From Adaptivity to Adaptation: Examining the Career Construction Model of Adaptation

The author examined a multiple mediation model and a moderation model for the relationships among adaptivity (in terms of future work self and proactivity), career adaptability, adapting responses (in terms of career decision self-efficacy [CDSE] and career engagement), and adaptation (in the form of academic satisfaction) among 282 university students. Results showed that career adaptability, CDSE, and career engagement fully mediated the relations of future work self and proactivity with academic satisfaction. Career adaptability also had a mediation role in the relationships of future work self and proactivity with CDSE and career engagement. The relationship between career adaptability and academic satisfaction was fully mediated by CDSE and career engagement. Career adaptability did not moderate the relationships between adaptivity indices and adapting indices. These results support the serial multiple mediation model, which is introduced in the adaptation model. Career education and career coaching could improve clients' agency through working on their self-regulation resources, self-efficacy beliefs, and overt career behaviors.     

Time series models based on the unrestricted skew-normal process

The standard location and scale unrestricted (or unified) skew-normal (SUN) family studied by Arellano-Valle and Genton [On fundamental skew distributions. J Multivar Anal. 2005;96:93–116] and Arellano-Valle and Azzalini [On the unification of families of skew-normal distributions. Scand J Stat. 2006;33:561–574], allows the modelling of data which is symmetrically or asymmetrically distributed. The family has a number of advantages suitable for the analysis of stochastic processes such as Auto-Regressive Moving-Average (ARMA) models, including being closed under linear combinations, being able to satisfy the consistency condition of Kolmogorov’s theorem and providing the guarantee of the existence of such a SUN stochastic process. The family is able to be represented in a hierarchical form which can be used for the ease of simulation. In addition, it facilitates an EM-type algorithm to estimate the model parameters. The performances and suitability of the proposed model are demonstrated on simulations and using two real data sets in applications.     

Reforming Land‐Tenure Systems in South Africa: Routes to Socio‐Economic and Agricultural Sustainability

Given the historical development of land tenure in South Africa, the aim of this article is to examine the best routes to alleviate poverty and retain sustainable agriculture in the country. First, a theoretical framework is presented that relates land tenure to sustainability, and three historical periods (pre‐colonial, colonial, and apartheid) are then considered to explain the changes in land tenure and their consequences. The progress and main limitations of post‐apartheid land reform to approach agricultural sustainability and alleviate poverty are discussed. Based on the analysis, different possibilities for future land reform are elaborated, followed by some recommendations for future land‐reform policies in South Africa.     

The beta skew-generalized normal distribution

In this article, we develop the skew-generalized normal distribution introduced by Arellano-Valle et al. (2004 Arellano-Valle, R.B., Gomez, H.W., Quintana, F.A. (2004). A new class of skew-normal distribution. Commun. Stat. - Theory Methods. 33(7):1465–1480.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) to a new family of the Beta skew-generalized normal (BSGN) distribution . Here, we present some theorems and properties of BSGN distribution and obtain its moment-generating function.     

University gatekeepers’ use of the rhetoric of citizenship to relegate the status of students with disabilities in Canada

Despite affirmation that students with disabilities should have equal access to education, individuals with disabilities are still not participating to the same degree as individuals without disabilities, particularly within postsecondary institutions. Students in Health and Human Service (HHS) programs experience many unique challenges and disadvantages. In-depth focus groups and interviews were conducted with 14 university stakeholders in HHS programs regarding their perceptions and experiences of working with students with disabilities. We found that the rhetoric of citizenship, specifically notions of rationality, autonomy, and productivity, interacts with beliefs about students with disabilities to allow stakeholders to justify their exclusion or limited participation. Our findings demonstrate how taken-for-granted beliefs can limit the inclusion of students with disabilities in ways that seem natural and unproblematic.     

Inferring gene regulatory networks by an order independent algorithm using incomplete data sets

Analyzing incomplete data for inferring the structure of gene regulatory networks (GRNs) is a challenging task in bioinformatic. Bayesian network can be successfully used in this field. k-nearest neighbor, singular value decomposition (SVD)-based and multiple imputation by chained equations are three fundamental imputation methods to deal with missing values. Path consistency (PC) algorithm based on conditional mutual information (PCA–CMI) is a famous algorithm for inferring GRNs. This algorithm needs the data set to be complete. However, the problem is that PCA–CMI is not a stable algorithm and when applied on permuted gene orders, different networks are obtained. We propose an order independent algorithm, PCA–CMI–OI, for inferring GRNs. After imputation of missing data, the performances of PCA–CMI and PCA–CMI–OI are compared. Results show that networks constructed from data imputed by the SVD-based method and PCA–CMI–OI algorithm outperform other imputation methods and PCA–CMI. An undirected or partially directed network is resulted by PC-based algorithms. Mutual information test (MIT) score, which can deal with discrete data, is one of the famous methods for directing the edges of resulted networks. We also propose a new score, ConMIT, which is appropriate for analyzing continuous data. Results shows that the precision of directing the edges of skeleton is improved by applying the ConMIT score.     

A sustainable supply chain network considering lot sizing with quantity discounts under disruption risks: centralized and decentralized models

This study proposes a framework for the main parties of a sustainable supply chain network considering lot-sizing impact with quantity discounts under disruption risk among the first studies. The proposed problem differs from most studies considering supplier selection and order allocation in this area. First, regarding the concept of the triple bottom line, total cost, environmental emissions, and job opportunities are considered to cover the criteria of sustainability. Second, the application of this supply chain network is transformer production. Third, applying an economic order quantity model lets our model have a smart inventory plan to control the uncertainties. Most significantly, we present both centralized and decentralized optimization models to cope with the considered problem. The proposed centralized model focuses on pricing and inventory decisions of a supply chain network with a focus on supplier selection and order allocation parts. This model is formulated by a scenario-based stochastic mixed-integer non-linear programming approach. Our second model focuses on the competition of suppliers based on the price of products with regard to sustainability. In this regard, a Stackelberg game model is developed. Based on this comparison, we can see that the sum of the costs for both levels is lower than the cost without the bi-level approach. However, the computational time for the bi-level approach is more than for the centralized model. This means that the proposed optimization model can better solve our problem to achieve a better solution than the centralized optimization model. However, obtaining this better answer also requires more processing time. To address both optimization models, a hybrid bio-inspired metaheuristic as the hybrid of imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) is utilized. The proposed algorithm is compared with its individuals. All employed optimizers have been tuned by the Taguchi method and validated by an exact solver in small sizes. Numerical results show that striking similarities are observed between the results of the algorithms, but the standard deviations of PSO and ICA–PSO show better behavior. Furthermore, while PSO consumes less time among the metaheuristics, the proposed hybrid metaheuristic named ICA–PSO shows more time computations in all small instances. Finally, the provided results confirm the efficiency and the performance of the proposed framework and the proposed hybrid metaheuristic algorithm.

     

Identifying Social Indicators for Sustainability Assessment of CCU Technologies: A Modified Multi-criteria Decision Making

Social Indicators Research - This paper analyses the trend and changes in Sri Lanka’s multidimensional poverty before the ethnic war in 2007, the transition through 2009 and after the war in...     

Predictive assessment of copula models

Copulas are powerful explanatory tools for studying dependence patterns in multivariate data. While the primary use of copula models is in multivariate dependence modelling, they also offer predictive value for regression analysis. This article investigates the utility of copula models for model‐based predictions from two angles. We assess whether, where, and by how much various copula models differ in their predictions of a conditional mean and conditional quantiles. From a model selection perspective, we then evaluate the predictive discrepancy between copula models using in‐sample and out‐of‐sample predictions both in bivariate and higher‐dimensional settings. Our findings suggest that some copula models are more difficult to distinguish in terms of their overall predictive power than others, and depending on the quantity of interest, the differences in predictions can be detected only in some targeted regions. The situations where copula‐based regression approaches would be advantageous over traditional ones are discussed using simulated and real data. The Canadian Journal of Statistics 47: 8–26; 2019 © 2018 Statistical Society of Canada     

The legitimization process of students with disabilities in Health and Human Service educational programs in Canada

Policy-makers have articulated the right to education for individuals with disabilities. Nevertheless, students with disabilities remain underrepresented in universities. Students in Health and Human Service (HHS) programs experience many unique challenges that disadvantage them, such as having to complete practicums. In-depth interviews were conducted with 12 students in HHS programs who identified as having a disability to explore the challenges that they experienced. We found that students had to legitimate their ability to perform the roles of student and future practitioner. This legitimization process involved three components: negotiation of the disability label, selective disclosure, and advocacy. Our findings demonstrate how subtle and insidious forms of marginalization and stigmatization work to create barriers for students with disabilities.