Explainable Graph-based Search for Lessons-Learned Documents in the Semiconductor Industry

Industrial processes produce a considerable volume of data and thus information. Whether it is structured sensory data or semi- to unstructured textual data, the knowledge that can be derived from it is critical to the sustainable development of the industrial process. A key challenge of this sustainability is the intelligent management of the generated data, as well as the knowledge extracted from it, in order to utilize this knowledge for improving future procedures. This challenge is a result of the tailored documentation methods and domain-specific requirements, which include the need for quick visibility of the documented knowledge. In this paper, we utilize the expert knowledge documented in chip-design failure reports in supporting user access to information that is relevant to a current chip design. Unstructured, free, textual data in previous failure documentations provides a valuable source of lessons-learned, which expert design-engineers have experienced, solved and documented. To achieve a sustainable utilization of knowledge within the company, not only the inherent knowledge has to be mined from unstructured textual data, but also the relations between the lessons-learned, uncovering potentially unknown links. In this research, a knowledge graph is constructed, in order to represent and use the interconnections between reported design failures. A search engine is developed and applied onto the graph to answer queries. In contrast to mere keyword-based searching, the searchability of the knowledge graph offers enhanced search results beyond direct matches and acts as a mean for generating explainable results and result recommendations. Results are provided to the design engineer through an interactive search interface, in which, the feedback from the user is used to further optimize relations for future iterations of the knowledge graph.Comment: Accepted in the "Computing2021" conference, 15-16 July 2021, London, U

Domain-specific greed

Greed, the insatiable and excessive desire and striving for more even at the expense of others, may be directed toward various goods. In this article, we propose that greed may be conceptualized as a domain-specific construct. Based on a literature review and an expert survey, we identified 10 domains of greed which we operationalized with the DOmain-SPEcific Greed (DOSPEG) questionnaire. In Study 1 (N = 725), we found support for the proposed structure and convergent validity with related constructs. Bifactor-(S-1) models revealed that generic greed is differentially related to the greed domains, indicating that generic greed primarily captures a striving for money and material things. In the second study (N = 591), we found that greed domains had incremental validity beyond generic greed with regard to corresponding criteria assessed via self- and other-reports. We conclude that greed can be conceptualized as a domain-specific construct and propose an onion model reflecting this structure

Predicting ground reaction forces of human gait using a simple bipedal spring-mass model

Aircraft design must be lightweight and cost-efficient on the condition of aircraft certification. In addition to standard load cases, human-induced loads can occur in the aircraft interior. These are crucial for optimal design but difficult to estimate. In this study, a simple bipedal spring-mass model with roller feet predicted human-induced loads caused by human gait for use within an end-to-end design process. The prediction needed no further experimental data. Gait movement and ground reaction force (GRF) were simulated by means of two parameter constraints with easily estimable input variables (gait speed, body mass, body height). To calibrate and validate the prediction model, experiments were conducted in which 12 test persons walked in an aircraft mock-up under different conditions. Additional statistical regression models helped to compensate for bipedal model limitations. Direct regression models predicted single GRF parameters as a reference without a bipedal model. The parameter constraint with equal gait speed in experiment and simulation yielded good estimates of force maxima (error 5.3%), while equal initial GRF gave a more reliable prediction. Both parameter constraints predicted contact time very well (error 0.9%). Predictions with the bipedal model including full GRF curves were overall as reliable as the reference

Modeling plasticity and dysplasia of pancreatic ductal organoids derived from human pluripotent stem cells

Personalized in vitro models for dysplasia and carcinogenesis in the pancreas have been constrained by insufficient differentiation of human pluripotent stem cells (hPSCs) into the exocrine pancreatic lineage. Here, we differentiate hPSCs into pancreatic duct-like organoids (PDLOs) with morphological, transcriptional, proteomic, and functional characteristics of human pancreatic ducts, further maturing upon transplantation into mice. PDLOs are generated from hPSCs inducibly expressing oncogenic GNAS, KRAS, or KRAS with genetic covariance of lost CDKN2A and from induced hPSCs derived from a McCune-Albright patient. Each oncogene causes a specific growth, structural, and molecular phenotype in vitro. While transplanted PDLOs with oncogenic KRAS alone form heterogenous dysplastic lesions or cancer, KRAS with CDKN2A loss develop dedifferentiated pancreatic ductal adenocarcinomas. In contrast, transplanted PDLOs with mutant GNAS lead to intraductal papillary mucinous neoplasia-like structures. Conclusively, PDLOs enable in vitro and in vivo studies of pancreatic plasticity, dysplasia, and cancer formation from a genetically defined background

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Mobility Data Science (Dagstuhl Seminar 22021)

This report documents the program and the outcomes of Dagstuhl Seminar 22021 "Mobility Data Science". This seminar was held January 9-14, 2022, including 47 participants from industry and academia. The goal of this Dagstuhl Seminar was to create a new research community of mobility data science in which the whole is greater than the sum of its parts by bringing together established leaders as well as promising young researchers from all fields related to mobility data science. Specifically, this report summarizes the main results of the seminar by (1) defining Mobility Data Science as a research domain, (2) by sketching its agenda in the coming years, and by (3) building a mobility data science community. (1) Mobility data science is defined as spatiotemporal data that additionally captures the behavior of moving entities (human, vehicle, animal, etc.). To understand, explain, and predict behavior, we note that a strong collaboration with research in behavioral and social sciences is needed. (2) Future research directions for mobility data science described in this report include a) mobility data acquisition and privacy, b) mobility data management and analysis, and c) applications of mobility data science. (3) We identify opportunities towards building a mobility data science community, towards collaborations between academic and industry, and towards a mobility data science curriculum

Kurzer Auszug aus dem Katalog der Kgl. Gymnasialbibliothek in Flensburg

von Wiegand und Krum

NN - Aus der Werkstatt eines Geodäten

Das Normal-Null (NN), bei Höhenangaben am Wegesrand als „Höhe über NN" mehr oder weniger bekannt, eröffnet zahlreiche Fragen: Was ist ein Normal-Null? Was bedeutet eine Höhenangabe über Normal-Null? Die Beantwortung dieser Fragen hat den Berufsstand der Geodäten mit dem Fachgebiet Geodäsie mitgeprägt. (yeco8mcmx: Teilung der Erde, historische Bezüge in Aristoteles: Metaphysik, Buch 2, 997 b, 26, 31). Die geodätische Höhenbezugsfläche als Normal-Null entstammt einem Vorschlag von C. F. Gauß (1828), im Detail ausgearbeitet von seinem Schüler J. B. Usting (1873, Göttinger Schule): Der mittlere Meeresspiegel ist das Normal-Null, genannt Geoid, physikalisch eine Äquipotentialfläche des Schwerepotentials zu einem Referenzzeitpunkt. Denken wir uns den mittleren Meeresspiegel, beispielsweise über einen Kanal, fortgesetzt „unter die feste Erde", so erhalten wir die Fläche des Normal-Null, auf welche sich lokale Höhenangaben beziehen. Neben der Höhenbezugsfläche Geoid ist auch die Höhenangabe eines Punktes auf der Erdoberfläche physikalisch, nämlich im Sinne dynamischer Höhen als gravitative Spannung gegen Erde, als Unterschied des Schwerepotentials W - W0 gegenüber dem geoidalen Potentialwert W0 definiert. Konkret sind metrische Höhenangaben im Sinne des obigen Beispiels „orthometrisch", das heißt sie geben die „Länge der Lotlinie" von einem Punkt der Erdoberfläche zu einem Projektionspunkt „unterhalb" auf dem Geoid an. Genauer gesagt bestimmt die Länge der geodätischen Linie zwischen Oberflächenpunkt und Geoid die geodätische Höhe eines Punktes, namentlich gerechnet mit einer konform-flachen Metrik und dem halbierten Quadrat des Schwerevektors als Konformfaktor. Der Gradient in einem derartig physikalisch definierten Höhensystem gibt an, „wohin das Wasser fließt". So ist eine topographische Situation vorstellbar, in der Wasser einen geometrischen Berg „hinauffließt"