Search Results (11,117)

The effects of pelleting a total mixed ration (TMR) for dairy sheep were investigated in an experiment involving 24 lactating Assaf ewes, which were assigned to two groups and fed the same TMR ad libitum, offered either in pelleted (PTMR group, n = 12) or in unpelleted form (CTMR group, n = 12). The experiment lasted 28 days, during which feed intake, eating behavior (including meal frequency and size, meal duration, eating rate, between-meal interval), and milk yield were recorded daily. Body weight (BW) was recorded on days 1 and 28 and milk samples were collected on days 1, 8, 15, 22 and 28 for milk composition analysis. Blood acid-base status was determined at the beginning and at the end of the trial. Ewes fed the CTMR diet exhibited (p < 0.05) a higher meal frequency and longer meal duration, along with a smaller meal size and slower eating rate. However, feed intake in this group was less than that in ewes fed PTMR only during the final two weeks of the experimental period. Total eating time was also longer (p < 0.001) in the CTMR group, whereas the average time between meals was shorter (p < 0.002). No differences (p > 0.05) were observed between dietary treatments in blood acid-base status, milk yield or milk composition. However, a diet x day interaction (p < 0.05) was detected for milk yield, as during the last 2 weeks of the experimental period the ewes fed the PTMR yielded more milk than those fed the CTMR. Feed conversion ratio did not differ between groups (p > 0.05), but body weight loss was greater in ewes fed the CTMR diet (−3.00 vs. −0.58 kg; p < 0.05). A trend toward improved feed efficiency was observed in the PTMR group when calculated based on milk yield corrected for that theoretically derived from the mobilization of body reserves (1.98 vs. 1.41 g DMI/kg milk; p = 0.077), with estimated contributions from body reserves of 485 g/day in the CTMR group and 70 g/day in the PTMR group. In conclusion, the use of pelleted total mixed rations in high-yielding dairy ewes enhances feed intake, feed efficiency, milk yield, and energy balance without adversely affecting milk composition or animal health in the short term. Full article

As space systems evolve towards cis-lunar missions and beyond, the demand for precise yet low-size, -weight, and -power (SWaP) clocks and synchronization methods becomes increasingly critical. We introduce a novel clock synchronization approach based on the Kuramoto oscillator model that facilitates the creation of an ensemble timescale for satellite constellations. Unlike traditional ensembling algorithms, the proposed Kuramoto method leverages nearest-neighbor interactions to achieve collective synchronization. This method simplifies the communication architecture and data-sharing requirements, making it well suited for dynamically connected networks such as proliferated low Earth orbit (pLEO) and lunar or Martian constellations, where intersatellite links may frequently change. Through simulations incorporating realistic noise models for small-scale atomic clocks, we demonstrate that the Kuramoto ensemble can yield an improvement in stability on the order of 1/√N, while mitigating the impact of constellation fragmentation and defragmentation. The results indicate that the Kuramoto oscillator-based algorithm can potentially deliver performance comparable to established techniques like Equal Weights Frequency Averaging (EWFA), yet with enhanced scalability and resource efficiency critical for future spaceborne PNT and communication systems. Full article

Banana (Musa spp.) fruit morphology is a key determinant of yield and quality, yet modeling its 3D structural dynamics across genotypes remains difficult. To address this challenge, we developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology. Field experiments were conducted over two growing seasons in Hainan, China, using three representative genotypes. Morphological traits, including outer and inner arc length, circumference, and pedicel length, along with dry (W_d) and fresh weight (W_f), were measured every 10 days after flowering until 110 days. Quantitative relationships between morphological traits and W_f, as well as between W_d and W_f, were fitted using linear or Gompertz functions with genotype-specific parameters. Based on these functions, a parameterized 3D reconstruction method was implemented in Python, combining biomass-driven growth equations, curvature geometry, and cross-sectional interpolation to simulate the fruit’s bending, tapering, and volumetric development. The resulting dynamic 3D models accurately reproduced genotype-specific differences in curvature, length, and shape with average fitting R² > 0.95. The proposed biomass-driven 3D structural model provides a methodological framework for integrating banana fruit morphology into functional–structural plant models. Full article

The ongoing advance of urbanization has increased the need for accurate monitoring of urban green space (UGS). However, existing remote-sensing UGS mapping still struggles with inconsistent data quality, diverse urban forms, and limited cross-city generalization. This study focuses on China’s Guangdong-Hong Kong-Macao Greater Bay Area as its research region, establishing a fully automated UGS mapping framework based on Sentinel-2 time-series imagery and standardized OpenStreetMap (OSM) data. This process achieves UGS mapping at 10 m resolution for 16 cities within the metropolitan area through a dynamic standardized OSM tagging system, a Sentinel-2 satellite image sample generation mechanism integrating spectral and textural features, multidimensional sample quality assessment and weighting strategies, as well as balanced cross-city sampling and weighted SVM classification. The results demonstrate that this method exhibits stable performance across multiple urban environments, achieving an average overall accuracy of approximately 0.83 and an average F1 score of approximately 0.82. The highest recorded F1 score reaches 0.96, highlighting the method’s strong generalization capability under diverse urban conditions. The mapping results reveal significant disparities in UGS distribution within the Guangdong-Hong Kong-Macao Greater Bay Area, reflecting the combined effects of varying urban development patterns and ecological contexts. The unified workflow proposed in this study demonstrates strong applicability in handling heterogeneous urban structures and enhancing cross-regional comparability. It provides consistent, transparent, and reusable foundational data for regional eco-urban planning, urban green infrastructure development, and policy evaluation. Full article

The Estimated Time of Arrival (ETA) of vessels is a vital operational indicator for voyage planning, fleet deployment, and resource allocation. However, most existing studies focus on short-distance liner services with fixed routes, while ETA prediction for long-distance tramp bulk carriers remains insufficiently accurate, often resulting in operational inefficiencies and charter party disputes. To fill this gap, this study proposes a data-driven stacking ensemble learning framework that integrates Light Gradient-Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) as base learners, combined with a Linear Regression meta-learner. This framework is specifically tailored to the unique complexities of tramp shipping, advancing beyond traditional single-model approaches by incorporating systematic feature engineering and model fusion. The study also introduces the construction of a comprehensive multi-dimensional AIS feature system, incorporating baseline, temporal, speed-related, course-related, static, and historical behavioral features, thereby enabling more nuanced and accurate ETA prediction. Using AIS trajectory data from bulk carrier voyages between Weipa (Australia) and Qingdao (China) in 2023, the framework leverages multi-feature fusion to enhance predictive performance. The results demonstrate that the stacking model achieves the highest accuracy, reducing the Mean Absolute Error (MAE) to 3.30 h—a 74.7% improvement over the historical averaging benchmark and an 11.3% reduction compared with the best individual model, XGBoost. Extensive performance evaluation and interpretability analysis confirm that the stacking ensemble provides stability and robustness. Feature importance analysis reveals that vessel speed, course stability, and remaining distance are the primary drivers of ETA prediction. Additionally, meta-learner weighting analysis shows that LightGBM offers a stable baseline, while systematic deviations in XGBoost predictions act as effective error-correction signals, highlighting the complementary strengths captured by the ensemble. The findings provide operational insights for maritime logistics and port management, offering significant benefits for port scheduling and maritime logistics management. Full article

Phishing emails continue to evade conventional detection systems due to their increasingly sophisticated, multi-faceted social engineering tactics. To address the limitations of single-modality or rule-based approaches, we propose SAHF-PD, a novel phishing detection framework that integrates multi-modal feature extraction with semantic-aware hierarchical fusion, based on large language models (LLMs). Our method leverages modality-specialized large models, each guided by domain-specific prompts and constrained to a standardized output schema, to extract structured feature representations from four complementary sources associated with each phishing email: email body text; open-source intelligence (OSINT) derived from the key embedded URL; screenshot of the landing page; and the corresponding HTML/JavaScript source code. This design mitigates the unstructured and stochastic nature of raw generative outputs, yielding consistent, interpretable, and machine-readable features. These features are then integrated through our Semantic-Aware Hierarchical Fusion (SAHF) mechanism, which organizes them into core, auxiliary, and weakly associated layers according to their semantic relevance to phishing intent. This layered architecture enables dynamic weighting and redundancy reduction based on semantic relevance, which in turn highlights the most discriminative signals across modalities and enhances model interpretability. We also introduce PhishMMF, a publicly released multimodal feature dataset for phishing detection, comprising 11,672 human-verified samples with meticulously extracted structured features from all four modalities. Experiments with eight diverse classifiers demonstrate that the SAHF-PD framework enables exceptional performance. For instance, XGBoost equipped with SAHF attains an AUC of 0.99927 and an F1-score of 0.98728, outperforming the same model using the original feature representation. Moreover, SAHF compresses the original 228-dimensional feature space into a compact 56-dimensional representation (a 75.4% reduction), reducing the average training time across all eight classifiers by 43.7% while maintaining comparable detection accuracy. Ablation studies confirm the unique contribution of each modality. Our work establishes a transparent, efficient, and high-performance foundation for next-generation anti-phishing systems. Full article

The objective was to evaluate the effect of the inclusion of avocado waste (AW) in the diet of rabbits on lipid composition, color, and lipid and protein oxidative stability in the meat of growing rabbits. For this purpose, 80 male rabbits (New Zealand × California) with an average initial weight of 945 ± 47 g were fed for 28 days, being randomly distributed to one of the four experimental treatments (T) (T1: 0%, T2: 4.32%, T3: 8.39%, and T4: 12.25% of waste avocado inclusion, respectively). A decrease (p < 0.05) in the amount of saturated fatty acids was observed in the meat of rabbits fed 8.39% and 12.25% AW, a lower (p < 0.05) concentration of malondialdehyde (mg MDA/kg) in the meat of rabbits fed the AW, and a lower (p < 0.05) concentration of dinitrophenylhydrazine (DNPH) in the treatments with 4.32 and 8.39% AW. The results suggest that the addition of AW in rabbit diets increases the content of n-3 polyunsaturated fatty acids and protects the meat from the products of lipid and protein oxidation, decreasing discoloration and delaying oxidation, generating a final product with a longer shelf life. Full article

Underwater optical images are the primary carriers of underwater scene information, playing a crucial role in marine resource exploration, underwater environmental monitoring, and engineering inspection. However, wavelength-dependent absorption and scattering severely deteriorate underwater images, leading to reduced contrast, chromatic distortions, and loss of structural details. To address these issues, we propose a U-shaped underwater image enhancement framework that integrates Swin-Transformer blocks with lightweight attention and residual modules. A Dual-Window Multi-Head Self-Attention (DWMSA) in the bottleneck models long-range context while preserving fine local structure. A Global-Aware Attention Map (GAMP) adaptively re-weights channels and spatial locations to focus on severely degraded regions. A Feature-Augmentation Residual Network (FARN) stabilizes deep training and emphasizes texture and color fidelity. Trained with a combination of Charbonnier, perceptual, and edge losses, our method achieves state-of-the-art results in PSNR and SSIM, the lowest LPIPS, and improvements in UIQM and UCIQE on the UFO-120 and EUVP datasets, with average metrics of PSNR 29.5 dB, SSIM 0.94, LPIPS 0.17, UIQM 3.62, and UCIQE 0.59. Qualitative results show reduced color cast, restored contrast, and sharper details. Code, weights, and evaluation scripts will be released to support reproducibility. Full article

Aiming at the core issues of the traditional YOLO11n model in rail surface defect detection—fine-grained feature loss of small defects, insufficient micro-target recognition accuracy, and the mismatch of existing downsampling/fusion methods for micro-defect feature extraction—this paper proposes an improved YOLO11n algorithm with two-dimensional network structure innovations. First, the Adaptive Downsampling (ADown) module is introduced into the backbone network for the first time, retaining global features via 2D average pooling and extracting local details through channel-split multi-path convolution/max pooling to avoid fine texture loss. Second, the original SOEP-RFPN-MFM neck network is designed, integrating SNI, GSConvE and MFM modules to achieve dynamic weighted fusion of multi-scale features and break the bottleneck of inefficient small-target feature aggregation. Trained and verified on a 4020-image rail dataset covering four defect types (Spalling, Squat, Wheel Burns, Corrugation), the improved algorithm achieves 93.7% detection accuracy, 92.4% recall and 95.6% mAP, realizing incremental improvements of 1.2, 2.6 and 0.8 percentage points, respectively, compared with the original YOLO11n, which is particularly optimized for rail micro-defect detection scenarios. This study provides a new deep learning method for rail transit micro-defect detection and a reference for scenario-specific improvement of lightweight YOLO11n models. Full article

To address the insufficient exploration ability, susceptibility to local optima, and limited convergence accuracy of the standard Student Psychology-Based Optimization (SPBO) algorithm in three-dimensional UAV trajectory planning, we propose an enhanced variant, Enhanced SPBO (ESPBO). ESPBO augments SPBO with three complementary strategies: (i) Time-Adaptive Scheduling, which uses normalized time ($\tau =t/T$) to schedule global step-size shrinking, Gaussian fine-tuning, and Lévy flight intensity, enabling strong early exploration and fine late-stage exploitation; (ii) Mentor Pool Guidance, which selects a top-K mentor set and applies time-varying guidance weights to reduce misleading attraction and improve directional stability; and (iii) Directional Jump Exploration, which couples a differential vector with Lévy flights to strengthen basin-crossing while keeping the differential step bounded for robustness. Numerical experiments on CEC2017, CEC2020 and CEC2022 benchmark functions compare ESPBO with Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Improved multi-strategy adaptive Grey Wolf Optimization (IAGWO), Dung Beetle Optimization (DBO), Snake Optimization (SO), Rime Optimization (RIME), and the original SPBO. We evaluate best path length, mean trajectory length, standard deviation, and convergence curves and assess statistical stability via Wilcoxon rank-sum tests (p = 0.05) and the Friedman test. ESPBO significantly outperforms the comparison algorithms in path-planning accuracy and convergence stability, ranking first on both test suites. Applied to 3D UAV trajectory planning in mountainous terrain with no-fly zones, ESPBO achieves an optimal path length of 199.8874 m, an average path length of 205.8179 m, and a standard deviation of 5.3440, surpassing all baselines; notably, ESPBO’s average path length is even lower than the optimal path length of other algorithms. These results demonstrate that ESPBO provides an efficient and robust solution for UAV trajectory optimization in intricate environments and extends the application of swarm intelligence algorithms in autonomous navigation. Full article

In this study, selenium nanoparticles (SeNPs) were synthesized using polysaccharides extracted from blueberry pomace (BP) as a stabilizing agent. BP was characterized as an acidic polysaccharide with a molecular weight of 5.4 × 10⁵ Da. The resulting BP-SeNPs were monodisperse spheres with an average size of 94.33 nm, as confirmed by TEM, DLS, FT-IR, XRD, and EDX analyses. Compared to SeNPs, BP-SeNPs demonstrated superior stability under varying conditions of storage time, temperature, pH, and ionic strength. Furthermore, in vitro evaluation using AAPH-induced rabbit erythrocytes revealed that BP-SeNPs offered enhanced protection against hemolysis. This protective effect was attributed to their ability to significantly bolster antioxidant enzyme activities (SOD, CAT, and GSH-Px) and preserve membrane integrity by maintaining ATPase function and sialic acid content. These results establish BP as an effective stabilizer for SeNPs and suggest the promising potential of BP-SeNPs as antioxidant agents in functional food or nutraceutical applications. Full article

Hand exoskeletons are increasingly used to support post-stroke reach-to-grasp, yet most intention-detection strategies trigger assistance from local hand events without considering the synergy between proximal arm transport and distal hand shaping. We evaluated whether proximal arm kinematics, alone or fused with EMG, can predict flexor and extensor digitorum activity for synergy-aligned hand assistance. We trained nine models per participant: linear regression (LINEAR), feedforward neural network (NONLINEAR), and LSTM, each under EMG-only, kinematics-only (KIN), and EMG+KIN inputs. Performance was assessed by RMSE on test trials and by a synergy-retention analysis, comparing synergy weights from original EMG versus a hybrid EMG in which extensor and flexor digitorum measure signals were replaced by model predictions. Results have shown that kinematic information can predict muscle activity even with a simple linear model (average RMSE around 30% of signal amplitude peak during go-to-grasp contractions), and synergy analysis indicated high cosine similarity between original and hybrid synergy weights (on average 0.87 for the LINEAR model). Furthermore, the LINEAR model with kinematics input has been tested in a real-time go-to-grasp motion, developing a high-level control strategy for a hand exoskeleton, to better simulate post-stroke rehabilitation scenarios. These results suggest the intrinsic synergistic motion of go-to-grasp actions, offering a practical path, in hand rehabilitation contexts, for timing hand assistance in synergy with arm transport and with minimal setup burden. Full article

Modular multilevel converter (MMC) is widely employed in high-voltage direct current (HVDC) systems for the long-distance renewable energy transmission, where the larger submodule (SM) capacitors significantly increase its size, weight and cost. Conventional capacitor voltage fluctuation suppression methods, such as composite harmonic injection (CHI) strategies, can achieve lightweight MMC. However, these approaches often neglect the dynamic constraints between harmonic injection parameters and their coupled effect on modulation wave, which not only leads to suboptimal global solutions but also increases the risk of system overshoot. Therefore, this paper proposes a comprehensive CHI parameters optimization method to minimize capacitor voltage fluctuations, thereby allowing for a smaller SM capacitor. First, the analytical expression of SM average capacitor voltage is developed, incorporating the injected second-order harmonic circulating current and third-order harmonic voltage. On this basis, an objective function is defined to minimize the sum of the fundamental and second-order harmonic components of the average capacitor voltage, with the harmonic injection parameters and modulation index as optimization variables. Then, these parameters are optimized using a particle swarm optimization (PSO) algorithm, where their constraints are set to prevent modulation wave overshoot and additional power loss. Finally, the optimization method is validated through a ±500 kV, 1500 MW MMC-HVDC system under various power conditions in PSCAD/EMTDC (version 4.6.3). In addition, simulation results demonstrate that the proposed method can achieve a 13.33% greater reduction in SM capacitance value compared to conventional strategies. Full article

This study characterizes the chemical defense system of the invasive longhorn beetle Aromia bungii, a destructive pest of Prunus trees, addressing the limited understanding of chemical defensive mechanisms in Cerambycidae. High-speed cameras, environmental scanning electron microscopy (ESEM), dissection, and micro-CT imaging were used to investigate defensive behavior, and the structure of the defense system, in this beetle. Both sexes of A. bungii possess a pair of triangular, sac-like defensive glands symmetrically located in the metathorax, attached to the metasternum. Upon mechanical stimulation, white liquid defensive substances are rapidly ejected through a pair of slit-shaped openings (~200 µm) at the metasternum corners, without gland eversion, reaching over 50 cm. The average weight of substances ejected in first sprays was 7.95 ± 0.79 mg for females and 8.62 ± 2.13 mg for males (mean ± se), with no significant difference between sexes. However, the weight in second sprays after 10 days was significantly lower, at 2.93 ± 0.54 mg for females and 2.22 ± 0.40 mg for males (mean ± se), suggesting that the beetles cannot re-synthesize the substances soon after spray. The weight of ejected substances had no correlation with beetle body weight. Our findings represent the first detailed morphological and functional description of a chemical defense system in Cerambycidae, revealing a specialized metasternal gland and spray mechanism. The substantial but likely non-renewable defensive substances reflect an adaptive trade-off in energy allocation between reproduction and defense in this species that exhibits high fecundity but a short lifespan at the adult stage. Full article

The objective of the present study was to evaluate the probiotic impact of Weizmannia faecalis (formerly Bacillus coagulans) DSM 32016 on the performance parameters and intestinal health of broiler chickens reared under high stocking density and mild heat stress conditions. The trial involved 320 day-old ROSS broiler chicks, randomly assigned to two experimental groups (8 pens per group). The control group received a standard commercial diet while the experimental group was supplemented with W. faecalis. At 42 days of age, 24 birds from each group were slaughtered for carcass composition analysis and evaluation of the weight of individual cuts. Probiotic supplementation significantly increased final body weight and improved feed conversion ratio, resulting in a significant increase in drumstick weight and breast meat yield, while the average feeding cost per kg broiler decreased by 5%. Collectively, the probiotic diet supplementation enhanced growth performance, alleviating the adverse effects of high stocking density and thermal stress. Full article