Empirical Bayes calculations of concordance between endpoints in environmental toxicity experiments

Hierarchical models are considered for estimating the probability of agreement between two outcomes or endpoints from an environmental toxicity experiment. Emphasis is placed on generalized regression models, under which the prior mean is related to a linear combination of explanatory variables via a monotone function. This function defines the scale over which the systematic effects are modelled as additive. Specific illustration is provided for the logistic link function. The hierarchical model employs a conjugate beta prior that leads to parametric empirical Bayes estimators of the individual agreement parameters. An example from environmental carcinogenesis illustrates the methods, with motivation derived from estimation of the concordance between two species carcinogenicity outcomes. Based on a large database of carcinogenicity studies, the inter-species concordance is seen to be reasonably informative, i.e. in the range 67–84%. Stratification into pertinent potency-related sub-groups via the logistic model is seen to improve concordance estimation: for environmental stimuli at the extremes of the potency spectrum, concordance can reach well above 90%.     

Conserving Connectivity: Some Lessons from Mountain Lions in Southern California

Abstract: Habitat corridors can be essential for persistence of wildlife populations in fragmented landscapes. Although much research has focused on identifying species and places critical for conservation action, the conservation literature contains surprisingly few examples of corridors that actually have been protected and so provides little guidance for moving from planning through implementation. We examined a case study from southern California that combines monitoring of radio‐collared mountain lions (Puma concolor) with an assessment of land‐protection efforts to illustrate lessons learned while attempting to maintain ecological connectivity in a rapidly urbanizing landscape. As in many places, conservation scientists have provided science‐based maps of where conservation efforts should focus. But implementing corridors is a business decision based not solely on ecological information but also on cost, opportunity cost, investment risk, and other feasibility considerations. Here, the type and pattern of development is such that key connections will be lost unless they are explicitly protected. Keeping pace with conversion, however, has been difficult, especially because conservation efforts have been limited to traditional parcel‐by‐parcel land‐protection techniques. The challenges of and trade‐offs in implementation make it clear that in southern California, connectivity cannot be bought one parcel at a time. Effective land‐use plans and policies that incorporate conservation principles, such as California's Natural Communities Conservation Planning program, are needed to support the retention of landscape permeability. Lessons from this study have broad application, especially as a precautionary tale for places where such extensive and intensive development has not yet occurred. Given how limiting resources are for biodiversity conservation, conservationists must be disciplined about where and how they attempt corridor protection: in rapidly fragmenting landscapes, the opportunity for success can be surprisingly fleeting.     

基于深度学习的地下水水质预测方法研究——以大湾区(广州)地下水多层监测基地氨氮为例

地下水水质预测可以准确反映水质的未来变化趋势,是地下水污染防治中的重要环节。为提高地下水水质自动监测的预测精度,以大湾区(广州)地下水多层监测基地2022年3层地下水的氨氮监测结果为例,进行数据预处理后,构建将集成经验模态分解(EEMD)与深度学习技术(RNN、LSTM及GRU)相结合的复合深度学习模型,开展传统机器学习模型与不同深度学习模型在水质预测效果方面的对比分析,并探讨深度学习模型多步长预测的预测效果。研究结果表明:①与单一深度学习模型相比,结合EEMD的复合深度学习模型解决了预测滞后性问题,具有更高的预测精度和拟合度。②复合深度学习模型的拟合度高于4种传统机器学习模型。4种传统机器学习模型中,仅MLR与RF的预测拟合度与单一深度学习模型接近。③多步长预测结果表明,复合深度学习模型可以准确地预测地下水水质在3 d内的变化趋势。综上,复合深度学习模型展现出更好的预测性能和泛化能力,可为地下水水质预测提供支撑。     

Using Measurements of Nitrogen Oxides to Estimate the Emission Controls Required to Meet the UK Nitrogen Dioxide Standard

In this paper we introduce a new method of analysing the relationship between nitrogen dioxide (NO₂) and oxides of nitrogen (NO_x) concentrations using data from the UK National Air Quality Archive. The study includes analyses of measurements from two different types of site in London, a kerbside site: Cromwell Rd, and three background sites: Bridge Place, London Bloomsbury and West London, over several years (1991–7). The data in some years showed that hourly NO₂ concentrations exceeded the UK Standard of 150 ppb. Data were binned, averaged, and polynomials fitted at each site. Analysis of the resulting polynomials was used to estimate reductions in NO_x emission required to achieve the National Air Quality Strategy Objective. Examination of the empirical ratio NO₂:NO_x (the 'yield') gives an indication of the sensitivity of the NO₂ to NO_x controls and the amount of NO₂ that would arise from modelled values of total oxides of nitrogen. The response of NO₂ to emission changes is very non-linear, implying 30–45% controls on NO_x may be required.     

Incorporating Collateral Data in Conservation Biology

Abstract:  Conservation biologists often need to set ecological modeling assumptions or estimate parameters from sparse data. In some cases this problem can be addressed by incorporating data from closely related species or from the same species at different sites (i.e., collateral data). Currently no structured methods exist for incorporating such information. An analogous problem in Actuarial science is to set premium rates in situations with little direct data on claim frequency or size. The rates are estimated using actuarial credibility theory, which incorporates collateral data with the direct data. actuarial credibility theory combined with the actuarial control cycle financial management process also provides an adaptive mechanism for updating assumptions. This theory may have some utility for ecologists wanting to incorporate collateral data in an adaptive management framework, a companion to approaches such as Bayesian updating. We describe the historical development of actuarial credibility theory from early ad hoc methods to empirical Bayes approaches. We explore some of the theory's strengths, such as relative simple formulae for incorporation collateral data, and we explore some of the theory's weaknesses, such as the use of the best linear approximation to the Bayes estimate. We illustrate potential applications of the theory using an example on the mortality rate of the Powerful Owl (  Ninox strenua ).     

出口贸易可持续发展:理论内涵、评价模型及经验研究

在人文贸易主义价值取向逐渐兴起的背景下，对外贸易可持续发展观越来越受到关注。选择以出口贸易可持续发展作为研究对象，在对出口贸易可持续发展内涵进行理论阐释的基础上尝试建立测度一国出口贸易可持续发展水平的指标体系和评价模型，最后对中国出口贸易可持续发展水平进行经验研究。结果显示1985-2003年中国出口可持续发展水平大致呈现出曲折缓慢上升趋势，通过灰色聚类可将各年份出口贸易可持续发展水平划分为优、良、中、劣四等。在多数年份中国出口社会经济效益的获取要以同期环境质量下降和资源过度消耗作为代价，且中国出口规模的持续加速上升并未带来出口经济效益水平的提升，这主要源于出口贸易条件的恶化，因此需警惕在中国出现“贫困化出口增长”问题。     

基于复合LSTM模型的PM2.5浓度预测

在对淄博市19个空气质量监测站点监测数据进行分析后,提出了一种基于机器学习的复合模型——灰色关联度分析(GRA)-改进的完备总体经验模态分解(ICEEMD)-长短期记忆网络(LSTM)模型。通过分析淄博市2019年大气污染物和气象数据,选用LSTM模型预测PM_2.5浓度。由于传统单一模块机器学习模型具有训练时间较长和预测精度较低的问题,提出了复合LSTM模型。该模型由3部分组成：GRA,用于PM_2.5浓度影响因素变量筛选;ICEEMD,用于PM_2.5分解、分量筛选和原始大气污染物及气象数据处理;LSTM,用于PM_2.5浓度预测。预测结果表明：淄博市中部丘陵地带PM_2.5浓度高于南部山区和北部平原,东部高于西部;淄博市逐月PM_2.5浓度呈“U”形分布,1月最高,8月最低;淄博市PM_2.5浓度受PM₁₀和CO影响较大,受湿度和温度影响较小。对比单一LSTM模型和GRA-LSTM模型,GRA-ICEEMD-LSTM模型...     

边坡位移的EEMD-PSO-ELM模型预测方法

为解决边坡变形位移预测难度大的问题,利用北斗监测系统获取边坡位移数据,引入集合经验模态分解(EEMD)法、粒子群优化(PSO)和极限学习机(ELM),建立边坡位移预测的EEMD-PSO-ELM模型;以攀钢集团石灰石矿5号监测点为例,对原始数据小波去噪,采用EEMD法将位移时间序列分解为波动项位移和趋势项位移;利用PSO-ELM优化模型预测下一时段位移,叠加2项位移预测结果,得到边坡累计位移预测值,并对比分析预测结果。结果表明:EEMD-PSO-ELM模型位移预测方法的平均相对误差(MRE)为0.15%,均方根误差(RMSE)为0.03,拟合优度为0.999 9,该模型具有一定的精确性和适用性。     

金属矿山员工风险认知与安全行为的关系

为探索金属矿山员工风险认知与安全行为之间的关系,选取金属矿山冒顶坍塌、中毒窒息和车辆伤害3类常见风险,从风险特征维度的视角提出研究假设.通过设计风险认知与安全行为量表开展调查,并对调查所获的397份问卷进行统计分析,利用相关分析、回归分析和中介效应对研究假设进行检验.研究结果表明:员工风险认知对安全行为有显著正向影响作...