Search Results (5,402)

As coal mining progresses to greater depths, the complex geological conditions significantly increase the risk of compound disasters. With increasing mining depth, elevated ground stress and gas pressure exacerbate the coupling effects of rockburst and gas outburst. This study employs laboratory tests and theoretical analysis to investigate gas disasters under varying gas and confining pressures. The experimental results are analyzed in terms of mechanical parameters, crack propagation, and acoustic emission (AE) time–frequency evolution. Under conventional compression, coal failure exhibits shear damage with axial splitting or debris ejection. The peak strength demonstrates a clear confining pressure strengthening effect and gas pressure weakening effect. At constant gas pressure, the elastic modulus increases with confining pressure, whereas at constant confining pressure, it decreases with rising gas pressure. Full article

Mycoplasmal pneumonia of sheep (MPS), caused by Mesomycoplasma (Mycoplasma) ovipneumoniae, profoundly impacts ovine productivity and survival. Although gut–lung microbiota interactions are increasingly recognized in respiratory diseases, whether similar crosstalk occurs between the lung and rumen microbiota in MPS-affected sheep remains unknown. To investigate alterations in the lung and rumen microbiota of sheep with MPS, the crosstalk between these microbial communities, and their impacts on growth phenotypes. From a cohort of 414 naturally infected six-month-old male Hu sheep, we selected 10 individuals with severe pulmonary pathology and 10 healthy controls for detailed phenotypic and microbiome analyses. Assessment of 359 phenotypic traits revealed that MPS significantly impairs feed efficiency and growth rate (p < 0.05). Through 16S rRNA gene sequencing, we found that MPS significantly altered the pulmonary microbiota community structure (p < 0.01), with a noticeable impact on the rumen microbiota composition (p = 0.059). Succinivibrionaceae_UCG-001 was significantly depleted in both the rumen and lungs of diseased sheep (p < 0.05) and strongly associated with reduced average daily feed intake (p < 0.05). In addition, pulmonary Pasteurella and ruminal Succinivibrionaceae_UCG-002 were significantly enriched in MPS-affected sheep, showed a strong positive correlation (p < 0.05), and were both negatively associated with feed efficiency (p < 0.05). Notably, Pasteurella multocida subsp. gallicida may act as a keystone species influencing feed efficiency. These findings point to a previously unrecognized rumen-lung microbial axis that may modulate host productivity in sheep affected by MPS. This work provides new insights into the pathogenesis of MPS and offers potential targets for therapeutic intervention and management. Full article

Background/Objectives: Ultrasound imaging is widely employed to assess kidney health and diagnose renal diseases. Accurate segmentation of renal structures in ultrasound images plays a critical role in the diagnosis and treatment of related kidney diseases. However, challenges such as speckle noise and low contrast still hinder precise segmentation. Methods: In this work, we propose an encoder–decoder architecture, named MAT-UNet, which incorporates two distinct attention mechanisms to enhance segmentation accuracy. Specifically, the multi-convolution pixel-wise attention module utilizes the pixel-wise attention to enable the network to focus more effectively on important features at each stage. Furthermore, the triple-branch multi-head self-attention mechanism leverages the different convolution layers to obtain diverse receptive fields, capture global contextual information, compensate for the local receptive field limitations of convolution operations, and boost the segmentation performance. We evaluate the segmentation performance of the proposed MAT-UNet using the Open Kidney US Data Set (OKUD). Results: For renal capsule segmentation, MAT-UNet achieves a Dice Similarity Coefficient (DSC) of 93.83%, a 95% Hausdorff Distance (HD95) of 32.02 mm, an Average Surface Distance (ASD) of 9.80 mm, and an Intersection over Union (IOU) of 88.74%. Additionally, MAT-UNet achieves a DSC of 84.34%, HD95 of 35.79 mm, ASD of 11.17 mm, and IOU of 74.26% for central echo complex segmentation; a DSC of 66.34%, HD95 of 82.54 mm, ASD of 19.52 mm, and IOU of 51.78% for renal medulla segmentation; and a DSC of 58.93%, HD95 of 107.02 mm, ASD of 21.69 mm, and IOU of 43.61% for renal cortex segmentation. Conclusions: The experimental results demonstrate that our proposed MAT-UNet achieves superior performance in multiple renal structure segmentation in ultrasound images. Full article

The integration of AI into online platforms is reshaping consumer experience and behavior. While existing research has largely focused on the role of AI in search services and experience services, few studies have examined the role of AI in the context of credence services. This study fills this gap by investigating an AI-powered preview tool in the context of online medical aesthetic platforms. Specifically, this study investigates how the AI Magic Mirror influences consumer purchase intentions in medical aesthetic services. Using secondary data analysis and two experimental studies, we examine the main effects, as well as mediation and moderation effects. The findings consistently demonstrate that the AI Magic Mirror significantly increases consumer purchase intentions. This relationship is positively mediated by perceived value and negatively mediated by perceived risk. In addition, the main effect is stronger for procedures with higher fit uncertainty and is more pronounced for those with lower popularity. These results provide theoretical insights into AI application in credence service contexts and offer practical implications for the design of AI-enhanced online service platforms. Full article

Silicone oil-based magnetic liquids containing carbon nanotubes (CNTs) were prepared using an in situ chemical coprecipitation method. The surface modification of Fe₃O₄/CNT composite particles was carried out by using three silane coupling agents: γ-aminopropyltriethoxysilane (550), γ-methacryloxypropyltrimethoxysilane (570), and phenyltrimethoxysilane (7030). Infrared Spectroscopy (IR), Transmission Electron Microscopy (TEM), and X-ray Diffraction (XRD) were used to confirm the successful doping of CNTs and the effective coating of the coupling agents. The rheological behavior of the magnetic liquids was systematically studied using an Anton Paar Rheometer. The results show that viscosity decreases exponentially with increasing temperature (fitting the Arrhenius equation), increases and tends to saturate with rising magnetic field intensity, and exhibits shear-thinning characteristics with increasing shear rate. Among the samples, Fe₃O₄@7030 has the best visco-thermal performance due to the benzene ring structure, which reduces the symmetry of the molecular chains. In contrast, Fe₃O₄@570 shows the most significant magneto-viscous effect (viscosity variation of 161.4%) as a result of the long-chain structure enhancing the steric hindrance of the magnetic dipoles. Full article

Accurate prediction of transformer oil temperature is crucial for load optimization scheduling and timely early warning of thermal faults in power transformers. This paper proposes a transformer oil temperature prediction method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD), Outlier-Robust Ensemble Deep Random Vector Functional Link Network (ORedRVFL), and error correction. CEEMD is used to decompose the oil temperature data into multiple subsequences, enhancing the regularity and predictability of the data. Regularization and norm improvements are introduced to edRVFL to obtain a more robust ORedRVFL model. The Tent initialization-based Differential Evolution algorithm (TDE) is employed to optimize the model parameters and predict each subsequence. Finally, error correction is applied to the prediction results. Taking the main transformer of a hydropower station in Yunnan, China as an example, the experimental results show that the proposed method improves the prediction accuracy by 5.05% and 4.13% in winter and summer oil temperature predictions, respectively. Moreover, the model’s degradation is significantly reduced when random noise is added, which verifies its robustness. This method provides an efficient and accurate solution for transformer oil temperature prediction. Full article

Omnidirectional mobile robots have gained extensive application across diverse fields due to their exceptional maneuverability and adaptability in confined spaces. However, structural and systemic uncertainties significantly compromise motion accuracy. To enhance motion control precision, this paper proposes a sliding mode control (SMC) method integrated with a radial basis function (RBF) neural network. The approach aggregates model uncertainties, nonlinear dynamics, and unknown disturbances into a composite disturbance term. An RBF neural network is employed to approximate this disturbance, with compensation embedded within the SMC framework. An online adaptive law for neural network optimization is derived using the Lyapunov stability theorem, thereby improving the disturbance rejection capability. Comparative simulations and experiments validate the proposed method against modern control strategies. Results demonstrate superior tracking performance and robustness, significantly enhancing trajectory tracking accuracy for the MY3 wheeled omnidirectional mobile robot. Full article

Fructose-1,6-bisphosphatase (FBP) is a crucial regulatory enzyme in sucrose synthesis and photosynthetic carbon assimilation, functioning through two distinct isoforms: cytosolic FBP (cyFBP) and chloroplastic FBP (cpFBP). However, the identification and functional characterization of FBP genes in Saccharum remains limited. In this study, we conducted a systematic identification and comparative genomics analyses of FBPs in three Saccharum species. We further examined their expression patterns across leaf developmental zones, spatiotemporal profiles, and responses to diurnal rhythms and hormonal treatments. Our analysis identified 95 FBP genes, including 44 cyFBPs and 51 cpFBPs. Comparative analyses revealed significant divergence in physicochemical properties, gene structures, and motif compositions between the two isoforms. Expression profiling indicated that both cyFBPs and cpFBPs were predominantly expressed in leaves, particularly in maturing and mature zones. During diurnal cycles, their expression peaked around the night–day transition, with cpFBPs exhibiting earlier peaks than cyFBPs. FBP genes in Saccharum spontaneum displayed greater diurnal sensitivity than those in Saccharum officinarum. Hormonal treatments further revealed significant regulatory divergence in FBP genes, both between isoforms and across species. Notably, cyFBP_2 and cpFBP_2 members consistently exhibited higher expression levels across all datasets, suggesting their pivotal roles in sugarcane physiology. These findings not only identify potential target genes for enhancing sucrose accumulation, but also highlight the breeding value of S. spontaneum and S. officinarum in sugarcane breeding. Full article

This study addresses the complex challenges associated with flexible mattress (soft mattress) installation in the sharply curved and deep-water sections of the Yangtze River, particularly in the Yaozui revetment reconstruction project. Under extreme hydrodynamic conditions—water depths exceeding 30 m and velocities over 2.5 m/s—the risk of structural failures such as displacement, flipping, or tearing of the mattress becomes significant. To improve construction safety and stability, the study integrates numerical modeling and on-site strain monitoring to analyze the mechanical response of flexible mattresses during deployment. A three-dimensional finite element model based on the catenary theory was developed to simulate stress distributions under varying flow velocities and angles, revealing stress concentrations at the mattress’s upper edge and reinforcement junctions. Concurrently, a real-time monitoring system using high-precision strain sensors was deployed on critical shipboard components, with collected data analyzed through a remote IoT platform. The results demonstrate strong correlations between mattress strain, flow velocity, and water depth, enabling the identification of high-risk operational thresholds. The proposed monitoring and early-warning framework offers a practical solution for managing construction risks in extreme riverine environments and contributes to the advancement of intelligent construction management for underwater revetment works. Full article

Background: Severe damage to one side of the brain often leads to adverse consequences and can also cause widespread changes throughout the brain, especially in the contralateral area. Studying molecular changes in the contralateral cerebral hemisphere, especially with regard to genetic regulation, can help discover potential treatment strategies to promote recovery after severe brain trauma on one side. Methods: In our study, the right motor cortex was surgically removed to simulate severe unilateral brain injury, and changes in glial cells and synaptic structure in the contralateral cortex were subsequently assessed through immunohistological, morphological, and Western blot analyses. We conducted transcriptomic studies to explore changes in gene expression levels associated with the inflammatory response. Results: Seven days after corticotomy, levels of reactive astrocytes and hypertrophic microglia increased significantly in the experimental group, while synapsin-1 and PSD-95 levels in the contralateral motor cortex increased. These molecular changes are associated with structural changes, including destruction of dendritic structures and the encapsulation of astrocytes by synapses. Genome-wide transcriptome analysis showed a significant increase in gene pathways involved in inflammatory responses, synaptic activity, and nerve fiber regeneration in the contralateral cortex after corticorectomy. Key transcription factors such as NF-κB1, Rela, STAT3 and Jun were identified as potential regulators of these contralateral changes. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) confirmed that the mRNA expression levels of Cacna1c, Tgfb1 and Slc2a1 genes related to STAT3, JUN, and NF-κB regulation significantly increased in the contralateral cortex of the experimental group. Conclusions: After unilateral brain damage occurs, changes in the contralateral cerebral hemisphere are closely related to processes involving inflammation and synaptic function. Full article

To improve the postharvest preservation of cherry tomatoes and combat pathogenic, both bacterial and fungal contamination (particularly Alternaria alternata), a novel biodegradable coating was developed based on a water-in-water (W/W) Pickering emulsion system. The emulsion was stabilized by L. plantarum (Lactobacillus plantarum), with maltodextrin (MD) as the dispersed phase and hydroxypropyl methylcellulose (HPMC) as the continuous phase. Characterization of emulsions at varying concentrations revealed that the optimized W/W-PL^8 film exhibited superior stability, smooth morphology, and low water vapor permeability (WVP = 220.437 g/(m²·24 h)), making it a promising candidate for fruit and vegetable preservation. Furthermore, the coating demonstrated strong antioxidant activity, with scavenging rates of 58.99% (ABTS) and 94.23% (DPPH), along with potent antimicrobial effects, showing inhibition rates of 12.8% against Escherichia coli and 23.7% against Staphylococcus aureus. Applied to cherry tomatoes, the W/W-PL^8 coating significantly reduced respiration rates, minimized decay incidence, and maintained nutritional quality during storage. Remarkably, the coating successfully controlled Alternaria alternata contamination, enhancing the storage duration of cherry tomatoes. These findings highlight the potential of W/W-PL^8 as an eco-friendly and functional packaging material for fresh produce preservation. Full article

In view of the lack of accurate model in the discrete element study during straw comprehensive utilization (crushing, mixing, and baling), wheat straw was taken as the research object to calibrate the simulation parameters using EDEM 2023. The intrinsic and contact mechanical parameters of wheat straw were measured, and a test of the angle of repose (AOR), extrusion test and bending test were carried out. On this basis, a discrete element model (DEM) of hollow flexibility by using cylindrical particles was developed. The optimal combination of contact mechanical parameters was obtained through AOR tests based on the Box–Behnken design (BBD), coefficients of static friction, rolling friction, and restitution between wheat straw and wheat straw-45 steel are separately 0.227, 0.136, 0.479, 0.271, 0.093, and 0.482, AOR is 18.66°. Meanwhile, optimal combinations of bond contact parameters were determined by the BBD. The calibrated parameters were used to conduct extrusion and bending tests. Results show that the average values of peak extrusion force and peak bending pressure are 23.20 N and 3.92 N, which have relative discrepancy of 3.25% and 3.59% compared to physical test measurements. The results can provide model reference for the optimization design such as feed processing equipment, baler, and mixer. Full article

To address the issues of loss of important detailed features, insufficient contrast enhancement, and high computational complexity in existing low-light image enhancing methodologies, this paper presents a low-light image enhancement network (MSF-ACA), which uses multi-scale feature fusion and adaptive contrast adjustment. Focus is placed on designing the local–global image feature fusion module (LG-IFFB) and the adaptive image contrast enhancement module (AICEB), in which the LG-IFFB adopts the local–global dual-branching structure to extract multi-scale image features, and utilizes the element-by-element multiplication method to fuse the local details with the global illumination distribution to alleviate the problem of serious loss of image details, while the AICEB incorporates linear contrast enhancement and confidence adaptive stopping mechanism, which dynamically adjusts the computational depth according to the confidence of the feature map, balancing the contrast enhancement and computational efficiency. According to the results of the experiment, the parameter count of MSF-ACA is 0.02 M, and compared with today’s mainstream algorithms, the suggested model attains 21.53 dB in PSNR when evaluated on the LOL-v2-real evaluation dataset, and the BRI is as low as 16.04 on the unpaired dataset DICM, which provides a better detail clarity and color fidelity in visual enhancement, and it is a highly efficient and robust low-light image model. Full article

The growth of rural residents’ disposable income is essential for narrowing the income gap between urban and rural areas and promoting integrated development. This study explores how China’s National Main Functional Zoning Plan influences rural household income through its regulatory impact on construction land expansion. Using data from county−level administrative units across China, the research identified the construction land regulation index as a key mediating variable linking zoning policy to changes in household income. By shifting the analytical perspective from a traditional urban–rural classification to a framework aligned with the National Main Functional Zoning Plan, the study reveals how spatial planning tools, particularly differentiated land quota allocations, influence household income. The empirical results confirm a structured causal chain in which zoning policy affects land development intensity, which in turn drives rural income growth. This relationship varies across different functional zones. In key development zones, strict land control limits income potential by constraining land supply. In main agricultural production zones, moderate regulatory control enhances land use efficiency and contributes to higher income levels. In key ecological function zones, ecological constraints require diverse approaches to value realization. The investigation contributes both theoretical and practical insights by elucidating the microeconomic effects of national spatial planning policies and offering actionable guidance for optimizing land use regulation to support income growth tailored to regional functions. Full article

Surface and sub-surface atomic configurations are critical for catalysis as they host the active sites governing electrochemical processes. This study employs density functional theory (DFT) calculations and Monte Carlo simulations combined with the cluster-expansion approach to investigate atom distribution and Pt segregation in Pt-Fe alloys across varying Pt/Fe ratios. Our simulations reveal a strong tendency for Pt atoms to segregate to the surface layer while Fe atoms enrich the sub-surface region. Crucially, the calculations predict the stability of a periodic Pt₂Fe alloy surface model, characterized by specific defect structures, at low platinum content and low annealing temperatures. Electronic structure analysis indicates that forming this Pt₂Fe surface alloy lowers the d-band center of Pt atoms, weakening CO adsorption and thereby enhancing resistance to CO poisoning. Although defect-induced strains can modulate the d-band center, crystal orbital Hamilton population (COHP) analysis confirms that such strains generally strengthen Pt-CO interactions. Therefore, the theoretical design of Pt₂Fe alloy surfaces and controlling defect density are predicted to be effective strategies for enhancing catalyst resistance to CO poisoning. This work highlights the advantages of periodic Pt₂Fe surface models for anti-CO poisoning and provides computational guidance for designing efficient Pt-based electrocatalysts. Full article