Neubrandenburg University of Applied Sciences

This study aimed to quantify patient preferences and assess how therapy success, copayment, and specific technical aspects influence acceptance of digital neurorehabilitation technologies. A Discrete Choice Experiment (DCE) was conducted among 1,259 individuals, including stroke survivors and members of the general population, to evaluate preferences for seven attributes of digital neurorehabilitation technologies: therapy success (within 6 months), monthly copayment, and five technical aspects (e.g., contact with professionals, therapy location, information provision, explanation format, and data processing). A fractional factorial design was applied, and data were analyzed using a Mixed Logit Model. Willingness to Pay (WTP) and predicted uptake probabilities (UP) were calculated based on individual preferences. Therapy success had the greatest influence on decision-making (β₁₀₀% = 1.47; p < .001), followed by monthly copayment (β₀€ = 0.86; p < .001). Relevant technical aspects included direct contact with professionals (β_direct = 0.54; p < .001) and choice of therapy location (β_place = 0.33; p < .001). Multimedia-based explanations were preferred over text (β_video = 0.26; β_move = 0.13; both p < .001). Despite equal therapy success, UP varied substantially across modeled interventions (44%, 65%, and 84%) due to differences in technical aspects alone, as calculated from estimated WTP values. Acceptance of digital neurorehabilitation technologies can be systematically assessed using stated preference methods. The findings demonstrate that specific technical aspects can decisively influence acceptance, even when therapy success remains unchanged. This underscores the importance of incorporating patient-valued features into digital health design and provides actionable insights for policy makers and payers aiming to support effective and accepted digital care models.

Fatigue is a prevalent and debilitating symptom in patients with liver cirrhosis (LC), characterized by persistent exhaustion that impairs both physical and mental function. Despite its significant impact fatigue is often overlooked in clinical practice due to its complex and subjective nature. Emerging evidence suggests central mechanisms underlying fatigue in LC, with potential links to malnutrition and sarcopenia. This study aimed to determine the prevalence of fatigue in LC patients, investigate its associations with malnutrition and sarcopenia, and identify independent indicators to aid in recognizing high-risk individuals. Additionally, the study explored the trajectory of fatigue in a structured nutritional intervention in malnourished LC patients. In a multicentric cross-sectional study, 91 LC patients and 92 matched healthy controls were recruited. Analysis focused on 56 LC patients, assessing both central and peripheral fatigue with the Fatigue Severity Scale (FSS), along with anxiety and depression (Hospital Anxiety and Depression Scale, HADS), and loneliness (De Jong Gierveld Scale). Body composition, malnutrition, and sarcopenia were evaluated using bioelectrical impedance, Global Leadership Initiative on Malnutrition (GLIM) and European Working Group on Sarcopenia in Older People (EWGSOP) II criteria. Laboratory parameters and waist circumference as indicator for ascites were assessed. Functional status was measured via handgrip strength (Jamar Dynamometer) and gait speed, and physical activity levels were assessed using the International Physical Activity Questionnaire short form (IPAQ SF). Dietary intake was evaluated with a validated Food Frequency Questionnaire (FFQ). The course of fatigue was investigated in a single-arm exploratory, intensified three-month nutritional intervention study with a 6-month follow-up in malnourished LC patients. Fatigue, affecting 68 % of LC patients, was more common than in controls and linked to both central and peripheral symptoms. LC patients also experienced higher rates of anxiety, depression, and mild loneliness. While 59 % had malnutrition and 23 % sarcopenia, neither were related to fatigue, nor anemia markers like hemoglobin or hematocrit. Fatigue was linked to increased use of proton pump inhibitors and antibiotics. Fatigue severity was correlated with increased anxiety, depression, loneliness, increased waist circumference, and lower gait speed and physical activity. Regression analysis showed increased waist circumference and reduced gait speed as independent predictors of fatigue. Nutritional intervention in malnourished LC patients led to improvements in fatigue, anxiety, and depression. Ascites and gait speed are practical clinical indicators of fatigue in LC patients. Nutritional therapy shows promise in alleviating fatigue and malnutrition in LC patients, offering a potential therapeutic approach. DRKS00021124, DRKS00021181.

Zusammenfassung Die Deutsche Rentenversicherung verzeichnet jährlich rund 165 000 Neuzugänge in die Erwerbsminderungsrente. Das Risiko einer Erwerbsminderung hängt von verschiedenen, nicht nur individuellen Faktoren ab. Vor dem Hintergrund der Bedeutung von Partnerschaften für die Gesundheit, der Herausforderungen bei der Beantragung einer Erwerbsminderungsrente und des wachsenden Anteils von Personen, die nicht in Partnerschaften leben, untersuchen wir, wie der Partnerschaftsstatus und Partnercharakteristika mit dem Zugangsrisiko in die Erwerbsminderungsrente zusammenhängen. Anhand von SOEP-RV-Daten zeigen ereignisanalytische Auswertungen, dass Personen, die mit dem/der Partner*in zusammenleben, ein geringeres Zugangsrisiko haben. Zudem stehen sowohl ein hohes Bildungsniveau als auch eine Erwerbstätigkeit des/der Partner*in im Zusammenhang mit einem geringeren Zugangsrisiko, während für dessen/deren Gesundheitsstatus kein signifikanter Zusammenhang festgestellt wird.

Structured quantitative data, such as survey responses in human resource management research, are often analysed using machine learning methods, including logistic regression. Although these methods provide accurate statistical predictions, their results are frequently abstract and difficult for non-specialists to comprehend. This limits their usefulness in practice, particularly in contexts where eXplainable Artificial Intelligence (XAI) is essential. This study proposes a domain-independent approach for the autonomous classification and interpretation of quantitative data using visual processing. This method transforms individual responses based on rating scales into visual representations, which are subsequently processed by Convolutional Neural Networks (CNNs). In combination with Class Activation Maps (CAMs), image-based CNN models enable not only accurate and reproducible classification but also visual interpretability of the underlying decision-making process. Our evaluation found that CNN models with bar chart coding achieved an accuracy of between 93.05% and 93.16%, comparable to the 93.19% achieved by logistic regression. Compared with conventional numerical approaches, exemplified by logistic regression in this study, the approach achieves comparable classification accuracy while providing additional comprehensibility and transparency through graphical representations. Robustness is demonstrated by consistent results across different visualisations generated from the same underlying data. By converting abstract numerical information into visual explanations, this approach addresses a core challenge: bridging the gap between model performance and human understanding. Its transparency, domain-agnostic design, and straightforward interpretability make it particularly suitable for XAI-driven applications across diverse disciplines that use quantitative response data.

The aim of this study was to determine claw health and lameness prevalence in cows housed in CBPs in southern Germany. Eight farms that housed their dairy cows in CBPs were visited for data collection once in the warm season and once in the cold season between January and December 2023. The first visit was during hoof trimming of the herd to identify claw disorders, score lameness, and assess the bedded pack resting area. Lameness was scored again and the bedded pack resting area assessed at the second visit. To compare claw health at cow and farm levels, a cow claw score (CCS) and a farm claw score (FCS) were calculated using geometric severity scores. The prevalence of lameness at cow level was 9.4% in the cold season and 11.1% in the warm season, which were lower than values reported in studies that investigated cubicle free-stall barns. The low prevalences of lameness and claw disorders were reflected in a CCS of 8 and FCS of 9, which are defined as excellent. Based on our results, CBPs are associated with low lameness prevalence and favourable claw health.

BackgroundThe present study aimed to investigate the psychometric properties of the German Hogg Eco-Anxiety Scale (HEAS) as a reliable and valid instrument to measure eco-anxiety, and to explore its associations with sociodemographic and psychological variables.Methods322 German speaking participants (67.4% female; M = 36.64 [SD = 14.77] years old) were recruited via the internet and social media. Confirmatory factor analyses, reliability and correlational analyses, independent sample t-tests, and a multiple regression analysis were conducted.ResultsBoth in confirmatory factor analyses tested models were acceptable with an even better model fit of the four-factorial structure (CFI = 0.96, TLI = 0.94, RMSEA = 0.08, SRMR = 0.04) than of the second-order model (CFI = 0.94, TLI = 0.93, RMSEA = 0.08, SRMR = 0.06). The HEAS total scale (α = 0.91, ω = 0.91) and the HEAS subscales Affective Symptoms (α = 0.87, ω = 0.86), Rumination (α = 0.84, ω = 0.84), Behavioral Symptoms (α = 0.79, ω = 0.79) and Personal Impact Anxiety (α = 0.90, ω = 0.90) had good to excellent internal consistency coefficients. Correlational analyses showed significant associations between the HEAS total scale and subscales and measures of climate anxiety, psychological distress, partially self-efficacy and social support as well as some sociodemographic variables. Some significant sociodemographic differences were found for the HEAS total scale and subscales regarding gender and parental status but not age groups. Our multiple regression analysis resulted in psychological distress as the only significant predictor of eco-anxiety.ConclusionThe German HEAS is a reliable and valid instrument to assess anxiety about ecological problems.

Abstract The effect of pulsed electric field (PEF) treatment strongly depends on specific product properties, e.g. the phenotype of the processed plant species. However, for food products it is crucial that quality parameters are maintained and that processing is reproducible. In the present study, PEF treatment was used for improving the extraction of hop compounds for beer production. Considering the fact that the flavor and aroma of a beer develops during the boiling process and is highly dependent on the hops used, PEF was applied as a pre-extraction procedure treating concentrated hop suspensions to improve the extractability of aroma compounds. A total of eight aroma and bitter hops were studied to consider putative variety-specific effects of PEF. Investigations of the bitterness and flavor (sensorial/instrumental) also showed putative and hop-dependent influences of the PEF treatment: Significant increases in the bittering units of between 48% (cv. ‘Polaris’) and 285% (cv. ‘Huell Melon’) were observed in three concentrated and PEF-treated hop extracts. A change of olfactory attributes was not perceived. Similarly, SPME-GC-MS was only able to determine negligible changes in 21 volatile aroma compounds, including four off-flavors. However, some larger increases in heptanoic and octanoic acid were observed in the PEF-treated cultivar ‘Huell Melon.’ The findings of the present study indicate that PEF can be successfully applied as a pre-extraction technique for treating hop components, suggesting its potential use in breweries for increasing the bittering units without negatively influencing the overall flavor, or for lowering costs by reducing the required quantities of expensive hops.

Landscape management, agricultural sciences and geoinformatics are inseparable nowadays, and this should also be reflected in teaching. &amp;#160;For example, green areas provide an important biodiversity, are positively associated with mental well-being and play a critical role in mitigating the impacts of climate change by absorbing carbon dioxide, reducing heat islands in urban areas, and providing shade and cooling. However, climate change is also affecting green areas.On the other hand, geoinformatics offers powerful tools for assessing, monitoring and sustainable planning for all types of landscapes such as urban, rural and natural areas, and deserts. It relates to all data with a spatial reference with the focus on data acquisition, management, analysis, visualization and dissemination. Geographic information systems and remote sensing (aerial photography, satellite and radar images) play a major role for the use of geoinformatics in the geosciences, such as geography, geology, agricultural sciences, and ecology.Prior to this, the Tunisian GEOMAG project was able to identify a lack of education in geomatics in the center of Tunisia, the Sahel region, which includes the governorates of Sousse, Mahdia, Monastir and Kairouan. This project included in particular an evaluation phase consisting of a self-assessment of 11 Tunisian universities (i.e. 51 &amp;#8220;institutional&amp;#8221; components) and a national survey of 66 public or private companies active in the geomatics sector.To address this proven problem, an intensive exchange has been taking place between the Institut sup&amp;#232;rieur agronomique de Chott Mariem (ISA CM ) in Tunisia and the Neubrandenburg University of Applied Sciences (HSNB). So far, the Tunisian students have attended the HSNB's Master's program &amp;#8220;Geomatics&amp;#8221; with a focus on spatial data analysis, landscape and risk management and remote sensing. The curricula are strongly oriented towards the use of Free and Open Source Software (FOSS), open data and open standards. Thanks to the high degree of interdisciplinarity, graduates are offered excellent and exciting career opportunities.The exchange is guaranteed by a contract between the two universities and is supported by the Erasmus program of the European Union. Various theses have been successfully completed to date. The main objectives of these efforts are:Promoting an interdisciplinary education that brings together the perspectives and skills of different disciplines; Integrating geospatial information technologies into land use planning; Fostering innovation and research and encouraging students to explore new approaches; Promote international cooperation between universities and organizations, including opportunities for students to study and work with colleagues from different countries. These objectives reflect the European Commission's priorities for interdisciplinary training, the integration of geoinformation technologies, innovation and research, and international cooperation in the field of territorial planning, agricultural sciences and in the European Green Deal strategy. In the future, these activities are to be expanded and a double Master's degree is to be targeted.The existing and planned activities in the field of higher education can be seen as a pilot project that can be extended to countries such as Morocco, with which there is also very close cooperation, or even beyond.