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The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.

The creep resistance of martensitic power plant steels depends strongly on the dispersion of various types of precipitates in the microstructure. Reliable prediction of rupture strength of such alloys thus demands accurate description of the microstructure evolution during service. A material model that calculates the simultaneous precipitation kinetics and hardening in these steels has been reported previously. The creep rupture model reported here is a natural extension of the previous research. This model has been validated against experimental creep rupture data of wide-used 9–12% Cr steels. It allows the effect of variation in alloy conditions, such as composition and heat treatments, on rupture strength to be quantitatively evaluated for a given alloy grade. Results show that such variations can lead to significant differences in the calculated rupture strength, which are believed to be largely responsible for the wide scatter in the experimental data. [ABSTRACT FROM AUTHOR]

Kathryn Curtis,1 Sander L Hitzig,2,3 Gitte Bechsgaard,4 Candice Stoliker,5 Charlene Alton,3 Nicole Saunders,3 Nicole Leong,6 Joel Katz1 1Department of Psychology, Faculty of Health, York University, 2St John’s Rehab Research Program, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, 3Lyndhurst Centre, Toronto Rehabilitation Institute, University Health Network, 4Vidya Institute, 5Student Life, University of Toronto, Toronto, 6Community Care Access Centre, London, ON, Canada Objectives: The purpose of this randomized controlled trial was to evaluate the effects of a specialized yoga program for individuals with a spinal cord injury (SCI) on pain, psychological, and mindfulness variables. Materials and methods: Participants with SCI (n=23) were outpatients or community members affiliated with a rehabilitation hospital. Participants were randomized to an Iyengar yoga (IY; n=11) group or to a 6-week wait-list control (WLC; n=12) group. The IY group participated in a twice-weekly 6-week seated IY program; the WLC group participated in the same yoga program, after the IY group’s yoga program had ended. Pain, psychological, and mindfulness measures were collected at two time points for both groups (within 1–2 weeks before and after program 1 and at a third time point for the WLC group (within 1 week after program 2).Results: Linear mixed-effect growth models were conducted to evaluate the main effects of group at T2 (postintervention), controlling for T1 (preintervention) scores. T2 depression scores were lower (F1,18=6.1, P

Patients with hematological malignancy (HM) are at higher risk of infection with SARS-CoV-2 and potentially severe outcomes. The rate was 15.2% (12/79) in patients without prior history of COVID-19 infection and 17.7% (11/62) in patients who did not receive a vaccine during the follow-up period. Within the last 12 months, 35/93 (37.6%) participants had received anti-CD20 therapy, 7/93 (7.5%) bispecific antibody therapy, and 6/93 (6.5%) Bruton tyrosine kinase inhibitor therapy (Table 1). [Extracted from the article]