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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.
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.
Downtimes (outages) are unfavorable and costly events in production. Although approaches exist, they have to be implemented mainly manually and with a huge effort. Language models could be useful to support the production root cause failure analysis and help to get production up and running again more quickly. However, sparse research focused on this point so far. Therefore, it is still unclear how the usage of language models for human assistance in production failure root case analysis should be implemented. A qualitative expert study was conducted to reveal the potentials of such an approach and to find suitable use cases for language models. Based on the insights triggering factors, use cases as well as benefits and risks were identified and summarized within a model.
Recent research has suggested that there is no general similarity measure, which can be applied on arbitrary databases without any parameterization. Hence, the optimal combination of similarity measures and parameters must be identified for each new image repository. This optimization loop is time consuming and depends on the experience of the designer as well as the knowledge of the medical expert. It would be useful if results that have been obtained for one data set can be transferred to another without extensive re-design. This transfer is vital if content-based image retrieval is integrated into complex environments such as picture archiving and communication systems. The image retrieval in medical applications (IRMA) project defines a framework that strictly separates data administration and application logic. This permits an efficient transfer of the data abstraction of one database on another without re-designing the software. In the ImageCLEF competition, the query performance was evaluated on the CasImage data set without optimization of the feature combination successfully applied to the IRMA corpus. IRMA only makes use of basic features obtained from grey-value representations of the images without additional textual annotations. The results indicate that transfer of parameterization is possible without time consuming parameter adaption and significant loss of retrieval quality.
The ImageCLEF 2006 medical automatic annotation task encompasses 11,000 images from 116 categories, compared to 57 categories for 10,000 images of the similar task in 2005. As a baseline for comparison, a run using the same classifiers with the identical parameterization as in 2005 is submitted. In addition, the parameterization of the classifier was optimized according to the 9,000/1,000 split of the 2006 training data. In particular, texture-based classifiers are combined in parallel with classifiers, which use spatial intensity information to model common variabilities among medical images. However, all individual classifiers are based on global features, i.e. one feature vector describes the entire image. The parameterization from 2005 yields an error rate of 21.7%, which ranks 13th among the 28 submissions. The optimized classifier yields 21.4% error rate (rank 12), which is insignificantly better.
An important aspect of achieving global climate neutrality and food security is transforming our food system. To support the goal, Germany has set a national target of reaching a 30% share in organic farming. When looking at the transformation process from conventional to organic farming, it becomes apparent that measures need to be taken to reach the anticipated goal. Using Design Science Research, we model and analyze the as-is farm-to-fork value chain of public out-of-home-eaten meals to identify the central barriers and drivers of organic transformation. From the insights gained in the modeling process, we derive a digital platform model that addresses the current issues. We propose a digitally supported value network instead of a hierarchical value chain to share the co-design opportunities for different stakeholders more equally. We then elaborate on the potential to overcome the barriers to organic transformation with the network-based platform. To specify the main functionalities of the digital platform architecture, we map user requirements with the proposed to-be value network. The results further emphasize the need for a change in the current value chain perspective. We conclusively propose to further develop existing approaches under consideration of our identified requirements and the overall sustainability goal, rather than focusing solely on individual dimensions or metrics.
Objectives: Content-based access (CBA) to medical image archives, i.e. data retrieval by means of image-based numerical features computed automatically, has capabilities to improve diagnostics, research and education. In this study, the applicability of CBA methods in dentomaxillofacial radiology is evaluated.
Methods: Recent research has discovered numerical features that were successfully applied for an automatic categorization of radiographs. In our experiments, oral and maxillofacial radiographs were obtained from the day-to-day routine of a university hospital and labelled by an experienced dental radiologist regarding the technique and direction of imaging, as well as the displayed anatomy and biosystem. In total, 2000 radiographs of 71 classes with at least 10 samples per class were analysed. A combination of co-occurrence-based texture features and correlation-based similarity measures was used in leaving-one-out experiments for automatic classification. The impact of automatic detection and separation of multi-field images and automatic separability of biosystems were analysed.
Results: Automatic categorization yielded error rates of 23.20%, 7.95% and 4.40% with respect to a correct match within the first, fifth and tenth best returns. These figures improved to 23.05%, 7.00%, 4.20%, and 20.05%, 5.65% and 3.25% if automatic decomposition was applied and the classifier was optimized to the dentomaxillofacial imagery, respectively. The dentulous and implant systems were difficult to distinguish. Experiments on non-dental radiographs (10 000 images of 57 classes) yielded 12.6%, 5.6% and 3.6%.
Conclusion: Using the same numerical features as in medical radiology, oral and maxillofacial radiographs can be reliably indexed by global texture features for CBA and data mining.
The medical automatic annotation task issued by the cross language evaluation forum (CLEF) aims at a fair comparison of state-of-the art algorithms for medical content-based image retrieval (CBIR). The contribution of this work is twofold: at first, a logical decomposition of the CBIR task is presented, and key elements to support the relevant steps are identified: (i) implementation of algorithms for feature extraction, feature comparison, and classifier combination, (ii) visualization of extracted features and retrieval results, (iii) generic evaluation of retrieval algorithms, and (iv) optimization of the parameters for the retrieval algorithms and their combination. Data structures and tools to address these key elements are integrated into an existing framework for image retrieval in medical applications (IRMA). Secondly, baseline results for the CLEF annotation tasks 2005–2007 are provided applying the IRMA framework, where global features and corresponding distance measures are combined within a nearest neighbor approach. Using identical classifier parameters and combination weights for each year shows that the task difficulty decreases over the years. The declining rank of the baseline submission also indicates the overall advances in CBIR concepts. Furthermore, a rough comparison between participants who submitted in only one of the years becomes possible.
