Aims:To assess the sex discrimination potential of permanent maxillary molar crown widths and cusp diameters.Materials and Methods:Measurements were made on plaster casts of 200 individuals of known sex (100 males, 100 females, aged 12-21 years). Eight parameters were measured on the first and second maxillary molars with a digital caliper [buccolingual, mesiodistal, mesiobuccal-distolingual and distobuccal-mesiolingual crown widths and cusp diameters (hypocone, protocone, paracone, and metacone)]. The percentage of sexual dimorphism for each parameter was calculated. Discriminant function analysis was used to determine the accuracy of sex determination for each molar separately and both the molars taken together.Results:The highest sexual dimorphism was shown by protocone in the first molar and hypocone in the second molar. Furthermore, the sex determination accuracy was highest when the first molar was taken alone than when the second molar or the first and second molars were taken together.Conclusion:Based on this study, odontometric measurements of maxillary molars provide low to moderate sex determination accuracy.
Domain-specific conceptual bases use key concepts to capture domain scope and relevant information. Conceptual bases serve as a foundation for various downstream tasks, including ontology construction, information mapping, and analysis. However, building conceptual bases necessitates domain awareness and takes time. Wikipedia navigational templates offer multiple articles on the same/similar domain. It is possible to use the templates to recognize fundamental concepts that shape the domain. Earlier work in this domain used Wikipedia's structured and unstructured data to construct open-domain ontologies, domain terminologies, and knowledge bases. We present a novel method for leveraging navigational templates to create domain-specific fuzzy conceptual bases in this work. Our system generates knowledge graphs from the articles mentioned in the template, which we then process using Wikidata and machine learning algorithms. We filter important concepts using fuzzy logic on network metrics to create a crude conceptual base. Finally, the expert helps by refining the conceptual base. We demonstrate our system using an example of RNA virus antiviral drugs.
In the multi-billion dollar formulated product industry, state of the art continues to rely heavily on experts during the generate, make and test steps of formulation design. We propose automation aids to each of these steps with a knowledge graph of relevant information as the central artefact. The generate step usually focuses on coming up with new recipes for intended formulation. We propose to aid the experts who generally carry out this step manually, by providing a recommendation system and a templating system on top of the knowledge graph. Using the former, the expert can create a recipe from scratch using historical formulations and related data. With the latter, the expert starts with a recipe template created by our system and substitutes the requisite constituents to form a recipe. In the current state of practice, the three steps mentioned above operate in a fragmented manner wherein observations from one step do not aid other steps in a streamlined manner. Instead of manually operated labs for the make and test steps, we assume the use of automated or robotic labs and in-silico testing respectively. Using two formulations namely, face cream and an exterior coating, we show how the knowledge graph may help integrate and streamline the communication between the generate, the make, and the test steps. Our initial exploration shows considerable promise.
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