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Ongoing research indicates the importance of explainabilty as it provides the rationale for results and decision of information systems to users. Explainability must be considered and implemented in software at the early stage in requirements engineering (RE). For the completeness of software requirements specifications, the elicitation and documentation of explainability requirements is essential. Although there are existing studies on explainability in RE, it is not clear yet, how to elicit and document such requirements in detail. Current software development projects miss a clear guidance, how explainability requirements should be specified. Through a review of literature, existing works for elicitation and documentation of explainability requirements are analyzed. Based on these findings, a template and additional guiding for capturing explainability requirements is developed. Following a design science approach, the template is applied and improved in a research project of the medical information domain. The overview of related work presents the current state of research for the documentation of explainability requirements. The template and additional guiding can be used in other information system context for RE elicitation and documentation. The application of the template and the elicitation guidance in a real world case show the refinement and an improved completeness of existing requirements.
Cells can sense geometrical cues with sizes of several tens of micrometers in their vicinity. Recent in vitro studies show that cells can adapt their shape, align along specific directions, or regulate other cellular functions when grown on surfaces with curvatures larger than their size. Although possible mechanisms for such responses like the alignment along axial cues have been suggested, a detailed understanding of the involved cellular processes remains open. This work addresses this gap by systematically investigating mesenchymal cell and nucleus orientation responses using a low‐cost model surface platform, the CurvChip. Using an array of cylindrically curved topographies with radii of curvatures ranging from tens to hundreds of micrometers, the contact guidance response of cells and nuclei is quantified in dependence on substratum curvature and manipulation of cytoskeletal components. Results suggest a desired perceived curvature for the investigated cells, and a very sensitive and robust curvature perception mechanism, as the effect of pharmacological manipulation of cytoskeletal components is relatively small. Furthermore, a comparison with previously published work strengthens the hypothesis of an involvement of the nucleus in the cell response to three‐dimensional (3D) curvatures.
Student academic success plays an important role in higher education institutions, as it is often used as a core measure of university performance. Internationally, there is an extensive literature on the predictors of academic success. In addition to intellectual variables, the effects of psychological factors have received increased attention. However, there is little literature on cross-cultural differences in the predictors of academic success. This study examines three psychological predictors (hope, self-efficacy, and motivation) and investigates differences between the scores of business students at a university in Germany and a university in Finland. The results indicate differences in students’ scores on the predictor hope. In addition, some differences were found in relation to the students’ demographics. Regarding the predictive power of the factors examined, for hope and self-efficacy this was higher in Finland than in Germany.
Background
Obesity is a major health problem with a significant impact on quality of life and is a major risk factor for other diseases such as diabetes. There is a growing body of evidence that time-restricted eating (TRE) as one form of intermittent fasting (IF) represents a promising weight management strategy. Given the lack of evidence-based weight management strategies in the primary health care setting, the aim of this study is to investigate the effect of TRE in general practice in Germany.
Methods
INDUCT is a type hybrid I, randomized-controlled study conducted in 41 practices within the newly established general practice-based research network (GPBRN) in Baden-Wuerttemberg (FoPraNet-BW). The study population consists of patients with a Body Mass Index between 30–45 kg (kg)/m2. The intervention group receives TRE at the scheme 16:8 (16 h fasting; 8 h energy intake) while the control group receives care as usual. The primary outcome is change in body weight under a 6-month period of TRE. Secondary outcomes are related to the patient (e.g. quality of life) and the practice (e.g. knowledge about research in own practice). As the INDUCT study represents one of the first four use-cases within the Research Network Baden-Wuerttemberg (FoPraNet-BW), feasibility is a further secondary outcome. The target sample size is 208 patients with a 1:1 randomization. An intention-to-treat approach is used for data analysis.
Discussion
INDUCT adds evidence on the effect of TRE as a weight management strategy in general practice. Relevant factors for a sustainable and successful implementation in general practice will be revealed and can be applied for future implementation of TRE interventions in general practice if proven successful. In addition, important lessons learned regarding the conduction of clinical research within FoPraNet-BW will be derived. This fosters a sustainable implementation of a research infrastructure in general practice in Germany.
Lange Rüstzeiten beeinträchtigen die CNC-Werkzeugherstellung durch reduzierte Flexibilität, niedrige Maschinenauslastung und gesteigerten Planungsaufwand, was Lieferverzögerungen und Produktionsengpässe begünstigt. Ökonomisch binden sie Kapital, erhöhen die Produktionskosten, limitieren die Auftragsflexibilität, treiben Lagerkosten hoch und lassen Marktchancen ungenutzt. Erklärbare KI-Modelle können durch die Analyse von Prozessdaten Muster in langen Rüstzeiten identifizieren und Anhaltspunkte für Verbesserungen transparent aufzeigen und somit die Effizienz steigern und Kosten reduzieren.
The application of transfer learning to predict sales demand is an emerging topic that has been attracting more and more attention recently. However, the selection of data to be used for the learning process is not trivial. Data resources are usually scarce and often anonymized to a certain extent, so their usability for successful training is not guaranteed. One solution is to use already developed categorization schemes that group time series based on certain calculated parameters, but the derived categories do not necessarily capture the process of time series formation. This research addresses the question of whether categorization schemes are beneficial for transfer learning approaches by conducting an experiment in which Syntetos’, Boylan’s and Croston’s categorization scheme is used in combination with two deep learning architectures for the transfer learning process. The results show that similar patterns are indeed beneficial for prediction, but that models using all available data perform quite similarly.
In order to evaluate the performance of different types of middle‐ear prostheses, a model of human ear was developed. The model was created using finite element (FE) method with the ossicles modeled as rigid bodies. First, the middle‐ear FE model was developed and validated using the middle‐ear transfer function measurements available in literature including pathological cases. Then, the inner‐ear FE model was developed and validated using tonotopy, impedance, and relative BM motion level curves from literature. Both models are based on preexisting research with some improvements and were combined into one coupled FE model. The stapes in the coupled FE ear model was replaced with a model of a stapes prosthesis to create a reconstructed ear model that can be used to estimate how different types of stapes protheses perform relative to each other as well as to the natural ear. The influence of the diameter of the prosthesis as well as the influence of the sealing and opening of the gap in the footplate were investigated along with different measures such as maximum basilar membrane displacement, intracochlear pressure, pressure in scala vestibuli, oval and round window volume displacements, and prosthesis displacement. This will help in designing new innovative types of stapes prostheses or any other type of middle‐ear prostheses, as well as to improve the ones that are already available on the market.
The geopolitics, government-business relations, and triangular cooperation of ‘Africa+1’ conferences
(2025)
Various countries have adopted Africa strategies and established high-level conferences with African nations over the past decades. We contribute to the interdisciplinary literature at the intersection of geopolitics and business by revealing the historical trajectory of government-business relations in the various Africa initiatives. We particularly aim to explore and compare how governments shape international business through their geopolitical initiatives. We conduct a qualitative analysis of 34 policy documents of the Forum on China–Africa Cooperation, the Tokyo International Conference on African Development, and the Türkiye-Africa Partnership conferences held between 1993 and 2022. Based on our longitudinal analysis, we find a complementarity of government-business relations: governments primarily act as facilitators and channelizers of business engagement, while firms are considered crucial enablers for government policies. We further discuss differences in the identified role and ownership of companies in the three Africa initiatives against the backdrop of the respective geopolitical agendas.
This paper proposes a novel framework – “Transparent Reasoning in Artificial intelligence Cause Explanation” (TRACE) – that combines root cause analysis, explainable artificial intelligence, and machine learning in an understandable way for the worker. The goal is to enhance transparency, interpretability, and explainability in AI-driven decision-making processes as well as to increase the acceptance of AI within an industrial manufacturing area. The paper outlines the need of such a framework, describes the design process, and shows a preliminary mockup, a possible underlying software architecture as well as an evaluation and integration plan in an industrial environment.
The maintenance of special tools is an expensive business. Either manual inspection by an expert costs valuable resources, or the loss of a tool due to irreparable wear is associated with high replacement costs, while reconditioning requires only a fraction. In order to avoid higher costs and drive forward the automation process in production, a German gear manufacturer wants to create an automatic evaluation of skiving gears. As a sub-step of this automated condition detection, it is necessary for wheels to be automatically aligned within a vision-based inspection cell. In extension to a study conducted last year, further image preprocessing steps are implemented in this publication and a new alignment algorithm from the autoencoder family is evaluated. By using an additional synthetic dataset, previous limitations could be clarified. The results show that thorough data preparation is beneficial for all solution approaches and that neural networks can even beat a brute force algorithm.
