Search Results (487)

Korean melon (K-melon, Cucumis melo L. var. makuwa) is a key horticultural crop in the Republic of Korea, but its short shelf life restricts long-distance export. This study evaluated the modified atmosphere (MA) films of varying oxygen transmission rates (OTR) at controlled atmosphere (CA) storage under real maritime export conditions to Vietnam. In the non-permeable OTR 0 (Control) treatment, internal O₂ rapidly declined below the anaerobic compensation point (1.67% at 10d and 0.47% at 10+3d) while CO₂ accumulated to 32–36%. This ultra-low oxygen environment induced anaerobic metabolism, evidenced by strong accumulation of fermentative metabolites such as lactic acid, acetoin, and 2,3-butanediol, along with glucose/fructose retention and increases in alanine and γ-Aminobutanoic acid (GABA). These changes disrupted glycolysis and the Tricarboxylic acid cycle (TCA), consistent with CA shock, and were accompanied by rind blackening, elevated weight loss, and hue angle shifts toward yellow-orange. By contrast, OTR 10,000 and OTR 30,000 films significantly suppressed weight loss and color changes. Partial least squares-discriminant analysis (PLS-DA) identified volatile organic compounds, namely acetoin, 2,3-butanediol, and hexanal, as key discriminant metabolites, with OTR 30,000 clearly separated from other treatments at 10+3d, indicating minimal fermentation and oxidative stress. Microbial assays revealed a dose-dependent reduction in bacterial counts with increasing OTR, while fungal growth was most strongly suppressed under OTR 10,000. Overall, OTR 30,000 maintained the lowest and most stable levels of stress-related metabolites, minimized microbial proliferation, and preserved metabolic stability throughout shipping. This study provides the first quantitative evidence of anaerobic metabolic transition and primary metabolite accumulation in K-melons under actual export trials. The findings demonstrate that optimizing MA film permeability, particularly OTR 30,000 films, offers a practical and cost-efficient strategy to extend shelf life, maintain quality stability, and enhance the global export potential of K-melons. Full article

The reliability of ocean-going ship engine fuel systems is crucial for the safety and continuous operation of vessels. Failure of this system can lead to serious operational and economic consequences; therefore, effective diagnostics and failure prediction are essential elements of modern fleet management. This paper presents an analysis of the reliability of fuel systems based on operational data from ten bulk carriers operated by Polska Żegluga Morska in Szczecin. The analysis combined classical statistical methods with artificial intelligence algorithms to develop a hybrid diagnostic and forecasting framework. The Weibull lifetime distribution was applied to estimate time-to-failure parameters, revealing mixed failure mechanisms—random failures (k < 1) and aging-related processes (k > 1). Using the k-means algorithm, ships were automatically classified into two reliability groups: high-failure-rate units and stable operational vessels. Individual linear regression models were then developed for each ship to forecast the time to the next failure, achieving satisfactory predictive performance (R² > 0.75 for most vessels). Sensitivity analysis quantified model robustness under different disturbance scenarios, yielding mean Relative Prediction Deviation (RPD) values of approximately 65% for Missing Data, 60% for False Failure, and 26% for Data Noise. These results confirm that the proposed hybrid reliability–AI framework is resistant to random noise but sensitive to incomplete or erroneous historical data. The developed approach provides an interpretable and effective tool for predictive maintenance, supporting reliability management and operational decision-making in marine engine systems. The article presents a hybrid model that has been developed to enable the detailed characterization of emergency processes and the identification of the most important factors that influence damage forecasting. For systems with variable failure risk, it was found that both classical probabilistic models and machine learning methods must be considered to interpret damage patterns correctly. Implementing data filtration and validation procedures before using data in artificial intelligence models has been shown to improve forecast stability and increase the usefulness of forecasts for planning repairs. Full article

This study addresses structural distortions in the IMO Carbon Intensity Indicator (CII) for short-voyage training vessels and proposes corrective strategies combining denominator adjustments with controllable pitch propeller (CPP) mode optimization. Using 2024 operational data from a training ship, we computed monthly and annual CII values, identifying significant inflation when time-at-sea fractions are low due to extensive port stays. Two correction methods were evaluated: a hybrid denominator approach converting port-stay CO₂ to equivalent distance, and a Braidotti functional correction. The CPP operating maps for combination and fixed modes revealed a crossover point at approximately 12 kn (~50% engine load), where the combination mode shows superior efficiency at low speeds and the fixed mode at higher speeds. The hybrid correction effectively stabilized CII values across varying operational conditions, while the speed-band CPP optimization provided additional reductions. Results demonstrate that combining optimized CPP mode selection with hybrid CII correction achieves compliance with required standards, attaining a B rating. The integrated framework offers practical solutions for CII management in short-voyage operations, addressing regulatory fairness while improving operational efficiency for training vessels and similar ship types. Full article

The rapid expansion of global shipping has led to continuously increasing vessel traffic density, making high-accuracy ship trajectory prediction particularly critical for navigational safety and traffic management optimization in complex waters such as ports and narrow channels. However, existing methods still face challenges in medium-to-long-term prediction and nonlinear trajectory modeling, including insufficient accuracy and low computational efficiency. To address these issues, this paper proposes an enhanced Informer model (WOA-Informer) based on the Whale Optimization Algorithm (WOA). The model leverages Informer to capture long-term temporal dependencies and incorporates WOA for automated hyperparameter tuning, thereby improving prediction accuracy and robustness. Experimental results demonstrate that the WOA-Informer model achieves outstanding performance across three distinct trajectory patterns, with an average reduction of 23.1% in Root Mean Square Error (RMSE) and 27.8% in Haversine distance (HAV) compared to baseline models. The model also exhibits stronger robustness and stability in multi-step predictions while maintaining a favorable balance in computational efficiency. These results substantiate the effectiveness of metaheuristic optimization for strengthening deep learning architectures and present a computationally efficient, high-accuracy framework for vessel trajectory prediction. Full article

Due to the complexity of the marine environment and the uncertainty of ship movements, altitude control of UAV is particularly important when approaching and landing on the deck of a ship. This paper focuses on unmanned helicopters as its research subject. Conventional altitude control systems may have difficulty in ensuring fast and stable landings under certain extreme conditions. Therefore, designing a new UAV altitude control method that can adapt to complex sea conditions has become a current problem to be solved. Designing a reinforcement learning based rotational speed compensator for UAV as a redundant controller to optimise UAV altitude control performance for the above problem. The compensator is capable of adjusting the UAV’s rotational speed in real time to compensate for altitude deviations due to external environmental disturbances and the UAV’s own dynamic characteristics. By introducing reinforcement learning algorithms, especially the DDPG algorithm, this compensator is able to learn the optimal RPM adjustment strategy in a continuous trial-and-error process, which improves the UAV’s rapidity and stability during the landing process. Full article

Under the context of “Energy Conservation and Emission Reduction,” low-sulfur fuel has become widely adopted in maritime operations, posing significant challenges to cylinder lubrication systems. Traditional oil injection strategies, heavily reliant on manual experience, suffer from instability and high costs. To address this, a lightweight image retrieval model for cylinder lubrication is proposed, leveraging deep learning and computer vision to support oiling decisions based on visual features. The model comprises three components: a backbone network, a feature enhancement module, and a similarity retrieval module. Specifically, EfficientNetB0 serves as the backbone for efficient feature extraction under low computational overhead. MobileViT Blocks are integrated to combine local feature perception of Convolutional Neural Networks (CNNs) with the global modeling capacity of Transformers. To further improve receptive field and multi-scale representation, Receptive Field Blocks (RFB) are introduced between the components. Additionally, the Convolutional Block Attention Module (CBAM) attention mechanism enhances focus on salient regions, improving feature discrimination. A high-quality image dataset was constructed using WINNING’s large bulk carriers under various sea conditions. The experimental results demonstrate that the EfficientNetB0 + RFB + MobileViT + CBAM model achieves excellent performance with minimal computational cost: 99.71% Precision, 99.69% Recall, and 99.70% F1-score—improvements of 11.81%, 15.36%, and 13.62%, respectively, over the baseline EfficientNetB0. With only a 0.3 GFLOP and 8.3 MB increase in model size, the approach balances accuracy and inference efficiency. The model also demonstrates good robustness and application stability in real-world ship testing, with potential for further adoption in the field of intelligent ship maintenance. Full article

Pure Loss of Stability is one of the five typical stability failure modes identified in the Second-Generation Intact Stability Criteria by the IMO. This study investigates the influence of hull line variations on the vulnerability of a saury fishing vessel to pure loss of stability. Hull forms were parametrically modified using the Free-Form Deformation method, and an in-house code was developed to evaluate stability performance. The numerical framework was validated against the commercial ICS-HydroSTAB software (Version 1.0), demonstrating high computational accuracy and engineering applicability. Parametric sensitivity analysis was then conducted to examine the effects of geometric characteristics under both calm-water and wave-induced conditions. The results indicate that vulnerability in calm water is primarily governed by the maximum sectional area curve and the bow portion of the DWL half-breadth curve, while in waves it is influenced by both the maximum sectional area curve and the fore and aft portions of the DWL half-breadth curve. The half angle of entrance (E = 0.08) exhibits a comparatively minor effect, but its increase reduces the initial metacentric height and significantly elevates the risk of capsizing in waves. These findings provide useful references for hull form optimization and stability design. Full article

Active helidecks systems (AHS) provide an effective solution scheme for the safe landing of helicopters on ships. This article proposes a novel adaptive antidisturbance prescribed performance control law of AHS subject to input saturation, ship motion-induced external disturbances. Specifically, we develop novel saturation-triggered boundaries to guarantee prescribed tracking error constraints under input saturation. This effectively addresses the control singularity issue inherent in traditional prescribed performance control, which occurs when input saturation causes the control error to exceed prescribed constraint boundaries. Subsequently, we design a continuous auxiliary dynamic system to further mitigate the effects of input saturation. Furthermore, leveraging the internal model principle and the periodic nature of ship motion, external disturbances are treated as the outputs of a linear exosystem with known structure but unknown parameters. These unknown parameters are then estimated using adaptive techniques, enabling asymptotic estimation of external disturbances. Building upon these developments and employing the backstepping design tool, we achieve adaptive antidisturbance stabilization of AHS. Both theoretical analysis and comparative simulations validate the proposed control law. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV) poses a significant challenge, costing annually approximately USD 1.2 billion to the U.S. swine industry due to production losses associated with, but not limited to, reproductive failure, abortion, and high pre-weaning mortality among piglets. PRRSV is endemic, with thirty percent of the U.S. breeding herd experiencing outbreaks annually. The shedding status of animals on a farm is typically assessed using serum or processing fluids from piglets, but tongue tips from deceased animals are emerging as a potential alternative specimen to support farm stability assessment. This study explored the impact of various processing and testing strategies on tongue tips to enhance the sensitivity and specificity of PRRSV detection in sow herds. We collected tongue tips from 20 dead piglets across seven sow farms, testing different pooling strategies (individual testing, and pools of n = 5 or n = 20) and laboratory processing methods (tongue tip fluid—TTF, versus tongue tissue homogenate—TTH). Additionally, we simulated storage and shipping conditions, comparing frozen samples to refrigerated ones tested at intervals of 1, 4, and 7 days post collection. RT-PCR testing revealed higher sensitivity and lower cycle threshold (Ct) values for TTF compared to TTH, suggesting that tongue tips are better tested as TTF rather than TTH for PRRSV detection. Pooling samples reduced diagnostic accuracy. Frozen samples had lower absolute Ct values, and Ct values increased by 0.2 Ct values each day post collection when the sample was kept refrigerated, emphasizing the importance of minimizing shipping delays. Tongue tips are a practical, easy-to-collect specimen that target potentially infected animals (dead piglets), offering valuable insights into swine herd health, but sample processing approaches significantly influence diagnostic outcomes. If tongue tips are used by veterinarians to assess viral presence on a farm, testing the TTF instead of TTH should be prioritized. Storage and shipment conditions should be considered to optimize laboratory results. Full article

As the core refrigeration equipment in cooling systems, the operational state of chiller units is crucial for ship support, equipment cooling, and mission stability. However, because of their sensitivity and the complexity of operating environments, obtaining large volumes of complete, fault-labeled data is difficult in practical engineering appli-cations. This limitation makes it challenging for traditional data-driven approaches to deliver accurate fault diagnoses. Furthermore, data collected from different devices or under varying operating conditions often differ significantly in both feature dimensions and distributions, i.e., data heterogeneity, which further complicates model transfer. To address these challenges, this study proposes a deep transfer learning–based fault di-agnosis method designed to leverage abundant knowledge from the source domain while adaptively learning features of the target domain. Given the persistent difficulties in collecting sufficient high-quality labeled fault data, traditional data-driven models continue to face restricted diagnostic performance on target equipment. At the same time, data heterogeneity across devices or operating conditions intensifies the challenge of cross-domain knowledge transfer. To overcome these issues, this study develops a heterogeneous transfer learning method that integrates a dual-channel autoencoder, domain adversarial training, and pseudo-label self-training. This combination enables precise small-sample knowledge transfer from the source to the target domain. Specifi-cally, the dual-channel autoencoder is first applied to align heterogeneous feature di-mensions. Then, a Gradient Reversal Layer (GRL) and a domain discriminator are in-troduced to extract domain-invariant features. In parallel, high-confidence pseu-do-labeled samples from the target domain are incorporated into joint training to im-prove generalization and robustness. Experimental results confirm that the method achieves high fault diagnosis accuracy in typical industrial application scenarios, ena-bling effective identification of common faults in various types of chiller units under conventional operating conditions, the proposed method achieves higher accuracy and F1-scores in multi-class fault diagnosis tasks compared with both traditional approaches and existing transfer learning methods. These findings provide a novel perspective for advancing the intelligent operation and maintenance of chiller units. Full article

The smoke toxicity of epoxy resin limits the application of its carbon fiber composites in marine interior structures. To address this issue, a novel epoxy resin (EZ) was synthesized by grafting phenyl propyl polysiloxane (PPPS) onto ortho-cresol novolac epoxy resin (EOCN), building upon the group’s earlier work on polysiloxane-modified epoxy resin (EB). The results confirmed successful grafting of PPPS onto EOCN, which significantly enhanced the thermal stability and char residue of EZ. Specifically, the peak heat release rate (PHRR), total heat release (THR), peak smoke production rate (PSPR), and total smoke production (TSP) of EZ were reduced by 68.5%, 35%, 73.1%, and 48.3%, respectively, attributable to the formation of a stable and compact char layer that suppressed smoke generation. By blending EZ with EB resin, a low-smoke epoxy system (LJF-2) was developed for prepreg applications. Carbon fiber composites (LJF-CF) prepared from LJF-2 exhibited minimal smoke emission and a unique bilayer char structure: a dense inner layer that hindered smoke transport and a thick outer layer that provided thermal insulation, delaying further resin decomposition. Silicon was uniformly distributed in the char residue as silicon oxides, improving its stability and compactness. Without adding any flame retardants or smoke suppressants, LJF-CF achieved a maximum smoke density (Ds,max) of 276.9, meeting the requirements of the FTP Code for ship deck materials (Ds,max < 400). These findings indicate that LJF-CF holds great promise for use in marine interior components where low smoke toxicity is critical. Full article

The International Maritime Organization (IMO) has announced regulations that affect many shipbuilding industries and related companies. They require building companies to demonstrate strict compliance with these regulations in construction activities going forward. In response, shipbuilding companies are testing various electrification methods, with the ultimate aim of making ships more eco-friendly. In large ships, in particular, constructors often take a gradual route by hybridizing the propulsion system. In many large cargo ships, the adoption of energy storage systems (ESSs) is expected as part of this transition. In practice, the most frequently operating units inside the ship are the generator engines (GEs). Therefore, this study targets the fluctuation rate characteristics of GEs, providing a more realistic basis for ESS sizing. By focusing on smoothing the GE output, this study determines the ESS capacity required to maintain system stability using a simple moving average (SMA) method and evaluates the fluctuation rate of the GEs under various load conditions. Full article

With the rapid growth of global shipping, accurate vessel traffic prediction is essential for waterway management and navigation safety. This study proposes the Fusion Spatio-Temporal Transformer (FSTformer) to address non-Gaussianity, non-stationarity, and spatiotemporal heterogeneity in traffic flow prediction. FSTformer incorporates a Weibull–Gaussian Transformation for distribution normalization, a hybrid Transformer encoder with Heterogeneous Mixture-of-Experts (HMoE) to model complex dependencies, and a Kernel MSE loss function to enhance robustness. Experiments on AIS data from the Fujiangsha waters of the Yangtze River show that FSTformer consistently outperforms baseline models across multiple horizons. Compared with the best baseline (STEAformer), it reduces MAE, RMSE, and MAPE by 3.9%, 1.8%, and 6.3%, respectively. These results demonstrate that FSTformer significantly improves prediction accuracy and stability, offering reliable technical support for intelligent shipping and traffic scheduling in complex waterways. Full article

In free-running ship simulations, PID control gains for rudder and propeller revolution are often selected based on empirical experience without a standardized procedure, leading to inconsistent results under varying operational conditions. This study examined PID control gains by implementing a simulation framework using STAR-CCM+. The Ziegler–Nichols tuning method was applied to derive control gains, and their behavior was analyzed across different wave conditions (calm, short, medium, and long waves), PID period condition, ship speeds (low and design speeds), and scale ratios. The simulations showed that the PID gains derived under moderate wave conditions provided stable and reliable control performance across various sea states. Furthermore, the influence of scale ratio changes on the control performance was evaluated, and a non-dimensional scaling formula for PID coefficients was proposed to enhance applicability across different model sizes. Validation against experimental data confirmed the reliability of the simulation setup. These findings offer a systematic guideline for selecting the PID control gains for free-running simulations, promoting improved accuracy and stability under diverse environmental and operational conditions. This research contributes to developing standardized practices for maneuvering performance evaluations in realistic maritime environments. Full article