Application of analytic hierarchy process for the selection of cotton fibers

In many engineering applications, the final decision is based on the evaluation of a number of alternatives in terms of a number of criteria. This problem may become very intricate when the selection criteria are expressed in terms of different units or the pertinent data are difficult to be quantified. The Analytic Hierarchy Process (AHP) is an effective way in dealing with such kind of complicated problems. Cotton fiber is selected or graded, in the spinning industries, based on several quality criteria. However, the existing selection or grading method based on Fiber quality Index (FqI) is rather crude and ambiguous. This paper presents a novel approach of cotton fiber selection using the AHP methodology of Multi Criteria Decision Making.     

Characterizing the tensile properties of spun yarns using bivariate normal distribution

The tensile properties of spun yarns decisively influence its performance in various mechanical processing stages. This study is primarily aimed at simultaneous analysis of two tensile properties of spun yarns namely tenacity and breaking strain, which play crucial role in determining the frequency of warping breaks. The threshold values of yarn tenacity and breaking strain required for 20’s Ne carded cotton yarn to sustain the imposed stresses and strains during warping process have been determined using a bivariate normal distribution model. This study opens up the possibility of minimizing end breakage rate in various manufacturing processes of textile industry by engineering of spun yarns devoid of potential weak spots which are responsible for breaks.     

Validation and Marker-Assisted Selection of Stem Rust Resistance Gene Sr2 in Indian Wheat Using Gel-Based and Gel-Free Methods

Journal of Crop Science and Biotechnology - Stem rust resistance gene Sr2 is an important slow-rusting gene which has provided resistance against stem rust for many years. The Sr2 gene has durable...     

SARS-CoV-2 in animals: potential for unknown reservoir hosts and public health implications

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously 2019-nCoV) is suspected of having originated in 2019 in China from a coronavirus infected bat of the genus Rhinolophus. Following the initial emergence, possibly facilitated by a mammalian bridge host, SARS-CoV-2 is currently transmitted across the globe via efficient human-to-human transmission. Results obtained from experimental studies indicate that animal species such as cats, ferrets, raccoon dogs, cynomolgus macaques, rhesus macaques, white-tailed deer, rabbits, Egyptian fruit bats, and Syrian hamsters are susceptible to SARS-CoV-2 infection, and that cat-to-cat and ferret-to-ferret transmission can take place via contact and air. However, natural infections of SARS-CoV-2 have been reported only in pet dogs and cats, tigers, lions, snow leopards, pumas, and gorillas at zoos, and farmed mink and ferrets. Even though human-to-animal spillover has been reported at several instances, SARS-CoV-2 transmission from animals-to-humans has only been reported from mink-to-humans in mink farms. Following the rapid transmission of SARS-CoV-2 within the mink population, a new mink-associated SARS-CoV-2 variant emerged that was identified in both humans and mink. The increasing reports of SARS-CoV-2 in carnivores indicate the higher susceptibility of animal species belonging to this order. The sporadic reports of SARS-CoV-2 infection in domestic and wild animal species require further investigation to determine if SARS-CoV-2 or related Betacoronaviruses can get established in kept, feral or wild animal populations, which may eventually act as viral reservoirs. This review analyzes the current evidence of SARS-CoV-2 natural infection in domestic and wild animal species and their possible implications on public health.     

The role of rpoS,hmp, and ssrAB in Salmonella enterica Gallinarum and evaluation of a triple-deletion mutant as a live vaccine candidate in Lohmann layer chickens

Salmonella enterica Gallinarum (SG) causes fowl typhoid (FT), a septicemic disease in avian species. We constructed deletion mutants lacking the stress sigma factor RpoS, the nitric oxide (NO)-detoxifying flavohemoglobin Hmp, and the SsrA/SsrB regulator to confirm the functions of these factors in SG. All gene products were fully functional in wild-type (WT) SG whereas mutants harboring single mutations or a combination of rpoS, hmp, and ssrAB mutations showed hypersusceptibility to H₂O₂, loss of NO metabolism, and absence of Salmonella pathogenicity island (SPI)-2 expression, respectively. A triple-deletion mutant, SGΔ3 (SGΔrpoSΔhmpΔssrAB), was evaluated for attenuated virulence and protection efficacy in two-week-old Lohmann layer chickens. The SGΔ3 mutant did not cause any mortality after inoculation with either 1 × 10⁶ or 1 × 10⁸ colony-forming units (CFUs) of bacteria. Significantly lower numbers of salmonellae were recovered from the liver and spleen of chickens inoculated with the SGΔ3 mutant compared to chickens inoculated with WT SG. Vaccination with the SGΔ3 mutant conferred complete protection against challenge with virulent SG on the chickens comparable to the group vaccinated with a conventional vaccine strain, SG9R. Overall, these results indicate that SGΔ3 could be a promising candidate for a live Salmonella vaccine against FT.     

Modelling of ring yarn unevenness by soft computing approach

This paper demonstrates the application of two soft computing approaches namely artificial neural network (ANN) and neural-fuzzy system to forecast the unevenness of ring spun yarns. The cotton fiber properties measured by advanced fiber information system (AFIS) and yarn count have been used as inputs. The prediction accuracy of the ANN and neural-fuzzy models was compared with that of linear regression model. It was found that the prediction performance was very good for all the three models although ANN and neural-fuzzy models seem to have some edge over the linear regression model. The linguistic rules developed by the neural-fuzzy system unearth the role of input variables on the yarn unevenness.     

Phosphorus Enriched Organic Amendments can Increase Nitrogen Use Efficiency in Wheat

Although nitrogen (N) has the highest requirement for plant growth, N use efficiency (NUE) seldom exceeds 40%. NUE may be improved by integrated application of fertilizer N and enriched organic amendments. The present experiment aimed to test the extent of increase in NUE by integrated application of fertilizer N farmyard manure (FYM) and rock phosphate enriched compost (RPEC). Mineralization kinetics and N release from FYM and RPEC were studied by an incubation experiment. Results revealed that maximum potentially mineralizable N as well as N release (283.9, 186.7 mg kg^?1 soil, respectively) were from RPEC + fertilizer N treated soils, followed by FYM + fertilizer N. Maximum yield, N uptake, and N recovery were obtained from RPEC + fertilizer N treated soils followed by FYM + fertilizer N. Soils treated with RPEC had shown significantly higher dehydrogenase activity than FYM treated soils. Thus, RPEC might increase yield as well as NUE over FYM. N uptake by plant at maximum tillering stage and flowering stage of wheat correlated positively (R² > 0.85) with the decay rate (k and kN₀) parameter of incubation experiment suggesting their relevance as indicators of plant available N.