A note on some characterizations of the hyperexponential distribution

In this paper, the hyperexponential distribution is characterized using relationships between two moments of order statistics. Also another characterization is introduced by considering the conditional expected spacings of order statistics.     

Selecting Indicators for Assessing Community Sustainable Resilience

Communities are complex systems subject to a variety of hazards that can result in significant disruption to critical functions. Community resilience assessment is rapidly gaining popularity as a means to help communities better prepare for, respond to, and recover from disruption. Sustainable resilience, a recently developed concept, requires communities to assess system‐wide capability to maintain desired performance levels while simultaneously evaluating impacts to resilience due to changes in hazards and vulnerability over extended periods of time. To enable assessment of community sustainable resilience, we review current literature, consolidate available indicators and metrics, and develop a classification scheme and organizational structure to aid in identification, selection, and application of indicators within a dynamic assessment framework. A nonduplicative set of community sustainable resilience indicators and metrics is provided that can be tailored to a community's needs, thereby enhancing the ability to operationalize the assessment process.     

Estimation and prediction of Marshall–Olkin extended exponential distribution under progressively type-II censored data

In this paper, we consider Marshall–Olkin extended exponential (MOEE) distribution which is capable of modelling various shapes of failure rates and aging criteria. The purpose of this paper is three fold. First, we derive the maximum likelihood estimators of the unknown parameters and the observed the Fisher information matrix from progressively type-II censored data. Next, the Bayes estimates are evaluated by applying Lindley’s approximation method and Markov Chain Monte Carlo method under the squared error loss function. We have performed a simulation study in order to compare the proposed Bayes estimators with the maximum likelihood estimators. We also compute 95% asymptotic confidence interval and symmetric credible interval along with the coverage probability. Third, we consider one-sample and two-sample prediction problems based on the observed sample and provide appropriate predictive intervals under classical as well as Bayesian framework. Finally, we analyse a real data set to illustrate the results derived.     

Hemorrhoids during pregnancy: Sitz bath vs. ano-rectal cream: A comparative prospective study of two conservative treatment protocols

Background

Hemorrhoids are a very common ano-rectal condition affecting pregnant females worldwide and representing a major medical and socioeconomic problem. In this paper, we aim to compare the effectiveness of the Sitz bath method with an ano-rectal cream as part of a conservative management protocol to treat hemorrhoids among pregnant Saudi Arabian females.

A consistent estimator of the smoothing operator in the functional Hodrick–Prescott filter

In this article, we consider a version of the functional Hodrick–Prescott filter for functional time series. We show that the associated optimal smoothing operator preserves the “noise-to-signal ratio” structure. Moreover, as the main result, we propose a consistent estimator of this optimal smoothing operator.     

Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages

The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross‐validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log‐likelihood and root mean squared error. The HBKM yields on average the highest out‐of‐sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.     

Inherent Costs and Interdependent Impacts of Infrastructure Network Resilience

Recent studies in system resilience have proposed metrics to understand the ability of systems to recover from a disruptive event, often offering a qualitative treatment of resilience. This work provides a quantitative treatment of resilience and focuses specifically on measuring resilience in infrastructure networks. Inherent cost metrics are introduced: loss of service cost and total network restoration cost. Further, “costs” of network resilience are often shared across multiple infrastructures and industries that rely upon those networks, particularly when such networks become inoperable in the face of disruptive events. As such, this work integrates the quantitative resilience approach with a model describing the regional, multi‐industry impacts of a disruptive event to measure the interdependent impacts of network resilience. The approaches discussed in this article are deployed in a case study of an inland waterway transportation network, the Mississippi River Navigation System.     

Penalized multidimensional fitting for protein movement detection

The three-dimensional structure of a given protein can take different conformations depending upon the reaction it undergoes and its substrate/cofactor/partners binding state. Various methods exist to study these conformational changes but only one, called DynDom, is clearly focused on movement detection. An alternative method is proposed, making use of multivariate data analysis, called ‘penalized Multidimensional Fitting (penalized MDF)’ based on penalized movements of points in order to approach the distances between points after movement to the distances given by the reference matrix. The objective is to detect the amino acids that undergo an important movement by fitting the distances of one conformation to the distances of the second one by modifying only the coordinates of the first one. This method is applied to three different proteins.     

Common criteria for usability review

The propose of this paper is to present a literature review, in a grouping of common criteria for usability approaches of Bastien and Scapin (1993), Nielsen (1994), Shnneiderman(1998), Dix et al (1998), Preece et al (2005) and ISO 9241-110 (2006). After establishment of prerequisites for knowledge of the general characteristics of the users who will use the system, are defined and explained the criteria in common: consistency, user control, ease of learning, flexibility, errors management, reduction of excess and visibility system status. Although there is no determination as to which criteria should be considered when developing an interface and each author presents some specificity in their approach, it is observed that there is equivalence in the measures adopted usability.