FPT-algorithms for some problems related to integer programming

This paper studies a new version of the location problem called the mixed center location problem. Let P be a set of n points in the plane. We first consider the mixed 2-center problem, where one of the centers must be in P, and we solve it in \(O(n^2\log n)\) time. Second, we consider the mixed k-center problem, where m of the centers are in P, and we solve it in \(O(n^{m+O(\sqrt{k-m})})\) time. Motivated by two practical constraints, we propose two variations of the problem. Third, we present a 2-approximation algorithm and three heuristics solving the mixed k-center problem (\(k>2\)).     

The Times They are A’changing: Multigenerational Family Simulations

Multigenerational households are increasingly affecting both the individual and family as well as community organizations and social policies. Social work and other family studies students can profit from educational modalities that use adult learning applications through a systems life-course perspective, the whole family aging over time. Family simulation software—addressing multigenerational families, such as two or more adult generations living together—builds on a previous paper (Marriage & Family Review, Feb. 2015). Social class, among other demographic and environmental variables, is emphasized. Agent-based family social network simulation of multigenerational families can facilitate experiential learning. An automatically generated life events report, based on both factual data and specific family characteristics, can be used as a classroom case study for role playing and assessing.     

Reclaiming the future: (In)visible dirt borders in Sammy Baloji’s mining photomontages

ABSTRACT

This paper analyses how Baloji’s photographic work Essay on Urban Planning and Mémoire/Kolwezi rely on the dialectic of visibility/invisibility of mining dirt to reveal similarities as well as discrepancies between past and present in capital and labour flows. Conflating archives of humans on display and photographs of abandoned mining sites, Baloji’s figurations of mining dirt unravel how perceptions of dirt were used by the colonial system to impose geographies of exclusion that have become invisible. While they expose how postindependence Congo has failed to appropriate its resources, Baloji’s images of artisanal miners working with mining dirt further epitomise how the disposable objects (often dematerialised via the web) consumed in rich nations involve the disposable lives of workers in the Congo within material dirt borders one can no longer see.     

Testing an Integrative Contextual Career Development Model With Adolescents From High‐Poverty Urban Areas

Variations in the economic recovery rate across the United States have led to even greater chasms that separate the employed, unemployed, and underemployed (DeSilver, 2017 ). Therefore, understanding and supporting the career development of future generations is critical—especially for those who live outside the context of social privilege. The authors examined the applicability of the integrative contextual model of career development (ICM; Lapan, 2004 ) to a sample of 220 adolescents (129 boys, 91 girls) from a high‐poverty urban area. Results indicated that a canonical variate comprising foundational ICM skills (e.g., career exploration, goal setting) predicted a variate composed of ICM outcomes (e.g., self‐efficacy, vocational identity), thus supporting the usefulness of the ICM framework for this population. The skill of setting viable career goals was an especially strong predictor of outcomes. Implications for career counseling with adolescents living in high‐poverty urban areas and directions for future research are discussed.     

Non-asymptotic sequential confidence regions with fixed sizes for the multivariate nonlinear parameters of regression

In this paper we consider a sequential design for the estimation of nonlinear parameters of regression with guaranteed accuracy. Non-asymptotic confidence regions with fixed sizes for the least squares estimates are used. The obtained confidence region is valid for finite numbers of data points when the distributions of the observations are unknown.     

Nonparametric control charts based on runs and Wilcoxon-type rank-sum statistics

In this article, we introduce three new distribution-free Shewhart-type control charts that exploit run and Wilcoxon-type rank-sum statistics to detect possible shifts of a monitored process. Exact formulae for the alarm rate, the run length distribution, and the average run length (ARL) are all derived. A key advantage of these charts is that, due to their nonparametric nature, the false alarm rate (FAR) and in-control run length distribution is the same for all continuous process distributions. Tables are provided for the implementation of the charts for some typical FAR values. Furthermore, a numerical study carried out reveals that the new charts are quite flexible and efficient in detecting shifts to Lehmann-type out-of-control situations.     

A Bayesian Approach for Analyzing Results of Vehicle Collision Tests

Certain motor vehicle safety standards stipulate a collision test speed and a set of performance criteria that vehicles must satisfy during or after the collision test. For example, Federal Motor Vehicle Safety Standard 301 requires a 30 mile per hour (mph) barrier collision and specifies a certain maximum allowable limit on the total spillage of fuel. Vehicle designs are required to meet this standard; however, when collision tests are conducted at speeds higher than the standard, vehicles do not always satisfy the performance criteria. This paper develops a mathematical model for estimating the probability of meeting the standard by using a Bayesian framework to incorporate engineering judgment with collision test results. The model is based on the idea that there are random features to a vehicle's ability to meet performance standards in a collision, especially at such elevated speeds. Example calculations are included to illustrate the estimation of the probability of meeting the standard and to compare it with a maximum likelihood approach.     

An intuitive risk factors search algorithm: usage of the Bayesian network technique in personalized medicine

The article focuses on the application of the Bayesian networks (BN) technique to problems of personalized medicine. The simple (intuitive) algorithm of BN optimization with respect to the number of nodes using naive network topology is developed. This algorithm allows to increase the BN prediction quality and to identify the most important variables of the network. The parallel program implementing the algorithm has demonstrated good scalability with an increase in the computational cores number, and it can be applied to the large patients database containing thousands of variables. This program is applied for the prediction for the unfavorable outcome of coronary artery disease (CAD) for patients who survived the acute coronary syndrome (ACS). As a result, the quality of the predictions of the investigated networks was significantly improved and the most important risk factors were detected. The significance of the tumor necrosis factor-alpha gene polymorphism for the prediction of the unfavorable outcome of CAD for patients survived after ACS was revealed for the first time.