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Introduction: Wernicke encephalopathy is a clinical diagnosis that requires a high degree of clinical suspicion to recognize. We report a case of a pregnant patient developing Wernicke encephalopathy in the setting of severe hyperemesis gravidarum. Case Report: The patient was a 22-year-old female 13 weeks pregnant presenting to the emergency department (ED) with neurological deficits after several weeks of hyperemesis gravidarum requiring hospitalization. Exam and workup ultimately revealed the diagnosis of Wernicke encephalopathy. Her symptoms improved after administration of thiamine. Conclusion: Wernicke encephalopathy is a consequence of thiamine deficiency, commonly seen in patients with alcohol use disorder but also with other causes of nutritional deficiency, such as hyperemesis gravidarum. Wernicke encephalopathy is a clinical diagnosis that requires a high degree of suspicion and is, therefore, often missed in the ED setting. Treatment is supplemental thiamine and management of the root cause for nutritional deficiency.

The Conference on Power System Engineering has a long-term tradition. It is an annual meeting of professors and experts from departments and universities where power system engineering is taught. The 21st year of this event was held in Pilsen (Czech Republic) between 7th and 9th September 2022 and was hosted by Department of Power System Engineering of Faculty of Mechanical Engineering, University of West Bohemia in Pilsen.

The Conference on Power System Engineering has a long-term tradition. It is an annual meeting of professors and experts from departments and universities where power system engineering is taught. The 20th year of this event was held in Pilsen (Czech Republic) between 7th and 9th September 2021 and was hosted by Department of Power System Engineering of Faculty of Mechanical Engineering, University of West Bohemia in Pilsen.

This article describes the results of a project whose task is to research and develop a screw expander through the inversion of a screw compressor. The article summarizes aspects of the construction of an experimental device that works with an oil-flooded air screw expander. The expander was subjected to experimental and numerical analysis, the results of which are presented in the article. Numerical analysis to examine the expansion process is performed both on the basis of the analytical geometric description of the working chamber of the expander and on the basis of the geometry obtained from a 3D scan of a real machine. The results of experimental and numerical analysis will be used to integrate an oil-flooded screw expander into an energy unit for the use of low-potential heat, for example in ORC systems.

In recent years, numerous proposals have been made with the aim of establishing a systematic hierarchy of relationships for languages for special purposes (LSP), and different approaches have been classified in terms of vertical and horizontal models. Recently, however LSP research has moved more and more towards Applied Linguistics and a segregation has emerged between studies focusing on systematic topics and those based on concrete case studies. While studies in applied LSP research have increased, work on systematic approaches has been neglected. The present study follows a different approach, in that instead of segregating the analysis of concrete case studies from the work on systematic approaches, examples from case studies will be used for testing and revising systematic approaches. In this paper we shall first address the question based on these examples of which long established classifications need to be revised, and consider further whether revising and applying classifications is the way to go in LSP research or whether there is a need for a new typology. In the present paper we shall thus be building on and developing the work on structuring LSP in (Roelcke 2014) and on project-specific terminology (Pelikan & Roelcke 2015).

Although adolescence is characterized by increasing individuation, parental support represents an important resource especially in early adolescence. This multi-informant study examined the role of parental self-efficacy in providing emotional and instrumental support when early adolescents partially learned from home during the COVID-19 pandemic. Based on a resources model of coping, we examined effects of parental self-efficacy on early adolescents' reports of self-regulated learning (SRL), learning self-efficacy, and positive emotions, mediated via early adolescents' problem-focused and emotion-focused coping. Assumptions were tested among 263 Austrian parent-child dyads. While the mediation assumption was rejected, we identified positive associations between emotional support and SRL, and between problem-focused coping and SRL, learning self-efficacy, and positive emotions. Instrumental support negatively related to SRL, suggesting benefits of emotional over instrumental support.

We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems are provided as support for the mathematical analysis of the approach. The experiments show that FGGA is capable of learning linkages and solving the optimization problems in polynomial time with a polynomial number of evaluations.

In the wake of COVID-19, university students have experienced fundamental changes of their learning and their lives as a whole. The present research identifies psychological characteristics associated with students' well-being in this situation. We investigated relations of basic psychological need satisfaction (experienced competence, autonomy, and relatedness) with positive emotion and intrinsic learning motivation, considering self-regulated learning as a moderator. Self-reports were collected from 6,071 students in Austria (Study 1) and 1,653 students in Finland (Study 2). Structural equation modeling revealed competence as the strongest predictor for positive emotion. Intrinsic learning motivation was predicted by competence and autonomy in both countries and by relatedness in Finland. Moderation effects of self-regulated learning were inconsistent, but main effects on intrinsic learning motivation were identified. Surprisingly, relatedness exerted only a minor effect on positive emotion. The results inform strategies to promote students' well-being through distance learning, mitigating the negative effects of the situation.