Study to determine serum vitamin D levels in patients with congestive heart failure

Background: It was to evaluate the association of serum levels of vitamin D in patients with congestive heart failure.Methods: The present study was conducted in the department of Medicine at Chattrapati Shivaji Subharti Hospital among 100 patients, aged 18 years and above diagnosed as congestive heart failure on the basis of clinical and echocardiographic evidence. Clinical manifestations looked for CHF were: Dyspnea, orthopnea, acute pulmonary edema, cerebral symptoms, cheyne-stokes respiration, cyanosis, sinus tachycardia, raised jugular venous pressure, congestive hepatomegaly and pedal edema. In the present study deficiency/ insufficiency of vitamin D was considered when the presence of levels of 25-hydroxyvitamin D was 30 respectively with statistically significant difference. The Mean±SD scores of CPK MB (IU/L) was found to be 33.1±20.8 and 18.6±13.3 among the subjects having vitamin D levels 30 respectively with statistically significant difference.Conclusions: The results of the present study suggest that low levels of vitamin D may adversely affect the cardiovascular system

AVFI: Fault Injection for Autonomous Vehicles

Autonomous vehicle (AV) technology is rapidly becoming a reality on U.S. roads, offering the promise of improvements in traffic management, safety, and the comfort and efficiency of vehicular travel. With this increasing popularity and ubiquitous deployment, resilience has become a critical requirement for public acceptance and adoption. Recent studies into the resilience of AVs have shown that though the AV systems are improving over time, they have not reached human levels of automation. Prior work in this area has studied the safety and resilience of individual components of the AV system (e.g., testing of neural networks powering the perception function). However, methods for holistic end-to-end resilience assessment of AV systems are still non-existent.Comment: Published in: 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W

The Optical Afterglow of GRB 011211

We present early-time optical photometry and spectroscopy of the optical afterglow of the gamma-ray burst GRB 011211. The spectrum of the optical afterglow contains several narrow metal lines which are consistent with the burst occurring at a redshift of 2.140 +/- 0.001. The optical afterglow decays as a power law with a slope of alpha = 0.83 +/- 0.04 for the first approximately two days after the burst at which time there is evidence for a break. The slope after the break is at least 1.4. There is evidence for rapid variations in the R-band light approximately 0.5 days after the burst. These variations suggest that there are density fluctuations near the gamma-ray burst on spatial scales of approximately 40--125 AU. The magnitude of the break in the light curve, the spectral slope, and the rate of decay in the optical, suggest that the burst expanded into an ambient medium that is homogeneous on large scales. We estimate that the local particle density is between approximately 0.1 and 10 cm^{-3} and that the total gamma-ray energy in the burst was 1.2--1.9 x 10^{50} erg. This energy is smaller than, but consistent with, the ``standard'' value of (5 +/- 2) x 10^{50} erg. Comparing the observed color of the optical afterglow with predictions of the standard beaming model suggests that the rest-frame V-band extinction in the host galaxy is less than approximately 0.03 mag.Comment: 17 pages, 4 figures, AASTeX 5.02, to appear in AJ Referee's report incorporated, minor changes in the tex

Improving the LSST dithering pattern and cadence for dark energy studies

The Large Synoptic Survey Telescope (LSST) will explore the entire southern sky over 10 years starting in 2022 with unprecedented depth and time sampling in six filters, $ugrizy$. Artificial power on the scale of the 3.5 deg LSST field-of-view will contaminate measurements of baryonic acoustic oscillations (BAO), which fall at the same angular scale at redshift $z \sim 1$. Using the HEALPix framework, we demonstrate the impact of an "un-dithered" survey, in which $17\%$ of each LSST field-of-view is overlapped by neighboring observations, generating a honeycomb pattern of strongly varying survey depth and significant artificial power on BAO angular scales. We find that adopting large dithers (i.e., telescope pointing offsets) of amplitude close to the LSST field-of-view radius reduces artificial structure in the galaxy distribution by a factor of $\sim$10. We propose an observing strategy utilizing large dithers within the main survey and minimal dithers for the LSST Deep Drilling Fields. We show that applying various magnitude cutoffs can further increase survey uniformity. We find that a magnitude cut of $r < 27.3$ removes significant spurious power from the angular power spectrum with a minimal reduction in the total number of observed galaxies over the ten-year LSST run. We also determine the effectiveness of the observing strategy for Type Ia SNe and predict that the main survey will contribute $\sim$100,000 Type Ia SNe. We propose a concentrated survey where LSST observes one-third of its main survey area each year, increasing the number of main survey Type Ia SNe by a factor of $\sim$1.5, while still enabling the successful pursuit of other science drivers.Comment: 9 pages, 6 figures, published in SPIE proceedings; corrected typo in equation

ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection

The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents. While AV development today involves verification, validation, and testing, end-to-end assessment of AV systems under accidental faults in realistic driving scenarios has been largely unexplored. This paper presents DriveFI, a machine learning-based fault injection engine, which can mine situations and faults that maximally impact AV safety, as demonstrated on two industry-grade AV technology stacks (from NVIDIA and Baidu). For example, DriveFI found 561 safety-critical faults in less than 4 hours. In comparison, random injection experiments executed over several weeks could not find any safety-critical faultsComment: Accepted at 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Network