Foramina of the anterior mandible in dentate and edentulous mandibles

The study provides a morphometric analysis of the foramina located at the anterior mandible according to dental status. The inner surface from the midline to the distal border of the second premolars of 70 dentate and 27 edentulous Greek adult dry mandibles was investigated. The lingual foramina were divided into medial and lateral foramina. Foramina located at the alveolar process and the midline were subdivided according to their location to genial tubercles. Moreover, the height of the mandible in the genial symphysis and the distances from the foramina to the alveolar crest and the lower border of the mandible were measured. Medial and lateral lingual foramina were presented in 97.9% and 78.4% of the mandibles, respectively. The alveolar medial and lateral lingual foramina were detected in 19.6% and 27.3%, respectively. The mean height of the genial symphysis was 32.06 ± ± 4.88 mm for the dentate and 23.87± 5.37 mm for the edentulous mandibles. The meticulous knowledge of the topography of the lingual foramina and their content is of paramount importance for dentists, oral and maxillofacial surgeons during dental implants placement. Middle and lateral lingual foramina are constant structures, while the alveolar foramina presented only in dentate mandibles. The foramina location is directly affected by dental status. The morphology of edentulous mandibles increases the risk of intraoperative complications at the anterior mandible.

Robust ordinal regression in preference learning and ranking

Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking

Qualification roadmap empowering the Greek building sector workforce in the field of energy

Summarization: A key factor hampering the delivery of high energy performance renovations in buildings is the under-qualification of the construction sector's workforce with regard to energy efficiency and renewable energy sources systems. Towards this direction, this paper proposes an integrated methodological framework, determining measures for the case of Greece, which are delivered in the form of a national qualification roadmap. These measures aim to enhance the qualifications of the blue collar workers and generally empower the Greek construction sector. They also facilitate the adherence of Greece to the European Energy Efficiency and Renewable Energy Sources Directives and support the attainment of the national energy objectives for 2020. The methodological framework is initiated with the analysis of the status quo of the Greek building sector and the skills and qualifications gap of the workforce, as well as the identification of the corresponding barriers that impede growth. Subsequently, a number of diverse measures are proposed and evaluated, using a synergy of decision analysis and evaluation methods. The measures that are assessed as of high priority are afterwards specified to concrete actions. Throughout the whole modular procedure, multiple relevant national bodies were actively engaged via transparent consultation procedures and multilateral discussions. Finally, 44 acknowledged organisations declared their endorsement to the roadmap by signing an official letter.Presented on: Renewable and Sustainable Energy Review