“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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A YOLOv8-CE-based real-time traffic sign detection and identification method for autonomous vehicles

Traffic sign detection in real scenarios is challenging due to their complexity and small size,often preventing existing deep learning models from achieving both high accuracy and real-time performance.An improved YOLOv8 model for traffic sign detection is proposed.Firstly,by adding Coordinate Attention(CA)to the Backbone,the model gains location information,improving detection accuracy.Secondly,we also introduce EIoU to the localization function to address the ambiguity in aspect ratio descriptions by calculating the width-height difference based on CIoU.Additionally,Focal Loss is incorporated to balance sample difficulty,enhancing regression accuracy.Finally,the model,YOLOv8-CE(YOLOv8-Coordinate Attention-EIoU),is tested on the Jetson Nano,achieving real-time street scene detection and outperforming the Raspberry Pi 4B.Experimental results show that YOLOv8-CE excels in various complex scenarios,improving mAP by 2.8%over the original YOLOv8.The model size and computational effort remain similar,with the Jetson Nano achieving an inference time of 96 ms,significantly faster than the Raspberry Pi 4B.

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.

Intrauterine midgut volvulus without malrotation:Diagnosis from the 'coffee bean sign'

Fetal midgut volvulus is quite rare, and most cases are associated with abnormalities of intestinal rotation or fixation. We report a case of midgut volvulus without malrotation, associated with a meconium pellet, during the gestation period. This 2.79 kg, 33-wk infant was born via a spontaneous vaginal delivery caused by preterm labor. Prenatal ultrasound showed dilated bowel loops with the appearance of a 'coffee bean sign'. This patient had an unusual presentation with a distended abdomen showing skin discoloration. An emergency laparotomy revealed a midgut volvulus and a twisted small bowel, caused by complicated meconium ileus. Such nonspecific prenatal radiological signs and a low index of suspicion of a volvulus during gestation might delay appropriate surgical management and result in ischemic necrosis of the bowel. Preterm labor, specific prenatal sonographic findings (for example, the coffee bean sign) and bluish discoloration of the abdominal wall could suggest intrauterine midgut volvulus requiring prompt surgical intervention.

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.

Continuous Sign Language Recognition Based on Spatial-Temporal Graph Attention Network

Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtemporal graph attention network to focus on essential features of video series.The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatialtemporal graph to reflect inter-frame relevance and physical connections between nodes.The graph-based multihead attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration,and short-term motion correlation modeling is completed via a temporal convolutional network.We adopted BLSTM to learn the long-termdependence and connectionist temporal classification to align the word-level sequences.The proposed method achieves competitive results regarding word error rates(1.59%)on the Chinese Sign Language dataset and the mean Jaccard Index(65.78%)on the ChaLearn LAP Continuous Gesture Dataset.

Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module

The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine Learning(ML)have been used in road infrastructure and construction,particularly with the Internet of Things(IoT)devices.Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing trafficrelated problems.This study aims to use You Only Look Once version 7(YOLOv7),Convolutional Block Attention Module(CBAM),the most optimized object-detection algorithm,to detect and identify traffic signs,and analyze effective combinations of adaptive optimizers like Adaptive Moment estimation(Adam),Root Mean Squared Propagation(RMSprop)and Stochastic Gradient Descent(SGD)with the YOLOv7.Using a portion of German traffic signs for training,the study investigates the feasibility of adopting smaller datasets while maintaining high accuracy.The model proposed in this study not only improves traffic safety by detecting traffic signs but also has the potential to contribute to the rapid development of autonomous vehicle systems.The study results showed an impressive accuracy of 99.7%when using a batch size of 8 and the Adam optimizer.This high level of accuracy demonstrates the effectiveness of the proposed model for the image classification task of traffic sign recognition.