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The objective of this research project is to develop a solar-powered refrigerator in the lower capacity range of up to 5 kW of cooling power. With the use of liquid pistons and one of the most efficient thermodynamic cycles known, the Stirling cycle, this product has the potential to outperform rival solar cooling technologies while providing inexpensive, reliable, quiet, environmentally-friendly, and efficient solar cooling for residential use, due to its straightforward manufacturing, simple design and inert working gas. Presented in this paper are the newest results of the theoretical and experimental investigation into deducing the key design parameters and system configuration of the so-called Liquid Piston Stirling Cooler (LPSC), which will help lead to optimal performance. Computer models of the complex unconstrained system have been constructed and validated using the modelling software Sage and shown to replicate system behavior with reasonable accuracy in experiments. The models have been used to predict system improvements and identify limitations imposed by the use of liquid pistons. The results to date provide a unique insight into a relatively little studied area in Stirling cycle research.
Predictive Modeling (PM) techniques are gaining importance in the worldwide health insurance business. Modern PM methods are used for customer relationship management, risk evaluation or medical management. This article illustrates a PM approach that enables the economic potential of (cost-)effective disease management programs (DMPs) to be fully exploited by optimized candidate selection as an example of successful data-driven business management. The approach is based on a Generalized Linear Model (GLM) that is easy to apply for health insurance companies. By means of a small portfolio from an emerging country, we show that our GLM approach is stable compared to more sophisticated regression techniques in spite of the difficult data environment. Additionally, we demonstrate for this example of a setting that our model can compete with the expensive solutions offered by professional PM vendors and outperforms non-predictive standard approaches for DMP selection commonly used in the market.
A DDR2 SDRAM test setup implemented on the Griffin III ATE test system from HILEVEL Technologies is used to analyse the row hammer bug. Row hammer pattern experiments are compared to standard retention tests.
The analysis confirms that the row hammer effect is caused by a charge excitation process depending on the number of stress activation cycles. The stress has to occur in the local neighborhood of the cells under test.
Shallow impurity levels support the responsible charge carrier transport process in the used DDR2 SDRAM technology
The International Spinal Cord Injury (InSCI) community survey has been developed to collect internationally comparable data on the lived experience of persons with spinal cord injury (SCI) in all 6 WHO regions.
The InSCI survey provides a crucial first step to generate evidence on functioning, health maintenance, and subjective well-being in persons with SCI globally.
A major challenge in setting up the InSCI community survey was to develop a data model and questionnaire that comprehensively captures what matters to people and, at the same time, is feasible and parsimonious in terms of participant’s burden.
This paper outlines the components of the InSCI data model and presents the question selection to operationalize the data model along the 4 guiding principles of efficiency, feasibility, comparability, and truth and discrimination.
The data model consists of 6 components operationalized with 125 questions including functioning (n = 28 body functions and structures; n = 42 activities and participation), contextual factors (n = 26 environmental; n = 19 personal factors), lesion characteristics (n = 2), and appraisal of health and well-being (n = 8).
The InSCI questionnaire presents an efficient and feasible solution with satisfying comparability to other populations; however, its validity and reliability still needs to be confirmed.
This study investigates the impact of generative AI systems like ChatGPT on semi-structured decision-making, specifically in evaluating undergraduate dissertations. We propose using Davis’ technology acceptance model (TAM) and Schulz von Thun’s four-sides communication model to understand human–AI interaction and necessary adaptations for acceptance in dissertation grading. Utilizing an inductive research design, we conducted ten interviews with respondents having varying levels of AI and management expertise, employing four escalating-consequence scenarios mirroring higher education dissertation grading. In all scenarios, the AI functioned as a sender, based on the four-sides model. Findings reveal that technology acceptance for human–AI interaction is adaptive but requires modifications, particularly regarding AI’s transparency. Testing the four-sides model showed support for three sides, with the appeal side receiving negative feedback for AI acceptance as a sender. Respondents struggled to accept the idea of AI, suggesting a grading decision through an appeal. Consequently, transparency about AI’s role emerged as vital. When AI supports instructors transparently, acceptance levels are higher. These results encourage further research on AI as a receiver and the impartiality of AI decision-making without instructor influence. This study emphasizes communication modes in learning-ecosystems, especially in semi-structured decision-making situations with AI as a sender, while highlighting the potential to enhance AI-based decision-making acceptance.
Functioning information constitutes a relevant component for determining patients’ service needs and respective resource use. Diagnosis-Related Group (DRG) systems can be optimized by integrating functioning information.
First steps toward accounting for functioning information in the German DRG (G-DRG) system have been made; yet, there is no systematic integration of functioning information. The G-DRG system is part of the health system; it is embedded in and as such dependent on various stakeholders and vested interests.
This study explores the stakeholder’s perspective on integrating functioning information in the G-DRG system. A qualitative interview study was conducted with national stakeholders in 4 groups of the G-DRG system (health policy, administration, development, and consultations).
Interviews were analyzed using inductive thematic analysis. In total, 14 interviews were conducted (4 administration and 10 consultation group). Three main themes were identified: (1) functioning information in the G-DRG system: opportunities and obstacles, (2) general aspects concerning optimizing G-DRG systems by integrating additional information, and (3) ideas and requirements on how to proceed.
The study offers insights into the opportunities and obstacles of integrating functioning information in the G-DRG system. The relevance of functioning information was evident. However, the value of functioning information for the G-DRG system was seen critically. Integrating functioning information alone does not seem to be sufficient and a systems approach is needed.
Outdoor performance analyses of photovoltaic modules can be advantageous compared to indoor investigations, as they take into account the influences of natural test conditions on the modules. However, such outdoor performance assessments usually suffer from poor accuracies due to undefined test conditions for the modules. This paper reports on a comprehensive concept for improved outdoor analysis which results in performance data with indoor laboratory precision. The approach delivers current-voltage characteristics for even more test conditions than required by the standard IEC 61853-1. Hence, curves of modules’ electrical parameters above irradiance can be deduced for any temperatures. The concept allows precise determination of temperature coefficients for user-defined irradiances taking into account outdoor effects like light-soaking or light-induced degradation. The calibration and measurement uncertainty of the presented outdoor analysis method is evaluated quantitatively. For the measurements an advanced outdoor set-up was used.