In this paper, in order to design a fast steering mirror(FSM) with large deflection angle and high linearity, a deflection angle detecting system(DADS) using quadrant detector(QD) is developed. And the mathematical mo...In this paper, in order to design a fast steering mirror(FSM) with large deflection angle and high linearity, a deflection angle detecting system(DADS) using quadrant detector(QD) is developed. And the mathematical model describing DADS is established by analyzing the principle of position detecting and error characteristics of QD. Based on this mathematical model, the variation tendencies of deflection angle and linearity of FSM are simulated. Then, by changing the parameters of the DADS, the optimization of deflection angle and linearity of FSM is demonstrated. Finally, a QD-based FSM is designed based on this method, which achieves ±2° deflection angle and 0.72% and 0.68% linearity along x and y axis, respectively. Moreover, this method will be beneficial to the design of large deflection angle and high linearity FSM.展开更多
A high-precision shape detecting system of cold rolling strip is developed to meet industrial application, which mainly consists of the shape detecting roller, the collecting ring, the digital signal processing （DSP...A high-precision shape detecting system of cold rolling strip is developed to meet industrial application, which mainly consists of the shape detecting roller, the collecting ring, the digital signal processing （DSP） shape signal processing board and the shape control model. Based on the shape detecting principle, the shape detecting roller is designed with a new integral structure for improving the precision of shape detecting and avoiding scratching strip surface. Based on the DSP technology, the DSP shape signal processing circuit board is designed and embedded in the shape detecting system for the reliability and stability of shape signal processing. The shape detecting system was successfully used in Angang 1 250 mm HC 6-high reversible cold rolling mill. The precision of shape detecting is 0.2 I and the shape deviation is controlled within 6 1 after the close loop shape control is input.展开更多
Target detection is one of the key technology of precision chemical application.Previously the digital coding modulation technique was commonly used to emit and receive the optical signal in the target detection syste...Target detection is one of the key technology of precision chemical application.Previously the digital coding modulation technique was commonly used to emit and receive the optical signal in the target detection systems previously in China.It was difficult to adjust the output power,and the anti-interference ability was weak in these systems.In order to resolve these problems,the target detection method based on analog sine-wave modulation was studied.The spectral detecting system was set up in the aspects of working principle,electric circuit,and optical path.Lab testing was performed.The results showed that the reflected signal from the target varied inversely with detection distances.It indicated that it was feasible to establish the target detection system using analog sine-wave modulation technology.Furthermore,quantitative measurement of the reflected optical signal for near-infrared and visible light could be achieved by using this system.The research laid the foundation for the future development of the corresponding instrument.展开更多
This paper is concerned with a high characteristic image processing and recognition system that is used for inspecting real-time blemishes, streaks and cracks on the inner walls of high accuracy pipes. As a regular de...This paper is concerned with a high characteristic image processing and recognition system that is used for inspecting real-time blemishes, streaks and cracks on the inner walls of high accuracy pipes. As a regular detector, the BP neural network is used for extracting features of the image inspected and classifying these images, it takes fully advantage of the function of artificial neural network, such as the information distributed memory, large scale self-adapting parallel processing, high fault-tolerant ability and so forth. Besides, an improved BP algorithm is used in the system for training the network, and making the learning procedure of the net converges to the minimum of overall situation at high rate.展开更多
Compared with the traditional scanning confocal microscopy,the effect of various factors on characteristic in multi-beam parallel confocal system is discussed,the error factors in multi-beam parallel confocal system a...Compared with the traditional scanning confocal microscopy,the effect of various factors on characteristic in multi-beam parallel confocal system is discussed,the error factors in multi-beam parallel confocal system are analyzed.The factors influencing the characteristics of the multi-beam parallel confocal system are discussed.The construction and working principle of the non-scanning 3D detecting system is introduced,and some experiment results prove the effect of various factors on the detecting system.展开更多
Objective Focusing on the problem such as slow scanning speed, complex system design and low light efficiency, a new parallel confocal 3D profile detecting method based on optical fiber technology, which realizes whol...Objective Focusing on the problem such as slow scanning speed, complex system design and low light efficiency, a new parallel confocal 3D profile detecting method based on optical fiber technology, which realizes whole-field confocal detecting, is proposed. Methods The optical fiber plate generates an 2D point light source array, which splits one light beam into N2 subbeams and act the role of pinholes as point source and point detecting to filter the stray light and reflect light. By introducing the construction and working principle of the multi-beam 3D detecting system, the feasibility is investigated. Results Experiment result indicates that the optical fiber technology is applicable in parallel confocal detecting. Conclusion The equipment needn't mechanical rotation. The measuring parameters that influence the detecting can easily be adapted to satisfy different requirments of measurement. Compared with the conventional confocal method, the parallel confocal detecting system using optical fiber plate is simple in the mechanism, the measuring field is larger and the speed is faster.展开更多
Using arylhydrocarbon hydroxylase (AHH),ethoxyre-sorufin-O-deethylase,ethoxycoumarin-O-deethylase andaminopyrine-N-demethylase as marker enzymes and 3-methylcholanthrene (3-MC),-naphthof1avon,norepine-phrine (NE) and ...Using arylhydrocarbon hydroxylase (AHH),ethoxyre-sorufin-O-deethylase,ethoxycoumarin-O-deethylase andaminopyrine-N-demethylase as marker enzymes and 3-methylcholanthrene (3-MC),-naphthof1avon,norepine-phrine (NE) and phenobarbita1 as inducers,it is con-firmed that there are inducib1e Cyt P450 IA and展开更多
The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT security.Deep learning(DL)-based intrusion detection(ID)h...The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT security.Deep learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT environments.To rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT networks.The DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT attacks.The BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal packets.The experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy rates.LSTMs are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack detection.This method,without feature selection,demonstrates advantages in training time and detection accuracy.Consequently,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.展开更多
A handwriting detecting system based on Micro- accelerometer and Micro-gyros is proposed. And the algorithm of the detecting system is also described in detail. And the error analysis of the detecting system is also d...A handwriting detecting system based on Micro- accelerometer and Micro-gyros is proposed. And the algorithm of the detecting system is also described in detail. And the error analysis of the detecting system is also described in de-tail. The motion contrail of the handwriting de-tecting in the 3-D space can be recognized through compute the matrix of attitude angles and the dynamic information of the handwriting detecting which is mapped on the 2-D plane. Then the information of contrail can be recurred on the writing plane by integral. There were good results in the actual experiment.展开更多
In this paper,a non-invasive detecting system for measuring blood flow parame-ters of cardiovascular system is described.The device employs a new unique methodwhich is based on the theory of hemodynamics,ordinary meas...In this paper,a non-invasive detecting system for measuring blood flow parame-ters of cardiovascular system is described.The device employs a new unique methodwhich is based on the theory of hemodynamics,ordinary measurement of blood pres-sure and pulse information of variation of pulse contour parameter Ko The sphygmo-gram is picked up from radial artery via sensor.As the blood pressure changes。展开更多
Indoor organization user activity’s (UA) direction detection monitoring system and also emergency prediction are major challenging tasks in the field of the typical body sensor and indoor fixed sensor networks. ...Indoor organization user activity’s (UA) direction detection monitoring system and also emergency prediction are major challenging tasks in the field of the typical body sensor and indoor fixed sensor networks. In this paper, indoor UA based direction detection monitoring system is achieved by the combination of both the orientation sensor and Bluetooth Low Energy (BLE) in user’s smartphones belonging to the Internet of Things (IoT). The orientation sensor senses the actual orientation of the user and BLE transmits the sensed BLE signals to monitoring system using star topology in IoT. In monitoring system, classification algorithm is used to identify the directions of the smartphone users. The emergency situation of the user is also predicted based on signal variation instantly in real time. The user activity’s signals are captured using LabVIEW toolkit then applied to various classification algorithms such asRF—91.42%, Ibk—90.55%, j48— 85.61%, K*—73.54% are the results obtained. An average of 85% was obtained in all the classifi- cation algorithims indicating the consistency and accuracy in detecting the directions of the users. RF was found to be the best among all the classification algorithms. IoT enabled devices have high demand in near coming future, moreover smartphones users increase day by day, hence implementing and maintaining the above said system would be much easier and cheaper compared to other conventional networks.展开更多
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
Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptibl...Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.展开更多
Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compro...Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.展开更多
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
Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted featur...Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.展开更多
The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threa...The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.展开更多
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
Microplastics are becoming well-known as chronic pollutants of terrestrial ecosystems,although their sources,dynamics of transportation,reliability of detection and ecological hazard are not evenly described.This revi...Microplastics are becoming well-known as chronic pollutants of terrestrial ecosystems,although their sources,dynamics of transportation,reliability of detection and ecological hazard are not evenly described.This review is a synthesis of the existing information about microplastics in soils,including analytical detection and characterization techniques,the major sources in the terrestrial environment,transport routes within the compartments and between compartments,and reported ecotoxicological consequences on soil biota,plants,and microbial communities.We also critically discuss the strengths and weaknesses of methodologies,making the distinction of sampling design differences,size detection limits,polymer identification methods,and quality assurance procedures on data comparability and uncertainty.An important outcome of this review is the systematic evaluation of the strength of evidence in three interrelated areas:measurement,environmental transport,and biological impacts,hence explaining which findings are strong and in which areas of research significant knowledge gaps still exist.We also suggest a conceptual framework that strongly connects the measurement uncertainty to the exposure estimation,interpretation of risk,and management relevance.This review uses mechanistic insights into transport and ecotoxicology alongside analysis constraints to add to the more comprehensive foundation of terrestrial risk assessment.Lastly,we determine research priorities,such as harmonized methodologies,realistic exposure scenarios,and cross-scale monitoring strategies,in order to assist in the science-based policies and mitigation action.展开更多
Illicit web ecosystems,encompassing phishing,illegal online gambling,scam platforms,and malicious advertising,have rapidly expanded in scale and complexity,creating severe social,financial,and cybersecurity risks.Trad...Illicit web ecosystems,encompassing phishing,illegal online gambling,scam platforms,and malicious advertising,have rapidly expanded in scale and complexity,creating severe social,financial,and cybersecurity risks.Traditional rule-based and blacklist-driven detection approaches struggle to cope with polymorphic,multilingual,and adversarially manipulated threats,resulting in increasing demand for Artificial Intelligence(AI)-based solutions.This review provides a comprehensive synthesis of research on AI-driven threat detection for illicit web environments.It surveys detection models across multiple modalities,including text-based analysis of Uniform Resource Locator(URL)and HyperText Markup Language(HTML),vision-based recognition of webpage layouts and logos,graphbased modeling of domain and infrastructure relationships,and sequence modeling using transformer architectures.In addition,the paper examines system architectures,data collection and labeling pipelines,real-time detection frameworks,and widely used benchmark datasets,while also discussing their inherent limitations related to imbalance,representativeness,and reproducibility.The review highlights critical challenges such as evasion strategies,cross-lingual detection barriers,deployment latency,and explainability gaps.Furthermore,it identifies emerging research directions,including the use of Generative Adversarial Network(GAN)for threat simulation,few-shot and self-supervised learning for data-scarce environments,Explainable Artificial Intelligence(XAI)for transparency,and predictive AI for proactive threat forecasting.By integrating technical,legal,and societal perspectives,this survey offers a structured foundation for researchers and practitioners to design resilient,adaptive,and trustworthy AI-based defense systems against illicit web threats.展开更多
基金supported by the National Natural Science Foundation of China(No.51605465)
文摘In this paper, in order to design a fast steering mirror(FSM) with large deflection angle and high linearity, a deflection angle detecting system(DADS) using quadrant detector(QD) is developed. And the mathematical model describing DADS is established by analyzing the principle of position detecting and error characteristics of QD. Based on this mathematical model, the variation tendencies of deflection angle and linearity of FSM are simulated. Then, by changing the parameters of the DADS, the optimization of deflection angle and linearity of FSM is demonstrated. Finally, a QD-based FSM is designed based on this method, which achieves ±2° deflection angle and 0.72% and 0.68% linearity along x and y axis, respectively. Moreover, this method will be beneficial to the design of large deflection angle and high linearity FSM.
基金Foundation item: Project(2009AA04Z143) supported by the National High Technology Research and Development Program of ChinaProject (E2011203004) supported by Natural Science Foundation of Hebei Province, ChinaProjects(2011BAF15B03, 2011BAF15B02) supported by the National Science Plan of China
文摘A high-precision shape detecting system of cold rolling strip is developed to meet industrial application, which mainly consists of the shape detecting roller, the collecting ring, the digital signal processing （DSP） shape signal processing board and the shape control model. Based on the shape detecting principle, the shape detecting roller is designed with a new integral structure for improving the precision of shape detecting and avoiding scratching strip surface. Based on the DSP technology, the DSP shape signal processing circuit board is designed and embedded in the shape detecting system for the reliability and stability of shape signal processing. The shape detecting system was successfully used in Angang 1 250 mm HC 6-high reversible cold rolling mill. The precision of shape detecting is 0.2 I and the shape deviation is controlled within 6 1 after the close loop shape control is input.
基金Supported by the National“863”Project of China(2010AA10A301)National Technology Support Project for the 12th Five-year Plan(2011BAD20B07)
文摘Target detection is one of the key technology of precision chemical application.Previously the digital coding modulation technique was commonly used to emit and receive the optical signal in the target detection systems previously in China.It was difficult to adjust the output power,and the anti-interference ability was weak in these systems.In order to resolve these problems,the target detection method based on analog sine-wave modulation was studied.The spectral detecting system was set up in the aspects of working principle,electric circuit,and optical path.Lab testing was performed.The results showed that the reflected signal from the target varied inversely with detection distances.It indicated that it was feasible to establish the target detection system using analog sine-wave modulation technology.Furthermore,quantitative measurement of the reflected optical signal for near-infrared and visible light could be achieved by using this system.The research laid the foundation for the future development of the corresponding instrument.
文摘This paper is concerned with a high characteristic image processing and recognition system that is used for inspecting real-time blemishes, streaks and cracks on the inner walls of high accuracy pipes. As a regular detector, the BP neural network is used for extracting features of the image inspected and classifying these images, it takes fully advantage of the function of artificial neural network, such as the information distributed memory, large scale self-adapting parallel processing, high fault-tolerant ability and so forth. Besides, an improved BP algorithm is used in the system for training the network, and making the learning procedure of the net converges to the minimum of overall situation at high rate.
基金supported by National Natural Science Foundation of China(No.50175024)Provincial Program for Young Teacher of Colleges and Universities of Anhui(No.2005jql019)Provincial Research Foundation of Key Laboratory of Anhui
文摘Compared with the traditional scanning confocal microscopy,the effect of various factors on characteristic in multi-beam parallel confocal system is discussed,the error factors in multi-beam parallel confocal system are analyzed.The factors influencing the characteristics of the multi-beam parallel confocal system are discussed.The construction and working principle of the non-scanning 3D detecting system is introduced,and some experiment results prove the effect of various factors on the detecting system.
文摘Objective Focusing on the problem such as slow scanning speed, complex system design and low light efficiency, a new parallel confocal 3D profile detecting method based on optical fiber technology, which realizes whole-field confocal detecting, is proposed. Methods The optical fiber plate generates an 2D point light source array, which splits one light beam into N2 subbeams and act the role of pinholes as point source and point detecting to filter the stray light and reflect light. By introducing the construction and working principle of the multi-beam 3D detecting system, the feasibility is investigated. Results Experiment result indicates that the optical fiber technology is applicable in parallel confocal detecting. Conclusion The equipment needn't mechanical rotation. The measuring parameters that influence the detecting can easily be adapted to satisfy different requirments of measurement. Compared with the conventional confocal method, the parallel confocal detecting system using optical fiber plate is simple in the mechanism, the measuring field is larger and the speed is faster.
文摘Using arylhydrocarbon hydroxylase (AHH),ethoxyre-sorufin-O-deethylase,ethoxycoumarin-O-deethylase andaminopyrine-N-demethylase as marker enzymes and 3-methylcholanthrene (3-MC),-naphthof1avon,norepine-phrine (NE) and phenobarbita1 as inducers,it is con-firmed that there are inducib1e Cyt P450 IA and
文摘The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT security.Deep learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT environments.To rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT networks.The DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT attacks.The BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal packets.The experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy rates.LSTMs are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack detection.This method,without feature selection,demonstrates advantages in training time and detection accuracy.Consequently,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.
文摘A handwriting detecting system based on Micro- accelerometer and Micro-gyros is proposed. And the algorithm of the detecting system is also described in detail. And the error analysis of the detecting system is also described in de-tail. The motion contrail of the handwriting de-tecting in the 3-D space can be recognized through compute the matrix of attitude angles and the dynamic information of the handwriting detecting which is mapped on the 2-D plane. Then the information of contrail can be recurred on the writing plane by integral. There were good results in the actual experiment.
文摘In this paper,a non-invasive detecting system for measuring blood flow parame-ters of cardiovascular system is described.The device employs a new unique methodwhich is based on the theory of hemodynamics,ordinary measurement of blood pres-sure and pulse information of variation of pulse contour parameter Ko The sphygmo-gram is picked up from radial artery via sensor.As the blood pressure changes。
文摘Indoor organization user activity’s (UA) direction detection monitoring system and also emergency prediction are major challenging tasks in the field of the typical body sensor and indoor fixed sensor networks. In this paper, indoor UA based direction detection monitoring system is achieved by the combination of both the orientation sensor and Bluetooth Low Energy (BLE) in user’s smartphones belonging to the Internet of Things (IoT). The orientation sensor senses the actual orientation of the user and BLE transmits the sensed BLE signals to monitoring system using star topology in IoT. In monitoring system, classification algorithm is used to identify the directions of the smartphone users. The emergency situation of the user is also predicted based on signal variation instantly in real time. The user activity’s signals are captured using LabVIEW toolkit then applied to various classification algorithms such asRF—91.42%, Ibk—90.55%, j48— 85.61%, K*—73.54% are the results obtained. An average of 85% was obtained in all the classifi- cation algorithims indicating the consistency and accuracy in detecting the directions of the users. RF was found to be the best among all the classification algorithms. IoT enabled devices have high demand in near coming future, moreover smartphones users increase day by day, hence implementing and maintaining the above said system would be much easier and cheaper compared to other conventional networks.
文摘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].
文摘Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.
文摘Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.
文摘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.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:1074-612-2024).
文摘Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.
文摘The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘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.
文摘Microplastics are becoming well-known as chronic pollutants of terrestrial ecosystems,although their sources,dynamics of transportation,reliability of detection and ecological hazard are not evenly described.This review is a synthesis of the existing information about microplastics in soils,including analytical detection and characterization techniques,the major sources in the terrestrial environment,transport routes within the compartments and between compartments,and reported ecotoxicological consequences on soil biota,plants,and microbial communities.We also critically discuss the strengths and weaknesses of methodologies,making the distinction of sampling design differences,size detection limits,polymer identification methods,and quality assurance procedures on data comparability and uncertainty.An important outcome of this review is the systematic evaluation of the strength of evidence in three interrelated areas:measurement,environmental transport,and biological impacts,hence explaining which findings are strong and in which areas of research significant knowledge gaps still exist.We also suggest a conceptual framework that strongly connects the measurement uncertainty to the exposure estimation,interpretation of risk,and management relevance.This review uses mechanistic insights into transport and ecotoxicology alongside analysis constraints to add to the more comprehensive foundation of terrestrial risk assessment.Lastly,we determine research priorities,such as harmonized methodologies,realistic exposure scenarios,and cross-scale monitoring strategies,in order to assist in the science-based policies and mitigation action.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea goverment(MSIT)(No.RS-2024-00439139,Development of a Cyber Crisis Response and Resilience Test Evaluation Systems)this research was supported by the MSIT(Ministry of Science and ICT),Korea,under the Graduate School of Virtual Convergence support program(IITP-2026-RS-2023-00254129)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)+1 种基金supported by the“Regional Innovation System&Education(RISE)”through the Seoul RISE Center,funded by the Ministry of Education(MOE)and the Seoul Metropolitan Government(2026-RISE-01-018-05)supported by QuadMiners Corp.
文摘Illicit web ecosystems,encompassing phishing,illegal online gambling,scam platforms,and malicious advertising,have rapidly expanded in scale and complexity,creating severe social,financial,and cybersecurity risks.Traditional rule-based and blacklist-driven detection approaches struggle to cope with polymorphic,multilingual,and adversarially manipulated threats,resulting in increasing demand for Artificial Intelligence(AI)-based solutions.This review provides a comprehensive synthesis of research on AI-driven threat detection for illicit web environments.It surveys detection models across multiple modalities,including text-based analysis of Uniform Resource Locator(URL)and HyperText Markup Language(HTML),vision-based recognition of webpage layouts and logos,graphbased modeling of domain and infrastructure relationships,and sequence modeling using transformer architectures.In addition,the paper examines system architectures,data collection and labeling pipelines,real-time detection frameworks,and widely used benchmark datasets,while also discussing their inherent limitations related to imbalance,representativeness,and reproducibility.The review highlights critical challenges such as evasion strategies,cross-lingual detection barriers,deployment latency,and explainability gaps.Furthermore,it identifies emerging research directions,including the use of Generative Adversarial Network(GAN)for threat simulation,few-shot and self-supervised learning for data-scarce environments,Explainable Artificial Intelligence(XAI)for transparency,and predictive AI for proactive threat forecasting.By integrating technical,legal,and societal perspectives,this survey offers a structured foundation for researchers and practitioners to design resilient,adaptive,and trustworthy AI-based defense systems against illicit web threats.
