Physical Literacy的本土化:概念厘定、实践反思及应对

Physical Literacy(简称PL)是身体活动与健康促进领域的重要理念,也是新时代学校体育的开拓点之一。长期以来,学界对PL的认知存在争议或模糊不清,导致其在实践中出现了一定的迟滞和偏差,故对相关认知加以厘定与反思具有重要意义。研究认为:(1)从形式逻辑来看,我国学者构建的PL概念在内涵与外延上存在争议或误用。内涵方面,“素养”作为属概念规定了PL的内在特性;“体育素养”的转译窄化了PL的内涵,而译为“身体素养”更能体现PL的身体本源性;作为面向全民的基础生活素养,PL并非核心素养在体育学科的具体化,不宜将其与体育学科核心素养相等同。外延方面,部分PL的外延体系存在偷换“Physical”概念、子素养客体化、子素养相容等问题。(2)在本土实践中,对PL的评价被横向割裂,且纵向衔接不畅;PL的发展情境囿于学校,家庭、社区等校外情境应用不足。针对以上问题,该研究建议未来既要构建横纵贯通的评价体系,也要注意营造多样化的发展情境。

The Relationship between Internet Addiction and Cyberbullying Perpetration: A Moderated Mediation Model of Moral Disengagement and Internet Literacy

Internet addiction and cyberbullying have emerged as significant global mental health concerns in recent years.Although previous studies have shown a close association between Internet addiction and cyberbullying,the underlying mechanisms connecting these two phenomena remain unclear.Therefore,this study aimed to reveal the mechanisms involved between Internet addiction and cyberbullying perpetration from the perspective of cognition function.This study recruited 976 Chinese youth through online survey,using the short version of Internet Addiction Test(s-IAT),Chinese Cyberbullying Intervention Project Questionnaire(C-CIPQ),Cyberbullying Moral Disengagement Scale(CMDS),and Internet Literacy Questionnaire(ILQ)to investigate the relationship between Internet addiction,moral disengagement,Internet literacy and cyberbullying perpetration.The keyfindings of this study were as follows:after controlling gender and age,(1)Internet addiction had a significant positive predictive effect on cyberbullying perpetration;(2)moral disengagement acted as a mediator between Internet addiction and cyberbullying perpetration;and(3)Internet literacy played a moderating role between moral disengagement and cyberbullying perpetration.In conclusion,there was a moderated mediating effect between Internet addiction and cyberbullying perpetration,contributing to a better understanding of the relationship between these two phenomena.

The impact of Internet access on household dietary quality:Evidence from rural China

Over the past few decades,the Internet has rapidly diffused across China.The spread of the Internet has had a profound economic and social impact on Chinese rural areas.Existing research shows that Internet access significantly impacts agricultural production and improves smallholder farmers’income.Beyond these,the Internet can affect other dimensions of social welfare.However,research about the impact of Internet access on dietary quality in rural China remains scarce.This study utilizes multi-period panel data from Fixed Observation Point in rural China from 2009 to 2015 to estimate the impact of Internet access on dietary quality and food consumption of rural households and conducts a causal analysis.Regression models with time and household fixed effects allow robust estimation while reducing potential issues of unobserved heterogeneity.The estimates show that Internet access has significantly increased rural household dietary quality(measured by the Chinese Diet Balance Index).Further research finds that Internet access has increased the consumption of animal products,such as aquatic and dairy products.We also examine the underlying mechanisms.Internet access improves dietary quality and food consumption mainly through increasing household income and food expenditure.These results encourage the promotion of Internet access as a valuable tool for nutritional improvements,especially in rural areas.

A credibility-aware swarm-federated deep learning framework in internet of vehicles

Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.

A Data Intrusion Tolerance Model Based on an Improved Evolutionary Game Theory for the Energy Internet

Malicious attacks against data are unavoidable in the interconnected,open and shared Energy Internet(EI),Intrusion tolerant techniques are critical to the data security of EI.Existing intrusion tolerant techniques suffered from problems such as low adaptability,policy lag,and difficulty in determining the degree of tolerance.To address these issues,we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas:(1)it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights;and (2)it combines a tournament competition model with incentive weights to obtain optimal strategies for each stage of the game process.Extensive experiments are conducted in the IEEE 39-bus system,whose results demonstrate the feasibility of the incentive weights,confirm the proposed strategy strengthens the system’s ability to tolerate aggression,and improves the dynamic adaptability and response efficiency of the aggression-tolerant system in the case of limited resources.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Mediating role of inner strength in the relationship between medication literacy and medication adherence among kidney transplant patients

Objective:Compared with long-term renal replacement therapy,kidney transplantation is the ideal treatment for end-stage renal disease(ESRD),significantly extending patient life and improving quality of life.Kidney transplant patients need to adhere to lifelong immunosuppressive medication regimens,but their medication adherence is generally poor compared with other organ transplant recipients.Medication adherence is closely related to medication literacy and psychological status,yet related studies are limited.This study aims to investigate the current status of medication adherence,inner strength,and medication literacy in kidney transplant patients,analyze the relationships among these 3 factors,and explore the mediating role of inner strength in the relationship between medication literacy and medication adherence.Methods:A cross-sectional survey was conducted from March to October 2023 involving 421 patients aged≥18 years who visited kidney transplantation outpatient clinics at 4 tertiary hospitals in Hunan Province.The inner strength,medication literacy,and medication adherence of kidney transplant patients were investigated using the Inner Strength Scale(ISS),the Chinese version of the Medication Literacy Assessment in Spanish and English(MedLitRxSE),and the Chinese version of the Morisky Medication Adherence Scale-8(C-MMAS-8),respectively.Univariate analysis was performed to examine the effects of demographic and clinical data on medication adherence.Correlation analysis was conducted to explore the relationships among medication literacy,medication adherence,and inner strength.Significant variables from univariate and correlation analyses were further analyzed using multiple linear regression,and the mediating effect of inner strength was explored.Results:Among the 421 questionnaires collected,408 were valid,with an effective rate of 96.91%.The scores of C-MMAS-8,MedLitRxSE,and ISS were 6.64±1.16,100.63±14.67,and 8.47±4.03,respectively.Among the 408 patients,only 86(21.08%)patients had a high level of medication adherence,whereas 230(56.37%)patients had a medium level of medication adherence,and 92(22.55%)patients had poor medication adherence.Univariate analysis indicated that the kidney transplant patients’age,marital status,education levels,years since their kidney transplant operation,number of hospitalizations after the kidney transplant,and adverse drug reactions showed significant differences in medication adherence(all P<0.05).Correlation analysis showed that inner strength positively correlated with both medication literacy(r=0.183,P<0.001)and medication adherence(r=0.201,P<0.001).Additionally,there was a positive correlation between medication adherence and medication literacy(r=0.236,P<0.001).Inner strength accounted for 13.22%of the total effect in the mediating role between medication literacy and medication adherence.Conclusion:The level of medication adherence among kidney transplant patients needs improvement,and targeted intervention measures are essential.Inner strength mediates the relationship between medication literacy and medication adherence in these patients.Healthcare professionals should focus on enhancing medication literacy and supporting patients’inner strength to improve medication adherence.

Digital Literacy in the Rural Environment of Rivers State, Nigeria

While literacy can generally be defined as the basic ability to read,write,and also count,digital literacy can be described as the ability to perform all these tasks using electronic means.These means would include modern electronic gadgets,such as mobile phones,tablets,computers,kindle books,and the like.To be digitally literate would therefore require the existence of modern technologies such as internet facilities that would make it possible to access online reading and writing.The rural environment,particularly in developing areas,is usually characterised by a seeming lack of modern amenities and even worse,digital internet networks.Yet,those who live in the rural areas of Rivers State belong to the modern digital era and deserve to be digitally literate.This paper examines the prerequisites for digital literacy and explores how these can be achieved for citizens who inhabit the rural areas of an industrially-nascent state like Rivers State.

Construction and Validity of Chinese Translation of the Universal Mental Health Literacy Scale for Adolescents

Background:In this study,the Universal Mental Health Literacy Scale for Adolescents(UMHL-A)was revised and tested for its reliability and validity in Chinese middle school students,thus establishing a useful tool for assessing the mental health of individuals in this occupation.Methods:Our sample comprised 1208 junior high school students(58.85%male),aged between 11 and 15 years old.The Chinese version of the scale includes a mental health attitude subscale and mental health knowledge subscale,including attitudes towards seeking help,attitudes related to stigma,general mental health knowledge,and knowledge about specific mental illnesses,encapsulated in a total of 17 items.A series of psychometric analyses such as exploratory factor analysis(EFA),confirmatory factor analysis(CFA),and internal consistency reliability estimation were carried out in this study.Results:The results of the CFA indicated that the two-factor model had an acceptable model fit(Attitude(UMHL-A Likert):χ^(2)/df=4.107;RMSEA=0.072;SRMR=0.045;TLI=0.932;CFI=0.954;Knowledge(UMHL-A T/F):χ^(2)/df=3.647;RMSEA=0.066;SRMR=0.044;TLI=0.923;CFI=0.945).The Cronbach’s alpha coefficient of subscales of the Chinese version UMHL-A were 0.80 and 0.78,respectively.Conclusion:In general,the Chinese version of the Universal Mental Health Literacy Scale for adolescents has good reliability and validity and can be used as a tool to measure the mental health literacy of Chinese adolescents.

Comparative Study of the Socioeconomic Characteristics and Digital Literacy Level of Agricultural Extension Personnel in Imo and Ebonyi States, South-East, Nigeria

The study comparatively analysed the socioeconomic characteristics and digital literacy level of Agricultural Extension personnel (AEP) in Ebonyi and Imo States, South-East, Nigeria. The specific objectives were to describe the socioeconomic characteristics of agricultural extension personnel in Ebonyi and Imo States, and to ascertain the digital literacy level of AEP in the studied states. Purposive sampling technique was used to select 312 Agricultural Extension personnel (132 from Ebonyi State Agricultural Development Program and 180 from Imo State Agricultural Development Program) for the study. Data were collected through the use of validated and structured questionnaire, and administered through the help of well-trained enumerators. Data were analysed using simple descriptive statistical tools such as percentages mean score, standard deviation and weighted mean. Findings indicated that they were more male in the both States (55.3% and 57.8%) for Ebonyi and Imo State respectively and that the average age of AEP in Ebonyi and Imo States were 44.7 years and 49.2 years respectively. It was further revealed that the majority (77.3% and 82.8%) had B.Sc./HND as their highest academic qualifications, belonged to professional organisations (62.1% and 75%), and were earning an average monthly income of N58,798 and N62,648 for Ebonyi and Imo State respectively. Also, it was revealed that their mean years of service were 12.4 years and 13.4 years for Ebonyi and Imo State respectively. Almost all of them (87.9% and 95.0%) own a smartphone, had access to the internet (80.3% and 90.0%), but do not own a laptop/ipad (82.6% and 72.8%) for Ebon-yi and Imo State respectively. Results further revealed that Agricultural extension personnel in both Ebonyi and Imo State respectively had low digital literacy level ( = 2.41 and 2.32). The study concluded that AEP in Ebonyi and Imo State respectively had similar socioeconomic characteristics and low level of digital literacy. The study recommended that the management of ADPs in both Ebonyi and Imo State should ensure the training of AEP in digital skills to enhance their digital literacy level to enable them use digital technologies in their work.

ResNeSt-biGRU: An Intrusion Detection Model Based on Internet of Things

The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device hascaught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. Thishas resulted in a myriad of security challenges, including information leakage, malware propagation, and financialloss, among others. Consequently, developing an intrusion detection system to identify both active and potentialintrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practicalintrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, andbidirectionalGated RecurrentUnitNetwork (biGRU).Our ResNeSt-biGRUframework diverges fromconventionalintrusion detection systems (IDS) by employing this dual-layeredmechanism that exploits the temporal continuityand spatial feature within network data streams, a methodological innovation that enhances detection accuracy.In conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, totrain and evaluate IDSmodels with a focus on identifying potential intrusion traffics. The effectiveness of proposedscheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on theN-BaIoT datasetas well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal thepotential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preservethe overall security of IoT ecosystems.

Introduction to the Special Issue on Advanced Security for FutureMobile Internet:A Key Challenge for the Digital Transformation

Mobile internet technologies have transformed our daily lives,allowing us to connect,communi-cate,and access various services and applications anytime and anywhere.These technologies are set to play a significant role in the next generation of digital transformation,further increasing their impact by integrating with emerging technologies like 6G,quantum computing,and generative AI.

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

Securing the Internet of Health Things with Certificateless Anonymous Authentication Scheme

Internet of Health Things(IoHT)is a subset of Internet of Things(IoT)technology that includes interconnected medical devices and sensors used in medical and healthcare information systems.However,IoHT is susceptible to cybersecurity threats due to its reliance on low-power biomedical devices and the use of open wireless channels for communication.In this article,we intend to address this shortcoming,and as a result,we propose a new scheme called,the certificateless anonymous authentication(CAA)scheme.The proposed scheme is based on hyperelliptic curve cryptography(HECC),an enhanced variant of elliptic curve cryptography(ECC)that employs a smaller key size of 80 bits as compared to 160 bits.The proposed scheme is secure against various attacks in both formal and informal security analyses.The formal study makes use of the Real-or-Random(ROR)model.A thorough comparative study of the proposed scheme is conducted for the security and efficiency of the proposed scheme with the relevant existing schemes.The results demonstrate that the proposed scheme not only ensures high security for health-related data but also increases efficiency.The proposed scheme’s computation cost is 2.88 ms,and the communication cost is 1440 bits,which shows its better efficiency compared to its counterpart schemes.

An Experimental Study on the Effectiveness of Individualized Online Intervention for Mental Health Literacy of Community Residents

Background:Understanding how to improve mental health literacy is conducive to maintaining and promoting individuals’mental health and well-being.However,to date,interventions for mental health literacy primarily depend on traditional education and contact interventions,which have limitations with regard to pertinence and individualization.Artificial intelligence(AI)and big data technology have influenced mental health services to be more intellectual and digital,and they also provide greater technical convenience for individualized interventions for promoting mental health literacy.However,there is relatively little research on the effectiveness of individualized online intervention for mental health literacy in the literature.This study aims to fill this void.To verify whether individualized online intervention can improve the level of mental health literacy.Methods:We conducted a pretest–post-test control experiment.The participants were recruited from a large community located in central China.A total of 152 participants completed the research.We use mixed linear model estimation and paired t-tests to analyze the data.Results:Individualized online intervention can effectively improve the mental health literacy level of participants.Specifically,we found that compared with the control group,the mental health literacy in the experimental group was significantly improved after receiving individualized online intervention.Likewise,the mental health literacy of the control group has also improved after receiving individualized online intervention.In addition,we compared the mental health literacy level of the experimental group at Time 3 to those at Time 2 and found that the mental health literacy level at Time 3 had not decreased one month later.This shows that individualized online intervention was not only momentarily effective,but also had long-term efficacy.Conclusion:This study illustrates that the individualized online intervention has both great momentary and long-term effectiveness in improving community residents’mental health literacy.

Call for Papers——Feature Topic Vol.22,No.2,2025 Efficient Cooperative Transmission over Satellite Internet for 6G

Satellite Internet,as a strategic public information infrastructure,can effectively bridge the limitations of traditional terrestrial network coverage,support global coverage and deep space exploration,and greatly enhance the range of network information services accessible to humans.With the transition of terrestrial mobile communication networks from the 5G era,which provides access to information anywhere,to the 6G era,which seeks to connect everything,the construction of satellite Internet,which promises a"network reaching everywhere and service is ubiquitous",has become the consensus of the industry's development and the focus of global scientific and technological innovation.

Intelligent Internet of Things with Reliable Communication and Collaboration Technologies

The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.

Assessing the effectiveness of internetbased interventions for mental health outcomes:an umbrella review

Internet-based interventions(IBIs)for behavioural health have been prevalent for over two decades,and a growing proportion of individuals with mental health concerns prefer these emerging digital alternatives.However,the effectiveness and acceptability of IBIs for various mental health disorders continue to be subject to scholarly debate.We performed an umbrella review of meta-analyses(MAs),conducting literature searches in PubMed,Web of Science,Embase,Cochrane and Ovid Medline from their inception to 17 January 2023.A total of 87MAs,reporting on 1683 randomised controlled trials and 295589 patients,were included.The results indicated that IBIs had a moderate effect on anxiety disorder(standardised mean difference(SMD)=0.53,95%CI 0.44 to 0.62)and post-traumatic stress disorder(PTSD)(SMD=0.63,95%CI 0.38 to 0.89).In contrast,the efficacy on depression(SMD=0.45,95%CI 0.39 to 0.52),addiction(SMD=0.23,95%CI 0.16 to 0.31),suicidal ideation(SMD=0.23,95%CI 0.16 to 0.30),stress(SMD=0.41,95%CI 0.33 to 0.48)and obsessive-compulsive disorder(SMD=0.47,95%CI 0.22 to 0.73)was relatively small.However,no significant effects were observed for personality disorders(SMD=0.07,95%CI-0.13 to 0.26).Our findings suggest a significant association between IBIs and improved mental health outcomes,with particular effectiveness noted in treating anxiety disorders and PTSD.However,it is noteworthy that the effectiveness of IBIs was impacted by high dropout rates during treatment.Furthermore,our results indicated that guided IBIs proved to be more effective than unguided ones,playing a positive role in reducing dropout rates and enhancing patient adherence rates.

Association between Mental Health Literacy and Workplace Well-Being of Chinese Grassroots Civil Servants:The Chain Mediating Effects of Regulatory Emotional Self-Efficacy and Resilience

This study aimed to investigate the relationship between mental health literacy(MHL)and workplace well-being(WWB)of Chinese grassroots civil servants,with regulatory emotional self-efficacy(RESE)and resilience as mediating variables.A questionnaire survey was conducted among Chinese grassroots civil servants,with a valid sample size of 2673 after excluding missing values and conducting relevant data processing.The PROCESS was used to examine the relationship between MHL,RESE,resilience,and WWB.The study found that MHL among grassroots civil servants was positively and significantly correlated with WWB(r=0.73,p<0.01).RESE partially mediated the relationship between MHL and WWB(β=0.25,95%CI[0.22,0.28]).Resilience partially mediated the relationship between MHL and WWB(β=0.22,95%CI[0.19,0.26]).MHL had a positive effect on WWB through the chain mediating effect of RESE and resilience(β=0.05,95%CI[0.03,0.07]).There is a close relationship between MHL and WWB,where Chinese grassroots civil servants with higher levels of MHL can develop stronger RESE and resilience,leading to higher WWB.The results of this study remind organizational institutions of Chinese grassroots civil servants that enhancing MHL,RESE,and resilience is an important pathway to promoting their WWB.

DTAIS:Distributed trusted active identity resolution systems for the Industrial Internet

In recent years,the Industrial Internet and Industry 4.0 came into being.With the development of modern industrial intelligent manufacturing technology,digital twins,Web3 and many other digital entity applications are also proposed.These applications apply architectures such as distributed learning,resource sharing,and arithmetic trading,which make high demands on identity authentication,asset authentication,resource addressing,and service location.Therefore,an efficient,secure,and trustworthy Industrial Internet identity resolution system is needed.However,most of the traditional identity resolution systems follow DNS architecture or tree structure,which has the risk of a single point of failure and DDoS attack.And they cannot guarantee the security and privacy of digital identity,personal assets,and device information.So we consider a decentralized approach for identity management,identity authentication,and asset verification.In this paper,we propose a distributed trusted active identity resolution system based on the inter-planetary file system(IPFS)and non-fungible token(NFT),which can provide distributed identity resolution services.And we have designed the system architecture,identity service process,load balancing strategy and smart contract service.In addition,we use Jmeter to verify the performance of the system,and the results show that the system has good high concurrent performance and robustness.