Relationships between Features of EmergingAdulthood, Situated Decisions toward PhysicalActivity, and Physical Activity among CollegeStudents: The Moderating Role of ExerciseIntensity Tolerance

A significant portion of emerging adults do not achieve recommended levels of physical activity (PA). Previous studies observedassociations between features of emerging adulthood and PA levels, while the potential psychological mechanisms that mightexplain this phenomenon are not fully understood. In this context, there is some evidence that situated decisions towardphysical activity (SDPA) and exercise-intensity tolerance might influence PA level. To provide empirical support for thisassumption, the current study investigated whether (i) features of emerging adulthood are linked to SDPA, which, in turn,might affect PA engagement;(ii) exercise-intensity tolerance moderate the relationship between SDPA and PA level;and (iii)SDPA is a mediator of the relationship between features of emerging adulthood and PA levels under the prerequisite thatexercise-intensity tolerance moderates the link between SDPA and PA engagement. In this study a group of 1,706 Chinesecollege students was recruited and asked to complete a set of questionnaires assessing their SDPA, PA levels, exercise-intensitytolerance, and features associated with emerging adulthood, namely Self-exploration, Instability, and Possibility. Our resultsindicated that SDPA positively predicted PA levels and this relationship became stronger when exercise-intensity tolerance wasused as a moderator. Furthermore, it was observed that individuals with a higher level of Instability and a lower level ofPossibility during emerging adulthood exhibited a lower level of SDPA. Taken together, the results of our study providefurther insights on a potential psychological mechanism linking features of emerging adulthood and physical activity.

Test Equipment Selection and Deployment of Materiel System Based on Grey Situation Decision

The situation is constructed when the tests are considered as events and the spare test equipments are considered as strategies. The model of grey situation decision for the test equipment selection and deployment （TESD） is founded. Through analyzing each decision objectives, their relative weights are calculated via analytic hierarchy process （AHP）. The grey situation effect matrix under each objective is computed so that the comprehensive effect measure matrix can be obtained when all of the objectives are traded off by their weights. Finally, the decision-maker can select the optimal situations by the value of the measures and such situations form the overall concept for TESD. The paper combines the AHP with grey situation decision to select and deploy the test equipments optimally. Our experimental results show that the proposed method is effective and efficient.

Role-based Bayesian decision framework for autonomous unmanned systems

In the process of performing a task,autonomous unmanned systems face the problem of scene changing,which requires the ability of real-time decision-making under dynamically changing scenes.Therefore,taking the unmanned system coordinative region control operation as an example,this paper combines knowledge representation with probabilistic decisionmaking and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences.Firstly,according to utility value decision theory,the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned.Then,multi-entity Bayesian network is introduced for situation assessment,by which scenes and their uncertainty related to the operation are semantically described,so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty.Finally,the effectiveness of the proposed method is verified in a virtual task scenario.This research has important reference value for realizing scene cognition,improving cooperative decision-making ability under dynamic scenes,and achieving swarm level autonomy of unmanned systems.

Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization

To reach a higher level of autonomy for unmanned combat aerial vehicle(UCAV) in air combat games, this paper builds an autonomous maneuver decision system. In this system,the air combat game is regarded as a Markov process, so that the air combat situation can be effectively calculated via Bayesian inference theory. According to the situation assessment result,adaptively adjusts the weights of maneuver decision factors, which makes the objective function more reasonable and ensures the superiority situation for UCAV. As the air combat game is characterized by highly dynamic and a significant amount of uncertainty,to enhance the robustness and effectiveness of maneuver decision results, fuzzy logic is used to build the functions of four maneuver decision factors. Accuracy prediction of opponent aircraft is also essential to ensure making a good decision; therefore, a prediction model of opponent aircraft is designed based on the elementary maneuver method. Finally, the moving horizon optimization strategy is used to effectively model the whole air combat maneuver decision process. Various simulations are performed on typical scenario test and close-in dogfight, the results sufficiently demonstrate the superiority of the designed maneuver decision method.

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.

Optimal Distribution of N-Team Interacting Decision Makers with Hierarchical Command Inputs That Are Predicated on Order Statistics

The spectral analysis of simulated N-team of interacting decision makers with bounded rationality constraints of Oladejo, which assumes triangular probability density function of command inputs is hereby restructured and analysed, to have hierarchical command inputs that are predicated on order statistics distributions. The results give optimal distributions.

脑卒中患者复发风险感知与行为决策情境理论模型的初步构建

目的构建脑卒中患者复发风险感知与行为决策情境理论模型,为脑卒中行为管理提供新的指导框架。方法遵循护理学科理论模型发展过程,综合采用文献研究、质性研究及理论分析,初步构建脑卒中复发风险感知与行为决策情境理论模型,包括概念、命题(概念间假设关系)及模型边界(适用范围)。结果脑卒中复发风险感知与行为决策模型包含复发风险感知、行为决策、自我效能、社会支持、情感反应(担忧)和健康行为等6个核心变量,一条核心路径为“复发风险感知→行为决策→健康行为”,三条桥梁路径为“复发风险感知社会支持→行为决策→健康行为”“复发风险感知→自我效能→行为决策→健康行为”和“复发风险感知复发担忧→行为决策→健康行为”。结论脑卒中复发风险感知与行为决策模型或可丰富脑卒中健康行为管理相关理论研究,进而为开展脑卒中患者健康管理及复发防控实践工作提供新的视角。

面向学科建设的高校图书馆决策情报服务分析

随着时代的发展,新时期高校图书馆有着新的发展目标,需要在“双一流”的背景下,做好针对学科建设的决策情报服务工作,有效提高学校中的学科管理质量,使高校具有较高的竞争力。不过从参考的部分高校图书馆的决策情报服务工作看,多方因素干扰下,决策情报服务容易受到影响,如何有效落实决策情报服务工作,也成为当前高校图书馆运行中需要考虑的问题。为此,分析决策情报服务的价值、现状,最后提出对应的处理策略,以期满足高校的发展需要。

基于DEMATEL-AISM的起重驾驶员态势感知与行为响应模型

为深入挖掘起重驾驶员态势感知信息-行为的因果关系,结合ENDSLEY态势感知模型与决策实验室分析(DEMATEL)-对抗解释结构模型(AISM)方法,构建起重驾驶员态势感知与行为响应模型。首先,以起重作业任务为研究对象,使用态势感知理论分析驾驶员行为响应过程,获取信息要素;其次,基于DEMATEL法,量化研究要素间的相互关系,得到综合影响矩阵,进而分析要素的属性及特征值,确定模型的关键要素;最后,采用AISM获得原因-结果属性稳定的层级结构,据此构建起重驾驶员态势感知与行为响应信息模型。结果表明:得到包含轨迹预测与规划、避碰等关键要素的22个要素及影响关系构成的5层信息模型;而且,模型明确了信息要素属性特征、影响关系及影响程度,较好地解释了驾驶员态势感知及行为响应过程。

面向大型数据集的高效决策树参数剪枝算法

决策树在数据分类上具有较好的效果,但容易产生过拟合的现象,解决方案是对决策树进行剪枝处理,然而传统剪枝算法普遍存在预剪枝容易欠拟合、后剪枝时间消耗多、网络搜索剪枝仅适用于小型数据集等问题。为了解决以上问题,提出一种高效的决策树参数剪枝算法。根据网络安全态势感知模型,建立剪枝决策树态势感知系统架构,分析网络数据流。在生成决策树的过程中,利用枚举与二分搜索算法找出决策树最大深度,采用深度优先搜索算法找到节点最小分裂数和最大特征数,最终结合这3个最优参数自上而下完成剪枝。实验结果表明,所提算法在大型数据集上的过拟合风险较小,训练集与测试集准确率都在95%以上,同时相比于后剪枝算法中表现较好的悲观错误剪枝算法快了近20倍。

超大型城市虚拟电厂的数字孪生框架设计及实践

超大型城市能源系统汇集了多种分布式能源资源(distributed energy resources,DER),包括具备不确定性的新能源发电单元(如光伏、风机),具备可调性的柔性用能单元(如空调、蓄冷)以及兼具充放功能的能源产消者(如电动汽车、储能),虚拟电厂(virtual power plant,VPP)已成为重塑上述DER生态关系并对其进行协同管控的有效途径。该文梳理了当前城市级VPP建设的目标与现状,明确了超大型城市VPP工程所面临的挑战,即对于由海量DER所诱发的多主体多目标高不确定性场景缺乏系统性的建模、仿真、推演、分析、决策与校核手段。为应对上述挑战,该文结合城市电网数字化整体架构与建设进程,提出了超大型城市VPP数字孪生技术框架,继而得以对城市能源系统的态势轨迹进行系统性地推演与分析;进一步,聚焦智能决策目标,探究了框架下的技术路径、关键技术、理论工具等。该框架已经助力临港新片区VPP工程示范落地,为城市级VPP建设提供参考。最后,展望了超大型城市下数字孪生VPP的研究方向与应用前景。

面向复杂决策的OODA环:智能赋能与认知增强

传统OODA环理论存在认知粒度低、单向循环、单一入口等缺点,无法满足未来以智能化、网络化、体系化为特征的多域联合作战需求,尚不具备在以跨域协同、体系聚优等为代表的复杂决策环境中应用的能力。通过梳理分析OODA环理论发展历程及演进路线,确定了面向复杂决策的OODA环理论的重点是突出认知环节与决策环节在整个OODA环中的作用;通过分析智能技术的赋能方式,构建了智能态势认知与智能复杂决策框架,并将这两个框架嵌入认知粒度提高的OODA环,形成面向复杂决策的智能CT-OODA环理论;基于Cynefin理论说明了复杂决策问题的环境分类方法,阐述了在不同复杂程度决策环境下智能CT-OODA环的运行方式,以及如何通过OODA的决策循环实现决策环境改变的动力学;提出了面向复杂决策的智能OODA及其具体结构和运行方式,并且提出了分类复杂决策环境的方法,为未来人机融合态势认知与复杂决策提供参考。

森林景观恢复土地适宜性评价

为探究县域森林景观恢复机制,确定优先恢复区域与适宜恢复措施,以秭归县为研究对象,采用物元模型与多目标灰色局势决策模型,分别从景观和立地尺度开展森林景观恢复土地适宜性评价研究。在景观尺度,构建评价指标体系,确定森林景观恢复的优先恢复地块,制定重点恢复策略;在立地尺度,构建以提升区域水源涵养功能、生物量、生产力为核心的恢复措施,以实现生态系统服务在一定时空范围内达到最优配置。结果表明,秭归县适宜森林景观恢复地块总面积18674.06 hm^(2)。在3643.74 hm^(2)适宜新造林地块中,41.74%适宜恢复为针叶林,29.93%适宜恢复为针阔混交林,24.53%适宜恢复为阔叶林,3.80%适宜恢复为灌木林。15030.32 hm^(2)地块适宜提升森林质量,根据其林地类型,提出补植补造、促进林下植被生长、被动恢复、森林经营管理、单一树种改造、栽植混交林、封山育林等具体干预措施及其组合。物元模型与多目标灰色局势决策模型定量评估,结合研究区域土地利用现状、森林资源普查数据等基础数据,可有效确定优先恢复地块和恢复策略,为县域森林景观恢复提供定量评估方法。

基于改进随机森林的工业互联网安全态势评估方法

针对工业互联网安全态势评估存在数据特征提取困难和安全态势评估准确率低等难题,提出一种基于改进随机森林的工业互联网安全态势评估方法.基于随机采样技术平衡原始数据集以减小不平衡数据集对实验的影响;利用梯度提升决策树确定工业互联网流量数据中不同特征的权重系数,结合递归特征消除法提取其关键特征;构建基于改进随机森林的工业互联网多分类攻击检测模型,识别网络受到的攻击类别,并结合安全态势量化指标确定其风险程度.实验结果表明,本文算法的检测准确率和F1值分别达到89.19%和89.68%,相较于传统随机森林算法、支持向量机和K最近邻算法,其准确率和F1值分别至少提高2.91%和1.7%,平均分别提高8.38%和9.33%.

混合式教学法在“医院感染控制”教学中的应用

探讨基于临床情景的翻转课堂在“医院感染控制”教学中的实践与效果。将2018级2个教学班的护理本科生做研究对象。教学班1(n=102)为试验组,采取基于临床情景的翻转课堂教学法;教学班2(n=97)为对照组,采取传统课堂授课的形式。对两组学生的临床决策能力、自主学习能力在教学前后进行比较。在教学后,试验组学生的临床决策能力、自主学习能力各维度评分及总分均明显高于对照组(p<0.05)。基于临床情景的翻转课堂教学方法能有效提高学生的临床决策能力与自主学习能力,增强学生的学习兴趣,适合用于本科学生的教学。

足球运动员个体与共享情境意识的特点及其关系

系统测量不同水平足球运动员个体情境意识(situation awareness,SA)的三层级(感知、理解、预测),并结合访谈法测评高水平足球队运动员之间及其与教练员之间的共享SA,考查足球运动中个体SA、共享SA和决策SA的特点及关系。发现:(1)高水平球员个体SA各层级和决策的水平均高于低水平球员,但高水平球员SA各层级之间及其与决策的表现均无显著相关;低水平球员个体SA的感知、理解、预测各层级的表现存在正相关,且与决策表现相关。(2)高水平球员之间及球员与教练员之间存在共享SA,且与个体SA的预测层级相关。认为:SA的各成分均能区分足球运动员的水平,高水平球员的SA可能更具有非线性以及个人特点,且个体对情境的预测有助于团体形成共享SA。

意图识别与态势感知驱动的路由策略系统研究

航空通信网络服务按需拓展、网络环境动态多变、网络对象异构多样与网络干扰强度和样式急剧增加,要求通信网络系统具备自动性和智能性。本文构建意图识别和态势感知联合驱动的路由策略系统,为指挥人员提供路由策略方案,提升决策的时效性和准确性,提高系统在复杂环境下的适应性。所述系统包含网络态势感知、用户意图识别和路由辅助决策等模块。网络态势感知模块通过分析航空通信网络拓扑结构,利用收发数据包,获取网络中链路时延、带宽、丢包率等网络服务质量指标;意图识别模块依据海量的航空通信数据,利用机器学习算法,实时对已经接入网络的航空单位依据其通信数据特征进行识别分类,给出其在通信网络中需要的服务质量指标权重;辅助决策模块以提供最方便快捷的航空通信网络服务为目的,利用前两个模块所提供的实时数据,使用强化学习算法为端到端的网络通信提供最佳路由,保障信息高效无失真地传输。

基于随机决策准则的空战算法比较研究

无人机正在逐步成为未来空中战场的主要作战力量,研究空战机动决策算法,建立准确高效且能用于军用无人机空战的辅助机动决策系统对现代空战具有重要的现实意义。研究了基于9种随机决策准则的自动空战算法性能,比较各决策准则在空战中中的决策倾向,通过设计仿真实验,给定不同的空战初始条件,分析9种决策准则的优劣。实验结果表明,基于随机决策准则的自动空战算法在多种情况下均表现出优异的性能,具有一定的潜力。

面向终端决策需求的化工储罐火灾态势感知模型构建

为解决重特大化工火灾应急救援现场信息融合和终端决策群体态势感知难题,基于储罐火灾态势演化特征和多层网络理论,提出大型储罐区火灾态势感知模型。通过构建化工储罐火灾态势信息集合,提出基于贝叶斯参数估计的网络边权值计算方法;以节点重要度和层间关联系数分别度量网络层内部节点的连接强度和网络层之间的关联强度,进而识别储罐自身、储罐与储罐之间关键态势的演化趋势;通过对某储罐区火灾应急救援态势演化过程进行分析。结果表明:基于多层网络的态势感知模型能够更加客观地反映大型储罐区火灾态势演化过程;节点的重要度越大,节点所对应的储罐内部态势演化速度越快;层间关联强度越强,相邻储罐之间的态势演化趋势越显著。

非完全信息下无人机集群对抗研究综述

无人机集群以其具备的应用优势及发展前景,成为当前人工智能领域研究者关注的热点之一。而非完全信息下的无人机集群对抗技术,因其集群结构变化的高动态性以及环境信息复杂多变且不能完全感知的特点,成为对集群协同性与智能性要求最高的研究方向之一。其研究成果可以促进智能化无人系统的快速发展和广泛应用。该文全面回顾了非完全信息环境下无人机集群对抗研究的最新进展,按照包以德循环理论的思路将无人机集群对抗过程划分为态势评估、意图推断、任务规划与机动决策4个相互衔接的关键组成部分,并进一步将其细分为8个子研究目标。通过分析比较近年来的相关研究,着重阐述了无人机集群对抗领域各项任务的研究重点和难点以及已取得的成果,并讨论了无人机集群对抗技术所面临的挑战,包括大规模异构集群的协同控制、非完全信息的处理、复杂决策过程的建模以及实际应用任务的应对等。