Reinforcing of Citizen’s Trust in E-Government: The Cameroon’s Case

The embracing of ICTs and related technologies has enhanced different approaches for governments worldwide to deliver services to their citizens in a smart way. However, the usage of e-government services by common citizens is recognized as one of the major setbacks of e-government development in both developed and developing countries. Moreover, government agencies in these countries are facing great challenges in keeping the citizens motivated enough to continue to use e-government services. This research aims to investigate the factors that influence citizens’ trust towards continue use of e-government services in Cameroon. The proposed research model consisted of three main constructs including technological, governmental, risk factors as well as six demographic characteristics (age, gender, educational level, income, internet experience and cultural perception). A five-point Likert scale questionnaire was designed to collect data physically and electronically, 352 valid questionnaires were retrieved. Simple and Multiple regression analysis methods were applied to build an adequate model based on the verification of hypotheses proposed. Based on results obtained, four demographic characteristics (age, education, occupation and income) have influence on citizens’ trust in e-government meanwhile gender and cultural affiliation have no influence. Furthermore, technological factors and governmental factors positively influence trust level in e-government, whereas risk factors have a negative influence on trust level. Deducing from the results, a list of recommendations is proposed to the government of Cameroon in order to reinforce citizens’ trust in e-government services.

Trust beyond Technology Algorithms: A Theoretical Exploration of Consumer Trust and Behavior in Technological Consumption and AI Projects

In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.

Implicit Trust Based Context-Aware Matrix Factorization for Collaborative Filtering

Matrix factorization （MF） has been proved to be a very effective technique for collaborative filtering （ CF）, and hence has been widely adopted in today＇s recommender systems, Yet due to its lack of consideration of the users＇ and items＇ local structures, the recommendation accuracy is not fully satisfied. By taking the trusts among users＇ and between items＇ effect on rating information into consideration, trust-aware recommendation systems （TARS） made a relatively good performance. In this paper, a method of incorporating trust into MF was proposed by building user-based and item-based implicit trust network under different contexts and implementing two implicit trust-based context-aware MF （]TMF） models. Experimental results proved the effectiveness of the methods.

Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

Recently,a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks(WSN).In routing,the trust system includes or avoids nodes related to the estimated trust values in the routing function.This article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol(EMTA-SRSP)for WSN.The presented EMTA-SRSP technique majorly involves the optimal selection of routes in WSN.To accomplish this,the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data communication.For the clustering process,the nodes with maximum residual energy will be considered cluster heads(CHs).In addition,the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy,distance,and trust degree.The experimental validation of the EMTA-SRSP technique is tested,and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.

基于SEM-ANN的数字人民币使用意愿影响因素研究

数字人民币是重塑我国支付新格局、加速数字经济进程的重要载体,但在用户推广方面仍存在诸多阻力,从用户的视角探究数字人民币使用意愿的影响因素具有重要的意义。本文以数字人民币为研究对象,在UTAUT2模型的基础上结合数字人民币的特点,构建数字人民币使用意愿理论模型;进而,以数字人民币首批试点城市的用户数据为样本,综合运用PLS-SEM和SEM-ANN方法实证分析数字人民币使用意愿的影响因素。结果表明,基于SEM的数字人民币使用意愿模型具有较好的解释和预测能力;信任、习惯、价格价值和社会影响对数字人民币使用意愿具有显著正向影响,而绩效期望、努力期望和创新意识对数字人民币使用意愿的影响不显著;此外,在作为调节变量的个人特征方面,价格价值与数字人民币使用意愿的关系受性别因素的调节;经验对价格价值、习惯、社会影响、信任与用户使用意愿的关系均具有调节作用;将结构方程模型与人工神经网络相结合能够提升模型的预测精度,并且影响因素的重要性有所改变,习惯最为重要、信任次之,其次为社会影响和价格价值。

可信拜占庭容错共识机制

针对实用拜占庭容错共识机制中存在共识效率低和激励机制不足的问题,提出一种可信拜占庭容错共识机制。首先,建立引入奖惩机制和时间衰减因子的节点信任评价模型,计算节点的信任值。然后,根据节点信任值进行排序,选择信任值高的节点组成共识节点集,利用可验证随机函数选取共识节点集中信任值高的节点作为主节点。此外,在拜占庭共识过程中利用BLS签名保证主节点不会作恶。实验仿真结果表明,信任模型可以有效惩罚作恶的节点,可信拜占庭容错共识相比于实用拜占庭容错共识交易吞吐量可以提高43%.

区块链技术对企业全要素生产率的影响研究--基于信任构建和降本增效的视角

为了研究区块链技术对企业的生产效率的作用,文章以2019—2021年A股上市公司中非ST制造业企业为样本,运用文本分析及机器学习word2vec方法基于上市公司年报对企业区块链应用程度进行度量,实证检验了其与制造业企业全要素生产率之间的关系。研究发现上市公司区块链技术应用程度越高,其全要素生产率越高。机制研究发现,信任构建和降本增效,是区块链应用赋能全要素生产率提高的主要路径。进一步研究发现,企业子行业类型以及企业代理成本,会影响区块链应用对生产率的促进作用。

膀胱癌患者参与医疗决策期望现状及影响因素

目的调查并分析膀胱癌患者参与医疗决策期望现状及相关影响因素。方法采用便利抽样法,选取2021年3月-2022年4月在武汉某三甲医院泌尿外科住院的240例膀胱癌患者为研究对象,采用一般资料调查表、病人参与能力测量量表、患者参与医疗决策期望量表、维克森林医师信任量表对患者进行调查。结果膀胱癌患者参与医疗决策期望总分为(35.36±6.62)分。多元线性回归分析结果显示,患者参与能力、对医师的信任水平、年龄、受教育程度是膀胱癌患者参与医疗决策期望的独立影响因素(R^(2)=0.931,△R^(2)=0.930,F=792.294,P<0.001)。结论膀胱癌患者参与医疗决策期望处于中等偏下水平,临床上医护人员应重点关注参与能力低、对医生信任水平高、高龄、受教育程度低的膀胱癌患者,对其加强知识宣教及病情沟通,提高患者参与医疗决策期望水平,改善患者治疗护理满意度。

基于直觉判断的初始信任:启发式线索及影响因素

初始信任是许多重要关系的开端,也是多数研究主要关注的信任形式。缺乏可靠线索的不确定性特征使得初始信任往往依赖于直觉。基于直觉判断的初始信任可从信任者自身和信任对象两方面获取启发式线索,同时还会受到来自个体和情境因素的影响。未来可以进一步深化启发式线索的相关研究、拓展直觉初始信任的影响因素研究,并结合启发式线索和影响因素提高初始信任准确性。

门诊患者对医生的信任及影响因素研究

为了解门诊患者对医生的信任情况及其影响因素,采用方便样本法对安徽某省级三甲医院门诊离诊患者进行问卷调查。结果显示,非首次就诊、感知到医疗服务过程尊重、对医生群体信任度高、系统整体信任度高对医生信任总分、仁爱、技术维度有正向影响;医疗环境得分高对医生信任总分、技术维度得分有正向影响;相较于认为医患关系是主动—被动模式的患者,认为医患关系应为指导—合作模式、共同参与模式的患者在仁爱维度得分更高。医疗机构层面应优化就医环境,注重对患者的隐私保护;社会层面应促进对医生群体的信任,提高对卫生系统的获得感。此外,要引导患者参与诊疗过程,正确认知医患关系。

基于节点行为的物联网动态综合信任评估方法

物联网节点由于所处环境复杂多变且无人监管,易受损或被捕获而成为内部攻击的源头。针对这种情况,提出基于物联网节点行为的动态综合信任评估方法。基于节点交互时的多种行为特征来计算节点的直接信任,并提出节点的自信度和推荐信任度作为权重系数参考。通过共同邻居节点获取推荐信任以缩短发现恶意节点的时间,并采用时间衰减因子和历史评估窗口来保证信任的时效性。实验结果表明,通过节点的异常行为变化能及时识别恶意节点,降低网络的能耗,具有良好的适应性和抵抗性。

MMC-HVDC交直流混联系统小扰动稳定域迭代求解和形态分析

随着高压直流输电技术的发展,区域电力系统将拓展为大规模交直流混联系统。因此,需要建立可以同时精确刻画交直流动态的大规模交直流混联系统模型来研究混联系统的动态与稳定性。文中考虑了交流侧和直流侧的详细动态,建立了异步联网的模块化多电平换流器型高压直流(MMC-HVDC)交直流混联系统的状态空间模型,给出了具体的解析解形式,并推导了多重迭代信赖域算法。所提算法建立了决策变量空间内的点与平衡点的映射关系,结合Hopf分岔识别边界,提出了一种多重迭代求解小扰动稳定域边界的方法。在利用Simulink仿真验证模型准确性后,以发电机出力作为决策变量绘制了交直流混联系统稳定域,利用特征根和参与因子定量研究了直流失稳和交流失稳的机理以及控制策略和控制参数对稳定域边界的影响。对比分析了多区域的混联系统与交流系统的稳定域,结果表明换流站和直流网络的引入可能使多区域系统稳定域边界呈现多约束共同决定的近似分段线性拓扑特性。

医患信任水平对就医选择的影响研究

目的拓宽影响就医选择的因素渠道,加入了社会因素进行考量,为医疗资源分配、加速分级医疗和公共网络建设升级提供参考。方法采用OLS回归模型,综合分析社会信任、个体因素、家庭因素、地区因素等对就医选择的影响,并通过倾向得分匹配模型(propensity score matching,PSM)进行异质性分析。结果医患信任水平与就医选择之间呈现反向相关,个体医患信任度越高越倾向基层医院就医。结论较高的医患信任水平可以引导医疗资源下沉,可以通过一定的政策支持和加强医疗合作加快基层医疗发展。

UAV-supported intelligent truth discovery to achieve low-cost communications in mobile crowd sensing

Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solved in the literature.In this paper,an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery(UAV-ITD)scheme is proposed to obtain truth data at low-cost communications for MCS.The main innovations of the UAV-ITD scheme are as follows:(1)UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization(DMF)to discover truth data based on the trust mechanism for an Information Elicitation Without Verification(IEWV)problem in MCS.(2)This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy,which saves more communication costs than most previous data collection schemes,where they collect n or kn data samples.Finally,we conducted extensive experiments to evaluate the UAV-ITD scheme.The results show that compared with previous schemes,our scheme can reduce estimated truth error by 52.25%–96.09%,increase the accuracy of workers’trust evaluation by 0.68–61.82 times,and save recruitment costs by 24.08%–54.15%in truth data discovery.

产业政策驱动生产要素“集聚—分流”的运行机制研究——社会信任的视角

生产要素有效集聚在特定生产空间范围内搭建生产链条对于一个地方的产业发展至关重要,而生产要素向一个产业链的集聚或分流不仅受到市场机制的调节作用,也受到政府产业政策的驱动作用。产业政策是地方政府优化营商环境、培育优质产业、促进经济社会发展的重要工具。以生产要素持有者对政府产业政策的信任关系演化为依据,选取W市集成电路产业发展政策和X市化工产业政策变迁为案例,从社会信任的视角可将产业政策驱动生产要素“集聚—分流”的运行机制概括为“信任—集聚型”“高信任—好口碑型”“低信任—流出型”“不信任—差口碑型”四种类型。

一种基于多因子信任机制的无线传感网络安全路由协议

为解决以往基于信任机制的无线传感网络平面式路由协议中存在的问题,如信任计算指标不全面、人工赋权导致权重不可靠、路由节点相关而导致丢包、延迟、能耗等等,提出了一种基于多因子信任机制的无线传感网络安全路由协议SRPMT,该信任机制考虑了节点直接信任、间接信任、剩余能量、节点间距离度量和汇聚节点sink距离等五项信任因子,采用熵权法进行动态赋值权重,采用改进的AODV协议节点不相关多径路由,sink根据信任的链路质量计算出最优路径及备用路径,当多条路径都不可靠之后网络发起路由重建,降低了路由重建频率。仿真结果表明,与TSRP与AODV相比,SRPMT有效降低了网络节点能耗、延迟与丢包率,提高了无线传感网络的安全性。

外界因素对TRUST测定结果的影响

目的探讨TRUST测定过程中,各种外界因素对结果的影响。方法收集30例TRUST阴性和69例TRUST阳性标本(经TPPA确认为阳性,并做TRUST滴度),观察保存时间和温度、振荡时间长短、反复冻融次数、试剂盒的平衡时间长短、pH值高低及电解质浓度等外界因素对TRUST测定结果的影响。结果阳性标本保存时间长达7 d及7 d以后,反复冻融次数3次及3次以上,均会使TRUST阳性标本的滴度明显下降(P<0.05);试剂盒在室温储存的时间过长,可导致TRUST测定结果发生假阴性的现象;水平旋转仪振荡的持续时间过长、强酸环境以及高电解质溶液浓度,都可使TRUST的测定结果产生假阳性现象。结论在TRUST测定过程中,检测人员应对外界影响因素和反应条件进行严格控制,才能保证TRUST测定结果的正确性和准确性。

ICU护士团队心理安全气氛现状及其影响因素分析

目的探讨ICU护士团队心理安全气氛影响因素,为后续干预提供借鉴。方法选择2023年6月—2024年1月安徽、江苏、浙江等城市210名ICU工作的护士作为研究对象进行问卷调查,包括社会一般资料、团队心理安全气氛量表、护士职业应对自我效能量表、信任领导调查表。结果210例ICU护士团队心理安全气氛总分为(55.51±8.20)分,得分率为69.40%;ICU护士团队心理安全气氛在平均月收入、婚姻状况、学历、职称、工作年限、是否有编制方面比较,差异有统计学意义(P<0.05);回归分析显示,对领导的信任状况、护士职业应对自我效能、职称、工作年限是ICU护士团队心理安全气氛的影响因素(调整后R^(2)=0.440,F=42.092,P<0.01),共计解释总变异的44%。结论医院管理者应重视ICU护士的团队心理安全气氛水平,尤其应注重低职称、工作年限短的护士;领导者应不断提高自身素质、护士应提高自我职业应对自我效能,从而提高ICU护士团队心理安全气氛。

A Distributed Trust Based Secure Communication Framework for Wireless Sensor Network

The wireless sensor network is an emerging technology, which is used to sense and monitor the environment. As the nodes are deployed in an open environment, the security is one of the essential factors. The cryptography techniques can ensure confidentiality, integrity and authentication. However, wireless sensor network also needs to deal with inside and outside attackers. To deal with outside attackers, attacks by compromised or malicious nodes, trust management system is suggested by many researchers in the area of wireless sensor network. Trust management system can be implemented in various applications for security management such as secure data aggregation, secure cluster head selection, trusted routing, access control, etc. Many researchers provide different kind of solutions for these secure applications based on trust management. However, to incorporate, all such applications on a single sensor node in the network, it is essential to design and develop a trust management system, which considers various aspects and applications of wireless sensor network. As a result, in this paper, we would like to propose a parameter and trust factor based secure communication framework and design a trust management system for wireless sensor networks. Our main contribution is to identify various parameters and trust factors which influences on trust in wireless sensor network and developing a framework for a trust management system based on various parameters and trust factors. The working of the proposed model is shown by simulation experiments conducted in MATLAB for the application of secure communication, data aggregation and intrusion detection in wireless sensor networks.

A Personalized Recommendation Algorithm with User Trust in Social Network

In the era of big data, personalized recommendation has become an important research issue in social networks as it can find and match user’s preference. In this paper, the user trust is integrated into the recommendation algorithm, by dividing the user trust into 2 parts: user score trust and user preference trust. In view of the common items in user item score matrix, the algorithm combines the number of items with the score similarity between users, and establishes an asymmetric trust relationship matrix so as to calculate the user’s score trust. For the non common score items, we use the attribute information of items and the scoring weight to calculate the user’s preference trust. Based on the user trust in social network, a new collaborative filtering recommendation algorithm is proposed. Besides, a new matrix factorization recommendation algorithm is proposed by combining the user trust with matrix factorization. We did the experiments comparing with the related algorithms on the real data sets of social network. The results show that the proposed algorithms can effectively improve the accuracy of recommendation.