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An NDVI-constrained multi-source remote sensing data fusion network for detecting the level of larch caterpillar(Dendrolimus superans)infestation 认领 引用
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作者 WU Linlin WANG Mingchang +1 位作者 WANG Fengyan MAO Dehua 《Journal of Mountain Science》 SCIE CSCD 2026年第7期3170-3191,共22页
Outbreaks of the larch caterpillar(Dendrolimus superans)cause severe ecological and economic damage to boreal forests,underscoring the urgent need for effective monitoring and early warning systems.However,the utility... Outbreaks of the larch caterpillar(Dendrolimus superans)cause severe ecological and economic damage to boreal forests,underscoring the urgent need for effective monitoring and early warning systems.However,the utility of space-borne multispectral imagery(MSI)for this purpose is often constrained by either coarse spatial resolution or insufficient spectral bands,limiting the accurate classification of pest occurrence levels.To address this challenge,we developed an NDVI-constrained Dynamic Ridge Polynomial Neural Network(NDRPNN)to fuse Sentinel-2 MSI data with Gaofen-2(GF-2)panchromatic imagery,thereby enhancing spatial detail while preserving spectral integrity.Timeseries spectral,textural,and polarimetric features derived from Sentinel-1/2 imagery were subsequently integrated,and correlation analysis was applied to identify the most sensitive indicators.Four classification models—Random Forest,Light Gradient Boosting Machine,Stacking Ensemble,and Soft Voting Ensemble(SVE)—were evaluated for detecting infestation levels,with Shapley(SHAP)analysis employed to interpret feature contributions.The NDRPNN exhibited robust fusion performance in forested landscapes.Ensemble methods outperformed single classifiers,with the SVE model achieving the highest accuracy(overall accuracy=87.6%,Kappa=0.83).SHAP analysis identified the mean and maximum Normalized Difference Vegetation Index(NDVI),minimum Anthocyanin Reflectance Index(ARI),minimum Normalized Burn Ratio(NBR),and seasonal amplitude of Enhanced Vegetation Index(EVI)as key contributing features,highlighting the critical role of time-series vegetation indices and textural metrics in early pest detection.This study demonstrates that the integration of high-quality Sentinel-2 and GF-2 imagery with ensemble learning enables rapid and precise assessment of pest occurrence,offering a robust foundation for the early warning and scientific management of forest pests in mountain regions. 展开更多
关键词 Larch caterpillar infestation Multisource data fusion Pansharpening Ensemble learning Early detecting
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Utility of the Vulnerable Elders Survey-13(VES-13) in detecting frailty and predicting prognosis in heart failure outpatients 认领 引用
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作者 Thaïs Roig Elisabet Zamora +15 位作者 Josep Lupón Beatriz González Ana Pulido Eva Crespo Patricia Velayos Carmen Rivas Violeta Díaz Yolanda López Andrea Borrellas Mar Domingo María Ruiz Pau Codina Evelyn Santiago-Vacas MiquelÀMas Ramón Miralles Antoni Bayes-Genis 《Journal of Geriatric Cardiology》 SCIE CAS CSCD 2026年第1期17-26,共10页
Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screenin... Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screening tool in HF outpatients. In this study, we prospectively evaluated VES-13 against a multimodal screening assessment in detecting frailty and predicting individual risk of adverse prognosis.Methods Frailty was assessed at the initial visit using both a multimodal approach, incorporating Barthel Index, Older American Resources and Services scale, Pfeiffer Test, abbreviated Geriatric Depression Scale, age > 85 years, lacking support systems,and VES-13. Patients scoring ≥ 3 on VES-13 or meeting at least one multimodal criterion were classified as frail. Endpoints included all-cause mortality, a composite of death or HF hospitalization, and recurrent HF hospitalizations.Results A total of 301 patients were evaluated. VES-13 identified 40.2% as frail and the multimodal assessment 33.2%. In Cox regression analyses, frailty identified by VES-13 showed greater prognostic significance than the multimodal assessment for allcause mortality(HR = 3.70 [2.15–6.33], P < 0.001 vs. 2.40 [1.46–4.0], P = 0.001) and the composite endpoint(HR = 3.13 [2.02–4.84], P< 0.001 vs. 1.96 [1.28–2.99], P = 0.002). Recurrent HF hospitalizations were four times more frequent in VES-13 frail patients while two times in those identified as frail by the multimodal assessment. Additionally, stratifying patients by VES-13 tertiles provided robust risk differentiation.Conclusions VES-13, a simple frailty tool, outperformed a comprehensive multimodal assessment and could be easily integrated into routine HF care, highlighting its clinical utility in identifying patients at risk for poor outcomes. 展开更多
关键词 barthel i frailty heart failure hf Heart Failure multimodal screening assessment multimodal approach detecting frailty Vulnerable Elders Survey
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Comparing line transect and Indian White-eye mobbing call playback for detecting forest birds in subtropical Yunnan 认领 引用
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作者 Yueqiang Liu Zehao Shen Fumin Lei 《Avian Research》 SCIE CAS CSCD 2026年第2期478-488,共11页
Detection bias in avian monitoring is a critical constraint on the accurate assessment of community structure.This study presents a detailed field-based comparative case study between the playback of the Indian White-... Detection bias in avian monitoring is a critical constraint on the accurate assessment of community structure.This study presents a detailed field-based comparative case study between the playback of the Indian White-eye(Zosterops palpebrosus)mobbing calls—which documents only those bird species and individuals that approached and responded to the playback stimulus—and the conventional line transect method,which records all birds detected visually or acoustically along the transect.The study was conducted in subtropical semi-humid evergreen broad-leaved forests on the central Yunnan Plateau.We conducted 23 replicate surveys using both methods and employed multivariate regression trees to systematically evaluate the detection efficiency of the two methods and their dominant influencing factors.The results indicate the following:(1)The two methods exhibit functional complementarity:the line transect method shows significant advantages in total species richness(84 vs.51 species)and the detection of larger-bodied birds(body mass≥15 g),while the specific mobbing call playback is more efficient for small-bodied species(particularly the 0–10 g group)and insectivorousectarivorous functional guilds.The combined application of both methods can increase the species detection rate by up to 46.9%;(2)Detection efficiency varies seasonally,with significant differences between the two methods during the non-breeding season(p<0.05)but no significant difference in the breeding season,reflecting the impact of seasonal changes in avian behavior;(3)Regression tree analysis reveals a hierarchical decision pathway governing detection differences:body mass is the primary differentiating factor(with a threshold of 15 g),followed by habitat type,and finally trophic niche.Specifically,detection rate differences for larger-bodied birds(≥15 g)are more pronounced in shrub and coniferous forests,while small-bodied birds(<15 g)respond more actively to playback in forest habitats,with insectivorous small birds showing the most obvious response.The phylogenetic signal is extremely weak(Blomberg's K=0.057),further indicating that functional traits rather than phylogenetic relationships dominate interspecific differences in responses to this specific mobbing call.This study provides an empirical case for targeted monitoring using specific mobbing calls and a basis for optimizing avian survey protocols combining multiple methods. 展开更多
关键词 Avian community Detection bias Functional traits Line transect method Mobbing call playback method Phylogenetic conservatism Species detection rate
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A Method for Detecting Spatio-Temporal Correlation Anomalies of WSN Nodes Based on Topological Information Enhancement and Time-Frequency Feature Extraction 认领 引用
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作者 Miao Ye Ziheng Wang +4 位作者 Qiuxiang Jiang Xingsi Xue Wenxi Liu Yu Ning Cheng Zhu 《Computers, Materials & Continua》 SCIE EI 2026年第8期1840-1870,共31页
In recent years,anomaly detection in Wireless Sensor Networks(WSNs)has been widely studied using Graph Neural Networks and Transformer-based methods.However,in multi-node and multi-modal data scenarios,these approache... In recent years,anomaly detection in Wireless Sensor Networks(WSNs)has been widely studied using Graph Neural Networks and Transformer-based methods.However,in multi-node and multi-modal data scenarios,these approaches still face challenges such as insufficient extraction of spatiotemporal correlation features,limited modeling capabilities when relying solely on either time-domain or frequency-domain information,and high computational overhead.To address these issues,this work aims to develop an anomaly detection model that balances detection performance with computational efficiency,enabling effective identification of complex anomaly patterns.Specifically,we propose a time–frequency feature extraction method with topological information enhancement,topology-enhanced multi-modal spatio-temporal anomaly detection(TE-MSTAD).Building upon the Receptance Weighted Key Value(RWKV)model with linear complexity,a cross-modal feature extraction module is introduced to strengthen the modeling of multi-modal correlations.Meanwhile,adaptive adjacency matrices are constructed by integrating time–frequency features and combining outputs from different Graph Neural Networks,thereby enhancing topological information.Furthermore,a dual-branch structure is designed to jointly model time-domain and frequency-domain features,improving the extraction of complex anomaly characteristics.Experiments on both publicly available datasets and real-world collected data demonstrate that the proposed method achieves F1-scores of 92.52%and 93.28%,respectively,outperforming existing methods in detection performance and generalization capability. 展开更多
关键词 Wireless sensor networks anomaly detection time-frequency domain fusion graph neural networks information enhancement
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Detecting subtle myocardial injury after percutaneous coronary intervention:Insights from electrocardiography and heart rate variability analysis 认领 引用
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作者 Maryam Salimi Khashayar Hematpour 《World Journal of Cardiology》 2026年第6期1-6,共6页
Percutaneous coronary intervention(PCI)remains one of the most effective therapies for coronary artery disease,yet even technically successful procedures can place short-lived or mild stress on the myocardium.In routi... Percutaneous coronary intervention(PCI)remains one of the most effective therapies for coronary artery disease,yet even technically successful procedures can place short-lived or mild stress on the myocardium.In routine practice,this early myocardial stress often goes unnoticed because standard 12-lead electrocardiography(ECG)and peri-procedural biomarkers are not well suited to detect small,transient,or patchy injury.In a recent observational study by Chaikovsky et al,published in World Journal of Cardiology,explored whether a more detailed ECG-based analysis could uncover these subtle changes.By integrating more than 240 ECG and heart rate variability parameters into composite indices,they identified distinct physiological response patterns in a small cohort of patients undergoing PCI.One subgroup demonstrated post-procedural changes suggestive of mild myocardial injury,including altered ventricular repolarization,increased electrical instability,and reduced autonomic balance–findings that were not captured by conventional ECG interpretation systems.These observations are preliminary and hypothesis-generating,but they highlight the potential of advanced ECG-heart rate variability analytics as a sensitive,noninvasive approach to assessing early myocardial stress after PCI.Larger prospective studies,with correlation to biomarkers,imaging,and clinical outcomes,are needed before such tools can be considered for routine clinical use. 展开更多
关键词 Percutaneous coronary intervention Myocardial injury Troponin Electrocardiography Heart rate variability Composite electrocardiography-heart rate variability analysis Subclinical ischemia Post-percutaneous coronary intervention monitoring Signal processing Micro-infarction detection
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Detecting Anomalies in FinTech: A Graph Neural Network and Feature Selection Perspective 认领 引用
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作者 Vinh Truong Hoang Nghia Dinh +3 位作者 Viet-Tuan Le Kiet Tran-Trung Bay Nguyen Van Kittikhun Meethongjan 《Computers, Materials & Continua》 SCIE EI 2026年第1期207-246,共40页
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce... The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems. 展开更多
关键词 GNN security ecommerce FinTech abnormal detection feature selection
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An on-site visual assay detecting Langya henipavirus based on recombinase polymerase amplification technology 认领 引用
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作者 Lin Cheng Haili Zhang +8 位作者 Xiaoai Zhang Pei Huang Xinlan Chen Fangxu Li Wujian Li Wei Liu Hualei Wang Sandra Chiu Zengguo Cao 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第7期3099-3101,共3页
Highlights·A visual assay of Langya henipavirus(LayV)nucleic acids was developed based on recombinase polymerase amplification technology(RPA),combined with an immunochromatographic test device.·The detectio... Highlights·A visual assay of Langya henipavirus(LayV)nucleic acids was developed based on recombinase polymerase amplification technology(RPA),combined with an immunochromatographic test device.·The detection limit of our assay reaches 1.22 copiesμL-1,with no observed cross-reactivity with other henipaviruses.·This assay is well-suited for the requirements of point-of-care and field detection and has the potential for broader application.The viruses of the genus Henipavirus within the family Paramyxoviridae are highly pathogenic and often associated with severe diseases in animals and humans(Basler 2012).For example,Nipah virus(NiV)and Hendra virus(HeV),two members of the genus Henipavirus,are known to infect humans and cause fatal disease(Field 2016;Singh et al.2019).Recently,a newly identified henipavirus,Langya henipavirus(LayV),was reported to be associated with respiratory symptoms in humans(Zhang et al.2022). 展开更多
关键词 point care detection recombinase polymerase amplification technology visual assay paramyxoviridae langya henipavirus layv nucleic immunochromatographic test device cross reactivity Langya henipavirus
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Analytical Modeling of Transcoding Artifacts for Detecting SIMBox-Routed Calls 认领 引用
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作者 Hyunghoon Kim Wonsuk Choi +1 位作者 Kyungho Joo Hyojin Jo 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1508-1521,共14页
As mobile networks evolve toward next-generation architectures in which cellular and IP-based voice services are increasingly converged,SIMBox-based call routing has emerged as an important issue in modern telecommuni... As mobile networks evolve toward next-generation architectures in which cellular and IP-based voice services are increasingly converged,SIMBox-based call routing has emerged as an important issue in modern telecommunication networks.By converting Voice over IP(VoIP)traffic into local cellular calls,SIMBox appliances allow IP-originated calls to appear as domestic cellular calls.Although SIMBox usage is not inherently fraudulent,detecting SIMBox-routed calls is important for identifying abnormal call-routing behavior and supporting network-side and client-side security applications.In this paper,we propose a client-side framework for SIMBox-routed call detection.Calls routed through SIMBox infrastructure are identified by exploiting acoustic artifacts introduced by VoIP-to-VoLTE codec transcoding.Variable-length call recordings are segmented into fixed-duration windows and analyzed using supervised classifiers to capture spectral patterns associated with transcoding operations.Unlike network-side SIMBox detection methods that rely on carrier-controlled metadata such as call detail records,subscriber identifiers,or cell-location patterns,our approach focuses on acoustic evidence observable from the call audio itself.We evaluate the proposed framework using two datasets:25706 codec-processed voice samples generated from 12853 source recordings with the ITU-T G.191 Software Tool,and 200 real-world call samples collected through a commercial SIMBox platform(DINSTAR UC2000-VE)under realistic call-routing conditions.Among five evaluated models,the CNN-based classifiers achieve the best performance,reaching an F1-score of 1.00.These results show that audio-level codec artifacts can serve as reliable indicators for detecting SIMBox-routed voice calls at the client side,and offer a promising direction for securing converged voice services in 5G and beyond. 展开更多
关键词 SIMBox-routed call detection VoIP-to-VoLTE transcoding client-side call analysis
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McIFAR:A Multi-Contextual Interaction-Based Framework for Detecting Context-Triggered Android Ransomware 认领 引用
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作者 Sonam Jain Tanya Gera +3 位作者 Rupali Gill Afnan Almegren Ateeq Ur Rehman Salil Bharany 《Computers, Materials & Continua》 SCIE EI 2026年第10期2499-2521,共23页
Android ransomware has emerged as a major threat to mobile ecosystems.Modern Android ransomware has evolved beyond the reach of traditional signature-based detection,often lying dormant until specific strategic trigge... Android ransomware has emerged as a major threat to mobile ecosystems.Modern Android ransomware has evolved beyond the reach of traditional signature-based detection,often lying dormant until specific strategic triggers activate its malicious payload.These strategic ransomware variants activate payloads only under specific device states,events,and conditions that are absent in a sandbox testing environment.To address these sophisticated evasion tactics,this article introduces a novel framework,McIFAR(Multi-contextual Interaction-based Detection Framework for Android Ransomware),that leverages in-context emulation within malware sandboxing to elicit dormant behaviours that are missed by conventional testing,thereby transcending the limitations of isolated static or dynamic analysis.A robust two-stage methodology is presented.In the first stage,the Cross-Validation Feature Selection Ensemble(CVFSE)identifies dominant indicators.This is followed by the Contextual Interaction Feature Orchestrator(CIFO),processing dominant features to encode complex behavioral interactions between features and context in the second stage.Unlike existing studies that rely solely on static and dynamic data,this approach prioritizes contextual interaction,thereby significantly enhancing detection accuracy.The experimental results on the KronoDroid dataset demonstrate that McIFAR achieves a 99.48%detection accuracy,outperforming traditional baselines.The statistical analysis using the Friedman and Nemenyi post-hoc tests confirms that the results are both significant and consistent.The future work includes enhancing the framework by incorporating richer contextual scenarios in in-context emulation,along with federated learning and real-time lightweight deployment. 展开更多
关键词 Android ransomware context detection feature interaction ransomware machine learning
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Procalcitonin and presepsin for detecting bacterial infection and spontaneous bacterial peritonitis in cirrhosis:A systematic review and meta-analysis 认领 引用 被引量：4
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作者 Salisa Wejnaruemarn Paweena Susantitaphong +2 位作者 Piyawat Komolmit Sombat Treeprasertsuk Kessarin Thanapirom 《World Journal of Gastroenterology》 SCIE CAS 2025年第6期89-103,共15页
BACKGROUND Diagnosing bacterial infections(BI)in patients with cirrhosis can be challenging because of unclear symptoms,low diagnostic accuracy,and lengthy culture testing times.Various biomarkers have been studied,in... BACKGROUND Diagnosing bacterial infections(BI)in patients with cirrhosis can be challenging because of unclear symptoms,low diagnostic accuracy,and lengthy culture testing times.Various biomarkers have been studied,including serum procal-citonin(PCT)and presepsin.However,the diagnostic performance of these markers remains unclear,requiring further informative studies to ascertain their diagnostic value.AIM To evaluate the pooled diagnostic performance of PCT and presepsin in detecting BI among patients with cirrhosis.INTRODUCTION Bacterial infections(BI)commonly occur in patients with cirrhosis,resulting in poor outcomes,including the development of cirrhotic complications,septic shock,acute-on-chronic liver failure(ACLF),multiple organ failures,and mortality[1,2].BI is observed in 20%-30%of hospitalized patients,with and without ACLF[3].Patients with cirrhosis are susceptible to BI because of internal and external factors.The major internal factors are changes in gut microbial composition and function,bacterial translocation,and cirrhosis-associated immune dysfunction syndrome[4,5].External factors include alcohol use,proton-pump inhibitor use,frailty,readmission,and invasive procedures.Spontaneous bacterial peritonitis(SBP),urinary tract infection,pneumonia,and primary bacteremia are the common BIs in hospit-alized patients with cirrhosis[6].Early diagnosis and adequate empirical antibiotic therapy are two critical factors that improve the prognosis of BI in patients with cirrhosis.However,early detection of BI in cirrhosis is challenging due to subtle clinical signs and symptoms,low sensitivity and specificity of systemic inflammatory response syndrome criteria,and low sensitivity of bacterial cultures.Thus,effective biomarkers need to be identified for the early detection of BI.Several biomarkers have been evaluated,but their efficacy in detecting BI is unclear.Procalcitonin(PCT)is a precursor of the hormone calcitonin,which is secreted by parafollicular cells of the thyroid gland[7].In the presence of BI,PCT gene expression increases in extrathyroidal tissues,causing a subsequent increase in serum PCT level[8].Changes in serum PCT are detectable as early as 4 hours after infection onset and peaks between 8 and 24 hours,making it a valuable diagnostic biomarker for BI.Several studies have demonstrated the favorable diagnostic accuracy of PCT in the diagnosis of BI in individuals with cirrhosis[9-13]and without cirrhosis[14-16].Since 2014,two meta-analyses have been published on the diagnostic value of PCT for SBP and BI in patients with cirrhosis[17,18].Other related studies have been conducted since then[10-12,19-33].Serum presepsin has recently emerged as a promising biomarker for diagnosing BI.This biomarker is the N-terminal fraction protein of the soluble CD14 g-negative bacterial lipopolysaccharide–lipopolysaccharide binding protein(sCD14-LPS-LBP)complex,which is cleaved by inflammatory serum protease in response to BI[34].Presepsin levels increase within 2 hours and peaks in 3 hours[35].This is useful for detecting BI since presepsin levels increase earlier than serum Our systematic review and meta-analysis was performed with adherence to PRISMA guidelines[37]. 展开更多
关键词 cirrhosis diagnosis detecting
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A simple and efficient in situ RT-PCR method for detecting gene expression in specific cells of tea plant(Camellia sinensis)root 认领 引用
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作者 Shijia Lin Xiaohan Hu +2 位作者 Zhaoliang Zhang Xiaochun Wan Shupei Zhang 《Beverage Plant Research》 CAS 2025年第1期381-386,共6页
Currently,the field of tea plant biology is rapidly advancing,with numerous significant scientific inquiries being raised and investigated.Meanwhile,a substantial number of functional genes have been reported.However,... Currently,the field of tea plant biology is rapidly advancing,with numerous significant scientific inquiries being raised and investigated.Meanwhile,a substantial number of functional genes have been reported.However,due to the lack of certain in vivo validation techniques,much of the expression information for these functional genes is at the tissue level in tea plants and remains unclear at the cell-type level.In this study,an in situ PCR method for detecting gene expression heterogeneity in tea plant root cells is presented.A detailed description of the procedure and precautions involved in this method is provided and suggestions offered for addressing potential experimental challenges.Finally,the expression patterns of CsGL3,CsCAT2,and CsAAP4 in tea plant root cells were taken as examples.The present results showed that CsGL3 was predominantly expressed in root epidermal cells,while CsCAT2 shows strong expression in pericycle and cortex.The expression of CsAAP4 was not detected in root cells.These findings are consistent with previous reports,indicating that this method is feasible for the detection of gene expression patterns in tea plant root cells. 展开更多
关键词 detecting gene expression heterogeneity functional genes tea plants situ pcr method situ RT PCR gene expression vivo validation techniquesmuch
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Detecting heart disease in 7 seconds 认领 引用
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作者 袁丹纯 《疯狂英语（新读写）》 2025年第8期28-31,77,共4页
At an age when most teens are figuring out high school,Siddharth is already shaping the future of medical tech.The 14⁃year⁃old boy from Dallas has created an AI⁃powered app,Circadian AI,capable of detecting heart dise... At an age when most teens are figuring out high school,Siddharth is already shaping the future of medical tech.The 14⁃year⁃old boy from Dallas has created an AI⁃powered app,Circadian AI,capable of detecting heart disease in just 7 seconds using only a smartphone's microphone. 展开更多
关键词 app heart disease microphone AI smartphone detecting heart disease circadian ai
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Collapse of Meilong Expressway as Seen from Space:Detecting Precursors of Failure with Satellite Remote Sensing 认领 引用 被引量：2
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作者 Zhuge Xia Chao Zhou +4 位作者 Wandi Wang Mimi Peng Dalu Dong Xiufeng He Guangchao Tan 《Journal of Earth Science》 SCIE CAS CSCD 2025年第2期835-838,共4页
INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This colla... INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This collapse resulted in a pavement failure of approximately 17.9 m in length and covering an area of about 184.3 m2(Chinanews,2024). 展开更多
关键词 failure detection satellite remote sensing pavement failure Meilong Expressway meilong expressway collapse precursors
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SPD-YOLO:A Method for Detecting Maize Disease Pests Using Improved YOLOv7 认领 引用 被引量：1
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作者 Zhunruo Feng Ruomeng Shi +3 位作者 Yuhan Jiang Yiming Han Zeyang Ma Yuheng Ren 《Computers, Materials & Continua》 SCIE EI 2025年第8期3559-3575,共17页
In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low... In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low resolution,and significant species variation across different growth stages.To improve the model’s ability to generalize and its robustness,we incorporate target background analysis,data augmentation,and processing techniques like Gaussian noise and brightness adjustment.In target detection,increasing the depth of the neural network can lead to the loss of small target information.To overcome this,we introduce the Space-to-Depth Convolution(SPD-Conv)module into the SPD-YOLOv7 framework,replacing certain convolutional layers in the traditional system backbone and head network.This modification helps retain small target features and location information.Additionally,the Efficient Layer Aggregation Network-Wide(ELAN-W)module is combined with the Convolutional Block Attention Module(CBAM)attention mechanism to extract more efficient features.Experimental results show that the enhanced YOLOv7 model achieves an accuracy of 98.38%,with an average accuracy of 99.4%,outperforming the original YOLOv7 model.These improvements represent an increase of 2.46%in accuracy and 3.19%in average accuracy.The results indicate that the enhanced YOLOv7 model is more efficient and real-time,offering valuable insights for maize pest control. 展开更多
关键词 Deep learning improved YOLOv7 attention mechanism SPD-Conv module insect pest detection
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Distortion-Free Zeeman Torque Sampling for Detecting Terahertz Magnetic Field Pulse 认领 引用
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作者 Chun-yan Geng Yi-chen Su +3 位作者 De-yin Kong Fei Dai Cheng-song Xiao-jun Wu 《Chinese Physics Letters》 SCIE EI CAS CSCD 2025年第11期383-392,共10页
Strong-field terahertz(THz) radiation holds significant potential in non-equilibrium state manipulation, electron acceleration, and biomedical effects. However, distortion-free detection of strong-field THz waveforms ... Strong-field terahertz(THz) radiation holds significant potential in non-equilibrium state manipulation, electron acceleration, and biomedical effects. However, distortion-free detection of strong-field THz waveforms remains an essential challenge in THz science and technology. To address this issue, we propose a ferromagnetic detection scheme based on Zeeman torque sampling, achieving distortion-free strong-field THz waveform detection in Py films. Thickness-dependent characterization(3–21 nm) identifies peak detection performance at 21 nm within the investigated range. Furthermore, by structurally engineering the Py ferromagnetic layer, we demonstrate strong-field THz detection in symmetric Ta(3 nm)/Py(9 nm)/Ta(3 nm) heterostructure while simultaneously resolving Zeeman torque responses and collective spin-wave dynamics in asymmetric W(4 nm)/Py(9 nm)/Pt(2 nm)heterostructure. We calculated spin wave excitations and spin orbit torque distributions in asymmetric heterostructures, along with spin wave excitations in symmetric modes. This approach overcomes the sensitivity limitations of conventional techniques in strong-field conditions. 展开更多
关键词 distortion free detection terahertz magnetic field ferromagnetic detection scheme py films strong field terahertz radiation zeeman torque sampling biomedical effects electron acceleration
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Online detecting living cells released TNF-αand studying intercellular communication using SuperDNA self-assembled conical nanochannel 认领 引用
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作者 Weiwei Liu Yu Liu +4 位作者 Zhaoyan Tian Zhaohan Wang Hui Liu Songqin Liu Yafeng Wu 《Chinese Chemical Letters》 SCIE CAS CSCD 2025年第5期668-672,共5页
Nanochannel technology based on ionic current rectification has emerged as a powerful tool for the detection of biomolecules owing to unique advantages.Nevertheless,existing nanochannel sensors mainly focus on the det... Nanochannel technology based on ionic current rectification has emerged as a powerful tool for the detection of biomolecules owing to unique advantages.Nevertheless,existing nanochannel sensors mainly focus on the detection of targets in solution or inside the cells,moreover,they only have a single function,greatly limiting their application.Herein,we fabricated SuperDNA self-assembled conical nanochannel,which was clamped in the middle of self-made device for two functions:Online detecting living cells released TNF-αand studying intercellular communication.Polyethylene terephthalate(PET)membrane incubated tumor associated macrophages and tumor cells was rolled up and inserted into the left and right chamber of the device,respectively.Through monitoring the ion current change in the nanochannel,tumor associated macrophages released TNF-αcould be in situ and noninvasive detected with a detection limit of 0.23 pg/mL.Furthermore,the secreted TNF-αinduced epithelial-mesenchymal transformation of tumor cells in the right chamber was also studied.The presented strategy displayed outstanding performance and multi-function,providing a promising platform for in situ non-destructive detection of cell secretions and related intercellular communication analysis. 展开更多
关键词 Nanochannel Ion transport In situ detection Cell secretions Intercellular communication
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Development and evaluation of a monoclonal antibody-based competitive ELISA for detecting porcine deltacoronavirus antibodies 认领 引用
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作者 Wei Wang Baochao Fan +7 位作者 Xuehan Zhang Shanshan Yang Junming Zhou Rongli Guo Yongxiang Zhao Jinzhu Zhou Jizong Li Bin Li 《Animal Diseases》 CAS CSCD 2025年第4期452-459,共8页
Porcine deltacoronavirus(PDCoV)is an emerging swine enteropathogenic coronavirus that can cause acute diarrhea and vomiting in newborn piglets and poses a potential risk for cross-species transmission.It is necessary ... Porcine deltacoronavirus(PDCoV)is an emerging swine enteropathogenic coronavirus that can cause acute diarrhea and vomiting in newborn piglets and poses a potential risk for cross-species transmission.It is necessary to develop an effective serological diagnostic tool for the surveillance of PDCoV infection and vaccine immunity effects.In this study,we developed a monoclonal antibody-based competitive ELISA(cELISA)that selected the purified recombinant PDCoV nucleocapsid(N)protein as the coating antigen to detect PDCoV antibodies.To evaluate the diagnostic performance of the cELISA,122 swine serum samples(39 positive and 83 negative)were tested and the results were compared with an indirect immunofluorescence assay(IFA)as the reference method.By receiver operating characteristic(ROC)curve analysis,the optimum cutoff value of percent inhibition(PI)was determined to be 26.8%,which showed excellent diagnostic performance,with an area under the curve(AUC)of 0.9919,a diagnostic sensitivity of 97.44%and a diagnostic specificity of 96.34%.Furthermore,there was good agreement between the cELISA and virus neutralization test(VNT)for the detection of PDCoV antibodies,with a coincidence rate of 92.7%,and theκanalysis showed almost perfect agreement(κ=0.851).Overall,the established cELISA showed good diagnostic performance,including sensitivity,specificity and repeatability,and can be used for diagnostic assistance,evaluating the response to vaccination and assessing swine herd immunity. 展开更多
关键词 Porcine deltacoronavirus(PDCoV) Competitive ELISA(cELISA) Antibody detection Monoclonal antibody Nucleocapsid(N)protein
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A Method for Detecting Depression in Adolescence Based on an Affective Brain‑Computer Interface and Resting‑State Electroencephalogram Signals 认领 引用 被引量：4
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作者 Zijing Guan Xiaofei Zhang +10 位作者 Weichen Huang Kendi Li Di Chen Weiming Li Jiaqi Sun Lei Chen Yimiao Mao Huijun Sun Xiongzi Tang Liping Cao Yuanqing Li 《Neuroscience Bulletin》 SCIE CAS CSCD 2025年第3期434-448,共15页
Depression is increasingly prevalent among adolescents and can profoundly impact their lives.However,the early detection of depression is often hindered by the timeconsuming diagnostic process and the absence of objec... Depression is increasingly prevalent among adolescents and can profoundly impact their lives.However,the early detection of depression is often hindered by the timeconsuming diagnostic process and the absence of objective biomarkers.In this study,we propose a novel approach for depression detection based on an affective brain-computer interface(aBCI)and the resting-state electroencephalogram(EEG).By fusing EEG features associated with both emotional and resting states,our method captures comprehensive depression-related information.The final depression detection model,derived through decision fusion with multiple independent models,further enhances detection efficacy.Our experiments involved 40 adolescents with depression and 40 matched controls.The proposed model achieved an accuracy of 86.54%on cross-validation and 88.20%on the independent test set,demonstrating the efficiency of multi-modal fusion.In addition,further analysis revealed distinct brain activity patterns between the two groups across different modalities.These findings hold promise for new directions in depression detection and intervention. 展开更多
关键词 Depression detection Brain-computer interface EEG Multimodal
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Upholding Academic Integrity amidst Advanced Language Models: Evaluating BiLSTM Networks with GloVe Embeddings for Detecting AI-Generated Scientific Abstracts 认领 引用 被引量：2
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作者 Lilia-Eliana Popescu-Apreutesei Mihai-Sorin Iosupescu +1 位作者 Sabina Cristiana Necula Vasile-Daniel Pavaloaia 《Computers, Materials & Continua》 SCIE EI 2025年第8期2605-2644,共40页
The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situati... The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situation is raising significant concerns regarding the integrity and authenticity of academic work.In light of the above,the current research evaluates the effectiveness of Bidirectional Long Short-TermMemory(BiLSTM)networks enhanced with pre-trained GloVe(Global Vectors for Word Representation)embeddings to detect AIgenerated scientific Abstracts drawn from the AI-GA(Artificial Intelligence Generated Abstracts)dataset.Two core BiLSTM variants were assessed:a single-layer approach and a dual-layer design,each tested under static or adaptive embeddings.The single-layer model achieved nearly 97%accuracy with trainable GloVe,occasionally surpassing the deeper model.Despite these gains,neither configuration fully matched the 98.7%benchmark set by an earlier LSTMWord2Vec pipeline.Some runs were over-fitted when embeddings were fine-tuned,whereas static embeddings offered a slightly lower yet stable accuracy of around 96%.This lingering gap reinforces a key ethical and procedural concern:relying solely on automated tools,such as Turnitin’s AI-detection features,to penalize individuals’risks and unjust outcomes.Misclassifications,whether legitimate work is misread as AI-generated or engineered text,evade detection,demonstrating that these classifiers should not stand as the sole arbiters of authenticity.Amore comprehensive approach is warranted,one which weaves model outputs into a systematic process supported by expert judgment and institutional guidelines designed to protect originality. 展开更多
关键词 AI-GA dataset bidirectional LSTM GloVe embeddings AI-generated text detection academic integrity deep learning overfitting natural language processing
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Highly selective QCM sensor based on functionalized hierarchical hollow TiO2nanospheres for detecting ppb-level 3-hydroxy-2-butanone biomarker at room temperature 认领 引用 被引量：1
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作者 Siqi Sun Cheng Zhao +6 位作者 Zhaohuan Zhang Ding Wang Xinru Yin Jingting Han Jinlei Wei Yong Zhao Yongheng Zhu 《Chinese Chemical Letters》 SCIE CAS CSCD 2025年第5期740-745,共6页
Listeria monocytogenes(LM)is a dangerous foodborne pathogen for humans.One emerging and validated method of indirectly assessing LM in food is detecting 3-hydroxy-2-butanone(3H2B)gas.In this study,the synthesis of 3-(... Listeria monocytogenes(LM)is a dangerous foodborne pathogen for humans.One emerging and validated method of indirectly assessing LM in food is detecting 3-hydroxy-2-butanone(3H2B)gas.In this study,the synthesis of 3-(2-aminoethylamino)propyltrimethoxysilane(AAPTMS)functionalized hierarchical hollow TiO2nanospheres was achieved via precise controlling of solvothermal reaction temperature and post-grafting route.The sensors based on as-prepared materials exhibited excellent sensitivity(480 Hz@50 ppm),low detection limit(100 ppb),and outstanding selectivity.Moreover,the evaluation of LM with high sensitivity and specificity was achieved using the sensors.Such stable three-dimensional spheres,whose distinctive hierarchical and hollow nanostructure simultaneously improved both sensitivity and responseecovery speed dramatically,were spontaneously assembled by nanosheets.Meanwhile,the moderate loadings of AAPTMS significantly improved the selectivity of sensors.Then,the gas-sensing mechanism was explored by utilizing thermodynamic investigation,Gaussian 16 software,and in situ diffuse reflectance infrared transform spectroscopy,illustrating the weak chemisorption between the-NHgroup and 3H2B molecules.These portable sensors are promising for real-time assessment of LM at room temperature,which will make a magnificent contribution to food safety. 展开更多
关键词 Hierarchical hollow TiO2nanospheres AAPTMS functionalization Gas sensor 3-Hydroxy-2-butanone detection Sensing mechanism
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