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.     

Older Adults: Work‐Related Issues and Implications for Counseling

In a period of business downsizing, older workers are at risk for unemployment. They often have difficulty finding suitable replacement jobs and frequently face emotional and financial distress. Employers, as well as older workers, may have perceptions and concerns that can create barriers to reemployment. Older workers are a valuable asset in the labor market. Their plight affects society as a whole. Counselors should be aware of work related issues of older workers, career theories and supporting research pertaining to this unique population, and suitable interventions with which to help older adults make smooth career and life transitions.     

On the Characterization of a Mixture of Generalized Power Function Distributions by Conditional Expectation of Order Statistics

In the present article, we give some theorems to characterize the mixture of two generalized power function distributions based on conditional expectation of order statistics.     

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.