On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rank...On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.展开更多
Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competi...Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.展开更多
Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of ...Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.展开更多
With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms ...With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.展开更多
The 2D sandwich model serves as a potent tool in exploring the influence of surface geometry on the combustion attributes of Ammonium perchlorate/Hydroxyl-terminated polybutadiene(AP/HTPB)propellant under rapid pressu...The 2D sandwich model serves as a potent tool in exploring the influence of surface geometry on the combustion attributes of Ammonium perchlorate/Hydroxyl-terminated polybutadiene(AP/HTPB)propellant under rapid pressure decay.The thickness of the sandwich propellant is derived from slicing the 3D random particle packing,an approach that enables a more effective examination of the micro-flame structure.Comparative analysis of the predicted burning characteristics has been performed with experimental studies.The findings demonstrate a reasonable agreement,thereby validating the precision and soundness of the model.Based on the typical rapid depressurization environment of solid rocket motor(initial combustion pressure is 3 MPa and the maximum depressurization rate is 1000 MPa/s).A-type(a flatter surface),B-type(AP recesses from the combustion surface),and C-type(AP protrudes from the combustion surface)propellant combustion processes are numerically simulated.Upon comparison of the evolution of gas-phase flame between 0.1 and 1 ms,it is discerned that the flame strength and form created by the three sandwich models differ significantly at the beginning stage of depressurization,with the flame structures gradually becoming harmonized over time.Conclusions are drawn by comparison extinction times:the surface geometry plays a pivotal role in the combustion process,with AP protrusion favoring combustion the most.展开更多
Field-free spin-orbit torque(SOT)switching of perpendicular magnetization is essential for future spintronic devices.This study demonstrates the field-free switching of perpendicular magnetization in an HfO_(2)/Pt/Co/...Field-free spin-orbit torque(SOT)switching of perpendicular magnetization is essential for future spintronic devices.This study demonstrates the field-free switching of perpendicular magnetization in an HfO_(2)/Pt/Co/TaO_(x) structure,which is facilitated by a wedge-shaped HfO_(2)buffer layer.The field-free switching ratio varies with HfO_(2)thickness,reaching optimal performance at 25 nm.This phenomenon is attributed to the lateral anisotropy gradient of the Co layer,which is induced by the wedge-shaped HfO_(2)buffer layer.The thickness gradient of HfO_(2)along the wedge creates a corresponding lateral anisotropy gradient in the Co layer,correlating with the switching ratio.These findings indicate that field-free SOT switching can be achieved through designing buffer layer,offering a novel approach to innovating spin-orbit device.展开更多
The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a c...The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.展开更多
The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current re...The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.展开更多
To explore the relationship between social influence,social comparison,clarity of self-concept,and psychological anxiety among young women during their usage of social networking sites,our study selected 338 young wom...To explore the relationship between social influence,social comparison,clarity of self-concept,and psychological anxiety among young women during their usage of social networking sites,our study selected 338 young women aged 14-34 from the social site platforms of Little Red Book and Weibo for questionnaire surveys.The Passive Social Network Utilization Scale,Social Comparison Scale(SCS),Social Influence Questionnaire,Self-Concept Clarity Scale(SCCS),and Generalized Anxiety Disorder Scale(GAD-7)were employed to measure the subjects.Our results show that the frequency of passive social media use is positively related to the level of psychological anxiety.Social comparison,social influence,and unclear self-concepts under social media use are negatively predictive of psychological anxiety.The chain mediation effects indicate that social comparison and social influence under social media use negatively predict the clarity of self-concept,thus having a negative impact on the psychological health of young women.Therefore,young women should strengthen their self-concepts,control their frequency of social media usage,avoid addiction,and pay special attention to their frequency of passive use,in order to protect their psychological health.Our study provides some practical implications and insights regarding the relationship between young women’s social media use and psychological health.展开更多
Flexible pressure sensors have many potential applications in the monitoring of physiological signals because of their good biocompatibil-ity and wearability.However,their relatively low sensitivity,linearity,and stab...Flexible pressure sensors have many potential applications in the monitoring of physiological signals because of their good biocompatibil-ity and wearability.However,their relatively low sensitivity,linearity,and stability have hindered their large-scale commercial application.Herein,aflexible capacitive pressure sensor based on an interdigital electrode structure with two porous microneedle arrays(MNAs)is pro-posed.The porous substrate that constitutes the MNA is a mixed product of polydimethylsiloxane and NaHCO3.Due to its porous and interdigital structure,the maximum sensitivity(0.07 kPa-1)of a porous MNA-based pressure sensor was found to be seven times higher than that of an imporous MNA pressure sensor,and it was much greater than that of aflat pressure sensor without a porous MNA structure.Finite-element analysis showed that the interdigital MNA structure can greatly increase the strain and improve the sensitivity of the sen-sor.In addition,the porous MNA-based pressure sensor was found to have good stability over 1500 loading cycles as a result of its bilayer parylene-enhanced conductive electrode structure.Most importantly,it was found that the sensor could accurately monitor the motion of afinger,wrist joint,arm,face,abdomen,eye,and Adam’s apple.Furthermore,preliminary semantic recognition was achieved by monitoring the movement of the Adam’s apple.Finally,multiple pressure sensors were integrated into a 33 array to detect a spatial pressure distribu-×tion.Compared to the sensors reported in previous works,the interdigital electrode structure presented in this work improves sensitivity and stability by modifying the electrode layer rather than the dielectric layer.展开更多
Mobile young white-collar workers not only have the characteristics of mobile young people,but also have the characteristics of general white-collar workers.Under the influence of both,their mental health may be suffe...Mobile young white-collar workers not only have the characteristics of mobile young people,but also have the characteristics of general white-collar workers.Under the influence of both,their mental health may be suffering from“double disadvantage”.So,based on an ecological model of the stress process,this paper tries to use the data of the questionnaire on the mental health of mobile young white-collar workers in Zhejiang Province to explore the influence of some factors in the middle workplace and residence place on the mental health of micro individuals.The results show that:(1)The working environment with high control and low freedom and the workplace discrimination against the mobile status will have a negative impact on the mental health of mobile young white-collar workers;(2)Financial anxiety in daily life will lead to a decline in the mental health level of mobile young white-collar workers;(3)Good organizational support and neighborhood social relations can significantly relieve life pressure,so as to effectively improve the mental health of mobile young white-collar workers.It can be seen that we also need to pay more attention to the mental health of mobile young white-collar workers in order to improve their situation.展开更多
This work presents an advanced and detailed analysis of the mechanisms of hepatitis B virus(HBV)propagation in an environment characterized by variability and stochas-ticity.Based on some biological features of the vi...This work presents an advanced and detailed analysis of the mechanisms of hepatitis B virus(HBV)propagation in an environment characterized by variability and stochas-ticity.Based on some biological features of the virus and the assumptions,the corresponding deterministic model is formulated,which takes into consideration the effect of vaccination.This deterministic model is extended to a stochastic framework by considering a new form of disturbance which makes it possible to simulate strong and significant fluctuations.The long-term behaviors of the virus are predicted by using stochastic differential equations with second-order multiplicative α-stable jumps.By developing the assumptions and employing the novel theoretical tools,the threshold parameter responsible for ergodicity(persistence)and extinction is provided.The theoretical results of the current study are validated by numerical simulations and parameters estimation is also performed.Moreover,we obtain the following new interesting findings:(a)in each class,the average time depends on the value ofα;(b)the second-order noise has an inverse effect on the spread of the virus;(c)the shapes of population densities at stationary level quickly changes at certain values of α.The last three conclusions can provide a solid research base for further investigation in the field of biological and ecological modeling.展开更多
Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often...Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.展开更多
The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation techniques.Biometric credentials are increasingly being used for verification due to their advant...The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation techniques.Biometric credentials are increasingly being used for verification due to their advantages over traditional methods,making it crucial to safeguard the privacy of people’s biometric data in various scenarios.This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems.It proposes a noble and thorough taxonomy survey for privacy-preserving techniques,as well as a systematic framework for categorizing the field’s existing literature.We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric modalities,such as face,fingerprint,and eye detection.The survey encompasses various categories of privacy-preserving mechanisms and examines the trade-offs between security,privacy,and recognition performance,as well as the issues and future research directions.It aims to provide researchers,professionals,and decision-makers with a thorough understanding of the existing privacy-preserving solutions in biometric recognition systems and serves as the foundation of the development of more secure and privacy-preserving biometric technologies.展开更多
In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,...In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,diagnosis and evaluation of kidney and urinary tract disease,providing insight into the specific type and severity.However,manual urine sediment examination is labor-intensive,time-consuming,and subjective.Traditional machine learning based object detection methods require hand-crafted features for localization and classification,which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments.Deep learning based object detection methods have the potential to address the challenges mentioned above,but these methods require access to large urine sediment image datasets.Unfortunately,only a limited number of publicly available urine sediment datasets are currently available.To alleviate the lack of urine sediment datasets in medical image analysis,we propose a new dataset named UriSed2K,which contains 2465 high-quality images annotated with expert guidance.Two main challenges are associated with our dataset:a large number of small objects and the occlusion between these small objects.Our manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this dataset.Specifically,our goal is to improve the accuracy and efficiency of the detection algorithm and,in doing so,provide medical professionals with an automatic detector that saves time and effort.We propose an improved lightweight one-stage object detection algorithm called Discriminatory-YOLO.The proposed algorithm comprises a local context attention module and a global background suppression module,which aid the detector in distinguishing urine sediment features in the image.The local context attention module captures context information beyond the object region,while the global background suppression module emphasizes objects in uninformative backgrounds.We comprehensively evaluate our method on the UriSed2K dataset,which includes seven categories of urine sediments,such as erythrocytes(red blood cells),leukocytes(white blood cells),epithelial cells,crystals,mycetes,broken erythrocytes,and broken leukocytes,achieving the best average precision(AP)of 95.3%while taking only 10 ms per image.The source code and dataset are available at https://github.com/binghuiwu98/discriminatoryyolov5.展开更多
Based on China Family Panel Studies(CFPS)2018 data,the multiple linear regression model is used to analyze the effects of Internet use on women’s depression,and to test the robustness of the regression results.At the...Based on China Family Panel Studies(CFPS)2018 data,the multiple linear regression model is used to analyze the effects of Internet use on women’s depression,and to test the robustness of the regression results.At the same time,the effects of Internet use on mental health of women with different residence,age,marital status and physical health status are analyzed.Then,we can obtain that Internet use has a significant promoting effect on women’s mental health,while the degree of Internet use has a significant inhibitory effect on women’s mental health.In addition,the study found that women’s age,education,place of residence,marital status,length of sleep,working status and physical health status are the main factors affecting the mental health of Chinese women.In the heterogeneity investigation of residence,age,marital status and physical health status,Internet use has a greater negative impact on the Center for Epidemiological Studies Depression Scale(CES-D8)scores of women in rural areas,has a significant positive impact on the mental health of middle-aged and elderly women or women with spouses,and has a positive impact on the mental health of physically unhealthy women.Therefore,in view of women’s mental health needs and the problems existing in the use of the Internet,this paper puts forward some suggestions to further improve the overall mental health level of women.展开更多
Contingent self-esteem captures the fragile nature of self-esteem and is often regarded as suboptimal to psychological functioning.Self-compassion is another important self-related concept assumed to promote mental he...Contingent self-esteem captures the fragile nature of self-esteem and is often regarded as suboptimal to psychological functioning.Self-compassion is another important self-related concept assumed to promote mental health and well-being.However,research on the relation of self-compassion to contingent self-esteem is lacking.Two studies were conducted to explore the role of selfcompassion,either as a personal characteristic or an induced mindset,in influencing the effects of contingent self-esteem on well-being.Study 1 recruited 256 Chinese college students(30.4%male,mean age=21.72 years)who filled out measures of contingent self-esteem,self-compassion,and well-being.The results found that self-compassion moderated the effect of contingent self-esteem on well-being.In Study 2,a sample of 90 Chinese college students(34%male,mean age=18.39 years)were randomly assigned to either a control or self-compassion group.They completed baseline trait measures of contingent self-esteem,self-compassion,and self-esteem.Then,they were led to have a 12-min break(control group)or listen to a 12-min self-compassion audio(self-compassion group),followed by a social stress task and outcome measures.The results demonstrated the effectiveness of the brief self-compassion training and its moderating role in influencing the effects of contingent self-esteem on negative affects after the social stress task.This research provides implications that to equip with a self-compassionate mindset could lower the risk of the impairment of well-being associated with elements of contingent selfesteem,which involves a fragile sense of self-worth.It may also provide insights into the development of an“optimal selfesteem”and the improvement of well-being.展开更多
On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rank...On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.展开更多
On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rank...On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.展开更多
According to UNESCO,China is the largest source country in terms of international students,contributing around 1 million international students per year(See the figure below).Choosing a study abroad destination as wel...According to UNESCO,China is the largest source country in terms of international students,contributing around 1 million international students per year(See the figure below).Choosing a study abroad destination as well as an international university is always a challenge to Chinese students as well as their parents.Most of them use the global university ranking as a tool during their decisionmaking process.展开更多
文摘On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.
基金supported by the NationalNatural Science Foundation of China(No.61972118)the Key R&D Program of Zhejiang Province(No.2023C01028).
文摘Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.
基金supported by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation“Research and System Development of Highway Asset Digitalization Technology inUse Based onHigh-PrecisionMap”(Project Number:202203)in part by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation:Research and Demonstration Application of Key Technologies for Precise Sensing of Expressway Thrown Objects(No.202204).
文摘Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.
基金supported in part by the Pioneer and Leading Goose R&D Program of Zhejiang Province under Grant 2022C01083 (Dr.Yu Li,https://zjnsf.kjt.zj.gov.cn/)Pioneer and Leading Goose R&D Program of Zhejiang Province under Grant 2023C01217 (Dr.Yu Li,https://zjnsf.kjt.zj.gov.cn/).
文摘With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.
基金supported by the National Natural Science Foundation of China(Grant No.51176076)。
文摘The 2D sandwich model serves as a potent tool in exploring the influence of surface geometry on the combustion attributes of Ammonium perchlorate/Hydroxyl-terminated polybutadiene(AP/HTPB)propellant under rapid pressure decay.The thickness of the sandwich propellant is derived from slicing the 3D random particle packing,an approach that enables a more effective examination of the micro-flame structure.Comparative analysis of the predicted burning characteristics has been performed with experimental studies.The findings demonstrate a reasonable agreement,thereby validating the precision and soundness of the model.Based on the typical rapid depressurization environment of solid rocket motor(initial combustion pressure is 3 MPa and the maximum depressurization rate is 1000 MPa/s).A-type(a flatter surface),B-type(AP recesses from the combustion surface),and C-type(AP protrudes from the combustion surface)propellant combustion processes are numerically simulated.Upon comparison of the evolution of gas-phase flame between 0.1 and 1 ms,it is discerned that the flame strength and form created by the three sandwich models differ significantly at the beginning stage of depressurization,with the flame structures gradually becoming harmonized over time.Conclusions are drawn by comparison extinction times:the surface geometry plays a pivotal role in the combustion process,with AP protrusion favoring combustion the most.
基金Project supported by the National Natural Science Foundation of China (Grant No.12274108)the Natural Science Foundation of Zhejiang Province,China (Grant Nos.LY23A040008 and LY23A040008)the Basic Scientific Research Project of Wenzhou,China (Grant No.G20220025)。
文摘Field-free spin-orbit torque(SOT)switching of perpendicular magnetization is essential for future spintronic devices.This study demonstrates the field-free switching of perpendicular magnetization in an HfO_(2)/Pt/Co/TaO_(x) structure,which is facilitated by a wedge-shaped HfO_(2)buffer layer.The field-free switching ratio varies with HfO_(2)thickness,reaching optimal performance at 25 nm.This phenomenon is attributed to the lateral anisotropy gradient of the Co layer,which is induced by the wedge-shaped HfO_(2)buffer layer.The thickness gradient of HfO_(2)along the wedge creates a corresponding lateral anisotropy gradient in the Co layer,correlating with the switching ratio.These findings indicate that field-free SOT switching can be achieved through designing buffer layer,offering a novel approach to innovating spin-orbit device.
基金support of the National Key R&D Program of China(No.2022YFC2803903)the Key R&D Program of Zhejiang Province(No.2021C03013)the Zhejiang Provincial Natural Science Foundation of China(No.LZ20F020003).
文摘The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.
基金supported by the National Social Science Fund of China(23BGL272)。
文摘The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.
基金funded by Zhejiang Xi Jinping Research Center for Socialist Thought with Chinese Characteristics in the New Era Project(Grant No.23CCG39).
文摘To explore the relationship between social influence,social comparison,clarity of self-concept,and psychological anxiety among young women during their usage of social networking sites,our study selected 338 young women aged 14-34 from the social site platforms of Little Red Book and Weibo for questionnaire surveys.The Passive Social Network Utilization Scale,Social Comparison Scale(SCS),Social Influence Questionnaire,Self-Concept Clarity Scale(SCCS),and Generalized Anxiety Disorder Scale(GAD-7)were employed to measure the subjects.Our results show that the frequency of passive social media use is positively related to the level of psychological anxiety.Social comparison,social influence,and unclear self-concepts under social media use are negatively predictive of psychological anxiety.The chain mediation effects indicate that social comparison and social influence under social media use negatively predict the clarity of self-concept,thus having a negative impact on the psychological health of young women.Therefore,young women should strengthen their self-concepts,control their frequency of social media usage,avoid addiction,and pay special attention to their frequency of passive use,in order to protect their psychological health.Our study provides some practical implications and insights regarding the relationship between young women’s social media use and psychological health.
基金supported in part by the National Natural Science Foundation of China(Grant No.62104056)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LQ21F010010)+4 种基金the National Natural Science Foundation of China(Grant Nos.62141409 and 62204204)the National Key R&D Program of China(Grant No.2022ZD0208602)the Zhejiang Provincial Key Research&Development Fund(Grant Nos.2019C04003 and 2021C01041)the Shanghai Sailing Program(Grant No.21YF1451000)the Key Research and Development Program of Shaanxi(Grant No.2022GY-001).
文摘Flexible pressure sensors have many potential applications in the monitoring of physiological signals because of their good biocompatibil-ity and wearability.However,their relatively low sensitivity,linearity,and stability have hindered their large-scale commercial application.Herein,aflexible capacitive pressure sensor based on an interdigital electrode structure with two porous microneedle arrays(MNAs)is pro-posed.The porous substrate that constitutes the MNA is a mixed product of polydimethylsiloxane and NaHCO3.Due to its porous and interdigital structure,the maximum sensitivity(0.07 kPa-1)of a porous MNA-based pressure sensor was found to be seven times higher than that of an imporous MNA pressure sensor,and it was much greater than that of aflat pressure sensor without a porous MNA structure.Finite-element analysis showed that the interdigital MNA structure can greatly increase the strain and improve the sensitivity of the sen-sor.In addition,the porous MNA-based pressure sensor was found to have good stability over 1500 loading cycles as a result of its bilayer parylene-enhanced conductive electrode structure.Most importantly,it was found that the sensor could accurately monitor the motion of afinger,wrist joint,arm,face,abdomen,eye,and Adam’s apple.Furthermore,preliminary semantic recognition was achieved by monitoring the movement of the Adam’s apple.Finally,multiple pressure sensors were integrated into a 33 array to detect a spatial pressure distribu-×tion.Compared to the sensors reported in previous works,the interdigital electrode structure presented in this work improves sensitivity and stability by modifying the electrode layer rather than the dielectric layer.
基金the National Social Science Fund of China(Grant No.20BTJ005).
文摘Mobile young white-collar workers not only have the characteristics of mobile young people,but also have the characteristics of general white-collar workers.Under the influence of both,their mental health may be suffering from“double disadvantage”.So,based on an ecological model of the stress process,this paper tries to use the data of the questionnaire on the mental health of mobile young white-collar workers in Zhejiang Province to explore the influence of some factors in the middle workplace and residence place on the mental health of micro individuals.The results show that:(1)The working environment with high control and low freedom and the workplace discrimination against the mobile status will have a negative impact on the mental health of mobile young white-collar workers;(2)Financial anxiety in daily life will lead to a decline in the mental health level of mobile young white-collar workers;(3)Good organizational support and neighborhood social relations can significantly relieve life pressure,so as to effectively improve the mental health of mobile young white-collar workers.It can be seen that we also need to pay more attention to the mental health of mobile young white-collar workers in order to improve their situation.
基金supported by the NSFC(12201557)the Foundation of Zhejiang Provincial Education Department,China(Y202249921).
文摘This work presents an advanced and detailed analysis of the mechanisms of hepatitis B virus(HBV)propagation in an environment characterized by variability and stochas-ticity.Based on some biological features of the virus and the assumptions,the corresponding deterministic model is formulated,which takes into consideration the effect of vaccination.This deterministic model is extended to a stochastic framework by considering a new form of disturbance which makes it possible to simulate strong and significant fluctuations.The long-term behaviors of the virus are predicted by using stochastic differential equations with second-order multiplicative α-stable jumps.By developing the assumptions and employing the novel theoretical tools,the threshold parameter responsible for ergodicity(persistence)and extinction is provided.The theoretical results of the current study are validated by numerical simulations and parameters estimation is also performed.Moreover,we obtain the following new interesting findings:(a)in each class,the average time depends on the value ofα;(b)the second-order noise has an inverse effect on the spread of the virus;(c)the shapes of population densities at stationary level quickly changes at certain values of α.The last three conclusions can provide a solid research base for further investigation in the field of biological and ecological modeling.
基金This research was supported by the National Natural Science Foundation of China No.62276086the National Key R&D Program of China No.2022YFD2000100Zhejiang Provincial Natural Science Foundation of China under Grant No.LTGN23D010002.
文摘Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.
基金The research is supported by Nature Science Foundation of Zhejiang Province(LQ20F020008)“Pioneer”and“Leading Goose”R&D Program of Zhejiang(Grant Nos.2023C03203,2023C01150).
文摘The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation techniques.Biometric credentials are increasingly being used for verification due to their advantages over traditional methods,making it crucial to safeguard the privacy of people’s biometric data in various scenarios.This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems.It proposes a noble and thorough taxonomy survey for privacy-preserving techniques,as well as a systematic framework for categorizing the field’s existing literature.We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric modalities,such as face,fingerprint,and eye detection.The survey encompasses various categories of privacy-preserving mechanisms and examines the trade-offs between security,privacy,and recognition performance,as well as the issues and future research directions.It aims to provide researchers,professionals,and decision-makers with a thorough understanding of the existing privacy-preserving solutions in biometric recognition systems and serves as the foundation of the development of more secure and privacy-preserving biometric technologies.
基金This work was partially supported by the National Natural Science Foundation of China(Grant Nos.61906168,U20A20171)Zhejiang Provincial Natural Science Foundation of China(Grant Nos.LY23F020023,LY21F020027)Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects(Grant Nos.2022SDSJ01).
文摘In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,diagnosis and evaluation of kidney and urinary tract disease,providing insight into the specific type and severity.However,manual urine sediment examination is labor-intensive,time-consuming,and subjective.Traditional machine learning based object detection methods require hand-crafted features for localization and classification,which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments.Deep learning based object detection methods have the potential to address the challenges mentioned above,but these methods require access to large urine sediment image datasets.Unfortunately,only a limited number of publicly available urine sediment datasets are currently available.To alleviate the lack of urine sediment datasets in medical image analysis,we propose a new dataset named UriSed2K,which contains 2465 high-quality images annotated with expert guidance.Two main challenges are associated with our dataset:a large number of small objects and the occlusion between these small objects.Our manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this dataset.Specifically,our goal is to improve the accuracy and efficiency of the detection algorithm and,in doing so,provide medical professionals with an automatic detector that saves time and effort.We propose an improved lightweight one-stage object detection algorithm called Discriminatory-YOLO.The proposed algorithm comprises a local context attention module and a global background suppression module,which aid the detector in distinguishing urine sediment features in the image.The local context attention module captures context information beyond the object region,while the global background suppression module emphasizes objects in uninformative backgrounds.We comprehensively evaluate our method on the UriSed2K dataset,which includes seven categories of urine sediments,such as erythrocytes(red blood cells),leukocytes(white blood cells),epithelial cells,crystals,mycetes,broken erythrocytes,and broken leukocytes,achieving the best average precision(AP)of 95.3%while taking only 10 ms per image.The source code and dataset are available at https://github.com/binghuiwu98/discriminatoryyolov5.
基金funded by the National Social Science Fund of China(Grant No.23BTJ069).
文摘Based on China Family Panel Studies(CFPS)2018 data,the multiple linear regression model is used to analyze the effects of Internet use on women’s depression,and to test the robustness of the regression results.At the same time,the effects of Internet use on mental health of women with different residence,age,marital status and physical health status are analyzed.Then,we can obtain that Internet use has a significant promoting effect on women’s mental health,while the degree of Internet use has a significant inhibitory effect on women’s mental health.In addition,the study found that women’s age,education,place of residence,marital status,length of sleep,working status and physical health status are the main factors affecting the mental health of Chinese women.In the heterogeneity investigation of residence,age,marital status and physical health status,Internet use has a greater negative impact on the Center for Epidemiological Studies Depression Scale(CES-D8)scores of women in rural areas,has a significant positive impact on the mental health of middle-aged and elderly women or women with spouses,and has a positive impact on the mental health of physically unhealthy women.Therefore,in view of women’s mental health needs and the problems existing in the use of the Internet,this paper puts forward some suggestions to further improve the overall mental health level of women.
基金the Jilin Science and Technology Department 20200201280JC,and Shanghai special fund for ideological and political work in Shanghai University of International Business and Economics.
文摘Contingent self-esteem captures the fragile nature of self-esteem and is often regarded as suboptimal to psychological functioning.Self-compassion is another important self-related concept assumed to promote mental health and well-being.However,research on the relation of self-compassion to contingent self-esteem is lacking.Two studies were conducted to explore the role of selfcompassion,either as a personal characteristic or an induced mindset,in influencing the effects of contingent self-esteem on well-being.Study 1 recruited 256 Chinese college students(30.4%male,mean age=21.72 years)who filled out measures of contingent self-esteem,self-compassion,and well-being.The results found that self-compassion moderated the effect of contingent self-esteem on well-being.In Study 2,a sample of 90 Chinese college students(34%male,mean age=18.39 years)were randomly assigned to either a control or self-compassion group.They completed baseline trait measures of contingent self-esteem,self-compassion,and self-esteem.Then,they were led to have a 12-min break(control group)or listen to a 12-min self-compassion audio(self-compassion group),followed by a social stress task and outcome measures.The results demonstrated the effectiveness of the brief self-compassion training and its moderating role in influencing the effects of contingent self-esteem on negative affects after the social stress task.This research provides implications that to equip with a self-compassionate mindset could lower the risk of the impairment of well-being associated with elements of contingent selfesteem,which involves a fragile sense of self-worth.It may also provide insights into the development of an“optimal selfesteem”and the improvement of well-being.
文摘On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.
文摘On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.
文摘According to UNESCO,China is the largest source country in terms of international students,contributing around 1 million international students per year(See the figure below).Choosing a study abroad destination as well as an international university is always a challenge to Chinese students as well as their parents.Most of them use the global university ranking as a tool during their decisionmaking process.
