Search Results (683)

The Medium Earth Orbit (MEO) spaceborne Synthetic Aperture Radar (SAR) has great coverage ability, which can improve maritime ship target surveillance performance significantly. However, due to the huge computational load required for imaging processing and the severe defocusing caused by ship motions, traditional ship recognition conducted in focused image domains cannot process MEO SAR data efficiently. To address this issue, a multi-level focusing-classification strategy for MEO SAR ship recognition is proposed, which is applied to the range-compressed ship data domain. Firstly, global fast coarse-focusing is conducted to compensate for sailing motion errors. Then, a coarse-classification network is designed to realize major target category classification, based on which local region image slices are extracted. Next, fine-focusing is performed to correct high-order motion errors, followed by applying fine-classification applied to the image slices to realize final ship classification. Equivalent MEO SAR ship images generated by real LEO SAR data are utilized to construct training and testing datasets. Simulated MEO SAR ship data are also used to evaluate the generalization of the whole method. The experimental results demonstrate that the proposed method can achieve high classification precision. Since only local region slices are used during the second-level processing step, the complex computations induced by fine-focusing for the full image can be avoided, thereby significantly improving overall efficiency. Full article

The maritime industry faces growing pressure to reduce greenhouse gas (GHG) emissions, reflected in the progressive adoption of stricter international energy regulations. Wind-Assisted Propulsion Systems (WAPS) offer a promising solution by significantly contributing to decarbonization. This paper presents a versatile simulation and optimization platform that supports the conceptual design of WAPS-equipped vessels and evaluates the viability of such investments. The platform uses a steady-state force equilibrium model to simulate vessel performance along predefined routes under realistic weather conditions, incorporating regulatory frameworks and economic assessments. A multi-objective optimization framework identifies optimal designs across user-defined criteria. To demonstrate its capabilities, the platform is applied to a bulk carrier operating between China and the USA, optimizing for capital expenditure, net present value (NPV), and CO₂ emissions. Results show the platform can effectively balance conflicting objectives, achieving substantial emissions reductions without compromising economic performance. The final optimized design achieved a 12% reduction in CO₂ emissions, a 7% decrease in capital expenditure, and a 6.6 million USD increase in net present value compared to the reference design with sails, demonstrating the platform’s capability to deliver balanced improvements across all objectives. The methodology is adaptable to various ship types, WAPS technologies, and operational profiles, offering a valuable decision-support tool for stakeholders navigating the transition to zero-carbon shipping. Full article

Gender-specific cardiology has gained increasing recognition in recent years, emphasizing the need for tailored management strategies for women with cardiovascular disease. Among these, cardiomyopathies—dilated, arrhythmogenic, hypertrophic, and restrictive—pose unique challenges throughout a woman’s reproductive life, affecting contraception choices, pregnancy outcomes, and breastfeeding feasibility. Despite significant advances in cardiovascular care, there is still limited guidance on balancing maternal safety and neonatal well-being in this complex setting. This review provides a comprehensive overview of the current evidence on reproductive counseling, pregnancy management, and postpartum considerations in women with cardiomyopathies. We discuss the cardiovascular risks associated with each cardiomyopathy subtype during pregnancy, highlighting risk stratification tools and emerging therapeutic strategies. Additionally, we address the safety and implications of breastfeeding, an often overlooked but increasingly relevant aspect of postpartum care. A multidisciplinary approach involving cardiologists, gynecologists, obstetricians, and anesthesiologists is crucial to optimizing maternal and fetal outcomes. Improved risk assessment, tailored patient counseling, and careful management strategies are essential to ensuring safer reproductive choices for women with cardiomyopathy. From now on, greater attention is expected to be given to bridging existing knowledge gaps, promoting a more personalized and evidence-based approach to managing these patients throughout different stages of reproductive life. Full article

Soils serve as critical carbon reservoirs, playing an essential role in climate change mitigation and agricultural sustainability. Accurate soil property determination relies on soil spectral reflectance data from Earth observation (EO), but current vegetation models often oversimplify soil conditions. This study introduces a novel approach that combines radiative transfer models (RTMs) with open-access soil spectral libraries to address this challenge. Focusing on conditions of low soil moisture content (SMC), photosynthetic vegetation (PV), and non-photosynthetic vegetation (NPV), the coupled Marmit–Leaf–Canopy (MLC) model is used to simulate early crop growth stages. The MLC model, which integrates MARMIT and PRO4SAIL2, enables the generation of mixed soil–vegetation scenarios. A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. The results demonstrated relatively high SOC prediction accuracy compared to previous approaches that rely only on RTMs and/or machine learning approaches. Incorporating soil moisture content significantly improved performance over bare soil alone, yielding an R² of 0.86 and RMSE of 4.05 g/kg, compared to R² = 0.71 and RMSE = 6.01 g/kg for bare soil. Adding PV slightly reduced accuracy (R² = 0.71, RMSE = 6.31 g/kg), while the inclusion of NPV alongside moisture led to modest improvement (R² = 0.74, RMSE = 5.84 g/kg). The most comprehensive model, incorporating bare soil, SMC, PV, and NPV, achieved a balanced performance (R² = 0.76, RMSE = 5.49 g/kg), highlighting the importance of accounting for all surface components in SOC estimation. While further validation with additional scenarios and SOC prediction methods is needed, these findings demonstrate, for the first time, using radiative-transfer simulations of mixed vegetation-soil-water environments, that an EO-DSSL approach enhances machine learning-based SOC modeling from EO data, improving SOC mapping accuracy. This innovative framework could significantly improve global-scale SOC predictions, supporting the design of next-generation EO products for more accurate carbon monitoring. Full article

This study presents a multi-objective predictive energy management system (EMS) for optimizing hybrid renewable energy systems (HRES) in autonomous marine vessels. The objective is to minimize fuel consumption and emissions while maximizing renewable energy usage and pure-electric sailing durations. The EMS combines nonlinear model predictive control (NMPC) with metaheuristic optimizers—Grey Wolf Optimization (GWO) and Genetic Algorithm (GA)—and is benchmarked against a conventional rule-based (RB) method. The HRES architecture comprises photovoltaic arrays, vertical-axis wind turbines (VAWTs), diesel engines, generators, and a battery storage system. A ship dynamics model was used to represent propulsion power under realistic sea conditions. Simulations were conducted using real-world operational and environmental datasets, with state prediction enhanced by an Extended Kalman Filter (EKF). Performance is evaluated using marine-relevant indicators—fuel consumption; emissions; battery state of charge (SOC); and emission cost—and validated using standard regression metrics. The NMPC-GWO algorithm consistently outperformed both NMPC-GA and RB approaches, achieving high prediction accuracy and greater energy efficiency. These results confirm the reliability and optimization capability of predictive EMS frameworks in reducing emissions and operational costs in autonomous maritime operations. Full article

This study investigates the structural behavior of a sailing catamaran subjected to wind, wave, and self-weight loads, with the ultimate goal of improving the structural design through redrawing techniques. A digital model was developed using Creo software 6 and analyzed through Finite Element Analysis (FEA), complemented by experimental deformation tests conducted under dry conditions and controlled loading. These tests provided a reliable dataset for calibrating and validating the numerical model. The analysis focused on the structural responses of key components—such as bulkheads, hulls, and beam-to-hull connections—under both isolated as well as combined load scenarios. Most structural elements demonstrated low deformation, confirming the robustness of the design; however, stress concentrations were observed at the connecting plates, highlighting areas for improvement. The vessel’s overall stiffness, though advantageous for structural integrity, was identified as a constraint in weight redrawing efforts. Consequently, targeted structural modifications were proposed and implemented, resulting in reduced material usage, construction time, and overall costs. The study concludes by proposing the integration of advanced composite materials to further enhance performance and efficiency, thereby laying the groundwork for future integration with digital and structural health monitoring systems. Full article

The maritime sector is under pressure to increase ship energy efficiency and reduce greenhouse gas (GHG) emissions as a part of global decarbonization goals. Various innovative technologies are being adopted in recent years, raising concerns not only about technological feasibility but also about the economic viability of such technologies in the context of sustainable maritime practices. This study evaluates the operational performance, potential to increase energy efficiency, and economic feasibility of wind-assisted propulsion technologies such as rotor sails across different vessel types and operational profiles. As a contribution to cleaner and more efficient shipping, energy savings produced by rotor thrust were analyzed in relation to vessel dimensions and rotor configuration. The results derived from publicly available industry data including shipowner reports, manufacturer case studies, and classification society publications on 25 confirmed rotor sail installations between 2010 and 2025 indicate that savings typically range between 4% and 15%, with isolated cases reporting up to 25%. A simulation model was developed to assess payback time based on varying fuel consumption, investment cost, CO₂ pricing, and operational parameters. Monte Carlo analysis confirmed that under typical assumptions rotor sail investments can reach payback in three to six years (as the ship is also liable for CO₂ payments). These findings offer practical guidance for shipowners and operators evaluating wind-assisted propulsion under current and emerging environmental regulations and contribute to advancing sustainability in maritime transport. The research contributes to bridging the gap between simulation-based and real-world performance evaluations of rotor sail technologies. Full article

This study addresses the persistent issue of gender bias in project management by developing and validating a practical survey tool for monitoring gender-related perceptions within project implementation frameworks. Using a Knowledge, Attitudes, and Practices (KAP) approach, a survey instrument was designed to assess awareness of gender equity policies, perceptions of inclusivity, and experiences related to sexual harassment (SASH) within project teams. The tool was piloted in a Horizon Europe project (Healthy Sailing), with responses collected from 66 participants (academics, maritime professionals, researchers, and government stakeholders). Exploratory Factor Analysis (EFA) revealed a five-factor structure explaining 72.29% of total variance, with the two dominant factors—Perceived Gender Bias and Organizational Safety—demonstrating excellent internal consistency (Cronbach’s alpha > 0.90). Confirmatory Factor Analysis (CFA) and bifactor modeling indicated areas for further refinement, with RMSEA values exceeding optimal thresholds. The results underscore the potential of the KAP-based tool to support gender-sensitive quality management practices in project-based environments, while highlighting the need for ongoing psychometric validation. The study contributes a novel, empirically grounded instrument for promoting inclusivity and equity in project management. Full article

Sailing Speed Optimization (SSO) is a crucial problem in shipping operations management, aiming to reduce both operating costs and carbon dioxide emissions. The ship’s sailing speed directly impacts fuel consumption, where fuel consumption is generally assumed to follow a power function with respect to sailing speed. Previous studies have used transformation-based fitting methods, such as logarithmic transformations, to investigate the relationship between sailing speed and fuel consumption using historical data. However, these methods introduce estimation bias and heteroskedasticity, violating the core assumptions of Ordinary Least Squares (OLS) used for general linear regression. To address these issues, we propose two novel fitting methods that directly optimize the original nonlinear model without relying on transformations. By analyzing the characteristics of the objective function, we derive parameter constraints and integrate them into a discrete optimization framework, resulting in improved fitting accuracy. Our methods are validated through extensive case studies, demonstrating their effectiveness in enhancing the reliability of SSO decisions. These methods offer a practical approach to optimizing fuel consumption in real-world maritime operations. Full article

With the rise of industry, river pollution has become increasingly severe. Countries worldwide now face the challenge of effectively and promptly detecting river pollution. Traditional river detection methods rely on manual sampling and subsequent data analysis at various sampling sites, requiring significant time and labor costs. This article proposes using an electric unmanned surface vehicle (USV) to replace manual river and lake water quality detection, utilizing a 2.4 G high-power wireless data transmission system, an M9N GPS antenna, and an automatic identification system (AIS) to achieve remote and unmanned control. The USV is capable of autonomously navigating along pre-defined routes and conducting water quality measurements without human intervention. The water quality detection system includes sensors for pH, dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP). This design uses a modular structure, it is easy to maintain, and it supports long-range wireless communication. These features help to reduce operational and maintenance costs in the long term. The data produced using this method effectively reflect the current state of river water quality and indicate whether pollution is present. Through practical testing, this article demonstrates that the USV can perform precise positioning while utilizing AIS to identify potential surrounding collision risks for the remote planning of water quality detection sailing routes. This autonomous approach enhances the efficiency of water sampling in rivers and lakes and significantly reduces labor requirements. At the same time, this contributes to the achievement of the United Nations Sustainable Development Goals (SDG 14), “Life Below Water”. Full article

During high-speed navigation, boat propellers often become partially exposed due to elevated sailing speeds. This condition results in a unique operational scenario where propellers are only partially submerged. Conducting computational studies on the excitation of propellers under such circumstances is essential for optimizing the dynamic performance of the shafting system. A theoretical calculation method for propeller performance was developed based on the principles of fluid dynamics relevant to water entry, leading to a computational method for determining excitation forces in this specific operational condition. This method was subsequently refined through appropriate adjustments using ANSYS Fluent software to simulate the behavior of partially submerged propellers. The findings highlighted the accuracy of the proposed model in predicting the pulsation of six force components across three distinct directions: along the propeller shaft, vertical, and lateral. Specifically, for a single blade (Blade 1), the pulsation amplitude of the vertical force (Fx) constituted 82.1% of its maximum peak magnitude and equated to 57.5% of the blade’s mean thrust. Analogously, the lateral force (Fz) pulsation amplitude represented 53.3% of its maximum peak magnitude and 40.0% of the mean thrust. These findings indicate the presence of significant unsteady hydrodynamic loads. Furthermore, a visualization approach was presented to analyze blade load phasing, offering insights relevant to the arrangement of blades on partially submerged propellers. Full article

Setting up anthropometric profiles for elite athletes in each sport, sport discipline, or specific sport positions could be a key element of sport selection processes. The main purpose of this study was to determine the anthropometric characteristics, body composition, and somatotype profiles of elite international ILCA 7 class sailors and to determine the differences contributing to different levels of competitive success. The subject sample included 97 elite ILCA 7 class sailors. A set of 25 anthropometric variables was applied. The sailors were divided into three groups according to their level of general competitive success according to the World Sailing Rankings. Differences between elite ILCA 7 sailors, separated into Higher, Medium, and Lower groups based on their success, were found in terms of age, body mass, muscle mass, trunk muscle mass, leg muscle mass, biepicondilar humerus width, sum of skinfolds, triceps skinfold, supraspinale skinfold, medial calf skinfold, and endomorphy rating. The most successful group of sailors was, on average, 4.9 years older than the least successful group. More highly successful sailors were also found to have an average of 2.73 kg more muscle mass but an 8.81 mm lower sum of skinfolds than those in the lower success group. Considering the average values of somatotype categories, ILCA 7 sailors fit the endomorphic–mesomorph somatotype category (3.23 ± 0.99–4.81 ± 0.90–2.25 ± 0.86). This research clearly identifies the anthropometric profile of elite ILCA 7 sailors, which can significantly contribute to a more informed choice of sailing class. Given the results of this research, current ILCA 7 sailors can easily compare their own anthropometric parameters with elite ILCA 7 sailors and eventually adjust their training process to obtain a more desirable anthropometric profile. Full article

Submarines are required to have good performance, which is influenced by their type of hull, hull conditions, and operational conditions. This study compares the resistance between a Modified-U209 (U209) submarine and the DARPA Suboff. The former is an older hull geometry with both surface and submerged operation considered, whereas the latter represents a modern nuclear-powered submarine designed for submerged operations only. The two geometries were scaled to give the same usable volume, and all results were non-dimensionalized using this to ensure consistency. A Computational Fluid Dynamics (CFD) method was utilized to predict resistance by employing the Reynolds-averaged Navier–Stokes (RANS) equations. The results show that the total resistance coefficient for the U209 bare hull is approximately 6% higher than the Suboff bare hull. When a casing was added to the U209 geometry the increase in total resistance coefficient was approximately 8%. The addition of the sail resulted in an increase in total resistance coefficient ranging from approximately 4% (Suboff sail added to U209) to approximately 14% (U209 sail added to U209). An existing empirical prediction technique was used to predict the resistance, with the total resistance coefficient predicted being consistently about 5% lower than the values obtained using CFD. Full article

The demand for maritime–coastal tourism has been intensifying, but its offerings are sometimes limited to a few activities. Some of these activities do not require specific skills or certifications, while others do. This study aimed to investigate what type of activities are carried out by tourism and recreational vessels in the coastal area of the central Algarve (Portugal). To this end, data from the automatic identification system (AIS) of recreational vessels was used to monitor and categorise these activities in a non-intrusive manner. A model (TORMA) was defined to facilitate the analysis of AIS data and relate them to five independent variables (distance from the coast, boat speed, bathymetry, seabed type, and number of pings). The results of the analysis of more than 11 thousand hourly AIS records for passenger, sailing, and charter vessels showed that the 14 most regular ones had strong seasonal patterns, greater intensity in summer, and spatial patterns with more records near some coastal cliffs. This study provides valuable information on the management of motorised nautical activities near the coast and at sea, contributing to more informed and effective tourism regulation and planning. Full article

The effects of leaf clumping on leaf area index (LAI, m²·m⁻²) retrieval have been proved by several studies. For dense and highly clumped Moso bamboo canopies, LAI is usually retrieved using the SAIL-series models that do not account for leaf clumping, although these retrievals are subsequently successfully validated by indirect ground-based methods that do account for leaf clumping. In order to explore these two seemingly contradictory results, LAIs of 21 Moso bamboo canopies retrieved by the GOST2 model (incorporating leaf clumping), the 4SAIL model and the SNAP tool (both without leaf clumping), respectively, were validated against ground-based LAI estimations, including the direct allometric method and indirect digital hemispherical photograph (DHP) methods. LAIs retrieved by GOST2 show strong agreement with the surrogate truth estimated by the allometric method (R² = 0.79, RMSE = 3.03), but underestimations of retrieved LAIs by 4SAIL and the SNAP tool reach up to 27.6 and 28.8, respectively, due to lack of consideration of leaf clumping. These results indicate the following: (1) Depending on gap analysis-based clumping index (Ω) algorithms, leaf clumping corrections in indirect ground-based LAI estimations are unsuccessful for highly clumped Moso bamboo canopies due to heavy overlapped leaves; (2) LAIs of dense and highly clumped Moso bamboo canopies can be retrieved from satellite remote sensing data through canopy reflectance models with leaf clumping consideration; (3) The misunderstanding of LAI ranges of Moso bamboo canopies by previous studies (2.2–6.5) can be attributed to the application of gap analysis-based Ω for indirect ground-based LAI estimations; and (4) Effective leaf area index (L_e) derived from satellite remote sensing data, and validated using gap analysis-based L_e/Ω, could be erroneously interpreted as LAI. Full article