Search Results (10,391)

Substantial advances have been made since the 1970s in reducing the environmental impacts of ammonia production. Renewable-driven electrochemical synthesis offers a promising pathway to decarbonize ammonia production. This review examines an integrated route in which hydrogen is generated photoelectrochemically under concentrated solar irradiation and subsequently used in electrochemical ammonia synthesis. Photoelectrochemical cells are fabricated by electrostatically depositing photosensitive particles onto cathodes to enhance light-driven hydrogen production. Hydrogen production rates and ammonia yield depend strongly on temperature and electrolyte composition. The synthesized hydrogen is fed into a molten salt electrochemical reactor that operates at atmospheric pressure and receives nitrogen from a dedicated supply. This combined solar–electrochemical approach can produce low-carbon ammonia with improved safety and reduced environmental impact, offering a scalable alternative to conventional processes. Full article

To address the widespread issue of abnormally high pump pressure during hydraulic fracturing of deep-to-ultra-deep reservoirs (burial depth > 4500 m) in the Junggar Basin, this study systematically reveals the mechanical mechanism underlying this phenomenon by integrating well logging curve analysis and elastoplastic mechanics theory. Statistical results demonstrate that the actual fracture initiation pressure of 60% of wells in the target block is significantly higher than the values predicted by traditional elastic theory, primarily attributed to plastic yielding and stress concentration effects around perforations induced by high in situ stress. An elastoplastic rock fracture initiation pressure model is established based on the Mohr–Coulomb criterion and the plastic zone radius criterion, which is applied to predict the fracture initiation pressure of selected wells in the target block. The relative error between the model predictions and field measurements is less than 2%, significantly improving the prediction accuracy of fracture initiation pressure in deep-to-ultra-deep formations. This provides precise guidance for subsequent optimization of operational parameters and selection of pressure ratings for wellhead equipment. The study further clarifies that in situ stress difference, rock yield stress, and the power-law hardening exponent are the key factors controlling the transition of fracture initiation modes. To mitigate the high pump pressure challenge in deep-to-ultra-deep reservoir fracturing, the field application of weighted fracturing fluid effectively increases the wellbore hydrostatic column pressure, reduces wellhead operational pressure, and ensures construction safety. The findings of this study provide critical theoretical and technical support for achieving the goal of “successful fracture initiation and effective fracture control” in deep-to-ultra-deep reservoir fracturing. Full article

Object detection in low-light scenes is difficult because weak illumination reduces local contrast, amplifies sensor noise, and makes small or occluded objects hard to localize. Existing enhancement-before-detection pipelines can improve visual brightness, but they are not always optimized for detection features, while transformer-style global reasoning is often too costly for lightweight detectors. To address this gap, we propose GE-Detection, a detector-side framework that integrates Global Sub-Sampled Attention (GSA), Efficient Multi-scale Attention (EMA), and dropout regularization into YOLO- and PicoDet-style architectures. GSA introduces lower-cost global context modeling through spatially reduced key-value tokens, EMA refines multi-scale fused features without aggressive channel compression, and dropout improves training-time regularization with no inference-time parameter overhead. Experiments on COCO, ExDark, BDD100K-Night, and NightOwls show that the method is most effective in low-light detection: on ExDark with YOLO11n, mAP50-95 improves from 34.39% to 36.74%, mAP50 from 56.24% to 59.27%, and Box (P) from 67.63% to 71.36%. The full YOLO11n variant uses 2.91M parameters and maintains 134.7 FPS on an RTX 2080 Ti under the tested setting. Cross-dataset and corruption experiments further indicate that the proposed modules improve localization under several nighttime domain shifts while retaining known limitations under severe noise and adverse weather. These results indicate that combining efficient global attention, multi-scale feature recalibration, and targeted regularization can improve low-light localization while keeping the detector practical for deployment. Full article

Unmanned Aerial Vehicle (UAV) image object detection holds significant application value in the low-altitude economy, traffic monitoring, intelligent agriculture, and disaster rescue. However, due to the top-down perspective, UAV images typically suffer from challenges such as small target scales, dense object distribution, severe occlusions, and complex backgrounds. These issues often limit the recall and localization accuracy of general-purpose detectors when they are directly applied to UAV small-object detection scenarios. To address these aforementioned challenges, this paper proposes an Adaptive Dynamic Aggregation YOLO network, termed ADA-YOLO. The novelty of ADA-YOLO lies in its highly efficient combinatorial design specifically tailored for UAV small object detection, while retaining the efficient backbone of YOLOv8, we systematically reconstruct the neck and detection head to improve accuracy. Specifically, a high-resolution P2 detection branch is incorporated to construct a P2–P5 multi-scale prediction structure. Furthermore, the lightweight DySample dynamic upsampling module is adopted to replace traditional upsampling methods, and an Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced to alleviate semantic conflicts and noise interference during multi-scale feature fusion. This synergistic combination explicitly addresses multi-scale representation challenges and enhances small-object detection performance in complex scenes. Comparative experiments with the baseline YOLOv8n on the VisDrone2019 dataset demonstrate that ADA-YOLO achieves an improvement of 11.3% in mAP@0.5 and 8.2% in mAP@0.5:0.95. The improved model achieves these performance gains with a modest parameter increase and acceptable computational complexity. Finally, ablation experiments further validate the effectiveness of each individual module and their synergistic gains. Full article

Excessive application of chemical preservatives has raised increasing concerns regarding food safety and human health, prompting the search for safer natural alternatives. Lavender essential oil (LEO), a plant-derived antimicrobial agent, has been considered a promising substitute for synthetic preservatives, but its high volatility and poor water solubility limit its practical application. In this study, LEO nanoemulsions were fabricated via high-pressure homogenization using sodium caseinate (SC) and tea polyphenols (TPs) as composite emulsifiers. The preparation process was optimized using a three-factor, three-level orthogonal design, and the physicochemical properties, storage stability, and antibacterial activity were systematically investigated. The optimal preparation conditions were determined as an SC/TP mass ratio of 2:1, homogenization pressure of 70 MPa, and 7 homogenization cycles. The optimized nanoemulsion exhibited a droplet size of 130–210 nm, zeta potential of −30.89 mV, and encapsulation efficiency of 98.61%, with typical shear-thinning behavior and excellent storage stability. The percentage of free LEO remained below 7.5% within 15 days, indicating high stability, and the release behavior followed a zero-order kinetic model. The prepared nanoemulsion showed significant antibacterial activity against Staphylococcus aureus and Escherichia coli, with a minimum inhibitory concentration (MIC) of 62.5 μg/mL for both strains. This study confirms that the SC/TP composite interface can effectively stabilize LEO nanoemulsions, providing a theoretical basis for the development of natural and efficient food preservatives. Full article

Identifying soil nutrient limiting factors and fertilization effects in the irrigated silty soil region of Ningxia is key to improving wheat (Triticum aestivum L.) quality and yield. A field experiment was conducted with five treatments: conventional fertilization (TF), recommended fertilization (RF), nitrogen deficiency (RF-N), phosphorus deficiency (RF-P), and potassium deficiency (RF-K). The results showed that under RF, soil nutrients remained at relatively high levels, with no significant differences compared with TF. In contrast, RF-N significantly reduced soil mineral nitrogen, total nitrogen, and organic matter compared with TF, and inhibited plant growth, photosynthesis, and plant accumulation of nitrogen, phosphorus, and potassium. Wheat yields under RF and RF-K showed no significant differences from those under TF, whereas RF-N and RF-P significantly reduced yields by 42.68% and 22.69%, respectively, relative to RF, mainly due to decreases in spike length and grain number per spike. The increase in yield was associated with synergistic increases in grain number per spike, spike number per hectare, and spike length. Yield components were significantly positively correlated with soil organic matter, total phosphorus, and mineral nitrogen, with soil total phosphorus identified as the environmental factor most strongly associated with wheat yield. Grain protein content was significantly positively correlated with soil mineral nitrogen, while starch content was significantly negatively correlated, indicating that mineral nitrogen is a key factor regulating grain quality. In summary, nitrogen fertilizer is the primary limiting factor in this region. Applying nitrogen, phosphorus, and potassium together synergistically enhances wheat yield by increasing soil total phosphorus levels and improves grain quality by regulating soil mineral nitrogen. Thus, this combined fertilization strategy provides a foundation for precise nutrient management and the simultaneous improvement of both yield and quality. Full article

Career adaptability has been linked to academic achievement, yet the mechanisms underlying this association remain insufficiently understood, particularly among adolescents in highly competitive, exam-oriented educational systems. Based on Career Construction Theory (CCT) and the performance model of Social Cognitive Career Theory (SCCT), this study tested whether academic self-efficacy, academic outcome expectations, and metacognitive strategies mediated this association. A three-wave longitudinal study was conducted with 519 students from two general high schools in central China (40.85% boys; mean age = 16.28 years, SD = 0.82). Career adaptability was measured at Time 1, the three mediators were measured at Time 2, and academic achievement was measured at Time 1 and Time 3 using standardized examination scores in Chinese, mathematics, and English. After controlling for baseline achievement and demographic covariates, structural equation modeling with bias-corrected bootstrapping showed that T1 career adaptability had a significant total effect on T3 academic achievement, whereas the direct effect was nonsignificant after the mediators were included. Significant indirect effects were found through academic self-efficacy and metacognitive strategies. Academic outcome expectations did not significantly mediate the association, and the pathway from academic self-efficacy to academic outcome expectations was not supported. These findings indicate that career adaptability may contribute to later academic achievement mainly through students’ academic self-efficacy and metacognitive strategy use. Full article

Although the Kazakh horse is a dual-purpose breed renowned for both milk and meat production, the extent to which surgical castration alters gene expression in its muscles has not yet been fully elucidated. In this study, left longissimus dorsi muscle (LDM) samples were obtained from six Kazakh stallions (W group) and six Kazakh geldings (S group) to comparatively evaluate meat quality parameters, examine histological characteristics in tissue sections, and apply transcriptomic profiling to comprehensively explore the principal regulatory pathways and candidate genes through which surgical castration modulates LDM growth. The results demonstrated that surgical castration did not induce significant alterations in meat color or pH-related parameters. However, cooking loss and shear force values were markedly diminished, accompanied by a marked decrease in muscle fiber cross-sectional area. Transcriptomic analysis identified 848 differentially expressed genes (DEGs) in total, comprising 415 upregulated and 433 markedly downregulated DEGs, which were predominantly enriched in key biological pathways, including actin cytoskeleton regulation. Moreover, eleven core candidate genes, including MYL2, MYL3, and TNNI1, were further screened and identified. Full article

Northern pike (Esox lucius) myofibrillar protein (MP) forms inherently weak gels due to endogenous proteolytic activity and the low thermal stability of fish myosin, limiting its application in surimi products. This study investigated the reinforcing effect and underlying mechanism of rice protein amyloid fibrils (RFs) on pike MP gels. Dynamic rheology revealed that RFs increased both the storage and loss moduli in a concentration-dependent manner, with the 5% group exhibiting an approximately threefold increase in the G′ at 100 rad/s relative to the control. The gel strength, hardness, and chewiness increased progressively with the RF content, whereas the water-holding capacity peaked at 1–3% RFs and declined sharply at 5% RFs. Microstructural imaging showed that moderate RF levels promoted a dense, homogeneous network architecture, while excessive RFs induced phase separation and structural heterogeneity. Hydrophobic interactions and hydrogen bonds were strengthened via RF incorporation, while disulfide bonds decreased monotonically with the increasing fibril concentration. FTIR spectroscopy revealed an α-helix-to-β-sheet transition, with the β-sheet content reaching a maximum of 49.37% at 3% RFs, and SDS-PAGE confirmed that the RF–MP interactions were predominantly non-covalent in nature. These results demonstrate that RFs reinforce pike MP gels through a molecular mechanism involving rigid fibrils acting as structural scaffolds within the protein network and a progressive shift from disulfide-mediated covalent crosslinking toward non-covalent stabilization via hydrophobic interactions and hydrogen bonding. The 1–3% RF range delivers the most balanced gel properties, while excessive fibril loading at 5% induces over-aggregation and impairs water retention. These findings establish amyloid fibrils as effective structural modifiers for freshwater fish gel products and provide a mechanistic basis for their application in surimi processing. Full article

The Gaizi River Basin, an alpine region in China crossed by the Karakoram Highway, is highly prone to glacier-related debris flows (GDF). Accurate debris flow susceptibility assessment in this high-altitude area remains challenging due to complex terrain, active tectonics, and dynamic glacial processes. This study develops a hybrid model integrating statistical methods and machine learning-based anomaly detection for debris flow susceptibility mapping. To address data noise, certainty factor (CF) distributions of debris flow predisposing factors (DFPFs) were derived via Locally Weighted Scatterplot Smoothing (LOWESS). The strength of the association between DFPFs and GDF susceptibility was evaluated using the mean residual between the raw and LOWESS-smoothed CF values. Multiple anomaly detection algorithms, including distance-based (L2 Norm), density-based (One-Class SVM), ensemble (Isolation Forest, RandNet), and GAN-based (WBiGAN-GP) methods, were tested on raw and CF-transformed data, using only the GDF inventory as the label. The CF-WBiGAN-GP model delivers the most balanced performance, excelling at identifying both high- and low-susceptibility zones. Results show that distance to stream, slope, and the topographic roughness and wetness indices are strongly associated with GDF susceptibility. Distance to glacier and precipitation appear less informative for direct susceptibility inference under our specific dataset and analytical setup. Full article

Volcanic rocks of the Qi’eshan Group, which are widely distributed in the Dananhu arc of the Eastern Tianshan, NW China, have long been debated in terms of their formation age and tectonic setting. In this study, we conducted an integrated study of U-Pb apatite geochronology, whole-rock major and trace element geochemistry, in situ major element analyses of clinopyroxene, and “Rhyolite-MELTS” thermodynamic modeling on the basalts from the Qi’eshan Group. Geochronological data show that the weighted mean of ²⁰⁶Pb/²³⁸U ages of apatite is 329 ± 10 Ma. The basalts belong to the tholeiitic series and are characterized by enrichment in large ion lithophile elements (LILEs), depletion in high field strength elements (HFSEs), and enrichment of light rare earth elements (LREEs) relative to heavy rare earth elements (HREEs) with weak negative Eu anomalies. They were derived by partial melting of garnet-spinel lherzolite in a depleted mantle source metasomatized by subduction-related fluids, followed by fractional crystallization of spinel, olivine, and clinopyroxene. Clinopyroxene is dominated by augite, characterized by high Mg and Ca contents and low Al and Na contents. Machine-learning-based thermobarometry indicates that clinopyroxene crystallized at temperatures of 1027–1033 °C and pressures of 1.1–1.6 kbar. “Rhyolite-MELTS” isobaric crystallization simulations suggest that mantle-derived magma, with an initial water content of 4 wt.% and oxygen fugacity of FMQ, can generate melts compositionally similar to the volcanic rocks of the Qi’eshan Group through fractional crystallization at a pressure of 1.5 kbar. Combined with previous studies, we propose that the Qi’eshan Group basalts formed in an extensional arc setting related to southward rollback of the northward-subducting Kanguer oceanic slab, which caused asthenosphere upwelling and lithospheric extension, thereby promoting partial melting of the subduction-metasomatized mantle. Our data provide new insights into the Carboniferous rollback of the Kanguer oceanic slab in the northern part of the Eastern Tianshan. Full article

This study investigated generation-specific heterosis in Chinese Simmental × Chinese Holstein crossbred cows by comparing lactation performance, reproductive performance, hematological traits, and blood transcriptomic profiles among Chinese Simmental (SIM), Chinese Holstein (HOL), F1 crossbreds, and first-generation backcrosses (BC1). Lactation analysis was based on 17,005 valid records, reproductive analysis was based on 5481 valid records, and transcriptomic analysis was conducted using blood samples from 31 cows. F1 showed the most favorable reproductive profile, whereas BC1 showed relatively strong milk-yield performance. Hematological analysis revealed group-level differences in leukocyte-related indicators. Main-text transcriptomic interpretation focused on SIM, F1, and BC1 comparisons because the HOL RNA-seq group included only three samples. These comparisons suggested that F1-related DEGs were mainly associated with immune and inflammatory processes, whereas BC1-related DEGs showed broader immune- and metabolism-related enrichment patterns. Candidate-gene expression heterosis and d/a-ratio analyses are presented as exploratory supplementary results with bootstrap uncertainty estimates because qPCR validation was not available. Overall, these results indicate that heterosis in this crossbreeding system is generation-specific rather than uniform. The integration of phenotypic and blood transcriptomic analyses provides candidate pathways and associated systemic expression patterns for future validation rather than definitive mechanistic evidence. Full article

The Kubuqi Desert serves as a critical zone for both renewable energy development and ecological management in China. Large-scale photovoltaic (PV) deployment has fundamentally altered the regional underlying surface, impacting near-surface wind–sand dynamics. To elucidate these disturbance mechanisms, we selected three representative surfaces—a PV area, a resource base, and Qixing Lake—and conducted field observations from September to December 2023 using meteorological towers and wind erosion sensors. Results indicate that all surfaces significantly attenuated near-surface wind speeds by over 30% through modified flow field structures. A strong linear positive correlation existed between wind speed and friction velocity (R² ≈ 0.99). Notably, for the same friction velocity, the actual wind speed required to initiate sand movement was lowest in the PV zone (high k) and highest at Qixing Lake (low k), signifying enhanced surface stability due to PV infrastructure and moisture. Threshold analysis revealed distinct initiation speeds: >6.0 m·s⁻¹ in peripheral quicksand, >4.3 m·s⁻¹ in inter-panel zones, and >4.6 m·s⁻¹ beneath panels. The tilted PV panels accelerate airflow downward, generating cyclonic vortices that intensify sand particle impacts under and between panels. This study reveals the tri-dimensional mechanism of wind regulation–sand suppression–stability enhancement, providing theoretical support for mitigating wind–sand disasters while advancing green energy in desert regions. Full article

Background/Objectives: Antipsychotic dosing for behavioral and psychological symptoms of dementia (BPSD) in Alzheimer’s disease remains empirical and variable. This study develops a deep learning model to predict individualized antipsychotic doses from structural MRI. Methods: A transfer learning approach with a cascaded ResNet (Cas-ResNet) was used. The model was first pre-trained on a large healthy aging dataset (CBMFM, n = 646) for brain age prediction, then fine-tuned on a BPSD dataset (SMHC, n = 86) to predict the defined daily dose (DDD) of antipsychotics. Model interpretability was performed using Grad CAM to identify predictive brain regions. Results: The proposed model achieved a mean absolute error of 0.19 and a Pearson correlation of 0.66 between predicted and actual doses, outperforming baseline 3DCNN, VGG, and DenseNet. Key contributing regions included the left inferior temporal gyrus, right parahippocampal gyrus, right putamen, left middle temporal gyrus, and left caudate. Conclusions: This proof-of-concept study demonstrates that deep learning can predict personalized antipsychotic doses from structural MRI, offering an objective tool to standardize BPSD pharmacotherapy and reduce empirical prescribing. The identified brain regions provide neurobiological insights into treatment response. Full article

This study used Poacynum hendersonii (Hook. f.) Woods. seedlings as experimental material. A soil drought group (gradual soil drying) and a PEG-simulated drought group (15% PEG-6000 treatment) were established. By combining physiological measurements, metabolomics, and transcriptomics, we investigated the physiological and molecular mechanisms of P. hendersonii in response to drought stress. The results showed that under drought stress, P. hendersonii alleviated oxidative damage by activating the antioxidant enzyme system (catalase, CAT; superoxide dismutase, SOD; peroxidase, POD), and enzyme activities recovered significantly after rehydration. In the osmotic stress group (PEG), hydrogen peroxide (H₂O₂) and malondialdehyde (MDA) contents increased significantly in the later stages, whereas membrane damage was milder in the soil drought group. Metabolomics analysis revealed significant enrichment of starch and sucrose metabolism pathways during early drought, shifting to unsaturated fatty acid biosynthesis and carbon metabolism in later stages. PEG-simulated drought specifically induced the accumulation of arachidonic acid, which may be associated with ferroptosis-like processes, although direct evidence is lacking. Transcriptomics analysis identified 23,623 differentially expressed genes (DEGs), with transcription factor families such as bHLH, MYB, and NAC playing key roles in drought response. Weighted Gene Co-expression Network Analysis (WGCNA) further revealed gene modules significantly correlated with physiological traits, indicating that enhanced respiratory metabolism (glycolysis, tricarboxylic acid (TCA) cycle) is an important strategy for P. hendersonii to adapt to drought. The study also found that while PEG-simulated drought could simulate the physiological effects of soil drought, significant differences existed in molecular pathways, particularly during later stress stages. This research provides a theoretical basis for elucidating the drought resistance mechanisms of P. hendersonii and offers potential targets for crop drought resistance breeding. Full article