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.

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.

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.

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.

Existence of Monotone Positive Solution for a Fourth-Order Three-Point BVP with Sign-Changing Green’s Function

This paper is concerned with the following fourth-order three-point boundary value problem , where , we discuss the existence of positive solutions to the above problem by applying to the fixed point theory in cones and iterative technique.

Geological Characteristics and Ore-controlling Factors of the Beiya Gold–Polymetallic Ore Deposit, Northwestern Yunnan Province

Based on comprehensive petrological, geochronological, and geochemical studies, this study analyzed the relationships between the Beiya gold-polymetallic skarn deposit and quartz syenite porphyries, and discussed the source（s） and evolution of magmas. Our results suggest that syenite porphyries（i.e. the Wandongshan, the Dashadi, and the Hongnitang porphyries）, which formed between the Eocene and the early Oligocene epochs, are the sources for the gold-polymetallic ores at the Beiya deposit. Carbonate rocks（T2 b） of the Triassic Beiya Formation in the ore district provide favorable host space for deposit formation. Fold and fault structures collectively play an important role in ore formation. The contact zone between the porphyries and carbonates, the structurally fractured zone of carbonate and clastic rocks, and the zone with well-developed fractures are the ideal locations for ore bodies. Four types of mineralization have been recognized： 1） porphyry-style stockwork gold–iron（copper） ore, 2） skarn-style gold-iron（copper and lead） ore in the near contact zone, 3） strata-bound, lense-type lead–silver–gold ore in the outer contact zone, and 4） distal vein-type gold–lead–silver ore. Supergene processes led to the formation of oxide ore, such as the weathered and accumulated gold–iron ore, the strata-bound fracture oxide ore, and the structure-controlled vein-type ore. Most of these ore deposits are distributed along the axis of the depressed basin, with the hypogene ore controlling the shape and characteristics of the oxide ore. This study provides critical geology understanding for mineral prospecting scenarios.

The Characteristics of Ore-Controlling Structure in Baoshan Cu-Pb-Zn Polymetallic Deposit,Hunan Province

The Baoshan Cu-Pb-Zn polymetallic deposit is lied in the central Nanling mineralization zone,and belongs to the junction area of the Chenzhou-Linwu fault zone and the Leiyang-Linwu fault zone.It is a significant part of Nanling polymetallic deposit belt.The outcropping stratas consist of upper Devonian Shetianqiao,Xikuangshan Formation,Lower Carboniferous Menggong’ao,Shidengzi,Ceshui,and Zimenqiao Formation.Igneous rocks in the Baoshan ore area mainly comprise granodiorite porphyry.Furthermore,the radio isotopic age ranges from 123 Ma to 183 Ma,belonging to the early to middle Yanshanian.

Mineralization and Ore-controlling Implications of Low-angle Faults

Abstract Low-angle faults include those occurring in thrust-nappe structures in a compressive setting and the detachment of metamorphic core complexes in an extensional setting. All low-angle faults have their own particularities. The low-angle fault plays an important role in controlling over some endogenetic metallic ore deposits. Based on studies of the Xiaoban gold deposit, Xinzhou gold deposit, and Longfengchang polymetallic ore deposit, and comparisons with other mines, the authors conclude the ore-controlling implications of low-angle faults as follows. (1) Because of high temperature and high pressure, as well as strong ductile deformation, the internal energy of the elements rises in the large-scale deep ductile low-angle faults, which causes the elements to activate and differentiate from the source rocks, forming ore-bearing hydrothermal solution, and bring mineralization to happen. (2) When rising from depths and flowing along the low-angle faults, the ore-bearing hydrothermal solution will alter and replace the tectonites in the fault zone. The rocks of the hanging side and the heading side differ in lithology, texture and structure, which results in changes or dissimilarities of the physical-chemical conditions. This destroys the balance of the hydrothermal solution system and causes the dissolved ore-forming elements to precipitate; as a result, a deposit is formed. Therefore, the meso-shallow ductile-brittle low-angle faults play the role of a geochemical interface in the process of mineralization. (3) Low-angle faults are often one of the important host structures.

Analysis of Ore-controlling Structure in the Qifengcha-Detiangou Gold Deposit,Huairou County,Beijing

The Qifengcha-Detiangou gold deposit is a medium-sized deposit recently found in Huairou County, Beijing. It belongs to the altered mylonite type with superimposed quartz vein type and is related to the early Yanshanian magmatic activity. Characterized by multiperiodic activity, the NE-trending Qifengcha fault is a regional ore-controlling structure in the area, and gold mineralization develops only in its southeastern part. Meanwhile, gold mineralization is controlled by the Yunmengshan metamorphic core complex. The nearly N-S- and E-W-trending low-angle detachment faults, reformed by the Qifengcha fault in the northwestern part of the core complex, are the main ore-bearing faults. All discovered gold deposits are located within an area 1.5–4.0 km away from the boundary of the upwelling centre. The N-S- (NNE-) and E-W-trending ore-bearing faults are ductile-brittle structural zones developing in shallow positions and subjected mainly to compressive deformation. The structural ore-controlling effects are as follows. (1) The attitude, shape, and distribution of gold orebodies are controlled by faults. (2) There is a negative correlation between the gold abundance and the magnetic anisotropy (P) of the altered mylonite samples from the deposit, which shows that the gold mineralization is later than the structural deformation. (3) Quartz vein type mineralization is superimposed on altered mylonite type mineralization. (4) In mineralized mylonite, the stronger the ductile shear deformation, the easier the late-stage gold mineralization to occur and the higher the gold abundance. The richest gold mineralization occurs only around the centre of the fault subjected to the strongest deformation.

Geophysical Features of the Ore-Controlling Fault in the Chang'an Gold Deposit, Southern Yunnan Province

The Ailao Mountain is one of the most important metallogenic belts ofpolymetallic deposits in the Sanjiang region, southwestern China. Located in the southern segment of this metallogenic belt, the newly-discovered Chang＇an gold deposit is large in scale （Fig. 1A）, and has attracted much attention among geologists. The ore-hosted rocks in the district include the Late Ordovician Xiangyang Fm. sandstone and clastic rocks and the Early Silurian Kanglang Fm. dolomite. Affected by the multistage tectonic activities, stocks and dykes of lamprophyre, dolerite, syenite porphyry and orthoclasite are widely exposed, and the orebodies are in symbiosis with or crosscut the dyke rocks.

Analysis of Ore-Controlling Structures of the Xincheng-Hexi Gold Deposit,Shandong Province,China

Based on quantitative and semi-quantitative mathematical and mechanical analysis of the shape, motion, structural factors, stress field and deformation field of the ore-hosting faults in the Xincheng-Hexi gold deposit, the ore-controlling features of faults and mineralization mechanism are discussed. It is concluded that the mineralization is controlled by the main faults, subsidiary fractures, joint density, mechanical features and deformation of the faults. The ore bodies are mainly located in the lower part of the convex crest and upper part of the concave trough of the main undulating fault surface. Mineralization is positively correlated to the development of subsidiary fractures and joints, which correspond to zones of low internal stress and high body strain and shear strain. They are favourable positions for mineralization and alteration.

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.

Research on Traffic Sign Detection Based on Improved YOLOv8

Aiming at solving the problem of missed detection and low accuracy in detecting traffic signs in the wild, an improved method of YOLOv8 is proposed. Firstly, combined with the characteristics of small target objects in the actual scene, this paper further adds blur and noise operation. Then, the asymptotic feature pyramid network (AFPN) is introduced to highlight the influence of key layer features after feature fusion, and simultaneously solve the direct interaction of non-adjacent layers. Experimental results on the TT100K dataset show that compared with the YOLOv8, the detection accuracy and recall are higher. .

Ore-controlling Regularitiesof Structureinthe Lehong Lead-zinc Deposit, Northeastern Yunnan

1 Introduction The Lehonglead-zincdeposit is a large-sized Pb-Zn depositnewly found in recent years in the Sichuan-Yunnan-Guizhou Lead-zinc Poly-metallic Mineralization Area,which occurrenceis strictly

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.

C2Net-YOLOv5: A Bidirectional Res2Net-Based Traffic Sign Detection Algorithm

Rapid advancement of intelligent transportation systems(ITS)and autonomous driving(AD)have shown the importance of accurate and efficient detection of traffic signs.However,certain drawbacks,such as balancing accuracy and real-time performance,hinder the deployment of traffic sign detection algorithms in ITS and AD domains.In this study,a novel traffic sign detection algorithm was proposed based on the bidirectional Res2Net architecture to achieve an improved balance between accuracy and speed.An enhanced backbone network module,called C2Net,which uses an upgraded bidirectional Res2Net,was introduced to mitigate information loss in the feature extraction process and to achieve information complementarity.Furthermore,a squeeze-and-excitation attention mechanism was incorporated within the channel attention of the architecture to perform channel-level feature correction on the input feature map,which effectively retains valuable features while removing non-essential features.A series of ablation experiments were conducted to validate the efficacy of the proposed methodology.The performance was evaluated using two distinct datasets:the Tsinghua-Tencent 100K and the CSUST Chinese traffic sign detection benchmark 2021.On the TT100K dataset,the method achieves precision,recall,and Map0.5 scores of 83.3%,79.3%,and 84.2%,respectively.Similarly,on the CCTSDB 2021 dataset,the method achieves precision,recall,and Map0.5 scores of 91.49%,73.79%,and 81.03%,respectively.Experimental results revealed that the proposed method had superior performance compared to conventional models,which includes the faster region-based convolutional neural network,single shot multibox detector,and you only look once version 5.

Ore-controlling Characteristics of Synchronous Faults of Dongshengmiao Polymetallic Sulfide Deposits in Inner Mongolia

1 Introduction The Dongshengmiao deposit is a super-large Zn-Pb polymetallic sulfide deposit which occurring in the Langshan-Zhaertaaishan metallogenic belt,and located in the western margin of the North China Platform.The ore-bodies of Dongshengmiao deposits are mainly hosted in the second Formation of Langshan Group.There are some studies on the geological characteristics(Peng et al.,2004),geological and

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.