Patient Participation in Communication about Treatment Decision-Making for Localized Prostate Cancer during Consultation Visits

Objectives: To describe the communication behaviors of patients and physicians and patient par-ticipation in communication about treatment decision-making during consultation visits for local-ized prostate cancer (LPCa). Methods: This is a secondary analysis of data from 52 men enrolled in the usual care control group of a randomized trial that focused on decision-making for newly diagnosed men with LPCa. We analyzed the patient-physician communication using the transcribed audio-recordings of real-time treatment consultations and a researcher-developed coding tool, including codes for communication behaviors (information giving, seeking, and clarifying/ verifying) and contents of clinical consultations (health histories, survival/mortality, treatment options, treatment impact, and treatment preferences). After qualitative content analysis, we categorized patient participation in communication about treatment-related clinical content, including “none” (content not discussed);“low” (patient listening only);“moderate” (patient providing information or asking questions);and “high” (patient providing information and asking questions). Results: Physicians mainly provided information during treatment decision consultations and patients frequently were not active participants in communication. The participation of patients with low and moderate cancer risk typically was: 1) “moderate and high” in discussing health histories;2) “low” in discussing survival/mortality;3) “low and moderate” in discussing treatment options;4) “none and low” in discussing treatment impacts;and 5) “low” in discussing treatment preferences. Conclusions: Findings suggest opportunities for increasing patient participation in communication about treatment decision-making for LPCa during clinical consultations.

Condition-Based Maintenance Decision-Making for Equipment

At present, condition monitoring and fault diagnosis technology and their application in engineering have been widely studied. Relatively little attention has been paid to condition-based maintenance decision-making for equipment. In this paper,based on the decision-making policy in traditional condition-based maintenance,the connotation of condition-based maintenance for equipment was defined, and its characteristics were analyzed.Working contents of condition-based maintenance for equipment were provided,which were divided into three stages. The influence factors in condition-based maintenance for equipment were analyzed. The key links of equipment maintenance contents and decision-making process were proposed. The condition-based maintenance decision-making policy presented in this paper can provide a practical reference for equipment maintenance.

RF-CLASS： A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

我国公路桥梁养护管理系统发展及展望

为促进我国公路桥梁管养智慧化转型,提高管养效率,综述了我国公路桥梁养护管理系统的研究历程与发展趋势。围绕桥梁养护管理系统发展的3个阶段:无纸信息化的桥梁数据库信息管理阶段,改善状态评估与决策方法的桥梁技术状态评估及维护决策阶段,融合可视化、巡检终端及健康监测系统等手段的桥梁高效化管理阶段,总结各阶段系统功能模块与特点、主要解决的问题及存在的弊端;结合当前桥梁智慧管养方面的研究热点,探讨与展望了以减少人为干预、实现经济高效养护为目标的公路桥梁智慧养护管理系统发展方向。结果表明:当代公路桥梁养护管理系统已基本满足桥梁管养部门的精细化管理需求,但仍存在智慧化程度不足、资源配置合理化欠缺等问题,而融合了智慧感知、智慧判别与决策、多源信息融合手段的桥梁智慧管养系统,将通过多种智能技术感知桥梁多源信息,采用大数据分析与挖掘方法评估桥梁状态,指导管理者实现经济高效的管养决策,达到公路桥梁智慧化养护目的。

社区慢性病共病药物治疗决策的困境与应对路径

慢性病共病是指个体同时患有2种及以上的慢性疾病。共病患者常联用多种药物治疗,给社区全科医生的科学用药决策带来了挑战。本文分析了社区共病药物治疗决策困境形成的原因与影响因素,并提出了一种动态的综合性决策框架,即共病药物治疗决策框架(MDMF)。该框架由社区全科医生接诊共病患者的5个阶段构成,包括“健康问题回顾”“用药综合评估”“医患共同决策”“药物治疗记录”和“安排随访计划”。MDMF能够促使社区全科医生为共病患者制定个体化的药物治疗方案,但也对其诊疗能力提出了一定要求。因此,建议对社区全科医生开展以MDMF为核心的培训、提供决策辅助支持,并采取合理的激励与监督措施,推动社区医疗机构提供以患者为中心的药物治疗方案,提升药物治疗的安全性和有效性,减轻患者的治疗负担。

共病药物治疗决策--全科医生接诊病案研究

慢性病共病患者常联合使用多种药物,疾病与药物之间潜在的复杂相互作用使全科医生面临着难以合理评估共病药物治疗获益与风险的决策困境。本文以1例在社区卫生服务中心就诊的慢性病共病患者为例,展示和阐述了全科医生运用共病药物治疗决策框架进行科学决策的过程,并基于阿里阿德涅原则为共病管理过程提出的多阶段目标,分析了全科医生在评估、沟通与制定治疗方案等方面的要点和注意事项。希望本文可以为改善社区慢性病共病药物治疗决策的质量提供借鉴。

汽车乘员舱个性化热舒适智能空调决策系统设计

为了进一步提高汽车乘员舱空调系统的智能化和舒适性水平,本文提出了一种基于热舒适理论的个性化智能空调决策系统设计方案。首先,针对汽车乘员舱改进了基于PMV(predicted mean vote)和PPD(predicted percentage of dissatisfaction)理论的热舒适性计算方法;进一步,利用人体画像技术实现了乘员舱驾乘人员的热舒适性特征提取,并在专家经验知识的基础上构建了具有理论计算依据的乘员舱热舒适数据集;然后,利用机器学习算法搭建了个性化热舒适空调系统随机森林决策模型,以此满足个性化热舒适智能决策需求;最后,给出了完整的系统框架和设计。测试结果显示所提出的系统模型决策准确率在90%以上,实车测试结果表明:本文系统能够识别驾乘人员特征,实时进行个性化热舒适性参数推荐,验证了本研究决策方法的有效性和实用价值。

基于模糊群决策的天津特色食品包装SPA–TOPSIS评价

目的针对地方特色食品包装设计需兼具功能性和文化性的特征,开展包装设计方案评价,有效辅助设计开发过程,丰富地方特色食品包装设计的思路。方法以天津特色食品——麻花包装为研究对象,构建分层次的包装评价准则框架;采用基于梯形模糊数的专家评价法来获得备选方案在各准则上的评分,通过群决策来集结多位专家的评判意见,进而求解得到备选方案的准则值;引入集对分析理论(SPA),采用连接向量距离来替换经典TOPSIS中的欧氏距离,提出SPA-TOPSIS评价方法来实现备选方案的评价和排序。结果针对麻花的6种包装设计方案,实施了基于模糊群决策的SPA-TOPSIS评价,6种包装设计方案与正理想解的贴近度依次为0.55、0.46、0.53、0.49、0.56、0.44,方案5为最优方案。结论通过天津特色食品包装评价,完善了城市文化内涵包装设计的理念,为包装的文化性设计提供了重要支持。

急危重症传染病护理决策能力评价量表的编制及信效度检验

目的编制急危重症传染病护理决策能力评价量表,并进行信效度检验。方法以Tanner临床判断模式为框架,通过文献分析、半结构访谈、德尔菲法和预调查形成量表终稿。于2023年3月-4月选取陕西省5家医院参与过急危重症传染病救治的434名护士进行调查,运用SPSS 26.0和AMOS 26.0软件对调查数据进行项目分析、效度分析和信度分析。结果急危重症传染病护理决策能力评价量表包括4个维度共27个条目。探索性因子分析共提取4个公因子,累计方差贡献率为72.537%;验证性因子分析结果显示,模型拟合度良好。量表水平内容效度指数为0.926,条目水平内容效度指数为0.810~1.000;总量表Cronbach’sα系数为0.958,各维度Cronbach’sα系数在0.856~0.945之间;量表总折半信度为0.842,各维度折半信度在0.767~0.902之间。结论急危重症传染病护理决策能力评价量表信效度良好,可用于评估护士急危重症传染病护理决策能力。

地方普通高校本科专业设置决策机制探讨

地方普通高校本科专业设置决策机制是地方高校对新专业设置问题做出决断时,决策要素间的相互关系及其运行方式。其运行逻辑是:为了实现决策目标,决策主体从制定和完善备选专业设置方案,到运用决策标准和方法对这些方案进行评价和排序,并在群体共识的基础上选定新专业的过程。在专业设置决策机制运行过程中,由于对决策目标的认识出现偏差,以及决策主体缺位、决策备选方案内容阙如、决策标准和方法科学性不足等问题,专业设置决策机制未能充分发挥其应有的效用。完善这一决策机制,需要进一步正确认识决策目标及其与其他决策要素之间的关系,优化决策主体的参与方式和权力分配,制定全面且具有可操作性的备选专业设置方案,并提升决策标准与方法的科学性。

基于地方旅游大数据的智慧旅游辅助决策服务平台设计

地方旅游大数据建设日趋完善和人工智能技术的不断发展为构建地方智慧旅游辅助决策平台提供了必要条件。文章提出基于地方旅游大数据的智慧旅游辅助决策服务平台的设计方案。一方面,对潜在游客的旅游出行规划提供智能参考,可增强游客的个性化体验。另一方面,为景区、涉旅企业及旅游管理部门提供辅助决策支持。该智慧旅游决策平台的设计方案和技术实现方案可用于指导智慧旅游平台建设,促进地方旅游智能化转型升级,推动地方全域旅游高质量发展。

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.

Novel Biological Based Method for Robot Navigation and Localization

The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.

Reactive Search Optimization;Application to Multiobjective Optimization Problems

During the last few years we have witnessed impressive developments in the area of stochastic local search techniques for intelligent optimization and Reactive Search Optimization. In order to handle the complexity, in the framework of stochastic local search optimization, learning and optimization has been deeply interconnected through interaction with the decision maker via the visualization approach of the online graphs. Consequently a number of complex optimization problems, in particular multiobjective optimization problems, arising in widely different contexts have been effectively treated within the general framework of RSO. In solving real-life multiobjective optimization problems often most emphasis are spent on finding the complete Pareto-optimal set and less on decision-making. However the com-plete task of multiobjective optimization is considered as a combined task of optimization and decision-making. In this paper, we suggest an interactive procedure which will involve the decision-maker in the optimization process helping to choose a single solution at the end. Our proposed method works on the basis of Reactive Search Optimization (RSO) algorithms and available software architecture packages. The procedure is further compared with the excising novel method of Interactive Multiobjective Optimization and Decision-Making, using Evolutionary method (I-MODE). In order to evaluate the effectiveness of both methods the well-known study case of welded beam design problem is reconsidered.

Public Participation Applied to the Environmental Planning

This paper focuses on the public participation in environmental planning. After the decade for inaccessible information related to the decision taken, actually, the program of public participation is the reference of all the decision making process. However, there are some factors that limit this process, such as poverty, illiteracy, ignorance and often the social inequality. Therefore, this study focuses first on the benefits of public participation in environmental planning, then the involvement of the local population, and finally the decision making access using a case study of Madagascar.

Necessary Conditions and Situational Variables of Payments for Ecosystem Services

Clarifying the necessary conditions for the emergence of payments for ecosystem services (PES) and the situational variables that affect PES is the basis for their interpretation, prediction, and selection. This article proposes an analytical framework for the emergence of PES and argues that the key to determining whether PES can occur and whether a selected PES program is appropriate is to evaluate the net gain. When payers anticipate that a PES program will provide a satisfactory number of ES and a net gain over the opportunity cost and will cover all costs, it is assumed that the program will be implemented. When it is difficult to accurately evaluate the net gain of PES, the situational variables that affect the costs and benefits need to be examined. The group characteristics, ES characteristics, spatial and temporal contacts between the suppliers and demanders, correlation with private goods and additionality are important situational variables that affect the emergence and choice of PES.

弱通信条件下计及暂态频率约束的小水电微电网紧急控制策略研究

小水电作为可再生清洁能源在生态环保、配网建设等方面的重要作用日益凸显,上级电网出现故障失电时小水电可独立自组网运行,提高供电可靠性。文章针对上级电网失电小水电独立组网切换的紧急控制策略展开研究,提出考虑暂态频率约束的“集中决策+就地控制”紧急控制策略,在不同源荷工况下,实现故障瞬间的微电网由并网运行向孤岛运行平滑过渡;集中决策阶段预先决策小水电与负荷开关状态并定期下发开关指令,就地控制阶段实时给出孤岛判别信号并综合集中控制器开关指令进行就地动作,保证弱通信条件下负荷连续供电、频率满足系统运行标准。同时,对频率进行数值积分求解并将其嵌入控制结果搜索过程,有效解决集中决策过程中的频率解析求解困难问题。最后,基于RTDS仿真平台搭建韶关某山区电网的电网模型并验证小水电微电网紧急控制策略的可行性与有效性。

考虑决策者心理行为的直觉模糊三支决策方法

针对传统的二支决策只能得到方案排序的不足,同时考虑到决策者具有参照依赖和损失规避行为,提出了考虑决策者心理行为的三支多属性决策方法。对于大多数三支决策模型中的代价损失函数以精确值形式给出,无法有效表达模糊信息,通过采用直觉模糊决策信息来处理这一问题,同时采用TODIM方法计算各方案的综合感知优势度,依据分类规则获得决策结果。以工业机器人的项目投资开发为例,验证所提方法的有效性与合理性。

考虑用户充电决策行为的电动汽车充电引导策略

快充站(fast charging station,FCS)是电动汽车(electric vehicle,EV)的重要能源供给设施。随着EV的推广应用,快充负荷也逐渐攀升,对配电网运行产生了一定影响。然而,快充负荷作为一种需求侧响应资源,可通过有序充电控制缓解EV接入给配电网运行带来的负面影响,因此文中提出考虑用户充电决策行为的EV充电引导策略。首先,考虑动态交通路况影响,利用出行链理论构建EV移动模型,进行用户出行模拟,并刻画剩余电量的时空分布。其次,考虑剩余电量、充电设施分布与充电服务价格,利用后悔理论构建用户充电决策模型,并刻画充电负荷时空分布。然后,以配电网网损最小为目标,构建充电服务价格优化模型,通过优化公共FCS的充电服务价格,引导充电负荷时空分布。最后,对不同服务价格方案进行对比,结果表明,文中方法对小容量车型的引导效果更好,且用户时间消耗等效折算系数越大,文中方法对充电负荷引导的效果越好。