A Shiny Update to an Old Experiment Game

ABSTRACT

Games can be a powerful tool for learning about statistical methodology. Effective game design involves a fine balance between caricature and realism, to simultaneously illustrate salient concepts in a controlled setting and serve as a testament to real-world applicability. Striking that balance is particularly challenging in response surface and design domains, where real-world scenarios often play out over long time scales, during which theories are revised, model and inferential techniques are improved, and knowledge is updated. Here, I present a game, borrowing liberally from one first played over 40 years ago, which attempts to achieve that balance while reinforcing a cascade of topics in modern nonparametric response surfaces, sequential design, and optimization. The game embeds a blackbox simulation within a shiny app whose interface is designed to simulate a realistic information–availability setting, while offering a stimulating, competitive environment wherein students can try out new methodology, and ultimately appreciate its power and limitations. Interface, rules, timing with course material, and evaluation are described, along with a “case study” involving a cohort of students at Virginia Tech. Supplementary materials for this article are available online.     

Risk Mapping of Groundwater‐Drawdown‐Induced Land Subsidence in Heterogeneous Soils on Large Areas

Groundwater leakage into subsurface constructions can cause reduction of pore pressure and subsidence in clay deposits, even at large distances from the location of the construction. The potential cost of damage is substantial, particularly in urban areas. The large‐scale process also implies heterogeneous soil conditions that cannot be described in complete detail, which causes a need for estimating uncertainty of subsidence with probabilistic methods. In this study, the risk for subsidence is estimated by coupling two probabilistic models, a geostatistics‐based soil stratification model with a subsidence model. Statistical analyses of stratification and soil properties are inputs into the models. The results include spatially explicit probabilistic estimates of subsidence magnitude and sensitivities of included model parameters. From these, areas with significant risk for subsidence are distinguished from low‐risk areas. The efficiency and usefulness of this modeling approach as a tool for communication to stakeholders, decision support for prioritization of risk‐reducing measures, and identification of the need for further investigations and monitoring are demonstrated with a case study of a planned tunnel in Stockholm.     

走进内心的森林--读宁明的诗歌

常人所不具有的飞行体验铸就和强化了诗人宁明的敏感善察、内敛理性和达观通透，这些成为其人其作的精神底色，令其诗歌具有独特的美学意义。     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Estimation of Sobol's sensitivity indices under generalized linear models

We derive explicit formulas for Sobol's sensitivity indices (SSIs) under the generalized linear models (GLMs) with independent or multivariate normal inputs. We argue that the main-effect SSIs provide a powerful tool for variable selection under GLMs with identity links under polynomial regressions. We also show via examples that the SSI-based variable selection results are similar to the ones obtained by the random forest algorithm but without the computational burden of data permutation. Finally, applying our results to the problem of gene network discovery, we identify through the SSI analysis of a public microarray dataset several novel higher-order gene–gene interactions missed out by the more standard inference methods. The relevant functions for SSI analysis derived here under GLMs with identity, log, and logit links are implemented and made available in the R package Sobol sensitivity.     

农产品品牌真实性对品牌推崇的影响研究

以S-O-R(刺激-机体-反应)模型为基础,构建了农产品品牌真实性与品牌推崇之间的关系模型,并以武汉市433名蔬菜消费者为样本进行实证检验。结果表明:农产品品牌真实性对品牌情感和品牌推崇具有显著的正向作用;“真有之情”和“应有之情”均对品牌推崇具有显著的正向影响;“真有之情”和“应有之情”在农产品品牌真实性与品牌推崇之间起到部分中介作用;品牌敏感在农产品品牌真实性和“真有之情”间起到负向调节作用。     

多元文化背景下民族地区教师的跨文化敏感性及其培育

进入21世纪以来,越来越多来自不同地区、具有不同文化背景的民族学生走向学校进入了课堂,教师与各民族学生间的 跨文化交流日益增多。面对来自不同民族、不同文化背景的学生,教师需具备一定的跨文化知识,才能正确指导文化多样的各 民族学生,才能正确处理好新时代教师与学生、学生与学生之间的关系,使教师与学生、学生与学生之间和谐相处,共同进步。 这是民族地区教师教育发展的关键,也是新时期处理好民族关系、加强民族团结进步教育以及铸牢中华民族共同体意识的时 代要求。     

Global sensitivity analysis with dependence measures

Global sensitivity analysis with variance-based measures suffers from several theoretical and practical limitations, since they focus only on the variance of the output and handle multivariate variables in a limited way. In this paper, we introduce a new class of sensitivity indices based on dependence measures which overcomes these insufficiencies. Our approach originates from the idea to compare the output distribution with its conditional counterpart when one of the input variables is fixed. We establish that this comparison yields previously proposed indices when it is performed with Csiszár f-divergences, as well as sensitivity indices which are well-known dependence measures between random variables. This leads us to investigate completely new sensitivity indices based on recent state-of-the-art dependence measures, such as distance correlation and the Hilbert–Schmidt independence criterion. We also emphasize the potential of feature selection techniques relying on such dependence measures as alternatives to screening in high dimension.     

Generalized Sobol sensitivity indices for dependent variables: numerical methods

The hierarchically orthogonal functional decomposition of any measurable function η of a random vector X=(X₁,?…?, X_p) consists in decomposing η(X) into a sum of increasing dimension functions depending only on a subvector of X. Even when X₁,?…?, X_p are assumed to be dependent, this decomposition is unique if the components are hierarchically orthogonal. That is, two of the components are orthogonal whenever all the variables involved in one of the summands are a subset of the variables involved in the other. Setting Y=η(X), this decomposition leads to the definition of generalized sensitivity indices able to quantify the uncertainty of Y due to each dependent input in X [Chastaing G, Gamboa F, Prieur C. Generalized Hoeffding–Sobol decomposition for dependent variables – application to sensitivity analysis. Electron J Statist. 2012;6:2420–2448]. In this paper, a numerical method is developed to identify the component functions of the decomposition using the hierarchical orthogonality property. Furthermore, the asymptotic properties of the components estimation is studied, as well as the numerical estimation of the generalized sensitivity indices of a toy model. Lastly, the method is applied to a model arising from a real-world problem.     

强水敏稠油油藏CO2 吞吐技术研究

郑408 块为典型的强水敏稠油油藏,由于储层能量不足和水敏性强,采用天然能量开发、注防膨水开发和蒸汽吞吐开发效果较差。通过室内实验研究了CO2 在郑408 原油中的溶解作用,认识了CO2 吞吐回采阶段渗流特征,基于数值模拟方法优化得到了郑408 块CO2 吞吐开发方案。研究表明,CO2 溶于稠油后,可使稠油的体积大幅度膨胀,原油黏度将大大降低;CO2 吞吐回采阶段,由于稠油黏度较高,CO2 在原油中析出后以小气泡形式分散在原油中,形成“泡沫油”渗流状态,“泡沫油”可以提高稠油的流动能力,增加原油的弹性能量,降低地层压力下降速度;数值模拟结果表明,郑408 块CO2 吞吐周期注入量优化值为100 t,注气速度优化值为50 t/d。