Background:The reputation system has been designed as an effective mechanism to reduce risks associated with online shopping for customers.However,it is vulnerable to rating fraud.Some raters may inject unfairly high ...Background:The reputation system has been designed as an effective mechanism to reduce risks associated with online shopping for customers.However,it is vulnerable to rating fraud.Some raters may inject unfairly high or low ratings to the system so as to promote their own products or demote their competitors.Method:This study explores the rating fraud by differentiating the subjective fraud from objective fraud.Then it discusses the effectiveness of blockchain technology in objective fraud and its limitation in subjective fraud,especially the rating fraud.Lastly,it systematically analyzes the robustness of blockchain-based reputation systems in each type of rating fraud.Results:The detection of fraudulent raters is not easy since they can behave strategically to camouflage themselves.We explore the potential strengths and limitations of blockchain-based reputation systems under two attack goals:ballot-stuffing and bad-mouthing,and various attack models including constant attack,camouflage attack,whitewashing attack and sybil attack.Blockchain-based reputation systems are more robust against bad-mouthing than ballot-stuffing fraud.Conclusions:Blockchain technology provides new opportunities for redesigning the reputation system.Blockchain systems are very effective in preventing objective information fraud,such as loan application fraud,where fraudulent information is fact-based.However,their effectiveness is limited in subjective information fraud,such as rating fraud,where the ground-truth is not easily validated.Blockchain systems are effective in preventing bad mouthing and whitewashing attack,but they are limited in detecting ballot-stuffing under sybil attack,constant attacks and camouflage attack.展开更多
With the complexity of integrated circuits is continually increasing, a local defect in circuits may cause multiple faults. The behavior of a digital circuit with a multiple fault may significantly differ from that of...With the complexity of integrated circuits is continually increasing, a local defect in circuits may cause multiple faults. The behavior of a digital circuit with a multiple fault may significantly differ from that of a single fault. A new method for the detection of multiple faults in digital circuits is presented in this paper, the method is based on binary decision diagram （BDD）. First of all, the BDDs for the normal circuit and faulty circuit are built respectively. Secondly, a test BDD is obtained by the XOR operation of the BDDs corresponds to normal circuit and faulty circuit. In the test BDD, each input assignment that leads to the leaf node labeled 1 is a test vector of multiple faults. Therefore, the test set of multiple faults is generated by searching for the type of input assignments in the test BDD. Experimental results on some digital circuits show the feasibility of the approach presented in this paper.展开更多
A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields.The proposed multi-attributes based quantum neural networks for hydrocarbon detection use data cluste...A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields.The proposed multi-attributes based quantum neural networks for hydrocarbon detection use data clustering and local wave decomposition based seismic attenuation characteristics,relative wave impedance features of prestack seismic data as the selected multiple attributes for one tight sandstone gas reservoir and further employ principal component analysis combined with quantum neural networks for giving the distinguishing results of the weak responses of the gas reservoir,which is hard to detect by using the conventional technologies.For the seismic data from a tight sandstone gas reservoir in the Sichuan basin,China,we found that multiattributes based quantum neural networks can effectively capture the weak seismic responses features associated with gas saturation in the gas reservoir.This study is hoped to be useful as an aid for hydrocarbon detections for the gas reservoir with the characteristics of the weak seismic responses by the complement of the multiattributes based quantum neural networks.展开更多
Surface-enhanced Raman scattering(SERS)is a powerful technology for obtaining vibrational information from molecules that present in different chemical or biological environments.This paper presents a 3D SERS substrat...Surface-enhanced Raman scattering(SERS)is a powerful technology for obtaining vibrational information from molecules that present in different chemical or biological environments.This paper presents a 3D SERS substrate based on nanocone forests.The substrates are prepared by using plasma treatment technique,which is a simple,fast and high-throughput approach.The SERS substrate based on nanocone forests exhibits high sensitivity.In the experiment,miRNA with a concentration as low as 10-10 M can be achieved.Meanwhile,the proposed SERS substrate shows a high uniformity over a large area.These experimental results demonstrate great potential of the 3D SERS substrate in wide applications.展开更多
To solve the problem that the conventional detections in DS-CDMA suffer from high complexity and poor robustness for the time-hopping pulse signals, the received pulse signals were remodeled, and a mulfipath-free dete...To solve the problem that the conventional detections in DS-CDMA suffer from high complexity and poor robustness for the time-hopping pulse signals, the received pulse signals were remodeled, and a mulfipath-free detection scheme, which provides a simple approach to select samples of received signals, was introduced. By this scheme, the subsequent multiuser detection （MUD） would get rid of the mis- match due to the correlative multipath signal in IR-UWB. In addition, a computationally efficient recur-sive least squares （RLS） type algorithm based on least mean fourth （LMF） criterion is derived to suppress multi-access interference. The proposed multiuser detection algorithm performs well at low complexity, even in dense muhipath environment.展开更多
This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human breast.The size of the current antenna is small enough(18mm×21mm×1.6mm)...This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human breast.The size of the current antenna is small enough(18mm×21mm×1.6mm)todistribute around the breast phantom.The operating frequency has been observed from6–14GHzwith a minimumreturn loss of−61.18 dB and themaximumgain of current proposed antenna is 5.8 dBiwhich is flexiblewith respectto the size of antenna.After the distribution of eight antennas around the breast phantom,the return loss curveswere observed in the presence and absence of tumor cells inside the breast phantom,and these observations showa sharp difference between the presence and absence of tumor cells.The simulated results show that this proposedantenna is suitable for early detection of cancerous cells inside the breast.展开更多
In the early-stage diagnosis of lung cancer,the low-concentration(<5 ppm)volatile organic compounds(VOCs)are extensively identified to be the biomarkers for breath analysis.Herein,the urchin-like sodium(Na)-doped z...In the early-stage diagnosis of lung cancer,the low-concentration(<5 ppm)volatile organic compounds(VOCs)are extensively identified to be the biomarkers for breath analysis.Herein,the urchin-like sodium(Na)-doped zinc oxide(ZnO)nanoneedles were synthesized through a hydrothermal strategy with the addition of different contents of citric acid.The Na-doped ZnO gas sensor with a 3:1 molar ratio of Na^(+)and citric acid showed outstanding sensing properties with an optimal selectivity to various VOCs(formaldehyde(HCOH),isopropanol,acetone,and ammonia)based on working temperature regulation.Specifically,significantly enhanced sensitivity(21.3@5 ppm)compared with pristine ZnO(~7-fold),low limit of detection(LOD)(298 ppb),robust humidity resistance,and long-term stability of formaldehyde sensing performances were obtained,which can be attributed to the formation of a higher concentration of oxygen vacancies(20.98%)and the active electron transitions.Furthermore,the improved sensing mechanism was demonstrated by the exquisite band structure and introduction of the additional acceptor level,which resulted in the narrowed bandgap of ZnO.展开更多
Dear Editor,Industrial Internet of things(IIoT) is a typical application of cyberphysical system(CPS). In the IIoT, wireless communication is an inevitable trend to replace the deployment-limited wired transmission fo...Dear Editor,Industrial Internet of things(IIoT) is a typical application of cyberphysical system(CPS). In the IIoT, wireless communication is an inevitable trend to replace the deployment-limited wired transmission for cases with large-scale and mobile devices. However, wireless communication gives rise to critical issues related to physical security, such as malicious detections and attacks [1].展开更多
Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num...Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.展开更多
Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable...Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.展开更多
With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a c...With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.展开更多
In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence ...In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.展开更多
In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approac...The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.展开更多
OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulati...OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulations,and De Qi sensation detection.The OptiTrack motion capture system is used to locate acupoints,which are then translated into coordinates in the robot control system.A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision.In addition,a De Qi sensation detection system is proposed to evaluate the effect of acupuncture.To verify the stability of the designed acupuncture robot,acupoints'coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests.RESULTS:Through repeated experiments for eight acupoints,the acupuncture robot achieved a positioning error within 3.3 mm,which is within the allowable range of needle extraction and acupoint insertion.During needle insertion,the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15°.The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm,which is within the recommended depth range for the Xingzhen operation.In addition,the average detection accuracy of the De Qi keywords is 94.52%,which meets the requirements of acupuncture effect testing for different dialects.CONCLUSION:The proposed acupuncture robot system streamlines the acupuncture process,increases efficiency,and reduces practitioner fatigue,while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects.The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.展开更多
Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer charact...Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft.In this paper,the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed,and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed.By modeling the spatial distribution of the disturbance density field,the scattered echo signal images of active light towards the disturbance field at long distance are simulated.On this basis,the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed.The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images.The disturbance signal can be further highlighted by differential processing of the adjacent scattering images.As the distance behind the aircraft increases,the diffusion range of the disturbance signal increases,and the signal intensity and contrast with the background decrease.Under the ground-based observation conditions of the aircraft at a height of 10000 m,a Mach number of1.6,and a detection distance of 100 km,the contrast between the disturbance signal and the back-ground was 30 d B at a distance of one time from the rear of the fuselage,and the diffusion diameter of the disturbance signal was 50 m.At a distance eight times the length of the aircraft,the contrast decreased to 10 dB,and the diameter increased to 290 m.The contrast was reduced to 3 dB at a distance nine times the length of the aircraft,and the diameter was diffused to 310 m.These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Liver cancer,particularly hepatocellular carcinoma(HCC),remains a significant global health challenge due to its high mortality rate and late-stage diagnosis.The discovery of reliable biomarkers is crucial for improvi...Liver cancer,particularly hepatocellular carcinoma(HCC),remains a significant global health challenge due to its high mortality rate and late-stage diagnosis.The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes.This review provides a comprehensive overview of current and emerging biomarkers for HCC,including alpha-fetoprotein,des-gamma-carboxy prothrombin,glypican-3,Golgi protein 73,osteopontin,and microRNAs.Despite advancements,the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages.The review emphasizes the importance of integrating multi-omics approaches,combining genomics,proteomics,and metabolomics,to develop more robust biomarker panels.Such integrative methods have the potential to capture the complex molecular landscape of HCC,offering insights into disease mechanisms and identifying targets for personalized therapies.The significance of large-scale validation studies,collaboration between research institutions and clinical settings,and consideration of regulatory pathways for clinical implementation is also discussed.In conclusion,while substantial progress has been made in biomarker discovery,continued research and innovation are essential to address the remaining challenges.The successful translation of these discoveries into clinical practice will require rigorous validation,standardization of protocols,and crossdisciplinary collaboration.By advancing the development and application of novel biomarkers,we can improve the early detection and management of HCC,ultimately enhancing patient survival and quality of life.展开更多
文摘Background:The reputation system has been designed as an effective mechanism to reduce risks associated with online shopping for customers.However,it is vulnerable to rating fraud.Some raters may inject unfairly high or low ratings to the system so as to promote their own products or demote their competitors.Method:This study explores the rating fraud by differentiating the subjective fraud from objective fraud.Then it discusses the effectiveness of blockchain technology in objective fraud and its limitation in subjective fraud,especially the rating fraud.Lastly,it systematically analyzes the robustness of blockchain-based reputation systems in each type of rating fraud.Results:The detection of fraudulent raters is not easy since they can behave strategically to camouflage themselves.We explore the potential strengths and limitations of blockchain-based reputation systems under two attack goals:ballot-stuffing and bad-mouthing,and various attack models including constant attack,camouflage attack,whitewashing attack and sybil attack.Blockchain-based reputation systems are more robust against bad-mouthing than ballot-stuffing fraud.Conclusions:Blockchain technology provides new opportunities for redesigning the reputation system.Blockchain systems are very effective in preventing objective information fraud,such as loan application fraud,where fraudulent information is fact-based.However,their effectiveness is limited in subjective information fraud,such as rating fraud,where the ground-truth is not easily validated.Blockchain systems are effective in preventing bad mouthing and whitewashing attack,but they are limited in detecting ballot-stuffing under sybil attack,constant attacks and camouflage attack.
基金Supported by the National Natural Science Foun-dation of China (60006002) Natural Science Research Project of Education Department of Guangdong Province of China (02019)
文摘With the complexity of integrated circuits is continually increasing, a local defect in circuits may cause multiple faults. The behavior of a digital circuit with a multiple fault may significantly differ from that of a single fault. A new method for the detection of multiple faults in digital circuits is presented in this paper, the method is based on binary decision diagram （BDD）. First of all, the BDDs for the normal circuit and faulty circuit are built respectively. Secondly, a test BDD is obtained by the XOR operation of the BDDs corresponds to normal circuit and faulty circuit. In the test BDD, each input assignment that leads to the leaf node labeled 1 is a test vector of multiple faults. Therefore, the test set of multiple faults is generated by searching for the type of input assignments in the test BDD. Experimental results on some digital circuits show the feasibility of the approach presented in this paper.
基金Supported in part by the Central Government Funds of Guiding Local Scientific and Technological Development for Sichuan Province(No.2021ZYD0030)in part by the National Natural Science Foundation of China(Nos.41804140,42074163,41974160,42030812).
文摘A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields.The proposed multi-attributes based quantum neural networks for hydrocarbon detection use data clustering and local wave decomposition based seismic attenuation characteristics,relative wave impedance features of prestack seismic data as the selected multiple attributes for one tight sandstone gas reservoir and further employ principal component analysis combined with quantum neural networks for giving the distinguishing results of the weak responses of the gas reservoir,which is hard to detect by using the conventional technologies.For the seismic data from a tight sandstone gas reservoir in the Sichuan basin,China,we found that multiattributes based quantum neural networks can effectively capture the weak seismic responses features associated with gas saturation in the gas reservoir.This study is hoped to be useful as an aid for hydrocarbon detections for the gas reservoir with the characteristics of the weak seismic responses by the complement of the multiattributes based quantum neural networks.
文摘Surface-enhanced Raman scattering(SERS)is a powerful technology for obtaining vibrational information from molecules that present in different chemical or biological environments.This paper presents a 3D SERS substrate based on nanocone forests.The substrates are prepared by using plasma treatment technique,which is a simple,fast and high-throughput approach.The SERS substrate based on nanocone forests exhibits high sensitivity.In the experiment,miRNA with a concentration as low as 10-10 M can be achieved.Meanwhile,the proposed SERS substrate shows a high uniformity over a large area.These experimental results demonstrate great potential of the 3D SERS substrate in wide applications.
基金the National Natural Science Foundation of China(No60432040)the Guangxi Key Laboratory Foundation(No,063006-5G)
文摘To solve the problem that the conventional detections in DS-CDMA suffer from high complexity and poor robustness for the time-hopping pulse signals, the received pulse signals were remodeled, and a mulfipath-free detection scheme, which provides a simple approach to select samples of received signals, was introduced. By this scheme, the subsequent multiuser detection （MUD） would get rid of the mis- match due to the correlative multipath signal in IR-UWB. In addition, a computationally efficient recur-sive least squares （RLS） type algorithm based on least mean fourth （LMF） criterion is derived to suppress multi-access interference. The proposed multiuser detection algorithm performs well at low complexity, even in dense muhipath environment.
基金the International Science and Technology Cooperation Project of the Shenzhen Science and Technology Commission(GJHZ20200731095804014).
文摘This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human breast.The size of the current antenna is small enough(18mm×21mm×1.6mm)todistribute around the breast phantom.The operating frequency has been observed from6–14GHzwith a minimumreturn loss of−61.18 dB and themaximumgain of current proposed antenna is 5.8 dBiwhich is flexiblewith respectto the size of antenna.After the distribution of eight antennas around the breast phantom,the return loss curveswere observed in the presence and absence of tumor cells inside the breast phantom,and these observations showa sharp difference between the presence and absence of tumor cells.The simulated results show that this proposedantenna is suitable for early detection of cancerous cells inside the breast.
基金the Outstanding Youth Foundation of Jiangsu Province of China(No.BK20211548)the Yangzhou Science and Technology Plan Project(No.YZ2023246)the Qinglan Project of Yangzhou University,and the Research Innovation Plan of Graduate Education Innovation Project in Jiangsu Province(No.KYCX23_3530).
文摘In the early-stage diagnosis of lung cancer,the low-concentration(<5 ppm)volatile organic compounds(VOCs)are extensively identified to be the biomarkers for breath analysis.Herein,the urchin-like sodium(Na)-doped zinc oxide(ZnO)nanoneedles were synthesized through a hydrothermal strategy with the addition of different contents of citric acid.The Na-doped ZnO gas sensor with a 3:1 molar ratio of Na^(+)and citric acid showed outstanding sensing properties with an optimal selectivity to various VOCs(formaldehyde(HCOH),isopropanol,acetone,and ammonia)based on working temperature regulation.Specifically,significantly enhanced sensitivity(21.3@5 ppm)compared with pristine ZnO(~7-fold),low limit of detection(LOD)(298 ppb),robust humidity resistance,and long-term stability of formaldehyde sensing performances were obtained,which can be attributed to the formation of a higher concentration of oxygen vacancies(20.98%)and the active electron transitions.Furthermore,the improved sensing mechanism was demonstrated by the exquisite band structure and introduction of the additional acceptor level,which resulted in the narrowed bandgap of ZnO.
基金partly supported by the National Natural Science Foundation of China(62273298,62273295)Hebei Natural Science Foundation(F2023203063,F2022203025)+1 种基金China Scholarship Council(CSC)(202308130180)Provincial Key Laboratory Performance Subsidy Project(22567612H)
文摘Dear Editor,Industrial Internet of things(IIoT) is a typical application of cyberphysical system(CPS). In the IIoT, wireless communication is an inevitable trend to replace the deployment-limited wired transmission for cases with large-scale and mobile devices. However, wireless communication gives rise to critical issues related to physical security, such as malicious detections and attacks [1].
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
文摘Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
文摘Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.
基金the Experimental Technology Research Project of Zhejiang University(SYB202138)National Natural Science Foundation of China(32000195)。
文摘With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.
文摘In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
基金supported by the National Natural Science Foundation of China(No.U21A20290)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011656)+2 种基金the Projects of Talents Recruitment of GDUPT(No.2023rcyj1003)the 2022“Sail Plan”Project of Maoming Green Chemical Industry Research Institute(No.MMGCIRI2022YFJH-Y-024)Maoming Science and Technology Project(No.2023382).
文摘The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.
基金Modernization of Traditional Chinese Medicine Project of National Key R&D Program of China:The construction of the theoretical system of Traditional Chinese Medicine nonpharmacological therapy based on body surface stimulation(2023YFC3502704)Sichuan Provincial Science and Technology Program Project:Research and Development of Chinese Medicine Intelligent Tongue Diagnosis Equipment for Digestive System Chinese Medicine Advantageous Diseases(2023YFS0327)+2 种基金Research and Development of Chinese Medicine Intelligent Detection System for Intestinal Functions(2024YFFK0044)Research and Application of Chinese Medicine Diagnosis and Treatment Program for Herpes Zoster Treated by Shu Pai Fire Acupuncture(2024YFFK0089)Major Research and Development Project of The China Academy of Chinese Medical Sciences Innovation:Construction and application of the theoretical research mode of Traditional Chinese Medicine diagnosis and treatment of modern diseases(CI2021A00104)。
文摘OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulations,and De Qi sensation detection.The OptiTrack motion capture system is used to locate acupoints,which are then translated into coordinates in the robot control system.A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision.In addition,a De Qi sensation detection system is proposed to evaluate the effect of acupuncture.To verify the stability of the designed acupuncture robot,acupoints'coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests.RESULTS:Through repeated experiments for eight acupoints,the acupuncture robot achieved a positioning error within 3.3 mm,which is within the allowable range of needle extraction and acupoint insertion.During needle insertion,the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15°.The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm,which is within the recommended depth range for the Xingzhen operation.In addition,the average detection accuracy of the De Qi keywords is 94.52%,which meets the requirements of acupuncture effect testing for different dialects.CONCLUSION:The proposed acupuncture robot system streamlines the acupuncture process,increases efficiency,and reduces practitioner fatigue,while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects.The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.
文摘Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft.In this paper,the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed,and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed.By modeling the spatial distribution of the disturbance density field,the scattered echo signal images of active light towards the disturbance field at long distance are simulated.On this basis,the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed.The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images.The disturbance signal can be further highlighted by differential processing of the adjacent scattering images.As the distance behind the aircraft increases,the diffusion range of the disturbance signal increases,and the signal intensity and contrast with the background decrease.Under the ground-based observation conditions of the aircraft at a height of 10000 m,a Mach number of1.6,and a detection distance of 100 km,the contrast between the disturbance signal and the back-ground was 30 d B at a distance of one time from the rear of the fuselage,and the diffusion diameter of the disturbance signal was 50 m.At a distance eight times the length of the aircraft,the contrast decreased to 10 dB,and the diameter increased to 290 m.The contrast was reduced to 3 dB at a distance nine times the length of the aircraft,and the diameter was diffused to 310 m.These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
文摘Liver cancer,particularly hepatocellular carcinoma(HCC),remains a significant global health challenge due to its high mortality rate and late-stage diagnosis.The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes.This review provides a comprehensive overview of current and emerging biomarkers for HCC,including alpha-fetoprotein,des-gamma-carboxy prothrombin,glypican-3,Golgi protein 73,osteopontin,and microRNAs.Despite advancements,the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages.The review emphasizes the importance of integrating multi-omics approaches,combining genomics,proteomics,and metabolomics,to develop more robust biomarker panels.Such integrative methods have the potential to capture the complex molecular landscape of HCC,offering insights into disease mechanisms and identifying targets for personalized therapies.The significance of large-scale validation studies,collaboration between research institutions and clinical settings,and consideration of regulatory pathways for clinical implementation is also discussed.In conclusion,while substantial progress has been made in biomarker discovery,continued research and innovation are essential to address the remaining challenges.The successful translation of these discoveries into clinical practice will require rigorous validation,standardization of protocols,and crossdisciplinary collaboration.By advancing the development and application of novel biomarkers,we can improve the early detection and management of HCC,ultimately enhancing patient survival and quality of life.
