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.

Decision-Making in Driver-Automation Shared Control:A Review and Perspectives

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.

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.

组织认同对城市公交驾驶员安全绩效的影响机制

为探究组织认同对城市公交驾驶员安全绩效的影响机制,基于理论分析,引入工作满意度和工作倦怠为中介变量,构建链式中介理论模型。借鉴成熟量表对633名城市公交驾驶员进行问卷调查,通过中介效应分析验证理论模型。研究结果表明:组织认同不仅可以直接正向影响城市公交驾驶员的安全绩效,还可以通过工作满意度和工作倦怠的独立中介作用,以及工作满意度、工作倦怠的链式中介作用间接影响城市公交驾驶员的安全绩效。研究结果可为城市公交企业提高公交驾驶员安全绩效提供理论支持。

互助、组织化与承认:劳动实践中志愿服务的多维动力机制研究以自雇卡车司机为例

本文以我国自雇卡车司机的志愿服务为例,结合其劳动实践探讨志愿服务的多维动力机制。研究发现,卡车司机志愿服务的动力机制是一个由微观劳动体制、中观组织化过程与宏观社会地位感知推动的多维框架,展现出从职业群体内部的互助救援扩展至外部社会公益的动态社会过程。其中,原子化劳动的“孤岛效应”与互帮互助是该机制的客观基础,公益组织内外的组织化过程与互动为之提供了制度保障和文化支撑,底层地位感知以及由其引发的承认需求则构成了持续的价值驱动。本文认为,只有从多维、动态的过程性视角出发,并使用纵贯性的研究方法,才能理解志愿服务的发端与持续。

勘测机器人SMA驱动器特性研究

为准确获取SMA驱动的勘测机器人驱动器性能,采用大变形多晶形状记忆合金马氏体相变的三维本构模型描述SMA力学关系,嵌入UMAT用户自定义材料子程序,建立了SMA驱动器的有限元分析模型。研究表明,SMA丝直径对驱动器性能影响较大,驱动器输出力随SMA丝直径增大而增大。研究方法可为同类型的SMA力学性能描述提供参考,研究结果可指导SMA驱动器的设计。

公共交通噪声性听力损失研究进展

公共交通噪声是指交通工具运行时产生的妨害人们正常生活和工作的声音。分为机动车噪声、飞机噪声、火车噪声等。一般指机动车辆在城市内交通干线上行驶时产生的噪声。在飞速发展的时代,因公共交通造成的听力损失不容小觑。造成听力损失的公共交通来源广泛,如公交车、地铁等,听力损失的占比也有初步统计,目前探究的可能影响因素有工龄和身体的健康情况等,可能的机制有声波对内耳机械刺激以及氧化应激等。听力损失的防治也主要是通过管理和保护措施来控制噪声源、切断噪声传播途径和加强个人防护等。本文就近年来公共交通驾驶员噪声性听力损失的特点、机制、防治等进行综述。以期对公共交通驾驶员噪声性听力损失的深入研究及减少我国因公共交通造成的噪声性听力损失的发病率有提供参考。

改进的目标检测算法在司机室场景的应用

针对司机室智能分析系统中的智能检测模块进行优化改进,提升检测算法在复杂场景下的准确率。提出一种YOLOv8-DR方法,在YOLOv8的基础上引入坐标注意力机制,增加一个大尺度检测头分支,更好地捕捉玩手机等小目标的细节信息。通过实验对比,该方法提升13.5%的准确率,可实时预警规范司机驾驶行为,保证机车安全运行。

一体化国家战略体系和能力建设赋能东北全面振兴:动能、机制和进路

为了突破长期以来“衰落—振兴—衰落—再振兴”的东北振兴怪圈,需要多视角研究和探讨东北全面振兴思路。把东北振兴战略有机融入一体化国家战略体系和能力建设之中,可为东北全面振兴提供强大的新动能,有助于更好对接国家战略和需求、融入全国统一大市场、陆海内外高水平开放和区域能级的整体跃迁。通过一体化国家战略体系和能力建设赋能东北全面振兴,能为东北全面振兴提供新优势、创造新机遇、注入新动力、发掘新潜能、增添新活力、提升新层次。为了使一体化国家战略体系和能力建设更好赋能东北全面振兴,需要军地军民各相关领域战略规划一体统筹、建设项目一体布局、制度体系一体设计、资源成果一体使用。

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.

基于深度学习的驾驶员分心检测理论研究

文章提出了一种基于改进Yolov8的驾驶员分心检测方法,在Yolov8的基础上设计了一种包含小目标检测层和新型连接结构的加权双向特征金字塔网络(BiFPN)。在骨干网络中,引入了高效多尺度注意力(EMA)机制模块,以加强对关键信息的学习和提取。提出了一种新型的C2f结构,将动态卷积、注意力机制融入到C2f中。改进模型的精度提高了4.6%,同时参数量降低了27.1%,一定程度上减小了模型的规模,提高了计算效率。最后在公开数据集上进行泛化实验,各项评价指标皆优于原模型。因此,文章研究为分心驾驶行为检测领域提供了一种更高效、更精确的解决方案,为道路安全和交通监管提供有力的支持。

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.

Vibrating Drivers for Transportation

The movement of objects by means of vibrations is a widely known idea, used for bodies transportation in automated industry based on vibrating bases, on which the transported elements are placed. We consider an inversed idea: vibration is applied to the movable element. Surface, on which this displacement must be realized, is unmovable. The asymmetry of the friction forces in the different phases of the vibration is the cause of motion in this case. A distinctive feature of the proposed device is a slope of the plane of vibration of the inertial mass, which leads to increasing of the asymmetry of friction. In this paper, we consider an example of application of the device to the lateral vehicle parking. The idea is numerically estimated and tested with a laboratory prototype. The movement along a straight line of the trolley with sloped vibrating mechanism under influence of asymmetric friction forces has been estimated and practically simulated with the laboratory prototype.

Research and implementation of I^2C device driver under embedded Linux system

As an universal bus technology at present time, I2C has been widely used in the interface between CPU and other devices such as EEPROM, RTC and small LCD. The architecture of I2C driver, the relationship of some important data structure and the operation mechanism of the I2C driver have been analyzed. Finally, by taking an EEPROM chip named AT24C08 as a specific example, the flow of how to develop an I2C device driver is illustrated. In addition, the design of a new driver mode of single device with multi-drivers is realized.

管卡板牙式锁紧机构轴向承载力的试验研究

板牙式锁紧机构提供全结构管卡加固管道的结构承载能力。为了解决在管卡设计中其轴向承载性能的有关结构计算问题,本文通过模型试验对4种规格的板牙式锁紧机构进行了轴向承载力测试,分析了内板牙齿挤毁管道表面并刮断累积金属屑层的锁紧机构失效模式,借鉴刨削力计算公式拟合得到了板牙式锁紧机构单齿轴向承载力与咬入深度的经验公式,并分析了齿间距、齿顶角对单齿承载力的影响。试验结果表明对于相同齿顶角的锁紧机构,齿间距越大的单齿承载力越大;对于相同齿间距的锁紧机构,90°齿顶角的单齿承载力比80°齿顶角的大。

基于自然驾驶数据的驾驶人跟驰行为内在异质性预测与建模

为了对交通流建模研究和高级辅助驾驶开发等提供可靠跟驰行为支持,本文针对同一位驾驶人个体内部发生的从正常到异常的跟驰行为模式的转变,提出一种以Transformer深度学习模型为基础的驾驶人跟驰行为内在异质性预测和建模方法。本文基于超过20万km的大规模自然驾驶实验,首先,利用对驾驶人的全要素长期行为观测,为41位驾驶人建立基线模型,完成3194次驾驶人内在异质性事件识别与提取;其次,设计基于Transformer多头自注意力机制的深度学习预测器,实现对驾驶人内在异质性事件的准确预测。结果表明,预测器在跟驰内在异质性三分类时间点预测的F1分数(F1-Score)相较于长短时记忆网络表现更优,达到了87.13%,基于预测结果的动态跟驰参数切换可以降低21.08%的驾驶行为建模误差。研究结果有助于更加准确地重现驾驶人跟驰行为,进一步提高交通流仿真精度和高级辅助驾驶控制策略拟人化水平。

基于3DCNN的驾驶员细微动作识别

针对驾驶员相似的背景下的细微动作的动作识别,提出了一种基于X3D卷积神经网络X3D-M-GC-AE。通过引入轻量级的自注意力网络GCNet,提高对时间和空间关键特征的关注度,不引入参数量的情况下,提高检测精度;设计了一种运动增强模块,使网络对时序上的运动信息更加敏感;引入知识蒸馏,将X3D-XL作为教师网络,X3D-M-GC-AE作为学生网络,可以使用较少的参数量和计算量,达到可以实车应用的程度。实验结果表明教师网络测试精度最高可以达到75.56%,学生网络最高可以达到71.13%,该框架在车载硬件设备要求较低的情况下能够实现较高精度的检测效果。

履带车辆液压机械差速转向控制策略研究

针对液压机械差速的履带车辆转向控制,在车辆动力学建模和驾驶员操控信号解析的基础上,提出一种基于驾驶员模型的模糊前馈-反馈控制策略。该控制策略将驾驶员模型输入的归一化方向盘转角及其变化率作为模糊前馈控制输入,对液压系统排量比进行补偿;将实际转向半径与目标转向半径的偏差及其变化率作为模糊反馈控制输入,对液压系统排量比进行修正,从而达到对两侧履带速度的补偿修正。仿真结果表明,与传统PID控制和模糊PID控制相比,模糊前馈-反馈控制能缩短转向动态响应时间,更好地跟踪驾驶员转向意图,且在转向阻力扰动下转向半径的波动明显减小,提高了转向轨迹的稳定性。

压电微纳定位平台自适应陷波滤波在线谐振抑制

压电驱动柔性微纳米定位平台通常呈现低阻尼谐振模态,高速运动时易激发机械谐振,从而严重影响控制系统稳定性、控制带宽和轨迹跟踪精度。为消除当前谐振控制器对平台动力学建模精度的依赖性,该文设计了一种自适应陷波滤波器来实现压电微纳定位平台的在线实时抑制谐振。首先,搭建了压电驱动柔性微纳定位平台系统并建立其机电耦合动力学模型;其次,利用快速傅里叶变换方法在线分析了闭环系统误差信号,设计平台频率特性提取算法,实现了对陷波滤波器参数的在线自适应整定;最后,利用所设计的自适应陷波滤波器对阶跃信号和三角波信号进行轨迹跟踪实验。实验结果表明,自适应陷波滤波器可以很好地实现谐振的在线抑制,能够有效提升平台的稳定性和轨迹跟踪精度。

基于改进YOLOV7与StrongSORT算法的列车司机手比行为检测

列车司机驾驶行为的规范性直接影响到列车行车安全与状态,但当前对列车司机手比行为的检测仍存在不足。为了对列车司机手比行为进行有效检测,本文利用动车组模拟驾驶系统所采集的司机乘务作业影像,结合采用融合注意力机制的You Only Look Once Version 7 (YOLOV7)神经网络模型与Strong Simple Online and Realtime Tracking (StrongSORT)算法,对动车组司机驾驶过程中的5种手比行为进行检测。实验结果表明:本文算法可以有效提升对列车司机乘务作业时不同类型手比行为的检测效果,其中检测精确率平均提升1.2%,检测召回率平均提升1.9%。本文提出的算法将有助于改进铁路院校、机务部门对列车司机日常训练、工作考评的效果,提高列车行车过程中的安全性。