Determining the Thin Film Thickness of Two Phase Flow using Optics and Image Processing

Gas- Liquid flows are by far the most important type of multiphase flow. This can be attributed to the wide range of industrial applications that the gas-liquid flow is discerned in. Popular examples of Gas-liquid flows are oil-gas mixtures, evaporators, boilers, condensers, refrigeration and cryogenics. The measurement of the liquid film thickness in two phase flows is prominent in various heat and mass transfer applications such as in boilers. To determine the thin film thickness is the aim of this study. A glass tube of diameter 4.7 mm is used for conducting the experiment and a laser pointer is used to obtain an image pattern on the screen. Using the principles of Optics, a method has been proposed to determine the thin film thickness and also to characterize the different types of flow. The thin film thickness obtained in the proposed method is validated using Image Processing

Dithieno[2,3-d;2',3'-d]benzo[2,1-b;3,4-b']dithiophene: a novel building-block for a planar copolymer

A planar heteroacene building block, dithieno[2,3-d;2′,3′-d′]benzo[1,2-b;3,4-b′]dithiophene (DTmBDT), is reported via a facile synthetic procedure.</p

Convergence of genetic influences in comorbidity

Abstract Background Predisposition to complex diseases is explained in part by genetic variation, and complex diseases are frequently comorbid, consistent with pleiotropic genetic variation influencing comorbidity. Genome Wide Association (GWA) studies typically assess association between SNPs and a single-disease phenotype. Fisher meta-analysis combines evidence of association from single-disease GWA studies, assuming that each study is an independent test of the same hypothesis. The Rank Product (RP) method overcomes limitations posed by Fisher assumptions, though RP was not designed for GWA data. Methods We modified RP to accommodate GWA data, and we call it modRP. Using p-values output from GWA studies, we aggregate evidence for association between SNPs and related phenotypes. To assess significance, RP randomly samples the observed ranks to develop the null distribution of the RP statistic, and then places the observed RPs into the null distribution. ModRP eliminates the effect of linkage disequilibrium and controls for differences in power at tested SNPs, to meet RP assumptions in application to GWA data. Results After validating modRP based on both positive and negative control studies, we searched for pleiotropic influences on comorbid substance use disorders in a novel study, and found two SNPs to be significantly associated with comorbid cocaine, opium, and nicotine dependence. Placing these SNPs into biological context, we developed a protein network modeling the interaction of cocaine, nicotine, and opium with these variants. Conclusions ModRP is a novel approach to identifying pleiotropic genetic influences on comorbid complex diseases. It can be used to assess association for related phenotypes where raw data is unavailable or inappropriate for analysis using other approaches. The method is conceptually simple and produces statistically significant, biologically relevant results.http://deepblue.lib.umich.edu/bitstream/2027.42/112931/1/12859_2012_Article_5068.pd

Study on awareness and perception towards adverse drug reactions among medical and paramedical students in South India

Background: Pharmacovigilance (PV) programme targets the monitoring of safety of drugs. It aims at promoting patient care and improving public health and also helps to assess the risk-benefit profile of medicines. The aim of the study was to assess public knowledge about medicine information, safety, and adverse drug reaction reporting (ADR) in medical and paramedical student community.Methods: It was a cross-sectional study conducted among medical and paramedical students for the period of six months from November 2021 to April 2022. The questionnaire was adopted from the literature and was validated. Content and face validities were established, and reliability was assessed. In this study a total of 364 participants returned completed questionnaires.Results: In this study, 364 students completely filled the questionnaire and out of 364 participants, 155 were males (42.58%) and 209 (57.41%) were females. Fourth year students 131 (35.98%) are highly participated in this study and indicated that final year B Pharmacy students having the perceptive knowledge towards ADR. Majority of medical and paramedical students known well about the ADRs.Conclusions: The results of this study highlighted that although the scores for knowledge of medicines, and tendency to report ADR were better, the score for knowledge regarding medication safety was unsatisfactory. There is a need for a regular training and the re-enforcement for the ADR reporting among the health care personnel both medical and paramedical students.

A comprehensive exploration on different machine learning techniques for state of charge estimation of EV battery

The State of Charge (SoC) is a measurement of the amount of energy available in a battery at a specific interval of time, mostly expressed as percentage. Proportional relationships between the electromotive force of a battery, current, terminal voltage and temperature determine the SoC. There can be a considerable error in the calculations due to a sharp drop of the terminal voltage at the end of discharge. This research has explored how important SoC is, as a factor in Battery Management Systems. The work focuses on using machine learning techniques to obtain an accurate and reliable status of battery charge, this includes Random Forest, Decision Tree, Gradient Boosting, Support Vector Regression, Polynomial Regression and Multilayer Perceptron. In this paper, these techniques are tested and compared with two real world captured datasets of Lithium-ion batteries which includes LG Battery and Unibo Powertools Battery. For supporting this study, statistical methods like K-fold cross validation and Grid Search cross validation techniques are used to estimate the skill of machine learning models. After implementing these techniques, it is found that Random Forest model returns the best Accuracy and Decision Tree returns the least Mean Absolute Error.</p

Genetic variability on leaf morpho-anatomical traits in relation to sterility mosaic disease (SMD) resistance in pigeonpea

Abstract Sterility mosaic disease (SMD) is a major biotic constraint in almost all pigeonpea growing areas caused by eriophyid mite transmitted pigeonpea sterility mosaic virus (PPSMV). Direct selection for resistance to SMD is expensive and laborious as it requires dependent of sick plots. Identification of easily assayable and simply inherited morphological traits such as leaf anatomical traits would enable increased efficiency of breeding pigeonpea for SMD resistance. A set of 70 pigeonpea accessions were evaluated for 12 leaf structural features such as leaf thickness (LT), upper epidermal thickness (UEPT), lower epidermal thickness (LEPT), upper cuticle cell wall complex (UCWC), lower cuticle cell wall complex (LCWC), trichome number on upper surface of leaf (TNUS), trichome number on lower surface of leaf (TNLS), trichome length on upper surface of leaf (TLUS) and on lower surface of leaf (TLLS) at experimental plots of Zonal Agricultural Research Station (ZARS), UAS, Bengaluru. The accessions differed significantly for most of the traits except for specific leaf area (SLA) and specific leaf weight (SLW). The accessions were grouped into four clusters, with significant differences in cluster means and variances. Principal component analysis (PCA) showed first three PCs explaining 69.70 % of the total variation and morpho-anatomical traits such as leaf thickness (LT), trichome length on upper (TLUS) and lower (TLLS) surface of leaf were the most important characters for disease incidence. Furthermore, correlation of all the leaf traits in relation to percent incidence (PDI) indicated only TLLS having significant negative correlation (-0.456*) with SMD incidence. While, trichome length also showed higher phenotypic (PCV) and genotypic (GCV) coefficient of variation 34.33 and 34.02, respectively and broad senesce heritability (98.2%) coupled with high genetic advance (69.45). Therefore, breeding for trichome length is very important to impart vector resistance. This may provide broad based resistance to all the isolates of SMD in pigeonpea

LeasyScan: 3D scanning of crop canopy plus seamless monitoring of water use to harness the genetics of key traits for drought adaptation

With the genomics revolution in full swing, relevant phenotyping is now a main bottleneck. New imaging technologies provide opportunities for easier, faster and more informative phenotyping of many plant parameters. However, it is critical that the development of automated phenotyping be driven by a clear framing of target phenotypes rather than by a technological push, especially for complex constraints. Previous studies on drought adaptation shows the importance of water availability during the grain filling period, which depends on traits controlling the plant water budget at earlier stages. We will then discuss “cause” and “consequence” in phenotypes. Drawing on this, a phenotyping platform (LeasyScan) was developed to target canopy development and conductance traits. Based on a novel 3D scanning technique to capture leaf area development continuously and a scanner-to-plant concept to increase imaging throughput, LeasyScan is also equipped with 1488 analytical scales to measure transpiration seamlessly. Examples of the first applications are presented: (i) to compare the leaf area development pattern of pearl millet breeding material targeted to different agro-ecological zones, (ii) for the mapping of QTLs for vigour traits in chickpea, shown to co-map with an earlier reported “drought tolerance” QTL; (iii) for the mapping of leaf area development in pearl millet; (iv) for assessing the transpiration response to high vapour pressure deficit in different crops. This new platform has the potential to phenotype traits controlling plant water use at a high rate and precision, opening the opportunity to harness their genetics towards breeding improved varieties

Resolved velocity profiles of galactic winds at Cosmic Noon

We study the kinematics of the interstellar medium (ISM) viewed "down the barrel" in 20 gravitationally lensed galaxies during Cosmic Noon ($z=1.5 - 3.5$). We use moderate-resolution spectra ($R\sim4000$) from Keck/ESI and Magellan/MagE to spectrally resolve the ISM absorption in these galaxies into $\sim$10 independent elements and use double Gaussian fits to quantify the velocity structure of the gas. We find that the bulk motion of gas in this galaxy sample is outflowing, with average velocity centroid \left=-141 km$\,$s$^{-1}$ ($\pm111$ km$\,$s$^{-1}$ scatter) measured with respect to the systemic redshift. 16 out of the 20 galaxies exhibit a clear positive skewness, with a blueshifted tail extending to $\sim -500$ km$\,$s$^{-1}$. We examine scaling relations in outflow velocities with galaxy stellar mass and star formation rate (SFR), finding correlations consistent with a momentum-driven wind scenario. Our measured outflow velocities are also comparable to those reported for FIRE-2 and TNG50 cosmological simulations at similar redshift and galaxy properties. We also consider implications for interpreting results from lower-resolution spectra. We demonstrate that while velocity centroids are accurately recovered, the skewness, velocity width, and probes of high velocity gas (e.g., $v_{95}$) are subject to large scatter and biases at lower resolution. We find that $R\gtrsim1700$ is required for accurate results for the gas kinematics of our sample. This work represents the largest available sample of well-resolved outflow velocity structure at $z>2$, and highlights the need for good spectral resolution to recover accurate properties.Comment: 42 pages, 37 figures (including appendix), Accepted for publication, Ap

A Glimpse of the Stellar Populations and Elemental Abundances of Gravitationally Lensed, Quiescent Galaxies at z≳1z\gtrsim 1 with Keck Deep Spectroscopy

Gravitational lenses can magnify distant galaxies, allowing us to discover and characterize the stellar populations of intrinsically faint, quiescent galaxies that are otherwise extremely difficult to directly observe at high redshift from ground-based telescopes. Here, we present the spectral analysis of two lensed, quiescent galaxies at $z\gtrsim 1$ discovered by the ASTRO 3D Galaxy Evolution with Lenses survey: AGEL1323 ($M_*\sim 10^{11.1}M_{\odot}$, $z=1.016$, $\mu \sim 14.6$) and AGEL0014 ($M_*\sim 10^{11.3}M_{\odot}$, $z=1.374$, $\mu \sim 4.3$). We measured the age, [Fe/H], and [Mg/Fe] of the two lensed galaxies using deep, rest-frame-optical spectra (S/N $\gtrsim$ 40\AA$^{-1}$) obtained on the Keck I telescope. The ages of AGEL1323 and AGEL0014 are $5.6^{+0.8}_{-0.8}$ Gyr and $3.1^{+0.8}_{-0.3}$ Gyr, respectively, indicating that most of the stars in the galaxies were formed less than 2 Gyr after the Big Bang. Compared to nearby quiescent galaxies of similar masses, the lensed galaxies have lower [Fe/H] and [Mg/H]. Surprisingly, the two galaxies have comparable [Mg/Fe] to similar-mass galaxies at lower redshifts, despite their old ages. Using a simple analytic chemical evolution model connecting the instantaneously recycled element Mg with the mass-loading factors of outflows averaged over the entire star formation history, we found that the lensed galaxies may have experienced enhanced outflows during their star formation compared to lower-redshift galaxies, which may explain why they quenched early.Comment: 18 pages, 11 figures, submitted to ApJ; comments welcom

Signatures of a spin-1/2 cooperative paramagnet in the diluted triangular lattice of Y2_2CuTiO6_6

We present a combination of thermodynamic and dynamic experimental signatures of a disorder driven dynamic cooperative paramagnet in a 50% site diluted triangular lattice spin-1/2 system, Y$_2$CuTiO$_6$. Magnetic ordering and spin freezing are absent down to 50 mK, far below the Curie Weiss scale of ~-134 K. We observe scaling collapses of the magnetic field- and temperature-dependent magnetic heat capacity and magnetisation data, respectively, in conformity with expectations from the random singlet physics. Our experiments establish the suppression of any freezing scale, if at all present, by more than three orders of magnitude, opening a plethora of interesting possibilities such as disorder-stabilized long range quantum entangled ground states.Comment: 18 pages, 9 figures, published in Physical Review Letter