analyzed. Based on this, the existing challenges in the GHG emissions characteristics analysis and emission reduction pathways are proposed, and future research directions are outlined. The analysis results indicate that the ship's GHG emissions characteristics are influenced by multiple factors. Future investigation is required to deeply probe the influence mechanism of different factors on ship GHG emissions, and to propose effective GHG emission reduction methods that are applicable to different conditions, to further foster the green and sustainable advancement of the maritime industry.

Reliable path planning for ship collision avoidance remains challenging in complex maritime environments with dense traffic. This study proposes a novel approach that integrates Belief-Rule-Based (BRB) reasoning with the Velocity Obstacle (VO) algorithm for multi-ship encounter scenarios. First, the BRB framework groups ships with similar movement trends and close proximity, reducing both the number of avoidance targets and decision-making complexity. Based on the grouping results, the safety domain radius for individual ships and ship groups is set proportionally to ship length. Subsequently, the VO algorithm generates safe and feasible avoidance trajectories toward the goal. The method was tested in two scenarios, showing improved navigational safety and operational efficiency in congested waters. This work provides a foundation for intelligent decision-making in maritime safety management and supports sustainable development in the shipping industry.

Sustained military and maritime operations demand continuous cognitive control and teamwork under conditions that restrict sleep opportunity and recovery. Nutritional constraints may further shape perceived recovery and functioning in these settings. This study examined relationships among hunger, diet quality, sleep, health, anxiety, burnout, command satisfaction and mishap risk among 9742 enlisted active-duty US Navy sailors across 155 ships. For Aim 1, guided by the Job Demands-Resources and Conservation of Resources frameworks, structural equation modelling evaluated whether hunger and the restorative sleep gap (sleep needed to feel well-rested minus sleep obtained while underway) are associated with downstream health and operational outcomes. Aim 2 evaluated whether fatigue-related outcomes were more strongly differentiated by occupational role than by department. Increased hunger and larger sleep gaps were associated with poorer sleep quality, greater anxiety, higher burnout and reduced command satisfaction, even after controlling for demographic and shipboard factors. Occupational differences were generally larger than departmental differences for multiple fatigue and health-related outcomes, underscoring that fatigue vulnerability is shaped by specific work roles in addition to broad organizational divisions. Findings highlight the need for targeted fatigue-risk management and attention to sleep and nutrition access within operational environments.

Quantitative structure-activity relationship (QSAR) modeling has conventionally relied on expert-designed molecular descriptors to encode chemical structures. DeepSnap is a descriptor-free QSAR approach that converts prepared three-dimensional molecular conformers into image representations and feeds them directly into convolutional neural networks for activity prediction. This focused narrative review traces DeepSnap from its introduction in 2018 to its current state and places it within the broader landscape of descriptor-based QSAR, topology-based and 3D-aware graph neural networks, and related image-based or semi-image-based molecular representation approaches. Previous studies applied DeepSnap to Tox21 nuclear receptor and molecular initiating event endpoints, rat hepatic clearance, blood-brain barrier penetration, acute oral toxicity, and cosmetics-pharmaceutical compound classification. Across the DeepSnap series, image-based and descriptor-based predictions have provided complementary information, particularly in ensemble or consensus models. However, high or near-ceiling ROC-AUC values reported for selected endpoints should not be interpreted as indicating deterministic or universally generalizable predictions; rather, they should be considered in the context of endpoint-specific model development, image-rendering parameter optimization, possible class imbalance, split dependence, limited matched external replication, and incomplete benchmarking against modern molecular representation models. Limitations include a dependence on nonphysical rendering parameters, single- or representative-conformer input, incomplete matched benchmarking against 2D and 3D molecular representation models, and an interpretability gap addressed in part by CAM-family visualization in the AI-based Substance Hazard Integrated Prediction System (AI-SHIPS) and S-COPHY (a model developed by Shiseido for cosmetics-pharmaceutical compound classification). Future directions include standardized image-generation protocols, conformer-ensemble extensions, systematic interpretability analysis, matched benchmarking, and potential integration with graph-based and 3D-aware molecular learning approaches.

Ship collision avoidance has become a focus issue in maritime navigation. Existing methods often struggle to simultaneously meet the hierarchical decision-making requirements of the International Regulations for Preventing Collisions at Sea (COLREGs), address the dynamic uncertainty of ship risk attitudes, and effectively cope with multi-ship coupling risks. To solve the above problems,this paper proposes an algorithm that combines multi-agent systems with game theory, and integrates ship collision avoidance rules into the reward function design. The algorithm constructs a two-stage framework: the risk attitude perception layer uses a Long Short-Term Memory (LSTM) network to predict the short-term motion states of target ships, and dynamically infers the probability distribution of target ships' risk attitudes through a Bayesian network combined with historical Automatic Identification System (AIS) data and encounter characteristics. The decision-making execution layer integrates Stackelberg game with the Multi-Agent Actor-Critic (MAAC) algorithm, and embeds COLREGs as rigid constraints into the action space to ensure the compliance of the algorithm. Experimental verification is carried out based on historical AIS data and simulation scenarios. The results show that the proposed algorithm has certain advantages in various key indicators,the collision rate, the COLREGs compliance rate, the trajectory smoothness, and the average risk. Statistical significance tests confirm the robustness and superiority of the algorithm. This study provides a reliable technical scheme for ship collision avoidance strategies in multi-ship waters.

The Surface Ocean CO2 Atlas (SOCAT) is a global scientific community effort to collate and provide additional quality control and standardisation for surface ocean carbon dioxide (CO2) data. Each year the international marine carbon community submit any new measurements collected on research vessels, ships of opportunity, moorings, uncrewed surface vehicles and sailing yachts for inclusion in the annual update of the SOCAT database. The data synthesis effort, which published its first data product in 2011, includes a variety of systems, sampling strategies, maintenance cycles and instrument calibrations. Each in-water CO2 gas measurement is paired, and linked, with a sea surface temperature (SST) measurement. However, the differences in measurement systems means that data pairs from different platforms are representative of differing depths in the ocean, whilst SST measurements can suffer from warming within the observation platform. These complexities can limit the accuracy and precision of any atmosphere-ocean CO2 assessments that use the SOCAT products. Here the SOCATv2025 database with an estimated uncertainty in the fugacity of CO2 in seawater (fCO2 (sw)) of less than 5 µatm is recalculated to a reference temperature at a consistent depth of 0.2 m using the European Space Agency (ESA) Climate Change Initiative (CCI) SST climate data record. This recalculation process of the fCO2 values does not assume isochemical conditions and so temperature driven carbonate speciation is captured. The data pairing is maintained so the resulting dataset is well suited for the analysis of atmosphere-ocean CO2 exchange. The synthesis cruise data and gridded data products, that include both the original and recalculated data, are provided and consistency with the original SOCAT data products and format is confirmed. The importance of robustly accounting for the observed warm bias is demonstrated as removing this signal by recalculation to a climate data record temperature shows a ~0.4 Pg C yr-1 (~12%) increase in the 2024 ocean CO2 sink (3.4 Pg C yr-1). These recalculated data products are needed for annual carbon assessments therefore these will be routinely provided each year following each annual SOCAT dataset release.

Single-cell multiomic profiling of RNA expression and chromatin accessibility is now a standard tool for resolving regulatory state in single cells, but existing analysis toolchains have lagged. Cell Ranger ARC, the proprietary multiomic pipeline, uses a custom broad peak caller rather than the MACS3 narrow peaks that the ATAC field has consolidated on, and its restrictive end-user licence forbids redistribution of analysis pipelines that include it. A fully open-source, permissively-licensed alternative anchored on community-standard methods-Chromap for ATAC alignment and MACS3 for narrow peak calling-has been impractical to assemble because the two codebases are written in different languages with incompatible runtimes, leaving practitioners to chain them together with ad-hoc scripts. We present Chromap Suite, the chromatin-accessibility side of an open-source multiomic stack built in support of the NIH Molecular Phenotypes of Null Alleles in Cells (MorPhiC) consortium's multiomic production pipeline. We extended Chromap with native BAM output and coordinate sorting, in-process narrow peak calling, optional Y-chromosome filtering, and native input from the compressed binary CBQ sequencing format alongside FASTQ, and hardened the result with a regression-test matrix that auto-validates the four upstream Chromap presets (bulk ATAC, scATAC, ChIP-seq, Hi-C). We reimplemented MACS3's narrow peak caller in portable C++ as libMACS3 , byte-identical to MACS3 v3.0.3 and free of any Python interpreter dependency. Finally, we extracted Chromap's alignment and fragment-generation paths into a callable C++ library ( libchromap ) and embedded both libchromap and libMACS3 into STAR Suite, so that one STAR invocation runs alignment, peak calling, and cell calling for both RNA and ATAC modalities concurrently-to our knowledge the first true single-binary RNA + ATAC multiomic implementation. On the public 3K PBMC Multiome at 32 threads, the platform completes in 18 minutes 55 seconds wall time and 44.6 GB peak resident memory, against 40 minutes 4 seconds and 79.1 GB resident memory for Cell Ranger ARC v2.2.0-a 2.12 × wall speedup with 1.8× less peak memory-and produces 50,274 peaks that are byte-identical to MACS3 v3.0.3. To support deployment by both research scientists and the AI agents increasingly used in bioinformatics analysis, Chromap Suite ships a Model Context Protocol (MCP) server and a browser-based Launchpad driven by a shared set of composable YAML recipes that humans and agents drive the same way. Chromap Suite delivers a unified, freely redistributable multiomic pipeline that produces the MACS3 narrow peaks downstream ATAC analyses already rely on, with substantially lower wall time and memory than the proprietary alternative. The MIT- and BSD-3-licensed code carries no redistribution restrictions, the constituent libraries are independently embeddable in other open-source tools, and the MCP server plus Launchpad recipes make the platform straightforward to drive both by humans and by AI agents.

Health promotion measures on board seagoing vessels are subject to special requirements due to limited access to health services on board. This study examines the needs and digital possibilities for modern health promotion and digital intervention on board merchant ships considering the different occupational groups. The data was collected using questionnaires completed by 903 seafarers (participation rate 99.4%) from 23 countries on 68 seagoing vessels belonging to two German shipping companies. A comparison of the responses was conducted to identify significant differences between ratings and officers, because this could be relevant for future specific interventions. Adjustments were made for cultural background and age using odds ratio. Ratings assessed the relevance of topics for well-being higher than officers did, especially regarding healthy food (p = 0.83), exercise (p = 0.05), and learning relaxation exercises (p = 0.04). The study shows that ratings are more than twice willing to do more sports (p = 0.04), learn relaxation exercises (p = 0.01), and practice measures against fatigue (p = 0.05). Health apps and sports competitions were highly valued in the communication of health information. Ratings and officers used smartphones to a high degree (98.3%) and would use a health app (87.5% ratings vs. 83.1% officers; OR 1.62 (1.07-2.43)) and both professional groups would like to participate in a bonus system based on a reward principle. In the multivariate analysis, interest in health promotion measures was essentially independent of age and cultural background. Key aspects for intervention measures on board include sports, learning relaxation techniques, offering healthy food, and raising awareness about exemplary health behavior of superiors. Modern digital intervention measures involving e-learning platforms, applications and wearables are of interest to seafarers and should be considered when planning health promotion measures at sea.

Methods for transferring nanoscale patterns include nanoimprinting, soft lithography, and nanosphere lithography. Soft lithography is a two-dimensional pattern transfer technique. In contrast, nanosphere lithography transfers the shape of arranged polystyrene spheres and is therefore a three-dimensional shape transfer technique; however, it offers limited freedom in terms of shape. Therefore, a technology to transfer the antireflective structure of a moth's eye onto a large surface area has been developed using nanoimprint lithography (NIL), which enables low-cost mass production of nanopatterns. For large-area transfer, a roll mold was used in conjunction with roll-to-roll (RTR) ultraviolet NIL (UV-NIL), which transfers in a printing-like manner. A UV-curable resin was used as the transfer resin, enabling high-speed transfer at room temperature. The moth-eye structure was created on the roll mold by irradiating glassy carbon (GC) with an oxygen-ion beam. To form the moth-eye structure in the roll mold, an aluminum roll with a diameter of 30 cm and a length of 1.5 m was prepared and a thin GC film was formed on the side of the roll by sputtering. The surface was subsequently roughened by oxygen reactive ion etching to form the moth-eye structure. The roll mold was subjected to a release treatment, and the moth-eye structure was transferred onto a triacetylcellulose film using RTR UV-NIL. The UV-curable resin used here contained a fluorine material that made the moth-eye structure water-repellent after transfer. The film with the transferred moth-eye structure exhibited a low reflectance of 0.1% across the visible light range, and its contact angle with water exceeded 150°. When this moth-eye-structured film was applied to both sides of a window, it was found to be antireflective and to prevent water droplets from adhering. This water-repellent moth-eye-structured film repels rain and water, thereby improving visibility, and is therefore expected to be used on ships.

Distributed acoustic sensing (DAS) has emerged as a powerful tool for passive whale monitoring, enabling both the detection of vocalizations and the simultaneous tracking of multiple individuals. However, a fundamental limitation of passive acoustic monitoring is that most methods rely on acoustic data, which is only available when whales vocalize. This clearly demonstrates the need for new sensing methods that can detect silent whales. In this paper, we detect hydrodynamic pressure and velocity fields in the low-frequency DAS data induced by a whale's motion and develop methods to analyze these signals. First, we use ships as proxies to demonstrate and calibrate the proposed method. Then, we show that a simple fluid mechanical model can be adapted to understand how whale swimming can be detected and analyzed using DAS. We detect multiple silent whales simultaneously, estimate their characteristics, and show that whale motion signals decay as one over distance cubed. Moreover, we demonstrate that we can observe hydrodynamic pressure and velocity signals from a cruise ship at 413 m water depth, and up to 550 m from the fiber cable. In comparison, the smaller blue whales can be observed when diving within 40 m of the fiber-optical cable. This sensing method enables an approach to monitoring one of the world's most endangered species.

To reduce greenhouse gas emissions, an increasing number of ships are expected in future to operate on ammonia and methanol. However, the effects of these chemicals on zooplankton in case of a spill are not yet fully understood. We conducted shipboard experiments in the northern Baltic Sea on seven species of copepods and cladocerans, differing in their size and feeding mode. Zooplankton were individually exposed to chemical concentrations representing realistic spill scenarios for 24-48&#x202f;h, and their mortality, faecal pellet production and hatching success (only Eurytemora affinis) were measured. Moreover, we assessed whether species traits and environmental parameters influenced zooplankton responses. Mortality was species- and population-specific. For ammonia, LC50 ranged from 0.5 to 1.2&#x202f;mg&#x202f;L-1 NH3-N, with the cladoceran Bosmina longispina and the copepod Temora longicornis as the most and least tolerant species, respectively. Ambient oxygen concentration was positively correlated with LC50, whereas ambient ammonium and total nitrogen were negatively correlated with LC50 (p&#x202f;<&#x202f;0.05). For methanol, Limnocalanus macrurus experienced elevated mortality (80%) at 25&#x202f;mL&#x202f;L-1, whereas mortality of all other species remained below 50%. Faecal pellet production increased at low concentrations (up to 0.5&#x202f;mg&#x202f;L-1 NH3-N and 2.5&#x202f;mL&#x202f;L-1 methanol) and decreased at higher concentrations for feeding-current feeding copepods, but was largely unaffected for ambush feeders. Hatching success was negatively impacted by ammonia. The results indicate that zooplankton response to ammonia and methanol varies between species and areas underscoring the importance of testing toxic effects on natural populations to determine risks under environmentally relevant conditions.

As pivotal interfaces of maritime trade and urban-industrial systems, ports are increasingly challenged by their dual roles in enabling economic activity and generating air pollution and carbon emissions. This review provides a structured assessment of emission characteristics, mitigation technologies, and governance strategies aimed at port sustainability. It first delineates the primary emission sources-ships, cargo handling equipment, land transport, and on-site energy systems-focusing on key pollutants such as NOx, SO2, PM2.5, VOCs, and CO2. Synergistic technological pathways are then examined, including ship-based alternatives such as shore power, low-carbon fuels, and carbon capture and storage, along with port-side electrification, operational optimization, and integration of renewables. The review further assesses how regulatory frameworks-encompassing mandatory rules, market-based incentives, and international cooperation-govern the adoption and scaling of these technologies. Drawing on case studies from both developed and developing economies, the review identifies enabling factors including institutional coordination and technological readiness, alongside persistent barriers such as fragmented governance and limited financing. It concludes by outlining four strategic imperatives: accelerating scalable technologies diffusion, enhancing multilevel governance alignment, embedding port transitions within broader urban and industrial systems, and promoting equity through just transition frameworks. By integrating technical and institutional dimensions, the review provides a forward-looking roadmap for low-carbon, clean, and resilient port development.

The retreat of Arctic sea ice is driving an increase in vessel traffic and associated underwater noise, which interferes with the frequency bands used by Arctic marine mammals. Detecting co-occurring vessel noise and marine mammal vocalizations in passive acoustic monitoring (PAM) data can help to assess their adverse impacts and guide mitigation strategies. This paper proposes two ship noise detection techniques: a modified variant of the Frequency Amplitude Variation (FAV) method, MFAV, which integrates signal processing with a simple statistical threshold to enhance both interpretability and detection performance; and a convolutional neural network (CNN) model specifically trained to advance ship detection in the Canadian Arctic. Comparative analysis of our PAM test dataset from the western Canadian Arctic, based on peak F1-scores, demonstrates that the CNN model generalizes well to unseen sites and, with one exception, consistently outperforms both MFAV and FAV by 1%-8%, maintaining scores above 91%. Furthermore, MFAV improves the detection of boats by up to 22% and of larger ships by 6%. The developed methods are publicly available as an open-source tool on GitHub, contributing to the advancement of acoustic vessel monitoring techniques in Canadian Arctic waters in support of conservation efforts aimed at protecting Arctic marine mammal habitats.

In January 1946, the Journal of Industrial Hygiene and Toxicology published "A Health Survey of Pipe Covering Operations in Constructing Naval Vessels." This cross-sectional epidemiological study is one of the most consequential in the history of industrial hygiene and occupational medicine as it errantly concluded that insulation work on ships using asbestos-containing materials was "&#x2026;not a dangerous occupation." As a consequence of this innocuous conclusion, the U.S. Navy and others neglected to protect insulators and other employees from asbestos dust for the next 25 years, leading to an epidemic of asbestos-related diseases in active and retired workers. Subsequently, attorneys and expert witnesses used this exculpating conclusion to mount "state of the art/science" defenses for asbestos manufacturers in tort actions, arguing that their clients relied on this publication when they failed to protect their own employees from asbestos or warn their customers of the hazard. Both the industrial hygiene and the medical components of this study were deeply flawed. The successor publisher to the original journal has refused to consider retracting this paper, so this catastrophic blunder may remain a part of the literature, available to asbestos tort defense attorneys and their experts. Publishing industry policies and precedent related to retracting very old articles are reviewed.

Sea lanes of communication (SLOC) are perceived as intricate systems comprising shipping routes, key straits, and canals, which are increasingly vulnerable to external disruptions. This study proposes a holistic framework for resilience assessment on the system by introducing an advanced framework incorporating fuzzy logic, a critic weight calculation approach, and the evidential reasoning (ER) algorithm to assess resilience. Second, a hierarchical influential index is created, assessing the system's capacity to absorb, adjust, and recover from disruptions while incorporating connectivity among key straits and canals to illustrate the risk performance and spatial relationships of the straits and canals within Sea Lanes of Communication. Third, a fuzzy ER algorithm integrates information from diverse sources, taking into account the significance and nonlinear relationships among the influential factors. Finally, we present techniques to assess the resilience performance and validate our models. This proposed framework is implemented through an empirical study of five primary Sea Lanes of Communications that connect the Far East and the rest of the world. This framework provides valuable insights into resilience performance in an environment with high uncertainties and offers guidance for relevant stakeholders.

Since the year 2000, oceanic research has seen a surge in data collection, with approximately 500,000 sets of measurements for a single variable (e.g., temperature) recorded annually. Yet, further advancements are essential to deepen understanding of climate change phenomena, pollutant propagation, and the ocean-human health nexus. Analyzing Essential Ocean Variables (EOVs) designated by the Global Ocean Observing System (GOOS), such as temperature, salinity, pH, dissolved oxygen, phytoplankton distribution, coral cover and many others, is critical to assessing oceanic responses to anthropogenic pressures. Equally vital are persistent pollutants, antimicrobial resistance genes, human viruses, pathogenic bacteria, and micro- and nano-plastics, which underscore the inseparability of human and environmental health under an Ocean and Human Health framework. The public health sector can contribute and provide support for the continuous expansion of EOVs and this review seeks to contribute to this process, endorsing collaboration between public health experts and oceanographers to inform health policies that recognize oceans' central role in human well-being. Over decades, a wide range of tools have been developed for the scientific community to measure EOVs, ranging from those requiring human intervention to automated systems. This narrative, not exhaustive review summarizes some of primary ocean observation technologies integrated within GOOS: satellites, drifting and moored buoys, research vessels and ships of opportunity (SOO), Animal Borne Ocean Sensors (AniBOS), and unmanned vehicles. Each one is examined in detail, highlighting characteristics and landmark projects like the Argo program and the FerryBox initiative, which have profoundly shaped oceanography. Accessing EOVs collected through these programs is fundamental to contextualize the new variables. Many databases are available to facilitate this process and key information will be provided. The review also explores emerging frontiers, particularly advancements in SOO through public-private collaborations. Framed from a public health perspective, it emphasizes the pivotal role of data collection and access in elucidating ocean-human health (OHH) interactions.