The complexity of living environment system demands higher requirements for the sensitivity and selectivity of the probe.Therefore,it is of great importance to develop a universal strategy for highperformance probe op...The complexity of living environment system demands higher requirements for the sensitivity and selectivity of the probe.Therefore,it is of great importance to develop a universal strategy for highperformance probe optimization.Herein,we propose a novel“Enrichment-enhanced Detection”strategy and use carbon dots-dopamine detection system as a representative model to evaluate its feasibility.The composite probe carbon dots (CDs)-encapsulated in glycol-chitosan (GC)(i.e.,CDs@GC) was obtained by simply mixing GC and CDs through noncovalent interactions,including electrostatic interactions and hydrogen bonding.Dopamine (DA) could be detected through internal filter effect (IFE)-induced quenching of CDs.In the case of CDs@GC,noncovalent interactions (electrostatic interactions) between GC and the formed quinone (oxide of DA) could selectively extract and enrich the local concentration of DA,thus effectively improving the sensitivity and selectivity of the sensing system.The nanosensor had a low detection limit of 3.7 nmol/L,which was a 12-fold sensitivity improvement compared to the bare CDs probes with similar fluorescent profiles,proving the feasibility of the“Enrichment-enhanced Detection”strategy.Further,to examine this theory in real case,we designed a highly portable sensing platform to realize visual determination of DA.Overall,our work introduces a new strategy for accurately detecting DA and provides valuable insights for the universal design and optimization of superior nanoprobes.展开更多
In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are qu...In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.展开更多
The relentless pursuit of advanced X-ray detection technologies has been significantly bolstered by the emergence of metal halides perovskites(MHPs)and their derivatives,which possess remarkable light yield and X-ray ...The relentless pursuit of advanced X-ray detection technologies has been significantly bolstered by the emergence of metal halides perovskites(MHPs)and their derivatives,which possess remarkable light yield and X-ray sensitivity.This comprehensive review delves into cutting-edge approaches for optimizing MHP scintillators performances by enhancing intrinsic physical properties and employing engineering radioluminescent(RL)light strategies,underscoring their potential for developing materials with superior high-resolution X-ray detection and imaging capabilities.We initially explore into recent research focused on strategies to effectively engineer the intrinsic physical properties of MHP scintillators,including light yield and response times.Additionally,we explore innovative engineering strategies involving stacked structures,waveguide effects,chiral circularly polarized luminescence,increased transparency,and the fabrication of flexile MHP scintillators,all of which effectively manage the RL light to achieve high-resolution and high-contrast X-ray imaging.Finally,we provide a roadmap for advancing next-generation MHP scintillators,highlighting their transformative potential in high-performance X-ray detection systems.展开更多
This paper deeply explores oversampling technology and its applications in biomedical signal detection.It first expounds on the significance of oversampling technology in biomedical signal detection,and then analyzes ...This paper deeply explores oversampling technology and its applications in biomedical signal detection.It first expounds on the significance of oversampling technology in biomedical signal detection,and then analyzes the application strategies of oversampling technology in this field.On this basis,it details the specific applications of oversampling technology in electrophysiological signal detection,biomedical imaging signal processing,and other biomedical signal detections,and verifies its effectiveness through practical case analysis,aiming to provide certain references for relevant researchers.展开更多
As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial ...As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical equipment.In response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis,we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once(GD-YOLO).Firstly,a partial convolution group is designed based on different convolution kernels.We combine the partial convolution group with deep convolution to propose a new Grouped Channel-wise Spatial Convolution(GCSConv)that compensates for the information loss caused by spatial channel convolution.Secondly,the Gather and Distribute Mechanism,which addresses the fusion problem of different dimensional features,has been implemented by aligning and sharing information through aggregation and distribution mechanisms.Thirdly,considering the limitations in current bounding box regression and the imbalance between complex and simple samples,Maximum Possible Distance Intersection over Union(MPDIoU)and Adaptive SlideLoss is incorporated into the loss function,allowing samples near the Intersection over Union(IoU)to receive more attention through the dynamic variation of the mean Intersection over Union.The GD-YOLO algorithm can surpass YOLOv5,YOLOv7,and YOLOv8 in infrared image detection for electrical equipment,achieving a mean Average Precision(mAP)of 88.9%,with accuracy improvements of 3.7%,4.3%,and 3.1%,respectively.Additionally,the model delivers a frame rate of 48 FPS,which aligns with the precision and velocity criteria necessary for the detection of infrared images in power equipment.展开更多
Tian et al investigated the diagnostic value of serum vascular endothelial growth factor(VEGF)and interleukin-17(IL-17)in primary hepatocellular carcinoma(PHC).Their retrospective study,published in the World Journal ...Tian et al investigated the diagnostic value of serum vascular endothelial growth factor(VEGF)and interleukin-17(IL-17)in primary hepatocellular carcinoma(PHC).Their retrospective study,published in the World Journal of Gastrointestinal Surgery,revealed that the serum levels of VEGF and IL-17 are significantly elevated in PHC patients compared with healthy controls.These biomarkers are closely associated with pathological features such as tumor metastasis and clinical tumor node metastasis stage.A receiver operating characteristic analysis further confirmed the diagnostic efficacy thereof,suggesting that VEGF and IL-17 could serve as valuable tools for early detection and treatment guidance.This study underscores the potential of integrating these biomarkers into clinical practice to increase diagnostic accuracy and improve patient management in PHC.展开更多
Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(...Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection performance.On the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features.To address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion detection.First,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic.Meanwhile,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic.In addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch.Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.展开更多
Quantitative oxygen detection,especially at low concentrations,holds significant importance in the realms of biology,complex environments,and chemical process engineering.Due to the high sensitivity and rapid response...Quantitative oxygen detection,especially at low concentrations,holds significant importance in the realms of biology,complex environments,and chemical process engineering.Due to the high sensitivity and rapid response of the triplet excitons of phosphorescence to oxygen,pure organic room-temperature phosphorescence(RTP)materials have garnered widespread attention in recent years for oxygen detection.However,simultaneously achieving ultralong phosphorescence at room temperature and quantitative oxygen detection from pure organic host-vip doped materials poses challenges.The d ensely packed materials may decrease non-radiative decay to increase the phosphorescence,but are unsuitable for oxygen diffusion in oxygen detection.Herein,the oxygen sensitivity of host-vip doped RTP materials using 4-bromo-N,N-bis(4-(tertbutyl)phenyl)aniline(TPABuBr)as the host and 6-bromo-2-butyl-1H-benzo[de]isoquinoline-1,3(2H)-dione(NIBr)as the vip was developed.The doped material exhibits fluorescence-phosphorescence dual-emission behavior at room temperature.The tert-butyl groups in TPABuBr facilitate appropriate intermolecular spacing in the crystal state,enhancing oxygen permeability.Therefore,oxygen penetration can quench the phosphorescence emission.The observed linear relationship between the phosphorescence intensity of the doped material and the oxygen volume fraction conforms to the Stern-Volmer equation,suggesting its potential for quantitative analysis of oxygen concentration.The calculated limit of detection is 0.015%(φ),enabling the analysis of oxygen with a volume fraction of less than 2.5%(φ).Moreover,the doped materials demonstrate rapid response and excellent photostability,indicating their potential utility as oxygen sensors.This study elucidates the design and characteristics of NIBr/TPABuBr doped materials,highlighting their potential application in oxygen concentration detection and offering insights for the design of oxygen sensors.展开更多
Aiming at the limitations of the existing knowledge representations in intelligent detection, a new method of Extension-based Knowledge Representation (EKR) was proposed. The definitions, grammar rules, and storage st...Aiming at the limitations of the existing knowledge representations in intelligent detection, a new method of Extension-based Knowledge Representation (EKR) was proposed. The definitions, grammar rules, and storage structure of EKR were presented. An Extension Solving Model (ESM) based on EKR was discussed in detail, including creation of the extension constraint graph, extended inference, calculation of relevant functions and generation of extension set. A knowledge base system based on EKR and ESM was developed, which was applied in extension repository system intelligent design of detection in photosynthesis process of D.huoshanense. More reasonable results were obtained than traditional rule-based system. EKR was feasible in intelligent design to solve the problem of intelligent detection knowledge representations.展开更多
Cryptojacking is a type of resource embezzlement attack,wherein an attacker secretly executes the cryptocurrency mining program in the target host to gain profits.It has been common since 2017,and in fact,it once beca...Cryptojacking is a type of resource embezzlement attack,wherein an attacker secretly executes the cryptocurrency mining program in the target host to gain profits.It has been common since 2017,and in fact,it once became the greatest threat to network security.To better prove the attack ability the harm caused by cryptojacking,this paper proposes a new covert browser-based mining attack model named Delay-CJ,this model was deployed in a simulation environment for evaluation.Based on the general framework of cryptojacking,Delay-CJ adds hybrid evasion detection techniques and applies the delayed execution strategy specifically for video websites in the prototype implementation.The results show that the existing detection methods used for testing may become invalid as result of this model.In view of this situation,to achieve a more general and robust detection scheme,we built a cryptojacking detection system named CJDetector,which is based on cryptojacking process features.Specifically,it identifies malicious mining by monitoring CPU usage and analyzing the function call information.This system not only effectively detects the attack in our example but also has universal applicability.The recognition accuracy of CJDetector reaches 99.33%.Finally,we tested the web pages in Alexa 50K websites to investigate cryptojacking activity in the real network.We found that although cryptojacking is indeed on the decline,it remains a part of network security threats that cannot be ignored.展开更多
This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patie...This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.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
Humanoid robots exhibit structures and movements akin to those of humans,enabling them to assist or substitute for humans in various operations without necessitating alterations to their typical environment and tools....Humanoid robots exhibit structures and movements akin to those of humans,enabling them to assist or substitute for humans in various operations without necessitating alterations to their typical environment and tools.Sustaining bal-ance amidst disturbances constitutes a fundamental capability for humanoid robots.Consequently,adopting efficacious strategies to manage instability and mitigate injuries resulting from falls assumes paramount importance in advancing the widespread adoption of humanoid robotics.This paper presents a comprehensive overview of the ongoing development of strategies for coping with falls in humanoid robots.It systematically reviews and discusses three critical facets:fall state detection,preventive actions against falls,and post-fall protection measures.The paper undertakes a thorough classifica-tion of existing coping methodologies across different stages of falls,analyzes the merits and drawbacks of each approach,and outlines the evolving trajectory of solutions for addressing fall-related challenges across distinct stages.Finally,the paper provides a succinct summary and future prospects for the current fall coping strategies tailored for humanoid robots.展开更多
MicroRNAs (miRNAs) are a family of endogenous, small (approximately 22 nucleotides in length), noncoding, functional RNAs. With the development of molecular biology, the research of miRNA biological function has attra...MicroRNAs (miRNAs) are a family of endogenous, small (approximately 22 nucleotides in length), noncoding, functional RNAs. With the development of molecular biology, the research of miRNA biological function has attracted significant interest, as abnormal miRNA expression is identified to contribute to serious human diseases such as cancers. Traditional methods for miRNA detection do not meet current demands. In particular, nanomaterial-based methods, nucleic acid amplification-based methods such as rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), strand-displacement amplification (SDA) and some enzyme-free amplifications have been employed widely for the highly sensitive detection of miRNA. MiRNA functional research and clinical diagnostics have been accelerated by these new techniques. Herein, we summarize and discuss the recent progress in the development of miRNA detection methods and new applications. This review will provide guidelines for the development of follow-up miRNA detection methods with high sensitivity and specificity, and applicability to disease diagnosis and therapy.展开更多
Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially in...Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.展开更多
Lung cancer is associated with a heavy cancer-related burden in terms of patients’physical and mental health worldwide.Two randomized controlled trials,the US-National Lung Screening Trial(NLST)and Nederlands-Leuvens...Lung cancer is associated with a heavy cancer-related burden in terms of patients’physical and mental health worldwide.Two randomized controlled trials,the US-National Lung Screening Trial(NLST)and Nederlands-Leuvens Longkanker Screenings Onderzoek(NELSON),indicated that low-dose CT(LDCT)screening results in a statistically significant decrease in mortality in patients with lung cancer,LDCT has become the standard approach for lung cancer screening.However,many issues in lung cancer screening remain unresolved,such as the screening criteria,high false-positive rate,and radiation exposure.This review first summarizes recent studies on lung cancer screening from the US,Europe,and Asia,and discusses risk-based selection for screening and the related issues.Second,an overview of novel techniques for the differential diagnosis of pulmonary nodules,including artificial intelligence and molecular biomarker-based screening,is presented.Third,current explorations of strategies for suspected malignancy are summarized.Overall,this review aims to help clinicians understand recent progress in lung cancer screening and alleviate the burden of lung cancer.展开更多
The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements,but they can’t accurately detect small ob...The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements,but they can’t accurately detect small objects and objects with obstructions.Therefore,we propose a helmet detection algorithm based on the attention mechanism(AT-YOLO).First of all,a channel attention module is added to the YOLOv3 backbone network,which can adaptively calibrate the channel features of the direction to improve the feature utilization,and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation between any positions in the feature map so that to increase the receptive field of the network.Secondly,we use DIoU(Distance Intersection over Union)bounding box regression loss function,it not only improving the measurement of bounding box regression loss but also increases the normalized distance loss between the prediction boxes and the target boxes,which makes the network more accurate in detecting small objects and faster in convergence.Finally,we explore the training strategy of the network model,which improves network performance without increasing the inference cost.Experiments show that the mAP of the proposed method reaches 96.5%,and the detection speed can reach 27 fps.Compared with other existing methods,it has better performance in detection accuracy and speed.展开更多
Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely dis...Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.展开更多
SSD(Single Shot Multibox Detector) is one of the best object detection algorithms with both high accuracy and fast speed, but fails to detect very small size object which lacks enough resolution and enough feature inf...SSD(Single Shot Multibox Detector) is one of the best object detection algorithms with both high accuracy and fast speed, but fails to detect very small size object which lacks enough resolution and enough feature information. In order to solve this problem, the majority of existing methods improve accuracy at the cost of a heavy loss of speed. In this paper, we propose SFE-SSD(Shallow Feature Enhancement SSD) to improve performance of SSD model on small object detection based on a novel and lightweight way of feature enhancement module. Firstly,we apply deconvolution on the shallowest feature map in SSD’s feature pyramid to enlarge the feature map size and recover more feature details. Then, we introduce semantic information to the enlarged feature map by multi-scale feature fusion. In addition, SFE-SSD is designed to a parallel network structure, which could reduce loss of speed in some degree. Experimental results show that our approach achieved 78.4%m AP and is higher than baseline SSD by 1.2%on PASCAL VOC2007, especially with significant improvement on small object detection. The testing speed of SFE-SSD is 81 FPS at the cost of a little loss of speed.展开更多
Smoke detection is the most commonly used method in early warning of fire and is widely used in forest detection.Most existing smoke detection methods contain empty spaces and obstacles which interfere with detection ...Smoke detection is the most commonly used method in early warning of fire and is widely used in forest detection.Most existing smoke detection methods contain empty spaces and obstacles which interfere with detection and extract false smoke roots.This study developed a new smoke roots search algorithm based on a multi-feature fusion dynamic extraction strategy.This determines smoke origin candidate points and region based on a multi-frame discrete confidence level.The results show that the new method provides a more complete smoke contour with no background interference,compared to the results using existing methods.Unlike video-based methods that rely on continuous frames,an adaptive threshold method was developed to build the judgment image set composed of non-consecutive frames.The smoke roots origin search algorithm increased the detection rate and significantly reduced false detection rate compared to existing methods.展开更多
基金the financial support from the National Natural Science Foundation of China(No.21904007)the Fundamental Research Funds for the Central Universities(China,No.2412022QD008)+1 种基金the Jilin Provincial Department of Education(China),the Key Laboratory of Nanobiosensing and Nanobioanalysis at Universities of Jilin Province(China)the Analysis and Testing Center of Northeast Normal University(China)。
文摘The complexity of living environment system demands higher requirements for the sensitivity and selectivity of the probe.Therefore,it is of great importance to develop a universal strategy for highperformance probe optimization.Herein,we propose a novel“Enrichment-enhanced Detection”strategy and use carbon dots-dopamine detection system as a representative model to evaluate its feasibility.The composite probe carbon dots (CDs)-encapsulated in glycol-chitosan (GC)(i.e.,CDs@GC) was obtained by simply mixing GC and CDs through noncovalent interactions,including electrostatic interactions and hydrogen bonding.Dopamine (DA) could be detected through internal filter effect (IFE)-induced quenching of CDs.In the case of CDs@GC,noncovalent interactions (electrostatic interactions) between GC and the formed quinone (oxide of DA) could selectively extract and enrich the local concentration of DA,thus effectively improving the sensitivity and selectivity of the sensing system.The nanosensor had a low detection limit of 3.7 nmol/L,which was a 12-fold sensitivity improvement compared to the bare CDs probes with similar fluorescent profiles,proving the feasibility of the“Enrichment-enhanced Detection”strategy.Further,to examine this theory in real case,we designed a highly portable sensing platform to realize visual determination of DA.Overall,our work introduces a new strategy for accurately detecting DA and provides valuable insights for the universal design and optimization of superior nanoprobes.
基金supported in part by the National Natural Science Foundation of China(61933007,62273087,62273088,U21A2019)the Shanghai Pujiang Program of China(22PJ1400400)+2 种基金the Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Royal Society of U.K.the Alexander von Humboldt Foundation of Germany
文摘In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.
基金supported by the National Nature Science Foundation of China(NSFC)(U2241236,1220041913,52473253)the National Key Research and Development Program of China(2022ZDZX0007)+1 种基金Fundamental Research Open Subject Grant Program of Yantai Advanced Materials and Green Manufacturing Laboratory of Shandong Province(AMGM2024F15)Yunnan Major Scientific and Technological Projects(202402AB080011).
文摘The relentless pursuit of advanced X-ray detection technologies has been significantly bolstered by the emergence of metal halides perovskites(MHPs)and their derivatives,which possess remarkable light yield and X-ray sensitivity.This comprehensive review delves into cutting-edge approaches for optimizing MHP scintillators performances by enhancing intrinsic physical properties and employing engineering radioluminescent(RL)light strategies,underscoring their potential for developing materials with superior high-resolution X-ray detection and imaging capabilities.We initially explore into recent research focused on strategies to effectively engineer the intrinsic physical properties of MHP scintillators,including light yield and response times.Additionally,we explore innovative engineering strategies involving stacked structures,waveguide effects,chiral circularly polarized luminescence,increased transparency,and the fabrication of flexile MHP scintillators,all of which effectively manage the RL light to achieve high-resolution and high-contrast X-ray imaging.Finally,we provide a roadmap for advancing next-generation MHP scintillators,highlighting their transformative potential in high-performance X-ray detection systems.
文摘This paper deeply explores oversampling technology and its applications in biomedical signal detection.It first expounds on the significance of oversampling technology in biomedical signal detection,and then analyzes the application strategies of oversampling technology in this field.On this basis,it details the specific applications of oversampling technology in electrophysiological signal detection,biomedical imaging signal processing,and other biomedical signal detections,and verifies its effectiveness through practical case analysis,aiming to provide certain references for relevant researchers.
基金Science and Technology Department of Jilin Province(No.20200403075SF)Education Department of Jilin Province(No.JJKH20240148KJ).
文摘As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical equipment.In response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis,we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once(GD-YOLO).Firstly,a partial convolution group is designed based on different convolution kernels.We combine the partial convolution group with deep convolution to propose a new Grouped Channel-wise Spatial Convolution(GCSConv)that compensates for the information loss caused by spatial channel convolution.Secondly,the Gather and Distribute Mechanism,which addresses the fusion problem of different dimensional features,has been implemented by aligning and sharing information through aggregation and distribution mechanisms.Thirdly,considering the limitations in current bounding box regression and the imbalance between complex and simple samples,Maximum Possible Distance Intersection over Union(MPDIoU)and Adaptive SlideLoss is incorporated into the loss function,allowing samples near the Intersection over Union(IoU)to receive more attention through the dynamic variation of the mean Intersection over Union.The GD-YOLO algorithm can surpass YOLOv5,YOLOv7,and YOLOv8 in infrared image detection for electrical equipment,achieving a mean Average Precision(mAP)of 88.9%,with accuracy improvements of 3.7%,4.3%,and 3.1%,respectively.Additionally,the model delivers a frame rate of 48 FPS,which aligns with the precision and velocity criteria necessary for the detection of infrared images in power equipment.
文摘Tian et al investigated the diagnostic value of serum vascular endothelial growth factor(VEGF)and interleukin-17(IL-17)in primary hepatocellular carcinoma(PHC).Their retrospective study,published in the World Journal of Gastrointestinal Surgery,revealed that the serum levels of VEGF and IL-17 are significantly elevated in PHC patients compared with healthy controls.These biomarkers are closely associated with pathological features such as tumor metastasis and clinical tumor node metastasis stage.A receiver operating characteristic analysis further confirmed the diagnostic efficacy thereof,suggesting that VEGF and IL-17 could serve as valuable tools for early detection and treatment guidance.This study underscores the potential of integrating these biomarkers into clinical practice to increase diagnostic accuracy and improve patient management in PHC.
基金sponsored by the Autonomous Region Key R&D Task Special(2022B01008)the National Key R&D Program of China(SQ2022AAA010308-5).
文摘Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection performance.On the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features.To address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion detection.First,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic.Meanwhile,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic.In addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch.Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.
文摘Quantitative oxygen detection,especially at low concentrations,holds significant importance in the realms of biology,complex environments,and chemical process engineering.Due to the high sensitivity and rapid response of the triplet excitons of phosphorescence to oxygen,pure organic room-temperature phosphorescence(RTP)materials have garnered widespread attention in recent years for oxygen detection.However,simultaneously achieving ultralong phosphorescence at room temperature and quantitative oxygen detection from pure organic host-vip doped materials poses challenges.The d ensely packed materials may decrease non-radiative decay to increase the phosphorescence,but are unsuitable for oxygen diffusion in oxygen detection.Herein,the oxygen sensitivity of host-vip doped RTP materials using 4-bromo-N,N-bis(4-(tertbutyl)phenyl)aniline(TPABuBr)as the host and 6-bromo-2-butyl-1H-benzo[de]isoquinoline-1,3(2H)-dione(NIBr)as the vip was developed.The doped material exhibits fluorescence-phosphorescence dual-emission behavior at room temperature.The tert-butyl groups in TPABuBr facilitate appropriate intermolecular spacing in the crystal state,enhancing oxygen permeability.Therefore,oxygen penetration can quench the phosphorescence emission.The observed linear relationship between the phosphorescence intensity of the doped material and the oxygen volume fraction conforms to the Stern-Volmer equation,suggesting its potential for quantitative analysis of oxygen concentration.The calculated limit of detection is 0.015%(φ),enabling the analysis of oxygen with a volume fraction of less than 2.5%(φ).Moreover,the doped materials demonstrate rapid response and excellent photostability,indicating their potential utility as oxygen sensors.This study elucidates the design and characteristics of NIBr/TPABuBr doped materials,highlighting their potential application in oxygen concentration detection and offering insights for the design of oxygen sensors.
文摘Aiming at the limitations of the existing knowledge representations in intelligent detection, a new method of Extension-based Knowledge Representation (EKR) was proposed. The definitions, grammar rules, and storage structure of EKR were presented. An Extension Solving Model (ESM) based on EKR was discussed in detail, including creation of the extension constraint graph, extended inference, calculation of relevant functions and generation of extension set. A knowledge base system based on EKR and ESM was developed, which was applied in extension repository system intelligent design of detection in photosynthesis process of D.huoshanense. More reasonable results were obtained than traditional rule-based system. EKR was feasible in intelligent design to solve the problem of intelligent detection knowledge representations.
基金This work is partially sponsored by National Key R&D Program of China(No.2019YFB2101700)National Science Foundation of China(No.62172297,No.61902276)+1 种基金the Key Research and Development Project of Sichuan Province(No.2021YFSY0012)Tianjin Intelligent Manufacturing Special Fund Project(No.20211097,No.20201159).
文摘Cryptojacking is a type of resource embezzlement attack,wherein an attacker secretly executes the cryptocurrency mining program in the target host to gain profits.It has been common since 2017,and in fact,it once became the greatest threat to network security.To better prove the attack ability the harm caused by cryptojacking,this paper proposes a new covert browser-based mining attack model named Delay-CJ,this model was deployed in a simulation environment for evaluation.Based on the general framework of cryptojacking,Delay-CJ adds hybrid evasion detection techniques and applies the delayed execution strategy specifically for video websites in the prototype implementation.The results show that the existing detection methods used for testing may become invalid as result of this model.In view of this situation,to achieve a more general and robust detection scheme,we built a cryptojacking detection system named CJDetector,which is based on cryptojacking process features.Specifically,it identifies malicious mining by monitoring CPU usage and analyzing the function call information.This system not only effectively detects the attack in our example but also has universal applicability.The recognition accuracy of CJDetector reaches 99.33%.Finally,we tested the web pages in Alexa 50K websites to investigate cryptojacking activity in the real network.We found that although cryptojacking is indeed on the decline,it remains a part of network security threats that cannot be ignored.
文摘This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.
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.
基金supported by the key research and development project of Science and Technology Department of Jilin Province(No.20230201102GX)the Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-MSX0278)the 2023 college students innovation and entrepreneurship training plan(202310183105).
文摘Humanoid robots exhibit structures and movements akin to those of humans,enabling them to assist or substitute for humans in various operations without necessitating alterations to their typical environment and tools.Sustaining bal-ance amidst disturbances constitutes a fundamental capability for humanoid robots.Consequently,adopting efficacious strategies to manage instability and mitigate injuries resulting from falls assumes paramount importance in advancing the widespread adoption of humanoid robotics.This paper presents a comprehensive overview of the ongoing development of strategies for coping with falls in humanoid robots.It systematically reviews and discusses three critical facets:fall state detection,preventive actions against falls,and post-fall protection measures.The paper undertakes a thorough classifica-tion of existing coping methodologies across different stages of falls,analyzes the merits and drawbacks of each approach,and outlines the evolving trajectory of solutions for addressing fall-related challenges across distinct stages.Finally,the paper provides a succinct summary and future prospects for the current fall coping strategies tailored for humanoid robots.
基金financial support from the National Natural Science Foundation of China(Grant 81573389)the National Key R&D Program of China(2017YFC0908600)
文摘MicroRNAs (miRNAs) are a family of endogenous, small (approximately 22 nucleotides in length), noncoding, functional RNAs. With the development of molecular biology, the research of miRNA biological function has attracted significant interest, as abnormal miRNA expression is identified to contribute to serious human diseases such as cancers. Traditional methods for miRNA detection do not meet current demands. In particular, nanomaterial-based methods, nucleic acid amplification-based methods such as rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), strand-displacement amplification (SDA) and some enzyme-free amplifications have been employed widely for the highly sensitive detection of miRNA. MiRNA functional research and clinical diagnostics have been accelerated by these new techniques. Herein, we summarize and discuss the recent progress in the development of miRNA detection methods and new applications. This review will provide guidelines for the development of follow-up miRNA detection methods with high sensitivity and specificity, and applicability to disease diagnosis and therapy.
基金supported by the National Natural Science Foundation of China(Nos.61771027,61071139,61471019,61671035)supported in part under the Royal Society of Edinburgh-National Natural Science Foundation of China(RSE-NNSFC)Joint Project(2017–2019)(No.6161101383)with China University of Petroleum(Huadong)partially supported by the UK Engineering and Physical Sciences Research Council(EPSRC)(Nos.EP/I009310/1,EP/M026981/1)
文摘Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.
基金This study was supported by the China National Science Foundation(Grant Nos.82022048 and 81871893)the Key Project of Guangzhou Scientific Research Project(Grant No.201804020030).
文摘Lung cancer is associated with a heavy cancer-related burden in terms of patients’physical and mental health worldwide.Two randomized controlled trials,the US-National Lung Screening Trial(NLST)and Nederlands-Leuvens Longkanker Screenings Onderzoek(NELSON),indicated that low-dose CT(LDCT)screening results in a statistically significant decrease in mortality in patients with lung cancer,LDCT has become the standard approach for lung cancer screening.However,many issues in lung cancer screening remain unresolved,such as the screening criteria,high false-positive rate,and radiation exposure.This review first summarizes recent studies on lung cancer screening from the US,Europe,and Asia,and discusses risk-based selection for screening and the related issues.Second,an overview of novel techniques for the differential diagnosis of pulmonary nodules,including artificial intelligence and molecular biomarker-based screening,is presented.Third,current explorations of strategies for suspected malignancy are summarized.Overall,this review aims to help clinicians understand recent progress in lung cancer screening and alleviate the burden of lung cancer.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61772561,author J.Q,http://www.nsfc.gov.cn/in part by the Degree&Postgraduate Education Reform Project of Hunan Province under Grant 2019JGYB154,author J.Q,http://xwb.gov.hnedu.cn/+6 种基金in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174,author X.X,http://kxjsc.gov.hnedu.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 19B584,author Y.T,http://kxjsc.gov.hnedu.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4140),author Y.T,http://kjt.hunan.gov.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4141),author X.X,http://kjt.hunan.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant 2019SK2022,author Y.T,http://kjt.hunan.gov.cn/in part by the Graduate Science and Technology Innovation Fund Project of Central South University of Forestry and Technology under Grant CX2020107,author Q.Z,https://jwc.csuft.edu.cn/。
文摘The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements,but they can’t accurately detect small objects and objects with obstructions.Therefore,we propose a helmet detection algorithm based on the attention mechanism(AT-YOLO).First of all,a channel attention module is added to the YOLOv3 backbone network,which can adaptively calibrate the channel features of the direction to improve the feature utilization,and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation between any positions in the feature map so that to increase the receptive field of the network.Secondly,we use DIoU(Distance Intersection over Union)bounding box regression loss function,it not only improving the measurement of bounding box regression loss but also increases the normalized distance loss between the prediction boxes and the target boxes,which makes the network more accurate in detecting small objects and faster in convergence.Finally,we explore the training strategy of the network model,which improves network performance without increasing the inference cost.Experiments show that the mAP of the proposed method reaches 96.5%,and the detection speed can reach 27 fps.Compared with other existing methods,it has better performance in detection accuracy and speed.
基金The authors are grateful to the Deanship of Scientific Research,King Saud University,Riyadh,Saudi Arabia,for funding this work through the Vice Deanship of Scientific Research Chairs:Research Chair of Pervasive and Mobile Computing.
文摘Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.
基金Supported by the National Natural Science Foundation of China(Grant Nos.07002157 U1811463)
文摘SSD(Single Shot Multibox Detector) is one of the best object detection algorithms with both high accuracy and fast speed, but fails to detect very small size object which lacks enough resolution and enough feature information. In order to solve this problem, the majority of existing methods improve accuracy at the cost of a heavy loss of speed. In this paper, we propose SFE-SSD(Shallow Feature Enhancement SSD) to improve performance of SSD model on small object detection based on a novel and lightweight way of feature enhancement module. Firstly,we apply deconvolution on the shallowest feature map in SSD’s feature pyramid to enlarge the feature map size and recover more feature details. Then, we introduce semantic information to the enlarged feature map by multi-scale feature fusion. In addition, SFE-SSD is designed to a parallel network structure, which could reduce loss of speed in some degree. Experimental results show that our approach achieved 78.4%m AP and is higher than baseline SSD by 1.2%on PASCAL VOC2007, especially with significant improvement on small object detection. The testing speed of SFE-SSD is 81 FPS at the cost of a little loss of speed.
基金supported by the National Natural Science Foundation of China(grants no.32171797 and 31800549)。
文摘Smoke detection is the most commonly used method in early warning of fire and is widely used in forest detection.Most existing smoke detection methods contain empty spaces and obstacles which interfere with detection and extract false smoke roots.This study developed a new smoke roots search algorithm based on a multi-feature fusion dynamic extraction strategy.This determines smoke origin candidate points and region based on a multi-frame discrete confidence level.The results show that the new method provides a more complete smoke contour with no background interference,compared to the results using existing methods.Unlike video-based methods that rely on continuous frames,an adaptive threshold method was developed to build the judgment image set composed of non-consecutive frames.The smoke roots origin search algorithm increased the detection rate and significantly reduced false detection rate compared to existing methods.
