The Close Binary Fraction of Dwarf M Stars

We describe a search for close spectroscopic dwarf M star binaries using data from the Sloan Digital Sky Survey to address the question of the rate of occurrence of multiplicity in M dwarfs. We use a template-fitting technique to measure radial velocities from 145,888 individual spectra obtained for a magnitude-limited sample of 39,543 M dwarfs. Typically, the three or four spectra observed for each star are separated in time by less than four hours, but for ~17% of the stars, the individual observations span more than two days. In these cases we are sensitive to large-amplitude radial velocity variations on timescales comparable to the separation between the observations. We use a control sample of objects having observations taken within a four-hour period to make an empirical estimate of the underlying radial velocity error distribution and simulate our detection efficiency for a wide range of binary star systems. We find the frequency of binaries among the dwarf M stars with a < 0.4 AU to be 3%-4%. Comparison with other samples of binary stars demonstrates that the close binary fraction, like the total binary fraction, is an increasing function of primary mass

Incident Ischemic Heart Disease After Long-Term Occupational Exposure to Fine Particulate Matter: Accounting for 2 Forms of Survivor Bias.

Little is known about the heart disease risks associated with occupational, rather than traffic-related, exposure to particulate matter with aerodynamic diameter of 2.5 µm or less (PM2.5). We examined long-term exposure to PM2.5 in cohorts of aluminum smelters and fabrication workers in the United States who were followed for incident ischemic heart disease from 1998 to 2012, and we addressed 2 forms of survivor bias. Left truncation bias was addressed by restricting analyses to the subcohort hired after the start of follow up. Healthy worker survivor bias, which is characterized by time-varying confounding that is affected by prior exposure, was documented only in the smelters and required the use of marginal structural Cox models. When comparing always-exposed participants above the 10th percentile of annual exposure with those below, the hazard ratios were 1.67 (95% confidence interval (CI): 1.11, 2.52) and 3.95 (95% CI: 0.87, 18.00) in the full and restricted subcohorts of smelter workers, respectively. In the fabrication stratum, hazard ratios based on conditional Cox models were 0.98 (95% CI: 0.94, 1.02) and 1.17 (95% CI: 1.00, 1.37) per 1 mg/m(3)-year in the full and restricted subcohorts, respectively. Long-term exposure to occupational PM2.5 was associated with a higher risk of ischemic heart disease among aluminum manufacturing workers, particularly in smelters, after adjustment for survivor bias

Ischemic Heart Disease Incidence in Relation to Fine versus Total Particulate Matter Exposure in a U.S. Aluminum Industry Cohort.

Ischemic heart disease (IHD) has been linked to exposures to airborne particles with an aerodynamic diameter &lt;2.5 μm (PM2.5) in the ambient environment and in occupational settings. Routine industrial exposure monitoring, however, has traditionally focused on total particulate matter (TPM). To assess potential benefits of PM2.5 monitoring, we compared the exposure-response relationships between both PM2.5 and TPM and incidence of IHD in a cohort of active aluminum industry workers. To account for the presence of time varying confounding by health status we applied marginal structural Cox models in a cohort followed with medical claims data for IHD incidence from 1998 to 2012. Analyses were stratified by work process into smelters (n = 6,579) and fabrication (n = 7,432). Binary exposure was defined by the 10th-percentile cut-off from the respective TPM and PM2.5 exposure distributions for each work process. Hazard Ratios (HR) comparing always exposed above the exposure cut-off to always exposed below the cut-off were higher for PM2.5, with HRs of 1.70 (95% confidence interval (CI): 1.11-2.60) and 1.48 (95% CI: 1.02-2.13) in smelters and fabrication, respectively. For TPM, the HRs were 1.25 (95% CI: 0.89-1.77) and 1.25 (95% CI: 0.88-1.77) for smelters and fabrication respectively. Although TPM and PM2.5 were highly correlated in this work environment, results indicate that, consistent with biologic plausibility, PM2.5 is a stronger predictor of IHD risk than TPM. Cardiovascular risk management in the aluminum industry, and other similar work environments, could be better guided by exposure surveillance programs monitoring PM2.5

Particle Size Distribution in Aluminum Manufacturing Facilities.

As part of exposure assessment for an ongoing epidemiologic study of heart disease and fine particle exposures in aluminum industry, area particle samples were collected in production facilities to assess instrument reliability and particle size distribution at different process areas. Personal modular impactors (PMI) and Minimicro-orifice uniform deposition impactors (MiniMOUDI) were used. The coefficient of variation (CV) of co-located samples was used to evaluate the reproducibility of the samplers. PM2.5 measured by PMI was compared to PM2.5 calculated from MiniMOUDI data. Mass median aerodynamic diameter (MMAD) and concentrations of sub-micrometer (PM1.0) and quasi-ultrafine (PM0.56) particles were evaluated to characterize particle size distribution. Most of CVs were less than 30%. The slope of the linear regression of PMI_PM2.5 versus MiniMOUDI_PM2.5 was 1.03 mg/m3 per mg/m3 (± 0.05), with correlation coefficient of 0.97 (± 0.01). Particle size distribution varied substantively in smelters, whereas it was less variable in fabrication units with significantly smaller MMADs (arithmetic mean of MMADs: 2.59 μm in smelters vs. 1.31 μm in fabrication units, p = 0.001). Although the total particle concentration was more than two times higher in the smelters than in the fabrication units, the fraction of PM10 which was PM1.0 or PM0.56 was significantly lower in the smelters than in the fabrication units (p &lt; 0.001). Consequently, the concentrations of sub-micrometer and quasi-ultrafine particles were similar in these two types of facilities. It would appear, studies evaluating ultrafine particle exposure in aluminum industry should focus on not only the smelters, but also the fabrication facilities

Contextual perception under active inference

Human social interactions depend on the ability to resolve uncertainty about the mental states of others. The context in which social interactions take place is crucial for mental state attribution as sensory inputs may be perceived differently depending on the context. In this paper, we introduce a mental state attribution task where a target-face with either an ambiguous or an unambiguous emotion is embedded in different social contexts. The social context is determined by the emotions conveyed by other faces in the scene. This task involves mental state attribution to a target-face (either happy or sad) depending on the social context. Using active inference models, we provide a proof of concept that an agent’s perception of sensory stimuli may be altered by social context. We show with simulations that context congruency and facial expression coherency improve behavioural performance in terms of decision times. Furthermore, we show through simulations that the abnormal viewing strategies employed by patients with schizophrenia may be due to (i) an imbalance between the precisions of local and global features in the scene and (ii) a failure to modulate the sensory precision to contextualise emotions

Low Emission Building Control with Zero-Shot Reinforcement Learning

Heating and cooling systems in buildings account for 31% of global energy use, much of which are regulated by Rule Based Controllers (RBCs) that neither maximise energy efficiency nor minimise emissions by interacting optimally with the grid. Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency, but existing solutions require access to building-specific simulators or data that cannot be expected for every building in the world. In response, we show it is possible to obtain emission-reducing policies without such knowledge a priori--a paradigm we call zero-shot building control. We combine ideas from system identification and model-based RL to create PEARL (Probabilistic Emission-Abating Reinforcement Learning) and show that a short period of active exploration is all that is required to build a performant model. In experiments across three varied building energy simulations, we show PEARL outperforms an existing RBC once, and popular RL baselines in all cases, reducing building emissions by as much as 31% whilst maintaining thermal comfort. Our source code is available online via https://enjeeneer.io/projects/pearl/Comment: Accepted at AAAI 2023. Code available via https://enjeeneer.io/projects/pearl

The “ABC model”: a non-hydrostatic toy model for use in convective-scale data assimilation investigations

In developing methods for convective-scale data assimilation (DA) it is necessary to consider the full range of motions governed by the compressible Navier-Stokes equations (including non-hydrostatic and ageostrophic flow). These equations describe motion on a wide range of time-scales with non-linear coupling. For the purpose of developing new DA techniques that suit the convective-scale problem it is helpful to use so-called ‘toy models’ that are easy to run, and contain the same types of motion as the full equation set. Such a model needs to permit hydrostatic and geostrophic balance at large-scales, but to allow imbalance at small-scales, and in particular, it needs to exhibit intermittent convection-like behaviour. Existing ‘toy models’ are not always sufficient for investigating these issues. A simplified system of intermediate complexity derived from the Euler equations is presented, which supports dispersive gravity and acoustic modes. In this system the separation of time scales can be greatly reduced by changing the physical parameters. Unlike in existing toy models, this allows the acoustic modes to be treated explicitly, and hence inexpensively. In addition, the non-linear coupling induced by the equation of state is simplified. This means that the gravity and acoustic modes are less coupled than in conventional models. A vertical slice formulation is used which contains only dry dynamics. The model is shown to give physically reasonable results, and convective behaviour is generated by localised compressible effects. This model provides an affordable and flexible framework within which some of the complex issues of convective-scale DA can later be investigated. The model is called the “ABC model” after the three tunable parameters introduced: A (the pure gravity wave frequency), B (the modulation of the divergent term in the continuity equation), and C (defining the compressibility)

Antitumour responses induced by short-term pretreatment with tumour cells.

The injection (s.c. or i.p.) of 10(6) live or lethally irradiated methylcholanthrene-induced fibrosarcoma cells into CBA/Ca mice one or 2 days before i.v. challenge with the same tumour inhibited the formation of artificial lung tumour metastases. In addition, it also frequently enhanced the cytostatic effect of peritoneal exudate cells on monolayers of the same tumour. The effects on lung tumour metastasis were not noted if X-irradiated tumour was injected i.v., or if s.c. administration was delayed until one day after i.v. challenge. Similar effects on tumour growth were also observed in C3Hf/Bu mice and (CBA/Ca x A/HeJ) F1 hybrids which were pretreated (s.c.) with tumour shortly before i.v. challenge with the same tumour. Further studies in CBA/Ca mice suggested that the protective effect was tumour-specific, for the growth of i.v. injected tumour was not significantly inhibited by pretreatement with a number of other MC-induced or spontaneous tumours from the same and different strains