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Purpose Additive manufacturing (AM) is an innovative printing technology that can manufacture 3-dimensional solid objects by adding layers of material from model data. AM in oral and maxillofacial surgery (OMFS) provides several clinical applications such as surgical guides and implants. However, the adoption of AM in OMFS is not well covered. The purpose was to study the adoption of AM in OMFS in university and non-university hospitals in Sweden. Three research questions were addressed: What is the degree of using AM solutions in university and non-university hospitals?; What are AM solutions used?; How are the AM solutions accessed (production mode) in university hospitals and non-university hospitals? Methods A survey was distributed to OMF surgeons in Sweden. The questionnaire consisted of 16 questions. Data were analyzed through descriptive and content analysis. Results A total of 14 university and non-university hospitals were captured. All 14 hospitals have adopted AM technology and 11 of the hospitals adopted AM in OMFS. Orthognathic and trauma surgery are two major types of surgery that involve AM technology where material extrusion and vat polymerization are the two most used AM technologies in OMFS. The primary application of AM was in medical models and guides. Conclusion Majority of Swedish university hospitals and non-university hospitals have adopted AM in OMFS. The type of hospital (university or non-university hospital) has no impact on AM adoption. AM in OMFS in Sweden can be perceived to be a mature clinical application.

ZusammenfassungDie Bearbeitung von Übungsaufgaben ist ein wichtiges Element in der Datenbank-Lehre. Lösungen der Studierenden lassen sich dabei häufig in strukturierten Ergebnisformaten festhalten, wie z. B. SQL-Anfragen oder die Spezifikation von Schemata und Relationen. Dieser Beitrag stellt das E‑Assessment-Tool DMT (Data Management Tester) vor, das sowohl eine automatische Bewertung als auch eine automatische Feedback-Generierung solcher strukturierter Lösungen ermöglicht. Es soll dabei insbesondere Studierende unterstützen, nicht ganz korrekte Lösungen zielgerichtet überarbeiten zu können. Dieser Betrag skizziert Konzept und Architektur von DMT und erläutert den Einsatz an der HTWK Leipzig und der Hochschule Mittweida.

The temporal analysis of evolving graphs is an important requirement in many domains but hardly supported in current graph database and graph processing systems. We therefore have started with extending the distributed graph analysis framework Gradoop for temporal graph analysis by adding time properties to vertices, edges and graphs and using them within graph operators. We outline these extensions and illustrate their use within analysis workflows. We further describe the implementation of the snapshot and diff operators and evaluated them.

This bibliometric analysis focuses on the general history of climate change research and, more specifically, on the discovery of the greenhouse effect. First, the Reference Publication Year Spectroscopy (RPYS) is applied to a large publication set on climate change of 222,060 papers published between 1980 and 2014. The references cited therein were extracted and analyzed with regard to publications, which are cited most frequently. Second, a new method for establishing a more subject-specific publication set for applying RPYS (based on the co-citations of a marker reference) is proposed (RPYS-CO). The RPYS of the climate change literature focuses on the history of climate change research in total. We identified 35 highly-cited publications across all disciplines, which include fundamental early scientific works of the 19th century (with a weak connection to climate change) and some cornerstones of science with a stronger connection to climate change. By using the Arrhenius (1896) paper as a RPYS-CO marker paper, we selected only publications specifically discussing the discovery of the greenhouse effect and the role of carbon dioxide. Also, we focused on the time period 1800-1850 to reveal the contributions of J.B.J Fourier in terms of cited references. Using different RPYS approaches in this study, we were able to identify the complete range of works of the celebrated icons as well as many less known works relevant for the history of climate change research. The analyses confirmed the potential of the RPYS method for historical studies: Seminal papers are detected on the basis of the references cited by the overall community without any further assumptions., in press at Scientometrics

Ein eigenes Themenheft zum Datenmanagement in der Cloud dient uns als Anlass, die Prasenz von Cloud-Themen in der akademischen Datenbanklehre zu erfassen. In diesem Artikel geben wir die Ergebnisse einer Umfrage innerhalb der Fachgruppe Datenbanksysteme durch den Arbeitskreis Datenmanagement in der Cloud wieder. Dozentinnen und Dozenten von uber zwanzig Hochschulen nahmen an der Umfrage teil. Es zeigt sich deutlich, dass sich das Thema „Cloud“ in der Hochschullehre zunehmend etabliert, jedoch uberwiegend als erganzendes Angebot, und seltener in der grundstandigen Lehre verankert. Wir fassen die Ergebnisse unserer Umfrage zusammen und wagen Deutungsversuche.

Cloud infrastructures enable the efficient parallel execution of data-intensive tasks such as entity resolution on large datasets. We investigate challenges and possible solutions of using the MapReduce programming model for parallel entity resolution using Sorting Neighborhood blocking (SN). We propose and evaluate two efficient MapReduce-based implementations for single- and multi-pass SN that either use multiple MapReduce jobs or apply a tailored data replication. We also propose an automatic data partitioning approach for multi-pass SN to achieve load balancing. Our evaluation based on real-world datasets shows the high efficiency and effectiveness of the proposed approaches.

Reconstruction of the temperomandibular joint with free fibula microvascular flaps without and with surgical guides

Recommendations are crucial for the success of large websites. While there are many ways to determine recommendations, the relative quality of these recommenders depends on many factors and is largely unknown. We present the architecture and implementation of AWESOME (Adaptive website recommendations), a data warehouse-based recommendation system. It allows the coordinated use of a large number of recommenders to automatically generate website recommendations. Recommendations are dynamically selected by efficient rule-based approaches utilizing continuously measured user feedback on presented recommendations. AWESOME supports a completely automatic generation and optimization of selection rules to minimize website administration overhead and quickly adapt to changing situations. We propose a classification of recommenders and use AWESOME to comparatively evaluate the relative quality of several recommenders for a sample website. Furthermore, we propose and evaluate several rule-based schemes for dynamically selecting the most promising recommendations. In particular, we investigate two-step selection approaches that first determine the most promising recommenders and then apply their recommendations for the current situation. We also evaluate one-step schemes that try to directly determine the most promising recommendations.