Analysis of gamma rays induced variability in lentil (Lens culinaris Medik.)

ArticleIn this study, a lentil variety, Idlib-3, was subjected to 100 Gy (LD50) gamma-ray irradiation. At M2, mutant families were characterized for the most beneficial agronomic traits. High genotypic coefficient of variation, broad sense heritability and genetic advance of the traits such as seed yield per plant and hundred-seed weight indicated expression of additive gene action and confirmed the response at early generation selection. Total number of pods per plant had positive correlation and the highest positive direct effect on seed yield per plant and hence the preference should be given for this trait during selection. The novel mutant families identified with early flowering, early maturity (families 5 and 90) in cluster I, and more first pod height (families 10,70 and 82) in cluster II could be utilized to breed short duration lentil varieties suitable for machine harvest

Is something better than nothing? automatically predicting stance-based arguments using deep learning and small labelled dataset

Online reviews have become a popular portal among customers making decisions about purchasing products. A number of corpora of reviews have been widely investigated in NLP in general, and, in particular, in argument mining. This is a subset of NLP that deals with extracting arguments and the relations among them from user-based content. A major problem faced by argument mining research is the lack of human-annotated data. In this paper, we investigate the use of weakly supervised and semi-supervised methods for automatically annotating data, and thus providing large annotated datasets. We do this by building on previous work that explores the classification of opinions present in reviews based on whether the stance is expressed explicitly or implicitly. In the work described here, we automatically annotate stance as implicit or explicit and our results show that the datasets we generate, although noisy, can be used to learn better models for implicit/explicit opinion classification

Comparative effects of glucose and water drinks on blood pressure and cardiac function in older subjects with and without postprandial hypotension

Postprandial hypotension (PPH) occurs frequently and is thought to reflect an inadequate increase in cardiac output to compensate for the rise in splanchnic blood flow after a meal. Gastric distension by water attenuates the postprandial fall in blood pressure (BP). Cardiac hemodynamics (stroke volume (SV), cardiac output (CO), and global longitudinal strain (GLS)) have hitherto not been measured in PPH We sought to determine the comparative effects of water and glucose drinks on cardiac hemodynamics in healthy older subjects and individuals with PPH Eight healthy older subjects (age 71.0 ± 1.7 years) and eight subjects with PPH (age 75.5 ± 1.0 years) consumed a 300 mL drink of either water or 75 g glucose (including 150 mg 13C-acetate) in randomized order. BP and heart rate (HR) were measured using an automatic device, SV, CO, and GLS by transthoracic echocardiography and gastric emptying by measurement of 13CO2 In both groups, glucose decreased systolic BP (P < 0.001) and increased HR, SV, and CO (P < 0.05 for all). The fall in systolic BP was greater (P < 0.05), and increase in HR less (P < 0.05), in the PPH group, with no difference in SV or CO Water increased systolic BP (P < 0.05) in subjects with PPH and, in both groups, decreased HR (P < 0.05) without affecting SV, CO, or GLS In subjects with PPH, the hypotensive response to glucose and the pressor response to water were related (R = -0.75, P < 0.05). These observations indicate that, in PPH, the hypotensive response to oral glucose is associated with inadequate compensatory increases in CO and HR, whereas the pressor response to water ingestion is maintained and, possibly, exaggerated.Laurence G. Trahair, Sharmalar Rajendran, Renuka Visvanathan, Matthew Chapman, Daniel Stadler, Michael Horowitz and Karen L. Jone

A preliminary study of crack initiation and growth at stress concentration sites

Crack initiation and propagation models for notches are examined. The Dowling crack initiation model and the E1 Haddad et al. crack propagation model were chosen for additional study. Existing data was used to make a preliminary evaluation of the crack propagation model. The results indicate that for the crack sizes in the test, the elastic parameter K gave good correlation for the crack growth rate data. Additional testing, directed specifically toward the problem of small cracks initiating and propagating from notches is necessary to make a full evaluation of these initiation and propagation models

Fertilizer responsiveness of chickpeas in India

Chickpea is one of the major pulse crops in India, occupying over 7 million hectares, India grows nearly 73% of the world and 80% of the total SAT chickpeas. The four states of Uttar Pradesh, Madhya Pradesh, Rajasthan and Haryana account for almost 82% of national production..

Application of machine learning in corrosion inhibition study

Artificial intelligence is a branch of science concerned with teaching machines to think and act like humans. Machine learning is concerned with enabling computers to perform tasks without the need for explicit programming. Machine Learning enables computers to learn without the need for explicit programming. Machine Learning is a broad field that encompasses a wide range of machine learning operations such as clustering, classification, and the development of predictive models. Machine Learning (ML) and Deep Learning (DL) research is now finding a home in both industry and academia. Machine Learning technologies are increasingly being used in medical imaging. To detect tumours and other malignant growths in the human body. Deep Learning is making significant contributions to the advancement of industrial robotics. Machine learning algorithms are used in the self-driving car industry to guide the vehicle to its destination. Deep Learning and Machine Learning are also used in corrosion science and engineering. They are used to choose the inhibitor molecules from a large pool of available molecules. © 2022 Authors

Beating dark-dark solitons in Bose-Einstein condensates

Motivated by recent experimental results, we study beating dark-dark solitons as a prototypical coherent structure that emerges in two-component Bose-Einstein condensates. We showcase their connection to dark- bright solitons via SO(2) rotation, and infer from it both their intrinsic beating frequency and their frequency of oscillation inside a parabolic trap. We identify them as exact periodic orbits in the Manakov limit of equal inter- and intra-species nonlinearity strengths with and without the trap and showcase the persistence of such states upon weak deviations from this limit. We also consider large deviations from the Manakov limit illustrating that this breathing state may be broken apart into dark-antidark soliton states. Finally, we consider the dynamics and interactions of two beating dark-dark solitons in the absence and in the presence of the trap, inferring their typically repulsive interaction.Comment: 13 pages, 14 figure