变电站操作不安全行为核心影响因素分析*

为分析变电站中操作不安全行为的核心影响因素,基于国家电网某省变电站员工安全心理自评数据,运用心理学模型确定9种主要心理维度,融合Bayesian Search和Peter Clark(PC)算法,并使用专家知识修正以训练贝叶斯网络模型结构,应用Expectation Maximization(EM)算法训练模型参数,并运用Mutual Information(MI)和预测推理2种方法分析核心影响因素。研究结果表明:训练模型的预测错误率为14.8%,AUC为0.945 8,变电站操作不安全行为的核心影响因素为情绪稳定性和心理承受力。     

基于多尺度排列熵和极限学习机的风机叶片覆冰故障检测方法*

针对风机叶片结冰故障检测中状态数据维度高和检测率低的问题,提出1种使用功率数据驱动的多尺度排列熵（multiscale permutation entropy,MPE）和极限学习机（extreme learning machine,ELM）的风机叶片结冰故障检测方法。首先,使用多尺度排列熵提取功率数据的多重尺度特征,得到特征向量;随后,采用极限学习机,结合环境温度,对结冰故障进行检测;最后,通过使用某风电场的数据采集与监视控制系统（supervisory control and data acquisition,SCADA）对数据进行仿真。研究结果表明:所提方法的故障检测率达到100％,同时虚警率仅有0.14％,表明所提方法在风机叶片的覆冰故障检测中的有效性。研究结果可为风机叶片覆冰故障检测提供1种有效方法。     

Inventory routing for dynamic waste collection

We consider the problem of collecting waste from sensor equipped underground containers. These sensors enable the use of a dynamic collection policy. The problem, which is known as a reverse inventory routing problem, involves decisions regarding routing and container selection. In more dense networks, the latter becomes more important. To cope with uncertainty in deposit volumes and with fluctuations due to daily and seasonal effects, we need an anticipatory policy that balances the workload over time. We propose a relatively simple heuristic consisting of several tunable parameters depending on the day of the week. We tune the parameters of this policy using optimal learning techniques combined with simulation. We illustrate our approach using a real life problem instance of a waste collection company, located in The Netherlands, and perform experiments on several other instances. For our case study, we show that costs savings up to 40% are possible by optimizing the parameters.     

Effects of Multiple Levels of Social Organization on Survival and Abundance

Abstract: Identifying how social organization shapes individual behavior, survival, and fecundity of animals that live in groups can inform conservation efforts and improve forecasts of population abundance, even when the mechanism responsible for group‐level differences is unknown. We constructed a hierarchical Bayesian model to quantify the relative variability in survival rates among different levels of social organization (matrilines and pods) of an endangered population of killer whales (Orcinus orca). Individual killer whales often participate in group activities such as prey sharing and cooperative hunting. The estimated age‐specific survival probabilities and survivorship curves differed considerably among pods and to a lesser extent among matrilines (within pods). Across all pods, males had lower life expectancy than females. Differences in survival between pods may be caused by a combination of factors that vary across the population's range, including reduced prey availability, contaminants in prey, and human activity. Our modeling approach could be applied to demographic rates for other species and for parameters other than survival, including reproduction, prey selection, movement, and detection probabilities.     

Estimating an homogeneous series of a population abundance indicator despite changes in data collection procedure: A hierarchical Bayesian modelling approach

Abundance indicators are required both to assess and to manage wild populations. As new techniques are developed and teams in charge of gathering the data change, data collection procedures (DCPs) can evolve in space and time. How to estimate an homogeneous series of abundance indicator despite changes in DCP? To tackle this question a hierarchical Bayesian modelling (HBM) approach is proposed. It integrates multiple DCPs in order to derive a single abundance indicator that can be compared over space and time irrespective of the DCP used. Compared to single DCP models, it takes further advantage for abundance estimation of the joint treatment of a larger set of spatio-temporal units. After presenting the general formulation of our HBM approach, it is applied to the juvenile Atlantic salmon (Salmo salar L.) population of the River Nivelle (France). Posterior model checking, using χ² discrepancy measure, do not reveal any inadequacy between the model and the data. Despite a change in the DCP used (successive removals to catch-per-unit of effort), a unique abundance indicator for the 425 spatio-temporal units (site × year) sampled over twenty-four years (1985-2008) is estimated. The HBM approach allows the assessment of precision of the abundance estimates and shows variation between DCPs: a reduction in precision is observed during the most recent years (2005-2008) when only the catch-per-unit of effort DCP was used. The merits and generality of our HBM approach are discussed. We contend it extends previous single DCP models or inter-calibration of two DCPs, and it could be applied to a wide range of specific situations (taxon and DCPs).     

Evidence-Based Knowledge Versus Negotiated Indicators for Assessment of Ecological Sustainability: The Swedish Forest Stewardship Council Standard as a Case Study

Assessing ecological sustainability involves monitoring of indicators and comparison of their states with performance targets that are deemed sustainable. First, a normative model was developed centered on evidence-based knowledge about (a) forest composition, structure, and function at multiple scales, and (b) performance targets derived by quantifying the habitat amount in naturally dynamic forests, and as required for presence of populations of specialized focal species. Second, we compared the Forest Stewardship Council (FSC) certification standards’ ecological indicators from 1998 and 2010 in Sweden to the normative model using a Specific, Measurable, Accurate, Realistic, and Timebound (SMART) indicator approach. Indicator variables and targets for riparian and aquatic ecosystems were clearly under-represented compared to terrestrial ones. FSC’s ecological indicators expanded over time from composition and structure towards function, and from finer to coarser spatial scales. However, SMART indicators were few. Moreover, they poorly reflected quantitative evidence-based knowledge, a consequence of the fact that forest certification mirrors the outcome of a complex social negotiation process.     

中国高新技术产业能源消耗与碳排放分析

基于2000-2007年中国高新技术产业经济发展与能源消耗(简称能耗)数据,统计分析了中国高新技术产业能耗强度变化趋势,并采用“学习曲线”方法论证了高新技术产业能耗的特征;在此基础上,采用政府间气候变化专门委员会(IPCC)温室气体指导方针的方法核算了中国高新技术产业的碳排放量及排放强度.结果表明:(1)中国高新技术产...     

A Bayesian-weighted approach to predicting the number of newly discovered rare species

In natural ecological communities, most species are rare and thus susceptible to extinction. Consequently, the prediction and identification of rare species are of enormous value for conservation purposes. How many newly found species will be rare in the next field survey? We took a Bayesian viewpoint and used observed species abundance information in an ecological sample to develop an accurate way to estimate the number of new rare species (e.g., singletons, doubletons, and tripletons) in an additional unknown sample. A similar method has been developed for incidence-based data sets. Five seminumerical tests (3 abundance cases and 2 incidence cases) showed that our proposed Bayesian-weight estimator accurately predicted the number of new rare species with low relative bias and low relative root mean squared error and, accordingly, high accuracy. Finally, we applied the proposed estimator to 6 conservation-directed empirical data sets (3 abundance cases and 3 incidence cases) and found the prediction of new rare species was quite accurate; the 95% CI covered the true observed value very well in most cases. Our estimator performed similarly to or better than an unweighted estimator derived from Chao et al. and performed consistently better than the naïve unweighted estimator. We recommend our Bayesian-weight estimator for conservation applications, although the unweighted estimator of Chao et al. may be better under some circumstances. We provide an R package RSE (r are s pecies e stimation) at https://github.com/ecomol/RSE for implementation of the estimators.     

公共危机管理中的组织学习研究

目前国外对危机管理的研究已经趋于成熟,已经进一步提出了在危机中学习的概念并取得一些进展。而在经历了几次大的危机之后,国内学者开始关注公共危机管理。随着危机种类越来越多,发生频率越来越高,政府的公共部门必须要具备组织学习的能力。但是国内对于组织学习研究在公共管理领域的延伸还十分有限,我国仅少量学者探讨了学习型政府,对其它类型公共部门的组织学习研究则更为罕见。显然,这种研究的缺失不利于公共组织构建组织学习能力和提高服务水平。未来的国内学习型组织研究需要在公共组领域有所突破,特别是危机情境下的公共组织学习。因此本文以分析文献的方式,借鉴国内外学者对公共危机管理,以及组织学习的研究,希望能为公共部门组织学习的深层探索提供思路。     

基于复合指纹图谱和贝叶斯模型的茅尾海悬浮颗粒物源解析

为明确茅尾海中悬浮颗粒物的来源,采集了茅尾海流域红树林土壤、堤岸土、河口颗粒物、茅尾海沉积物以及湾外颗粒物等悬浮颗粒物潜在源样品.基于多元统计复合指纹图谱方法,筛选出最佳指纹因子组合,进而通过贝叶斯混合模型得出五种潜在源对茅尾海悬浮颗粒物的贡献率.结果表明:Mg、Al、Mn、Pb、Fe五种指纹元素可作为最佳指纹因子组合,累计判别正确率为78%.贝叶斯混合模型结果显示,茅尾海悬浮颗粒物主要来源于河口和湾外输送,贡献率最高达到58.9%和68.6%.其中,靠近河口区域主要受河流汇入影响,其贡献率达到42.2%~58.9%;靠近湾外区域则以湾外颗粒物贡献为主,贡献率达到44.9%~68.6%.各点位的沉积物贡献率均较低,红树林土壤和堤岸土的贡献率都在10%左右.总的来说,由河口汇入和潮汐作用带入的颗粒物是茅尾海悬浮颗粒物的主要来源.