Neural mechanism of proposer's decision-making in the ultimatum and dictator games

Previous studies have demonstrated that reactions to unfair offers in the ultimatum game are correlated with negative emotion. However, little is known about the difference in neural activity between a proposer＇s decision-making in the ultimatum game compared with the dictator game. The present functional magnetic resonance imaging study revealed that proposing fair offers in the dictator game elicited greater activation in the right supramarginal gyrus, right medial frontal gyrus and left anterior cingulate cortex compared with proposing fair offers in the ultimatum game in 23 Chinese undergraduate and graduate students from Beijing Normal University in China. However, greater activation was found in the right superior temporal gyrus and left cingulate gyrus for the reverse contrast. ＂The results indicate that proposing fair offers in the dictator game is more strongly associated with cognitive control and conflicting information processing compared with proposing fair offers in the ultimatum game.

The modulation recognition based on decision-making mechanism and neural network integrated classifier

A neural network integrated classifier(NNIC) designed with a new modulation recognition algorithm based on the decision-making tree is proposed in this paper.Firstly,instantaneous parameters are extracted in the time domain by the coordinated rotation digital computer(CORDIC) algorithm based on the extended convergence domain and feature parameters of frequency spectrum and power spectrum are extracted by the time-frequency analysis method.All pattern identification parameters are calculated under the I/Q orthogonal two-channel structure,and constructed into the feature vector set.Next,the classifier is designed according to the modulation pattern and recognition performance of the feature parameter set,the optimum threshold is selected for each feature parameter based on the decision-making mechanism in a single classifier,multi-source information fusion and modulation recognition are realized based on feature parameter judge process in the NNIC.Simulation results show NNIC is competent for all modulation recognitions,8 kinds of digital modulated signals are effectively identified,which shows the recognition rate and anti-interference capability at low SNR are improved greatly,the overall recognition rate can reach 100%when SNR is12dB.

A CASE STUDY ON CONCENTRATION AND DECENTRALIZATION:BEHAVIOR AND DYN AMIC MECHANISM OF SPATIAL EVOLUTION IN METROPOLITAN AREA,NANJING,CHINA

In rapid socio-economic developme nt,the process of concentration and dispersal of various elements tends to be more dramatic,tremendously in fluencing the shaping and transform ation of the space in metropolitan area.Survey of spatial concentration and decent ralization has thus become a basic me thod in examining metropolitan spatial evolution.In this research,three elements were selected as the essential indicato rs of the process:demographic density distribu-tion,employment density distribut ion and business office location.Performance of these elements in Nanji ng City was exam-ined historically.As Nanjing City c ould be regarded as a representative of metropolitan areas in China,its s ituation large-ly suggestes the general characteristics in similar areas of China.Hence based on the investigation of Nanji ng City,four general implications were highligh ted.First,metropolitan areas inChina are in a violentprocess and shift of spatialconcentra-tion and decentralization.Second,from now to at least the near future,c oncentration will continue to be the central fea-ture.Third,the landscape of metrop olitan areas basically exhibits a dual structure character.The gap in en vironmental and ecological qualities among different districts will continue for a l ong time.Fourth,Central Business District(CBD)is playing an important role in helpi ng to convert the traditionally single-centered city structure into a polycentric one.

Finite-time decentralized event-triggered state estimation for coupled neural networks under unreliable Markovian network against mixed cyberattacks

This article investigates the issue of finite-time state estimation in coupled neural networks under random mixed cyberattacks,in which the Markov process is used to model the mixed cyberattacks.To optimize the utilization of channel resources,a decentralized event-triggered mechanism is adopted during the information transmission.By establishing the augmentation system and constructing the Lyapunov function,sufficient conditions are obtained for the system to be finite-time bounded and satisfy the H∞ performance index.Then,under these conditions,a suitable state estimator gain is obtained.Finally,the feasibility of the method is verified by a given illustrative example.

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data.However,the training mechanism for passing model parameters is still threatened by gradient inversion,inference attacks,etc.With a lightweight encryption overhead,function encryption is a viable secure aggregation technique in federation learning,which is often used in combination with differential privacy.The function encryption in federal learning still has the following problems:a)Traditional function encryption usually requires a trust third party(TTP)to assign the keys.If a TTP colludes with a server,the security aggregation mechanism can be compromised.b)When using differential privacy in combination with function encryption,the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible.In this paper,we propose a hybrid privacy-preserving scheme for federated learning,called Fed-DFE.Specifically,we present a decentralized multi-client function encryption algorithm.It replaces the TTP in traditional function encryption with an interactive key generation algorithm,avoiding the problem of collusion.Then,an embedded incentive mechanism is designed for function encryption.It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy.Subsequently,we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm.The experimental results demonstrate the effectiveness and efficiency of our scheme.

Fuzzy synthetic evaluation on layoff mechanism for human resource

According to the modern theory of human resource management, the condition of buyout mainly depends on two variables of an employee: his interior value of product and exterior work reward. There exist shortages in evaluating the value of the employees in an enterprise. Consequently, a lot of employees might be laid off. Hence, this paper puts forward a two-stage method for decision-making to carry out a selective plan of buyout, based on the fuzzy synthetic evaluation involing many factors impacting on human resource. Moreover, a positive analysis is also given.

Key Technologies for Decentralized Control Platform with Dynamic Evolution for Railway Transportation System

Key technologies as well as their principles were discussed for a decentralized control platform capable of dynamic evolution. The primary content includes the automatic decision-making mechanism and the algorithm of the control center migration, the principle and technology of system self-monitoring, the principle and technology of the switch-mode of remote control station, the information transmission technology, and the data consistency technology. These key technologies have shown a series of advanced characteristics for decentralized control platform.

Use of Research Evidence in Policy and Decision-Making: Views, Attitudes and Practices of Health Policy Makers in Lusaka Province of Zambia

Background: The integration of relevant high-quality research evidence into the health decision and policy formulation process is a key strategy for improving health systems especially in developing countries such as Zambia. However, the lack of capacity to understand and value research evidence by policy and decision makers makes it difficult for them to find and use research evidence in a timely manner even when motivated to do so. This study aimed to establish the views, attitudes and practices of policy makers on the use of research evidence in policy and decision-making process in Zambia. Methodology: This descriptive cross-sectional study was conducted in Lusaka, Zambia among selected public health decision and policy making institutions. A purposive sample of 21 consenting policy makers who were working in different positions in the Ministry of Health Headquarters, Provincial and District Health Offices, Health Professions Regulatory Bodies, United Nations Agencies, International Non-Governmental Organizations and University Deans from the University of Zambia participated in the study. A self-administered questionnaire was used to collect data. The IBM? SPSS? Statistics for Windows Version 20.0 was used for data analysis. Results: The concept of Evidence Informed Health Policy was not well understood such that only less than half (47.5%) of the participants reported having heard specifically about Evidence Informed Health Policy meanwhile almost two thirds (61.9%) reported that they used research evidence in decision making and policy formulation. Similar discrepancy was expressed in the understanding of and use of rapid response mechanisms such that although (47.6%) of the participants reported having heard about it, (57%) had never used rapid response mechanisms for deci-sion-making. With regard to the sources of information, about half (52.3) of the participants reported scholarly articles as their main source of evidence. Con-clusion and Recommendations: There is need for more sensitization and ca-pacity building among the decision and policy makers on the importance of using research evidence in decision and policy making process as incorporation of relevant high-quality research evidence into the health policy making pro-cess is a key strategy for improving health systems.

China's Development Modeli:the Costs and Benefits of the Decentralization Approach to Transition

This article provides a coherent framework within which to understand China’s development model,as well as the successes and the failures of China’s decentralization approach to reform.The combination of political centralization and economic decentralization provide local government with enough incentives to develop local economies,in particular incentives to promote market privatisation locally.However,the relative evaluation-based incentive schemes lead to inter- regional market segmentation,increasing inter-regional development gaps and the unequal provision of certain public goods.The success of early-stage reform can be attributed to the benefits of the decentralization approach.The next stage reform should however focus on minimizing the associated costs.China’s gradualist reform can be seen as a mechanism design issue under the control of central government.Therefore,it is essential to take both the costs and benefits of the decentralization approach into account in the design of the next-stage reform package.

Research on self-adaptive decision-making mechanism for competition strategies in robot soccer

In the robot soccer competition platform, the cur- rent confrontation decision-making system suffers from dif- ficulties in optimization and adaptability. Therefore, we pro- pose a new self-adaptive decision-making （SADM） strategy. SADM compensates for the restrictions of robot physical movement control by updating the task assignment and role assignment module using situation assessment techniques. It designs a self-adaptive role assignment model that assists the soccer robot in adapting to competition situations similar to how humans adapt in real time. Moreover, it also builds an accurate motion model for the robot in order to improve the competition ability of individual robot soccer. Experimental results show that SADM can adapt quickly and positively to new competition situations and has excellent performance in actual competition.

Path analysis on educational fiscal decision-making mechanism in China

In China’s current educational fiscal decision making,problems are as follows:no law to trust or not abiding by available laws,absence of equity and efficiency,as well as the standardization of decision-making procedures.It is necessary to set up effective fiscal decision-making mechanism in education and rationally devise reliable paths.

基于DeFi消息传递机制的跨链交易关联方法

跨链桥作为一种新型区块链基本服务,实现了区块链间的资产和数据流通,极大地促进了区块链去中心化金融(DeFi)的建设。然而在实际应用过程中,由于跨链桥的交易不透明与资产难追踪问题,许多犯罪分子开始将跨链技术作为一种洗钱手段,在链间进行非法资产的转移。因此,跨链交易追踪问题正逐渐成为学术界与相关监管部门关注的焦点。现有方法主要通过规则匹配的启发式算法来实现跨链交易的关联,这种方法虽能有效匹配特定区块链间的交易,但仅面向中心化金融(CeFi)跨链桥服务,且高度依赖于中心化的内部应用程序编程接口(API),难以广泛支持多种跨链桥的交易关联。为解决以上问题,提出一种面向DeFi跨链桥服务的交易关联方法。根据跨链桥消息传递机制获取源链的存款事件日志,并基于专家规则提取关键跨链信息,利用数据爬取技术构造目标链交易搜索空间。在此基础上,通过多种业务规则实现跨链交易的关联。实验结果表明,该方法在Celer cBridge、Multichain和Poly Network这3座跨链桥的真实交易数据集(以太坊为源链,币安智能链为目标链)上准确率分别达到93.6%、95.3%和91.6%,具有较好的关联效果。

生态文明建设示范区对企业全要素生产率的影响研究——基于财政分权视角

生态文明建设示范区是中国政府推进生态文明建设的一项制度创新。本文以2011~2022年沪深A股制造业上市公司为研究样本,采用多期双重差分模型实证探究生态文明建设示范区对企业全要素生产率的影响。研究发现:(1)生态文明建设示范区提升了企业全要素生产率,且这一结果在经过一系列稳健性检验后仍然成立;(2)生态文明建设示范区对企业全要素生产率的促进作用随着财政分权程度的提高而降低;(3)进一步分析表明,生态文明建设示范区对企业全要素生产率的促进作用在非国有企业、东部企业和大规模企业中更显著。生态文明建设示范区主要是通过缓解企业面临的融资约束来促进企业全要素生产率的提升。本文的研究对于全面建设生态文明、促进企业转型、提升企业全要素生产率和经济的高质量发展具有一定的参考意义。

去中心化分布式控制系统设计研究

针对上万个被控对象,传统的分布式控制系统无法实现控制器与服务器去中心化,导致效率低且故障率高,设计了一种去中心化分布式控制系统。该系统中的控制器、被控对象、传感器及执行器、服务器通过控制网络进行通信传输,利用冗余备份机制进行控制器动态切换,从而实现控制器去中心化,利用映射表动态更新机制使服务器对相应的被控对象进行数据运算,从而实现服务器去中心化;利用工具箱Truetime2.0及MATLAB/Simulink搭建去中心化分布式电机控制系统仿真平台,以电机为被控对象,研究冗余备份机制对去中心化分布式控制系统的性能影响以及去中心化分布式控制系统的网状网架构能否支持大规模电机正常运行。仿真结果表明,冗余备份机制有效地提高了系统运行的平稳性,系统的网状网架构足以支持上万个电机运行。

基于投票机制的暖通空调空气处理单元传感器故障诊断

现有暖通空调(HVAC)空气处理单元(AHU)的故障诊断研究往往是集中式的。少量的分布式方法大多需要求解大量耗时的优化问题,使得无法及时完成故障诊断。针对以上挑战,该文提出一种基于投票机制的分布式故障诊断方法。在该方法中,建立一个玻尔兹曼机来描述传感器网络,通过传感器之间的相互投票来确定玻尔兹曼机的边权值,基于边权值对玻尔兹曼机的状态也就是传感器的状态进行迭代,从而定位传感器的故障。设计了一种基于欧氏距离的投票策略确定投票值。开发了一种方法,通过在玻尔兹曼机中增加一个额外的节点来重置其权值矩阵,在将玻尔兹曼机对称化的同时,保持原来各传感器之间的投票关系,以保证玻尔兹曼机状态的迭代收敛。该方法不需要求解大量的优化问题,相较于当前的分布式方法计算量小。使用ASHRAE Project RP-1312提供的实际数据对所提方法进行验证。实验结果表明所提方法可以精确且高效地诊断出空气处理单元传感器的偏差故障和漂移故障。

基于去中心化身份的跨域数据交易系统

在数字化时代,数据作为重要的生产要素,对经济增长、科技创新和企业竞争力提升至关重要.传统的中心化的数据交易系统存在一系列数据安全、交易安全风险.虽然当前已经出现了基于区块链实现的去中心化数据交易系统,可以为数据交易提供安全保障,但基于区块链的数据交易系统仍难以处理跨地区、跨地域、跨国家的数据交易,其主要面临3个挑战:1)跨域身份解析结果的一致性问题;2)跨域身份解析服务的可用性问题;3)面向跨域交易的数据交易系统的安全与合规问题.为解决上述3个问题,基于去中心化身份技术设计了一套安全高效的身份跨域解析机制,并构建了一种安全、可监管的去中心化数据交易系统.身份跨域解析机制包括确保解析结果一致性的身份跨域协议和保障该协议执行服务可用性的激励机制.该去中心化数据交易系统支持基于身份属性的细粒度监管,并保证跨域数据交易的原子性.实验验证表明,该系统满足设计目标,解析延迟仅为跨域解析延迟的45%,缓存命中率通过主动和被动更新机制提高72%.系统兼容多种区块链,跨域数据交易平均在2.3个区块内完成,效率在可接受范围内.

财政分权、金融分权与乡村全面振兴——基于中国省际面板数据的实证检验

财政和金融是当前我国实施乡村振兴战略的重要资金来源,而财政分权、金融分权是“三农”领域央地间经济权力配置的重要模式。本文基于我国2006—2022年的省际面板数据,实证分析了财政分权、金融分权的乡村振兴效应。研究发现,财政分权和金融分权对乡村振兴具有显著的抑制效应;财政压力对于财政分权、金融分权的乡村振兴效应具有调节作用,而财政金融协同支农具有显著的乡村振兴助推效应,且此助推效应的区域异质性特征较明显;财政分权、金融分权的乡村振兴抑制作用具有空间溢出效应。因此,本文建议完善央地间乡村振兴财政支出责任划分,适度提高中央财政和国有金融的支农责任;着力优化财政金融协同支农机制,不断创新财政金融协同支农模式;建立乡村振兴全国基金并向欠发达地区适度倾斜;构建乡村振兴中的地方合作机制,不断探索共同推进乡村振兴新模式。

区块链技术在电商物流安全及信任机制中的应用探索

为了解决互联网和电子商务快速发展带来的电商物流行业面临的越来越多的安全与信任问题,本文探讨了区块链技术在电商物流安全及信任机制中的应用,通过提高数据安全性、增强供应链透明度与创新业务模式等方式,有助于区块链技术构建一个更加安全、透明和高效的电商物流体系。

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

基于区块链技术的高校毕业生实习管理应用研究

我国高校毕业生就业、实习管理过程中存在诸多问题,如管理过于中心化、学生实习择业困难、校企沟通不畅、信息不对称、可信度低等问题。文章设计一种基于区块链技术的高校毕业生实习管理系统模型,该系统能够促进学校、用人单位以及学生三方打破信息壁垒,实现信息透明共享、建立可信任机制、激励合法行为,为各利益相关方提供一个可靠的交互平台,具有广阔的应用前景。