基于马尔可夫随机场的人群密度监测技术

针对目前人群密度估计和人群统计方法的不足,提出基于马尔可夫随机场的人群密度监测技术,将这一方法归纳为两个主要步骤,并详细阐述了其计算原理;讨论存在严重遮挡情况下,通过给定域值,如何将其实际应用于公共场所人群聚集风险管理中的方法.     

On Limits to the Use of Linear Markov Strategies in Common Property Natural Resource Games

We derive conditions that must be satisfied by the primitives of the problem in order for an equilibrium in linear Markov strategies to exist in some common property natural resource differential games. These conditions impose restrictions on the admissible form of the natural growth function, given a benefit function, or on the admissible form of the benefit function, given a natural growth function.

民航事故征候的灰色马尔可夫预测

民航事故征候的分析和预测是民航安全研究的重要内容.掌握民航事故征候的发展状况并据此提出相应的安全措施,可减少民航事故的发生.在民航事故征候灰色预测基础上,引入马尔可夫链(Markov Chains)预测理论,建立事故征候的灰色马尔可夫预测模型.该模型具有灰色预测和马尔可夫链预测的优点,不仅提高了对波动性较大的随机变量的预测精度,同时还拓宽了灰色预测的应用范围.对某一航空公司过载大事件的灰色马尔可夫预测和检验分析表明,事故征候的灰色马尔可夫预测模型精度高于GM(1,1)模型预测精度.研究表明,灰色马尔可夫预测模型可用于民航事故征候的预测.     

Regional Scale Stressor‐Response Models in Aquatic Ecosystems1

Abstract: A systematic method for identification and estimation of regional scale stressor‐response models in aquatic ecosystems will be useful in monitoring and assessment of aquatic resources, determination of regional nutrient criteria and for increased understanding of the differences between regions. The model response variable is chlorophyll a, a measure of algal density, while the stressors include nutrient concentrations from the USEPA Nutrient Criteria Database (NCD) for lakes/ponds and reservoirs of the continental United States. The NCD has observations for both stressors and biological responses determined using methods that are not consistently available at the continental scale. To link multiple environmental stressors to biological responses and quantify uncertainty in model predictions, we take a multilevel modeling approach to the estimation of a linear model for prediction of log Chlorophyll a using predictors log TP and log TN. The multilevel modeling approach allows us to adjust the impact of covariates at all levels (observation, higher level groups) for the simultaneous operation of contextual and individual variability in the outcome. Here, we wish to allow separate regression coefficients for inference regarding similarities and differences between each of 14 ecoregions, and between the two water‐body types, lakes/ponds and reservoirs. We are also interested in the nuisance effects of the categorical variables indicating the type of nitrogen measurements (three levels) and the type of chlorophyll a measurements (four levels) used. Model‐based determination of nutrient criteria points to an apparent incompatibility of criteria developed for nutrient stressors and eutrophication responses using current Environmental Protection Agency’s guidance.     

Modeling flood induced interdependencies among hydroelectricity generating infrastructures

This paper presents a new kind of integrated modeling method for simulating the vulnerability of a critical infrastructure for a hazard and the subsequent interdependencies among the interconnected infrastructures. The developed method has been applied to a case study of a network of hydroelectricity generating infrastructures, e.g., water storage concrete gravity dam, penstock, power plant and transformer substation. The modeling approach is based on the fragility curves development with Monte Carlo simulation based structural–hydraulic modeling, flood frequency analysis, stochastic Petri net (SPN) modeling, and Markov Chain analysis. A certain flood level probability can be predicted from flood frequency analysis, and the most probable damage condition for this hazard can be simulated from the developed fragility curves of the dam. Consequently, the resulting interactions among the adjacent infrastructures can be quantified with SPN analysis; corresponding Markov Chain analysis simulates the long term probability matrix of infrastructure failures. The obtained results are quite convincing to prove the novel contribution of this research to the field of infrastructure interdependency analysis which might serve as a decision making tool for flood related emergency response and management.     

多点激励下拱桥竖向地震反应的简化计算方法

以某大跨公路拱桥为例,通过对拱桥在三种行波输入模式下地震反应的对比计算,提出了拱脚行波输入的简化输入方式;利用拱桥结构的对称性特点,提出了多点输入下半拱叠加的简化计算方法。经验证,这一计算方法具有良好的计算精度,可将较为复杂的拱桥多点输入地震反应计算问题,转化为工程技术人员熟悉的一致输入下地震反应计算问题。     

内蒙古东乌珠穆沁旗草甸草原生态环境时空演变分析

选取内蒙古东乌珠穆沁旗满都胡宝拉格草甸草原为研究区,在遥感和GIS技术的支撑下,以景观生态学理论为指导,通过分析该地区近15年来四个时期景观类型的空间格局、演化态势和变化规律,从景观视角挖掘出格局信息与生态环境变化之间的内在关联性,运用马尔科夫链模型对未来草原景观格局变化的趋势进行了模拟和预测,并分析了该地区生态环境变化的驱动机制,为科学利用和保护草地资源、保证区域可持续发展提供了科学依据。     

基于全产业链视角实施生态资产管理

生态资产是人类赖以生存的基本条件,当前研究尚未充分关注人类活动对生态资产的影响。本文从社会经济系统的全产业链视角出发,探讨产业链不同环节的生产消费活动对生态资产的影响以及所对应的不同政策内涵。分析结果表明,产业链的不同环节对应不同政策内涵,识别不同环节上的关键区域/行业能够为不同类型的政策决策指出着力点。初始投入环节的核算服务于人力资本投入行为的调控;初始生产环节的核算服务于末端控制手段,包括资源节约与污染物减排;中间生产环节和末端生产环节的核算服务于提升生产效率的政策手段;最终消费环节的核算服务于消费行为优化。最终得出应该从全产业链视角实施多环节生态资产管理的结论,并提出,为了从全产业链视角实施生态资产管理,应考虑建立一套标准化、全产业链视角的生态资产需求核算框架,建设支持全产业链视角核算生态资产需求的基础数据库,将全产业链视角管理生态资产的思维融入政策决策等建议。     

Spatiotemporal trends of illegal activities from ranger‐collected data in a Ugandan national park

Within protected areas, biodiversity loss is often a consequence of illegal resource use. Understanding the patterns and extent of illegal activities is therefore essential for effective law enforcement and prevention of biodiversity declines. We used extensive data, commonly collected by ranger patrols in many protected areas, and Bayesian hierarchical models to identify drivers, trends, and distribution of multiple illegal activities within the Queen Elizabeth Conservation Area (QECA), Uganda. Encroachment (e.g., by pastoralists with cattle) and poaching of noncommercial animals (e.g., snaring bushmeat) were the most prevalent illegal activities within the QECA. Illegal activities occurred in different areas of the QECA. Poaching of noncommercial animals was most widely distributed within the national park. Overall, ecological covariates, although significant, were not useful predictors for occurrence of illegal activities. Instead, the location of illegal activities in previous years was more important. There were significant increases in encroachment and noncommercial plant harvesting (nontimber products) during the study period (1999–2012). We also found significant spatiotemporal variation in the occurrence of all activities. Our results show the need to explicitly model ranger patrol effort to reduce biases from existing uncorrected or capture per unit effort analyses. Prioritization of ranger patrol strategies is needed to target illegal activities; these strategies are determined by protected area managers, and therefore changes at a site‐level can be implemented quickly. These strategies should also be informed by the location of past occurrences of illegal activity: the most useful predictor of future events. However, because spatial and temporal changes in illegal activities occurred, regular patrols throughout the protected area, even in areas of low occurrence, are also required.     

Hierarchical Bayesian space-time models

Space-time data are ubiquitous in the environmental sciences. Often, as is the case with atmo- spheric and oceanographic processes, these data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations. These factors can limit the effectiveness of traditional space- time statistical models and methods. In this article, we propose the use of hierarchical space-time models to achieve more flexible models and methods for the analysis of environmental data distributed in space and time. The first stage of the hierarchical model specifies a measurement- error process for the observational data in terms of some 'state' process. The second stage allows for site-specific time series models for this state variable. This stage includes large-scale (e.g. seasonal) variability plus a space-time dynamic process for the anomalies'. Much of our interest is with this anomaly proc ess. In the third stage, the parameters of these time series models, which are distributed in space, are themselves given a joint distribution with spatial dependence (Markov random fields). The Bayesian formulation is completed in the last two stages by speci- fying priors on parameters. We implement the model in a Markov chain Monte Carlo framework and apply it to an atmospheric data set of monthly maximum temperature.