Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for...Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for SLRT. However, making a large-scale and diverse sign language dataset is difficult as sign language data on the Internet is scarce. In making a large-scale and diverse sign language dataset, some sign language data qualities are not up to standard. This paper proposes a two information streams transformer(TIST) model to judge whether the quality of sign language data is qualified. To verify that TIST effectively improves sign language recognition(SLR), we make two datasets, the screened dataset and the unscreened dataset. In this experiment, this paper uses visual alignment constraint(VAC) as the baseline model. The experimental results show that the screened dataset can achieve better word error rate(WER) than the unscreened dataset.展开更多
Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition ...Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition algorithms often relies on expert knowledge to enhance the image feature extraction networks,necessitating image preprocessing and model parameter tuning.This increases the complexity of the model design process.This study introduces an evolutionary neural architecture search(ENAS)algorithm for the automatic design of neural network models tailored for traffic sign recognition.By integrating the construction parameters of residual network(ResNet)into evolutionary algorithms(EAs),we automatically generate lightweight networks for traffic sign recognition,utilizing blocks as the fundamental building units.Experimental evaluations on the German traffic sign recognition benchmark(GTSRB)dataset reveal that the algorithm attains a recognition accuracy of 99.32%,with a mere 2.8×10^(6)parameters.Experimental results comparing the proposed method with other traffic sign recognition algorithms demonstrate that the method can more efficiently discover neural network architectures,significantly reducing the number of network parameters while maintaining recognition accuracy.展开更多
Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emerg...Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emergency medicine poses a significant challenge.To address this,we propose a novel approach based on the inter-learning of visual features between global and local information.Specifically,our method enhances the perception capabilities of the visual feature extractor by strategically leveraging the strengths of convolutional neural network(CNN),which are adept at capturing local features,and visual transformers which perform well at perceiving global features.Furthermore,to mitigate the issue of overfitting caused by the limited availability of sign language data for emergency medical applications,we introduce an enhanced short temporal module for data augmentation through additional subsequences.Experimental results on three publicly available sign language datasets demonstrate the efficacy of the proposed approach.展开更多
A graph whose edges are labeled either as positive or negative is called a signed graph.Hameed et al.introduced signed distance and distance compatibility in 2021,initially to characterize balanced signed graphs which...A graph whose edges are labeled either as positive or negative is called a signed graph.Hameed et al.introduced signed distance and distance compatibility in 2021,initially to characterize balanced signed graphs which have nice spectral properties.This article mainly studies the conjecture proposed by Shijin et al.on the distance compatibility of the direct product of signed graphs,and provides necessary and sufficient conditions for the distance compatibility of the direct product of signed graphs.Some further questions regarding distance compatibility are also posed.展开更多
To explore the influence of emergency evacuation signs on passenger behavior during subway fires and improve evacuation efficiency in emergencies,this paper proposes a dynamic emergency evacuation sign system.A simula...To explore the influence of emergency evacuation signs on passenger behavior during subway fires and improve evacuation efficiency in emergencies,this paper proposes a dynamic emergency evacuation sign system.A simulation platform integrating building information modeling(BIM)and virtual reality(VR)technologies was em-ployed to create subway fire evacuation scenarios using both the current and proposed dynamic emergency evacuation signage systems.Through simulation experiments,fine-grained microscopic data on passenger behavior was collected.Seven indicators were selected to assess evacuation efficiency and wayfinding difficulty.The analysis explored the influence of evacuation signs on passenger behavior in both overall and decision-making areas,thereby validating the effectiveness of the new emergency evacuation signage system.The results show that the dynamic evacuation signage system significantly improves overall passenger evacuation efficiency and reduces decision-making errors.It also improves wayfinding efficiency in critical decision areas by reducing the need for direction identification,minimizing stopping times,and lowering the frequency of decision errors.The method for evaluating the effects of emergency evacuation signs on passenger evacuation behavior proposed in this study provides a robust theoretical basis for the design and optimization of emergency-oriented signs.展开更多
Background:Physical diagnosis is the first course of clinical medicine and the first contact with patients during clinical practice.Evidence-based physical diagnosis is a helpful tool in both education and practice.We...Background:Physical diagnosis is the first course of clinical medicine and the first contact with patients during clinical practice.Evidence-based physical diagnosis is a helpful tool in both education and practice.We aim to compare the accuracy of physical signs based on practice experience and research evidence.Methods:A questionnaire was designed and administered using convenience sampling to collect empirical estimates of physical signs.The literature search was conducted using Medline,EMBASE,and the Cochrane Library up to December 2023.Included studies independently compared diagnostic performance between physical signs and acceptable diagnostic standards.Hierarchical summary receiver operating characteristic models were used to summarize test accuracy data across studies.The pooled sensitivity,specificity,likelihood ratios,and diagnostic odds ratio were calculated.Results from the two parts were compared.Results:A total of 1971 respondents completed the questionnaire,and 37 studies reporting the performance of physical signs were included in the review.Compared with synthesized results of meta-analysis,estimations of palpable liver(sensitivity and specificity),palpable spleen(sensitivity and specificity),Traube space dullness(sensitivity and specificity),shifting dullness(sensitivity),right upper quadrant(RUQ)tenderness(sensitivity and specificity),Murphy sign(specificity),and distended abdomen(sensitivity)had relatively good concordance.The sensitivity of bulging flanks and the specificity of the distended abdomen were underestimated.The specificity of bulging flanks,shifting dullness,and sensitivity of the Murphy sign were overestimated.Conclusions:Variable results were observed in the comparison between clinical experience and evidence-based medicine.Evidence-based physical diagnosis should be introduced during teaching and emphasized throughout a career as a guide for accumulating expertise.展开更多
The unique structure of signed networks,characterized by positive and negative edges,poses significant challenges for analyzing network topology.In recent years,various statistical algorithms have been developed to ad...The unique structure of signed networks,characterized by positive and negative edges,poses significant challenges for analyzing network topology.In recent years,various statistical algorithms have been developed to address this issue.However,there remains a lack of a unified framework to uncover the nontrivial properties inherent in signed network structures.To support developers,researchers,and practitioners in this field,we introduce a Python library named SNSAlib(Signed Network Structure Analysis),specifically designed to meet these analytical requirements.This library encompasses empirical signed network datasets,signed null model algorithms,signed statistics algorithms,and evaluation indicators.The primary objective of SNSAlib is to facilitate the systematic analysis of micro-and meso-structure features within signed networks,including node popularity,clustering,assortativity,embeddedness,and community structure by employing more accurate signed null models.Ultimately,it provides a robust paradigm for structure analysis of signed networks that enhances our understanding and application of signed networks.展开更多
Once a train stops in a tunnel section and requires emergency evacuation,the large distance between stations and long walking distances in the underground spaces of suburban railway systems pose potential risks to the...Once a train stops in a tunnel section and requires emergency evacuation,the large distance between stations and long walking distances in the underground spaces of suburban railway systems pose potential risks to the evacuation process on tunnel platforms,especially in complex environments.This study utilized Virtual Reality(VR)technology to construct a virtual experimental platform for tunnel evacuation in suburban railway systems,simulating different combinations of smoke and obstacle conditions.By requiring participants to wear VR glasses and walk on an omnidirectional treadmill for moving,as well as complete psychological questionnaires,the study reveals the influences of No Guiding(NG)signs,Wall-Guided(WG)signs,and Central axis Guidance(CG)signs on the movement abilities and psychological behaviors of participants contrastively.The results show that either smoke conditions or obstacle positions affect the mental stress of participants,and the guidance sign has a positive effect on reducing the mental stress.There is an inverse relationship between mental stress and movement abilities.WG and CG signs respectively lead participants to walk closer to walls and along the central axis,which is conducive to reducing the variation in participants’behavior characteristics when circumventing obstacles on the wall side or track side under smoke conditions,respectively.Additionally,CG signs reduce the speed fluctuations of participants before circumventing obstacles,improving the stability of the distance from the wall and speed under smoke conditions,compared to NG and WG signs.These findings contribute to understanding the evacuation psychological-behavioral-movement characteristics of pedestrians on evacuation platforms in suburban railway tunnels and provide a basis for improving the safety design of evacuation guidance signs.展开更多
Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dyn...Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dynamic sign language requires identifying keyframes that best represent the signs, and missing these keyframes reduces accuracy. Secondly, some methods do not focus enough on hand regions, which are small within the overall frame, leading to information loss. To address these challenges, we propose a novel Video Transformer Attention-based Network (VTAN) for dynamic sign language recognition. Our approach prioritizes informative frames and hand regions effectively. To tackle the first issue, we designed a keyframe extraction module enhanced by a convolutional autoencoder, which focuses on selecting information-rich frames and eliminating redundant ones from the video sequences. For the second issue, we developed a soft attention-based transformer module that emphasizes extracting features from hand regions, ensuring that the network pays more attention to hand information within sequences. This dual-focus approach improves effective dynamic sign language recognition by addressing the key challenges of identifying critical frames and emphasizing hand regions. Experimental results on two public benchmark datasets demonstrate the effectiveness of our network, outperforming most of the typical methods in sign language recognition tasks.展开更多
Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakt...Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.展开更多
Dear Editor,I am responding to Zou and Li's,The missing perilymph sign on MRI indicates a perilymphatic fistula:A case report Zou J,Li H.Journal of Otology,2025, 20(1):1-4.https://doi.org/10.26599/JOTO.2025.954000...Dear Editor,I am responding to Zou and Li's,The missing perilymph sign on MRI indicates a perilymphatic fistula:A case report Zou J,Li H.Journal of Otology,2025, 20(1):1-4.https://doi.org/10.26599/JOTO.2025.9540001 proposing the"missing perilymph"sign on MRI as a novel radiological indicator of perilymphatic fistula(PLF).This study adds to the growing body of work seeking objective,non-invasive diagnostic methods for PLF,a condition that has long eluded definitive radiological confirmation.The avoidance of gadolinium contrast in the imaging technique is an additional strength,given increasing awareness of gadoliniumassociated risks (Starekova et al.,2024).展开更多
The arterial pulse tapping artifact,known as Aslanger’s sign,is an electrocardiographic artifact resulting from the transmission of arterial pulsation onto the limb electrodes of the standard 12-lead electrocardiogra...The arterial pulse tapping artifact,known as Aslanger’s sign,is an electrocardiographic artifact resulting from the transmission of arterial pulsation onto the limb electrodes of the standard 12-lead electrocardiograph(ECG)which are placed near the radial or posterior tibial arteries.[1-16]This electromechanical artifact is of cardiac origin and is synchronous with the cardiac cycle.[17]Nearly all reported cases of Aslanger’s sign exhibit an unusual waveform morphology in all 12 leads except one limb lead.[1-14,16]However,we previously reported a case of Aslanger’s sign that showed distorted waveforms from the ST to TP segments observed only in five limb leads among 12 leads.展开更多
Dear Editor,I am writing in response to Jamil's letter,"Interpretative Challenges of the Missing Perilymph'Sign in PLF Diagnosis."I concur with the author's emphasis on the necessity for cautious...Dear Editor,I am writing in response to Jamil's letter,"Interpretative Challenges of the Missing Perilymph'Sign in PLF Diagnosis."I concur with the author's emphasis on the necessity for cautious interpretation of low-signal areas as evidence of active perilymph leakage,requiring correlation with clinical findings,surgical confirmation,and longitudinal imaging changes.展开更多
This paper presents a predefined-time controller for Multiple Space transportation Robots System (MSRS), which can be applied in on-orbit assembly tasks to transport modules to pre-assembly configuration quickly. Firs...This paper presents a predefined-time controller for Multiple Space transportation Robots System (MSRS), which can be applied in on-orbit assembly tasks to transport modules to pre-assembly configuration quickly. Firstly, to simplify the analysis and design of predefined-time controller, a Predefined-time Stability Criterion is proposed in the form of Composite Lyapunov Function (CLF-PSC). Besides simplicity, the CLF-PSC also has the advantage of less conservativeness due to utilization of initial state information. Secondly, a concept of Lp-Norm-Normalized Sign Function (LPNNSF) is proposed based on the CLF-PSC. Different from traditional norm-normalized sign function, the Lp-norm of LPNNSF can be selected arbitrarily according to practical control task requirements, which means that the proposed LPNNSF is more generalized and more convenient for calculation. Thirdly, a predefined-time disturbance observer and predefined-time controller are designed based on the LPNNSF. The observer has the property of predefined-time convergence to achieve quicker and more accurate estimation of the lumped disturbance. The controller has less control input and chattering phenomenon than traditional predefined-time controller. In addition, by introducing the observer into the controller, the closed-loop system enjoys high precision and strong robustness. Finally, the effectiveness of the proposed controller is verified by numerical simulations. By employing the controller, the MSRS can carry assembly modules to the desired pre-assembly configuration accurately within predefined time.展开更多
This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolu...This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).展开更多
Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;howev...Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;however,these methods face multiple challenges that include high gesture variability,occlusions,limited signer diversity,and the scarcity of large annotated datasets.Existing methods,often relying solely on either skeletal data or video-based features,struggle with generalization and robustness,especially in dynamic and real-world conditions.This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint distances and angles with temporal features extracted from RGB videos using the Inflated 3D ConvNet(I3D).By fusing these complementary modalities at the feature level and applying a majority-voting ensemble of XGBoost,Random Forest,and Support Vector Machine classifiers,the framework robustly captures both spatial configurations and motion dynamics of sign gestures.Feature selection using the Pearson Correlation Coefficient further enhances efficiency by reducing redundancy.Extensive experiments on the ArabSign dataset,which includes RGB videos and corresponding skeletal data,demonstrate that the proposed approach significantly outperforms state-of-the-art methods,achieving an average F1-score of 97%using a majority-voting ensemble of XGBoost,Random Forest,and SVM classifiers,and improving recognition accuracy by more than 7%over previous best methods.This work not only advances the technical stateof-the-art in ArSL recognition but also provides a scalable,real-time solution for practical deployment in educational,social,and assistive communication technologies.Even though this study is about Arabic Sign Language,the framework proposed here can be extended to different sign languages,creating possibilities for potentially worldwide applicability in sign language recognition tasks.展开更多
This paper proposes a model-based control framework for vehicle platooning systems with secondorder nonlinear dynamics operating over switching signed networks,time-varying delays,and deception attacks.The study inclu...This paper proposes a model-based control framework for vehicle platooning systems with secondorder nonlinear dynamics operating over switching signed networks,time-varying delays,and deception attacks.The study includes two configurations:a leaderless structure using Finite-Time Non-Singular Terminal Bipartite Consensus(FNTBC)and Fixed-Time Bipartite Consensus(FXTBC),and a leader—follower structure ensuring structural balance and robustness against deceptive signals.In the leaderless model,a bipartite controller based on impulsive control theory,gauge transformation,and Markovian switching Lyapunov functions ensures mean-square stability and coordination under deception attacks and communication delays.The FNTBC achieves finite-time convergence depending on initial conditions,while the FXTBC guarantees fixed-time convergence independent of them,providing adaptability to different operating states.In the leader—follower case,a discontinuous impulsive control law synchronizes all followers with the leader despite deceptive attacks and switching topologies,maintaining robust coordination through nonlinear corrective mechanisms.To validate the approach,simulations are conducted on systems of five and seventeen vehicles in both leaderless and leader—follower configurations.The results demonstrate that the proposed framework achieves rapid consensus,strong robustness,and high resistance to deception attacks,offering a secure and scalable model-based control solution for modern vehicular communication networks.展开更多
A signed graph S=(S^(u),σ)has an underlying graph Suand a functionσ:E(S^(u))-→{+,-}.Let E^(-)(S)denote the set of negative edges of S.Then S is eulerian signed graph(or subeulerian signed graph,or balanced eulerian...A signed graph S=(S^(u),σ)has an underlying graph Suand a functionσ:E(S^(u))-→{+,-}.Let E^(-)(S)denote the set of negative edges of S.Then S is eulerian signed graph(or subeulerian signed graph,or balanced eulerian signed graph,respectively)if Suis eulerian(or subeulerian,or eulerian and|E-(S)|is even,respectively).We say that S is balanced subeulerian signed graph if there exists a balanced eulerian signed graph S′such that S′is spanned by S.The signed line graph L(S)of a signed graph S is a signed graph with the vertices of L(S)being the edges of S,where an edge eiej is in L(S)if and only if the edges e_(i)and e_(j)of S have a vertex in common in S such that an edge eiej in L(S)is negative if and only if both edges ei and ej are negative in S.In this paper,two families of signed graphs S and S′are identified,which are applied to characterize balanced subeulerian signed graphs and balanced subeulerian signed line graphs.In particular,it is proved that a signed graph S is balanced subeulerian if and only if S∈S,and that a signed line graph of signed graph S is balanced subeulerian if and only if S∈S′.展开更多
基金supported by the National Language Commission to research on sign language data specifications for artificial intelligence applications and test standards for language service translation systems (No.ZDI145-70)。
文摘Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for SLRT. However, making a large-scale and diverse sign language dataset is difficult as sign language data on the Internet is scarce. In making a large-scale and diverse sign language dataset, some sign language data qualities are not up to standard. This paper proposes a two information streams transformer(TIST) model to judge whether the quality of sign language data is qualified. To verify that TIST effectively improves sign language recognition(SLR), we make two datasets, the screened dataset and the unscreened dataset. In this experiment, this paper uses visual alignment constraint(VAC) as the baseline model. The experimental results show that the screened dataset can achieve better word error rate(WER) than the unscreened dataset.
基金supported by the National Natural Science Foundation of China(No.62066041).
文摘Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition algorithms often relies on expert knowledge to enhance the image feature extraction networks,necessitating image preprocessing and model parameter tuning.This increases the complexity of the model design process.This study introduces an evolutionary neural architecture search(ENAS)algorithm for the automatic design of neural network models tailored for traffic sign recognition.By integrating the construction parameters of residual network(ResNet)into evolutionary algorithms(EAs),we automatically generate lightweight networks for traffic sign recognition,utilizing blocks as the fundamental building units.Experimental evaluations on the German traffic sign recognition benchmark(GTSRB)dataset reveal that the algorithm attains a recognition accuracy of 99.32%,with a mere 2.8×10^(6)parameters.Experimental results comparing the proposed method with other traffic sign recognition algorithms demonstrate that the method can more efficiently discover neural network architectures,significantly reducing the number of network parameters while maintaining recognition accuracy.
基金supported by the National Natural Science Foundation of China(No.62376197)the Tianjin Science and Technology Program(No.23JCYBJC00360)the Tianjin Health Research Project(No.TJWJ2025MS045).
文摘Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emergency medicine poses a significant challenge.To address this,we propose a novel approach based on the inter-learning of visual features between global and local information.Specifically,our method enhances the perception capabilities of the visual feature extractor by strategically leveraging the strengths of convolutional neural network(CNN),which are adept at capturing local features,and visual transformers which perform well at perceiving global features.Furthermore,to mitigate the issue of overfitting caused by the limited availability of sign language data for emergency medical applications,we introduce an enhanced short temporal module for data augmentation through additional subsequences.Experimental results on three publicly available sign language datasets demonstrate the efficacy of the proposed approach.
基金Supported by the National Natural Science Foundation of China(Grant No.12071260)。
文摘A graph whose edges are labeled either as positive or negative is called a signed graph.Hameed et al.introduced signed distance and distance compatibility in 2021,initially to characterize balanced signed graphs which have nice spectral properties.This article mainly studies the conjecture proposed by Shijin et al.on the distance compatibility of the direct product of signed graphs,and provides necessary and sufficient conditions for the distance compatibility of the direct product of signed graphs.Some further questions regarding distance compatibility are also posed.
基金Beijing Natural Science Foundation-Fengtai Rail Transit Frontier Research Joint Foundation(No.L211024),the National Natural Science Foundation of China(No.52072012).
文摘To explore the influence of emergency evacuation signs on passenger behavior during subway fires and improve evacuation efficiency in emergencies,this paper proposes a dynamic emergency evacuation sign system.A simulation platform integrating building information modeling(BIM)and virtual reality(VR)technologies was em-ployed to create subway fire evacuation scenarios using both the current and proposed dynamic emergency evacuation signage systems.Through simulation experiments,fine-grained microscopic data on passenger behavior was collected.Seven indicators were selected to assess evacuation efficiency and wayfinding difficulty.The analysis explored the influence of evacuation signs on passenger behavior in both overall and decision-making areas,thereby validating the effectiveness of the new emergency evacuation signage system.The results show that the dynamic evacuation signage system significantly improves overall passenger evacuation efficiency and reduces decision-making errors.It also improves wayfinding efficiency in critical decision areas by reducing the need for direction identification,minimizing stopping times,and lowering the frequency of decision errors.The method for evaluating the effects of emergency evacuation signs on passenger evacuation behavior proposed in this study provides a robust theoretical basis for the design and optimization of emergency-oriented signs.
基金supported by the National Natural Science Foundation of China(32170788)the National High Level Hospital Clinical Research Funding(2022-PUMCH-B-023)+2 种基金the Beijing Natural Science Foundation(7232123)the Beijing Natural Science Foundation(L232016)funding from Peking Union Medical College(2023zlgc0501).
文摘Background:Physical diagnosis is the first course of clinical medicine and the first contact with patients during clinical practice.Evidence-based physical diagnosis is a helpful tool in both education and practice.We aim to compare the accuracy of physical signs based on practice experience and research evidence.Methods:A questionnaire was designed and administered using convenience sampling to collect empirical estimates of physical signs.The literature search was conducted using Medline,EMBASE,and the Cochrane Library up to December 2023.Included studies independently compared diagnostic performance between physical signs and acceptable diagnostic standards.Hierarchical summary receiver operating characteristic models were used to summarize test accuracy data across studies.The pooled sensitivity,specificity,likelihood ratios,and diagnostic odds ratio were calculated.Results from the two parts were compared.Results:A total of 1971 respondents completed the questionnaire,and 37 studies reporting the performance of physical signs were included in the review.Compared with synthesized results of meta-analysis,estimations of palpable liver(sensitivity and specificity),palpable spleen(sensitivity and specificity),Traube space dullness(sensitivity and specificity),shifting dullness(sensitivity),right upper quadrant(RUQ)tenderness(sensitivity and specificity),Murphy sign(specificity),and distended abdomen(sensitivity)had relatively good concordance.The sensitivity of bulging flanks and the specificity of the distended abdomen were underestimated.The specificity of bulging flanks,shifting dullness,and sensitivity of the Murphy sign were overestimated.Conclusions:Variable results were observed in the comparison between clinical experience and evidence-based medicine.Evidence-based physical diagnosis should be introduced during teaching and emphasized throughout a career as a guide for accumulating expertise.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72371031,62173065,62476045)Fundamental Research Funds for the Central Universities(Grant No.124330008)。
文摘The unique structure of signed networks,characterized by positive and negative edges,poses significant challenges for analyzing network topology.In recent years,various statistical algorithms have been developed to address this issue.However,there remains a lack of a unified framework to uncover the nontrivial properties inherent in signed network structures.To support developers,researchers,and practitioners in this field,we introduce a Python library named SNSAlib(Signed Network Structure Analysis),specifically designed to meet these analytical requirements.This library encompasses empirical signed network datasets,signed null model algorithms,signed statistics algorithms,and evaluation indicators.The primary objective of SNSAlib is to facilitate the systematic analysis of micro-and meso-structure features within signed networks,including node popularity,clustering,assortativity,embeddedness,and community structure by employing more accurate signed null models.Ultimately,it provides a robust paradigm for structure analysis of signed networks that enhances our understanding and application of signed networks.
基金supported by the National Natural Science Foundation of China(Grant Number 52472322)the Shanghai Sailing Program(Grant Number 21YF1415800)+1 种基金the Open Project of Key Laboratory of Advanced Public Transportation Science(Grant Number 2023-APTS-05)the Shanghai SASAC Enterprise Innovation and Capability Enhancement Project(Grant Number 2022016,2023020).
文摘Once a train stops in a tunnel section and requires emergency evacuation,the large distance between stations and long walking distances in the underground spaces of suburban railway systems pose potential risks to the evacuation process on tunnel platforms,especially in complex environments.This study utilized Virtual Reality(VR)technology to construct a virtual experimental platform for tunnel evacuation in suburban railway systems,simulating different combinations of smoke and obstacle conditions.By requiring participants to wear VR glasses and walk on an omnidirectional treadmill for moving,as well as complete psychological questionnaires,the study reveals the influences of No Guiding(NG)signs,Wall-Guided(WG)signs,and Central axis Guidance(CG)signs on the movement abilities and psychological behaviors of participants contrastively.The results show that either smoke conditions or obstacle positions affect the mental stress of participants,and the guidance sign has a positive effect on reducing the mental stress.There is an inverse relationship between mental stress and movement abilities.WG and CG signs respectively lead participants to walk closer to walls and along the central axis,which is conducive to reducing the variation in participants’behavior characteristics when circumventing obstacles on the wall side or track side under smoke conditions,respectively.Additionally,CG signs reduce the speed fluctuations of participants before circumventing obstacles,improving the stability of the distance from the wall and speed under smoke conditions,compared to NG and WG signs.These findings contribute to understanding the evacuation psychological-behavioral-movement characteristics of pedestrians on evacuation platforms in suburban railway tunnels and provide a basis for improving the safety design of evacuation guidance signs.
基金supported by the National Natural Science Foundation of China under Grant Nos.62076117 and 62166026the Jiangxi Provincial Key Laboratory of Virtual Reality under Grant No.2024SSY03151.
文摘Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dynamic sign language requires identifying keyframes that best represent the signs, and missing these keyframes reduces accuracy. Secondly, some methods do not focus enough on hand regions, which are small within the overall frame, leading to information loss. To address these challenges, we propose a novel Video Transformer Attention-based Network (VTAN) for dynamic sign language recognition. Our approach prioritizes informative frames and hand regions effectively. To tackle the first issue, we designed a keyframe extraction module enhanced by a convolutional autoencoder, which focuses on selecting information-rich frames and eliminating redundant ones from the video sequences. For the second issue, we developed a soft attention-based transformer module that emphasizes extracting features from hand regions, ensuring that the network pays more attention to hand information within sequences. This dual-focus approach improves effective dynamic sign language recognition by addressing the key challenges of identifying critical frames and emphasizing hand regions. Experimental results on two public benchmark datasets demonstrate the effectiveness of our network, outperforming most of the typical methods in sign language recognition tasks.
基金funded by Key research and development Program of Henan Province(No.251111211200)National Natural Science Foundation of China(Grant No.U2004163).
文摘Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.
文摘Dear Editor,I am responding to Zou and Li's,The missing perilymph sign on MRI indicates a perilymphatic fistula:A case report Zou J,Li H.Journal of Otology,2025, 20(1):1-4.https://doi.org/10.26599/JOTO.2025.9540001 proposing the"missing perilymph"sign on MRI as a novel radiological indicator of perilymphatic fistula(PLF).This study adds to the growing body of work seeking objective,non-invasive diagnostic methods for PLF,a condition that has long eluded definitive radiological confirmation.The avoidance of gadolinium contrast in the imaging technique is an additional strength,given increasing awareness of gadoliniumassociated risks (Starekova et al.,2024).
文摘The arterial pulse tapping artifact,known as Aslanger’s sign,is an electrocardiographic artifact resulting from the transmission of arterial pulsation onto the limb electrodes of the standard 12-lead electrocardiograph(ECG)which are placed near the radial or posterior tibial arteries.[1-16]This electromechanical artifact is of cardiac origin and is synchronous with the cardiac cycle.[17]Nearly all reported cases of Aslanger’s sign exhibit an unusual waveform morphology in all 12 leads except one limb lead.[1-14,16]However,we previously reported a case of Aslanger’s sign that showed distorted waveforms from the ST to TP segments observed only in five limb leads among 12 leads.
文摘Dear Editor,I am writing in response to Jamil's letter,"Interpretative Challenges of the Missing Perilymph'Sign in PLF Diagnosis."I concur with the author's emphasis on the necessity for cautious interpretation of low-signal areas as evidence of active perilymph leakage,requiring correlation with clinical findings,surgical confirmation,and longitudinal imaging changes.
基金co-supported by the National Natural Science Foundation of China(Nos.12372048,12102343)the Key Program of the National Natural Science Foundation of China(No.U2013206)+1 种基金the China Postdoctoral Science Foundation(No.2023M742835)the Guangdong Basic and Applied Basic Research Foundation,China(No.2023A1515011421).
文摘This paper presents a predefined-time controller for Multiple Space transportation Robots System (MSRS), which can be applied in on-orbit assembly tasks to transport modules to pre-assembly configuration quickly. Firstly, to simplify the analysis and design of predefined-time controller, a Predefined-time Stability Criterion is proposed in the form of Composite Lyapunov Function (CLF-PSC). Besides simplicity, the CLF-PSC also has the advantage of less conservativeness due to utilization of initial state information. Secondly, a concept of Lp-Norm-Normalized Sign Function (LPNNSF) is proposed based on the CLF-PSC. Different from traditional norm-normalized sign function, the Lp-norm of LPNNSF can be selected arbitrarily according to practical control task requirements, which means that the proposed LPNNSF is more generalized and more convenient for calculation. Thirdly, a predefined-time disturbance observer and predefined-time controller are designed based on the LPNNSF. The observer has the property of predefined-time convergence to achieve quicker and more accurate estimation of the lumped disturbance. The controller has less control input and chattering phenomenon than traditional predefined-time controller. In addition, by introducing the observer into the controller, the closed-loop system enjoys high precision and strong robustness. Finally, the effectiveness of the proposed controller is verified by numerical simulations. By employing the controller, the MSRS can carry assembly modules to the desired pre-assembly configuration accurately within predefined time.
基金supported by the Shanxi Agricultural University Science and Technology Innovation Enhancement Project。
文摘This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).
基金funding this work through Research Group No.KS-2024-376.
文摘Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;however,these methods face multiple challenges that include high gesture variability,occlusions,limited signer diversity,and the scarcity of large annotated datasets.Existing methods,often relying solely on either skeletal data or video-based features,struggle with generalization and robustness,especially in dynamic and real-world conditions.This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint distances and angles with temporal features extracted from RGB videos using the Inflated 3D ConvNet(I3D).By fusing these complementary modalities at the feature level and applying a majority-voting ensemble of XGBoost,Random Forest,and Support Vector Machine classifiers,the framework robustly captures both spatial configurations and motion dynamics of sign gestures.Feature selection using the Pearson Correlation Coefficient further enhances efficiency by reducing redundancy.Extensive experiments on the ArabSign dataset,which includes RGB videos and corresponding skeletal data,demonstrate that the proposed approach significantly outperforms state-of-the-art methods,achieving an average F1-score of 97%using a majority-voting ensemble of XGBoost,Random Forest,and SVM classifiers,and improving recognition accuracy by more than 7%over previous best methods.This work not only advances the technical stateof-the-art in ArSL recognition but also provides a scalable,real-time solution for practical deployment in educational,social,and assistive communication technologies.Even though this study is about Arabic Sign Language,the framework proposed here can be extended to different sign languages,creating possibilities for potentially worldwide applicability in sign language recognition tasks.
基金Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP.2/103/46”Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia for funding this research work through project number“NBU-FFR-2025-871-15”funding from Prince Sattam bin Abdulaziz University project number(PSAU/2025/R/1447).
文摘This paper proposes a model-based control framework for vehicle platooning systems with secondorder nonlinear dynamics operating over switching signed networks,time-varying delays,and deception attacks.The study includes two configurations:a leaderless structure using Finite-Time Non-Singular Terminal Bipartite Consensus(FNTBC)and Fixed-Time Bipartite Consensus(FXTBC),and a leader—follower structure ensuring structural balance and robustness against deceptive signals.In the leaderless model,a bipartite controller based on impulsive control theory,gauge transformation,and Markovian switching Lyapunov functions ensures mean-square stability and coordination under deception attacks and communication delays.The FNTBC achieves finite-time convergence depending on initial conditions,while the FXTBC guarantees fixed-time convergence independent of them,providing adaptability to different operating states.In the leader—follower case,a discontinuous impulsive control law synchronizes all followers with the leader despite deceptive attacks and switching topologies,maintaining robust coordination through nonlinear corrective mechanisms.To validate the approach,simulations are conducted on systems of five and seventeen vehicles in both leaderless and leader—follower configurations.The results demonstrate that the proposed framework achieves rapid consensus,strong robustness,and high resistance to deception attacks,offering a secure and scalable model-based control solution for modern vehicular communication networks.
基金Supported by the National Natural Science Foundation of China(Grant No.12261016)。
文摘A signed graph S=(S^(u),σ)has an underlying graph Suand a functionσ:E(S^(u))-→{+,-}.Let E^(-)(S)denote the set of negative edges of S.Then S is eulerian signed graph(or subeulerian signed graph,or balanced eulerian signed graph,respectively)if Suis eulerian(or subeulerian,or eulerian and|E-(S)|is even,respectively).We say that S is balanced subeulerian signed graph if there exists a balanced eulerian signed graph S′such that S′is spanned by S.The signed line graph L(S)of a signed graph S is a signed graph with the vertices of L(S)being the edges of S,where an edge eiej is in L(S)if and only if the edges e_(i)and e_(j)of S have a vertex in common in S such that an edge eiej in L(S)is negative if and only if both edges ei and ej are negative in S.In this paper,two families of signed graphs S and S′are identified,which are applied to characterize balanced subeulerian signed graphs and balanced subeulerian signed line graphs.In particular,it is proved that a signed graph S is balanced subeulerian if and only if S∈S,and that a signed line graph of signed graph S is balanced subeulerian if and only if S∈S′.
