数字技术支持服务增进老年人社会联结的路径

老龄化社会与数字化时代共振,引发各界对老年人数字融入的关注。文章以南京图书馆老年智能手机培训项目为例,采用深度访谈、焦点小组和参与式观察等方法获取资料,重点探究公共服务部门所提供的数字技术支持服务对促进老年群体的数字融入,进而增进数字化时代老年社会联结起到了怎样的作用。研究发现:公共服务部门为老年人提供数字技术支持服务的核心价值不仅在于从个体层面提高老年人的数字素养和数字使用技能,更重要的是以数字技术培训项目为依托,增进老年人与更广泛的社会系统之间的互动,进而促进老年人的社群联系和社会融入,从个体—社群—社会三个层面增进了老年群体的社会联结。文章为挖掘公共服务部门在积极应对人口老龄化战略中的价值提供了新的理论思路和实践支撑。

绿色债券与其他金融市场间的风险溢出研究——基于TVP-VAR频域溢出模型

基于TVP-VAR频域溢出模型的风险溢出结果表明:绿色债券与其他金融市场之间的总溢出主要由短期溢出驱动;在不同的时间尺度,绿色债券和传统债券市场间存在显著的双向溢出效应,绿色债券市场与股票市场、能源市场、新能源市场、外汇市场之间的风险溢出均不显著;在重大事件冲击下,绿色债券市场与股票市场、能源市场、新能源市场间的风险溢出显著增加。

绿色变革型领导对员工自愿绿色行为的影响:自然情感联结与权力距离取向的作用

基于情感事件理论,探究绿色变革型领导对员工自愿绿色行为的影响机制。本研究分三个时点搜集了83个团队305份领导-员工配对数据,并进行多层次分析。结果表明:绿色变革型领导正向预测员工自愿绿色行为,员工自然情感联结在上述关系中发挥中介作用。此外,员工的权力距离取向正向调节绿色变革型领导与员工自然情感联结的关系,以及绿色变革型领导经由自然情感联结对员工自愿绿色行为的积极作用。本研究拓展了学界对员工自愿绿色行为激发因素的认识,同时为企业的绿色管理实践提供了新启示。

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

自然康复中室外环境、设施和项目标准的质量保证框架

【目的】在瑞典斯科讷省,自然康复(nature-based rehabilitation, NBR)项目的创新性体现在医疗保健方面,该项目倡导自然环境的疗愈潜力。作为基础医疗保健服务的组成部分,NBR项目旨在满足人们面对与压力相关的复杂心理健康挑战时的个人需求。【方法/过程】在将NBR概念应用于医疗保健之前,基于在阿尔纳普康复花园(Alnarp Rehabilitation Garden)生活实验室进行的全面的实证和描述性研究以及一项为期2年的试点研究,对NBR项目进行了测试,并根据在NBR项目中获取到的个人从与压力相关的精神疾病中恢复的最新证据对其定期更新。该项目由斯科讷省区域办事处(Region Sk?ne)促成,旨在促进与10家精选的NBR服务供应商的合作,战略性地提供全面和整体的恢复方法。【结果/结论】与传统医疗不同,NBR项目侧重于康复,希望通过自然(即支持性户外环境)支持的活动,增益身体、心理和社会福祉。NBR体系强调参与者的休息、有意义的参与和支持性户外环境。与自然相结合的日常活动为个人喜好提供了灵活的选择。NBR体系化的恢复方法包括晨会、参与者自己的时间、基于自然的活动、闭幕集会。维持NBR的质量需要详细的记录,定期现场访问以鼓励公开对话,并确保NBR项目的合规性。这一质量要求可以确保参与者在他们的自然疗愈(nature-based intervention, NBI)之旅中获得高质量的服务。

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

Approximately Bi-Similar Symbolic Model for Discretetime Interconnected Switched System

Dear Editor,This letter concerns the development of approximately bi-similar symbolic models for a discrete-time interconnected switched system(DT-ISS).The DT-ISS under consideration is formed by connecting multiple switched systems known as component switched systems(CSSs).Although the problem of constructing approximately bi-similar symbolic models for DT-ISS has been addressed in some literature,the previous works have relied on the assumption that all the subsystems of CSSs are incrementally input-state stable.

FADSF:A Data Sharing Model for Intelligent Connected Vehicles Based on Blockchain Technology

With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is the key to organically maximizing their efficiency.However,in the context of increasingly strict global data security supervision and compliance,numerous problems,including complex types of connected vehicle data,poor data collaboration between the IT(information technology)domain and OT(operation technology)domain,different data format standards,lack of shared trust sources,difficulty in ensuring the quality of shared data,lack of data control rights,as well as difficulty in defining data ownership,make vehicle data sharing face a lot of problems,and data islands are widespread.This study proposes FADSF(Fuzzy Anonymous Data Share Frame),an automobile data sharing scheme based on blockchain.The data holder publishes the shared data information and forms the corresponding label storage on the blockchain.The data demander browses the data directory information to select and purchase data assets and verify them.The data demander selects and purchases data assets and verifies them by browsing the data directory information.Meanwhile,this paper designs a data structure Data Discrimination Bloom Filter(DDBF),making complaints about illegal data.When the number of data complaints reaches the threshold,the audit traceability contract is triggered to punish the illegal data publisher,aiming to improve the data quality and maintain a good data sharing ecology.In this paper,based on Ethereum,the above scheme is tested to demonstrate its feasibility,efficiency and security.

Call for Papers——Feature Topic Vol.22,No.3,2025 Special Issue of China Communications:Convergence of 6G empowered Edge Intelligence and Generative AI:Theories,Algorithms,and Applications

Generative artificial intelligence(AI),as an emerging paradigm in content generation,has demonstrated its great potentials in creating high-fidelity data including images,texts,and videos.Nowadays wireless networks and applications have been rapidly evolving from achieving“connected things”to embracing“connected intelligence”.Generative AI has been recognized as a fundamentally innovative technology to drive the advancement of intelligent wireless communications and networks.

Current optimization-based control of dual three-phase PMSM for low-frequency temperature swing reduction

In this paper,a control scheme based on current optimization is proposed for dual three-phase permanent-magnet synchronous motor(DTP-PMSM)drive to reduce the low-frequency temperature swing.The reduction of temperature swing can be equivalent to reducing maximum instantaneous phase copper loss in this paper.First,a two-level optimization aiming at minimizing maximum instantaneous phase copper loss at each electrical angle is proposed.Then,the optimization is transformed to a singlelevel optimization by introducing the auxiliary variable for easy solving.Considering that singleobjective optimization trades a great total copper loss for a small reduction of maximum phase copper loss,the optimization considering both instantaneous total copper loss and maximum phase copper loss is proposed,which has the same performance of temperature swing reduction but with lower total loss.In this way,the proposed control scheme can reduce maximum junction temperature by 11%.Both simulation and experimental results are presented to prove the effectiveness and superiority of the proposed control scheme for low-frequency temperature swing reduction.

Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected Vehicles

With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points(NCPs).Considering the amount of task data and the idle resources of NCPs,a computing resource scheduling model for NCPs is established.Taking the heterogeneous task execution delay threshold as a constraint,the optimization problem is described as the problem of maximizing the utilization of computing resources by NCPs.The proposed problem is proven to be NP-hard by using the method of reduction to a 0-1 knapsack problem.A many-to-many matching algorithm based on resource preferences is proposed.The algorithm first establishes the mutual preference lists based on the adaptability of the task requirements and the resources provided by NCPs.This enables the filtering out of un-schedulable NCPs in the initial stage of matching,reducing the solution space dimension.To solve the matching problem between ICVs and NCPs,a new manyto-many matching algorithm is proposed to obtain a unique and stable optimal matching result.The simulation results demonstrate that the proposed scheme can improve the resource utilization of NCPs by an average of 9.6%compared to the reference scheme,and the total performance can be improved by up to 15.9%.

MSADCN:Multi-Scale Attentional Densely Connected Network for Automated Bone Age Assessment

Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.

Vehicle Head and Tail Recognition Algorithm for Lightweight DCDSNet

In the model of the vehicle recognition algorithm implemented by the convolutional neural network,the model needs to compute and store a lot of parameters.Too many parameters occupy a lot of computational resources making it difficult to run on computers with poor performance.Therefore,obtaining more efficient feature information of target image or video with better accuracy on computers with limited arithmetic power becomes the main goal of this research.In this paper,a lightweight densely connected,and deeply separable convolutional network(DCDSNet)algorithmis proposed to achieve this goal.Visual Geometry Group(VGG)model is improved by utilizing the convolution instead of the fully connected module,the deeply separable convolution module,and the densely connected network module,with the first two modules reducing the parameters and the third module allowing the algorithm to have more features in a limited number of parameters.The algorithm achieves better results in the mine vehicle recognition dataset.Experiments show that the recognition accuracy is improved by 4.41% compared to VGG19 and the amount of parameters is reduced by 71% compared to VGG19.

感恩和学校联结在家庭功能与心理韧性间的链式中介作用:普高生和职高生的多群组分析

目的:探究普高生与职高生在心理韧性、家庭功能、学校联结与感恩及其关系上的差异。方法:使用问卷法对广东、甘肃1224名普通高一学生与693名职业高一学生进行调查,对数据进行结构方程模型与多群组路径分析。结果:1普高生在自强(t=5.98,P<0.001)与人物取向的感恩(t=4.95,P<0.001)上显著高于职高生,而在韧性维度(t=-2.09,P<0.05)与适应性维度(t=-3.84,P<0.001)上则显著低于职高生。2感恩和学校联结在家庭功能和心理韧性之间的链式中介效应的95%置信区间为[0.04,0.06],感恩和学校联结的链式中介效应成立。3根据嵌套模型比较的结果,结构系数相等模型与基线模型存在显著差异(Δχ2=123.78,Δdf=12,P<0.001),两个群体的模型在系数上存在显著的差异。结论:普高生比职高生更自强,对他人更有感恩心;但职高生在韧性和家庭功能适应性上比普高生更胜一筹;家庭功能通过感恩与学校联结作用于个体的心理韧性,但家庭功能对职高生心理韧性的影响更大,感恩对普高生心理韧性的影响更大。研究为两个群体的韧性干预提供了实证证据。

A New Malicious Code Classification Method for the Security of Financial Software

The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software.The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients.Nevertheless,present detection models encounter limitations in their ability to identify malevolent code and its variations,all while encompassing a multitude of parameters.To overcome these obsta-cles,we introduce a lean model for classifying families of malevolent code,formulated on Ghost-DenseNet-SE.This model integrates the Ghost module,DenseNet,and the squeeze-and-excitation(SE)channel domain attention mechanism.It substitutes the standard convolutional layer in DenseNet with the Ghost module,thereby diminishing the model’s size and augmenting recognition speed.Additionally,the channel domain attention mechanism assigns distinctive weights to feature channels,facilitating the extraction of pivotal characteristics of malevolent code and bolstering detection precision.Experimental outcomes on the Malimg dataset indicate that the model attained an accuracy of 99.14%in discerning families of malevolent code,surpassing AlexNet(97.8%)and The visual geometry group network(VGGNet)(96.16%).The proposed model exhibits reduced parameters,leading to decreased model complexity alongside enhanced classification accuracy,rendering it a valuable asset for categorizing malevolent code.

非正则赋权有向图A_(α)谱半径的上界

Let D be a weighted digraph with n vertices in which each arc has been assigned a positive number.Let A(D)be the adjacency matrix of D and W(D)=diag(w_(1)^(+),w_(2)^(+),...,w_(n)^(+)).In this paper,we study the matrix A_(α)(D),which is defined as Aα(D)=αW(D)+(1−α)A(D),0≤α≤1.The spectral radius of A_(α)(D)is called the Aαspectral radius of D,denoted byλα(D).We obtain some upper bounds on the Aαspectral radius of strongly connected irregular weighted digraphs.

学校联结与抑郁的关系:一项三水平元分析

以往关于学校联结与抑郁关系的理论和实证研究结果均不一致。为明确两者间的整体关系,探索造成分歧的原因,对纳入的87项研究进行了三水平元分析。结果发现,学校联结与抑郁存在显著负相关(r=-0.39, df=205, p <0.001)。此外,学校联结和抑郁的关系受被试性别、年龄、抑郁测量工具、研究数据属性的调节,但不受学校联结测量工具、文化类型、发表年份的调节。本研究首次使用三水平元分析技术整合了学校联结与抑郁的关系,理论上为两者关系提供了阶段性定论,实践上为预防和干预个体抑郁提供了参考依据。

城市公园环境要素对居民心理健康的影响

为探讨城市公园中自然要素、感知要素、休息要素和活动要素对居民正向情绪、认知功能及心理症状和负向情绪三个心理健康维度的影响,以及自然联结在其中的作用,调查了911名北京市居民,并构建了结构方程模型。分析结果表明,城市公园的感知要素可以直接促进居民的正向情绪和认知功能,也可以通过自然联结间接促进居民的正向情绪和认知功能,并缓解居民的心理症状和负向情绪。另外,城市公园的活动要素可以直接促进居民的正向情绪,缓解居民的心理症状和负向情绪。然而,自然要素和休息要素对居民心理健康各维度的预测作用不显著。因此,应注重增加城市公园中的感知要素和活动要素,以及增强人与自然的互动,提高居民与自然的联结。

文化价值观对国家公园游客亲环境行为的影响路径研究

以大熊猫国家公园为例,基于“认知-情感-行为”理论框架,结合问卷调查和实地访谈法收集游客数据,运用结构方程模型和bootstrap中介效应检验,研究文化价值观、自然联结和大熊猫国家公园游客亲环境行为之间的关联。研究发现:(1)文化价值观对游客的自然联结有显著正向影响;(2)自然联结对游客的亲环境行为有显著的正向影响;(3)自然联结在文化价值观与游客的亲环境行为之间产生显著中介作用。研究结果一方面丰富和拓展了国家公园游客亲环境行为的研究视域,为理解游客行为动机提供了重要的理论依据,另一方面为国家公园的生态保护和可持续发展提供了理论支撑和实践参考。

父母自主支持与高中生学习投入:意向性自我调节和学校联结的作用

为探讨父母自主支持对高中生学习投入的影响及其内在作用机制,使用父母自主支持量表、意向性自我调节问卷、学校联结问卷和中文版学习投入量表对977名高中生进行施测。结果表明:(1)父母自主支持能够显著正向预测高中生的学习投入;(2)意向性自我调节在父母自主支持与学习投入关系中的中介作用显著;(3)父母自主支持影响学习投入的直接效应以及意向性自我调节中介作用的前半路径均受到学校联结的调节。研究结果凸显了家校协同在促进学生发展适应中的重要作用并揭示了其内在机制,对于推动家庭教育、提升学生学习投入和发展适应水平具有现实启示。