Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.     

Centerline-SMED integration for machine changeovers improvement in food industry

This paper demonstrates how it is possible to combine SMED with Centerlining in complex production environments with numerous changes of format, product, raw materials and tools. This research proposes a methodology involving an initial application of Centerlining and, once the machinery and the process are stable, the subsequent implementation of SMED. Furthermore, it aims to show that it is possible to achieve improved output and reduced machinery changeover time while ensuring that the effect achieved by SMED is more stable. This increases the organizational advantages with respect to the implementation of SMED without prior application of Centerlining. The most important overall conclusion is that a successful application of SMED must always be accompanied by another type of tool or technique to maximize the results of its application. And, as presented in this work, one technique that enables significant improvements to SMED is the application of Centerlining.     

Reliable error estimation for Sobol’ indices

In the field of sensitivity analysis, Sobol’ indices are sensitivity measures widely used to assess the importance of inputs of a model to its output. The estimation of these indices is often performed through Monte Carlo or quasi-Monte Carlo methods. A notable method is the replication procedure that estimates first-order indices at a reduced cost in terms of number of model evaluations. An inherent practical problem of this estimation is how to quantify the number of model evaluations needed to ensure that estimates satisfy a desired error tolerance. This article addresses this challenge by proposing a reliable error bound for first-order and total effect Sobol’ indices. Starting from the integral formula of the indices, the error bound is defined in terms of the discrete Walsh coefficients of the different integrands. We propose a sequential estimation procedure of Sobol’ indices using the error bound as a stopping criterion. The sequential procedure combines Sobol’ sequences with either Saltelli’s strategy to estimate both first-order and total effect indices, or the replication procedure to estimate only first-order indices.     

选择试剂容器减少废液处理费用以提高实验效率和质量

实验室科学家用于完成研究的工作时间是有限的。在如今现代化、实验仪器密集的实验室里,每年用于一位科学家和实验室场地的开销可能超过＄250000或＄120/h。由于完成研究的成本高,而且时间有限,因此需要考虑许多因素来最优化的利用科学家时间。这篇文章以一个实际调查为例讨论了如下因素,包括实验室工作流程,高效液相色谱(HPLC)分离中的溶剂质量以及如何避免重复性工作,如何使用简单自动化设备来监测实验状态从而除去多步骤和减少工作频率以节省科学家的时间。当将所有小细节纳入考虑范围中时,因节省细碎的时间而对实验成本的节约和实验效率的提升具有非常重要的潜在影响。     

Bias Correction in the Type I Generalized Logistic Distribution

Four strategies for bias correction of the maximum likelihood estimator of the parameters in the Type I generalized logistic distribution are studied. First, we consider an analytic bias-corrected estimator, which is obtained by deriving an analytic expression for the bias to order n ^?1; second, a method based on modifying the likelihood equations; third, we consider the jackknife bias-corrected estimator; and fourth, we consider two bootstrap bias-corrected estimators. All bias correction estimators are compared by simulation. Finally, an example with a real data set is also presented.     

Asymptotic efficiency of a competing risks model: A two-sample case

A generalized Cox regression model is studied for the covariance analysis of competing risks data subject to independent random censoring. The information of the maximum partial likelihood estimates is compared with that of maximum likelihood estimates assuming a log linear hazard function.The method of generalized variance is used to define the efficiency of estimation between the two models. This is then applied to two-sample problems with two exponentially censoring rates. Numerical results are summarized ane presented graphically.The detailed results indicate that the semi-parametric model wrks well for a higher rate of censoring. A method of generalizing the result to type 1 censoring and the efficiency of estimating the coefficient of the covariate are discussecd. A brief account of using the results to help design experiments is also given.     

Learning Style Characteristics of Adult Dependent Decision Makers: Counseling and Instructional Implications

Dependent decision makers form a significant population of students and adults who seek help from career counselors. This study investigated the learning style characteristics of dependent decision makers in an urban community college setting. The results suggest that the dependent decision makers as a group may have reduced motivation for learning, show limited persistence, and take less responsibility for their learning. Because of these learning style characteristics, we recommend that these characteristics be considered when making counseling interventions and instructional modifications when working with dependent decision makers.