震后伤员救援车辆两阶段规划模型及算法研究

不确定情景下的应急物资配送和车辆路径优化问题是目前国内研究的一大热点,其中 震后伤员运送车辆调度及路径优化问题随着近几年地震等自然灾害的频繁发生也逐渐引起人 们的重视． 本文采用聚合优化算法对灾区进行应急救援区域划分,建立了以总救援时间最短和 相对综合救援权重值最大为目标的救援车辆两阶段数学规划模型,设计了带三角函数变异的 离散型萤火虫优化算法进行求解,并与常规算法求解结果做了对比分析． 通过算例进行模拟实 验表明,所提出的模型和算法可以有效解决地震情景下的伤员救援车辆调度问题,且改进算法 的全局搜索性能更优．     

工匠精神在当代应用型人才职业价值观的缺失与培育研究

工匠精神是当代职业教育中的重要理念,也是顺应时代发展需求,推进产业升级、经济转型的重要职业精神。文章围绕工匠精神的内涵、如何挖掘职业教育的工匠精神、如何将工匠精神纳入到当代职业人才价值观培育实践中等问题展开论述,并提出积极的建议和对策。     

XML信息查询优化研究

为了提高XML信息查询效率,根据XML信息查询的特点,提出了信息查询优化的一些思想汨解决方法．     

西部地区社会保障体系的模式设计与理论创新

西部地区的经济发展需要社会保障制度为其构筑一道“安全网”。回顾西部地区社会保障的发展历史,从社会保障中几个重要指标出发对西部地区社会保障制度作出绩效评估、分析其原因,并且提出西部地区建立覆盖城乡居民的社会保障体系的基本框架、实现路径、步骤与措施。     

厦门竹坝华侨农场社会保障问题调查研究

以厦门同安竹坝华侨农场社会保障制度改革的研究为标的,成文过程查阅了竹坝农场1960年以来四十多年的相关档案资料,并做了深入细致的实地考察,在此基础上进行了探讨、研究.首先对农场的社会保障制度改革的路径进行了总体描述,揭示了不同阶段不同的特征表现,尤其突出了现时特征;其次提出了改革至今仍存在的问题,诸如历史旧帐不清、政策落实不到位、管理体制不顺等等,并揭示其原因;最后对于农场的社会保障制度的进一步健全与完善提出建议.     

高校服务农村经济建设的路径研究

建设社会主义新农村的核心是增强农村经济建设,提高农民收入。高校肩负着服务区域经济的神圣历史使命,高校要发挥育人功能,培养农村经济建设人才;发挥科研优势,为农村经济建设提供智力支撑;突出服务社会职能,积极探索科技扶贫新途径;加强信息沟通与交流,提高农民综合素质;强化政策支持,提高师生服务农村的积极性,强化对农村经济建设的服务功能。     

Two-dimensional generalisations of dynamic programming for image analysis

Dynamic programming (DP) is a fast, elegant method for solving many one-dimensional optimisation problems but, unfortunately, most problems in image analysis, such as restoration and warping, are two-dimensional. We consider three generalisations of DP. The first is iterated dynamic programming (IDP), where DP is used to recursively solve each of a sequence of one-dimensional problems in turn, to find a local optimum. A second algorithm is an empirical, stochastic optimiser, which is implemented by adding progressively less noise to IDP. The final approach replaces DP by a more computationally intensive Forward-Backward Gibbs Sampler, and uses a simulated annealing cooling schedule. Results are compared with existing pixel-by-pixel methods of iterated conditional modes (ICM) and simulated annealing in two applications: to restore a synthetic aperture radar (SAR) image, and to warp a pulsed-field electrophoresis gel into alignment with a reference image. We find that IDP and its stochastic variant outperform the remaining algorithms.     

Ecological research can augment restoration practice in urban areas degraded by invasive species—examples from Chicago Wilderness

Urban biodiversity conservation needs a firm scientific foundation, one that draws upon active and regionally calibrated research programs. Until recently this foundation has not existed. In this paper we examine the way in which the emerging discipline of restoration ecology in an urban context can learn from the experiences of ongoing restoration projects and in turn how novel insights from research of urban soils can help these projects define and reach their goals. Limitations on collaboration between academic researchers and practitioners continues be problematic. We discuss a model whereby this impediment may be removed. A case study of Rhamnus cathartica, an invader of Midwestern woodlands which modifies some important soil ecosystem properties will be used to illustrate the potential for engagement between research and practice.     

网络反腐视角下政府信息公开存在的问题及完善路径

政府信息公开在网络反腐中具有重要的意义和作用,其不仅能够加强公民对政府权力的监督和制约,保障公民的监督权和知情权,而且有利于政府深入开展反腐败工作,树立廉洁形象,有效地预防和遏制腐败。目前,政府信息公开在网络反腐中还存在着许多问题：如政府机构信息公开意识淡薄,对网络反腐存在抵触心理;政府机构滥用自由裁量权,垄断网络信息资源;信息公开质量不高,形式主义严重;政府网站建设存在不足,公开内容和方式需要完善等。为保证政府信息公开在网络反腐中的有效实施,应加快网络反腐立法建设、推行官员财产网上公开、加强政府网站平台建设、积极开通政府微博、不断改进网络发言人公开方式,从而推进网络反腐的进程。     

Progressive Learning

We study a dynamic principal–agent relationship with adverse selection and limited commitment. We show that when the relationship is subject to productivity shocks, the principal may be able to improve her value over time by progressively learning the agent's private information. She may even achieve her first‐best payoff in the long run. The relationship may also exhibit path dependence, with early shocks determining the principal's long‐run value. These findings contrast sharply with the results of the ratchet effect literature, in which the principal persistently obtains low payoffs, giving up substantial informational rents to the agent.