Search Results (2,311)

Urban nature and nature-based solutions are increasingly promoted to enhance public space experience and urban climate resilience. In Public and semi-public indoor settings, biophilic design is considered beneficial for stress reduction and mental health restoration through the introduction of natural elements such as plants. However, research focusing on the specific visual features of plants and the underlying mechanisms remains limited. Based on 200 indoor greenery images and their multi-dimensional feature vectors, and combined with questionnaire data from 253 valid participants, this study developed a quantitative framework of plant visual features and adopted a two-level analytical approach. At the image level, linear mixed-effects models (LMMs) were used to identify how plant features influenced immediate responses. At the group level, partial least squares structural equation modelling (PLS-SEM) was employed to examine how cumulative restorative experience translated into affective states, perceived restorativeness, and behavioural intention. The results showed that Green View Index (GVI) and species richness were the most stable positive features, while plant health status, certain planting modes, and spatial layer-related features also showed significant effects. Restorative experience influenced behavioural intention mainly through positive affect and perceived restorativeness. These findings provide evidence for biophilic design, offering quantitative support for incorporating indoor public space into broader urban nature and public space framework. Full article

The decarbonization of international shipping has accelerated interest in ammonia as a zero-carbon marine fuel. However, its acute toxicity poses safety challenges fundamentally different from those associated with LNG. This study presents a structured comparative regulatory analysis of the IGF Code and the IMO Interim Guidelines for Ships Using Ammonia as Fuel through a chapter-by-chapter review of key safety domains. The results show that, despite structural similarities, the two frameworks diverge significantly in their underlying safety logic: LNG regulation is primarily oriented toward flammability and explosion prevention, whereas ammonia regulation adopts a toxicity-driven safety architecture. This shift is reflected in ppm-level gas detection thresholds, ammonia release mitigation systems (ARMS), toxic area and Safe Haven concepts, broader secondary containment measures, and enhanced personnel protection requirements. These findings suggest that ammonia safety cannot be adequately addressed through incremental extensions of LNG-based rules alone. Instead, it requires a dedicated regulatory approach that explicitly incorporates toxic exposure management into ship design and operation. Full article

Shafting load directly reflects shafting alignment quality and is critical to ship safety and reliability, yet remains difficult to measure directly in engineering practice. To address this, we propose a load identification and prediction method based on a Digital–Physical hybrid model. This approach integrates shafting load inversion with the time-series dependency characteristics of LSTM networks to construct an interpretable framework comprising physical, data, and decision layers. Modal testing calibrates the finite element model, while Tikhonov regularization addresses the ill-posed nature of frequency response function inversion. Additionally, a weight allocation strategy is designed during preprocessing to enhance training data quality. Validation experiments for load identification and prediction are conducted using an optimized dataset fused from measured and simulation data. Results show that, compared with purely physical or purely simulation-based models, the proposed hybrid model reduces prediction errors (RMSE, MAE, MSE) by 32–48.4% and increases the goodness of fit of prediction curves by 4%. This demonstrates superior predictive capability and interpretability, providing a new avenue for the monitoring of shafting conditions and load prediction in complex mechanical structures. Full article

To address ship-sourced water pollutant issues resulting from shipping industry growth and achieve precise supervision and effective management in coastal ports, this study develops a method for calculating and predicting the generation volume of oily sewage, domestic sewage and solid waste based on Automatic Identification System (AIS) data. First, a questionnaire survey (“Survey on Ship Water Pollutants”) is designed and implemented. Through analysis of questionnaire data, the ranges of values for the generation of oily sewage, domestic sewage, and solid waste from different ship types at China’s coastal ports are established. Additionally, onboard sampling is conducted to determine average emission factors for domestic sewage and oily sewage from typical ship types. Second, ship activities are derived from AIS data and combined with the established generation volume ranges for spatiotemporal calculation. Finally, a ConvLSTM (Convolutional Long Short-Term Memory) model is developed to predict the generation volume of water pollutant based on their spatiotemporal characteristics. Taking a major Chinese port area as a case study, the results indicate that pollutant generation volumes are significant in coastal port zones and main navigation channels, particularly between 15:00 and 16:00. chemical oxygen demand (COD), suspended solids (SS), and 5-day biochemical oxygen demand (BOD₅) levels in domestic sewage exceeded China’s national regulatory limits by 0.35 times, 2.88 times and 1.07 times, respectively, which can easily lead to a decrease in dissolved oxygen content in the water, affecting the respiration and survival of aquatic organisms. Petroleum content in oily sewage remained below the standard threshold. For pollutant generation volume prediction, the proposed ConvLSTM model achieved MAE and RMSE values of 0.0824 and 0.1433, respectively, outperforming other prediction models such as LSTM and CNN-LSTM. This research provides technical support for the prevention and control of water pollution from ships in coastal ports. The proposed AIS-driven framework and ConvLSTM prediction method are transferable and globally applicable, offering a reference for the environmental sustainability of port ecosystems, the global maritime pollution prevention, and the sustainable development of the shipping industry worldwide. Full article

With the accelerated transformation of green shipping and the advancement of ship electrification, lithium-ion batteries have become the core solution for ship propulsion due to their advantages of high energy density and zero emission. Efficient thermal management serves as a key technical support to ensure the safe and stable operation of batteries, extend their service life, and mitigate the risk of thermal runaway. Lithium-ion batteries accumulate heat during discharge, and pure phase change material (PCM) cooling systems are limited by low thermal conductivity, leading to excessive battery temperature rise and poor temperature uniformity. To address this problem, RT42 (a paraffin-based PCM with a melting temperature range of 311.15–316.15 K) was selected as the PCM in this study. The battery thermal management system (BTMS) coupling RT42 with a three-dimensional fin network structure was designed. Numerical simulations were conducted via ANSYS Fluent, and the enthalpy-porosity method was adopted to simulate the PCM phase change process. The effects of fin distribution, spacing and layer number on BTMS performance were systematically investigated and compared. Results show that the heat transfer process in the PCM can be significantly improved due to the three-dimensional fin network, and the battery maximum temperature can be reduced by 7.53 K compared with the pure PCM system. This study provides theoretical support for the design and optimization of high-efficiency BTMS. Full article

Synthetic Aperture Radar (SAR) ship target detection is crucial for marine monitoring, offering vital support for maritime security, navigation safety, and environmental surveillance. However, deploying advanced deep learning models on resource-constrained edge devices like UAVs and spaceborne platforms is challenging due to the high computational complexity and large parameter counts, hindering real-time performance. To address this, we propose the HGXES model, a lightweight SAR ship detection network. This model integrates efficient structural design, feature enhancement mechanisms, and an attention mechanism to reduce computational costs while preserving feature extraction capabilities. It employs factorized convolutions, a cross-level feature reuse module, and an attention mechanism to dynamically adjust feature weights, enhancing sensitivity to ship targets. A lightweight detection head ensures rapid and accurate target classification and localization. Experiments on benchmark SAR datasets show that based on the lightweight HGNetV2 backbone, our incremental designs (Xfeat, ELA, LWDetect) further compress the model and achieve a 70% reduction in parameters compared with traditional models, with a model size of just 1.9 MB, 2.3 M parameters, and 3.9 G FLOPs, achieving 49.7 fps detection speed. Comparative analyses reveal the superiority of the ELA attention mechanism and ShapeIoU loss function in enhancing performance. Thus, the HGXES model successfully achieves lightweight SAR ship detection, supporting real-time marine monitoring on resource-limited platforms with high accuracy and reduced computational costs. Full article

This study addresses the question of how augmented reality (AR) technologies can be designed and integrated into maritime navigation systems to meet the needs of young navigators within contemporary socio-technical bridge environments. The article is based on a qualitative, literature-based research methodology involving a structured analysis and synthesis of peer-reviewed journal articles and conference proceedings related to AR interfaces, human performance, decision support, and maritime training. The reviewed studies indicate that AR can enhance perceptual and situational awareness by overlaying navigational information directly into the navigator’s field of view, thereby reducing head-down time, improving spatial alignment of information, and supporting performance in low-visibility and high-traffic conditions. The literature also shows that AR-enabled visualizations and shared displays can support individual and team-based decision-making by facilitating real-time, context-aware information exchange on the ship’s bridge. Safety-related benefits are identified as indirect outcomes of improved perception and cognitive support rather than as isolated technological effects. Simultaneously, the findings highlight that these benefits depend strongly on human-centered interface design and appropriate training. The study concludes that AR has significant potential to enhance maritime navigation for young navigators when integrated as part of a balanced socio-technical system combining technology, human factors, and structured education. Full article

Synthetic aperture radar (SAR) ship detection in complex inshore scenarios has long been constrained by the coupled effects of speckle noise and small–scale weak scattering targets. Although feature–level frequency–domain denoising methods partially alleviate noise interference, existing studies predominantly focus on spatial frequency modeling and implicitly assume consistent spectral responses and discriminative contributions across channels. This assumption may lead to over–suppression of weak ship targets under complex backgrounds. To address the incomplete dimensionality of current frequency–domain modeling, this paper proposes FSMD–Net, a joint spatial–channel spectral modeling framework for SAR ship detection. During multi–scale feature fusion, a coordinated modulation mechanism integrating multi–spectral channel attention with spatial frequency–domain denoising is introduced. This design enables channel discriminability and frequency–subspace denoising to act synergistically, enforcing structurally consistent spectral constraints throughout multi–scale feature propagation. Extensive experiments on SARDet–100K, HRSID, and AIR–SARShip–2.0 demonstrate that FSMD–Net achieves consistent performance improvements, particularly in small–target and strong–clutter scenarios, exhibiting enhanced detection accuracy and robustness. Full article

The increasing availability of microwave bistatic remote sensing data highlights the need for reliable and computationally efficient scattering models to support data interpretation, system design, and mission planning. This is particularly relevant in urban and maritime environments, where the electromagnetic (EM) interaction between buildings and ships with the surrounding environment significantly affects the observed bistatic signatures. This paper presents a fully analytical model for EM bistatic scattering from a canonical target, represented as a parallelepiped with smooth dielectric faces located over a lossy random rough surface. The formulation is developed within the framework of the Kirchhoff Approximation and accounts for both single- and multiple-bounce scattering mechanisms arising from the mutual interaction between the target and the underlying surface. Reflections from the target walls are modeled using the Geometrical Optics solution, while scattering from the rough surface is described through the zeroth-order Physical Optics approximation. The resulting closed-form expressions provide both coherent and incoherent components of the scattered field as explicit functions of system and scene parameters. The proposed closed-form model enables fast and reliable evaluation of bistatic scattering from parallelepiped-like structures, such as buildings and large ships interacting with surrounding rough surfaces. This capability is particularly beneficial for the design and optimization of bistatic remote sensing missions in urban and maritime contexts as well as the development and assessment of inversion methods and large-scale analyses. Validation against numerical simulations and experimental results available in the literature demonstrates the effectiveness of the proposed approach across different operating conditions. Full article

The dynamic response of the secondary circuit system in nuclear propulsion plants is critical to the power output, safety, and energy efficiency of nuclear-powered ships. High-fidelity thermo-hydraulic simulation models can accurately capture system transients but are computationally expensive and unsuitable for real-time applications. To address this limitation, this study proposes a reduced-order dynamic parameter prediction method that integrates high-fidelity simulation with deep learning. A multi-operating-condition simulation model of a typical nuclear-powered ship secondary circuit system is developed to generate time-series data covering load ramping and propulsion mode switching. Based on this dataset, a conventional recurrent neural network (RNN) and a multilayer long short-term memory (LSTM) network are constructed for multivariate autoregressive prediction of 17 key dynamic parameters, and their performances are systematically compared. Results show that the LSTM significantly outperforms the RNN in capturing long-term temporal dependencies, achieving average RMSE and MAPE values of 0.0228% and 0.365%, respectively. The proposed model completes 50-step-ahead prediction within 0.84 s, satisfying real-time requirements. The hybrid simulation-driven and data-driven framework provides a practical solution for intelligent monitoring and control optimization of nuclear-powered ship propulsion systems. Full article

To address the challenges of constrained grid-like compartments, a motion framework integrating adaptive obstacle avoidance planning and active disturbance rejection control is proposed. First, an Adaptive Rapidly exploring Random Tree Star (Adaptive RRT*) algorithm based on multi-source state feedback is developed. Scaled-down model simulations show that, compared to conventional algorithms, its path length (374.28 mm), planning time (0.30 s), and node count (50.83) are reduced by at least 29.5%, 64.7%, and 28.6%, respectively, achieving a 100% planning success rate. Next, a control scheme based on Extended State Observer–Model Predictive Control (ESO-MPC) is designed. Simulations indicate that under nominal conditions, tracking errors are reduced by 5.78–84.35% compared to traditional MPC. Under a 20% link mass perturbation, the scheme effectively eliminates phase lag. Under complex scenarios involving parameter perturbation and a 0.6 N·m step torque disturbance, the tracking error reduction ranges from 25.27% to 87.59%, exhibiting excellent disturbance rejection robustness. Physical experiments conducted on a scaled-down experimental platform further verify that the maximum tracking errors of the manipulator end-effector along the x, y, and z axes under ESO-MPC are 0.88 mm, 0.85 mm, and 0.89 mm, respectively, significantly outperforming the 2.41 mm, 2.39 mm, and 2.47 mm observed with MPC. Finally, obstacle avoidance and trajectory-tracking simulations of an industrial manipulator in a full-scale ship compartment environment validate the engineering feasibility of the proposed framework. Full article

Ship classification in optical remote sensing requires balancing discriminative representation and model efficiency. Standard convolutional neural network (CNN) bottlenecks rely on local spatial kernels and may emphasize high-frequency texture cues, while stronger backbones increase parameter cost. We propose a frequency-aware lightweight bottleneck (FALB) that couples enhanced wavelet convolution (WTsConv) and contextual anchor attention (CAA) in a cascaded design. WTsConv adopts Sym4 wavelets and a learnable symmetric fusion weight between spatial and wavelet-reconstructed features to improve frequency-aware feature mixing. CAA is then applied to the refined features for contextual aggregation. Integrated into ResNet-50 bottlenecks, FALB is evaluated on FGSCM-52 and achieves 97.88% top-1 accuracy with 17.78 M parameters, compared with 96.92% and 25.56 M for the ResNet-50 baseline, surpassing ResNet-50 by 0.96% and outperforming compared general-purpose baselines while reducing parameters by 30.4%. Under this experimental setting, FALB improves the observed accuracy–parameter trade-off for remote sensing ship classification. Full article

Nonlinear model predictive control (NMPC) delivers high tracking accuracy for industrial processes but requires solving a nonlinear program at each sampling instant, limiting its applicability under tight real-time constraints. The Koopman operator provides a principled route to circumvent this limitation by embedding nonlinear dynamics into a higher-dimensional space where the evolution becomes linear, thereby reducing the online optimization to a convex quadratic program. This paper presents a Koopman-based MPC framework (K-MPC) that incorporates three algorithmic contributions. First, an adaptive radial basis function dictionary learning procedure selects lifting functions from process data, eliminating manual basis selection and improving approximation fidelity for systems with localized nonlinearities. Second, a recursive least-squares update rule adjusts the Koopman matrix online as new measurements arrive, enabling the controller to track slow parameter drifts without full model recomputation. Third, a tube-based constraint tightening strategy accounts for the residual linearization error, preserving recursive feasibility under bounded Koopman approximation mismatch. Simulations on a Van der Pol oscillator, a continuous stirred-tank reactor (CSTR), and a four-state Tennessee Eastman-inspired distillation column demonstrate that K-MPC achieves root-mean-square tracking errors within 11–16% of NMPC while reducing average per-step computation time by a factor of 14 to 18. The recursive update mechanism reduces prediction error by 80% compared to the fixed offline Koopman model when reactor feed concentration drifts by 15% from its nominal value. Ablation experiments confirm that adaptive dictionary learning and online updating each contribute measurably to closed-loop performance. Full article

Offshore wind turbine operation and maintenance in complex sea states is influenced by the coupled effects of low-frequency vessel drift and high-frequency wave-induced disturbances. In practical operations, the ship dynamic positioning system primarily regulates low-frequency motion through vessel position control, whereas a boarding compensation system is required to attenuate high-frequency six-degrees-of-freedom motions to ensure safe personnel transfer. This study establishes coupled kinematic mapping among the ship dynamic positioning system, the Stewart platform, and a three-degrees-of-freedom gangway and proposes a hierarchical cooperative control architecture. At the upper layer, an extended Kalman filter and an exponential moving average low-pass filter are employed for online state estimation and for separating low-frequency and high-frequency components. A Kalman filter lookahead predictor is then used to generate a short-horizon prediction of the high-frequency component and to construct a feedforward reference signal. At the middle layer, the feedforward reference and the gangway end error feedback are coordinated at the velocity level, and a quadratic programming-based allocation strategy distributes compensation tasks between the Stewart platform and the gangway under safety-related constraints, including actuator stroke limits and singularity avoidance. At the lower layer, a robust feedback controller is designed for the gangway to mitigate modeling uncertainties and environmental disturbances and to ensure stable tracking. MATLAB R2024a-based simulations under representative wave conditions demonstrate that the proposed architecture improves end effector tracking accuracy and closed-loop stability compared with baseline strategies, providing a feasible engineering solution for shipboard boarding operations in complex sea states. Full article

Although air resistance represents a relatively small portion of a ship’s total resistance, it can vary significantly for containerships due to loading conditions and container configurations. As containerships grow, their exposed projected windage area increases, leading to higher wind loads that affect maneuverability, heading control, and operational efficiency. Accurately assessing these aerodynamic effects is therefore crucial for both ship design and operational planning. This study investigates the aerodynamic contribution to ship resistance by evaluating the impact of random container configurations on wind loads for a post-Panamax 6750 TEU containership. Numerical simulations are performed at full scale under open-sea conditions using the Reynolds-averaged Navier-Stokes equations with the Realizable k-ε Two-Layer turbulence model. The resulting aerodynamic forces and moments are expressed as non-dimensional wind load coefficients, following ITTC recommendations. Numerical results are compared with methods provided by Blendermann and Isherwood, which are based on systematic wind tunnel measurements. Full article