Artificial Intelligence-Based Sentiment Analysis of Dynamic Message Signs that Report Fatality Numbers Using Connected Vehicle Data

This study presents results from sentiment analysis of Dynamic message sign (DMS) message content, focusing on messages that include numbers of road fatalities. As a traffic management tool, DMS plays a role in influencing driver behavior and assisting transportation agencies in achieving safe and efficient traffic movement. However, the psychological and behavioral effects of displaying fatality numbers on DMS remain poorly understood;hence, it is important to know the potential impacts of displaying such messages. The Iowa Department of Transportation displays the number of fatalities on a first screen, followed by a supplemental message hoping to promote safe driving;an example is “19 TRAFFIC DEATHS THIS YEAR IF YOU HAVE A SUPER BOWL DON’T DRIVE HIGH.” We employ natural language processing to decode the sentiment and undertone of the supplementary message and investigate how they influence driving speeds. According to the results of a mixed effect model, drivers reduced speeds marginally upon encountering DMS fatality text with a positive sentiment with a neutral undertone. This category had the largest associated amount of speed reduction, while messages with negative sentiment with a negative undertone had the second largest amount of speed reduction, greater than other combinations, including positive sentiment with a positive undertone.

Correlation between abdominal computed tomography signs and postoperative prognosis for patients with colorectal cancer

BACKGROUND Patients with different stages of colorectal cancer(CRC)exhibit different abdominal computed tomography(CT)signs.Therefore,the influence of CT signs on CRC prognosis must be determined.AIM To observe abdominal CT signs in patients with CRC and analyze the correlation between the CT signs and postoperative prognosis.METHODS The clinical history and CT imaging results of 88 patients with CRC who underwent radical surgery at Xingtan Hospital Affiliated to Shunde Hospital of Southern Medical University were retrospectively analyzed.Univariate and multivariate Cox regression analyses were used to explore the independent risk factors for postoperative death in patients with CRC.The three-year survival rate was analyzed using the Kaplan-Meier curve,and the correlation between postoperative survival time and abdominal CT signs in patients with CRC was analyzed using Spearman correlation analysis.RESULTS For patients with CRC,the three-year survival rate was 73.86%.The death group exhibited more severe characteristics than the survival group.A multivariate Cox regression model analysis showed that body mass index(BMI),degree of periintestinal infiltration,tumor size,and lymph node CT value were independent factors influencing postoperative death(P<0.05 for all).Patients with characteristics typical to the death group had a low three-year survival rate(log-rankχ2=66.487,11.346,12.500,and 27.672,respectively,P<0.05 for all).The survival time of CRC patients was negatively correlated with BMI,degree of periintestinal infiltration,tumor size,lymph node CT value,mean tumor long-axis diameter,and mean tumor short-axis diameter(r=-0.559,0.679,-0.430,-0.585,-0.425,and-0.385,respectively,P<0.05 for all).BMI was positively correlated with the degree of periintestinal invasion,lymph node CT value,and mean tumor short-axis diameter(r=0.303,0.431,and 0.437,respectively,P<0.05 for all).CONCLUSION The degree of periintestinal infiltration,tumor size,and lymph node CT value are crucial for evaluating the prognosis of patients with CRC.

Effectiveness of aromatherapy with lavender compared to progressive muscle relaxation on anxiety and vital signs in patients under spinal anesthesia:A randomized clinical trial

Objective:This study aimed to determine the effectiveness of aromatherapy with lavender essential oil compared to progressive muscle relaxation(PMR)on anxiety and vital signs of patients under spinal anesthesia.Materials and Methods:This clinical trial was conducted on 120 spinal anesthesia candidates who were randomly assigned into three groups of 40 including control,PMR(Jacobsen group),and aromatherapy.The state-trait anxiety inventory was completed on surgery day and 15 min after the end of the intervention by the samples of all three groups,and at the same time as completing the questionnaire,vital signs were also measured and recorded.Results:The mean score of anxiety after intervention was lower than that before the intervention in the aromatherapy group(P<0.001).The mean score of anxiety in the aromatherapy group was significantly lower than that in the Jacobsen group(P<0.001).Moreover,data analysis showed a significant decrease in the mean arterial blood pressure scores of the PMR(P=008)and aromatherapy(P<0.001)groups and a statistically significant increase in the mean heart rate scores in the control group(P=0.002).Conclusion:The use of aromatherapy with lavender is more effective than PMR therapy in reducing the anxiety level of patients undergoing spinal anesthesia.Due to the high level of anxiety and its serious effects on the patient’s hemodynamics,aromatherapy with lavender can be used as an easy and cheap method to reduce anxiety in operation rooms.

Text Type and C-E Translation of Public Signs

This paper contends that the public sign is a kind of text with such vocative functions as indicating,instructing,restricting,prohibiting,persuading,and publicizing,so it falls into the type of vocative texts.The paper suggests that conveying the vocative function of the public sign is the essential task of the translator,so as to achieve the intended effect of the public sign.

Research on Preschoolers’Comprehension of Safety Signs and Its Influencing Factors

As an integral part of children’s safety education,safety signs hold significant importance for preschoolers’safety.This study aims to investigate the comprehension level of safety signs and its influencing factors among preschoolers and explore the role of background factors such as safety education in children’s learning of safety signs.Sixty-seven preschoolers participated in the questionnaire investigation on 11 safety signs.The results were encoded by a binary method and subjected to descriptive analysis and multiple correspondence analysis.The results indicated that preschoolers can understand symbols,but there is a certain degree of arbitrariness.The existing thematic education fails to improve their understanding of safety signs.This study provides a theoretical basis for improving and optimizing child safety education.

Simulation based on a modified social force model for sensitivity to emergency signs in subway station

The subway is the primary travel tool for urban residents in China. Due to the complex structure of the subway and high personnel density in rush hours, subway evacuation capacity is critical. The subway evacuation model is explored in this work by combining the improved social force model with the view radius using the Vicsek model. The pedestrians are divided into two categories based on different force models. The first category is sensitive pedestrians who have normal responses to emergency signs. The second category is insensitive pedestrians. By simulating different proportions of the insensitive pedestrians, we find that the escape time is directly proportional to the number of insensitive pedestrians and inversely proportional to the view radius. However, when the view radius is large enough, the escape time does not change significantly, and the evacuation of people in a small view radius environment tends to be integrated. With the improvement of view radius conditions, the escape time changes more obviously with the proportion of insensitive pedestrians. A new emergency sign layout is proposed, and the simulations show that the proposed layout can effectively reduce the escape time in a small view radius environment. However, the evacuation effect of the new escape sign layout on the large view radius environment is not apparent. In this case, the exit setting emerges as an additional factor affecting the escape time.

Epidemiology and Clinical Signs of Gynecological Cancers in an African Country South of the Sahara: Case of the Republic of Benin in 2022

Introduction: Gynaecological cancers are the deadliest of the women’s cancers in the Republic of Benin. Late diagnosis is the most common reason. Objective: This paper aims to describe the epidemiological characteristics, and clinical and pathological signs of gynaecological cancers treated in the Republic of Benin between 2018 and 2022. Patients and Methods: This was a cross-sectional, descriptive, retrospectively collected study of patient data treated between 2018 and 2022 in two university gynaecology departments in Cotonou. All gynaecological cancers that have histological evidence were included. The epidemiological, clinical and pathological characteristics of the cancers were assessed. Results: Cervical, endometrial and ovarian cancers were the most common in the proportions of 62.0%, 24.1%, 12.0% and 1.8% respectively. The mean age at diagnosis was 54 years. The victims were uneducated and had low economic power in 81% and 85% of cases, respectively. The consultation was late in 82.1% of cases. Metrorrhagia, postmenopausal metrorrhagia and pelvic cluster headache were the common reasons for consultation for cervical, endometrial and ovarian cancer, respectively. Diagnosis was late in 66.7% (n = 71). The most common histological types were squamous cell carcinoma, endometrioid adenocarcinoma, and serous cystadenocarcinoma for cervical, endometrial, and ovarian cancers, respectively. Conclusion: Gynaecological cancers were common and their consultation time was delayed. The diagnosis was made at the advanced stage and there were several reasons for this.

Selective his bundle pacing eliminates crochetage sign:A case report

BACKGROUND Crochetage sign is a specific electrocardiographic manifestation of ostium secundum atrial septal defects(ASDs),which is associated with the severity of the left-to-right shunt.Herein,we reported a case of selective his bundle pacing(SHBP)that eliminated crochetage sign in a patient with ostium secundum ASD.CASE SUMMARY A 77-year-old man was admitted with a 2-year history of chest tightness and shortness of breath.Transthoracic echocardiography revealed an ostium secundum ASD.Twelve-lead electrocardiogram revealed atrial fibrillation with a prolonged relative risk interval,incomplete right bundle branch block,and crochetage sign.The patient was diagnosed with an ostium secundum ASD,atrial fibrillation with a second-degree atrioventricular block,and heart failure.The patient was treated with selective his bundle pacemaker implantation.After the procedure,crochetage sign disappeared during his bundle pacing on the electrocardiogram.CONCLUSION S-HBP eliminated crochetage sign on electrocardiogram.Crochetage sign may be a manifestation of a conduction system disorder.

A Deep Learning Model of Traffic Signs in Panoramic Images Detection

To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent,the traffic signs detection system has become one of the necessary topics in recent years and in the future.The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image.To accomplish this goal,the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for com-prehensive traffic sign classification and Mask Region-based Convolutional Neural Networks(R-CNN)implementation for identifying and extracting signs in panoramic images.Data augmentation and normalization of the images are also applied to assist in classifying better even if old traffic signs are degraded,and considerably minimize the rates of discovering the extra boxes.The proposed model is tested on both the testing dataset and the actual images and gets 94.5%of the correct signs recognition rate,the classification rate of those signs discovered was 99.41%and the rate of false signs was only around 0.11.

Recognizing Early Warning Signs (EWS) in Patients Is Critically Important

Introduction: Monitoring vital signs is a basic indicator of a patient’s health status and allows prompt detection of delayed recovery or adverse effects and early intervention. Patients with adverse events during hospitalization often display clinical decline for several hours before the event is observed. Non-critical care Nurses’ inconsistent recognition and response to patient deterioration lead to an increase in the length of hospital stay, unexpected admissions to the ICU, and increased morbidity and mortality. Aim: The study aimed to assess the factors that facilitate or impede the detection of early warning signs among adult patients hospitalized in tertiary care settings. Training should be provided to improve nurses’ knowledge, practice and attitude toward early warning signs of deteriorating patients leading to enhanced clinical judgment, skills and decision-making in addressing alerts. Methodology: A literature search was carried out in various databases;these were Cumulative Index to Nursing and Allied Health Literature (CINHAL), Google Scholar, PubMed, Science Direct, and Sage. The search area was narrowed from 2017 to 2022. The keywords used were “prevalence” AND “unplanned ICU admission”, “the importance of early warning signs” “outcome failure in rescue” “patient deterioration, communication” “improvement in early detection” AND “patient outcome admission” AND “early warning signs” AND “Pakistan”. After the analysis process, around 33 articles that met the inclusion criteria and were most relevant to the scope and context of the current study were considered. Conclusion: Most of the studies had reviewed literature in a qualitative retrospective observational study, content analysis, mixed method, and quasi-experimental study. The literature review identified that long hours of shift, nurse staffing levels, missed vital signs, lack of nursing training and education, and communication impact nurses’ ability to recognize and respond to early warning signs.

“Keyboard sign”and“coffee bean sign”in the prenatal diagnosis of ileal atresia:A case report

BACKGROUND Ileal atresia is a congenital abnormality where there is significant stenosis or complete absence of a portion of the ileum.The overall diagnostic accuracy of prenatal ultrasound in detecting jejunal and ileal atresia is low.We report a case of ileal atresia diagnosed prenatally by ultrasound examination with the“keyboard sign”and“coffee bean sign”.CASE SUMMARY We report a case of ileal atresia diagnosed in utero at 31 weeks'of gestation.Prenatal ultrasound examination revealed two rows of intestines arranged in an‘S’shape in the middle abdomen.The inner diameters were 1.7 cm and 1.6 cm,respectively.A typical“keyboard sign”was observed.The intestine canal behind the“keyboard sign”showed an irregular strong echo.There was no normal intestinal wall structure,showing a typical“coffee bean sign”.Termination of the pregnancy and autopsy findings confirmed the diagnosis.CONCLUSION The prenatal diagnosis of ileal atresia is difficult.The sonographic features of the“keyboard sign”and“coffee bean sign”are helpful in diagnosing the location of congenital jejunal and ileal atresia.

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

DC-SIGN靶向的载铜绿假单胞菌DNA疫苗纳米粒的构建及免疫效力评价

目的构建一种靶向树突状细胞(dendritic cells,DC)乳-N-岩藻糖戊糖(lacto-N-fucopentoseⅢ,Lewis X)修饰的载铜绿假单胞菌(Pseudomonas aeruginosa,PA)PcrV和OprF联合DNA疫苗的PLGA纳米粒,为预防PA临床感染提供新思路。方法利用双乳化-溶剂挥发法制备载PcrV和OprF联合DNA的PLGA纳米粒(PLGA+PcrV/OprF)或载pEGFP的PLGA纳米粒(PLGA+pEGFP);在此基础上,利用酰胺缩合反应将DC-SIGN靶向配体Lewis X连接至PLGA纳米粒表面,制备Lewis X修饰的PLGA+PcrV/OprF(Lewis X-PLGA+PcrV/OprF)、Lewis X修饰的PLGA-pEGFP(Lewis X-PLGA+pEGFP);以水化直径、Zeta电位、包封率与载药量为指标对Lewis X-PLGA+PcrV/OprF进行表征分析;用CCK-8考察其细胞毒性;通过Lewis X-PLGA+pEGFP体外转染进行DC靶向验证;进一步通过Lewis X-PLGA+PcrV/OprF溶酶体逃逸评价Lewis X修饰的携载DNA的PLGA纳米粒的体外靶向性能;通过检测该纳米粒的淋巴细胞增殖水平、体液免疫水平和免疫保护水平,评价其免疫效力。结果制备的Lewis X-PLGA+PcrV/OprF水化直径为(201.17±1.6)nm,包封率为(85.72±5.3)%,Zeta电位为+(31.17±1.8)mV;Lewis X-PLGA+PcrV/OprF在DC2.4中的细胞毒性试验显示细胞存活率均在85%以上;荧光显微镜观察Lewis X-PLGA+pEGFP体外转染结果表明,DC2.4更能摄取表达Lewis X-PLGA+pEGFP,具有DC-SIGN特异性靶向性能;激光共聚焦观察溶酶体逃逸结果表明,Lewis X-PLGA+PcrV/OprF发生溶酶体逃逸后有更多的DNA进入细胞质;体内免疫结果显示,靶向DNA疫苗的淋巴细胞增殖水平和抗体滴度水平显著增加,进一步提高了感染急性肺炎小鼠的生存率,减少了小鼠肺部细菌负荷。结论成功构建DC-SIGN靶向的载PA DNA疫苗纳米粒Lewis X-PLGA;促进了其携载的DNA转染进入DC;促进了更多PA DNA疫苗内吞进入DC溶酶体,逃逸出更多PA DNA至细胞质,从而引起体内显著的免疫应答,增强了疫苗保护效力。

Traffic Sign Detection Model Based on Improved RT-DETR

The correct identification of traffic signs plays an important role in automatic driving technology and road safety driving.Therefore,to address the problems of misdetection and omission in traffic sign detection due to the variety of sign types,significant size differences and complex background information,an improved traffic sign detection model for RT-DETR was proposed in this study.Firstly,the HiLo attention mechanism was added to the Attention-based Intra-scale Feature Interaction,which further enhanced the feature extraction capability of the network and improved the detection efficiency on high-resolution images.Secondly,the CAFMFusion feature fusion mechanism was designed,which enabled the network to pay attention to the features in different regions in each channel.Based on this,the model could better capture the remote dependencies and neighborhood feature correlation,improving the feature fusion capability of the model.Finally,the MPDIoU was used as the loss function of the improved model to achieve faster convergence and more accurate regression results.The experimental results on the TT100k-2021 traffic sign dataset showed that the improved model achieves the performance with a precision value of 90.2%,recall value of 88.1%and mAP@0.5 value of 91.6%,which are 4.6%,5.8%,and 4.4%better than the original RT-DETR model respectively.The model effectively improves the problem of poor traffic sign detection and has greater practical value.

Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual language.Many works based on rich source language have been proposed;however,the work using poor resource language is still lacking.Unlike other SLs,the visuals of the Urdu Language are different.This study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this purpose.Unlike existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited resources.We conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and prediction.To enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise filtering.Comparative analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of 0.95.Additionally,our model exhibited superior performance in Precision,Recall,and F1-score evaluations.This work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

Recent Advances on Deep Learning for Sign Language Recognition

Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automatically recognizing and interpreting sign language gestures,has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world.The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR.This paper presents a comprehensive and up-to-date analysis of the advancements,challenges,and opportunities in deep learning-based sign language recognition,focusing on the past five years of research.We explore various aspects of SLR,including sign data acquisition technologies,sign language datasets,evaluation methods,and different types of neural networks.Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)have shown promising results in fingerspelling and isolated sign recognition.However,the continuous nature of sign language poses challenges,leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition(CSLR).Despite significant advancements,several challenges remain in the field of SLR.These challenges include expanding sign language datasets,achieving user independence in recognition systems,exploring different input modalities,effectively fusing features,modeling co-articulation,and improving semantic and syntactic understanding.Additionally,developing lightweight network architectures for mobile applications is crucial for practical implementation.By addressing these challenges,we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community.

A Hybrid Feature Fusion Traffic Sign Detection Algorithm Based on YOLOv7

Autonomous driving technology has entered a period of rapid development,and traffic sign detection is one of the important tasks.Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced,and traffic sign targets are small and have unclear features,which makes detection more difficult.Therefore,we propose aHybrid Feature Fusion Traffic Sign detection algorithmbased onYOLOv7(HFFTYOLO).First,a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales;Secondly,the cross-scale fusion part of the neck introduces a bottom-up multi-path fusion method.Design reuse paths at the end of the neck,paying particular attention to cross-scale fusion of highlevel features.In addition,we found the appropriate channel width through a lot of experiments and reduced the superfluous parameters.In terms of training,a newregression lossCMPDIoUis proposed,which not only considers the problem of loss degradation when the aspect ratio is the same but the width and height are different,but also enables the penalty term to dynamically change at different scales.Finally,our proposed improved method shows excellent results on the TT100K dataset.Compared with the baseline model,without increasing the number of parameters and computational complexity,AP0.5 and AP increased by 2.2%and 2.7%,respectively,reaching 92.9%and 58.1%.

Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing.

Multi-scale context-aware network for continuous sign language recognition

The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information.In addition,the signs have different lengths,whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling,which disturbs the perception of complete signs.In this study,we propose a Multi-Scale Context-Aware network(MSCA-Net)to solve the aforementioned problems.Our MSCA-Net contains two main modules:(1)Multi-Scale Motion Attention(MSMA),which uses the differences among frames to perceive information of the hands and face in multiple spatial scales,replacing the heavy feature extractors;and(2)Multi-Scale Temporal Modeling(MSTM),which explores crucial temporal information in the sign language video from different temporal scales.We conduct extensive experiments using three widely used sign language datasets,i.e.,RWTH-PHOENIX-Weather-2014,RWTH-PHOENIX-Weather-2014T,and CSL-Daily.The proposed MSCA-Net achieve state-of-the-art performance,demonstrating the effectiveness of our approach.