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Abstract Background The management of male infertility continues to encounter an array of challenges and constraints, necessitating an in-depth exploration of novel therapeutic targets to enhance its efficacy. As an eight-carbon medium-chain fatty acid, octanoic acid (OCA) shows promise for improving health, yet its impact on spermatogenesis remains inadequately researched. Methods Mass spectrometry was performed to determine the fatty acid content and screen for a pivotal lipid component in the serum of patients with severe spermatogenesis disorders. The sperm quality was examined, and histopathological analysis and biotin tracer tests were performed to assess spermatogenesis function and the integrity of the blood-testis barrier (BTB) in vivo. Cell-based in vitro experiments were carried out to investigate the effects of OCA administration on Sertoli cell dysfunction. This research aimed to elucidate the mechanism by which OCA may influence the function of Sertoli cells. Results A pronounced reduction in OCA content was observed in the serum of patients with severe spermatogenesis disorders, indicating that OCA deficiency is related to spermatogenic disorders. The protective effect of OCA on reproduction was tested in a mouse model of spermatogenic disorder induced by busulfan at a dose 30 mg/kg body weight (BW). The mice in the study were separated into distinct groups and administered varying amounts of OCA, specifically at doses of 32, 64, 128, and 256 mg/kg BW. After evaluating sperm parameters, the most effective dose was determined to be 32 mg/kg BW. In vivo experiments showed that treatment with OCA significantly improved sperm quality, testicular histopathology and BTB integrity, which were damaged by busulfan. Moreover, OCA intervention reduced busulfan-induced oxidative stress and autophagy in mouse testes. In vitro, OCA pretreatment (100 µM) significantly ameliorated Sertoli cell dysfunction by alleviating busulfan (800 µM)-induced oxidative stress and autophagy. Moreover, rapamycin (5 µM)-induced autophagy led to Sertoli cell barrier dysfunction, while OCA administration exerted a protective effect by alleviating autophagy. Conclusions This study demonstrated that OCA administration suppressed oxidative stress and autophagy to alleviate busulfan-induced BTB damage. These findings provide a deeper understanding of the toxicology of busulfan and a promising avenue for the development of novel OCA-based therapies for male infertility.

In this work, a tapered fed circular-shaped monopole antenna for ultrawideband (UWB) applications integrated with four frequently-used band-notched properties is designed. By the design of notch structures inside the radiation patch, on the feedline, and on either side of the feedline, four notch bands covering from3.3–3.8 GHz, 5.25–5.75 GHz, 7.25–7.75 GHz, and 8.01–8.5 GHz are obtained possibly, which suppress the interference with WiMax, WLAN, satellite downlink and ITU applications, respectively. Based on these, the proposed antenna is operable from 1.4 GHz to 25 GHz with maximum coupling value of −20 dB covering almost operational band, and it has compact dimensions of $43\times 36\times 1.59$ mm3. Evolution and accomplishment of final results, involving the reduction of mutual coupling suppression, are illustrated gradually. The property of the monopole antenna considering reflection coefficient, isolation among different ports, realized gain, radiation pattern, efficiency, and envelope correlation coefficient (ECC) are studied. Moreover, experimental results are verified with the simulations. In addition, using the antenna inserted in chest badge, the chance of infection will greatly reduce. Ultimate, the proposed MIMO monopole antenna has a potential application in the medical, high precision machining, and biopharming relevant domains.

Abstract Background Polycystic ovary syndrome (PCOS) is a common reproductive endocrine disorder that frequently exhibits low-grade inflammation, pro-oxidant activity, and gut dysbiosis. PCOS has become one of the leading causes of female infertility worldwide. Recently, omega-3 polyunsaturated fatty acids (PUFAs) have been proven to benefit metabolic disorders in PCOS patients. However, its roles in the regulation of metabolic and endocrinal balances in PCOS pathophysiology are not clear. In the present study, we aimed to explore how omega-3 PUFAs alleviate ovarian dysfunction and insulin resistance in mice with dehydroepiandrosterone (DHEA)-induced PCOS by modulating the gut microbiota. Methods We induced PCOS in female mice by injecting them with DHEA and then treated them with omega-3 PUFAs. 16S ribosomal DNA (rDNA) amplicon sequencing, fecal microbiota transplantation (FMT) and antibiotic treatment were used to evaluate the role of microbiota in the regulation of ovarian functions and insulin resistance (IR) by omega-3 PUFAs. To further investigate the mechanism of gut microbiota on omega-3-mediated ovarian and metabolic protective effects, inflammatory and oxidative stress markers in ovaries and thermogenic markers in subcutaneous and brown adipose tissues were investigated. Results We found that oral supplementation with omega-3 PUFAs ameliorates the PCOS phenotype. 16S rDNA analysis revealed that omega-3 PUFA treatment increased the abundance of beneficial bacteria in the gut, thereby alleviating DHEA-induced gut dysbiosis. Antibiotic treatment and FMT experiments further demonstrated that the mechanisms underlying omega-3 benefits likely involve direct effects on the ovary to inhibit inflammatory cytokines such as IL-1β, TNF-α and IL-18. In addition, the gut microbiota played a key role in the improvement of adipose tissue morphology and function by decreasing multilocular cells and thermogenic markers such as Ucp1, Pgc1a, Cited and Cox8b within the subcutaneous adipose tissues. Conclusion These findings indicate that omega-3 PUFAs ameliorate androgen-induced gut microbiota dysbiosis. The gut microbiota plays a key role in the regulation of omega-3-mediated IR protective effects in polycystic ovary syndrome mice. Moreover, omega-3 PUFA-regulated improvements in the ovarian dysfunction associated with PCOS likely involve direct effects on the ovary to inhibit inflammation. Our findings suggest that omega-3 supplementation may be a promising therapeutic approach for the treatment of PCOS by modulating gut microbiota and alleviating ovarian dysfunction and insulin resistance.

Abstract In recent years, the postponement of childbearing has become a critical social issue. Male fertility is negatively associated with age because of testis aging. Spermatogenesis is impaired with age, but the molecular mechanism remains unknown. The dynamic posttranslational modification O-linked N-acetylglucosamine (O-GlcNAc), which is a type of monosaccharide modification, has been shown to drive the process of aging in various systems, but it has not yet been investigated in the testis and male reproductive aging. Thus, this study aims to investigate the alteration of O-GlcNAc with aging and explore the role of O-GlcNAc in spermatogenesis. Here, we demonstrate that the decline in spermatogenesis in aged mice is associated with elevation of O-GlcNAc. O-GlcNAc is specifically localized in differentiating spermatogonia and spermatocytes, indicating its crucial role in meiotic initiation and progression. Mimicking the age-related elevation of O-GlcNAc in young mice by disabling O-GlcNAcase (OGA) using the chemical inhibitor Thiamet-G can recapitulate the impairment of spermatogenesis in aged mice. Mechanistically, the elevation of O-GlcNAc in the testis leads to meiotic pachytene arrest due to defects in synapsis and recombination. Furthermore, decreasing O-GlcNAc in aged testes using an O-GlcNAc transferase (OGT) inhibitor can partially rescue the age-related impairment of spermatogenesis. Our results highlight that O-GlcNAc, as a novel posttranslational modification, participates in meiotic progression and drives the impairment of spermatogenesis during aging.

Abstract Background Exosomes are 50–150 nm endocytic vesicles secreted by almost all type of cells that carry bioactive molecules from host. These small vesicles are considered to be novel cross-talk circuits established by tumor cells and tumor microenvironment. Previous studies have shown certain biological influence of exosomal programmed cell-death ligand 1 (Exo-PD-L1) on immune suppression and dysfunction. The aim of the current study was to investigate the impact of Exo-PD-L1 and soluble PD-L1 (sPD-L1) on non-small cell lung cancer (NSCLC) and explore the concordance between Exo-PD-L1 and PD-L1 expression in matched tumor tissues in NSCLC patients. Methods 85 consecutive patients from April 2017 to December 2017 at General Hospital of Eastern Command Theatre who were primarily diagnosed with NSCLC and 27 healthy individuals were enrolled in this study. Two milliliters of whole blood samples were collected from each participant and further centrifuged. Exosomes were derived from serum using the commercial kit (Total Exosome Isolation Kit), which was further identified by Western blotting analysis (CD63/TSG101), transmission electron microscope analysis (TEM) and nanoparticle tracking analysis (NTA). Exosomes were next solubilized for Exo-PD-L1 detection by enzyme-linked immuno-sorbent assay (ELISA). PD-L1 expression in matched tissue were assessed by PD-L1 immunohistochemistry (IHC) (clone 28-8) assay. Tumor proportion score (TPS) ≥ 1% was deemed as “positive” in this study and TPS 2.5 cm) (p 2.5 cm) (p

Since remote sensing images are captured from the top of the target, such as from a satellite or plane platform, ship targets can be presented at any orientation. When detecting ship targets using horizontal bounding boxes, there will be background clutter in the box. This clutter makes it harder to detect the ship and find its precise location, especially when the targets are in close proximity or staying close to the shore. To solve these problems, this paper proposes a deep learning algorithm using a multiscale rotated bounding box to detect the ship target in a complex background and obtain the location and orientation information of the ship. When labeling the oriented targets, we use the five-parameter method to ensure that the box shape is maintained rectangular. The algorithm uses a pretrained deep network to extract features and produces two divided flow paths to output the result. One flow path predicts the target class, while the other predicts the location and angle information. In the training stage, we match the prior multiscale rotated bounding boxes to the ground-truth bounding boxes to obtain the positive sample information and use it to train the deep learning model. When matching the rotated bounding boxes, we narrow down the selection scope to reduce the amount of calculation. In the testing stage, we use the trained model to predict and obtain the final result after comparing with the score threshold and nonmaximum suppression post-processing. Experiments conducted on a remote sensing dataset show that the algorithm is robust in detecting ship targets under complex conditions, such as wave clutter background, target in close proximity, ship close to the shore, and multiscale varieties. Compared to other algorithms, our algorithm not only exhibits better performance in ship detection but also obtains the precise location and orientation information of the ship.

A compact antipodal Vivaldi antenna (AVA) integrated with circularly shaped loads and some elliptic slits etched in tapered slots is proposed in this paper. First, the elliptic slits etched in two tapered slots are employed for wideband application in X-band. Then, the value of max power capacity increasing from 0.47 MW to 0.82 MW is mainly due to two circularly shaped loads. Moreover, the size of the antenna is decreased to 0.44λmiddle x 0.42λmiddle. The configuration is measured to confirm the simulated results. Based on these, a novel antipodal Vivaldi antenna with compact size is successfully designed and applied in high-power field at X-band. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a severe global public health emergency that has caused a major crisis in the safety of human life, health, global economy, and social order. Moreover, COVID-19 poses significant challenges to healthcare systems worldwide. The prediction and early warning of infectious diseases on a global scale are the premise and basis for countries to jointly fight epidemics. However, because of the complexity of epidemics, predicting infectious diseases on a global scale faces significant challenges. In this study, we developed the second version of Global Prediction System for Epidemiological Pandemic (GPEP-2), which combines statistical methods with a modified epidemiological model. The GPEP-2 introduces various parameterization schemes for both impacts of natural factors (seasonal variations in weather and environmental impacts) and human social behaviors (government control and isolation, personnel gathered, indoor propagation, virus mutation, and vaccination). The GPEP-2 successfully predicted the COVID-19 pandemic in over 180 countries with an average accuracy rate of 82.7%. It also provided prediction and decision-making bases for several regional-scale COVID-19 pandemic outbreaks in China, with an average accuracy rate of 89.3%. Results showed that both anthropogenic and natural factors can affect virus spread and control measures in the early stages of an epidemic can effectively control the spread. The predicted results could serve as a reference for public health planning and policymaking.

Abstract The high stretchability of two-dimensional (2D) materials has facilitated the possibility of using external strain to manipulate their properties. Hence, strain engineering has emerged as a promising technique for tailoring the performance of 2D materials by controlling the applied elastic strain field. Although various types of strain engineering methods have been proposed, deterministic and controllable generation of the strain in 2D materials remains a challenging task. Here, we report a nanoimprint-induced strain engineering (NISE) strategy for introducing controllable periodic strain profiles on 2D materials. A three-dimensional (3D) tunable strain is generated in a molybdenum disulfide (MoS2) sheet by pressing and conforming to the topography of an imprint mold. Different strain profiles generated in MoS2 are demonstrated and verified by Raman and photoluminescence (PL) spectroscopy. The strain modulation capability of NISE is investigated by changing the imprint pressure and the patterns of the imprint molds, which enables precise control of the strain magnitudes and distributions in MoS2. Furthermore, a finite element model is developed to simulate the NISE process and reveal the straining behavior of MoS2. This deterministic and effective strain engineering technique can be easily extended to other materials and is also compatible with common semiconductor fabrication processes; therefore, it provides prospects for advances in broad nanoelectronic and optoelectronic devices.

Glycosphingolipids (GSLs) are abundant glycolipids on cells and essential for cell recognition, adhesion, signal transduction, and so on. However, their lipid anchors are not long enough to cross the membrane bilayer. To transduce transmembrane signals, GSLs must interact with other membrane components, whereas such interactions are difficult to investigate. To overcome this difficulty, bifunctional derivatives of II3-β-N-acetyl-D-galactosamine-GA2 (GalNAc-GA2) and β-N-acetyl-D-glucosamine-ceramide (GlcNAc-Cer) were synthesized as probes to explore GSL-interacting membrane proteins in live cells. Both probes contain photoreactive diazirine in the lipid moiety, which can crosslink with proximal membrane proteins upon photoactivation, and clickable alkyne in the glycan to facilitate affinity tag addition for crosslinked protein pull-down and characterization. The synthesis is highlighted by the efficient assembly of simple glycolipid precursors followed by on-site lipid remodeling. These probes were employed to profile GSL-interacting membrane proteins in HEK293 cells. The GalNAc-GA2 probe revealed 312 distinct proteins, with GlcNAc-Cer probe-crosslinked proteins as controls, suggesting the potential influence of the glycan on GSL functions. Many of the proteins identified with the GalNAc-GA2 probe are associated with GSLs, and some have been validated as being specific to this probe. The versatile probe design and experimental protocols are anticipated to be widely applicable to GSL research.

Tumor mutation burden (TMB) has emerged as an essential biomarker for assessing the efficacy of cancer immunotherapy. However, due to the inherent complexity of tumors, TMB is not always correlated with the responsiveness of immune checkpoint inhibitors (ICIs). Thus, refining the interpretation and contextualization of TMB is a requisite for enhancing clinical outcomes. In this study, we conducted a comprehensive investigation of the relationship between TMB and multi-omics data across 33 human cancer types. Our analysis revealed distinct biological changes associated with varying TMB statuses in STAD, COAD, and UCEC. While multi-omics data offer an opportunity to dissect the intricacies of tumors, extracting meaningful biological insights from such massive information remains a formidable challenge. To address this, we developed and implemented the PGLCN, a biologically informed graph neural network based on pathway interaction information. This model facilitates the stratification of patients into subgroups with distinct TMB statuses and enables the evaluation of driver biological processes through enhanced interpretability. By integrating multi-omics data for TMB prediction, our PGLCN model outperformed previous traditional machine learning methodologies, demonstrating superior TMB status prediction accuracy (STAD AUC: 0.976 ± 0.007; COAD AUC: 0.994 ± 0.007; UCEC AUC: 0.947 ± 0.023) and enhanced interpretability (BA-House: 1.0; BA-Community: 0.999; BA-Grid: 0.994; Tree-Cycles: 0.917; Tree-Grids: 0.867). Furthermore, the biological interpretability inherent to PGLCN identified the Toll-like receptor family and DNA repair pathways as potential combined biomarkers in conjunction with TMB status in gastric cancer. This finding suggests a potential synergistic targeting strategy with immunotherapy for gastric cancer, thus advancing the field of precision oncology.

Wafer-scale fabrication of periodic nanostructures with spatially modulated feature sizes is achieved by combining interference lithography and grayscale-patterned secondary exposure, opening new pathways toward large-area nanophotonic devices.

Pay for performance, as one of the most important means of motivating employees, has attracted the attention of many scholars and managers. However, controversy has continued regarding whether it promotes or undermines job performance. Drawing on a meta-analysis of 108 independent samples (N = 71,438) from 100 articles, we found that pay for performance was positively related to job performance. That pay for performance had a more substantial positive effect on task performance than contextual performance in workplace settings. From the cognitive evaluation perspective, we found that pay for performance enhanced employees' task performance and contextual performance by enhancing intrinsic motivation and weakened task performance and contextual performance by increasing employee pressure. From the equity perspective, our results indicated that the relationship between pay for performance and task performance was partially mediated by employee perceptions of distributive justice and procedural justice, with distributive justice having a more substantial mediating effect than procedural justice. However, the relationship between pay for performance and contextual performance was only partially mediated by procedural justice. Further tests of moderating effects indicated that the varying impacts of pay for performance are contingent on measures of pay for performance and national culture. The findings contributed to understanding the complex mechanisms and boundary conditions of pay-for-performance's effects on job performance, which provided insights for organizations to maximize its positive effects.

Rhus chinensis Mill. (RCM) is the host plant of Galla chinensis, which is valued in traditional medicine. Environmental temperature directly determines the probability of gallnut formation and RCM growth. At present, there is no experiment to systematically analyse the stability of internal reference gene (RG) expression in RCM. In this experiment, leaves that did not form gallnuts were used as the control group, while leaves that formed gallnuts were used as the experimental group. First, we conducted transcriptome experiments on RCM leaves to obtain 45 103 differential genes and functional enrichment annotations between the two groups. On this basis, this experiment established a transcriptional gene change model of leaves in the process of gallnut formation after being bitten by aphids, and RCM reference candidate genes were screened from RNA sequencing (RNA‐seq) data. This study is based on RCM transcriptome data and evaluates the stability of 11 potential reference genes under cold stress (4 °C) and heat stress (34 °C), using three statistical algorithms (geNorm, NormFinder, and BestKeeper). The results show that GAPDH1 + PP2A2/UBQ are stable reference genes under heat stress, while GAPDH1 + ACT are the most stable under cold stress. This study is the first to screen candidate reference genes in RCM and could help guide future molecular studies in this genus.

India suffered from a devastating 2021 spring outbreak of coronavirus disease 2019 (COVID-19), surpassing any other outbreaks before. However, the reason for the acceleration of the outbreak in India is still unknown. We describe the statistical characteristics of infected patients from the first case in India to June 2021, and trace the causes of the two outbreaks in a complete way, combined with data on natural disasters, environmental pollution and population movements etc. We found that water-to-human transmission accelerates COVID-19 spreading. The transmission rate is 382% higher than the human-to-human transmission rate during the 2020 summer outbreak in India. When syndrome coronavirus 2 (SARS-CoV-2) enters the human body directly through the water-oral transmission pathway, virus particles and nitrogen salt in the water accelerate viral infection and mutation rates in the gastrointestinal tract. Based on the results of the attribution analysis, without the current effective interventions, India could have experienced a third outbreak during the monsoon season this year, which would have increased the severity of the disaster and led to a South Asian economic crisis.

Anthropogenic dust (AD), as a crucial component of particulate matter, is defined as dust emitted through modifying or disturbing soil particles directly or indirectly associated with human activities in urban areas, croplands, pasturelands and dry lakes. The sources, characteristics, and impacts of AD remain poorly studied, in contrast to the large body of research on natural dust (ND). This review summarizes scientific findings published since the 1990s regarding the emissions, physical-chemical characteristics, and spatio-temporal distributions of AD from the micro to the global scale. AD accounts for 5%–60% of the global dust loading, with notable spread in existing estimates. Compared with ND, AD has more complex and variable compositions and physical-chemical properties. Influenced by human disturbances, AD exhibits small particle sizes, easily accessible critical friction velocity, and large emissions. Further research should improve the observations and simulations to investigate the complex interactions among AD, climate change, and human health.

In the past two decades, the post-genomic era envisaged high-throughput technologies, resulting in more species with available genome sequences. In-depth multi-omics approaches have evolved to integrate cellular processes at various levels into a systems biology knowledge base. Metabolomics plays a crucial role in molecular networking to bridge the gaps between genotypes and phenotypes. However, the greater complexity of metabolites with diverse chemical and physical properties has limited the advances in plant metabolomics. For several years, applications of liquid/gas chromatography (LC/GC)-mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been constantly developed. Recently, ion mobility spectrometry (IMS)-MS has shown utility in resolving isomeric and isobaric metabolites. Both MS and NMR combined metabolomics significantly increased the identification and quantification of metabolites in an untargeted and targeted manner. Thus, hyphenated metabolomics tools will narrow the gap between the number of metabolite features and the identified metabolites. Metabolites change in response to environmental conditions, including biotic and abiotic stress factors. The spatial distribution of metabolites across different organs, tissues, cells and cellular compartments is a trending research area in metabolomics. Herein, we review recent technological advancements in metabolomics and their applications in understanding plant stress biology and different levels of spatial organization. In addition, we discuss the opportunities and challenges in multiple stress interactions, multi-omics, and single-cell metabolomics.

Identification of kinase substrates is a prerequisite for elucidating the mechanism by which a kinase transduces internal or external stimuli to cellular responses. Conventional methods to profile this type of protein-protein interaction typically deal with one kinase-substrate pair at a time. Mass spectrometry-based proteomics, on the other hand, can determine putative kinase-substrate pairs at a large-scale in an unbiased manner. In this study, we identified the interacting partners of SNF1-related protein kinase 2.4 (SnRK2.4) via immunoprecipitation coupled with mass spectrometry. Proteins from stable transgenic Arabidopsis plants overexpressing a FLAG-tagged SnRK2.4 (cloned from Brassica napus) were pulled down using an anti-FLAG antibody. The protein components were then identified by mass spectrometry. In parallel, proteins from wild type plants were also analyzed, providing a list of nonspecific binding proteins that were further removed from the candidate SnRK2.4-interacting protein list. Our data identified over 30 putative SnRK2.4 interacting partners, which included many key players in stress responses, transport, and cellular metabolic processes.

In contrast to emotional and behavioral problems (EBPs), which can disrupt normal adolescent development, resilience can buffer the effects of stress and adverse childhood experiences and can help youth overcome adversity. While research has looked at the relationship between adolescent resilience and EBPs, current literature relatively lack a discussion of a strengths-based approach of resilience framework, nor discuss non-western sociocultural contexts. In this study, we utilized the resilience theory to examine the effects of individual mindfulness and life skills on resilience and consequently on EBPs in a group of low-income and gifted adolescents in China. A secondary data of 152 adolescents from a specialized school for low-income and gifted students in Guangzhou, China was used for the analysis. The findings from structural equation modeling indicated that mindfulness and life skills were associated with heightened resilience and reduced EBPs. In addition, resilience reduced EBPs for this group of adolescents. These findings underscore the promise of mindfulness and life skills training on increasing resilience and reducing EBPs in gifted adolescents.

Mitogen-activated protein kinase 4 (MPK4) was first identified as a negative regulator of systemic acquired resistance. It is also an important kinase involved in many other biological processes in plants, including cytokinesis, reproduction, and photosynthesis. Arabidopsis thaliana mpk4 mutant is dwarf and sterile. Previous omics studies including genomics, transcriptomics, and proteomics have revealed new functions of MPK4 in different biological processes. However, due to challenges in metabolomics, no study has touched upon the metabolomic profiles of the mpk4 mutant. What metabolites and metabolic pathways are potentially regulated by MPK4 are not known. Metabolites are crucial components of plants, and they play important roles in plant growth and development, signaling, and defense. Here we used targeted and untargeted metabolomics to profile metabolites in the wild type and the mpk4 mutant. We found that in addition to the jasmonic acid and salicylic acid pathways, MPK4 is involved in polyamine synthesis and photosynthesis. In addition, we also conducted label-free proteomics of the two genotypes. The integration of metabolomics and proteomics data allows for an insight into the metabolomic networks that are potentially regulated by MPK4.

Deep convolutional neural network model has achieved excellent results in many computer vision applications. How to use deep learning technology to complete auxiliary guidance and aiming points positioning in complex battlefield environment is the key for army to win the modern information war. To solve this problem, this paper proposes a recognition algorithm for types and aiming point of the time-sensitive target to improve the quality of time-sensitive target detection, and provides the strike value of various components of military resources for subsequent modules. This algorithm redesigns the YOLOv3 trunk network and uses the residual block of the depth-wise separable convolutional neural network to extract the features of the input image, then sents the obtained feature maps into the attention model, and assigns corresponding weights to feature maps with important semantic information such as target components. Finally, feature maps processed by the attention model is sent into the regression network for the recognition of time-sensitive target types and aiming points. The experimental results on COCO and VOC data sets show that the feature extraction network and attention module used in this algorithm effectively improve the mean average precision of deep convolutional neural network in common target detection. The experimental results on the data set of the battlefield military resource model established in this paper show that this algorithm can accurately recognize the aiming points of non-cooperative time-sensitive targets.