珠三角部分企业职工体育弱化趋势亟待扭转

职工是企业的灵魂,是企业在竞争中取胜的重要因素之一.通过对珠江三角洲地区部分企业职工体育活动情况的调查,提出在新的经济形势下,职工体育发展的新思路.     

第三方物流企业约定作业能力成本控制研究

随着第三方物流企业应用高科技的速度不断加快以及模仿能力的普遍提高,对成本进行有效控制已成为竞争的焦点。由于第三方物流企业已经具备了应用作业成本法的条件．在传统成本法对成本控制呈现边际效用递减的情况下,其对成本控制的重点应逐渐转移到对作业能力的控制与管理上。探讨了作业成本法在第三方物流企业作业能力成本控制中的应用,研究企业如何根据未来物流服务的需求,确定未来期间最优的约定作业能力水平．从而为企业的经营管理决策提供科学的依据。     

浙江省生态交通五项行动保障政策评价

建设生态交通对推进浙江交通运输行业转型升级和科学发展具有重要意义,分析了浙江省生态交通五项行动政策保障体系构建的原则,从资源集约利用的角度出发,通过目标分解法提取一系列生态交通评价指标,提出以最小的投入,最大限度地提高浙江交通运输业生态化水平。     

城郊结合部社区文化活动现状及对策研究——以武汉经济技术开发区滨湖社区为个案

社区文化活动作为社区建设的重要内容受到党和政府的高度重视。在加快推进城镇化的过程中,城郊结合部社区文化活动既有独特性,又有差异性。对城乡结合部社区文化活动的嬗变及其现状进行调查,探寻外部环境条件的改变对社区文化活动的影响,挖掘提升居民精神文化生活的有效渠道,对造就和培养具有现代公民意识的城市居民具有重要的现实意义。     

AlOOH与MgO固相反应合成MAS粉体的热力学分析

根据热力学计算得到镁铝尖晶石固相合成的临界温度为800 K,说明在较低温度下固相反应合成镁铝尖晶石是可行的.DTA曲线上大约在750℃处稍微出现一点放热凸起,以及XRD图谱出现几个尖晶石的馒头峰,都说明此时已经生成少量尖晶石相.在固相反应中,A lOOH分解为高活性的A l2O3,是降低合成温度的关键.将高纯A lOOH与MgO球磨混合30 h后,于1 400℃下焙烧保温3 h,得到纯相MAS粉体.     

炮兵某型通信系统抗太阳活动干扰对策分析

文章分析了昼夜变化、季节变化、太阳黑子和耀斑等太阳活动对天波通信的干扰,并提出路相应对策。     

基于信道状态信息的船舶驾驶台人员检测及活跃度评价方法

船舶驾驶台人员包括按照规定要求的常规值班人员和特殊情况下额外的瞭望人员或船长、引航员等,驾驶台人员活跃度是判断其工作状态的重要指标之一。传统的基于计算机视觉的人员检测方法在面对船舶驾驶台遮挡物多、夜间或恶劣天气下光线不足等问题时,精度明显降低。为解决该问题,提出了1种基于普通商用Wi-Fi设备的活跃度感知方法。由于船体材质、结构特点以及变化的运动状态导致动态多径多、信号噪声强,对Wi-Fi设备造成干扰,为此设计了值班高关联度数据（duty high correlation data,DHCD）选择模块及基于信道状态信息（channel state information,CSI）的多层级特征提取模块。DHCD选择模块分析驾驶台人员不同航行、值班情况下的CSI特点,对比0~5人在驾驶台内值班、工作时的信道变化,利用模糊C-means聚类算法提取CSI中对值班人员行为反应最灵敏的信道,去除对信号噪声反应敏感的信道信息;通过多层级特征提取模块计算去噪后CSI数据的幅值与相位离散度、多链路融合离散度、变异指数等多层特征,作为活跃度评价基础参数。依据驾驶台值班要求设计了驾驶台人员活跃度评价模块,采用支持向量机算法判断驾驶台人员数量,采用客观赋权法得到基础参数权重,结合人数信息与权重信息评价驾驶台人员活跃度。实验结果表明：使用DHCD选择模块和多层级模块处理后的多层级特征将驾驶台人员数量检测精度提升至89.6%,对比直接使用原始数据时检测精度提升7.1%。在夜间、雨雾天气等光照不足情况下,基于计算机视觉方法的检测精度会由光线充足时的96.2%降至60.3%,而该方法监测精度不会降低。因此,基于CSI的驾驶台人员活跃度检测方法丰富了驾驶台人员检测算法,能有效识别船舶驾驶台人员是否符合安全值班的基本要求。     

An analysis of the social context of children’s weekend discretionary activity participation

This paper examines the discretionary time-use of children, including the social context of children’s participations. Specifically, the paper examines participation and time investment in in-home leisure as well as five different types of out-of-home discretionary activities: (1) shopping, (2) social, (3) meals, (4) passive recreation (i.e., physically inactive recreation, such as going to the movies or a concert), and (5) active recreation (i.e., physically active recreation, such as playing tennis or running). The social context of children’s activity participation is also examined by focusing on the accompanying individuals in children’s activity engagement. The accompanying arrangement is classified into one of six categories: (1) alone, (2) with mother and no one else, (3) with father and no one else, (4) with both mother and father, and no one else, (5) with other individuals, but no parents, and (6) with other individuals and one or both parents. The utility-theoretic Multiple Discrete-Continuous Extreme Value (MDCEV) is employed to model time-use in one or more activity purpose–company type combinations. The data used in the analysis is drawn from the 2002 Child Development Supplement (CDS) to the U.S. Panel Study Income Dynamics (PSID). The results from the model can be used to examine the time-use choices of children, as well as to assess the potential impacts of urban and societal policies on children’s activity participation and time-use decisions. Our findings also emphasize the need to collect, in future travel surveys, more extensive and higher quality data capturing the intra- and inter-household interactions between individuals (including children). To our knowledge, the research in this paper is the first transportation-related study to rigorously and comprehensively analyze the social dimension of children’s activity participation.

Network-oriented household activity pattern problem for system optimization

The recently emerging trend of self-driving vehicles and information sharing technologies, made available by private technology vendors, starts creating a revolutionary paradigm shift in the coming years for traveler mobility applications. By considering a deterministic traveler decision making framework at the household level in congested transportation networks, this paper aims to address the challenges of how to optimally schedule individuals’ daily travel patterns under the complex activity constraints and interactions. We reformulate two special cases of household activity pattern problem (HAPP) through a high-dimensional network construct, and offer a systematic comparison with the classical mathematical programming models proposed by Recker (1995). Furthermore, we consider the tight road capacity constraint as another special case of HAPP to model complex interactions between multiple household activity scheduling decisions, and this attempt offers another household-based framework for linking activity-based model (ABM) and dynamic traffic assignment (DTA) tools. Through embedding temporal and spatial relations among household members, vehicles and mandatory/optional activities in an integrated space-time-state network, we develop two 0–1 integer linear programming models that can seamlessly incorporate constraints for a number of key decisions related to vehicle selection, activity performing and ridesharing patterns under congested networks. The well-structured network models can be directly solved by standard optimization solvers, and further converted to a set of time-dependent state-dependent least cost path-finding problems through Lagrangian relaxation, which permit the use of computationally efficient algorithms on large-scale high-fidelity transportation networks.     

Towards a common measure of greenhouse gas related logistics activity using data envelopment analysis

Monitoring company emissions from freight transport is essential if future greenhouse gas (GHG) reductions are to be realised. Modern economies are characterised increasingly by lower density freight movements. However, weight-based measures of freight transport activity (tonne-kilometre, tonnes lifted) are not good at describing volume-limited freight. After introducing the need for performance measurement, the problem of benchmarking is outlined in more detail. A context-dependent undesirable output data envelopment analysis (DEA) model, designed to be sensitive to business context, is then tested on a simulated set of fleet profiles. DEA can produce more consistent measures of good-practice, compared to ratio-based key performance indicators (KPI), providing emission reduction targets for companies and an aggregate reporting tool.