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Dieser Band der Reihe „Potsdamer Geographische Praxis“ enthält drei Beiträge, die sich mit dem Handlungskonzept „Tolerantes Brandenburg“ der Brandenburgischen Landesregierung befassen. In allen Beiträgen wird auf der Grundlage empirischer Erhebungen analysiert, wie dieses Konzept zum Umgang mit Rechtsextremismus und Rechtspopulismus sowie zur Demokratieförderung in den letzten Jahren umgesetzt wurde. Die ersten beiden Beiträge haben die sogenannten Zukunftsdialoge „Tolerantes Brandenburg“ zum Gegenstand, die in den Jahren 2015 bis 2017 in allen kreisfreien Städten und Landkreisen Brandenburgs durchgeführt wurden. Der erste Beitrag von Schubarth, Kohlstruck und Rolfes beinhaltet die Ergebnisse der wissenschaftlichen Beobachtung der Zukunftsdialoge; die Ergebnisse beruhen überwiegend auf teilnehmenden Beobachtungen der Zukunftsdialoge und qualitativen Interviews mit Teilnehmenden. Der zweite Beitrag von Bode und Rolfes basiert auf einer quantitativen Methodik und enthält die Auswertungen einer standardisierten Befragung der Teilnehmer/innen der Zukunftsdialoge. Die Ergebnisse beider Untersuchungen liefern wichtige Erkenntnisse und gute Ansatzpunkte, wie einerseits die Institutionen des Beratungsnetzwerks „Tolerantes Brandenburg“ und das Handlungskonzept auf lokaler Ebene eine größere Bekanntheit erlangen könnten und andererseits, welche Schritte hilfreich wären, um eine (noch) stärkere Verankerung des Handlungskonzeptes in den Regionen zu erreichen. Beim dritten Beitrag von Schubarth, Kohlstruck und Rolfes handelt es sich eine Expertise aus dem Jahr 2019. Der Beitrag liefert einen mehrdimensionalen Blick auf das Handlungskonzept aus unterschiedlichen internen wie externen Perspektiven. Dabei wird vor allem auf die gesellschaftlichen und politischen Veränderungen fokussiert, die sich seit 2014 im Handlungsfeld „Demokratiestärkung und Auseinandersetzung mit dem Rechtsextremismus“ ergeben haben. Grundlage der Expertise waren leitfadenzentrierte Interviews.
Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and the resulting interactions with the self-healing actions. In this paper, we first study the state-of-the-art for evaluating the performances of self-healing systems by means of a systematic literature review. We provide a classification of different input types for such systems and analyse the limitations of each input type. A main finding is that the employed inputs are often not sophisticated regarding the considered characteristics for failure occurrences. To further study the impact of the identified limitations, we present experiments demonstrating that wrong assumptions regarding the characteristics of the failure occurrences can result in large performance prediction errors, disadvantageous design-time decisions concerning the selection of alternative self-healing approaches, and disadvantageous deployment-time decisions concerning parameter tuning. Furthermore, the experiments indicate that employing multiple alternative input characteristics can help with reducing the risk of premature disadvantageous design-time decisions.
Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.
Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.
In his essay, Mel Ainscow looks at inclusion and equity from an international perspective and makes suggestions on how to develop inclusive education in a ‘whole-system approach’. After discussing different conceptions of inclusion and equity, he describes international policies which address them. From this international macro-level, Ainscow zooms in to the meso-level of the school and its immediate environment, defining dimensions to be considered for an inclusive school development. One of these dimensions is the ‘use of evidence’. In my comment, I want to focus on this dimension and discuss its scope and the potential to apply it in inclusive education development. As a first and important precondition, Ainscow explains that different circumstances lead to different linguistic uses of the term ‘inclusive education’. Thus, the term ‘inclusive education’ does not refer to an identical set of objectives across countries, and neither does the term ‘equity’.
In his essay, Mel Ainscow looks at inclusion and equity from an international perspective and makes suggestions on how to develop inclusive education in a ‘whole-system approach’. After discussing different conceptions of inclusion and equity, he describes international policies which address them. From this international macro-level, Ainscow zooms in to the meso-level of the school and its immediate environment, defining dimensions to be considered for an inclusive school development. One of these dimensions is the ‘use of evidence’. In my comment, I want to focus on this dimension and discuss its scope and the potential to apply it in inclusive education development. As a first and important precondition, Ainscow explains that different circumstances lead to different linguistic uses of the term ‘inclusive education’. Thus, the term ‘inclusive education’ does not refer to an identical set of objectives across countries, and neither does the term ‘equity’.