基于网络组织视角的粮食产业化模式与优化路径

在全球粮食安全状况严峻以及中国人多地少的形势下,探讨粮食的产业化发展具有重要意义。从网络组织视角出发,梳理了中国粮食产业化模式并提出优化路径。研究表明：粮食产业化发展是一个网络化的成长过程。基于网络主体控制与参与网络的能力、融资形式以及立足或服务的空间尺度,可以将粮食产业化模式划分为“国家调控跨国公司组织模式”“地方主导龙头企业带动模式”“政府主导社会多元主体参与模式”和“市场主导私营企业推动模式”这四类主要模式,分别对应介于市场组织与等级制组织之间的全球—区域—本地网络组织,并以跨国公司、本地龙头企业、产业化联合体、产业园区、产业集群等产业化组织形式呈现。进一步建议从提升网络主体开始,通过优化网络结构、强化网络载体、完善网络治理来指导粮食产业化的高质量发展。     

Comparison of a two-stage sampling design and its composite sample alternative: An application to soil studies

The objective of a long-term soil survey is to determine the mean concentrations of several chemical parameters for the pre-defined soil layers and to compare them with the corresponding values in the past. A two-stage random sampling procedure is used to achieve this goal. In the first step, n subplots are selected from N subplots by simple random sampling without replacement; in the second step, m sampling sites are chosen within each of the n selected subplots. Thus n · m soil samples are collected for each soil layer. The idea of the composite sample design comes from the challenge of reducing very expensive laboratory analyses: m laboratory samples from one subplot and one soil layer are physically mixed to form a composite sample. From each of the n selected subplots, one composite sample per soil layer is analyzed in the laboratory, thus n per soil layer in total. In this paper we show that the cost is reduced by the factor m — 1 when instead of the two-stage sampling its composite sample alternative is used; however, the variance of the composite sample mean is increased. In the case of positive intraclass correlation the increase is less than 12.5%; in the case of negative intraclass correlation the increase depends on the properties of the variable as well. For the univariate case we derive the optimal number of subplots and sampling sites. A case study is discussed at the end.     

Presence–Absence versus Abundance Data for Monitoring Threatened Species

Abstract:  Effective detection of population trend is crucial for managing threatened species. Little theory exists, however, to assist managers in choosing the most cost-effective monitoring techniques for diagnosing trend. We present a framework for determining the optimal monitoring strategy by simulating a manager collecting data on a declining species, the Chestnut-rumped Hylacola ( Hylacola pyrrhopygia parkeri ), to determine whether the species should be listed under the IUCN (World Conservation Union) Red List. We compared the efficiencies of two strategies for detecting trend, abundance, and presence–absence surveys, under financial constraints. One might expect the abundance surveys to be superior under all circumstances because more information is collected at each site. Nevertheless, the presence–absence data can be collected at more sites because the surveyor is not obliged to spend a fixed amount of time at each site. The optimal strategy for monitoring was very dependent on the budget available. Under some circumstances, presence–absence surveys outperformed abundance surveys for diagnosing the IUCN Red List categories cost-effectively. Abundance surveys were best if the species was expected to be recorded more than 16 times/year; otherwise, presence–absence surveys were best. The relationship between the strategies we investigated is likely to be relevant for many comparisons of presence–absence or abundance data. Managers of any cryptic or low-density species who hope to maximize their success of estimating trend should find an application for our results.     

基于最优分割理论的酸雨监控点位的分类研究

Fisher最优分割法是用离差平方和来表示同类样本之间的差异程度,通过简便的计算步骤和作图,确定最优分类数,使同类样本间的差异最小,各类别样本间的差异最大,并用F检验法检验最优分类数的合理性。文章根据Fisher最优分割法的理论内涵,研究其应用于酸雨环境监测优化布点的可行性。以漳州市沿海区域监测点为研究实例,用Fisher最优分割法确定最优的分类情况和最优的优化布点方案。结果表明,原布设的9个监测点用Fisher最优分割法优化为3个典型点位,可客观地反映漳州市酸雨控制区的整体污染状况和污染趋势,该方法理论内涵直观、计算简便、重复性好,对环境质量监测有重要的意义。     

区域大气环境容量与基础工业布局研究──以镇江为例

大气环境容量是工业布局的影响因素之一。基于大气科学理论及运筹学的线性优化，创建了一个由三维中央尺度风场动力学模式、三维大气扩散数值模式及线性优化容量模式构成的动态优化区域大气环境容量模拟系统，并用该系统就镇江市区的大气环境容量及各污染源的最优排污量进行了计算，由此对镇江市现有重点污染企业的最优排污量及发展提出意见，并对未来基础工业的布局提供依据。该模拟系统具有普适性，故只要收集到模式所需要的长江沿岸地区的地理、气象及污染源资料，即可计算沿江地区的大气环境容量及基础工业企业的布局与规模，为长江产业带建设提供科学依据。     

危险品道路运输过程风险管理体系探讨

危险品道路运输是一个复杂的系统,风险管理涉及危险品运输规划和事故应急响应。本文介绍了现有的危险品运输风险管理模式和基本管理原则,基于运输风险评估、运输路径优化、应急单位优化选址和选线、人员疏散管理以及事件决策管理等构建了危险品道路运输过程风险管理体系,阐述了系统基本要素之间的相互关系。危险品道路运输过程风险管理是一个持续改进的结构化过程风险管理体系,有助于减少危险品运输事故概率和降低运输沿线影响人员风险,为政府监管部门和危险品生产经营单位的运输安全管理、优化选线以及应急救援等工作提供技术依据,合理规划危险品运输系统。     

最优指标法在环境监测优化布点中的应用

采用最优指标法对郑州市17个大气环境采样点的监测数据进行优选,得到的优化点位基本能反映城市空气质量的总体情况,可以达到环境监测优化布点的目的.     

半结构决策方法在松花江蓄洪区选址问题中的研究

研究松花江蓄洪区选址问题的决策方法.松花江蓄滞洪区选址问题涉及诸多定性指标和定量指标,属于半结构决策问题.本文综合层次分析法和模糊优选法,提出求解这类问题的一种半结构决策方法.基本方法是:将评价指标分为定性指标和定量指标;对于定性应用层次分析法,求其评价矩阵;对于定量指标应用相对隶属度方法,求其评价矩阵;二者合成得到全体指标的评价矩阵;最后利用模糊优选法求得最优决策.应用该方法于松花江流域蓄滞洪区的方案选择中.     

Control of TAME reactive distillation using Non-Dominated Sorting Genetic Algorithm-II

The control system performs poor in characteristics and even it becomes unstable, if improper values of the controller tuning constants are used. So it becomes necessary to tune the controller parameters to achieve good control performance with the proper choice of tuning constants. Many control problems involve simultaneous optimization of multiple variables that competing with each other. In this paper, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has been successfully applied to optimization of dynamic state of t-amyl-methyl-ether (TAME) reactive distillation process. This paper presents the tuning of Proportional-Integral-Derivative (PID) controllers by minimizing of two objective functions (overshoot and Integral of Absolute Error (IAE)) through the NSGA-II. Results show that genetic algorithm is more suitable method for optimal control of the TAME reactive distillation columns than traditional methods such as Tyreus-Luyben.     

Uncertainty, learning, and the optimal management of wildlife

Wildlife management is limited by uncontrolled and often unrecognized environmental variation, by limited capabilities to observe and control animal populations, and by a lack of understanding about the biological processes driving population dynamics. In this paper I describe a comprehensive framework for management that includes multiple models and likelihood values to account for structural uncertainty, along with stochastic factors to account for environmental variation, random sampling, and partial controllability. Adaptive optimization is developed in terms of the optimal control of incompletely understood populations, with the expected value of perfect information measuring the potential for improving control through learning. The framework for optimal adaptive control is generalized by including partial observability and non-adaptive, sample-based updating of model likelihoods. Passive adaptive management is derived as a special case of constrained adaptive optimization, representing a potentially efficient suboptimal alternative that nonetheless accounts for structural uncertainty.