Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Lever...Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT termi...With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.展开更多
The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expo...The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems.展开更多
Background Ongoing debates question the harm of internet use with the evolving technology,as many individuals transition from regular to problematic internet use(PIU).The habenula(Hb),located between the thalamus and ...Background Ongoing debates question the harm of internet use with the evolving technology,as many individuals transition from regular to problematic internet use(PIU).The habenula(Hb),located between the thalamus and the third ventricle,is implicated in various psychiatric disorders.In addition,personality features have been suggested to play a role in the pathophysiology of PIU.Aims This study aimed to investigate Hb volumetry in individuals with subclinical PIU and the mediating effect of personality traits on this relationship.Methods 110 healthy adults in this cross-sectional study underwent structural magnetic resonance imaging.Hb segmentation was performed using a deep learning technique.The Internet Addiction Test(IAT)and the NEO Five-Factor Inventory were used to assess the PIU level and personality,respectively.Partial Spearman's correlation analyses were performed to explore the reiationships between Hb volumetry,IAT and NEO.Multiple regression analysis was applied to identify personality traits that predict IAT scores.The significant trait was then treated as a mediator between Hb volume and IAT correlation in mediation analysis with a bootstrap value of 5000.Results Relative Hb volume was negatively correlated with IAT scores(partial rho=-0.142,p=0.009).The IAT score was positively correlated with neuroticism(partial rho=0.430,p<0.001)and negatively correlated with extraversion,agreeableness and conscientiousness(partial rho=-0.213,p<0.001;partial rho=-0.279,p<0.001;and partial rho=-0.327,p<0.001).There was a significant indirect effect of Hb volume on this model(β=-0.061,p=0.048,boot 95%confidence interval:-0.149 to-0.001).Conclusions This study uncovered a crucial link between reduced Hb volume and heightened PIU.Our findings highlight neuroticism as a key risk factor for developing PIU.Moreover,neuroticism was shown to mediate the relationship between Hb volume and PIU tendency,offering valuable insight into the complexities of this interaction.展开更多
China is a fast-developing nation,especially in traditional concepts of emphasis on agricultural production,with millions of highly educated college students as new generations of workers enter the workforce,while pro...China is a fast-developing nation,especially in traditional concepts of emphasis on agricultural production,with millions of highly educated college students as new generations of workers enter the workforce,while promoting the booming agriculture industry in China.Concerning these new generations of ambitious college students,it is a pretty attractive career to leverage their knowledge to spread their local special rural agricultural products(agri-products)to well-known places around the nation,even the world.Meanwhile,the Chinese government also supports rural products branding via internet marketing as well as the exploitation of online technologies.Su et al.pointed out that governments in China are expected to take more effective measures to enhance adoption rates of online purchases and sales technology,in particular for entrepreneurial farmers[1].Currently,the most existing phenomenon in China is that quantities of regional rural products with excellent quality but without national popularity.Thereby,it is significant to enhance the popularity of various brands in regional agricultural products using internet marketing,and also contribute to the nation’s strategy of rural revitalization.To appeal to the nations’strategy,we are supposed to make use of brand personality(BP)traits,which probably contribute to robust internet branding of regional agricultural products.Our research will focus on the influences of differential dimensions of brand personality(BP)in terms of common rural products,additionally,we also attempt to design a BP model for internet branding of agricultural products in China.Furthermore,from the two perspectives of characteristics in rural areas(agricultural producers and agricultural consumers),measures to assist agricultural producers in building their brands through the application of internet tools and marketing should be recognized.On the other side,methods to enhance agricultural consumers’brand loyalty also need to be captured.展开更多
In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong Unive...In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong University)and Prof.Xing Zhang(Wuhan Textile University)have published the timely book Datadriven Internet Health Platform Service Value Co-creation through China Science Press.The book focuses on the provision of medical and health services from doctors to patients through Internet health platforms,where the service value is co-created by three parties.展开更多
The rapid evolution of satellite constellation projects(e.g.,SpaceX)and the standardization of 3rd Generation Partnership Project(3GPP)non-terrestrial networks(NTNs)have positioned satellite Internet networking(SIN)as...The rapid evolution of satellite constellation projects(e.g.,SpaceX)and the standardization of 3rd Generation Partnership Project(3GPP)non-terrestrial networks(NTNs)have positioned satellite Internet networking(SIN)as a cornerstone of future communication systems.The demand for ubiquitous connectivity,resilient infrastructures,and intelligent network services has never been greater,driven by applications ranging from global broadband access to emergency response and space-air-ground integration.展开更多
Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These...Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
Objective Systematically integrate nurses’experience with“Internet Nursing Service”to analysis the nurses’experiences with“Internet Nursing Service”,and to provide a theoretical reference for formulating a more ...Objective Systematically integrate nurses’experience with“Internet Nursing Service”to analysis the nurses’experiences with“Internet Nursing Service”,and to provide a theoretical reference for formulating a more rational“Internet Nursing Service”model.Methods A systematic search in PubMed,Embase,Web of Science,the Cochrane Library,CINAHL,China National Knowledge Infrastructure(CNKI),Wanfang Database,and Chinese Biomedical Literature Database was conducted to collect qualitative research on nurses’experiences with“Internet Nursing Service,”with a retrieval time limit from December 2019 to June 2024.Qualitative meta-synthesis was performed through line-by-line coding of relevant quotes,organization of codes into descriptive themes,and development of analytical themes.Results A total of 19 studies were included,one study was rated as Grade A in quality evaluation,and the remaining studies were rated as Grade B.Collectively synthesized into three integrated results:Harvest and growth,Difficulties and challenges,and Expectations and support.Harvest and growth,include 1)manifestation of self-value,2)enhancing nursing capabilities,3)optimizing nursing resources;Difficulties and challenges,include 1)lack of safety guarantee,2)role conflict;Expectations and support include,1)expectation for professional knowledge and skill training,2)expectations for service platform optimization,3)expectation for reasonable charges,4)expectation for related policy support.Conclusion“Internet Nursing Service”model benefits both nurses and patients,but still full of challenges.It aids in the decentralization of medical resources.Management departments still need to encourage nurses to actively invest in“Internet Nursing Service”while ensuring their safety and interests.展开更多
The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and ...The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and advanced services.However,this rapid expansion also heightens the vulnerability of the IoT ecosystem to security threats.Consequently,innovative solutions capable of effectively mitigating risks while accommodating the unique constraints of IoT environments are urgently needed.Recently,the convergence of Blockchain technology and IoT has introduced a decentralized and robust framework for securing data and interactions,commonly referred to as the Internet of Blockchained Things(IoBT).Extensive research efforts have been devoted to adapting Blockchain technology to meet the specific requirements of IoT deployments.Within this context,consensus algorithms play a critical role in assessing the feasibility of integrating Blockchain into IoT ecosystems.The adoption of efficient and lightweight consensus mechanisms for block validation has become increasingly essential.This paper presents a comprehensive examination of lightweight,constraint-aware consensus algorithms tailored for IoBT.The study categorizes these consensus mechanisms based on their core operations,the security of the block validation process,the incorporation of AI techniques,and the specific applications they are designed to support.展开更多
In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),dee...In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.展开更多
Objectives:This study aimed to explore the effectiveness and advantages of an“Internet+”nursing model based on user profilingin the rehabilitation of postoperative breast cancer patients.Methods:Breast cancer patien...Objectives:This study aimed to explore the effectiveness and advantages of an“Internet+”nursing model based on user profilingin the rehabilitation of postoperative breast cancer patients.Methods:Breast cancer patients admitted to the hospital from July 2023 to September 2024 were enrolled.These patients were randomly assigned to a control group and an intervention group,with 52 patients in each group.The control group received routine nursing care,while the intervention group received an“Internet+”nursing intervention based on user profilingin addition to routine care.The intervention period lasted for one month following discharge.Before and one month after the intervention,the Fear of Progression Questionnaire-Short Form(FOP-Q-SF),the Fear of Cancer Recurrence Inventory-Short Form(FCRI-SF),Chinese Posttraumatic Growth Inventory(C-PTGI),and the Functional Assessment of Cancer Therapy-Breast(FACT-B)were applied to assess the effects of interventions.Results:A total of 104 patients were analyzed.After the intervention,FOP-Q-SF and FCRI-SF scores were significantlylower in the intervention group compared to the control group,with statistical significance(t=3.98,P<0.001;t=-7.59,P<0.001),and Cohen’s d of 0.781 and 1.49,respectively.Additionally,CPTGI and FACT-B scores in the intervention group were significantly higher than those in the control group(t=-6.534,P<0.001;t=-4.579,P<0.001),with Cohen’s d of 0.585 and 0.656.Conclusions:An“Internet+”nursing model based on user profilingcould reduce postoperative breast cancer patients fear of disease progression and cancer recurrence,also enhancing posttraumatic growth and overall quality of life.展开更多
基金supported in part by National key R&D projects(2024YFB4207203)National Natural Science Foundation of China(52401376)+3 种基金the Zhejiang Provincial Natural Science Foundation of China under Grant(No.LTGG24F030004)Hangzhou Key Scientific Research Plan Project(2024SZD1A24)“Pioneer”and“Leading Goose”R&DProgramof Zhejiang(2024C03254,2023C03154)Jiangxi Provincial Gan-Po Elite Support Program(Major Academic and Technical Leaders Cultivation Project,20243BCE51180).
文摘Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金supported by National Key R&D Program of China(No.2022YFB3105101).
文摘With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting ProjectNumber(PNURSP2025R97),PrincessNourah bint AbdulrahmanUniversity,Riyadh,Saudi Arabia.
文摘The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems.
基金funded by a Grant-in-Aid for Scientific Research(B)(Japan Society for The Promotion of Science,21H02849)Grant-in-Aid for Scientific Research(C)(Japan Society for The Promotion of Science,23K07013)+2 种基金Grant-in-Aid for Transformative Research Areas(A)(Japan Society for The Promotion of Science,JP21H05173)Grant-in-Aid by the Smoking Research FoundationGrant-in-Aid by the Telecommunications Advancement Foundation.
文摘Background Ongoing debates question the harm of internet use with the evolving technology,as many individuals transition from regular to problematic internet use(PIU).The habenula(Hb),located between the thalamus and the third ventricle,is implicated in various psychiatric disorders.In addition,personality features have been suggested to play a role in the pathophysiology of PIU.Aims This study aimed to investigate Hb volumetry in individuals with subclinical PIU and the mediating effect of personality traits on this relationship.Methods 110 healthy adults in this cross-sectional study underwent structural magnetic resonance imaging.Hb segmentation was performed using a deep learning technique.The Internet Addiction Test(IAT)and the NEO Five-Factor Inventory were used to assess the PIU level and personality,respectively.Partial Spearman's correlation analyses were performed to explore the reiationships between Hb volumetry,IAT and NEO.Multiple regression analysis was applied to identify personality traits that predict IAT scores.The significant trait was then treated as a mediator between Hb volume and IAT correlation in mediation analysis with a bootstrap value of 5000.Results Relative Hb volume was negatively correlated with IAT scores(partial rho=-0.142,p=0.009).The IAT score was positively correlated with neuroticism(partial rho=0.430,p<0.001)and negatively correlated with extraversion,agreeableness and conscientiousness(partial rho=-0.213,p<0.001;partial rho=-0.279,p<0.001;and partial rho=-0.327,p<0.001).There was a significant indirect effect of Hb volume on this model(β=-0.061,p=0.048,boot 95%confidence interval:-0.149 to-0.001).Conclusions This study uncovered a crucial link between reduced Hb volume and heightened PIU.Our findings highlight neuroticism as a key risk factor for developing PIU.Moreover,neuroticism was shown to mediate the relationship between Hb volume and PIU tendency,offering valuable insight into the complexities of this interaction.
文摘China is a fast-developing nation,especially in traditional concepts of emphasis on agricultural production,with millions of highly educated college students as new generations of workers enter the workforce,while promoting the booming agriculture industry in China.Concerning these new generations of ambitious college students,it is a pretty attractive career to leverage their knowledge to spread their local special rural agricultural products(agri-products)to well-known places around the nation,even the world.Meanwhile,the Chinese government also supports rural products branding via internet marketing as well as the exploitation of online technologies.Su et al.pointed out that governments in China are expected to take more effective measures to enhance adoption rates of online purchases and sales technology,in particular for entrepreneurial farmers[1].Currently,the most existing phenomenon in China is that quantities of regional rural products with excellent quality but without national popularity.Thereby,it is significant to enhance the popularity of various brands in regional agricultural products using internet marketing,and also contribute to the nation’s strategy of rural revitalization.To appeal to the nations’strategy,we are supposed to make use of brand personality(BP)traits,which probably contribute to robust internet branding of regional agricultural products.Our research will focus on the influences of differential dimensions of brand personality(BP)in terms of common rural products,additionally,we also attempt to design a BP model for internet branding of agricultural products in China.Furthermore,from the two perspectives of characteristics in rural areas(agricultural producers and agricultural consumers),measures to assist agricultural producers in building their brands through the application of internet tools and marketing should be recognized.On the other side,methods to enhance agricultural consumers’brand loyalty also need to be captured.
文摘In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong University)and Prof.Xing Zhang(Wuhan Textile University)have published the timely book Datadriven Internet Health Platform Service Value Co-creation through China Science Press.The book focuses on the provision of medical and health services from doctors to patients through Internet health platforms,where the service value is co-created by three parties.
文摘The rapid evolution of satellite constellation projects(e.g.,SpaceX)and the standardization of 3rd Generation Partnership Project(3GPP)non-terrestrial networks(NTNs)have positioned satellite Internet networking(SIN)as a cornerstone of future communication systems.The demand for ubiquitous connectivity,resilient infrastructures,and intelligent network services has never been greater,driven by applications ranging from global broadband access to emergency response and space-air-ground integration.
文摘Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
基金supported by the General Project of the Cultivation Project of the Chinese Hospital Reform and Development Research Institute of Nanjing University(NDYG2022072)。
文摘Objective Systematically integrate nurses’experience with“Internet Nursing Service”to analysis the nurses’experiences with“Internet Nursing Service”,and to provide a theoretical reference for formulating a more rational“Internet Nursing Service”model.Methods A systematic search in PubMed,Embase,Web of Science,the Cochrane Library,CINAHL,China National Knowledge Infrastructure(CNKI),Wanfang Database,and Chinese Biomedical Literature Database was conducted to collect qualitative research on nurses’experiences with“Internet Nursing Service,”with a retrieval time limit from December 2019 to June 2024.Qualitative meta-synthesis was performed through line-by-line coding of relevant quotes,organization of codes into descriptive themes,and development of analytical themes.Results A total of 19 studies were included,one study was rated as Grade A in quality evaluation,and the remaining studies were rated as Grade B.Collectively synthesized into three integrated results:Harvest and growth,Difficulties and challenges,and Expectations and support.Harvest and growth,include 1)manifestation of self-value,2)enhancing nursing capabilities,3)optimizing nursing resources;Difficulties and challenges,include 1)lack of safety guarantee,2)role conflict;Expectations and support include,1)expectation for professional knowledge and skill training,2)expectations for service platform optimization,3)expectation for reasonable charges,4)expectation for related policy support.Conclusion“Internet Nursing Service”model benefits both nurses and patients,but still full of challenges.It aids in the decentralization of medical resources.Management departments still need to encourage nurses to actively invest in“Internet Nursing Service”while ensuring their safety and interests.
文摘The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and advanced services.However,this rapid expansion also heightens the vulnerability of the IoT ecosystem to security threats.Consequently,innovative solutions capable of effectively mitigating risks while accommodating the unique constraints of IoT environments are urgently needed.Recently,the convergence of Blockchain technology and IoT has introduced a decentralized and robust framework for securing data and interactions,commonly referred to as the Internet of Blockchained Things(IoBT).Extensive research efforts have been devoted to adapting Blockchain technology to meet the specific requirements of IoT deployments.Within this context,consensus algorithms play a critical role in assessing the feasibility of integrating Blockchain into IoT ecosystems.The adoption of efficient and lightweight consensus mechanisms for block validation has become increasingly essential.This paper presents a comprehensive examination of lightweight,constraint-aware consensus algorithms tailored for IoBT.The study categorizes these consensus mechanisms based on their core operations,the security of the block validation process,the incorporation of AI techniques,and the specific applications they are designed to support.
文摘In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.
基金funded by the 2023 Hospital Management Innovation Research Project by the Jiangsu Hospital Association(No.JSYGY-2-2023-551)。
文摘Objectives:This study aimed to explore the effectiveness and advantages of an“Internet+”nursing model based on user profilingin the rehabilitation of postoperative breast cancer patients.Methods:Breast cancer patients admitted to the hospital from July 2023 to September 2024 were enrolled.These patients were randomly assigned to a control group and an intervention group,with 52 patients in each group.The control group received routine nursing care,while the intervention group received an“Internet+”nursing intervention based on user profilingin addition to routine care.The intervention period lasted for one month following discharge.Before and one month after the intervention,the Fear of Progression Questionnaire-Short Form(FOP-Q-SF),the Fear of Cancer Recurrence Inventory-Short Form(FCRI-SF),Chinese Posttraumatic Growth Inventory(C-PTGI),and the Functional Assessment of Cancer Therapy-Breast(FACT-B)were applied to assess the effects of interventions.Results:A total of 104 patients were analyzed.After the intervention,FOP-Q-SF and FCRI-SF scores were significantlylower in the intervention group compared to the control group,with statistical significance(t=3.98,P<0.001;t=-7.59,P<0.001),and Cohen’s d of 0.781 and 1.49,respectively.Additionally,CPTGI and FACT-B scores in the intervention group were significantly higher than those in the control group(t=-6.534,P<0.001;t=-4.579,P<0.001),with Cohen’s d of 0.585 and 0.656.Conclusions:An“Internet+”nursing model based on user profilingcould reduce postoperative breast cancer patients fear of disease progression and cancer recurrence,also enhancing posttraumatic growth and overall quality of life.
