Timing Offset Calibration of CZTI instrument aboard ASTROSAT

The radio as well as the high energy emission mechanism in pulsars is yet not understood properly. A multi-wavelength study is likely to help in better understanding of such processes. The first Indian space-based observatory, ASTROSAT, has five instruments aboard, which cover the electromagnetic spectrum from infra-red (1300 $\AA$) to hard X-ray (380 KeV). Cadmium Zinc Telluride Imager (CZTI), one of the five instruments is a hard X-ray telescope functional over an energy range of 20-380 KeV. We aim to estimate the timing offset introduced in the data acquisition pipeline of the instrument, which will help in time alignment of high energy time series with those from two other ground-based observatories, viz. the Giant Meterwave Radio Telescope (GMRT) and the Ooty Radio Telescope (ORT). PSR B0531+21 is a well-studied pulsar with nearly aligned radio and hard X-ray pulse profiles. We use simultaneous observations of this pulsar with the ASTROSAT, the ORT and the GMRT. The pulsar was especially observed using the ORT with almost daily cadence to obtain good timing solutions. We also supplement the ORT data with archival FERMI data for estimation of timing noise. The timing offset of ASTROSAT instruments was estimated from fits to arrival time data at the ASTROSAT and the radio observatories. We estimate the offset between the GMRT and the ASTROSAT-CZTI to be -4716 $\pm$ 50 $\mu s$. The corresponding offset with the ORT was -29639 $\pm$ 50 $\mu s$. The offsets between the GMRT and Fermi-LAT -5368 $\pm$ 56 $\mu s$. (Abridged)Comment: 6 pages, 5 figures, 2 tables, Revised and Updated, accepted for publication in A&

Synthesis, structural, DFT calculations and biological studies of rhodium and iridium complexes containing azine Schiff-base ligands

The reaction of [Cp*MCl2]2 (M = Rh/Ir) with N-Nʹ azine Schiff-base ligands (L1-L4) leads to the formation of mononuclear cationic half-sandwich complexes having the general formula [Cp*M(L)Cl]+ (1–8), (M = Rh/Ir and L = (2-hydroxy-4-methoxybenzylidene)2- pyridylamidrazone (L1), (2-hydroxybenzylidene)2-pyridylamidrazone (L2), (1-(2-hydroxyphenyl)ethylidene)2-pyridylamidrazone (L3) and (1-phenylethylidene)2-pyridylamidrazone (L4). All these complexes were isolated as their hexafluorophosphate salts and fully characterized by spectroscopic and analytical techniques. The molecular structure of complexes (1), (3), (4), (7) and (8) have been determined by single crystal X-ray crystallographic studies which displayed the coordination of the ligand to the metal in a bidentate N∩N fashion through nitrogen atom of pyridine and one azine nitrogen. The chemo-sensitivity activities of the complexes were evaluated against HT-29 (human colorectal cancer) cell line and non-cancer cell line ARPE-19 (human retinal epithelial cells) which revealed that the complexes are moderately cytotoxic to cancer cells over human cells although complex 5 was the most potent among all the compounds. Theoretical studies carried out using DFT and TD-DFT at B3LYP level shows good agreement with the experimental results

Magnetodielectric effect of Graphene-PVA Nanocomposites

Graphene-Polyvinyl alcohol (PVA) nanocomposite films with thickness $120 \mu m$ were synthesized by solidification of PVA in a solution with dispersed graphene nanosheets. Electrical conductivity data were explained as arising due to hopping of carriers between localized states formed at the graphene-PVA interface. Dielectric permittivity data as a function of frequency indicated the occurrence of Debye-type relaxation mechanism. The nanocomposites showed a magnetodielectric effect with the dielectric constant changing by 1.8% as the magnetic field was increased to 1 Tesla. The effect was explained as arising due to Maxwell-Wagner polarization as applied to an inhomogeneous two-dimensional,two-component composite model. This type of nanocomposite may be suitable for applications involving nanogenerators.Comment: 13 pages, 11 figure

The Extended Solar Cycle: Muddying the Waters of Solar/Stellar Dynamo Modeling or Providing Crucial Observational Constraints?

In 1844 Schwabe discovered that the number of sunspots increased and decreased over a period of about 11 years, that variation became known as the sunspot cycle. Almost eighty years later, Hale described the nature of the Sun's magnetic field, identifying that it takes about 22 years for the Sun's magnetic polarity to cycle. It was also identified that the latitudinal distribution of sunspots resembles the wings of a butterfly—showing migration of sunspots in each hemisphere that abruptly start at mid-latitudes (about ±35o) toward the Sun's equator over the next 11 years. These sunspot patterns were shown to be asymmetric across the equator. In intervening years, it was deduced that the Sun (and sun-like stars) possess magnetic activity cycles that are assumed to be the physical manifestation of a dynamo process that results from complex circulatory transport processes in the star's interior. Understanding the Sun's magnetism, its origin and its variation, has become a fundamental scientific objective—the distribution of magnetism, and its interaction with convective processes, drives various plasma processes in the outer atmosphere that generate particulate, radiative, eruptive phenomena, and shape the heliosphere. In the past few decades, a range of diagnostic techniques have been employed to systematically study finer scale magnetized objects, and associated phenomena. The patterns discerned became known as the “Extended Solar Cycle” (ESC). The patterns of the ESC appeared to extend the wings of the activity butterfly back in time, nearly a decade before the formation of the sunspot pattern, and to much higher solar latitudes. In this short review, we describe their observational patterns of the ESC and discuss possible connections to the solar dynamo as we depart on a multi-national collaboration to investigate the origins of solar magnetism through a blend of archived and contemporary data analysis with the goal of improving solar dynamo understanding and modeling

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning Based Approach

In medical practice, the X-ray Computed tomography-based scans expose a high radiation dose and lead to the risk of prostate or abdomen cancers. On the other hand, the low-dose CT scan can reduce radiation exposure to the patient. But the reduced radiation dose degrades image quality for human perception, and adversely affects the radiologist\u27s diagnosis and prognosis. In this paper, we introduce a GAN based auto-encoder network to de-noise the CT images. Our network first maps CT images to low dimensional manifolds and then restore the images from its corresponding manifold representations. Our reconstruction algorithm separately calculates perceptual similarity, learns the latent feature maps, and achieves more accurate and visually pleasing reconstructions. We also showed the effectiveness of our model on a number of patient abdomen CT images, and compare our results with existing deep learning and iterative reconstruction methods. Experimental results demonstrate that our model outperforms other state-of-the-art methods in terms of PSNR, SSIM, and statistical properties of the image regions. https://github.com/ShibaPrasad/CT-Image-Reconstruction