Micro-electromechanical systems (MEMS) have reduced drastically in size, cost, and power consumption, while improving accuracy. The combination of different sensor technologies is considered a promising step in the monitoring of athletes. Those “wearables” enable the capturing of relevant physiological and tactical information in individual and team sports and thus replacing subjective, time-consuming and qualitative methods with objective, quantitative ones. Prior studies mainly comprised sports categories such as: targeting sports, batting and fielding games as well as net and wall games, focusing on the detection of individual, non-locomotive movements. The increasing capabilities of wearables allow for more complex and integrative analysis expanding research into the last category: invasion sports. Such holistic approaches allow the derivation of metrics, estimation of physical conditions and the analysis of team strategic behavior, accompanied by integrative knowledge gains in technical, tactical, physical, and mental aspects of a sport. However, prior and current researchers find the precise measurement of the actual movement within highly dynamic and non-linear movement difficult. Thus, the present article showcases an overview of the environments in which the wearables are employed. It elaborates their use in individual as well as team-related performance analyses with a special focus on reliability and validity, challenges, and future directions.

Videos have emerged as a dominant medium for educational purposes in many Massive Open Online Course (MOOC) platforms. Online learning videos are visual components of the online learning platforms. They are particularly interesting for students who are visual learners and who learn best by watching the short format engaging videos rather than by just reading or listening to course materials. The paper focuses on the categorization of factors that are responsible for the success/popularity of online learning videos. The research is based on the literature review and video observations of the MOOC platforms, in particular of the Khan Academy and Coursera. In this paper, we propose a tabular and a spider graph as a condensed overview of categories that should commonly be utilized by the online learning video production to make them successful.

Diabetes is a life-threatening disease that should be diagnosed and treated as early as possible. In this paper, Recursive Feature Elimination (RFE) and a Genetic Algorithm (GA) have been used for the Feature Selection (FS) of two different diabetes datasets of different patient heritages, in combination with K-Nearest Neighbors (KNN) and Random Forest (RF) classifiers for an optimized diabetes prediction. In our paper, RF shows a better performance compared to KNN. The level of accuracy also highly depends on the dataset used. The Iraqi Society Diabetes (ISD) dataset results in a notably higher accuracy than the Pima Indian Diabetes (PID) dataset using the same FS and classification method. The performance of KNN has been improved by combining it with RFE or GA for the FS, while RF deteriorates when applied in combination with. GA is computationally less efficient than RFE and shows a lower accuracy.

Resource management and planning in railway operating companies is a highly complex task. Based on simulation, service planning, vehicle rotation planning, personnel rotation planning and operations management, the resources (e.g. people, vehicles, and infrastructure) must be combined to timetables and rosters. In this work, the development of an optimization agent, using a genetic algorithm, for an existing distributed software product for resource planning (RailOpt) is treated. Main aspects are the description of an underlying real world problem, the definition of a fitness function in order to solve a sub-problem, the particular implementation of the GA and its configuration.