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［Objective］ Currently, when metro trains operate on mainline tracks under the ATO (automatic train operation) mode and stop at stations, the rail transit signaling system must confirm that all braking signals are applied previously before issuing a door-opening command, which ensures passenger safety. Failure to issue this command would prevent the door system from opening in coordination, significantly impacting mainline train operation. Therefore, it is essential to explore new optimization solutions to enhance train operational service quality. ［Method］ Using a real fault case on Ningbo Rail Transit Line 4 as research object, where a train door failed to open in coordination due to unapplied braking during mainline operation, the problem is analyzed from multiple dimensions, including train door-opening logic, braking control principles, fault reporting logic, and data. The goal is to develop effective optimization solutions. The feasibility and reliability of the proposed solutions are verified through multiple trials. ［Result & Conclusion］ In the braking system, the optimization of braking software improves the fault reporting mechanism, significantly reducing the fault reporting rate. Additionally, enhancements to the braking application mechanism in fault scenarios effectively mitigate the negative impact of such faults on operations. For the LCU (logic control unit), the optimized software improves the output logic of the ′all braking applied′ signal, successfully preventing door coordination failures caused by braking issues. Results from multiple trials demonstrate that the optimization scheme significantly reduces fault impacts, improves the reliability of metro train operations, and enhances service quality, showcasing a high practical value.

Researching the deformation prediction of domestic 7050 aluminum alloy aviation thin-walled parts during turning processing and establishing an accurate finite element model for predicting turning deformation are of great significance for ensuring the machining precision and dimensional stability of aviation thin-walled parts，as well as improving the structural strength and reliability of aircraft. This thesis employs simulation and experimental demonstration methods to study the influence of the coupling effect between the initial residual stress of domestic 7050 aluminum alloy and the turning residual stress on the deformation of thin-walled bearing frames during processing. Firstly，based on the machining process of aviation thin-walled bearing frames，a turning deformation simulation model of the bearing frame is established using the birth and death element method. Next，the initial residual stress field of the bearing frame blank and the turning surface residual stress are obtained through the blind hole method and turning experiments，respectively. Finally，based on the turning deformation simulation model，the deformation of the bearing frame under the coupling effect of the initial residual stress field and turning residual stress is predicted and experimentally verified. The results show that the error of the bearing frame deformation simulation prediction model is less than 15%.

HP-1 is an eco-friendly hydrofluoroolefin (HFO) refrigerant with favorable thermodynamic and environmental properties. The critical parameters and saturated vapor pressure of HP-1 are similar to those of R245fa, with HP-1 serving as a potential replacement for R245fa, which has a high global warming potential (GWP) in high-temperature heat pumps. The flammability, solubility, and material compatibility of HP-1 were mainly determined experimentally, with the results demonstrating that HP-1 has a flammability range of 9.75%-16.1%, exhibiting excellent solubility with MK220 lubricating oil at elevated temperatures, and good compatibility with materials used in high-temperature heat pump systems. When HP-1 is applied to high-temperature heat pump units, the condensing temperature of the unit can reach up to 125 ℃ when the evaporating temperature ranges between 50 ℃ and 70 ℃, with a heating capacity of 99.27-153.14 kW and a coefficient of performance (COP) of 2.25-4.85.

Considering the uncertainty associated with structural boundary conditions, a method for modifying the boundary constraint static model of beam structures was proposed based on the homotopy stochastic finite element method. The overall modification of both the beam body elements and boundary elements was achieved using uncertain static measurement data. By employing the static condensation method, computational degrees of freedom were ensured to match measured degrees of freedom. Regularization methods were applied to mitigate ill-conditioned solutions in modification equations for stochastic models. The probabilistic residual minimization method enables optimal selection of homotopy coefficients, ensured accurate identification of boundary constraints and precise overall modification. Finally, simulations on variable-section concrete beams and static loading tests on aluminum alloy beams were conducted to verify the effectiveness of this approach.

Aiming at the phenomenon of soil metal pollution in Beijing Wanzhuang gold tailings reservoir, 182 soil samples from the mining area, tailings pond and concentrator were tested and analyzed. The Nemero index method was used to evaluate the pollution of heavy metal elements in the soil of the mining area, and the spatial distribution of metal pollution elements in the soil of the mining area was mapped by using Arc GIS software. The results show that the pollution degree in the study area is mining area > tailings pond > concentrator. As and Pb in the polluted elements in the mining area seriously exceed the standard. Nearly 22.8% of the samples constitute heavy pollution, and as in the tailings pond constitutes heavy pollution. Secondly, Cd is moderately polluted, which is common in the study area. While Cu and Zn are slightly polluted, Cr and Ni are almost free of pollution. According to the spatial distribution and pollution degree of pollution elements, it is inferred that heavy metal elements in the soil in the study area continuously migrate from the mining area, concentrator and tailings pond to 500 m upstream and 2 km downstream, but do not affect the living area. This study provides a basis for metal pollution and treatment of gold tailings pond, and also provides reference value for other mining areas.

Memory refers to the physiological process in which the brain is able to keep the information obtained from both internal and external sources over an extended period of time. In the brain, neuronal ensembles distributed across multiple regions encode information about past scenes or events and form long-term memories, which then can guide individuals' behavior in similar scenes or events. Unless been consolidated, these newly-formed memories decay quickly. Sleep is a brain state with multiple physiological functions, including non-rapid eye movement sleep (NREM) and rapid eye movement (REM) sleep. Memory formation involves 3 processes: encoding, consolidation, and retrieval. Both reduced sleep time and fragmented sleep can lead to the impaired consolidation of various types of memory, indicating that one of the important physiological functions of sleep is beneficial for the memory consolidation. In this article, we summarizes the critical evidence that NREM sleep and REM sleep are involved in memory consolidation, and discuss how sleep promotes memory consolidation through the mechanisms such as ensemble reactivation and synaptic modification. Future researches may include further exploration on how sleep coordinates synaptic "down-scaling" with "up-scaling", how REM sleep complements with NREM sleep, the involvement of glial cells activity during sleep in memory consolidation, and whether and how brain regions controlling NREM and REM sleep directly participates in the regulation of memory consolidation.

Objective‍ ‍To prepare neutrophil-responsive luminescent nanomicelles based on self-luminating compound, luminol, and investigate their imaging capability for a mouse burn model at early stage of inflammation. Methods‍ ‍Hexachlorotripolyphosphazene (HCCP) was utilized as the skeleton material to synthesize amphiphilic LHP luminescence materials through chemical synthesis of luminol and polyethylene glycol (PEG). The chemical structure of LHP was characterized using infrared spectrum and nuclear magnetic hydrogen spectrum. Nanomicelle LHP NM was formed by self-assembly of LHP in deionized water, and then its particle size and potential were measured. Transmission electron microscopy (TEM) was applied to observe the morphology of nanomicelles. Optical fiber spectrometer, weak luminescence measuring instrument, and small animal living imager were employed to evaluate the spectroscopic properties, chemiluminescence rules, and in vitro luminescence imaging ability of the nanomicelles. After that, female Balb/c mice were subjected to scalding with hot water at temperatures of 70 ℃, 80 ℃, and 90 ℃, respectively to establish a mouse burn model ranging from degree I to degree II burns. The depth of skin scalds in the model mice was determined through HE staining, while the expression levels of TNF-α, IL-1β, IL-6 and myeloperoxidase (MPO) in the scalded skin tissues were assessed with fluorescence quantitative qPCR. The changes in reactive oxygen species (ROS) levels over time and burn depth in the skin tissues were determined with ROS detection kit. Additionally, the neutrophils within the skin tissues of model mice were labeled with FITC-Ly6G antibody to count the neutrophil number. Finally, a small animal imaging system was utilized to examine the imaging capability of nanomicelle LHP NM in a mouse burn model to analyze the correlation between luminous intensity and number of recruited neutrophil in order to evaluate the effectiveness of luminous nanomicelles for monitoring early inflammatory response and diagnosing burn depth in a mouse burn model. Results Nuclear magnetic resonance (NMR) spectroscopy confirmed the bonding of approximately 5 luminol units and a polyethylene glycol (PEG) chain to 1 HCCP molecule. TEM and particle size determination results demonstrated that the prepared nanomicelles were spherical, hollow structures in a diameter of around 120 nm. In vitro luminescence experiments revealed that the nanomicelles exhibited high brightness and sustained chemiluminescence under varying concentrations of ROS and MPO levels, with luminescence intensity dependent on both ROS level and nanomicelle concentration. The in vitro cellular experiments demonstrated that the nanomicelles exhibited neutrophil-responsive imaging capability. The luminescence intensity was positively correlated with both the number of neutrophils and the dose of LHP NM, with a linear correlation coefficient (r) of 0.98 and 0.99, respectively. In vivo animal study revealed a significant increase (P

Objective To investigate the clinical efficacy and prognosis of cross-line immunotherapy for driver gene-negative advanced non-small cell lung cancer (NSCLC).Methods Clinical data of patients with advanced NSCLC in Zhongnan Hospital of Wuhan University from June 2019 to December 2023 were retrospectively analyzed. For first-line treatment for the patients, programmed cell death receptor-1 (PD-1) monoclonal antibody combined with platinum-based doublet chemotherapy was adopted, and for second-line treatment, PD-1 monoclonal antibody combined with chemotherapy was used. The Kaplan-Meier method was employed to draw survival curves, and the Log-rank test was used to evaluate the differences in survival. The Cox proportional hazards regression model was used to analyze the risk factors that affect prognosis and subgroup analyses were conducted to explore the impact on patients' prognosis.Results A total of 112 advanced NSCLC patients with negative driver genes were included. The overall response rate (ORR) of cross-line immunotherapy was 20.54%, and the disease control rate (DCR) of cross-line immunotherapy reached 49.11%. The median overall survival (OS) was 26.6 months. The median progression free survival of first-line treatment (PFS-1) was 7.3 months, and that of second-line treatment (PFS-2) was 5.4 months. The subgroup analysis showed that, compared with patients with PFS-1≤10 months, patients with PFS-1>10 months had longer median OS [44.3 months vs. 13.8 months, P

Accurate recognition of working conditions can optimize the zinc flotation process and improve its efficiency. Traditionally, this recognition heavily relies on manual observations of froth appearance, a method prone to human error and subjective judgment. To address this issue and improve recognition accuracy, a sparse attention convolution-ViT model is proposed. This model leverages machine vision techniques to investigate the relationship between froth visual features and the working conditions using real-time froth images from industrial sites. The model aims to recognize zinc flotation working conditions in real time, thereby providing guidance for operations. First, it combines the strengths of convolutional neural networks (CNNs) and vision transformers (ViT) to effectively extract both local and global features from froth images. Specifically, CNNs are adept at capturing local features, such as texture, color, and fine details of the froth, while ViT excels at identifying global features, such as the froth size distribution. By combining these two architectures, the sparse attention convolution-ViT model comprehensively analyzes the froth images. To enhance the global feature processing of froth images, a sparse multi-head attention mechanism is introduced into the ViT component. This mechanism allows the model to process global features with different sparsity levels, reducing computational costs and improving the model’s adaptability to different froth appearances. Each attention head in the sparse multi-head attention mechanism targets different aspects of global features, allowing the model to extract various information from the froth images while maintaining efficiency. Furthermore, an attention gated unit is introduced to refine the feature processing. This unit allows adaptive weighting of extracted features in the image, enhancing model interpretability and optimizing feature transfer. By effectively capturing the relevant features, the attention-gated unit helps the model to focus on critical features of the froth images that can indicate the working conditions. Experimental results demonstrated the effectiveness of the proposed sparse attention convolution-ViT model in recognizing zinc flotation working conditions. The model achieved a recognition accuracy of 88.62% on the zinc flotation froth image dataset, surpassing traditional CNN and ViT models. Ablation experiments highlighted the critical role of the sparse multi-head attention mechanism and the attention-gated unit, contributing to accuracy improvements of 0.92% and 2.63%, respectively. Moreover, gradient-weighted class activation mapping was used to visualize feature weights, confirming the model’s capability to effectively characterize froth images by identifying both local and global features. This accurate recognition of zinc flotation conditions underscores the potential of the model in providing reliable real-time recognition, supporting the optimization of the flotation process, thereby improving efficiency and resource utilization in zinc flotation.

Background and objective Ferroptosis-related genes play a crucial role in regulating intracellular iron homeostasis and lipid peroxidation, and they are involved in the regulation of tumor growth and drug resistance. The expression of ferroptosis-related genes in tumor tissues can be used to predict patients' future survival times, aiding doctors and patients in anticipating disease progression. Based on the sequencing data of lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) database, this study identified genes involved in the regulation of ferroptosis, constructed a prognostic model, and evaluated the predictive performance of the model. Methods A total of 1467 ferroptosis-related genes were obtained from the GeneCards database. Gene expression profiles and clinical data from 541 LUAD patients were collected from the TCGA database. The expression data of all ferroptosis-related genes were extracted, and differentially expressed genes were identified using R software. Survival analysis was performed on these genes to screen for those with prognostic value. Subsequently, a prognostic risk scoring model for ferroptosis-related genes was constructed using LASSO regression model. Each LUAD patient sample was scored, and the patients were divided into high-risk and low-risk groups based on the median score. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. Kaplan-Meier survival curves were generated to assess model performance, followed by validation in an external dataset. Finally, univariate and multivariate Cox regression analyses were conducted to evaluate the independent prognostic value and clinical relevance of the model. Results Through survival analysis, 121 ferroptosis-related genes associated with prognosis were initially identified. Based on this, a LUAD prognostic risk scoring model was constructed using 12 ferroptosis-related genes (ALG3, C1QTNF6, CCT6A, GLS2, KRT6A, LDHA, NUPR1, OGFRP1, PCSK9, TRIM6, IGF2BP1 and MIR31HG). The results indicated that patients in the high-risk group had significantly shorter survival time than those in the low-risk group (P

[Purposes] Catastrophic Optical Damage (COD) is a major limiting factor for the reliability and lifespan of high-power semiconductor lasers, making effective defect detection crucial for optimizing the manufacturing processes and structural designs of laser chips. In this study, a lightweight laser chip defect detection algorithm based on an improved YOLOv7-Tiny is proposed, aiming at addressing the high computational and parameter demands of deep learning applications in defect detection. [Methods] By employing a lightweight convolutional neural network as the feature extraction backbone and integrating multi-branch reparameterized convolution blocks, this algorithm not only significantly reduces resource consumption but also enhances feature representation capabilities. Additionally, the introduced coordinate attention mechanism improves the precision of defect localization. Pruning experiments and model deployment are conducted to further verify the algorithm practicality. [Findings] Experimental results on the electroluminescence dataset demonstrate that this method can accurately detect chip defects with lower parameter and computational costs, showing excellent performance.

5G ultra-dense network (UDN) was introduced to improve throughput, especially for static and low-speed scenarios, and thus cannot meet the demands that require high throughput and high mobile speed at the same time. For the new 6G scenarios that need to support both high-capacity and high-speed mobility in the future, a homocentric sphere model (HSM) network architecture was proposed, which adopted the control-plane/user-plane data separation and multiple transmission and reception point (TRP) cooperative transmission method to deal with the problem in densely deployed networks. The HSM was an effective method to improve the network capacity and cope with high-speed mobility in dense deployment networks by adopting control-plane/user-plane data separation and multiple TRP co-transmission to deal with the problems of high Doppler frequency shift effect and frequent switching of TRP in the dense deployment networks. The results demonstrate that the proposed homocentric sphere model can effectively reduce the Doppler shift effect in the high-speed mobility scenario of a densely organized network, and can provide higher network traversal spectrum efficiency.

The construction of smart borders has become a key issue in ensuring China's border security, maintaining the country's geopolitical stability, and promoting sustainable development. Against the backdrop of a changing global security landscape, particularly the increasing likelihood of non-traditional security threats, such as cybercrime, terrorism, and environmental risks, the need for a more sophisticated technology-driven approach to border management has become ever-more pressing. By quantitatively analyzing the literature related to smart borders between 2001 and 2022, using CiteSpace software and the Web of Science database as a data source, we aim in this paper to provide a comprehensive review of the development of the field of smart border research, to assess the hotspots and themes of current research, and consider future directions in the field. The results of the study revealed that smart border research is still very much in its infancy, and although there has been a general upward trend in the number of published articles, the volume of relevant research is still comparatively low. Furthermore, the distribution of smart border research groups appears to be relatively decentralized, with only a small number of collaborative initiatives to date, and, as yet, no prominent networked group of authors, with a large proportion of the research being conducted in the United States, the United Kingdom, China, Canada, and Germany. Among the topics addressed in smart border research, border security appears to be the subject of most concern, with immigration management and intelligent surveillance being particular focuses of smart border construction. On the basis of our review, we identified six areas that are the primary focus of current smart border research, namely, the connotation of smart borders, smart border demand, smart border systems, early warning mechanism construction, re-bordering and intelligence, and border strategy and cross-border cooperation. As an emerging research field, despite the preliminary results, smart border studies are still facing problems relating to practical application, such as weak theoretical foundations, an insufficiently developed methodological basis, and conflict between "human defense" and "smart defense," Our findings highlight the need for future studies to focus on establishing a sound theoretical and methodological framework underpinning smart border research, building a smart border control system adapted to China's national conditions, strengthening the precise control and early warning research of border corridors, and examining the modes of multi-subject linkage and humanized governance. Through interdisciplinary integration, drawing on international experience, and promoting the innovation of technology and governance models, the construction of China's smart borders can effectively respond to both traditional and non-traditional security threats, and enhance the precision and efficiency of border management. Having surveyed the current state of smart border research, we propose the following steps to promote the development of China's smart borders. Firstly, smart borders should be comprehensively integrated into the national security strategy to ensure that the system meets China's specific border security needs. A smart border management model with Chinese characteristics should be constructed, emphasizing data sharing, technological interoperability, and coordination and cooperation among all parties. Secondly, with a view toward enhancing border management efficiency, it is recommended that interdisciplinary cooperation be strengthened, international best practices be drawn upon, and technological innovations be promoted. Given the efficient implementation of these measures, China will be able to respond more effectively to traditional and non-traditional security threats, ensure border security and efficient management, and thus maintain stability and sustainable development within an increasingly complex global security environment.

[Objective] To explore the effects of Sanqitongshu capsule combined with argatroban on neurological deficits and peripheral blood endothelin-1 (ET-1), CD41, CD62P and soluble intercellular adhesion molecule-1 (sICAM-1) expressions in elderly patients with acute ischemic stroke. [Methods] A total of 106 elderly patients with acute ischemic stroke from Handan Central Hospital were divided into a study group and a control group according to the random number table method, each had 53 cases.Both groups were given basic treatment, the control group was treated with argatroban, the study group was treated with Sanqitongshu capsule on the basis of the control group.Both groups were treated for 7 days.The clinical efficacy, the National Institutes of Health Stroke Scale (NIHSS) score, hemorheology index, the expression levels of ET-1, CD41, CD62P and sICAM-1 in the two groups before and after treatment, and adverse reactions of the two groups were compared. [Results] After treatment, the total effective rate of the study group was significantly higher than that in the control group (P0.05). [Conclusion] Sanqitongshu capsule combined with argatroban has a significant effect in the treatment of elderly acute ischemic stroke, it can reduce the degree of neurological deficits, and reduce the expressions of ET-1, CD41, CD62P and sICAM-1.

The dried fruit of Ningxia goji berry (Lycium barbarum L.) with the homology of medicine and food was used as raw material, and fermented by Lactobacillus rhamnosus to prepare fermented goji berry juice in this study. The fermentation process was optimized by one-way and response surface experiments using the inhibition rate of xanthine oxidase as the evaluation index. The fermentation characteristics, nutritional and flavor properties, and antioxidant activities of the fermented goji berry juice were investigated. The results showed that the optimal fermentation process for the fermentation of goji berry juice by Lactobacillus rhamnosus was 28 h of fermentation time, 34 ℃ of fermentation temperature and 4% of inoculum. Under these conditions, the inhibition rate of xanthine oxidase by fermented goji berry juice was 79.40%, the pH value of fermented goji berry juice was 4.44±0.04, and the number of viable bacteria could reach 9.5±0.005 lg CFU/mL. Compared with unfermented goji berry juice, the content of soluble protein, total phenols, and total flavonoids in fermented goji berry juice was significantly reduced (P

ObjectiveTo compare the differences in clinical features and treatment choices between peripheral spondyloarthritis(pSpA) and axial spondyloarthritis(axSpA), and better understand the clinical characteristics and medication needs of pSpA.MethodsOur study is a retrospective cohort study. The patients who first visited the First Medical Center of Chinese PLA General Hospital between January 2016 and December 2022 and were diagnosed with axSpA or pSpA according to the classification criteria established by the Assessment of SpondyloArthritis International Society were selected as the study subjects. Demographic data, clinical characteristics, laboratory tests, and treatment information of these patients were obtained through the electronic medical records management system and the intelligent management system for spondyloarthritis. The research compared the distribution of swollen and tender joints between pSpA and axSpA patients, as well as that between pSpA1(excluding patients with psoriatic arthritis) and axSpA patients. Additionally, we analyzed differences in clinical features and treatment options among these groups.ResultsA total of 1639 patients were included in the study, of which 184 had pSpA(including 97 with psoriatic arthritis), and 1455 had axSpA. Compared to axSpA patients, pSpA patients had fewer male patients(62.5% vs. 79.7%, P < 0.001), later onset age(33.8 years vs. 22.0 years, P < 0.001), shorter diagnostic delays(6.0 months vs. 14.2 months, P=0.004), more associated peripheral arthritis(71.7% vs. 9.3%, P < 0.001) and dactylitis(6.5% vs. 0.3%, P < 0.001), more cases of psoriasis(52.7% vs. 1.1%, P < 0.001) and a more common family history of psoriasis(11.4% vs. 3.4%, P < 0.001). pSpA patients had higher levels of inflammatory markers but a lower positive rate of human leukocyte antigen(HLA)-B27(43.5% vs. 87.4%, P < 0.001). A positive HLA-B27 was associated with an earlier onset age, fewer cases of psoriasis, and a family history of ankylosing spondylitis. pSpA patients had a higher proportion of using conventional synthetic disease-modifying antirheumatic drugs(csDMARDs), biologic disease-modifying antirheumatic drugs(bDMARDs), and oral glucocorticoids, and they also more frequently used a combination of bDMARDs and csDMARDs(19.0% vs. 12.2%, P=0.009) or multiple csDMARDs(65.8% vs. 12.5%, P < 0.001). Compared to axSpA patients, pSpA1 patients(excluding psoriatic arthritis) did not show significant differences in the prevalence of psoriasis, uveitis, family history of psoriasis, or the use of bDMARDs, but the subgroup analysis of other variables was consistent with the results of pSpA patients.ConclusionspSpA patients tend to have a later onset of disease, a lower proportion of male and HLA-B27 positivity, more associated peripheral arthritis, dactylitis, psoriasis, and a more common family history of psoriasis. The disease burden in terms of treatment for pSpA is not lower than that for axSpA. Due to the presence of more peripheral symptoms, psoriasis, and higher levels of inflammation, they also require more medication.

Federated computing power Internet of things (IoT) is designed to deeply integrate computing power with IoT resources, facilitating the efficient utilization of vast and ubiquitously dispersed IoT data and heterogeneous resources through federated learning. Faced with the threats of emerging privacy attacks, e.g., model inversion attacks and gradient leakage attacks, the academic and industrial communities have widely investigated and applied differential privacy (DP) as an effective privacy protection technique. However, two severe challenges have not been taken into account in the existing DP budget settings, i.e., data heterogeneity issue of local computing power nodes and the fairness of privacy budget allocation, which lead to a significant loss in model accuracy. Therefore, an adaptive optimization scheme for privacy budget was proposed in federated computing power IoT, which was called federated learning based on Cramér-Rao lower bound differential privacy (FedCDP). In specific, to adaptively adjust privacy budgets, the privacy budget estimates for edge computing power nodes based on the Cramér-Rao lower bound theory were analyzed. Furthermore, by assessing the similarity between the local model and the aggregated model, as well as their respective privacy budget proportions, the global contribution of each node was determined, which was used to fairly, also in real time, optimize and adjust the privacy budget settings in conjunction with the estimated privacy budget. Through rigorous theoretical analysis, FedCDP achieves ε-DP for local models, and ensures the convergence of the global model. Experimental results on multiple public datasets show that the proposed scheme improves the accuracy of the global model by up to 10.19% under the premise of satisfying the same privacy protection requirements.

Molybdenum disilicide (MoSi2) coating has the excellent high-temperature oxidation resistance which is widely used as a protective coating of various materials. Based on the current research status of MoSi2 coatings at home and abroad, the preparation methods of MoSi2 coatings, such as plasma spraying, pack cementation, slurry method, molten salt method, magnetron sputtering, chemical vapor deposition, laser cladding method, and spark plasma sintering method, were introduced in detail, and the advantages, differences, and shortcomings of those preparation methods for the MoSi2 coatings were summarized. The improvement and development of the MoSi2 coating preparation process were also prospected.

The defects on TB6 titanium alloy were repaired using pulsed TIG（tungsten inert gas） additive manufacturing technology， and the effects of process parameters （pulse current and pulse time） and heat treatment on the microstructure and mechanical properties of the repaired TB6 titanium alloy were studied to determine the optimal heat treatment process parameters. The results show that the mechanical properties are relatively better in the as-repaired state when the pulse current is 50 A and the pulse time is 40 ms， with a tensile strength of 1113 MPa and an elongation of 5.26%. These samples are sequentially subjected to solid solution and aging heat treatment. When the samples are solid solution treated for 2 h under different temperatures （740， 760， 780 ℃）， the primary α phase is increasingly dissolved， while the β phase gradually grows and evenly distributes in the matrix. After water quenching， the growth of the β phase is inhibited， and acicular rhombic martensite α'' phase is precipitated in β grains， resulting in a decrease in tensile strength and a remarkable increase in elongation. Under different aging temperatures （500， 520， 540 ℃） for 8 h， the α'' phase continuously grows and gradually transforms to equiaxial grain， and the mechanical properties are greatly improved. The optimal microstructure and mechanical properties are achieved under the conditions of 780 ℃/2 h WC+520 ℃/8 h AC， with a tensile strength of 1119 MPa and an elongation of 7.36%.

The flying-gangue hazard has long threatened the safety of personnel and mining equipment in steeply dipping/inclined coal seams. A flying gangue maybe produces a large number of “subgangues” after impacting on the longwall floor, it’s damage is sharply increased. Revealing the rupture response and fragmentation mechanism of the flying gangue under dynamic loading is one of the important contents to grasp the characteristics of dynamical damage for flying gangue hazard. This study was carried out through the combination of Split Hopkinson Pressure Bar test, model test and numerical simulation test. The results show: The failure mode of coal-rock under high-speed impact can be divided into three types: intact, single crack, and severe fragmentation. Fractal dimension D the coal specimen has a logarithmic relationship with the strain rate. Coal with lower uniaxial compression strength is more severely broken and has a larger fractal dimension D under the same strain rate conditions. The larger the coal size, the smaller the fractal dimension D is. The fractal dimension D of coal has a logarithmic relationship with strain rate regardless of whether the shape of the specimen is a disk or a cube. The “shape-rounded” phenomenon of the cube block is prone to appear during the movement. This phenomenon can be attributed to three parts: First, because the impact orientation between the block and slope or equipment is not strictly perpendicular compared with a drop test, the stress wave propagation within the specimen is dispersed to some extent during an impact. Second, because the block shape is not strictly regular, the stress is easily concentrated on a non-smooth surface. Finally, due to local shear failure in the contact area, a damaged area is formed. The corners of the flying-gangue are more susceptible to collision and direct loading, resulting in localized disintegration. This study provides a method for risk assessment and for determining the principles of protective systems in underground steep coal seams. In contrast, the flying-gangue involves material, geometry and other sources of uncertainty, stratification, fracture distribution and the degree of development by mining disturbance has a greater impact on the fractal characteristics of its crushing tends to be more complex. In the next step, the effect of the above factors on the crushing characteristics of the flying-gangue will be considered.

With the increasingly widespread application of optical communication technology in modern social production and life, the application scenarios of optical communication technology are becoming increasingly diverse and challenging. In energy sensitive scenarios such as data centers and aerospace, traditional digital coherent optical communication reception methods are difficult to reduce complexity and power consumption, and have obvious limitations. Analog coherent technology is a simplified coherent technology that utilizes analog devices to achieve part or all of the functions of the traditional digital coherent optical communication receiver, in order to simplify the receiver structure and reduce power consumption. Compared to traditional digital coherent optical communication receiving technology, analog coherent technology has obvious advantages in power consumption, complexity, and cost. This paper first introduces the typical architecture of analog coherent technology and demonstrates its main components. Then, the principles, specific structures, and implementation methods of each part are specifically introduced, including clock and carrier recovery, optical domain polarization demultiplexing (polarization rotation compensation), and analog adaptive equalization. Finally, other simplified coherent technology similar to analog coherent technology are introduced, including quasi coherent reception technology, Kramers-Kronig (KK) reception technology, and differential reception technology. Through the analysis of the principles and current research status of analog coherent and other simplified coherent techniques, this paper compares the advantages and disadvantages of various simplified coherent methods, elucidates the future development directions of simplified coherent technology, and provides a reference for future research on simplified coherent technology.

Experimental diagnostics is a crucial component of medical education, setting high demands on students' theoretical knowledge and practical skills. However, traditional teaching models is no longer suitable for the evolving needs of modern education.Through an in-depth analysis of the current state of experimental diagnostics education, this study identifies several issues inherent in traditional teaching models, such as a lack of diversity in teaching methods, insufficient emphasis on practical training, and inadequate development of experimental skills. The introduction of the college students' innovation and entrepreneurship training program offers a novel approach to teaching reform. Project-based practical teaching has shown significant improvements in students' practical abilities, research-oriented thinking, and teamwork skills. The study proposes a series of teaching reform recommendations, including the implementation of project-based teaching models, integration of multidisciplinary content, and enhancement of self-directed learning abilities, with the goal of comprehensively improving students' overall competence and innovation potential. These reform measures are anticipated to effectively advance the teaching of experimental diagnostics, foster students' innovative spirit and practical skills, and provide an effective pathway for training high-level innovative medical talents for the new era.

ObjectiveTo analyze the surveillance status of acute respiratory tract infection pathogens in Fengxian District of Shanghai, and to determine the pathogen spectrum and epidemiological characteristics of acute respiratory tract infection in the district.MethodsCase surveillance data from Fengxian Central Hospital were collected through the Shanghai Integrated Surveillance System for Acute Respiratory Infections, and respiratory specimens were collected from the 50th week of 2022 to the 49th week of 2023 for nucleic acid detection of 25 pathogens and bacterial culture testing. Case surveillance data from Fengxian Central Hospital were collected through the National Influenza Sentinel Surveillance System, and respiratory specimens were collected for nucleic acid detection of 5 pathogens.ResultsThe percentage seeking for medical visits of influenza-like illness (ILI) and severe acute respiratory infection (SARI) were 4.74% and 0.81%, respectively, with epidemic peaks in winter and spring. A total of 1 610 samples were detected, among which 354 were SARI cases, whose age of P25 and P75 quartiles were 44 years and 71 years, respectively, with a positive detection rate of 48.31%. Among the 1 256 ILI cases, the age of P25 and P75 quartiles were 21 years and 39 years, respectively. The positive detection rate of pathogens was 65.04% in 123 ILI cases under comprehensive surveillance of respiratory tract infection and 66.11% in 1 133 ILI cases under influenza surveillance. The positive rates of influenza and SARS-CoV-2 in SARI samples were 12.99% and 29.66%, respectively. The positive rates of influenza and SARS-CoV-2 in ILI case samples were 33.20% and 31.53%, respectively. Influenza virus concentrated in the winter and spring. The ILI and SARI cases had the highest positivity rates in the 6‒14 years old group and 15‒24 years old group, respectively.ConclusionThe main pathogens of respiratory tract infection in Fengxian District of Shanghai were SARS-CoV-2 and influenza virus. Special efforts should be made to monitor respiratory disease pathogens in the population under 24 years old, and surveillance of SARI cases aged ≥60 years should be strengthened. The influenza virus and SARS-CoV-2 are alternating, which is different from the past and requires a change in the previous prevention and control strategies.

ObjectiveTo analyze the level and trend of infant mortality rate (IMR) in Tongzhou from 2017 to 2023, and to provide a scientific basis for policies aimed at further reducing IMR.MethodsA descriptive epidemiological analysis was conducted on the causes of death and their rankings for 106 infant deaths with locally registered household between 2017 and 2023 in Tongzhou District. Chi-square test was used to compare the differences between groups, and the chi-square trend test was applied to analyze the year-by-year trend of relevant indicators.ResultsFrom 2017 to 2023, the IMR in Tongzhou District was 1.87‰, accounting for 72.11% of the deaths among children under 5 years old. A downward trend in IMR was observed from 2017 to 2023 (χ2trend=6.734, Ptrend=0.009). Early neonatal deaths accounted for 53.77% all neonatal deaths, with an early neonatal mortality rate of 1.01‰. The late neonatal mortality rate showed a declining trend (χ2trend=6.194, Ptrend=0.013). The IMR gender ratio of male to female was 1.25, and the IMR for females showed a declining trend (χ2trend=8.628, Ptrend=0.003). The top three causes of death were neonatal diseases, congenital anomalies, and pneumonia, accounting for 68.87% of all deaths. Among infants who died within the first three days, the primary causes of death were neonatal diseases and congenital anomalies, accounting for 92.31% of all deaths within this time frame. For infants aged 6 to 12 months, accidents and tumors were the leading causes of death, accounting for 50.00%.ConclusionEarly neonates, extremely premature infants, and very low birth weight infants are key groups for the prevention and reduction of infant mortality. It is crucial to standardize the management of high-risk pregnancies and explore the development of early warning assessments and graded management for high-risk fetuses.

ObjectiveTo investigate reinfection or the third time infection with SARS-CoV-2 among the people tested for positive from December 2022 to January 2023 and the influencing factors through a follow-up survey on previous novel coronavirus nucleic acid positive individuals between March to May, 2022.MethodsEpidemiological data of 2 583 novel coronavirus nucleic acid test positive cases were analyzed from March to May, 2022, following a follow-up survey at the 8th and 12th month after the first nucleic acid test positivity. Pearson chi-square method was used to analyze the differences of reinfection and the third time infection rates among first-positive patients with different characteristics. Kaplan-Meier survival analysis and Cox regression were used to analyze the influencing factors of reinfection.ResultsA total of 2 264 valid questionnaires were collected in the 8th month after nucleic acid tested positive, with a recovery rate of 87.7% and a reinfection rate of 9.7%. The third time infection was investigated among the individuals infected twice at the 12th month after the first nucleic acid test positivity, with a third time infection rate of 4.6%. The median interval (P25, P75) between reinfection and the first nucleic acid test positive for the novel coronavirus was 261 (252, 268) days and the interval (P25, P75) between the third time infection and reinfection was 135 (111, 157) days. Gender, age, occupation, smoking, drinking and underlying diseases were not statistically associated with the risk of reinfection (P>0.05). However, the the third time infection rate for medical staffs (20.0%) was higher than that for student /teachers (14.3%) and corporate employees (9.5%), with a statistically significant difference in the third infection rate between different occupations (P

ObjectiveTo construct an evaluation index system in accordance with the practical implementation of county-level medical treatment and prevention integration in China, and to provide guiding principles and evaluation tools for its scientific development. MethodsUsing the rainbow model as a theoretical foundation, a theoretical framework and a pool of candidate indicators for evaluating county-level medical-preventive integration were established through policy analysis and literature review. The Delphi method and analytic hierarchy process (AHP) were used to determine the index system and corresponding weights. ResultsThe response rates for the two rounds of expert consultation were 87% and 100%, respectively, with an average authority coefficient of 0.79. The coordination coefficients of the expert opinions were 0.222 and 0.359 (both P < 0.05). After two rounds of expert consultation, an evaluation index system was established, which included 6 first-level, 15 second-level, and 37 third-level indicators. ConclusionsThe construction of the index system was guided by appropriate theoretical principles, followed scientific methodology, adhered to systematic procedures, maintained rigorous processes, and produced reliable results to promote and enhance the development and sustainability of county-level medical-preventive integration in China.

At present, in the complex real-world application scenairos, the task of small face detection encounters numerous challenges, which include small face scale, abrupt lighting changes and low accuracy. In order to address the concerns of overlooking small face detection within existing models, this study introduced a novel small face detection model termed SK-YOLOv5s which was based on convolutional kernel attention mechanism. Firstly, we proposed a small face enhancement module to fuse and upsample multi-layer features, which enhances the resolution of small face feature maps and strengthens their distinctiveness. Subsequently, we incorporated the SKNet attention mechanism into the model, which can adaptively adjust receptive field sizes across multiple scales and enhance the detection efficacy of small face. Finally, EIoU was utilized as the loss function, which directly reduced the width and height discrepancies between predicted and actual bounding boxes, and FReLU was utilized as the activation function, which could enhance the nonlinear expressiveness of feature maps to improve the precision and stability of small face detection. The performance of the enhanced model on the WIDER FACE dataset demonstrated mean average precision improvement of 7.9% over YOLOv5s. The experimental results demonstrate the viability of the enhanced model for small face detection in real-world scenarios.

The application and funding were statistically analyzed in the area of information and communication system in 2024, including general programs, young scientists funds, funds for less developed regions of National Natural Science Foundation of China (NSFC) in 2024. Detailed statistics were provided among different institutions, professional titles, research topics and keyword frequencies. Key programs were analyzed in terms of application and funding statistics as well as of different research topics in recent five years. Moreover, ages and professional titles of applicants for excellent young scientist funds and national science funds for distinguished young scholar were also revealed, while the impact of the relevant NSFC reforms in 2024 on scholar funds was also analyzed. Finally, major programs and young student funds were briefly introduced, in order to provide landscape of NSFC in the area of information and communication systems and offer references for scientists’ applications in 2025.

South China （SC） is one of the regions with the most annual precipitation in China.Under the background of global warming， there had been significant changes in precipitation at regional and scale levels， the area of dry- and wet-season precipitation was expanding， and regional extreme precipitation events showed a significant upward trend in SC， but the changes and impacts of different types of precipitation were not the same.To better understand how different types of precipitation in SC respond to global warming， this paper investigated the characteristics of convective precipitation （CP） and large-scale precipitation （LSP） in SC based on ERA5 reanalysis precipitation dataset from 1960 to 2022 using linear correlation， trend analysis and wavelet analysis.The results showed that： （1） SC was dominated by LSP in winter while CP in other seasons.（2） CP and LSP in SC showed an increasing trend in winter， but most of CP showed a decreasing trend in other seasons.CP in SC had a relatively significant 2~4 years cycle from the 1980s to the beginning of the 21st century in winter， as well as there was an interdecadal characteristic of shifting from more to less in the 1990s to the beginning of the 21st century in spring， but the significant cyclical variations were mainly found in the period before the 1990s in autumn.In all seasons except winter， the changes in LSP were consistent with CP.（3） The total precipitation （TP） from April to October in Guangdong and Guangxi was mainly CP （CP accounts for about 65% of TP）， with the largest proportion in August （71.8% in Guangdong and 69.0% in Guangxi）.On the other hand， the proportion of CP in the first rainy season （up to 80% in May） was significantly higher than in the second rainy season in Hainan.Additionally， the proportion of CP remained at about 50% even in autumn and winter.（4） The diurnal variation intensity of CP and LSP was strongest in Hainan while the intensity of CP was the weakest in Guangxi.The peak periods of the strongest precipitation in Guangdong， Guangxi， and Hainan occured respectively from 13:00 to 17:00 （Beijing time， the sameas followed）， 15:00 to 17:00， and 14:00 to 16:00.The LSP peaked between 09:00 and 17:00 in Guangdong， and it started to strengthen from 04:00 and weakened in the afternoon， with the strongest period around 10:00 in Guangxi， for Hainan， the strongest period was 12:00 -17:00 before August， but 15:00 -16:00 after late September.In conclusion， the characteristics of precipitation varied with different seasons and types.Therefore， it was necessary to continue considering the impact of different types of precipitation in future research.

Objective To analysis disease burden and its change trend of benign prostatic hyperplasia (BPH) in China from 1990 to 2021. Methods Data from the Global Burden of Disease Study 2021 (GBD 2021) database were obtained to analysis the incidence, prevalence, disability-adjusted life years (DALYs) of BPH in China from 1990 to 2021. Assessing temporal trends in the burden of disease by calculating estimated annual percentage changes (EAPC) in age standardised rates and comparative analysis of trends in the burden of BPH in China over time by social demographic index (SDI) were conducted. Results In 2021, the incident cases of BPH in China were 3.244 million, with age-standardized incidence rate (ASIR) of 229.14 per 100,000. The prevalent cases were 2.311 million, with age-standardized prevalence rate (ASPR) of 2,315.23 per 100,000. The number of DALYs were 460,200 person year, with age-standardized DALYs rate (ASDR) of 45.84 per 100,000. The incidence, prevalence and DALYs rates of BPH in China in 2021 initially increased with age and then decreased, peaking in the 75-79 and 80-84 age groups, respectively. The ASIR [EAPC=-0.21, 95%CI(-0.36, -0.06)], ASPR [EAPC=-0.24, 95%CI(-0.41, -0.07)], and ASDR [EAPC=-0.25, 95%CI(-0.43, -0.09)] of BPH in China maintained a decreasing trend from 1990 to 2021, whereas the number of incident cases (122.16%), prevalent cases (132.50%), and DALYs (130.15%) showed an increasing trend. The ASIR, ASPR, and ASDR of BPH in China over the period 1990—2021 were much higher than those in high SDI countries. Conclusion The burden of BPH is high in China. Efforts should be made to enhance prostate health education for middle-aged and older men and to explore a BPH prevention and control system suitable for China's conditions. By promoting early detection and intervention, the progression of the disease can be delayed, reducing the overall disease burden.

The increasing penetration of distributed generation （DG） in distribution networks has exposed poor adaptation of conventional differential current protection schemes. To address this issue， an analysis of the positive-sequence current components output by various types of DGs during faults is conducted. A differential current protection scheme for active distribution networks with dual proportional compensation coefficients is proposed. The scheme constructs braking coefficients and compensation coefficients for fault current phasors， adjusting the phase and magnitude of the positive-sequence current fault components on both sides. This adjustment helps the positive-sequence current components move more easily into the action region of the amplitude-phase plane during internal faults， while ensuring reliable non-operation during external faults. Finally， the proposed protection scheme’s effectiveness under different fault conditions is tested using a model of an active distribution network established in PSCAD/EMTDC. The simulation results demonstrate that the proposed scheme improves both the sensitivity and reliability of differential current protection.

In engineering applications， the use of energy storage to support grid-forming photovoltaic （GFM PV） units faces challenges such as high construction costs and difficulties in land negotiations. However， without energy storage， grid-forming control struggles to find the operating point of maximum power point tracking （MPPT） during weather changes. To address this， a grid-forming control strategy that considers reserved power and integrates virtual synchronous control （VSC） is proposed. Additionally， a controller is designed to prevent DC bus voltage collapse， stabilizing the DC bus voltage. A control model for GFM PV units without energy storage， considering reserved power， is developed in Simulink， and simulations are conducted on the RT-LAB platform in conjunction with physical power electronics to obtain the simulation waveform. The simulation results demonstrate that the proposed strategy can find the operating point of MPPT in various scenarios， ensuring stable outputs of active and reactive power， thereby verifying the effectiveness of the control strategy.

Non-intrusive load monitoring plays a significant role in supporting applications such as load forecasting and demand response. To address the challenge of independently decomposing sub-equipment in industrial load users with production lines， a non-intrusive load monitoring （NILM） scheme is proposed， using the production line as the decomposition unit， based on the interlinked operation of equipment within the line. A factorized hidden Markov model （FHMM）， based on Gaussian mixture model （GMM）， is employed to achieve a fine-grained representation of load at the production line level. Additionally， a time-segmented state transition probability decomposition model is developed， leveraging the stable overall load patterns of industrial production lines， to further enhance load monitoring accuracy. Experimental results demonstrate that the proposed model significantly improves performance in both multi-state modeling and time segmentation， with load monitoring error metrics on some production lines ultimately reduced by nearly 20%.

Prior research has demonstrated significant deficits in sensory attenuation in patients with schizophrenia， which is a phenomenon that pertains to their inability to adequately modulate the intensity of sensory feedback associated with self-initiated voluntary actions. Sensory attenuation， a fundamental aspect of the sense of agency， is crucial for individuals to accurately perceive their own actions and effectively adapt to the external environment. Investigation on this phenomenon sheds light on the intricate mechanisms underlying sensory information processing deficits in patients with schizophrenia. This paper comprehensively examines the intricate relationship between abnormal sensory attenuation and the early identification of schizophrenia， delves into the underlying mechanisms that give rise to these abnormalities， and explores intervention strategies specifically designed to address them. Ultimately， our aim is to offer novel perspectives and insights that can contribute to the advancement of diagnosis and treatment of schizophrenia. ［Funded by Regional Project of National Natural Science Foundation of China （number， 32160202）］

Objective·To construct a cervical vertebra image segmentation model by using a diffusion model with the Transformer deep learning algorithm, and evaluate its segmentation performance, to address the clinical challenge of accurately assessing complex changes in skeletal morphology during the growth and developmental peaks of malocclusion.Methods·Accurate cervical vertebra segmentation was performed on cephalometric radiographs from 185 orthodontic patients (44 cases from the Stomatological Hospital of Chongqing Medical University and 141 cases from the Stomatological Hospital of Xi'an Jiaotong University) by using a method combining Transformer and diffusion models. First, the images were preprocessed to crop out the cervical vertebra region of interest, and all data were randomly divided into a training set (79.6%) and a test set (20.4%). The diffusion model and a conditional model based on U-Net were utilized for feature extraction, with a Transformer module introduced to learn the interaction between noise and semantic features. Multi-scale images were fused to enhance fine structure and boundary texture features in low-contrast images. The proposed method was compared with U-Net and SOLOv2 methods. The segmentation performance was quantitatively evaluated by two metrics, Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), and also qualitatively assessed through physicians' manual annotations and model visualization results.Results·The cervical vertebra segmentation method based on Transformer and diffusion models achieved DSC and IoU scores of 93.3% and 87.5%, respectively, significantly outperforming the U-Net and SOLOv2 methods (with improvements of 3.0% and 4.1% in DSC, and 5.2% and 7.1% in loU, respectively). Despite the longer processing time for a single image, segmentation accuracy was significantly improved. Compared with U-Net and SOLOv2, the proposed method also showed higher stability and robustness in processing complex, low-contrast and blurred-boundary images, and was able to accurately segment the cervical vertebrae with clear boundaries and complete structures.Conclusion·The Transformer-based diffusion model for cervical vertebra segmentation can enhance the edge and texture features in cervical vertebra images and recognize the boundaries of different vertebrae more easily. Thus, automatic, accurate, and robust cervical vertebra segmentation results are achieved, which can assist in cervical vertebral maturation analysis.

Magma intrusion into coal seams is widely existed in China, and the structure and components of the coal body are changed after intrusion to form a high-order metamorphic coal, but its spontaneous combustion characteristics are not clear. It is a great significance to explore the effect of magma intrusion on the low-temperature oxidization characteristics of coal, and to prevent and control spontaneous combustion disasters in mining magma-eroded coal seams. Taking the −350 working face in the second mining area of the Xin'an Coal Mine as the engineering background, the coal samples were collected at different distances from the magma intrusion on the spot. By testing the industrial composition and analyzing the differences in the surface morphology of the samples, it was concluded that magma intrusion metamorphosed the raw coal and increased the number of pores and fissures on the surface, mainly through contact thermal evolution. The metamorphic coals were categorized into strongly eroded coals, weakly eroded coals, and high rank coals of the homogeneous group. Based on fixed carbon, ash, specular group reflectance, and paleo temperature, the distribution range of different intrusive metamorphic coals was calculated by curve backpropagation, and the influence range of magmatic intrusion was predicted to be about 172.34%−234.68% of the width of the intrusive body. A comparative study of the oxidative thermal effect of magma intrusion on metamorphic coal and raw coal was carried out by using C80 microcalorimeter. And the results showed that under different air supply conditions, compared with the original coal, the initial exothermic and thermal equilibrium temperature points in the low-temperature oxidation stage were advanced by 0.2−3.4 ℃ and 1.9−18.2 ℃ for strongly eroded, weakly eroded, and homogeneous high-order coals, the total exothermic capacity was increased by 1.72%−135.75%, and the apparent activation energy was reduced by 1.62%−69.02%, and the closer the metamorphic coal was to the magma intrusion, the more oxidizing and exothermic it was, and the shorter the natural ignition period was. Under the condition of sufficient oxygen supply, magma intrusion enhanced the exothermic capacity of oxidation reaction of the strongly eroded coal within the range of 1.01−2.68 m from the magma intrusion, and its mining volume only accounted for about 6.23%−12.15% of the influence range of the magma intrusion. Therefore, when mining back into the magma intrusion area, the prevention and control of spontaneous combustion of strongly eroded coal should be strengthened, and the critical value of the coal spontaneous combustion forecast index in this area should be corrected in time, so as to guarantee the safe mining of coal seams.

The brake pad is one of the most critical safety part in the braking system, and it plays a vital role in ensuring drivers safety. In order to investigate the effect of braking conditions on the wear loss of disc brake pads, using Ansys Workbench simulation software, the wear simulation model was built based on the Archard wear model; Archard wear commands were added through the APDL command flow to realize the wear simulation of the brake pad, so as to analyze and calculate the wear loss of the brake pad. The brake pad friction and wear test bench was built and five groups of different braking conditions were randomly selected for brake pad wear test, and the test results were compared and analyzed with the simulation results to verify the correctness of the simulation model. The results show that within the set parameter range, the effect of the brake pressure on the wear loss of brake pad is smaller compared to the effect of the initial brake speed; without the intervention of the external heat source, the individual effect of the brake pad surface temperature on the wear loss is smaller compared to the combined effect of other parameters such as initial brake speed and brake pressure on the wear loss of the brake pad.

Zeolite adsorbents were synthesized through an alkali fusion-hydrothermal process, using coal fly ash as a raw material. Firstly, the coal fly ash was mixed with sodium hydroxide at a mass ratio of 1∶1.2 and then roasted at 850 ℃ for 2 h to obtain the alkali-fused ash. The effects of the Si/Al molar ratio of the precursor, reaction time, temperature, and liquid/solid ratio on the structure of the zeolite adsorbents derived from coal fly ash were investigated through single-factor experiments conducted during the hydrothermal reaction. The synthesized zeolite adsorbents derived from coal fly ash were systematically characterized using X-ray diffractometer (XRD), scanning electron microscope (SEM) and specific surface area and pore size analyzer (BET). Under the optimized conditions of a Si/Al molar ratio of the precursor set at 1.5, a reaction time of 16 h, a reaction temperature of 70 ℃, and a liquid/solid ratio of 8 mL/g in hydrothermal reaction, the relative mass fraction of type X zeolite in the synthesized product was 96.3%, the specific surface area was 489.9 m2/g, and the pore volume was 0.28 cm3/g. Subsequently, the gas adsorption performance of benzene, a typical volatile organic compound, by zeolite adsorbent derived from coal fly ash was investigated and compared with commercial activated carbon. The results showed that at an initial pollutant concentration of 800 μg/L, an adsorption temperature of 100 ℃, a gas flow rate of 200 mL/min, and an adsorbent loading of 0.1 g, the zeolite adsorbents derived from coal fly ash exhibited a saturated adsorption capacity of benzene at 41.5 mg/g, which was higher compared to commercial activated carbon (31.3 mg/g). This study provides a reference for the resource utilization of coal fly ash.

With China′s "dual carbon" goals continuously deepening, the wind power and photovoltaic industries are experiencing steady growth, with the new energy vehicle sector rapidly developing. Meanwhile, battery energy storage technology is nearing maturity. Recently, clean energy and new energy technologies have seen rapid development, with the application market continuing to expand. The new energy sector will soon be confronted with a large number of retired or scrapped products and equipment, posing significant challenges to achieving "zero-waste cities". These wastes have dual characteristics: They are both resources and environmental concerns, and are composed of complex, diverse materials. Recovering resources and controlling pollution from these wastes have become key research areas and challenges. This article reviews the latest research, industry practices, and challenges in recovering and utilizing renewable energy solid waste, comparing and analyzing the technological characteristics of recycling solar panels, wind turbine blades, and electric vehicle batteries. It identifies suitable technologies for different product recovery needs and outlines directions for developing resource recovery and pollution control technologies for renewable energy solid waste. The goal is to support the sustainable development of the renewable energy industry and help address the challenges of renewable energy waste management.

To address the ineffectiveness of single-base radar in suppressing adjoint main-lobe interference, an equivalent large-aperture array can be designed by deploying sparse auxiliary arrays to separate main-lobe interference from targets in the spatial domain. However, this method is prone to generating spatial grating lobes. To overcome this problem, this study proposes a dual-parameter iterative optimization framework comprising two parts: array configuration optimization and subarray element number optimization. Array configuration optimization caters to the number of subarray elements and creates nulls in the main-lobe interference direction on the basis of the minimum variance distortionless response criterion. To suppress grating lobes of the beam an improved adaptive genetic particle swarm algorithm is used to optimize the array configuration under constraints, such as aperture size, minimum subarray spacing, and null depth in the main-lobe interference direction. Subarray element number optimization uses the above-mentioned algorithm to optimize the number of subarray elements under constraints, such as a limited number of subarray elements and null depth in the main-lobe interference direction, further suppressing beam grating lobes. Finally, numerical simulations confirmed the effectiveness of the dual-parameter iterative optimization framework for array configuration and element number under the same parameter conditions. Additionally, this study explores the performance boundaries of main-lobe interference suppression and grating lobe suppression for typical distributed mobile platform cooperative detection scenarios.

The 12th annual meeting of China Earthquake Prediction Forum (CEPF), which was co-sponsored by Prediction Committee of China Seismological Society and Hainan Earthquake Agency, was held on Aug. 13—17, 2024 in Haikou City, Hainan Province. More than 130 persons from 17 universities (one from abroad), 7 institutes from Chinese Academy of Sciences and China Earthquake Administration (CEA), 23 Provincial Earthquake Agencies of CEA attended this conference. 13 special topics had been set up for academic exchange. 96 conference articles were collected, where the pier reviewed articles will be published by the journal of South China Earthquake in Supplementary Issue of 2024. During the 2 days room meeting, there were 26 orals and 25 posters presented, where 7 invited key note speakers presented the research current advances and future trend of earthquake prediction practice in China. There were 6 young presenters got excellent poster awards who obtained the permission of oral presentation in the following annual conferences of China Earthquake Prediction Forum. Popular science lectures on 1605 Hainan M7.5 earthquake and the disaster and monitoring techniques of volcano respectively were organized for the one day field training course held in the source area of Hainan M7.5 earthquake and the monitoring station of volcano creator in Hainan after the room meeting of this conference. A short panel discussion on how to enhance the capability of hunting precursors and prediction efficiency in high risk area was conducted. All the attendees enjoyed the successful conference both in scientific exchange and in onsite training courses.

Indica and Japonica rice were selected as test materials to investigate the difference formation in textural perception, particle size, water content, leakage solid content and reducing sugar content of rice bolus during chewing process were analyzed by using in vivo oral masticatory properties as a reference and setting salivary addition amounts, chewing times in vitro. The results showed that the three types of rice were similar in composition, but their gelatinization characteristics and crystalline structures were different. Monitoring the dynamic textural perception of rice, it was found that the force worked on rice decreased from 44~66 N to 20~21 N, Daohuaxiang No.2 rice had a higher chewiness and adhesiveness, while Yexiangyoulisi rice had a lower adhesiveness and loose texture perception during in vitro chewing. Through analyzing the characteristics of rice bolus in simulated chewing process, results indicated that the moisture content of rice bolus of Yexiangyoulisi rice increased by 1.84%, significantly higher than the other Japonica rice (P

In the dynamic field of the internet in modern life, networks are increasingly vulnerable to a diverse range of cyberattacks. Conventional intrusion detection systems based on machine learning techniques require a large number of samples for training. However, in some scenarios, only a limited number of malicious samples can be collected. To address the issue of insufficient training samples and unbalanced sample classes for intrusion detection system in real network environments, this paper proposes an intrusion detection method named DeepInsight–transfer learning–convolutional neural network (DI–TL–CNN), which is based on DI and TL. First, the DI method is used to convert the intrusion dataset into an image form suitable for CNN model input. The DI method can transform text while maintaining the semantic relationships between data points, thereby providing high-quality images. In this step, we map the 1D feature vector representation of the input data onto the 2D image representation using T-SNE and construct 2D grayscale images. In the second step, we train and optimize the VGG16 model through TL and fine-tuning, enhancing the model’s adaptability and performance. We propose six TL schemes by freezing and fine-tuning the parameters of different modules in the CNN model to enhance intrusion detection performance. In the TL process, the VGG16 model, pretrained on the ImageNet dataset, demonstrates promising results for generic image classification tasks. The bottom layers of CNN models often learn basic feature patterns that are applicable to various tasks, while the features acquired by the top layers of the model are specific to the target domain intrusion dataset. Fine-tuning allows the model to adjust the pretrained architecture’s higher-order features to better match the targeted dataset. During the training process, the bottom layers of the pretrained architecture are frozen, whereas the top layers are unfrozen for fine-tuning. The optimal intrusion detection model is determined through a comparison of the performance of the six TL schemes. Finally, the correctness and effectiveness of the proposed DI–TL–CNN method are validated on a dataset with insufficient training samples, using metrics such as accuracy, precision, recall, and F1-score. In the experiments, compared with existing state-of-the-art models for intrusion detection, the proposed method considerably enhances accuracy in the detection of network traffic data. The experimental results show that the DI–TL–CNN method is suitable for intrusion detection with small samples and unbalanced data, demonstrating the good application prospects of the method in complex networks.

Eriocheir sinensis, commonly known as the Chinese mitten crab, is widely distributed in China from the Liaohe River in the north to the Pearl River in the south and in the shallow waters of the estuary into the sea, among which the population in the Yangtze River system has the largest yield and the most delicious taste, and is favored by domestic consumers. It is an important breeding species in the Chinese fishing industry. Since artificial breeding began in the 1980s, the production of Chinese mitten crabs has increased, and this species has become one of the most economically important crabs in the world and the most economically important freshwater crab in China. From the perspective of aquaculture areas, the Yangtze River Basin region accounts for over 80% of the total output in China, with Jiangsu and Anhui provinces ranking first and third, respectively. Therefore, the culture of Chinese mitten crabs in Jiangsu and Anhui plays a pivotal role in the country. After more than 30 years of development, China has established ecological and large-scale breeding systems for juvenile crabs. Therefore, artificial seedlings dominate the market but are mainly distributed in coastal areas such as Jiangsu. However, to reduce production costs, some breeding farms blindly introduce Chinese mitten crabs to one another, and do not perform germplasm detection during breeding, causing germplasm resources to be random and mixed, which directly affects the quality of juvenile crabs and results in the risk of germplasm decline. To evaluate the genetic background of cultured Chinese mitten crab populations in Jiangsu and Anhui in odd-number years, and to provide a scientific basis for subsequent genetic improvement and breeding of new varieties, 10 pairs of highly polymorphic microsatellite markers (simple sequence repeats) were used to analyze the genetic diversity and genetic structure of six cultured populations with 177 individuals in total. Each population contained 28–30 adult individuals with an approximate sex ratio. The leg muscle of the sampled crabs was extracted, and genomic DNA was extracted according to the instructions of the genome extraction kit (TianGen DP304). The microsatellite primers were modified using fluorescent labels (FAM and HEX) prior to PCR, and the PCR products were subjected to capillary electrophoresis. Genemarker 2.2 software was used to interpret typing results. The number of alleles (Na), effective number of alleles (Ne), expected heterozygosity (He), observed heterozygosity (Ho), inbreeding coefficient (Fis), and genetic distance (Dn) were calculated using Popgene 1.32 software, and Hardy-Weinberg equilibrium analysis was performed using the Markov chain method. PIC_CALC 0.6 software calculates polymorphism information content (PIC). Analysis of molecular variance (AMOVA) and the coefficient of genetic differentiation (Fst) were performed using Arlequin 3.5 software. A UPGMA tree was constructed based on inter-population genetic distance using MEGA 4.0 software. According to the frequency of alleles, mutation-drift equilibrium was detected using Bottleneck software. Genetic structure analysis was performed using the Structure 2.3.4 software. The results showed that the genetic diversity levels of the six cultured populations of E. sinensis were high and similar (Na=16.0–18.4, Ne=10.1–12.4, Ho=0.759–0.836, He=0.897–0.916, PIC=0.870–0.892). Genetic distance among populations (0.015–0.277) and the coefficient of genetic differentiation (Fst: 0.001–0.011) were low, and genetic variation among populations accounted for only 0.47% of the total variation in AMOVA. These analyses consistently indicated no significant genetic differentiation among the populations. The phylogenetic tree showed that the six cultured populations had a common ancestor and that the Gaochun population had further relationships with the other populations. Owing to its high genetic diversity, the Gaochun population can be used as a base population for mass selection. Genetic structure analysis showed that the genetic composition of each cultured population was diverse, and the proportions were similar. In this study, the inbreeding coefficient (Fis) of 10 loci in six populations had 43 positive values and 17 negative values, and there were both positive and negative values in each population, indicating that there was not only inbreeding to a certain extent but also a small amount of distant breeding in the six cultured populations of Chinese mitten crabs.The results of genetic structure analysis showed that the number of optimal genetic cluster groups was K=3. However, the chaotic genetic structures among the three groups failed to gather into a relatively independent group. Furthermore, each population had a genetic composition similar to those of the three genetic lineage sources. Considering the high level of genetic diversity in these populations, it is possible that the parental population sources are more complex. This indicated that the cultured population of Chinese mitten crabs had a large number of exogenous genetic hybrids.In conclusion, the genetic diversity of cultured Chinese mitten crab populations in the Jiangsu and Anhui areas is still high, which has potential development and utilization value, but there may be germplasm confounding. Therefore, it is necessary to conduct further studies on the breeding population during the subsequent breeding of improved varieties, such as using molecular markers with high sensitivity that can be used to identify different water systems to analyze the sources of mixed germplasm, and conducting germplasm detection before breeding to purify the germplasm and make reasonable and sustainable use of its germplasm resources.

ObjectiveTo investigate the trends in diabetes mortality rate and the characteristics of decreased population in Fengxian District, Shanghai from 2012 to 2021.MethodsData from the death registration records of the residents in Fengxian District between 2012 and 2021, sourced from the Shanghai Death Surveillance System, were analyzed. Indicators such as the crude mortality rate due to diabetes, the standardized mortality rate, years of life lost (YLL), and the probability of premature death were estimated. Annual percentage change (APC) was used to analyze the temporal trends of mortality and the probability of premature death due to diabetes. Rate decomposition analysis was used to assess the contributions of demographic and non-demographic factors to diabetes mortality.ResultsFrom 2012 to 2021, there were 1 471 deaths due to diabetes in Fengxian District, with a crude mortality rate of 27.51/100 000 and a standardized mortality rate of 17.58/100 000. The crude mortality rate showed an overall increasing trend (APC=4.58%, Z=3.49, P

ObjectiveTo investigate the prevalence of abnormal bone mineral density (BMD) and its influencing factors among adults in Jiangsu province, to provide a reference for the development of prevention and control measures for osteoporosis. MethodsUsing stratified multistage cluster random sampling, we recruited 6 000 adult permanent residents (≥ 20 years old and living locally for at least 5 years) without disability or serious illness and, for women, not pregnant or breastfeeding, in urban and rural areas of Jiangsu province. Participants underwent a face-to-face interview with a self-designed questionnaire, physical examination, laboratory tests, and measurement of calcaneal BMD using the Pegasus ultrasound bone densitometer between October and November 2023. A multifactorial unconditional logistic regression model was used to analyze the main factors influencing abnormal BMD in the participants. ResultsA total of 5 786 adults were included in the final analysis. The numbers of participants with abnormal BMD, osteopenia, and osteoporosis were 1 280, 1 148, and 132, with prevalence rates of 22.12%, 19.84%, and 2.28%, respectively. Among 2 265 male participants, the numbers with abnormal BMD, osteopenia, and osteoporosis were 624, 560, and 64, with prevalence rates of 27.55%, 24.72%, and 2.83%, respectively; among 3 521 female participants, the numbers with abnormal BMD, osteopenia, and osteoporosis were 656, 588, and 68, with prevalence rates of 18.63%, 16.70%, and 1.93%, respectively. The prevalence rates of abnormal BMD, osteopenia, and osteoporosis were all significantly higher in male participants than in female participants (χ2 = 63.625, 55.792, and 4.944, all P < 0.05). Multivariate unconditional logistic regression analysis showed that age ≥ 60 years and history of fragility fracture were risk factors for abnormal BMD in male adults, whereas education of college degree or higher was a protective factor. Age ≥ 60 years, hypertension, and menopause were risk factors for abnormal BMD in female adults, whereas high school education and moderate physical work were protective factors. ConclusionsThe prevalence of abnormal BMD in adult residents of Jiangsu province is relatively low, and the prevalence of abnormal BMD is higher in male adult residents than in female adult residents. Age and education level are common factors influencing abnormal BMD in both male and female adult residents in the region.

Long oil and gas pipelines inevitably cross mountainous regions with complex geological environment and are threatened by various geological disasters, the most severe of which is landslides. To achieve early-warning of pipeline landslide hazards, multiple monitoring and early-warning indicators are typically established. However, the failure to account for the correlation between various indicators has led to a high frequency of false alarms. Based on the analysis of the dynamic evolutionary process of pipeline landslide hazards, this study proposes four stages of pipeline landslide hazards: (1) rainfall on the landslide leads to surface infiltration; (2) the groundwater level rises increasing the slope self-weight increases and reducing the geotechnical parameter; (3) the slope has undergone local deformation; (4) the landslide slides entirely and threatens pipeline safety. Three key monitoring indicators of landslide hazard deformations, external induced indicators, and pipeline stress-strain were extracted to construct a monitoring index system for pipeline landslide hazards. On this basis, a multifactor coupled early-warning model for pipeline landslide hazards is proposed. This model integrates the relationships between landslide deformation indicators, external triggers, and pipeline mechanical responses, creating a multidimensional early-warning criterion. This early-warning model was applied in a natural gas pipeline landslide in Guizhou Province, and successfully issued blue, yellow, and red early-warning information, providing sufficient time for emergency response and rescue of the landslide, ensuring the safe operation of the pipeline. This model can avoid the problem of high early-warning levels due to excessive rainfall, further improving the accuracy of pipeline landslide hazards early-warning and making the early-warning results more realistic. This study can provide a valuable reference for the monitoring and early-warning work on pipeline landslide hazards.

【Objective】High-temperature and rainy weather lead to pre-harvest sprouting (PHS), severely impacting yield and quality traits of rice. Identification and screening of germplasm or genetic resources is a fundamental pathway for developing new varieties resistant to PHS and eliminating PHS damage.【Method】In the study, a set of chromosome segment substitution lines (CSSLs) derived from Dongxiang wild rice ('C35') as the donor parent and 'Nipponbare' ('NIP') as the recipient parent were used as the experimental materials, and then PHS resistance were evaluated and QTLs were mapped in 2021-2023, with an aim to screen PHS germplasm and identify major QTLs.【Result】Dongxiang wild rice 'C35' exhibited strong dormancy under different environments with a pre-harvest sprouting rate (PHSR) of 0.00%; 'Nipponbare' showed significant PHS with an average PHSR of 31.95%. The PHSR varied widely among CSSL populations, the phenotypic repeatability of PHSR was relatively high in different years, and ten lines from the CSSL populations with strong dormancy and resistance to PHS were screened. A total of 14 QTLs controlling the PHSR were detected, and four QTLs were repeatedly detected under different environments. These QTLs formed four QTL clusters (qPHSRC1, qPHSRC2, qPHSRC8 and qPHSRC9), among which qPHSRC2 and qPHSRC9 had higher LOD values, phenotypic contribution rate (%) and additive effect, and qPHSRC2 was a newly discovered major QTL cluster.【Conclusion】A batch of PHS resistant germplasms were screened and 14 QTLs were mapped, four QTL clusters were repeatedly identified, and a new major QTL cluster qPHSRC2 controlling the PHSR was discovered.

[Objective]Spraying calcium can effectively prevent the occurrence of dry burning heart disease in Chinese cabbage. Accurately targeting spraying calcium can more effectively improve the utilization rate of calcium. Since the sprayer needs to move rapidly in the field, this can lead to over-application or under-application of the pesticide. This study aims to develop a targeted spray control system based on deep learning technology, explore the relationship between the advance speed, spray volume, and coverage of the sprayer, thereby addressing the uneven application issues caused by different nebulizer speeds by studying the real scenario of calcium administration to Chinese cabbage hearts.[Methods]The targeted spraying control system incorporates advanced sensors and computing equipment that were capable of obtaining real-time data regarding the location of crops and the surrounding environmental conditions. This data allowed for dynamic adjustments to be made to the spraying system, ensuring that pesticides were delivered with high precision. To further enhance the system's real-time performance and accuracy, the YOLOv8 object detection model was improved. A Ghost-Backbone lightweight network structure was introduced, integrating remote sensing technologies along with the sprayer's forward speed and the frequency of spray responses. This innovative combination resulted in the creation of a YOLOv8-Ghost-Backbone lightweight model specifically tailored for agricultural applications. The model operated on the Jetson Xavier NX controller, which was a high-performance, low-power computing platform designed for edge computing. The system was allowed to process complex tasks in real time directly in the field. The targeted spraying system was composed of two essential components: A pressure regulation unit and a targeted control unit. The pressure regulation unit was responsible for adjusting the pressure within the spraying system to ensure that the output remains stable under various operational conditions. Meanwhile, the targeted control unit played a crucial role in precisely controlling the direction, volume, and coverage of the spray to ensure that the pesticide was applied effectively to the intended areas of the plants. To rigorously evaluate the performance of the system, a series of intermittent spray tests were conducted. During these tests, the forward speed of the sprayer was gradually increased, allowing to assess how well the system responded to changes in speed. Throughout the testing phase, the response frequency of the electromagnetic valve was measured to calculate the corresponding spray volume for each nozzle.[Results and Conclusions]The experimental results indicated that the overall performance of the targeted spraying system was outstanding, particularly under conditions of high-speed operation. By meticulously recording the response times of the three primary components of the system, the valuable data were gathered. The average time required for image processing was determined to be 29.50 ms, while the transmission of decision signals took an average of 6.40 ms. The actual spraying process itself required 88.83 ms to complete. A thorough analysis of these times revealed that the total response time of the spraying system lagged by approximately 124.73 ms when compared to the electrical signal inputs. Despite the inherent delays, the system was able to maintain a high level of spraying accuracy by compensating for the response lag of the electromagnetic valve. Specifically, when tested at a speed of 7.2 km/h, the difference between the actual spray volume delivered and the required spray volume, after accounting for compensation, was found to be a mere 0.01 L/min. This minimal difference indicates that the system met the standard operational requirements for effective pesticide application, thereby demonstrating its precision and reliability in practical settings.[Conclusions]In conclusion, this study developed and validated a deep learning-based targeted spraying control system that exhibited excellent performance regarding both spraying accuracy and response speed. The system serves as a significant technical reference for future endeavors in agricultural automation. Moreover, the research provides insights into how to maintain consistent spraying effectiveness and optimize pesticide utilization efficiency by dynamically adjusting the spraying system as the operating speed varies. The findings of this research will offer valuable experiences and guidance for the implementation of agricultural robots in the precise application of pesticides, with a particular emphasis on parameter selection and system optimization.

[Objective]Apple Alternaria leaf spot can easily lead to premature defoliation of apple tree leaves, thereby affecting the quality and yield of apples. Consequently, accurately detecting of the disease has become a critical issue in the precise prevention and control of apple tree diseases. Due to factors such as backlighting, traditional image segmentation-based methods for detecting disease spots struggle to accurately identify the boundaries of diseased areas against complex backgrounds. There is an urgent need to develop new methods for detecting apple Alternaria leaf spot, which can assist in the precise prevention and control of apple tree diseases.[Methods]A novel detection method named Deep Semi-Non-negative Matrix Factorization-based Mahalanobis Distance Anomaly Detection (DSNMFMAD) was proposed, which combines Deep Semi-Non-negative Matrix Factorization (DSNMF) with Mahalanobis distance for robust anomaly detection in complex image backgrounds. The proposed method began by utilizing DSNMF to extract low-rank background components and sparse anomaly features from the apple Alternaria leaf spot images. This enabled effective separation of the background and anomalies, mitigating interference from complex background noise while preserving the non-negativity constraints inherent in the data. Subsequently, Mahalanobis distance was employed, based on the Singular Value Decomposition (SVD) feature subspace, to construct a lesion detector. The detector identified lesions by calculating the anomaly degree of each pixel in the anomalous regions. The apple tree leaf disease dataset used was provided by PaddlePaddle AI-Studio. Each image in the dataset has a resolution of 512×512 pixels, in RGB color format, and was in JPEG format. The dataset was captured in both laboratory and natural environments. Under laboratory conditions, 190 images of apple leaves with spot-induced leaf drop were used, while 237 images were collected under natural conditions. Furthermore, the dataset was augmented with geometric transformations and random changes in brightness, contrast, and hue, resulting in 1 145 images under laboratory conditions and 1 419 images under natural conditions. These images reflect various real-world scenarios, capturing apple leaves at different stages of maturity, in diverse lighting conditions, angles, and noise environments. This diversed dataset ensured that the proposed method could be tested under a wide range of practical conditions, providing a comprehensive evaluation of its effectiveness in detecting apple Alternaria leaf spot.[Results and Discussions]DSNMFMAD demonstrated outstanding performance under both laboratory and natural conditions. A comparative analysis was conducted with several other detection methods, including GRX (Reed-Xiaoli detector), LRX (Local Reed-Xiaoli detector), CRD (Collaborative-Representation-Based Detector), LSMAD (LRaSMD-Based Mahalanobis Distance Detector), and the deep learning model Unet. The results demonstrated that DSNMFMAD exhibited superior performance in the laboratory environment. The results demonstrated that DSNMFMAD attained a recognition accuracy of 99.8% and a detection speed of 0.087 2 s/image. The accuracy of DSNMFMAD was found to exceed that of GRX, LRX, CRD, LSMAD, and Unet by 0.2%, 37.9%, 10.3%, 0.4%, and 24.5%, respectively. Additionally, the DSNMFMAD exhibited a substantially superior detection speed in comparison to LRX, CRD, LSMAD, and Unet, with an improvement of 8.864, 107.185, 0.309, and 1.565 s, respectively. In a natural environment, where a dataset of 1 419 images of apple Alternaria leaf spot was analysed, DSNMFMAD demonstrated an 87.8% recognition accuracy, with an average detection speed of 0.091 0 s per image. In this case, its accuracy outperformed that of GRX, LRX, CRD, LSMAD, and Unet by 2.5%, 32.7%, 5%, 14.8%, and 3.5%, respectively. Furthermore, the detection speed was faster than that of LRX, CRD, LSMAD, and Unet by 2.898, 132.017, 0.224, and 1.825 s, respectively.[Conclusions]The DSNMFMAD proposed in this study was capable of effectively extracting anomalous parts of an image through DSNMF and accurately detecting the location of apple Alternaria leaf spot using a constructed lesion detector. This method achieved higher detection accuracy compared to the benchmark methods, even under complex background conditions, demonstrating excellent performance in lesion detection. This advancement could provide a valuable technical reference for the detection and prevention of apple Alternaria leaf spot.