基于贝叶斯网络的气井井筒完整性风险评价

井筒完整性失效是气井生产中的主要风险,为有效评价井筒完整性风险,应用贝叶斯网络的推理与学习能力,建立了基于贝叶斯网络和Noisy-OR gate模型的井筒完整性失效概率计算方法和风险评价模型。由故障树分析将井筒分为管柱、水泥环密封性、井口装置、水力屏障和其他部件5个评价单元,确定了各单元的主要风险因素,建立了井筒完整性失效的贝叶斯网络拓扑结构;由Noisy-OR gate模型和历史数据,确定了贝叶斯网络的条件概率参数;将基于贝叶斯网络的失效概率与层次分析法相结合,确定了风险评价指标和等级划分标准;建立了气井井筒完整性风险评价方法。结果表明,该方法实现了井筒完整性失效概率的定量计算、风险的定量评价和主要风险因素的反向推理,可为预防和控制井筒完整性失效提供决策依据,有助于降低井筒完整性失效风险。     

栖息地适宜度评价方法研究进展

栖息地模拟法是根据指示物种所需的物理生境条件确定河流流量,评价栖息地适宜度,为水生生物提供一个适宜的物理生境。与其他方法相比,栖息地模拟法考虑了生物本身对物理生境的要求,需要建立物种-生境评价指标。文章总结了目前用于描述物种—生境关系的栖息地适宜度评价方法,包括栖息地适宜度指数、多元统计方法、模糊逻辑方法、人工神经网络、对多物种和群落的统计分析,归纳了各个方法的进展和应用情况,重点分析了统计学方法在评估栖息地适宜度时的优势和不足。栖息地适宜度指数有二元、单变量、多变量3种格式,前2种方法只考虑单一因子,实际应用中多变量格式应用较多。多变量格式方法直观、所需数据容易获取、实际操作性强,是栖息地定量化的经典方法,也是目前应用最多的方法,然而其对专家经验依赖较多,主观性较强。近些年来,多元统计方法在栖息地适宜度评价方面的应用不断增加,它考虑物理变量之间的相互作用和相关性。模糊逻辑法在处理栖息地模拟中的不确定性方面具有优势,能更好地利用专家知识,更合理的处理建模过程中测量的不准确性和不确定性,同时也考虑了多个变量之间的相互作用,但是当考虑的变量数增加时,模糊规则的数量会迅速增加,给计算带来不便。人工神经网络能够隐性地找出响应变量和环境变量之间的复杂关系,但是其解释能力不足,并需要大量的实测数据对其进行训练,实际应用受到限制。通过排序分析或梯度分析可以对多物种和群落进行统计分析。这些方法都各有优劣,在实际应用中,应结合实际情况选择最合适的栖息地适宜度评价方法。     

基于区间活性数据的定量结构-活性关系

利用贝叶斯统计方法构建了基于区间活性数据的取代苯胺和苯酚类化合物对大型溞(Daphniamagna)24 h急性毒性的定量结构-活性关系模型,并与基于平均数和中位数的点估计活性数据的定量结构-活性关系模型进行了比较.结果表明,前者可以充分利用化合物的活性数据信息,模型具有更好的拟合效果与预测能力以及较宽的应用范围.基于区间活性数据的定量结构-活性关系模型可为生态风险评价等提供更加可靠的预测数据.     

A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources

We demonstrate a density projection approximation method for solving resource management problems with imperfect state information. The method expands the set of partially-observed Markov decision process (POMDP) problems that can be solved with standard dynamic programming tools by addressing dimensionality problems in the decision maker's belief state. Density projection is suitable for uncertainty over both physical states (e.g. resource stock) and process structure (e.g. biophysical parameters). We apply the method to an adaptive management problem under structural uncertainty in which a fishery manager's harvest policy affects both the stock of fish and the belief state about the process governing reproduction. We solve for the optimal endogenous learning policy—the active adaptive management approach—and compare it to passive learning and non-learning strategies. We demonstrate how learning improves efficiency but typically follows a period of costly short-run investment.     

BP和Elman神经网络在砂土液化预测中的研究

基于砂土液化的影响因素具有非线性关系,而神经网络模型能够逼近任意非线性函数和适合于动态系统辨识的特性,分别建立输入层为4,隐含层神经元为2,输出层为1的三层BP神经网络和Elman网络,并且通过matlab软件运算,实例比较得出Elman模型比BP模型收敛速度快、精度高,在砂土液化的预测中效果更好。     

Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error

Measurement errors in spawner abundance create problems for fish stock assessment scientists. To deal with measurement error, we develop a Bayesian state-space model for stock-recruitment data that contain measurement error in spawner abundance, process error in recruitment, and time series bias. Through extensive simulations across numerous scenarios, we compare the statistical performance of the Bayesian state-space model with that of standard regression for a traditional stock-recruitment model that only considers process error. Performance varies depending on the information content in data, as determined by stock productivity, types of harvest situations, and amount of measurement error. Overall, in terms of estimating optimal spawner abundance S_MSY, the Ricker density-dependence parameter β, and optimal harvest rate h_MSY, the Bayesian state-space model works best for informative data from low and variable harvest rate situations for high-productivity salmon stocks. The traditional stock-recruitment model (TSR) may be used for estimating α and h_MSY for low-productivity stocks from variable and high harvest rate situations. However, TSR can severely overestimate S_MSY when spawner abundance is measured with large error in low and variable harvest rate situations. We also found that there is substantial merit in using h_MSY (or benchmarks derived from it) instead of S_MSY as a management target.     

On the basic reproduction number and the topological properties of the contact network: An epidemiological study in mainly locally connected cellular automata

We study the spreading of contagious diseases in a population of constant size using susceptible-infective-recovered (SIR) models described in terms of ordinary differential equations (ODEs) and probabilistic cellular automata (PCA). In the PCA model, each individual (represented by a cell in the lattice) is mainly locally connected to others. We investigate how the topological properties of the random network representing contacts among individuals influence the transient behavior and the permanent regime of the epidemiological system described by ODE and PCA. Our main conclusions are: (1) the basic reproduction number (commonly called _R0$R_0$) related to a disease propagation in a population cannot be uniquely determined from some features of transient behavior of the infective group; (2) _R0$R_0$ cannot be associated to a unique combination of clustering coefficient and average shortest path length characterizing the contact network. We discuss how these results can embarrass the specification of control strategies for combating disease propagations.     

Nutrient criteria for lakes,ponds, and reservoirs: A Bayesian TREED model approach

We develop regional-scale eutrophication models for lakes, ponds, and reservoirs to investigate the link between nutrients and chlorophyll-a. The Bayesian TREED (BTREED) model approach allows association of multiple environmental stressors with biological responses, and quantification of uncertainty sources in the empirical water quality model. Nutrient data for lakes, ponds, and reservoirs across the United States were obtained from the Environmental Protection Agency (EPA) National Nutrient Criteria Database. The nutrient data consist of measurements for both stressor variables (such as total nitrogen and total phosphorus), and response variables (such as chlorophyll-a), used in the BTREED model. Markov chain Monte Carlo (McMC) posterior exploration guides a stochastic search through a rich suite of candidate trees toward models that better fit the data. The Bayes factor provides a goodness of fit criterion for comparison of resultant models. We randomly split the data into training and test sets; the training data were used in model estimation, and the test data were used to evaluate out-of-sample predictive performance of the model. An average relative efficiency of 1.02 between the training and test data for the four highest log-likelihood models suggests good out-of-sample predictive performance. Reduced model uncertainty relative to over-parameterized alternative models makes the BTREED models useful for nutrient criteria development, providing the link between nutrient stressors and meaningful eutrophication response.     

Association networks in spider monkeys (<Emphasis Type="Italic">Ateles geoffroyi</Emphasis>)

We use two novel techniques to analyze association patterns in a group of wild spider monkeys (Ateles geoffroyi) studied continuously for 8 years. Permutation tests identified association rates higher or lower than chance expectation, indicating active processes of companionship and avoidance as opposed to passive aggregation. Network graphs represented individual adults as nodes and their association rates as weighted edges. Strength and eigenvector centrality (a measure of how strongly linked an individual is to other strongly linked individuals) were used to quantify the particular role of individuals in determining the network's structure. Female–female dyads showed higher association rates than any other type of dyad, but permutation tests revealed that these associations cannot be distinguished from random aggregation. Females formed tightly linked clusters that were stable over time, with the exception of immigrant females who showed little association with any adult in the group. Eigenvector centrality was higher for females than for males. Adult males were associated mostly among them, and although their strength of association with others was lower than that of females, their association rates revealed a process of active companionship. Female–male bonds were weaker than those between same-sex pairs, with the exception of those involving young male adults, who by virtue of their strong connections both with female and male adults, appear as temporary brokers between the female and male clusters of the network. This analytical framework can serve to develop a more complete explanation of social structure in species with high levels of fission–fusion dynamics. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James)     

Potential banana skins in animal social network analysis

Social network analysis is an increasingly popular tool for the study of the fine-scale and global social structure of animals. It has attracted particular attention by those attempting to unravel social structure in fission–fusion populations. It is clear that the social network approach offers some exciting opportunities for gaining new insights into social systems. However, some of the practices which are currently being used in the animal social networks literature are at worst questionable and at best over-enthusiastic. We highlight some of the areas of method, analysis and interpretation in which greater care may be needed in order to ensure that the biology we extract from our networks is robust. In particular, we suggest that more attention should be given to whether relational data are representative, the potential effect of observational errors and the choice and use of statistical tests. The importance of replication and manipulation must not be forgotten, and the interpretation of results requires care. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).