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Hintergrund: Trotz einer allgemeinen Krankenversicherungspflicht in Deutschland seit dem Jahr 2009 lebt eine Vielzahl von Menschen in Deutschland ohne bzw. mit eingeschränktem Krankenversicherungsschutz. Zu den Betroffenen zählen wohnungslose Menschen, Bürger:innen aus den Staaten der [zum vollständigen Text gelangen Sie über die oben angegebene URL]

There are continuous research and practical interest to combine different renewable sources within one Smart Grid network. The paper aims to estimate the influence of key economic and social drivers of renewable energy and Smart Grid promotion in OECD member countries. The random effect of the generalized least squares method was used to estimate the empirical model based on the World Bank, OECD, Heritage Foundation, and World Energy Council datasets for a panel of 36 OECD counties. For the empirical estimation, the dependent variables considered are energy renewable electricity output and energy trilemma index, taken as two proxies for Smart Grid development. The results suggest that an increase in GDP p. c. in national economies by 10,000 USD leads on average to a 3.9% decrease in renewable electricity output during 2001–2015. The richer the society, the less renewable energy sources were used for power generation in a group of OECD countries. The last is also supported by the fact that gross fixed capital formation treated as a percentage value of GDP is negatively correlated with structural changes in renewable energy output. The empirical conclusion is that during the study period, OECD countries were mainly oriented to economic growth, which was achieved by consuming non-renewable energy resources, and limited attention was paid to sustainability and Millennium Development Goals. The paper provides policy recommendations for Smart Grid development and points in the future research within OECD countries. AcknowledgmentsComments from the Editor and anonymous referees have been gratefully acknowledged. Leonid Melnyk gratefully acknowledges financial support from the Ministry of Education and Science of Ukraine (0118U003578). Oleksandra Kubatko gratefully acknowledges financial support from the Ministry of Education and Science of Ukraine (0119U100766) and National Research Foundation of Ukraine (2020.01/0135).

Upper limb spasticity(ULS)is a common pathophysiological changes manifest by a structural damage towards the central nervous system (CNS) that includes brain and spinal cord.The current clinical practice of spasticity assessment utilizes Modified Ashworth Scale (MAS) as a subjective tool to measure the severity of spasticity.Lack of objective value, poor sensitivity in detecting minimal changes,and dependency to the interpretation by the assessing clinicians are the several reasons ofthe inter and intra-rater variabilityof the measurement using MAS.These limit the use of MAS in diagnosing, treating,and monitoring spasticity especially in inexperiencedclinicians, hence leadingto inadequate spasticity management.Toovercome this problem, a study is carried out to quantify and develop a data-drivenmodelof ULS detectionbased on MAS. The characteristics that detect the existence of ULSaccording to MAS are identified and adopted to train the machine learning models for smart diagnosis purpose to assist the physicians to effectively manage spasticity.

The article is devoted to the problem of psychological well-being of a personality during the COVID-19 pandemic. The essence of the phenomenon of personality’s psychological well-being and its connection with the social situation and social stress is determined. Subjective severity of the impact of the quarantine on their personal lives and psychological well-being differs between respondents in the first group (72 who rated such impact as high) and the second group (149 people who rated the impact as low). It is impossible to draw unambiguous conclusions about the influence of certain socio-psychological and demographic predictors on the perception of psychological well-being. There is a statistically significant relationship between the subjective high assessment of the impact of the coronavirus situation (first group) and the level of education (almost the same for both groups), which may indicate an indirect effect and requires additional verification. The results of the study show that the respondents of the first and second groups experience the period of the COVID-19 pandemic differently, which in general affects the degree of their psychological well-being. The subjects of the first group have less differentiated expectations about the peculiarities of the pandemic situation in the near future and its impact on their lives; they tend to experience asthenic emotions, depressive states, and use ways to overcome stress based on avoidance and social distraction. This is also manifested in the growth of their anxiety and reduces the level of psychological well-being. The subjects of the second group have more differentiated expectations about the peculiarities of the pandemic situation, tend to analyze the circumstances and develop a specific action plan, not prone to anxiety and depression; use problem-oriented ways to overcome stress, which determines in general their fairly high level of psychological well-being., Стаття присвячена проблемі психологічного благополуччя особистості під час пандемії COVID-19. Визначається сутність феномену психологічного благополуччя особистості та його зв’язку з соціальною ситуацією та соціальним стресом. Описуються результати дослідження особливостей психологічного благополуччя громадян України в умовах пандемії коронавірусу.

Providing optimal rehabilitation services to the broad public is one of the greatest challenges in the healthcare sector due to the shortage of rehabilitation physicians and facilities. Recent advances in digitalization and sophisticated data analytics offers new innovative ways in delivering rehabilitation services to enhance the quality of life of people with disabilities. Currently, the development of data-driven solutions for rehabilitation in Malaysia is limited due to multiple factors: medical and rehabilitation data is not digitally stored; the knowledge for the interpretation of clinical data is distributed; in particular, there is a lack of expertise in the field of medical data science. Thus, a data science platform is proposed so that medical expertise can be made available through digital services and is not dependent on human resource, location, time or financial ability. This platform is applied for the smart diagnosis of upper limb spasticity in compliance with clinical practice, and extensible for the other smart rehabilitation applications. By collaborating with the prestigious Fraunhofer Society, their knowhow in industrial data science can be brought to Malaysia towards improved health care in the country. The smart diagnosis system provides advice in classifying the severity level of upper limb spasticity based on the Modified Ashworth Scale and the Modified Tardieu scale. The basis is a measurement system for muscle signal, muscle tone and elbow motion. Users of the smart diagnosis application include rehabilitation physicians, doctors from other specialities, nurses, psychologists, physiotherapists and occupational therapists. The tools provided by the data science platform is deployed to store and analyze the clinical data. Further, the expertise of a rehabilitation physician is emulated in the form of an expert system to determine the severity level of upper limb spasticity.

In this paper, we simulate allocation policies for a two-stage inventory system that receives perfect advance demand information (ADI) from customers belonging to different demand classes. Demands for each customer class are generated by independent Poisson processes while the processing times are deterministic. All customers in the same class have the same demand lead time (the difference between the due date and the requested date) and back-ordering costs. Each stage in the inventory system follows order-base-stock-policies where the replenishment order is issued upon arrival of a customer order. The problem requires a fast and reliable method that determines the system performance under different policies and ADI. Thus, we employ discrete-event simulation to obtain output parameters such as inventory costs, fill rates, waiting time, and order allocation times. A numerical analysis is conducted to identify a reasonable policy to use in this type of system.

This paper discusses the development of an efficient algorithm that minimizes overproduction in the allocation of wafers to customer orders prior to assembly at a semiconductor production facility. This study is motivated by and tested at Nexperia’s assembly and test facilities, but its potential applications extend to many manufacturers in the semiconductor industry. Inspired by the classic bin covering problem, the wafer allocation problem is formulated as an integer linear program (ILP). A novel heuristic is proposed, referred to as the multi-start swap algorithm, which is compared to current practice, other existing heuristics and benchmarked with a commercial optimization solver. Experiments with real-world data sets show that the proposed solution method significantly outperforms current practice and other existing heuristics, and that the overall performance is generally close to optimal. Furthermore, some data processing steps andheuristics are presented to make the ILP applicable to real-world applications.

The semi-online bin covering problem is a NP-hard problem that occurs in a batching processes in a high-end poultry processing line. The objective is to form batches of items with minimal giveaway, which is the difference between the target and realized batch weight. The items in this process are allocated in the order of arrival, and the weight of the first set of items is assumed to be known. We develop a novel hybrid genetic algorithm, combining a genetic algorithm and several local search methods. Simulation experiments based on real-world data are performed to gain managerial insights. These simulations suggest that the proposed algorithm produces high quality solutions within a reasonable time limit.