The precision of DXA VAT mass measurements increase with BMI, and caution should be used with estimates in adults with obesity.

No abstract

Good governance practices are pivotal to the success of Open Source Software (OSS) projects. However, the decision‐making processes that are made available to stakeholders are at times incomplete and may remain buried and hidden in large amounts of software repository data. This work bridges this gap by unearthing enacted decision‐making processes available for Python Enhancement Proposals (PEPs) from 1.54 million email messages that embody decisions made during the evolution of the Python language. This work employs a design science approach in operationalizing a framework called DeMaP miner that is used to discover hidden processes using information retrieval and information extraction techniques. It also uses process mining techniques to visualize the processes, and comparative structural analysis techniques to compare different decision processes. The work identifies a richer set of decision‐making activities than those reported on the Python website and in prior research work (48 new decision activities, 199 new pathways and 6 new stages). The extracted decision process has been positively evaluated by a prominent member of the Python steering council. The extracted process can be used for process compliance checking and process improvement in OSS communities. Additionally, the DeMaP Miner framework can be extended and customized to suit other OSS projects, such as the OpenJDK project.

A sound Decision-Making (DM) process is key to the successful governance of software projects. In many Open Source Software Development (OSSD) communities, DM processes lie buried amongst vast amounts of publicly available data. Hidden within this data lie the rationale for decisions that led to the evolution and maintenance of software products. While there have been some efforts to extract DM processes from publicly available data, the rationale behind 'how' the decisions are made have seldom been explored. Extracting the rationale for these decisions can facilitate transparency (by making them known), and also promote accountability on the part of decisionmakers. This work bridges this gap by means of a large-scale study that unearths the rationale behind decisions from Python development email archives comprising about 1.5 million emails. This paper makes two main contributions. First, it makes a knowledge contribution by unearthing and presenting the rationale behind decisions made. Second, it makes a methodological contribution by presenting a heuristics-based rationale extraction system called Rationale Miner that employs multiple heuristics, and follows a data-driven, bottom-up approach to infer the rationale behind specific decisions (e.g., whether a new module is implemented based on core developer consensus or benevolent dictator's pronouncement). Our approach can be applied to extract rationale in other OSSD communities that have similar governance structures.

Scientific literature is an important medium for disseminating scientific knowledge. However, in recent times, a dramatic increase in research output has resulted in challenges for the research community. An increasing need is felt for tools that exploit the full content of an article and provide insightful services with value beyond quantitative measures such as impact factors and citation counts. However, the intricacies of language and thought, and the unstructured format of research articles present challenges in providing such services. The identification of sentence contexts that encode the role of specific sentences in advancing an article's scientific argument can facilitate in developing intelligent tools for the research community. This paper describes our research work in this direction. First, we investigate the possibility of identifying contexts associated with sentences and propose a scheme of thirteen context type definitions for sentences, based on the generic rhetorical pattern found in scientific articles. We then present the results of our experiments using sequential classifiersconditional random fields -for achieving automatic context identification. We also describe our Semantic Web application developed for providing citation context based information services for the research community. Finally, we present a comparison and analysis of our results with similar studies and explain the distinct features of our application.