Search Results (514)

Agribusiness is the main economic activity in northeastern Yucatán, México. The production of forage grasses for livestock feed requires weed control, achieved through chemical methods, with the herbicide 2,4-D being the most widely used in the region. This research quantified the presence of 2,4-D in groundwater in Sucilá, Yucatán, during 2023. Twenty ranches with different stocking rates (organisms/hectare) were evaluated. Groundwater samples and soil cores were collected in two climatic seasons (dry and rainy) to relate soil properties and herbicide concentration to the weather. The results showed that the concentration of 2,4-D was higher during the rainy season (September 2023), with values between 1.6 and 3.6 mg 2,4-D/L, but no differences were observed among stocking rates. Soil organic matter was different between seasons, and cation exchange capacity changed among stocking rates. Continuous irrigation, coupled with the application of agrochemicals, is likely impairing the water quality of groundwater in this area of the Yucatan. Full article

Thermochemical conversion of nitrogen- and sulfur-rich biomass-derived wastes, such as sewage sludge, algae, and agricultural residues, is a promising route for renewable fuel production and waste valorization. However, fuel-bound nitrogen can be released as NH₃, HCN, and HNCO, while sulfur species such as H₂S, SO₂, and COS accelerate catalyst deactivation and generate NO_x/SO_x precursors. Metal-loaded ZSM-5 catalysts are attractive for clean catalytic pyrolysis because they combine the MFI pore confinement and tunable Brønsted/Lewis acidity of ZSM-5 with the hydrogen transfer, dehydrogenation, cracking, redox, and sulfur-tolerance functions of metal species. This review critically summarizes recent advances in metal-loaded ZSM-5 catalysts for catalytic denitrogenation of biomass-derived solid wastes. The formation and migration of NH₃, HCN, HNCO, tar-N, and char-N are first discussed to clarify the chemical basis of fuel-N conversion. The effects of ZSM-5 pore structure, acid-site distribution, Si/Al ratio, hierarchical porosity, and synergy on adsorption, diffusion, C-N bond cleavage, heterocyclic-N ring-opening, aromatization, and nitrogen redistribution are then analyzed. Catalyst deactivation under realistic pyrolysis atmospheres is also highlighted, including coke deposition, metal sintering, framework dealumination, mineral poisoning, and H₂S/SO₂/COS-induced sulfur poisoning. Finally, future directions are proposed for designing multifunctional ZSM-5-based catalysts integrating denitrogenation activity, sulfur resistance, coke resistance, regenerability, and quantitative nitrogen/sulfur mass balance. Full article

In large-scale cattle farming, accurate behavior recognition is central to achieving intensive health monitoring and animal welfare assessment. To address challenges such as background interference from fences, feed troughs, and stains in real-world barns, and overlapping of cattle coupled with the inability of single-RGB modalities to capture physical spatial structure, which leads to issues like blurred detection boundaries and significant noise interference—we propose a cattle behavior detection method based on RGB-Depth dual-modal information fusion. This approach jointly models the texture information from RGB images and the spatial structural information from depth images. Within this framework, this paper constructs three collaborative optimization modules: first, the CDSAM module is developed, which evaluates neuron importance through a parameter-free attention mechanism and combines dynamic convolutions to adapt to the cattle’s variable postures, effectively suppressing complex background noise. Second, we propose the C2BRA module based on a two-layer routed attention mechanism. By adopting a two-stage modeling approach of “region-level routing—intra-region fine-grained attention,” it adapts to changes in target scale and enhances the model’s ability to represent spatial context for multi-scale semantic information. Finally, in the prediction stage, a lightweight shared convolutional detection head (LSCD) is introduced. By sharing convolutional parameters across scales and decoupling the classification and regression architectures, it reduces computational overhead while maintaining accuracy. Experimental results show that the improved model achieves a mAP@0.5 of 90.3% on our self-built cattle behavior dataset, representing a 4.3 percentage point increase compared to the baseline model, while reducing GFLOPs from 11.0 G to 9.6 G, a decrease of 12.7%; Visualization results indicate that the improved model can focus more accurately on cattle body contours and key behavioral regions, thereby reducing false negatives and enhancing detection accuracy. Concurrently, the model achieves an optimal balance between detection performance and computational complexity, providing robust technical support for automated cattle behavior monitoring on smart farms. Full article

Cross-domain underwater object recognition is essential for intelligent visual monitoring in marine ranching, yet domain shift caused by varying water conditions and imaging devices severely degrades model performance. Existing adversarial domain adaptation methods align feature distributions to mitigate domain shift, but they often fail to preserve the fine-grained discriminative structure required for distinguishing visually similar marine species. Discriminator-free adversarial domain adaptation constrains only the sum of singular values, leading to projection distortion, thereby degrading target discriminability. We observe that the discriminative structure of the source domain is encoded in the singular value distribution of classifier outputs, and aligning this distribution across domains, rather than its sum, preserves discriminability during knowledge transfer. Based on this insight, we propose Adversarial Spectral Distribution Alignment (ASDA). ASDA consists of Spectral Distribution Alignment (SDA), which minimizes the Wasserstein distance between source and target singular value distributions, and a Dynamic Feature Queue (DFQ) with an adaptive length schedule that provides stable spectral distribution estimates across mini-batches. By enforcing singular value ratio consistency across all principal directions, SDA achieves fine-grained alignment, which reduces domain discrepancy while preserving discriminative features. Experimental results on two underwater image datasets demonstrate that ASDA outperforms existing domain adaptation methods. Full article

Fish schooling is a common phenomenon in nature, and offers valuable inspiration for the design of underwater biomimetic propulsors. To investigate the fluid interaction mechanisms among individuals in an oblique tandem arrangement, this study conducts two-dimensional numerical simulations of three airfoil-based biomimetic fish models. Using Computational Fluid Dynamics, the effects of streamwise spacing ($L_x=0.8$–$1.2$) and orientation angle ($\theta =10$–$30°$) on the hydrodynamic performance and wake characteristics of each fish are examined for a selected set of five representative cases. The results show that, within the present parameter range, the overall time-averaged thrust coefficient of the three-fish school in an oblique tandem arrangement is higher than that of a single fish, with an increase ranging from 16% to 22%. As the streamwise spacing increases, the mutual interference among individuals weakens, and the overall thrust gain gradually decreases. With increasing orientation angle, the thrust gain of the leading and middle fish decreases, whereas that of the trailing fish transitions from negative to positive. At a small orientation angle ($\theta =10°$), the leading and middle fish show a substantial thrust increase. This preliminary study indicates that, within the examined parameter space, appropriately adjusting the streamwise spacing and orientation angle can effectively enhance the overall thrust of a fish school in an oblique tandem arrangement and enable the trailing fish to achieve a thrust gain. Full article

This study investigates the flow field around a multi-cage aquaculture platform under wave–current coupling. Using STAR-CCM+ with fifth-order Stokes wave theory and a porous media model, the effects of incident wave height and net solidity ratio on wave propagation and energy dissipation are analyzed. Results show the platform exerts a significant damping effect on incident waves. Inside the platform, wave height distribution follows a characteristic pattern: it is relatively high at the front, attenuated in the front cage, recovered in the central cage, and stabilized in the rear cage. Pronounced wave diffraction and energy concentration around the columns cause a marked wave height reduction at the center of the first cage, while wave heights in the middle and rear cages tend to stabilize. The overall wave attenuation pattern remains consistent under different incident wave heights. Higher waves induce greater attenuation in the central culture area, with the transmission coefficient showing a stable variation trend. A higher net solidity ratio enhances front-edge wave reflection and rear wave-height reduction, improving overall damping; a lower ratio results in weaker damping and a higher wave response at the rear. The findings provide practical references for platform design and application. Full article

Sea surface temperature (SST) anomalies represent a critical threat to the operational safety and productivity of marine ranching systems. Taking a typical marine ranching area in the Northern Yellow Sea as the study area, this study developed a lightweight two-stage field-to-site forecasting framework. In the first stage, a Convolutional Long Short-Term Memory network (ConvLSTM) was employed to generate 1–5 days regional SST forecasts. Through experiments involving 24 input configurations, the combination of Optimum Interpolation Sea Surface Temperature (OISST), seasonal and trend components, and ERA5 meteorological variables was identified as an optimal configuration, providing spatial-evolution constraints for the target location. In the second stage, the 5-day target-site forecasts were concatenated with OISST to construct a 60-day sequence, which was then used to drive a lightweight Gated Recurrent Unit (GRU) for extended forecasts from days 6 to 15. This strategy reformulates the forecasting task into a spatially constrained 10-day extension forecast, thereby suppressing long-lead error accumulation. The results showed that, in the regional forecasting stage, an RMSE of 0.83 °C and MAE of 0.63 °C were achieved on Day 5, while in the extended-forecasting stage, an RMSE of 0.89 °C and MAE of 0.69 °C were achieved on Day 15. Compared with single-stage ConvLSTM and GRU models performing direct 15-day forecasting, the field-to-site strategy reduced RMSE by approximately 25.21% and 9.18%, respectively, and reduced MAE by approximately 24.18% and 8.00%, respectively. Independent validation against in situ buoy observations further showed that the proposed framework introduced only limited additional errors comparable to the inherent discrepancy between OISST and buoy observations. Additional tests under representative marine heatwave (MHW) conditions showed that the framework retained useful forecasting skill under anomalous warming conditions. Furthermore, its extended application experiments at five additional marine ranching sites in the Northern Yellow Sea produced consistent forecasting performance, with mean RMSE and MAE ranging from 0.71 °C to 0.75 °C and from 0.53 °C to 0.56 °C, respectively. The proposed method can therefore provide a risk-warning window of at least two weeks for marine ranching management and support timely operational decisions for temperature-related risk mitigation and refined aquaculture management. Full article

Grass carp (Ctenopharyngodon idella) is an important freshwater aquaculture species in China. However, high-density farming often leads to significant growth differentiation among individuals, seriously affecting yield and product quality. Here, we conducted an integrated transcriptomic and data-independent acquisition (DIA) proteomic analysis across the brain, liver, and muscle tissues of fast-growing (FG) and slow-growing (SG) grass carp after 9 months of high-density culture. Our analysis revealed that the core mechanism driving growth differentiation is a deep decoupling of transcription and translation, where enhanced muscle translational efficiency dictates the fast-growth phenotype, while slow growth is constrained by central stress and hepatic energy depletion. In the slow growth group, the brain exhibited translational arrest and neuroinflammation, and the liver entered a state of hypermetabolism mediated by AMPK, evidenced by the post-transcriptional up-regulation of key sensors such as CAB39 (PRM ratio = 1.634) and CAMKK2 (PRM ratio = 1.295). Conversely, in the fast-growing group, the mTOR–ribosome signaling axis was strongly activated at the post-transcriptional level in the muscle (GSEA NES = −1.852), triggering myofibrillar protein deposition despite transcriptional silence in classical growth pathways. These findings elucidate the systemic molecular mechanisms of growth divergence and provide high-confidence molecular targets for developing fast-growing, stress-resilient grass carp strains for precision aquaculture breeding. Full article

Foggy ship detection frequently suffers from image degradation, blurred object contours and a high missed detection rate. Moreover, most existing maritime datasets lack adequate real fog samples. To solve the above problems, in this paper, the Fog-SMD is constructed on the basis of atmospheric scattering principles and fractal theory in combination with a diffusion model to enrich samples covering various fog scenarios. On this basis, we develop an improved SF-YOLO model that takes YOLOv12 as the basic framework. By embedding the shallow–deep adaptive feature fusion module, scattering-guided refinement module and spatial-frequency dual feature attention module, the model can effectively alleviate feature loss resulting from image degradation in foggy environments. Weighted-EIoU loss is introduced to optimize the bounding box regression and reduce the localization deviation of slender ship targets. The experimental results show that SF-YOLO achieves mAP@50 and mAP@50:95 values of 79.3% and 61.6%, respectively, and outperforms mainstream detection algorithms; compared with YOLOv12n, it improves mAP@50:95 from 59.6% to 61.6%, with only a slight increase in parameters from 2.5 M to 2.8 M, providing a new solution for the practical deployment of detection systems and all-weather maritime monitoring in low-visibility foggy scenarios. Full article

Nutrient stoichiometry provides a powerful framework for linking nutrient limitation to plant community biomass, especially in grasslands undergoing degradation in the agro-pastoral ecotone, where nitrogen (N) fertilization and mowing have become two widespread key management practices. However, their influence on nutrient stoichiometry has received little attention, especially beyond the leaf carbon (C):N:phosphorus (P) ratio. Here, we conducted a field experiment on the Mongolian Plateau wherein we quantified 19 nutrient ratios for soils and three plant components (aboveground plants, litter, and belowground roots) following 12 years of N addition (0, 2, and 10 g N m⁻² year⁻¹) combined with mowing, and grouped these ratios into four sets with C, N, P, and potassium (K) as the numerators. Under N addition, nutrient stoichiometry in plant components and soils changed markedly, whereas mowing management resulted in negligible changes in most C-, N-, and K-based nutrient ratios. Furthermore, mowing and N addition interactively and significantly affect P-based nutrient ratios. The responses of nutrient stoichiometry differed among plant components and soils, and also depended on the level of N input, and these ratios with C and N as numerators generally showed greater variability than those with P and K in the plant–soil system. Plant community biomass was associated with nutrient ratios in both plant components and in soils, although the relationships were not always significant. Long-term N addition resulted in rate-dependent shifts in nutrient stoichiometry, whereas mowing had only weak modifying effects. Extending nutrient stoichiometry framework (including neglected ratios, e.g., N:K) beyond leaf C:N:P to encompass entire plant–soil systems can help local government and ranch owners manage grasslands more cost-effectively because of simple assessment procedures, and could further provide more comprehensive insights into nutrient limitation and main hypotheses of ecological stoichiometry in grassland ecosystems. Full article

Grass carp (Ctenopharyngodon idella) is a major freshwater aquaculture species in China, but its growth is limited under intensive high-density farming. This study aimed to investigate the characteristics of plasma exosomes associated with distinct growth performance by isolating and characterizing exosomes from fast- and slow-growing grass carp after nine months of culture. Exosomes showed typical morphology and expressed characteristic markers (CD63, CD81, TSG101). Small RNA sequencing identified 3325 miRNAs, with 177 highly abundant miRNAs differentially expressed: immune-related miRNAs were upregulated, while development-inhibitory miRNAs were downregulated in fast-growing fish. Target gene enrichment highlighted pathways in neural and skeletal development and amino acid metabolism. Integrative analysis across tissues revealed 26 miRNAs with coordinated expression patterns between plasma exosomes and brain, liver, or muscle, validated by qPCR. DIA proteomics quantified 4203 proteins, identifying 843 differentially enriched proteins linked to immune response, energy metabolism, and endoplasmic reticulum stress. Notably, TYMP was upregulated in muscle and exosomes, while several proteins (e.g., GYG2, BHMT) showed coordinated downregulation across tissues and exosomes in large fish. These results provide comprehensive evidence of exosome-mediated cross-tissue communication in teleosts and suggest a potential role for plasma exosomal miRNAs and proteins as non-invasive biomarkers correlated with growth status in aquaculture. Full article

Seasonal hydrology reshapes large-river food webs by altering habitat connectivity and basal resource availability. Trophic baselines from before the 2021 Yangtze ten-year fishing ban are now valuable because monitoring has shifted from fish abundance alone toward food-web function and ecological recovery. We analysed carbon (δ¹³C) and nitrogen (δ¹⁵N) stable isotopes of fish and the baseline bivalve Corbicula fluminea collected in March (dry season) and August (wet season) 2016 from the Jingjiang section of the lower Yangtze River. In the dry season, 100 individuals of 27 species were analysed; species mean δ¹³C ranged from −30.52‰ (Micropercops swinhonis) to −21.19‰ (Aristichthys nobilis) and δ¹⁵N from 6.30‰ (Hypophthalmichthys molitrix) to 14.90‰ (Lophiogobius ocellicauda). In the wet season, 187 individuals of 47 species were analysed; species mean δ¹³C ranged from −32.07‰ (Pseudobrama simoni) to −20.84‰ (Salanx ariakensis) and δ¹⁵N from 6.27‰ (Misgurnus anguillicaudatus) to 14.87‰ (Saurogobio gymnocheilus). Among 24 shared species, δ¹³C differed significantly between seasons (paired t = 4.30, p < 0.001), but δ¹⁵N did not (t = 1.52, p = 0.143). Mean trophic level fell from 3.07 to 2.74 (t = 3.85, p < 0.001). This decline remained significant in a trophic-enrichment-factor sensitivity analysis using 2.5–4.0‰. Community-wide carbon range (CR), nitrogen range (NR), total convex-hull area (TA), mean nearest-neighbour distance (NND), and the standard deviation of nearest-neighbour distance (SDNND) showed larger wet-season CR (9.08 vs. 7.51), slightly larger NR, TA and NND, and lower SDNND. Seasonal hydrology thus mainly altered basal carbon pathways and relative trophic positions rather than reorganising feeding guilds. The dataset provides a pre-ban isotopic baseline for assessing whether post-ban recovery in the lower Yangtze includes restoration of trophic structure and energy-flow pathways. Full article

CoCrFeNi/WC-Nb high-entropy alloy (HEA) composite coating was prepared on the surface of Q235 steel by LC (laser cladding) technology, and the effects of WC and in situ NbC reinforcement on the coating were studied. The phase composition, phase characteristics, microhardness, and wear resistance of the cladding coatings were characterized by scanning electron microscope (SEM), X-ray diffraction (XRD), friction and wear tester, and X-ray photoelectron spectroscopy (XPS), and the corrosion resistance was tested by a three-electrode electrochemical workstation. The results show that the CoCrFeNi/WC-Nb HEA coating consists of FCC, WC, NbC, and Laves phases, and the reinforcing phase causes grain refinement and lattice distortion. The microhardness reached (418.29 ± 16.72) HV, which was about 2.64-times higher than that of the CoCrFeNi HEA coating. The wear rate decreased to (1.150 ± 0.11) × 10⁻⁴ mm³N⁻¹m⁻¹, which was about 0.25 times that of the CoCrFeNi HEA coating, and the wear of the coating changed from abrasive wear to adhesive wear. The corrosion current density and corrosion voltage of the CoCrFeNi/WC-Nb HEA coating are (3.3820 ± 0.2103) × 10⁻⁶ A/cm² and −(0.7650 ± 0.0850) V, respectively. Full article

Accurate beef cattle body measurement data are crucial for growth assessment, phenotypic analysis, breeding management, and precision livestock farming. Traditional manual measurements are labor-intensive, time-consuming, and likely to cause stress in animals, making it difficult to meet the demands of large-scale livestock farming. This paper employs a structured systematic literature review method, in accordance with the PRISMA 2020 guidelines, to summarize research progress in vision-based beef cattle body measurement. This paper focuses on reviewing technical approaches such as 2D image-based measurement, 3D measurement using RGB-D and LiDAR, and multi-view fusion. It analyzes key technologies including image segmentation, keypoint detection, point cloud processing, 3D reconstruction, and geometric calculations, and compares the advantages and disadvantages of different methods in terms of measurement accuracy, robustness, cost, and farm applicability. The results indicate that 2D image-based methods are low-cost and flexible to deploy but have limited expressiveness for 3D body measurement parameters; RGB-D and LiDAR methods can provide spatial information but are affected by point cloud noise, occlusion, equipment costs, and data processing complexity; multi-view fusion can improve the completeness of body surface information but places high demands on calibration, registration, and system integration. Current research still faces challenges such as a lack of public datasets, inconsistent annotation standards, uncertainty regarding ground truth, insufficient cross-ranch generalization validation, and limited practical applications. Future research should focus on developing standardized datasets, conducting cross-scenario validation, advancing multimodal perception, creating lightweight models, and applying edge computing to drive the evolution of visual body measurement toward continuous monitoring and intelligent decision-making. Full article

In complex ranch environments, ReID of cattle faces remains highly challenging due to postmortem appearance degradation, severe occlusion, environmental interference, and the extremely limited number of samples available for each individual. These factors substantially undermine the reliability and robustness of existing visual ReID models. To address these challenges, we propose SG-NSA (Semantic-guided NFC Set Aggregation), a training-free inference-stage enhancement framework built upon a trained and fixed ReID backbone. Without introducing additional learnable parameters, modifying the backbone architecture, or fine-tuning the trained model, SG-NSA improves dead-cattle face ReID performance through feature-level correction, set-level aggregation, and semantic-guided filtering. SG-NSA consists of three synergistic modules: Neighborhood Feature Correction (NFC), which alleviates feature drift; Image Set Aggregation (ISA), which constructs stable set-level identity representations; and Semantic-guided Filtering (SF), which constrains and progressively narrows the candidate identity space. Together, these modules form a progressively enhanced inference mechanism. Furthermore, we construct a real-world live-to-dead paired cattle-face dataset collected over two years from ranches in Inner Mongolia, comprising 19,809 images of 1385 individual cattle. Experimental results demonstrate that SG-NSA consistently yields substantial gains across multiple baseline models. When applied to TransReID-SSL, it achieves 81.6% mAP and 80.7% Rank-1 accuracy, corresponding to improvements of 65.2% and 64.8%, respectively, over the baseline. Full article