Search Results (1,289)

Groundwater quantification is essential for sustainable water resources management, yet it is often hampered by limited data availability and difficulties in measuring spring discharges. This study investigates three carbonate aquifers in Central Italy’s Abruzzo region: the Genzana–Greco, Morrone, and Marsicano mountains. The aim is to resolve uncertainties in spring attribution, and groundwater flow patterns using isotopic analyses combined with field surveys. The Genzana–Greco aquifer was examined to clarify the sources of the Acquachiara spring and the previously unreported Germina spring, assessing whether recharge occurs locally or from the carbonate massif. In this case, the results indicate that the Germina, together with a similar known spring of Capolaia, share a common recharge sector, while the Acquachiara spring is mainly fed by higher-elevation carbonate areas, excluding significant contributions from local alluvial deposits. In the Morrone mountain aquifer, discharge gains along the Pescara River through the Gole di Popoli were quantified, and spring isotopic compositions were compared to the main basal spring Giardino to better define groundwater contributions. In this case study, the stable isotopes and tritium data confirm recharge from the central–southern massif and support the identification of basal springs and Pescara River gains as primary discharge points, with minimal influence from surface water. For the Marsicano mountain aquifer, the role of Lake Scanno in feeding the Villalago springs was investigated through isotopic analysis of inflows, downstream springs, and basal aquifer discharge points to constrain the hydrogeological water budget. The results highlight Lake Scanno’s role in the recharge of Villalago springs and delineate the Cavuto group as a major discharge system receiving inputs from central and northern sectors of the massif. Overall, the integration of isotopic tracers with hydrological measurements allowed a more precise characterization of aquifer recharge areas, Mean Residence Times, and groundwater flow paths, improving the understanding of regional water resources in a complex carbonate setting. Full article

Modern genetic selection for high productivity has created a physiological conflict in ruminants, where the metabolic demands of lactation compete directly with the energy requirements of reproduction. This review provides a mechanistic synthesis of how key nutritional factors modulate the endocrine and cellular pathways governing reproductive success in cattle and sheep. Negative energy balance (NEB), characteristic of the early postpartum period, suppresses the hypothalamic–pituitary–gonadal (HPG) axis by impairing the pulsatile secretion of gonadotropin-releasing hormone (GnRH) and luteinizing hormone (LH), mediated through reduced kisspeptin signaling, growth hormone (GH) resistance, and decreased circulating insulin, insulin-like growth factor-1 (IGF-1), and leptin. At the macronutrient level, excess rumen-degradable protein elevates blood urea nitrogen and impairs the uterine environment, while omega-3 polyunsaturated fatty acids inhibit prostaglandin F2α synthesis to support corpus luteum maintenance. At the micronutrient level, selenium, copper, and zinc are essential antioxidant cofactors protecting gametes and embryos from oxidative stress, while vitamins A, D, and E regulate gene expression in reproductive tissues. Furthermore, maternal nutrition during critical gestational windows programs the reproductive capacity of offspring through epigenetic modifications, with profound implications for long-term herd fertility. Understanding these nutritional–reproductive interactions is crucial for developing precision feeding strategies that optimize herd fertility, improve animal welfare, and ensure the economic sustainability of livestock management. A thorough understanding of these nutritional–reproductive interactions is essential for developing precision feeding strategies that optimize fertility in high-producing ruminants. Full article

Pacific white shrimp (Litopenaeus vannamei) is a major aquaculture product worldwide. For consumers, discriminating domestic from imported sources of shrimp meat, and individual domestic sources, can be highly desirable because of the different meat quality and environmental contamination from geographically different origins of shrimp. This study evaluated the potential of stable isotope analysis (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O) with chemometric models to authenticate the origins of Pacific white shrimp sold in China. Shrimp samples from domestic (Guangxi, Fujian, Shandong, Inner Mongolia) and foreign (Ecuador) sources were analyzed, using statistical analyses. The four-isotope model achieved 89.3% cross-validation accuracy in distinguishing domestic and foreign shrimp, with an overall prediction Area Under the Curve (AUC) of 0.901 (95% CI: 0.819–0.983)—significantly outperforming single-isotope models. Differences in δ¹³C and δ¹⁵N reflected feed source variations, while δ²H and δ¹⁸O (Variable Importance in the Projection (VIP) > 1, key discriminatory indicators) mirrored geographic environmental difference. Although δ¹⁵N did not differ significantly among groups, the combination of all four isotopes reduced limitations of individual δ²H/δ¹⁸O use. This approach enhanced the precision, reliability, and applicability of stable isotope analysis for origin authentication by leveraging complementary isotopic data and robust statistical frameworks. These findings demonstrate the proposed model’s potential as a cost-effective, copyright-compliant framework for shrimp origin authentication, with implications for isotopic traceability across food science fields. Full article

The tomato hind (Cephalopholis sonnerati) is a marine aquaculture fish species with high economic value. Elucidating the mechanisms underlying its growth regulation is crucial for the development of the aquaculture industry. To analyze the biological mechanisms underlying growth differences, individuals with extreme body sizes at 8 months of age from the same batch were selected in this study. A combined experiment of “body size × feeding status” was constructed, and transcriptome sequencing and weighted gene co-expression network analysis (WGCNA) were performed on brain and muscle tissues. The results showed that 2553 differentially expressed genes (DEGs) were identified between individuals with distinct body sizes, which were significantly enriched in growth regulation pathways such as PI3K–Akt, MAPK, and FoxO. Feeding differences affected 4480 genes, which were significantly enriched in signaling pathways including the insulin signaling pathway. WGCNA further identified co-expression modules (brown4, blue, coral1) significantly correlated with growth, as well as hub genes including pik3r1 and eif4ebp2. Comprehensive analysis demonstrated that the growth regulation of C. sonnerati operates as a cascade network. Brain tissues perceive signals through neuroactive ligand–receptor interactions and integrate and transduce these signals via core pathways including Ras–MAPK and PI3K–Akt. Finally, growth processes are executed in muscle tissues by regulating glycogen metabolism, protein synthesis, and other processes, which are precisely regulated by terminal processes such as cellular senescence. Among them, pik3r1 and eif4ebp2, as key molecular switches, play a central role in integrating upstream signals and precisely regulating downstream growth programs. This study preliminarily clarifies the molecular mechanism network of growth differences in C. sonnerati, providing a theoretical basis and candidate genes for the genetic improvement of its growth traits. Full article

Faba bean-fed crispy tilapia represents a commercially valuable aquaculture product, renowned for its exceptional muscle firmness. However, the dynamic changes in muscle metabolite profiles during the tilapia crisping process remain largely unelucidated. In this study, proton nuclear magnetic resonance spectroscopy (¹H-NMR) combined with multivariate statistical analysis was employed to characterize and compare the muscle metabolomes of tilapia subjected to different crispness grades (CD0, CD2, CD4). A total of 11 differential metabolites were successfully identified, among which glycine, threonine, and trans-4-hydroxy-L-proline were demonstrated to be potential crispness-related biomarkers. Specifically, as the crispness grade increased from 0 to 4, the muscle contents of these key metabolites exhibited a consistent downward trend: glycine decreased significantly from 19.86 mM to 7.15 mM, threonine from 1.21 mM to 0.58 mM, and trans-4-hydroxy-L-proline from 2.25 mM to 0.89 mM. Subsequent metabolic pathway enrichment analysis further revealed that the glycine-serine-threonine metabolic pathway represented the most significantly perturbed pathway associated with the crisping process. Collectively, our findings demonstrate that faba bean-based feeding regimens enhance tilapia muscle crispness by orchestrating metabolite signatures involved in collagen biosynthesis and lipid metabolism. These results not only provide novel insights into the intrinsic molecular mechanisms underlying tilapia crisping but also establish a solid theoretical framework for the precise quality control and standardized production of high-quality crispy tilapia. Full article

Accurate, non-contact monitoring of rumination behavior is essential for assessing dairy cow health and welfare, as well as for optimizing feeding strategies and herd management in modern precision livestock farming. However, practical deployment in commercial barns faces challenges such as occlusions, variable lighting, and dynamic cow movements. To address this, we developed a robust, automated vision-based framework for continuous rumination monitoring. The core of our system integrates an enhanced object detection algorithm with a robust tracking module, specifically improved to capture subtle behavioral features and maintain identity under complex conditions. Evaluated on a comprehensive dataset collected from commercial settings under various lighting and occlusion scenarios, our framework achieved high detection accuracy (mAP of 96.26%) and reliable tracking performance (multi-object tracking accuracy of 99.2%). This demonstrates its suitability for real-time, on-farm deployment. The study provides a practical, end-to-end solution for fine-grained behavioral analysis in complex environments, offering a tool that can enhance welfare assessment and support decision-making in dairy farm management. The methodological approach is also adaptable to other precision livestock monitoring tasks. Full article

To address the existing challenges in mold slag thickness measurement—such as the susceptibility of contact sensors to high-temperature degradation and the limitation of non-contact methods to detecting only the upper slag surface—this study proposes an integrated approach that fuses millimeter-wave radar and eddy current sensors for measuring mold slag thickness in a continuous casting mold. The method innovatively combines two sensing principles: the millimeter-wave radar employs an improved FFT-CZT² high-precision ranging algorithm to perform high-resolution scanning of the solid slag upper surface, reconstructing its topography (error: $\pm$1 mm), while Mel-frequency cepstral coefficients (MFCC) are applied to extract features from the radar intermediate-frequency signals, combined with an enhanced PSO-BP neural network algorithm to predict the thickness of the solid slag layer (error: $\pm$5 mm). Concurrently, an eddy current sensor monitors the liquid slag–molten steel interface position (error: $\pm$1 mm). Through dual-sensor data fusion, the upper surface topography data and solid slag thickness obtained from the radar are spatially registered in three dimensions with the molten steel level information derived from the eddy current sensor. This integration ultimately enables the non-contact synchronous measurement of three key parameters within the mold: solid slag layer thickness, liquid slag layer thickness inversion, and molten steel level. Furthermore, by reconstructing the upper slag surface morphology, the method successfully resolves practical issues such as uneven material distribution, local material deficiency, or excessive feeding. Preliminary experimental verification confirms that the proposed method maintains stable performance even under high-temperature and complex environmental conditions. It thus provides a real-time, accurate, and full-cross-section monitoring solution for mold slag in continuous casting, offering significant practical value for the development of smart steel plants. Full article

Remote sensing images are highly susceptible to spatially non-uniform haze under complex atmospheric conditions, leading to contrast degradation and structural detail loss. Moreover, remote sensing scenes usually exhibit complex spatial structures, highly uneven haze distribution, and significant statistical variability, which further increases the difficulty of haze removal. To address this issue, we revisit the haze degradation mechanism of remote sensing imagery and propose a dynamic histogram-sorted Transformer dehazing method from the perspectives of statistical distribution modeling and region-adaptive restoration. Specifically, a Histogram-Sorted Adaptive Attention is designed to map spatial features into the statistical distribution domain through a dynamic histogram sorting mechanism, enabling explicit discrimination and precise modeling of regions with different haze densities. Meanwhile, a Perception-Adaptive Feed-Forward Network is constructed, which incorporates a stable routing-based mixture-of-experts mechanism to adaptively select restoration strategies according to local texture characteristics and global haze density, thereby significantly enhancing the adaptability of the model in complex remote sensing scenarios. Extensive experimental results demonstrate that the proposed method achieves superior performance over existing approaches across multiple remote sensing benchmark datasets, effectively improving both visual quality and robustness of remote sensing imagery. Full article

This study investigates the factors affecting surface integrity during the machining of near-β Ti-5553, a critical material in the aerospace and defense industries. Considering this alloy as a difficult-to-machine material, the turning process was examined by analyzing the effects of cutting speed, feed rate, and cooling strategy (dry, conventional, and 30 MPa/High-Pressure cooling) on cutting force, temperature, surface roughness, and residual stress. The primary novelty of this research lies in its integrated approach: rather than evaluating surface integrity metrics in isolation, it simultaneously models interrelated responses to residual stress, cutting temperature, cutting force, and surface roughness under high-pressure coolant (HPC) conditions. Furthermore, it introduces a robust machine learning framework that uniquely applies data augmentation (Gaussian jittering and interpolation) to overcome the conventional constraints of limited experimental machining data, providing a highly accurate predictive tool. The experimental data were expanded using data augmentation methods (Gaussian jittering and interpolation) and modeled using five different machine learning algorithms (Extra Trees, Random Forest, Gradient Boosting, KNN, and AdaBoost). The results revealed that cooling pressure plays a dominant role, particularly in residual stress (importance score: 0.926) and cutting temperature (0.657). It was observed that high-pressure cooling (HPC) reduces thermal gradients, thereby lowering tensile stresses and improving surface integrity. When algorithm performances were compared, the Extra Trees and Random Forest models achieved the most accurate predictions after hyperparameter optimization. Specifically, the optimized Extra Trees regressor demonstrated exceptional predictive capability for residual stress, achieving an accuracy of 98.47%, a remarkably high coefficient of determination (R² = 0.9997), and a minimal Mean Squared Error (MSE = 6.8289). These quantitative results confirm that the proposed machine learning framework provides a highly reliable and precise tool for controlling surface quality in HPC- assisted machining. Full article

Fully maintenance-free smart collars for range cattle, sheep and deer must survive years of uncontrolled grazing under highly variable shade and motion conditions. This paper presents an ultra-low-power buck converter governed by a fast integral terminal sliding mode controller (FITSMC) with a fixed-time observer. A new reaching law retains the initial sliding manifold and a negative-power term maintains the constant switching gain to preserve robustness near the surface while attenuating chattering without widening the bandwidth. The fixed-time observer estimates the irradiance and load changes and provides a feed-forward correction, tightening the output regulation regardless of initial conditions. Load step tests with moderate resistance swings showed the proposed method recovers noticeably faster and exhibits slightly lower overshoot than a recent method based on a two-phase power reaching law, while visible inductor current spikes are also suppressed. Simulations under daily grazing profiles confirmed tight output regulation adequate for microwatt data logging and periodic long-range (LoRa) bursts. The sleep mode quiescent current remained in the 9 microamps range, eliminating the need for manual recharge across multi-season field deployments. By integrating robust power electronics with collar-grade solar harvesting, the circuit offers a truly maintenance-free energy path for untethered livestock wearables and supports sustainable precision agriculture. Full article

Acoustic microactuation technology has emerged as an effective approach for fabrication of micro- and nanoscale objects, enabling precise processing and shaping control of microscale materials by efficiently transmitting ultrasonic vibration energy and focusing energy locally. In this work, the proposed platform is regarded as an acoustically driven micromachine, in which ultrasonic excitation acts as the primary microactuation mechanism. Micrometer-scale copper wires are widely used in microelectronics and precision manufacturing. However, their small dimensions and low rigidity make fixation and forming particularly challenging. To achieve controllable forming of fine copper wires, this study introduces an ultrasonic vibration energy-focusing principle and investigates an ultrasonic post-processing method tailored for such materials, with the aim of enhancing processing stability and forming accuracy. An ultrasonic processing experimental platform for copper wires was established, and multiple micro-tool designs—including glass fiber, 304 stainless steel wire with support, and elastic hard 304 stainless steel—were evaluated. Single-point and continuous processing experiments were conducted by varying micro-tool materials and support configurations, and the influence of feed speed on processing width and depth was systematically analyzed. The results indicate that a hard 304 stainless steel micro-tool supported by a hard plastic ring provides the best overall performance. Feed speed has a significant effect on processing depth, with a maximum average depth of approximately 0.95 μm achieved at a feed speed of 1 mm/min. These findings demonstrate the feasibility of ultrasonic processing for the effective forming of fine copper wires and confirm that appropriate micro-tool design and feed speed are critical for achieving stable and reliable processing results. The proposed system employs an ultrasonically actuated micro-tool to perform post-processing on micrometer-scale copper wires. The ultrasonic vibration serves as a microactuation mechanism that enhances local deformation and material response during micro-machining. Full article

Gonad loss triggers severe endocrine disorders and altered energy metabolism, yet the precise mechanisms remain poorly understood. In swine production, surgical castration is widely performed to eliminate boar taint and aggressive behavior, but it impairs feed efficiency, increases fat deposition, and raises animal welfare issues. Castration reduces testosterone and estrogen levels, leading to elevated gonadotropin-releasing hormone (GnRH) and its downstream follicle-stimulating hormone (FSH) and luteinizing hormone (LH). Traditionally viewed as a reproductive hormone, FSH has recently emerged as a critical regulator of peripheral metabolism. Based on these findings, we designed and developed a novel FSH vaccine comprising an FSHβ13AA-tandem-ovalbumin conjugate, which has been demonstrated to effectively regulate growth and metabolism in castrated boars. In conclusion, this review underscores the previously underrecognized metabolic functions of follicle-stimulating hormone (FSH) and proposes a novel immunomodulatory strategy targeting FSH for fine-tuning organ function and energy metabolism. This approach shows considerable potential for advancing sustainable, welfare-oriented swine production. Full article

Precise 3D reconstruction of non-cooperative space targets is a prerequisite for active debris removal and on-orbit servicing. However, this task is impeded by severe environmental challenges. Specifically, the limited dynamic range of visible light cameras leads to frequent overexposure or underexposure under extreme space lighting. Compounded by sparse textures and strong specular reflections, these factors significantly constrain reconstruction accuracy. While existing general-purpose feed-forward models such as MapAnything offer efficient inference, their geometric recovery capabilities degrade sharply when facing significant domain shifts. To address these issues, this paper proposes an enhanced 3D reconstruction framework tailored for the space environment named In-Orbit MapAnything. First, to mitigate data scarcity, we construct a high-quality space target dataset incorporating extreme illumination characteristics, which provides comprehensive auxiliary modalities including accurate camera poses and dense point clouds. Second, we propose the SatMap-Adapter module to mitigate feature degradation caused by severe specular reflections. This architecture employs a hierarchical cascade sampling strategy to align multi-level backbone features and utilizes a lightweight adaptive fusion module to dynamically integrate shallow photometric cues, intermediate structural information, and deep semantic features. Finally, we employ a weight-decomposed low-rank adaptation strategy to achieve parameter-efficient fine-tuning while strictly freezing the pre-trained backbone. Experimental results demonstrate that the proposed method decreases the absolute relative error and Chamfer distance by 15.23% and 20.02% respectively compared to the baseline MapAnything model, while maintaining a rapid inference speed. The proposed approach effectively suppresses reconstruction noise on metallic surfaces and recovers fine geometric structures, validating the effectiveness of our feature-enhanced framework in extreme space environments. Full article

Heat stress is a major constraint to global egg production, as rising temperatures increasingly challenge the physiological limits of commercial layer chickens. This review integrates current advances in stress physiology to demonstrate that heat stress is not merely a thermoregulatory problem but a multi-systemic disruption involving neuroendocrine overload, metabolic imbalance, oxidative damage, immune suppression, and gastrointestinal barrier breakdown. These interacting pathways collectively impair egg production, shell quality, feed efficiency, and hen welfare. The review also synthesizes emerging mitigation strategies across environmental control, nutritional interventions, genetic and breeding innovations, welfare-oriented housing systems, and precision monitoring technologies. Studies indicate that targeted cooling, antioxidant, and electrolyte supplementation, the selection of heat-tolerant strains, enriched environments, and sensor-based early-warning systems can significantly enhance egg-laying hen resilience. Remaining gaps include a limited understanding of multi-stressor interactions, microbiome-mediated thermal tolerance, and the large-scale implementation of precision tools. The review provides a forward-looking framework for improving heat resilience in modern layer systems. Full article

Gallbladder diseases spanning cholelithiasis, cholecystitis, and gallbladder cancer represent a clinically heterogeneous continuum in which type 2 diabetes mellitus (T2DM) acts as a key metabolic modifier. Conventional models centered on bile supersaturation alone do not sufficiently account for the persistent inflammation and inter-individual variability frequently observed in practice. Here, we synthesize emerging evidence implicating the gut microbiota–bile acid (BA) axis as an integrative mechanism linking metabolic dysregulation, barrier dysfunction, and biliary pathobiology in the diabetic host. Hyperglycemia and insulin resistance, together with impaired mucosal resilience, are associated with shifts in microbial community structure and BA-transforming functions (e.g., bile salt hydrolase and 7α-dehydroxylation), favoring a more hydrophobic BA pool. These changes may disrupt BA receptor signaling, including FXR–FGF15/19 and TGR5-related pathways, thereby amplifying metabolic inflammation, promoting lithogenic bile formation, and impairing gallbladder motility. In parallel, barrier vulnerability may facilitate microbial translocation and LPS-driven immune activation, reinforcing a feed-forward loop that supports the gallstone–inflammation–carcinogenesis trajectory. Translationally, microbiome- and BA-oriented strategies (dietary patterns, bile acid therapeutics, and targeted microbiome modulation) are promising adjuncts, yet precision management should explicitly consider medication- and weight loss–related confounding—particularly with incretin-based therapies—to optimize biliary outcomes across disease stages. Full article