Courses in the language and culture of origin and their impact on youth development in cultural transition: A study among immigrant and dual heritage youth in Switzerland

Elena Makarova traces how the concept of intercultural education in German-speaking European countries promotes the inclusion of courses in the Language and Culture of Origin (LCO) for immigrant youth in the school curriculum of host countries. Such courses are assumed to have positive effects on the development of immigrant youth in the host country. Particularly, it has been suggested that participation in LCO courses increases the self-esteem of immigrant youth, facilitates the development of their bicultural identity and improves their integration in the host society. However, there is a lack of empirical evidence on the nature of the effects of LCO course attendance on the acculturation of immigrant youth and their cultural identity. Accordingly, the aim of the study detailed in the chapter is to examine the impact of immigrant youth’s attitudes towards LCO courses and of their attendance of such courses on their acculturation and cultural identity.

A mixed-integer linear programming approach to the optimization of event-bus schedules: a scheduling application in the tourism sector

This paper deals with “The Enchanted Journey,” which is a daily event tour booked by Bollywood-film fans. During the tour, the participants visit original sites of famous Bollywood films at various locations in Switzerland; moreover, the tour includes stops for lunch and shopping. Each day, up to five buses operate the tour. For operational reasons, however, two or more buses cannot stay at the same location simultaneously. Further operative constraints include time windows for all activities and precedence constraints between some activities. The planning problem is how to compute a feasible schedule for each bus. We implement a two-step hierarchical approach. In the first step, we minimize the total waiting time; in the second step, we minimize the total travel time of all buses. We present a basic formulation of this problem as a mixed-integer linear program. We enhance this basic formulation by symmetry-breaking constraints, which reduces the search space without loss of generality. We report on computational results obtained with the Gurobi Solver. Our numerical results show that all relevant problem instances can be solved using the basic formulation within reasonable CPU time, and that the symmetry-breaking constraints reduce that CPU time considerably.

The relationship between competencies acquired through Swiss academic sports science courses and the job requirements

In view of the changes in and growing variety of sports-related occupations, it is highly relevant for educational institutions 10 to know how well the educational contents of their sport science courses meet the professional requirements. This study analyses the relationship between the competencies acquired through academic sports science courses and the requirements of the relevant jobs in Switzerland. The data for this empirical analysis were drawn from a sample of n = 1054 graduates of different academic sport science programmes at all eight Swiss universities. The results show that academic sport science courses primarily communicate sports-specific expertise and practical sports skills. On the other hand, most graduates consider that the acquisition of interdisciplinary competencies plays a comparatively minor role in sport science education, even though these competencies are felt to be an important requirement in a variety of work-related environments and challenges.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.