PLIF Study of Mars Science Laboratory Capsule Reaction Control System Jets

Nitric-oxide planar laser-induced fluorescence (NO PLIF) was used to visualize the flow in the wake of a Mars Science Lab (MSL) entry capsule with activated reaction control system (RCS) jets in NASA Langley Research Center s 31-Inch Mach 10 Air Tunnel facility. Images were processed using the Virtual Diagnostics Interface (ViDI) method, which brings out the three-dimensional nature of the flow visualization data while showing the relative location of the data with respect to the model. Comparison of wind-on and wind-off results illustrates the effect that the hypersonic crossflow has on the trajectory and structure of individual RCS jets. The visualization and comparison of both single and multiple activated RCS jets indicate low levels of jet-jet interaction. Quantitative streamwise velocity was also obtained via NO PLIF molecular tagging velocimetry (MTV)

Cost-benefit analysis of BIM-enabled design clash detection and resolution

Building Information Modelling (BIM) is increasingly deployed as part of the processes in Architecture, Engineering and Construction (AEC) industry projects. While the benefits of BIM have been extensively proclaimed, explicit justification in terms of direct cost savings for BIM implementation on real-life projects, particularly for clash detection BIM workstream, are not well documented. This paper proposes and demonstrates a methodology to prove how BIM-based clash detection leads to cost savings. A schema is developed based on literature review and industrial expertise to quantify cost savings achieved by the utilisation of BIM-based clash detection and resolution. This paper provides validation of the proposed schema on a major infrastructure project. The developed schema includes the categorisation of identified clashes based on stakeholder involvement and required actions. The validation used the estimated cost of clashes were those not resolved before site operations took place. This schema simplifies both the categorisation and cost estimation of clashes in design. Estimated savings yielded 20% of contract value using the schema, for the multi-million-dollar project case study, thus extending evidence of BIM savings and benefits. The schema improves the existing process and valorises clash detection, thus allowing stakeholders to conduct a cost-benefit analysis. In addition, the categorisation methodology allows prioritising on the most costly clashes, and draw lessons learnt for further projects. This schema opens the path towards a systematic methodology to appraise the benefits of different BIM uses or processes

The KELT Follow-Up Network And Transit False-Positive Catalog: Pre-Vetted False Positives For TESS

The Kilodegree Extremely Little Telescope (KELT) project has been conducting a photometric survey of transiting planets orbiting bright stars for over 10 years. The KELT images have a pixel scale of ~23\u27\u27 pixel⁻¹—very similar to that of NASA\u27s Transiting Exoplanet Survey Satellite (TESS)—as well as a large point-spread function, and the KELT reduction pipeline uses a weighted photometric aperture with radius 3\u27. At this angular scale, multiple stars are typically blended in the photometric apertures. In order to identify false positives and confirm transiting exoplanets, we have assembled a follow-up network (KELT-FUN) to conduct imaging with spatial resolution, cadence, and photometric precision higher than the KELT telescopes, as well as spectroscopic observations of the candidate host stars. The KELT-FUN team has followed-up over 1600 planet candidates since 2011, resulting in more than 20 planet discoveries. Excluding ~450 false alarms of non-astrophysical origin (i.e., instrumental noise or systematics), we present an all-sky catalog of the 1128 bright stars (6 \u3c V \u3c 13) that show transit-like features in the KELT light curves, but which were subsequently determined to be astrophysical false positives (FPs) after photometric and/or spectroscopic follow-up observations. The KELT-FUN team continues to pursue KELT and other planet candidates and will eventually follow up certain classes of TESS candidates. The KELT FP catalog will help minimize the duplication of follow-up observations by current and future transit surveys such as TESS

Does psychopathology at admission predict the length of inpatient stay in psychiatry? Implications for financing psychiatric services

Background: The debate on appropriate financing systems in inpatient psychiatry is ongoing. In this context, it is important to control resource use in terms of length of stay (LOS), which is the most costly factor in inpatient care and the one that can be influenced most easily. Previous studies have shown that psychiatric diagnoses provide only limited justification for explaining variation in LOS, and it has been suggested that measures such as psychopathology might be more appropriate to predict resource use. Therefore, we investigated the relationship between LOS and psychopathological syndromes or symptoms at admission as well as other characteristics such as sociodemographic and clinical variables. Methods: We considered routine medical data of patients admitted to the Psychiatric University Hospital Zurich in the years 2008 and 2009. Complete data on psychopathology at hospital admission were available in 3,220 inpatient episodes. A subsample of 2,939 inpatient episodes was considered in final statistical models, including psychopathology as well as complete datasets of further measures (e.g. sociodemographic, clinical, treatment-related and psychosocial variables). We used multivariate linear as well as logistic regression analysis with forward selection procedure to determine the predictors of LOS. Results: All but two syndrome scores (mania, hostility) were positively related to the length of stay. Final statistical models showed that syndromes or symptoms explained about 5% of the variation in length of stay. The inclusion of syndromes or symptoms as well as basic treatment variables and other factors led to an explained variation of up to 25%. Conclusions: Psychopathological syndromes and symptoms at admission and further characteristics only explained a small proportion of the length of inpatient stay. Thus, according to our sample, psychopathology might not be suitable as a primary indicator for estimating LOS and contingent costs. This might be considered in the development of future costing systems in psychiatry

Facilitating Organisational Fluidity with Computational Social Matching

Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Peer reviewe