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August Zillmer (1831–1893) was a German life insurance actuary in Berlin. He is credited for one of the first German textbooks on actuarial mathematics. His name is associated with the Zillmer method of calculating life insurance reserves. In this paper, August Zillmer’s early contribution to demographic analysis, which is virtually unknown, is described and appreciated. In 1863 he published a discrete population model, and produced several age distributions using a given life table and different population growth rates. He showed that the resulting age distributions will eventually become stable. Although the stable model in demography can be traced back to Euler in 1760, Zillmer’s model is the first dynamic analysis of the influence of changes in population growth rates on the age distribution and population parameters such as mean age. His results and conclusions are discussed and compared with modern demographic methods. Finally, Zillmer’s model is considered as a tool for special population forecasts, where new inputs (births) do not depend on the population size of other age-groups.
The rapidly developing concept of carsharing is an essential and scalable part of sustainable, multimodal mobility in urban environments. There is a clear need for carsharing operators to understand their users and how they use different transportation modes to intensify the development of carsharing and its positive impacts on the environment and urban cohabitation. We foster this understanding by analyzing usage data of carsharing in a medium-sized German city. We compare user groups based on individual characteristics and their carsharing usage behavior. We focus on a station-based two-way carsharing scheme and its relation to free-floating carsharing. Based on different clustering and segmentation approaches, we defined 20 particularly interesting user groups among the carsharing users and analyzed noticeable usage patterns. Additionally, we examined these partially overlapping user groups in the spatial dimension. With these results, we support research and operators in understanding carsharing customers and assessing users’ individual behavior.
Case study teaching is a state-of-the-art didactical method for strategy and management classes. It is a very effective way to combine theory with practice and to involve students in a very active way. Different to the use of existing, secondary teaching cases, this chapter focuses on the production and use of your own case study. Writing and integrating your own teaching case is very beneficial in terms of the alignment between teaching content and the case as well as the overall involvement of both sides, the lecturer and the class. An important chapter objective is about the description of an effective case study writing process as well as about methods to integrate the case into your teaching concept. The writing processes and didactical methods are exemplified by a case study itself (teaching case about Nespresso). Here it is important to give pragmatic advices and to show outcomes in terms of learning and teaching success.
The present study investigated the mental workload associated with driving a vehicle equipped with Lane Keeping Assistance System (LKAS). Specifically, an experiment was carried out with16participants driving with LKAS in four real-world scenarios. Effects on mental workload were evaluated with psychophysiological measures such as heart rate and skin conductance response (SCR). The driving performance, which is also a measure of evaluating mental workload, was assessed by measure such as steering reversal rate, variation of lateral position and steering effort. The result suggested that LKAS has reduced physical workload in the steering task. However, the lane keeping performance was not improved. Moreover, the NASA-TLX showed that participants perceived higher mental workload while driving with LKAS. This effect was mirrored in the SCR. The objective data showed that LKAS was associated with higher steering reversal rate, which might explain the reason of participants perceiving higher mental workload. Overall, it was suggested that the mental workload was higher with the tested LKAS.
Learning how to create UML class diagrams from requirements specifications in textual format is one of the fundamental competences of students in Information Technologies. However, students seem to struggle creating those with all the requested elements. This process is not only challenging for students, but for teachers as well. Teachers, who correct the students solutions, have to take a deeper look at each created diagram to verify whether it is correct or not. Created diagrams can differ from each other or a given solution, but can nevertheless be correct. Also we observed, that the manner of creating class diagrams differ from student to student. Some create them using tools like the Enterprise Architect, others draw them by hand. To support students in the progress of learning how to create UML class diagrams and support teachers, we began realizing a prototype which can visually detect UML class diagram elements and compare them to a given solution. Visual recognition enables universal support no matter how a student created the diagram. Deep learning technologies have lead to major achievements in visual image processing in the recent years. Therefore this paper investigates that they are of use in UML element detection and syntactical analysis.
Work-In-Progress: Converting textual software engineering class diagram exercises to UML models
(2022)
Class diagram exercises are an important part in the development of software engineering students in higher computer science education. Generating textual exercises with sample solutions for such courses is time-consuming for educators, especially with multiple courses and different contexts. According to literature, the automatic generation of diagrams from structured text is possible. However, students often do not receive template based exercise texts but descriptions in natural language, which is still not a closed research topic. To address this problem, this paper discusses a model that analyses real exercise texts used for software engineering education, considers each individual sentences components and provides a class diagram. Due to the complexity of natural language, the model does not deliver perfect results so far, but is a great work in progress for the attempt to generate sample solutions for given exercise texts.
This contribution discusses problems of existing gamified learning platforms as a result of an analysis and reveals a research gap that is addressed using a domain-specific modeling (DSM) approach. Foundations of the DSM approach are described and our vision to use it for improving the creation, adaptability, and extensibility of platforms as well as the exchange of promising gamified learning concepts. Early results are presented and steps ahead are shown.
Introduction:
Across Europe, informal care is an important source of long-term care provision. Governments and companies offer an increasing supply of online support and digital solutions for the domains of health and care. Large-scale evidence on the diffusion of digital technologies among older persons involved in family care is scarce. The article aims to investigate digital inequalities in the context of informal care and to explore the role of socio-economic aspects, health-related factors, and social-environmental factors.
Methods:
Data source is the Survey of Health, Ageing and Retirement in Europe (SHARE), waves 5, 6, 7, and 8. Samples for analysis include 14,059 care recipients and 15,813 caregivers aged 50 years and older. Multivariate logistic regressions model the probability of not using the internet.
Results:
For both caregivers and care recipients, the following characteristics are significantly associated with a higher likelihood of being offline: older age, cognitive limitations, severe impairment of close-up vision, and living in a rural area. In contrast, individuals with a higher level of education, a good financial situation, who are active in the labour market, living with a partner, and have children are more likely to be onliners.
Conclusions:
To ensure that all population groups benefit equally from digital transformation, knowledge about the characteristics of the target users and non-users is crucial. Experts and policy-makers, who consider digital solutions as one remedy for reducing the burden of care and tackling the care crisis, should consider that a large proportion of people involved in informal care are currently offliners.
The COVID-19 pandemic has necessitated support for continued learning in frontline practitioners through online digital mediums that are convenient and fast to maintain physical distancing. Nurses are already neglected professionals for support in training for infection control, leadership, and communication in Pakistan and other developing countries. For that reason, we aimed to deliver a WhatsApp-based intervention for continued learning in nurses who are currently working in both private and public sector. A 12-week intervention was delivered to 208 nurses (102 in the control group and 106 in the intervention group) who had been employed in the clinical setting during data collection. The analysis reveals that nurses in the intervention group show significantly better results for learning in “infection prevention and control” and “leadership and communication.” Results of a content analysis based on participant's feedback also confirm that the WhatsApp-based intervention is a valuable tool for education. This study highlights the effectiveness of online-based digital interventions as a convenient training tool for awareness and management of infectious diseases, leadership, and communication during COVID-19 and beyond. Furthermore, this study emphasizes that group interventions with other healthcare practitioners and the role of on-going longer WhatsApp-based interventions can become integral tools to support continued learning and patient safety practices.