Search Results (100)

The hydrostatic guideway has been widely used in ultra-precision machine tools. The flow stability of the hydrostatic guideway has a significant impact on its bearing characteristics, and the flow controller is critical to safeguard the flow stability of the hydrostatic guideway. Currently, most engineering applications use fixed, fluid-resistance flow controllers, which have a simple structure, low cost, and high reliability. However, when facing complex working conditions, the fixed, fluid-resistance flow controller cannot maintain the flow stability of the hydrostatic guide. In this study, a membrane-type flow controller with variable fluid resistance is designed, and a theoretical model of the flow controller’s bearing characteristics is established, which is verified by fluid–solid coupling simulation and flow rate experiments. Analyzing the influence of the design parameters of the membrane-type flow controller on the performance according to the theoretical model, the design guidelines of the membrane-type flow controller are established, the key structure of the flow controller is clarified, and the design range of the key structure dimensions is given. The results show that the gasket thickness of the membrane-type flow controller has the greatest impact on the performance of the hydrostatic guideways, which should be ensured to have a machining error of less than 0.005 mm. This study is a guide for the design and manufacture of flow controllers, as well as for engineering applications. Full article

Vessel detection technology based on marine remote sensing imagery is of great importance. However, it often faces challenges, such as small vessel targets, cloud occlusion, insufficient data volume, and severely imbalanced class distribution in datasets. These issues result in conventional models failing to meet the accuracy requirements for practical applications. In this paper, we first construct a novel remote sensing vessel image dataset that includes various complex scenarios and enhance the data volume and diversity through data augmentation techniques. Secondly, we address the class imbalance between foreground (small vessels) and background in remote sensing imagery from two perspectives: the sensitivity of IoU metrics to small object localization errors and the innovative design of a cost-sensitive loss function. Specifically, at the dataset level, we select vessel targets appearing in the original dataset as templates and randomly copy–paste several instances onto arbitrary positions. This enriches the diversity of target samples per image and mitigates the impact of data imbalance on the detection task. At the algorithm level, we introduce the Normalized Wasserstein Distance (NWD) to compute the similarity between bounding boxes. This enhances the importance of small target information during training and strengthens the model’s cost-sensitive learning capabilities. Ablation studies reveal that detection performance is optimal when the weight assigned to the NWD metric in the model’s loss function matches the overall proportion of small objects in the dataset. Comparative experiments show that the proposed NWD-YOLO achieves Precision, Recall, and ${\mathrm{AP}}_{50}$ scores of 0.967, 0.958, and 0.971, respectively, meeting the accuracy requirements of real-world applications. Full article

As electric multiple unit (EMU) operations and maintenance demands have expanded, spare parts supply chain management has become increasingly crucial. This study emphasizes the supply challenges of EMU spare parts, including inadequate minimum inventory levels and prolonged response times. Redesigning the OEM–railway bureau vendor-managed inventory (VMI) model contract incentive and penalty system is the key goal. Connecting the spare parts supply system with its characteristics yields a game theory model. This study analyzes and compares the equilibrium strategies and profits of supply chain members under different mechanisms for managing critical spare parts. The findings demonstrate that mechanism contracts can enhance supply chain performance in a Pareto-improving manner. An in-depth analysis of downtime loss costs, procurement challenges, and order losses reveals their effects on supply chain coordination and profit allocation, providing railway bureaus and OEMs with a theoretical framework for supply chain decision-making. This study offers theoretical justification and a framework for decision-making on cooperation between OEMs and railroad bureaus in the management of spare parts supply chains, particularly for extensive EMU operations. Full article

Maize (Zea mays L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. Conventional manual stomatal detection methods are inefficient, subjective, and inadequate for high-throughput plant phenotyping research. To address this, we curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO, tailored for small stomatal targets and subtle features in microscopic images. Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. Our model achieved a remarkable 91.8% mean average precision (mAP) and 98.5% precision, surpassing numerous mainstream detection models while maintaining computational efficiency. Ablation and comparative analyses demonstrated that the Small Object Detection layer, dynamic convolutional module, multi-task training, and knowledge distillation strategies substantially enhanced detection performance. Integrating all four strategies yielded a nearly 9% mAP improvement over the baseline model, with computational complexity under 8.4 GFLOPS. Our findings underscore the superior detection capabilities of StomaYOLO compared to existing methods, offering a cost-effective solution that is suitable for practical implementation. This study presents a valuable tool for maize stomatal phenotyping, supporting crop breeding and smart agriculture advancements. Full article

The glass transition temperature (T_g) was a pivotal parameter governing the thermal and mechanical properties of bismaleimide-based polyimide (BMI) resins. However, limited experimental data for BMI systems posed significant challenges for predictive modeling. To address this gap, this study introduced a hybrid modeling framework leveraging transfer learning. Specifically, a multilayer perceptron (MLP) deep neural network was pre-trained on a large-scale polymer database and subsequently fine-tuned on a small-sample BMI dataset. Complementing this approach, six interpretable machine learning algorithms—random forest, ridge regression, k-nearest neighbors, Bayesian regression, support vector regression, and extreme gradient boosting—were employed to construct transparent predictive models. SHapley Additive exPlanations (SHAP) analysis was further utilized to quantify the relative contributions of molecular descriptors to T_g. Results demonstrated that the transfer learning strategy achieved superior predictive accuracy in data-scarce scenarios compared to direct training on the BMI dataset. SHAP analysis identified charge distribution inhomogeneity, molecular topology, and molecular surface area properties as the major influences on T_g. This integrated framework not only improved the prediction performance but also provided feasible insights into molecular structure design, laying a solid foundation for the rational engineering of high-performance BMI resins. Full article

Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and important method for human activity recognition. However, most WiFi-based activity recognition methods have limitations, such as using WiFi fingerprints to identify human activities. They either require extensive sample collection and training, are constrained by a fixed environmental layout, or rely on the precise positioning of transmitters (TXs) and receivers (RXs) within the space. If the positions are uncertain, or change, the sensing performance becomes unstable. To address the dependency of current WiFi indoor human activity trajectory reconstruction on the TX-RX position, we propose WiPIHT, a stable system for tracking indoor human activity trajectories using a small number of commercial WiFi devices. This system does not require additional hardware to be carried or locators to be attached, enabling passive, real-time, and accurate tracking and trajectory reconstruction of indoor human activities. WiPIHT is based on an innovative CSI channel analysis method, analyzing its autocorrelation function to extract location-independent real-time movement speed features of the human body. It also incorporates Fresnel zone and motion velocity direction decomposition to extract movement direction change patterns independent of the relative position between the TX-RX and the human body. By combining real-time speed and direction curve features, the system derives the shape of the human movement trajectory. Experiments demonstrate that, compared to existing methods, our system can accurately reconstruct activity trajectory shapes even without knowing the initial positions of the TX or the human body. Additionally, our system shows significant advantages in tracking accuracy, real-time performance, equipment, and cost. Full article

γ-aminobutyric acid (GABA) is a four-carbon non-protein amino acid, with many regulatory effects in humans. It aids in regulating blood glucose levels and pressure and is widely recognized for its ability to promote cognitive balance through the alleviation of stress and improvements in sleep quality. The GABA content of longan pulp is higher than that of many other fruits and vegetables; however, much is still unknown about GABA’s biosynthesis in longan. In this study, we found that the GABA content of ‘Baoshi No. 1’ (BS1) pulp was significantly higher than that of ‘Chunxiang’ (CX) pulp. The GAD activity was higher in BS1 pulp than CX pulp, while there was no significant difference in the GABA-T activity. Additionally, five GAD genes were identified in longan, and an analysis of their transcriptional levels showed that only the expression level of DlGAD3 corresponded to the GABA content and GAD activity. DlGAD3 was localized in the cytoplasm, and its transient overexpression promoted an increase in the GABA content in Nicotiana benthamiana leaves. Overall, our results show that DlGAD3 is able to promote the accumulation of GABA and may play a major role in its biosynthesis in longan pulp. Full article

Emerging evidence highlights the gut microbiota as a pivotal determinant of pharmacological efficacy. While Enterococcus faecalis (E. faecalis)-derived tyrosine decarboxylases (tyrDCs) are known to decarboxylate levodopa (L-dopa), compromising systemic bioavailability, the causal mechanisms underlying microbiota-mediated pharmacodynamic variability remain unresolved. In our study, we employed antibiotic-induced microbiota depletion and fecal microbiota transplantation (FMT) to interrogate microbiota-L-dopa interactions in MPTP-induced Parkinson’s disease (PD) mice. The study demonstrated that antibiotic-mediated microbiota depletion enhances L-dopa bioavailability and striatal dopamine (DA) level, correlating with improved motor function. To dissect clinical heterogeneity in the L-dopa response, PD patients were stratified into moderate responders and good responders following standardized L-dopa challenges. In vitro bioconversion assays revealed greater L-dopa-to-DA conversion in fecal samples from moderate responders versus good responders. FMT experiments confirmed mice receiving good-responder microbiota exhibited enhanced L-dopa bioavailability, higher striatal DA concentrations, and a heightened therapeutic effect of L-dopa relative to moderate-responder recipients. Collectively, our study provided evidence that the gut microbiota directly modulates L-dopa metabolism and microbial composition determines interindividual therapeutic heterogeneity. Targeted microbial modulation—through precision antibiotics or donor-matched FMT—is a viable strategy to optimize PD pharmacotherapy, supporting the potential for microbiota-targeted adjuvant therapies in PD management. Full article

NAC transcription factors form a plant-specific family essential for growth, development, and stress responses. NTLs, a subfamily of the NAC transcription factor family, belong to the membrane-bound transcription factors (MTFs). These proteins contain transmembrane domains that enable rapid nuclear translocation in response to environmental stimuli, thereby regulating target gene expression. As a major sugar crop, sugar beet is primarily cultivated in arid and semi-arid regions, where drought stress significantly impairs yield and quality, underscoring the urgent need to improve its drought tolerance. This study identified the NTL gene family in sugar beet and analyzed its gene structure, evolutionary relationships, cis-regulatory elements, drought-induced expression patterns, and BvNTL2’s role in drought resistance. The BvNTLs family comprises five members located on five distinct chromosomes. Their promoters harbor cis-regulatory elements related to ABA and drought stress, and their expression is drought-responsive. Under drought stress, BvNTL2 translocates to the nucleus, where its transmembrane domain is cleaved, resulting in its direct nuclear localization. Functional validation in Arabidopsis demonstrated that BvNTL2 overexpression enhances drought tolerance by increasing antioxidant enzyme activities and promoting the expression of ABA-related genes. This study highlights BvNTL2 as a promising candidate gene for the genetic improvement of drought-resistant sugar beet. Full article

First reported in 1987, the porcine reproductive and respiratory syndrome virus (PRRSV) has significantly disrupted the major regions affected by PRRSV in the pig breeding industry. Recently, outbreaks of disease caused by recombinant PRRSV strains in China have raised serious concerns. Effective immunization and infection control in pig populations is critical, as the virus frequently undergoes mutation and recombination. This study characterized a novel recombinant PRRSV strain, BX/CH/22, isolated from Northeast China. Genetic analysis revealed that BX/CH/22 is a recombinant of JXA1, NADC 30-like, and NADC 34-like strains. Phylogenetic analysis of the non-structural protein (NSP) 2 region classified BX/CH/22 as JXA1 PRRSV-like, with a characteristic deletion of 30 discontinuous amino acids in NSP2. However, Open Reading Frame (ORF) 5 analysis classified it as NADC 30-like PPRSV, while whole-genome phylogenetic analysis classified it as NADC 34-like PPRSV. Recombination analysis revealed that BX/CH/22 contains an NADC 34-like PRRSV backbone, an NSP-coding region from NADC 30-like PRRSV, and an ORF2-ORF6 region from NADC 34-like PRRSV. The strain was isolated from serum samples obtained from commercial swine farms undergoing active PRRS outbreaks. In animal experiments, all BX/CH/22-challenged piglets exhibited persistent fever, with peak temperatures >40.5 °C at 4–9 dpi resolving by 11 dpi, accompanied by cough, anorexia, and lethargy. A significant reduction in daily weight gain was observed in infected groups compared to asymptomatic controls, with a 100% survival rate. Our findings provide early warning for PRRSV immune control strategies. Full article

In recent years, the frequency and severity of large-scale forest fires have increased globally, threatening forest ecosystems, human lives, and property while potentially triggering cascading ecological and social crises. Despite significant advancements in remote sensing-based forest fire monitoring, early warning systems, and fire risk zoning, post-fire thermal damage assessment remains insufficiently addressed. This study introduces an innovative approach combining multispectral imaging with a dual annealing constrained optimization algorithm to enable dynamic monitoring of fire temperature distribution. Based on this method, we develop a dynamic thermal damage assessment model to quantify thermal impacts during forest fires. The proposed model provides valuable insights for defining thermal damage zones, optimizing evacuation strategies, and supporting firefighting operations, ultimately enhancing emergency response and forest fire management efficiency. Full article

Identifying the most suitable areas for developing forest health care in Hainan Tropical Rainforest National Park (HTRNP) is of great significance to its ecological protection and development. This study selected 107 health care points in HTRNP as research objects to monitor environmental factors, a forest health care evaluation system was constructed based on those environmental factors, and the health care resource points were rated. Kernel density analysis and buffer zone analysis were used to analyze other factors such as roads, villages, and water inside and outside of the national park. Multi-factor superposition analysis of the first-level health care points with other impact factors was performed to obtain a map of the distribution of health care potential in different sub-areas of HTRNP. A total of 67 first-level health care points were selected through the forest health care evaluation system. Through superposition analysis, it was found that, among the seven sub-areas of HTRNP, there are 42 first-level health care points within the 5 km buffer zone for roads and waterways, including 11 in Diaoluo Mountain, 10 in Limu Mountain, 6 in Yingge Ridge, 5 in Jianfeng Ridge, 4 in Bawang Ridge, 4 in Maorui, and 2 in Wuzhi Mountain. There are nine first-level health care points located in the area with a village kernel density greater than 3000, including three in Diaoluo Mountain, two in Limu Mountain, two in Yingge Ridge, and two in Maorui. At the same time, to meet the above two conditions of the first level of health care points, there are six, including three in Diaoluo Mountain, two in Maorui, and one in Yingge Ridge. Through the results analysis, Diaoluo Mountain is considered to be the area with the greatest potential for developing forest health care in HTRNP. In addition, the comprehensive performance of Limu Mountain is second only to Diaoluo Mountain, and Limu Mountain, Maorui, and Yingge Ridge are listed as areas with great potential for developing forest health care. Full article

Precise modulation of the cell cycle via electromagnetic (EM) control presents a groundbreaking approach for cancer therapy, especially in the development of personalized treatment strategies. EM fields can precisely regulate key cellular homeostatic mechanisms such as proliferation, apoptosis, and repair by finely tuning parameters like frequency, intensity, and duration. This review summarizes the mechanisms through which EM fields influence cancer cell dynamics, highlighting recent developments in high-throughput electromagnetic modulation platforms that facilitate precise cell cycle regulation. Additionally, the integration of electromagnetic modulation with emerging technologies such as artificial intelligence, immunotherapy, and nanotechnology is explored, collectively enhancing targeting precision, immune activation, and therapeutic efficacy. A systematic analysis of existing clinical studies indicates that EM modulation technology significantly overcomes key challenges such as tumor heterogeneity, microenvironment complexity, and treatment-related adverse effects. This review summarizes the prospects of electromagnetic modulation in clinical translation and future research directions, emphasizing its critical potential as a core element in individualized and multimodal cancer treatment strategies. Full article

This article focuses on the prescribed performance sliding mode fault-tolerant control problem for an unmanned morphing flight vehicle (MFV) with actuator faults and composite disturbances during wing deformation. Firstly, the longitudinal nonlinear dynamic model of the unmanned MFV is introduced. Then, a control framework is proposed by decomposing the integrated dynamic model into attitude and velocity subsystems, effectively simplifying controller architecture and improving fault tolerance. Further, the constrained tracking errors are systematically transformed into unconstrained counterparts via projection operators to facilitate controller design. For each subsystem, a prescribed performance sliding mode fault-tolerant controller is developed, ensuring both transient performance and steady-state tracking accuracy. Finally, the simulation results verify the feasibility and effectiveness of the proposed fault-tolerant control strategy. Full article

Sulfur hexafluoride (SF₆), the strongest greenhouse gas, has great challenges in degradation because of its stable structure, posing significant environmental concerns. Photocatalysis offers an environmentally friendly, low-energy solution, but the fluoride deposition on catalysts reduces their activity, thus limiting their large-scale application. To prevent catalyst fluoride poisoning, we report a thin-layer graphitic carbon nitride (CN) material loaded with MoO_x (CNM) that resists fluoride deposition for long-term SF₆ degradation. By combining molecular structure design and nanostructure regulation, we construct a photocatalyst with enhanced charge carrier mobility and reduced transport distances. We find that the CNM exhibits a high specific surface area, increased contact between reactants and active sites, and efficient electron–hole separation due to the Mo-N bonds, achieving an SF₆ degradation efficiency of 1.73 mmol/g after one day due to the prolonged catalytic durability of the catalyst, which is eight times higher than pristine g-C₃N₄ (0.21 mmol/g). We demonstrate the potential of CNMs for low-energy, high-efficiency SF₆ degradation, offering a new approach to mitigate the environmental impact of this potent greenhouse gas. We envision that this study will inspire further research into advanced photocatalytic materials for environmental remediation, contributing to global efforts in combating climate change. Full article