Cultural, Morphological and Pathogenic Variability in Alternaria cyamopsidis Causing Alternaria Blight of Clusterbean in Rajasthan

Background: Alternaria cyamopsidis (Rang. and Rao) causes Alternaria blight of clusterbean and it is one of the significant disease of clusterbean. Studies were conducted to compare the Cultural, morphological and pathogenic variability among ten isolates of Alternaria cyamopsidis from clusterbean, in five districts of Rajasthan viz., Bikaner, Barmer, Churu, Hanumangarh and Jaipur. Methods: During 2016-17 exhaustive survey was conducted in clusterbean growing areas of Rajasthan and collected diseased samples of clusterbean caused by Alternaria. All the samples were processed for isolation, purification and their pathogenicity was proved in cagehouse and laboratory and standard methods were adopted for cultural and morphological variability study. Result: All the isolates showed variation in their morphological characters, i.e., colony color and shape; conidial number, size, width, length, shape and septation on PDA. Out of ten isolates two isolate, viz., AlcyJp1 and AlcyJp2 showed maximum colony diameter 89.50 and 86.30 mm, respectively. All the isolates varied in their spore length and width and virulent on the tested variety of clusterbean for virulence. AlcyJp1 was the most virulent and produced maximum (65.50%) disease intensity, followed by AlcyJp2 isolate (61.22%).

Parallel approach of Schrödinger-based quantum corrections for ultrascaled semiconductor devices

In the current technology node, purely classical numerical simulators lack the precision needed to obtain valid results. At the same time, the simulation of fully quantum models can be a cumbersome task in certain studies such as device variability analysis, since a single simulation can take up to weeks to compute and hundreds of device configurations need to be analyzed to obtain statistically significative results. A good compromise between fast and accurate results is to add corrections to the classical simulation that are able to reproduce the quantum nature of matter. In this context, we present a new approach of Schrödinger equation-based quantum corrections. We have implemented it using Message Passing Interface in our in-house built semiconductor simulation framework called VENDES, capable of running in distributed systems that allow for more accurate results in a reasonable time frame. Using a 12-nm-gate-length gate-all-around nanowire FET (GAA NW FET) as a benchmark device, the new implementation shows an almost perfect agreement in the output data with less than a 2% difference between the cases using 1 and 16 processes. Also, a reduction of up to 98% in the computational time has been found comparing the sequential and the 16 process simulation. For a reasonably dense mesh of 150k nodes, a variability study of 300 individual simulations can be now performed with VENDES in approximately 2.5 days instead of an estimated sequential execution of 137 days.

γ-Ray Flux and Spectral Variability of Blazar Ton 599 during Its 2021 Flare

Blazars are known to emit exceptionally variable non-thermal emission over the wide range (from radio to γ-rays) of electromagnetic spectrum. We present here the results of our γ-ray flux and spectral variability study of the blazar Ton 599, which has been recently observed in the γ-ray flaring state. Using 0.1−300 GeV γ-ray data from the Fermi Gamma-ray Space Telescope (hereinafter Fermi), we generated one-day binned light curve of Ton 599 for a period of about one-year from MJD 59,093 to MJD 59,457. During this one year period, the maximum γ-ray flux detected was 2.24 ± 0.25 ×10−6 ph cm−2 s−1 at MJD 59,399.50. We identified three different flux states, namely, epoch A (quiescent), epoch B (pre-flare) and epoch C (main-flare). For each epoch, we calculated the γ-ray flux variability amplitude (Fvar) and found that the source showed largest flux variations in epoch C with Fvar∼ 35%. We modelled the γ-ray spectra for each epoch and found that the Log-parabola model adequately describes the γ-ray spectra for all the three epochs. We estimated the size of the γ-ray emitting region as 1.03 ×1018 cm and determined that the origin of γ-ray radiation, during the main-flare, could be outside of the broad line region.

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Enset (Enset ventricosum (Welw.) Cheesman) Breeding for Various Purposes - A Review

Enset (Enset ventricosum (Welw) Cheesman) is a well-known cultivated crop in Ethiopia and a cultural staple food for over 20 million humans in different parts of Ethiopia. It bears flowers that developed into fruits and seeds. It bears seeds and propagated vegetativly. Attempts have been done for maintenance, conservation, improving cultivars, diversity, and variability study. About 623 Enset germplasm were collected from 12 main growing areas and ex-situ conserved at Areka. Seeds of Enset were stored at the millennium gene bank. Seedlings and specimens are held in the garden of Cambridge Cottage and Wakehurst. In the research effort 6 cultivars: 3 early set cultivars: Yanbulle, Gewada, Endale, and 3 late set cultivars: Kelisa, Zerita, Mesena were released. Variability study revealed that corn yields were significantly affected by location, cultivar, and cultivar Vs location. Genotypes: Suite, Warke, Bidu, Astera, and Kekari showed 100% disease symptoms after 30 days of inoculation. Symptoms weren&#39;t observed on Meziya, Bedadet, Hiniba, and Nech Enset clones after 90 days of inoculation. ISSRs primers revealed that, in all parameters, Kefficho genotypes have been more diversified than genotypes from Essera areas. The Partitioning of Shannon&#39;s diversity index indicated that the major variations were occurred within populations than between the two populations.

Parallel Approach of Schr¨Odinger Based Quantum Corrections for Ultrascaled Semiconductor Devices

In the current technology node, purely classical numerical simulators lack the precision needed to obtain valid results. At the same time, the simulation of fully quantum models can be a cumbersome task in certain studies such as device variability analysis, since a single simulation can take up to weeks to compute and hundreds of device configurations need to be analyzed to obtain statistically significative results. A good compromise between fast and accurate results is to add corrections to the classical simulation that are able to reproduce the quantum nature of matter. In this context, we present a new approach of Schrödinger equation-based quantum corrections. We have implemented it using Message Passing Interface (MPI) in our in-house built semiconductor simulation framework called VENDES, capable of running in distributed systems that allow for more accurate results in a reasonable time frame. Using a 12 nm gate length Gate-AllAround Nanowire FET (GAA NW FET) as an application example, the new implementation shows an almost perfect agreement in the output data with less than a 2% difference between the cases using 1 and 16 processes. Also, a reduction of up to 98% in the computational time has been found comparing the sequential and the 16 process simulation. For a reasonably dense mesh of 150k nodes, a variability study of 300 individual simulations, can be now performed with VENDES in approximately 2.5 days instead of an estimated sequential execution of 137 days.

Variability Study of Bond Work Index and Grindability Index on Various Critical Metal Ores

It is a well-known fact that the value of the Bond work index (wi) for a given ore varies along with the grinding size. In this study, a variability bysis is carried out with the Bond standard grindability tests on different critical metal ores (W, Ta), ranging from coarse grinding (rod mills) to fine grinding (ball mills). The relationship between wiand grinding size did not show a clear correlation, while the grindability index (gpr) and the grinding size showed a robust correlation, fitting in all cases to a quadratic curve with a very high regression coefficient. This result suggests that, when performing correlation studies among ore grindability and rock mechanics parameters, it is advised to use the grindability index instead of the Bond work index.