Search Results (72)

The tight conglomerate oil reservoir in Xinjiang’s Mahu area is situated on the northwestern margin of the Junggar Basin. The reservoir comprises five stacked fan bodies, with the Triassic Baikouquan Formation serving as the primary pay zone. To delineate the study scope and conduct a field validation, the Ma-X well block was selected for investigation. Through triaxial compression tests and large-scale true triaxial hydraulic fracturing simulations, we analyzed the failure mechanisms of tight conglomerates and identified key factors governing hydraulic fracture propagation. The experimental results reveal several important points. (1) Gravel characteristics control failure modes: Larger gravel size and higher content increase inter-gravel stress concentration, promoting gravel crushing under confining pressure. At low-to-medium confining pressures, shear failure primarily occurs within the matrix, forming bypassing fractures around gravel particles. (2) Horizontal stress differential dominates fracture geometry: Fractures preferentially propagate as transverse fractures perpendicular to the wellbore, with stress anisotropy being the primary control factor. (3) Injection rate dictates fracture complexity: Weakly cemented interfaces in conglomerates lead to distinct fracture morphologies—low rates favor interface activation, while high rates enhance penetration through gravels. (4) Stimulation strategy impacts SRV: Multi-cluster perforations show limited effectiveness in enhancing fracture network complexity. In contrast, variable-rate fracturing significantly increases stimulated reservoir volume (SRV) compared to constant-rate methods, as evidenced by microseismic data demonstrating improved interface connectivity and broader fracture coverage. Full article

The cytokinin response regulator (ARR) gene is essential for cytokinin signal transduction, which plays a crucial role in plant growth and development. However, the functional mechanism of ARR genes in cotton leaf abscission remains incompletely understood. In this study, a total of 86 ARR genes were identified within the genome of Gossypium hirsutum. These genes were categorized into four distinct groups based on their phylogenetic characteristics, supported by analyses of gene structures and conserved protein motifs. The GhARR genes exhibited an uneven distribution across 25 chromosomes, with three pairs of tandem duplication events observed. Both segmental and tandem duplication events significantly contributed to the expansion of the ARR gene family. Furthermore, numerous putative cis-elements were identified in the promoter regions, with hormone and stress-related elements being common among all 86 GhARRs. Transcriptome expression profiling screening results demonstrated that GhARRs may play a mediating role in cotton’s response to TDZ (thidiazuron). The functional validation of GhARR16, GhARR43, and GhARR85 using virus-induced gene silencing (VIGS) technology demonstrated that the silencing of these genes led to pronounced leaf wilting and chlorosis in plants, accompanied by a substantial decrease in petiole fracture force. Overall, our study represents a comprehensive analysis of the G. hirsutum ARR gene family, revealing their potential roles in leaf abscission regulation. Full article

Aiming at the problems of visual occlusion, reduced positioning accuracy and pose loss in the dynamic scanning process of aviation large components, this paper proposes a binocular vision dynamic positioning method based on magnetic target. This method detects the spatial coordinates of the magnetic target in real time through the binocular camera, extracts the target center to construct a unified reference system of the measurement platform, and uses MATLAB simulation to analyze the influence of different target layouts on the scanning stability and positioning accuracy. On this basis, a dual-objective optimization model with the objectives of ‘minimizing the number of targets’ and ‘spatial distribution uniformity’ is established, and Monte Carlo simulation is used to evaluate the robustness under Gaussian noise and random frame loss interference. The experimental results on the C-Track optical tracking platform show that the optimized magnetic target layout reduces the rotation error of the dynamic scanning from 0.055° to 0.035°, the translation error from 0.31 mm to 0.162 mm, and the scanning efficiency is increased by 33%, which significantly improves the positioning accuracy and tracking stability of the system under complex working conditions. This method provides an effective solution for high-precision dynamic measurement of aviation large components. Full article

To address the challenge of insufficient spatial resolution in remote sensing precipitation data, this paper proposes a novel Multi-Scale Residual Generative Adversarial Network (MSRGAN) for reconstructing high-resolution precipitation images. The model integrates multi-source meteorological information and topographic priors, and it employs a Deep Multi-Scale Perception Module (DeepInception), a Multi-Scale Feature Modulation Module (MSFM), and a Spatial-Channel Attention Network (SCAN) to achieve high-fidelity restoration of complex precipitation structures. Experiments conducted using Weather Research and Forecasting (WRF) simulation data over the continental United States demonstrate that MSRGAN outperforms traditional interpolation methods and state-of-the-art deep learning models across various metrics, including Critical Success Index (CSI), Heidke Skill Score (HSS), False Alarm Rate (FAR), and Jensen–Shannon divergence. Notably, it exhibits significant advantages in detecting heavy precipitation events. Ablation studies further validate the effectiveness of each module. The results indicate that MSRGAN not only improves the accuracy of precipitation downscaling but also preserves spatial structural consistency and physical plausibility, offering a novel technological approach for urban flood warning, weather forecasting, and regional hydrological modeling. Full article

Layered transition metal oxide (LTMO) cathode materials for sodium-ion batteries (SIBs) have attracted extensive attention due to their unique structural stability and excellent electrochemical performance. However, their poor stability in air has significantly impeded their practical application, as exposure to moisture and carbon dioxide can lead to Na⁺ loss, phase transitions, and decreased electrochemical performance. This paper reviews the application of high-entropy strategies in sodium-ion LTMO cathode materials, focusing on the optimization of air stability and electrochemical performance through approaches including high-entropy cation regulation, P2/O3 dual-phase synergistic structures, and fluorine ion doping. Studies have shown that high-entropy design can effectively inhibit phase transitions, alleviate Jahn–Teller distortion, enhance oxygen framework stability, and markedly enhance the cycle life and rate performance of materials. Furthermore, future research directions are proposed, including the use of advanced characterization techniques to reveal failure mechanisms, the integration of machine learning to optimize material design, and the development of high-performance mixed-phase structures. High-entropy strategies provide new perspectives for the development of SIBs cathode materials with enhanced air stability, potentially promoting the practical application of SIBs in large-scale energy storage systems. Full article

With the widespread adoption of highways in the mountainous regions of southwestern China, the electricity load of numerous tunnels and service areas has increased rapidly. Constructing photovoltaic (PV) microgrids in service areas has become an important means of energy conservation, consumption reduction, and carbon emission mitigation. However, constrained by mountainous terrain, the PV power generation conditions in highway service areas exhibit significant micro-terrain variations, making it difficult to effectively evaluate PV utilization efficiency. This paper proposes a dynamic block optimization model for PV microgrids that considers regional layout constraints. The model utilizes an intelligent adjustment mechanism to plan PV panel layouts in highway service areas, optimizing energy utilization efficiency and economic benefits. Additionally, long short-term memory (LSTM) networks are employed for short-term PV output prediction to address the challenges posed by varying weather and seasonal changes. This approach comprehensively considers the intermittency and instability of PV power generation, enabling dynamic block optimization to autonomously adjust the PV power output in response to load fluctuations. Through simulation case studies, the model is validated to effectively improve the utilization rate and economic performance of PV microgrids under various environmental conditions and demonstrates superior performance compared with traditional static block methods. Full article

In modern large-scale pig farming, accurately identifying sow estrus and ensuring timely breeding are crucial for maximizing economic benefits. However, the short duration of estrus and the reliance on subjective human judgment pose significant challenges for precise insemination timing. To enable non-contact, automated estrus detection, this study proposes an improved algorithm, Enhanced Context-Attention YOLO (ECA-YOLO), based on YOLOv11. The model utilizes ocular appearance features—eye’s spirit, color, shape, and morphology—across different estrus stages as key indicators. The MSCA module enhances small-object detection efficiency, while the PPA and GAM modules improve feature extraction capabilities. Additionally, the Adaptive Threshold Focal Loss (ATFL) function increases the model’s sensitivity to hard-to-classify samples, enabling accurate estrus stage classification. The model was trained and validated on a dataset comprising 4461 images of sow eyes during estrus and was benchmarked against YOLOv5n, YOLOv7tiny, YOLOv8n, YOLOv10n, YOLOv11n, and Faster R-CNN. Experimental results demonstrate that ECA-YOLO achieves a mean average precision (mAP) of 93.2%, an F1-score of 88.0%, with 5.31M parameters, and FPS reaches 75.53 frames per second, exhibiting superior overall performance. The findings confirm the feasibility of using ocular features for estrus detection and highlight the potential of ECA-YOLO for real-time, accurate monitoring of sow estrus under complex farming conditions. This study lays the groundwork for automated estrus detection in intensive pig farming. Full article

Hydroxytyrosol (HT), a principal bioactive phytochemical abundant in Mediterranean dietary sources, has emerged as a molecule of significant scientific interest owing to its multifaceted health-promoting properties. Accumulating evidence suggests that HT’s therapeutic potential in metabolic disorders extends beyond conventional antioxidant capacity to encompass mitochondrial regulatory networks. This review synthesizes contemporary evidence from our systematic investigations and the existing literature to delineate HT’s comprehensive modulatory effects on mitochondrial homeostasis. We systematically summarized the impact of HT on mitochondrial dynamics (fusion/fission equilibrium), biogenesis and energy metabolism, mitophagy, inter-organellar communication with the endoplasmic reticulum, and microbiota–mitochondria crosstalk. Through this multidimensional analysis, we established HT as a mitochondrial homeostasis modulator with potential therapeutic applications in metabolic syndrome (MetS) and its related pathologies including type 2 diabetes mellitus, obesity-related metabolic dysfunction, dyslipidemia, non-alcoholic steatohepatitis, and hypertension-related complications. Moreover, we further discussed translational challenges in HT research, emphasizing the imperative for direct target identification, mitochondrial-targeted delivery system development, and combinatorial therapeutic strategies. Collectively, this review provides a mechanistic framework for advancing HT research and accelerating its clinical implementation in MetS and its related diseases. Full article

Nanomaterials (NMs) hold significant potential for enhancing agricultural production, extending the shelf life, and maintaining the quality of postharvest vegetables and fruits. In this study, after foliar spraying with 1, 10, and 50 mg of L⁻¹ Fe-P NMs at different stages (seedling, flowering, and fruit stage), the pepper plant growth was significantly improved. In particular, the foliar application of 10 mg of L⁻¹ Fe-P NMs during the flowering stage was found to be an optimal cultivation approach to promote the growth, yield, and freshness of peppers. Compared with the control group, Fe-P NMs increased net photosynthetic rate, plant height, and fruit number by 132.7%, 40.4%, and 265.7%, respectively. The applied Fe-P NMs, at the flowering stage, altered the capsaicin metabolic pathway, upregulating the genes for the synthesis of total phenols, flavonoids, lignans, and capsaicinoids. Consequently, these metabolites, which are beneficial for maintaining the freshness of pepper fruits, were increased. Furthermore, Fe-P NMs at the flowering stage downregulated the abundance of rot-causing microorganisms (Enterobacter and Chryseobacterium) and upregulated beneficial microorganisms (Pseudomonas, Arthrobacter, Sphingobacterium, and Paenibacillus) to change the microbial community structure. This ultimately created a micro-ecological environment conducive to the preservation of pepper fruits. For comparison, during pepper fruit storage, dipping and spraying with Fe-P NM suspensions effectively delayed weight loss and enhanced the growth of beneficial bacteria. Nevertheless, the effect was less pronounced than preharvest foliar application. This study provides insights into the pre- or postharvest application of NMs for improving the preservation performance of pepper fruits. Full article

Variable-coefficient viscous dampers (VVDs) have a variable annular gap, allowing them to dynamically adjust the damping coefficient at different displacement stages and provide higher damping forces during large displacement phases. This study evaluates the seismic performance of a steel frame equipped with VVDs. A shaking table test was conducted on a two-story, single-span steel frame with the VVDs to assess its seismic response, and the results were compared with those of the same frame equipped with conventional viscous dampers (VD). The experimental results demonstrated that the VVDs significantly reduced the structural dynamic response at various levels of earthquake intensity, consistently outperforming the VDs in terms of the seismic reduction effectiveness. Subsequently, a constitutive model for the VVD element was developed using the open-source finite element software OpenSees3.3.0. The accuracy of the developed element was validated by comparing the finite-element analysis results with mechanical performance tests of the VVD. Based on the developed VVD element, a numerical model of test structure was established in OpenSees for time–history analyses. The results showed good agreement between the numerical simulations and shaking table test data. Finally, a parametric study was conducted on the effects of the ratio $r$ of the second-order damping coefficient to the first-order damping coefficient and the velocity index α of the VVD on the seismic response of the numerical model of the tested structure. The results indicated that the seismic reduction rate of the tested structure increased with $r$, with a maximum improvement of 24%, while it decreased with increasing α, with a maximum reduction of 27%. Full article

This study introduces a novel framework that simultaneously addresses the challenges of performance accuracy and result interpretability in transcriptomic-data-based classification. Background/objectives: In biological data classification, it is challenging to achieve both high performance accuracy and interpretability at the same time. This study presents a framework to address both challenges in transcriptomic-data-based classification. The goal is to select features, models, and a meta-voting classifier that optimizes both classification performance and interpretability. Methods: The framework consists of a four-step feature selection process: (1) the identification of metabolic pathways whose enzyme-gene expressions discriminate samples with different labels, aiding interpretability; (2) the selection of pathways whose expression variance is largely captured by the first principal component of the gene expression matrix; (3) the selection of minimal sets of genes, whose collective discerning power covers 95% of the pathway-based discerning power; and (4) the introduction of adversarial samples to identify and filter genes sensitive to such samples. Additionally, adversarial samples are used to select the optimal classification model, and a meta-voting classifier is constructed based on the optimized model results. Results: The framework applied to two cancer classification problems showed that in the binary classification, the prediction performance was comparable to the full-gene model, with F1-score differences of between −5% and 5%. In the ternary classification, the performance was significantly better, with F1-score differences ranging from −2% to 12%, while also maintaining excellent interpretability of the selected feature genes. Conclusions: This framework effectively integrates feature selection, adversarial sample handling, and model optimization, offering a valuable tool for a wide range of biological data classification problems. Its ability to balance performance accuracy and high interpretability makes it highly applicable in the field of computational biology. Full article

Accurate estrus detection and optimal insemination timing are crucial for improving sow productivity and enhancing farm profitability in intensive pig farming. However, sows’ estrus typically lasts only 48.4 ± 1.0 h, and interference from false estrus further complicates detection. This study proposes an enhanced YOLOv8 model, Integrated Convolution and Attention Enhancement (ICAE), for vulvar detection to identify the estrus stages. This model innovatively divides estrus into three phases (pre-estrus, estrus, and post-estrus) and distinguishes five different estrus states, including pseudo-estrus. ICAE-YOLO integrates the Convolution and Attention Fusion Module (CAFM) and Dual Dynamic Token Mixing (DDTM) for improved feature extraction, Dilation-wise Residual (DWR) for expanding the receptive field, and Focaler-Intersection over Union (Focaler-IoU) for boosting the performance across various detection tasks. To validate the model, it was trained and tested on a dataset of 6402 sow estrus images and compared with YOLOv8n, YOLOv5n, YOLOv7tiny, YOLOv9t, YOLOv10n, YOLOv11n, and the Faster R-CNN. The results show that ICAE-YOLO achieves an mAP of 93.4%, an F1-Score of 92.0%, GFLOPs of 8.0, and a model size of 4.97 M, reaching the highest recognition accuracy among the compared models, while maintaining a good balance between model size and performance. This model enables accurate, real-time estrus monitoring in complex, all-weather farming environments, providing a foundation for automated estrus detection in intensive pig farming. Full article

High-Mn steels are widely used in various fields. However, the FCC structure is not conducive to improving strength, limiting their development and application. In this work, hot-rolled Fe-25Mn-1Al-3Si-1C (wt.%) steel was annealed at various temperatures to tailor the cementite particles and recrystallized grains, thus achieving a balance between strength and ductility. As the annealing temperature increased from 550 to 650 °C, the volume fraction of recrystallized grains slightly increased and the volume fraction of cementite particles initially increased and then decreased, which was explained and verified by the quantitative calculation. Especially, the high-density pre-dislocation and finely dispersed cementite particles in sample AN550 resulted in a relatively low volume fraction of recrystallized grains. Interestingly, secondary deformation twinning was activated during the subsequent tensile deformation in addition to the dislocations, stacking faults, and previous deformation twinning. This complex interaction among various deformation mechanisms indued a good balance between strength and ductility, achieving an outstanding result (58.9 GPa%) regarding tensile strength and total elongation. This work offers an effective route for developing a high-Mn TWIP steel with outstanding strength–ductility balance. Full article

Purpose: The aims of this study were to explore the differences in total body water and hydration status among Chinese children aged 6–17 years. Methods: A cross-sectional study was implemented among children aged 6–17 years in China. The total body water (TBW), intracellular water (ICW), and extracellular water (ECW) were determined by bioelectrical impedance analysis (BIA). The participants were divided according to age—age 6–8 years, age 9–11 years, age 12–14 years, age 15–17 years—and body mass index (BMI) of China—underweight, normal weight, overweight, and obese groups. The differences of variables of groups were compared using analysis of variance, Student’s t-test, and Kruskal–Wallis test. Significance levels were set at 0.05 (p < 0.05). Results: A total of 59,643 participants (30,103 males and 29,540 females) completed the study. As children became older, the TBW, ICW, ECW, ICW/TBW, and TBW/FFM (TBW to fat free mass ratio) increased simultaneously (all p < 0.05); concurrently, the ECW/TBW decreased with age (all p < 0.05). Boys had higher TBW, ICW, ECW, ICW/TBW, TBW/BW, and TBW/FFM than those of girls at each age (all p < 0.05). For all BMI groups, increases in TBW, ICW, ECW were observed from the underweight group to the obese group, both in boys and girls (all p < 0.001). For the increase in BMI in all age groups, the values of TBW made a significantly lower percentage compared to BW. The higher BMI groups showed higher levels of TBW/FFM, both in girls and boys (all p < 0.001). Conclusions: The body water contents of children aged 6–17 years varied according to their age, sex, and BMI. Overweight and obese individuals may have inferior hydration status compared to those with normal weight. Full article

Considering the anti-swing control and output constraint problems of shipboard cranes, a nonlinear anti-swing controller based on asymmetric barrier Lyapunov functions (BLFs) is designed. First, model transformation mitigates the explicit effects of ship roll on the desired position and payload fluctuations. Then, a newly constructed BLF is introduced into the energy-based Lyapunov candidate function to generate nonlinear displacement and angle constraint terms to control the rope length and boom luffing angle. Among these, constraints with positive bounds are effectively handled by the proposed BLF. For the swing constraints of the unactuated payload, a carefully designed relevant constraint term is embedded in the controller by constructing an auxiliary signal, and strict theoretical analysis is provided by using a reductio ad absurdum argument. Additionally, the auxiliary signal effectively couples the boom and payload motions, thereby improving swing suppression performance. Finally, the asymptotic stability is proven using LaSalle’s invariance principle. The simulation comparison results indicate that the proposed method exhibits satisfactory performance in swing suppression control and output constraints. In all simulation cases, the payload swing angle complies with the 3° constraint and converges to the desired range within 6 s. This study provides an effective solution to the control challenges of shipboard crane systems operating in confined spaces, offering significant practical value and applicability. Full article