Stochastic Modelling and Simulation of SIR Model for COVID-2019 Epidemic Outbreak in India

Coronavirus disease 2019 (COVID-19) emerged in Wuhan city, China, at the end of December 2019. As of July 26, 2020, 16258353 COVID-19 cases were confirmed worldwide, including  649848 deaths. The spread of COVID-19 is currently very high. Under the classical SIR (Susceptible-Infected-Recovered) model, epidemiological data for India up to 26th July 2020 were used to forecast the COVID-19 outbreak. For controlling the spreading of the virus, we have to prepare for precaution and futuristic calculation for infection spreading. We used the data from the COVID-2019 Outbreak of India on July 26th, 2020 in this report. In these results, for the initial level of experimental intent, we used 16291331 susceptible cases, 481248 infectious cases, and 910298 rewards / removed cases. Through the aid of the SIR model, data on a wide range of infectious diseases have been analyzed.  SIR model is one of the most effective models which can predict the spreading rate of the virus. We have validated the model with the current spreading rate with this SIR model. The findings of the SIR model can be used to forecast transmission and avoid the outbreak of COVID-2019 in India. The results of the study will shed light on understanding the outbreak patterns and indicate those regions' epidemiological points. Finally, from this study, we have found that the outbreak of the COVID-2019 epidemic in India will be at its peak on 09 August 2020 and after that, it will work slowly and on the verge of ending in the second or third week of November 2020

Properties of an anti vague filter in BL-algebras

ABSTRACTIn this paper, we introduce the notion of an anti vague filter of a BL-algebra with illustration, and obtain some related properties. Further, we investigate some equivalent conditions of anti vague filter. 

Drugs modulating apoptosis: current status

Apoptosis (programmed cell death) is a natural process that helps in removing potentially harmful cells from the body and replacing it with normal ones. Like any other process, it is also subjected to lots of deregulations and can lead to diseases like cancer, neurodegenerative conditions, multiple sclerosis, Parkinson’s disease, autoimmune disorders and inappropriate death of cells after liver failure, stroke and myocardial infarction. The knowledge of the molecular mechanisms involved in apoptosis has been progressed tremendously. Thus, therapeutics targeting apoptosis have been emerged as a novel approach for treating various disease conditions. Current approaches induce or inhibit apoptosis by targeting the key regulators of apoptosis such as Bcl2 family of proteins, TRAIL, caspases, MDM2, IAPs and p53. While many apoptotic drugs proved its efficacy in preclinical studies, some are already approved and entered the clinical setting. Numerous novel approaches such as antisense therapy, gene therapy, recombinant biologics and combinatorial chemistry are being employed to target these regulators. This review focused on the pathways of apoptosis, various therapeutic targets in apoptosis and the drugs modulating these targets

A new variety of Gymnosporia emarginata (Celastraceae) from the Coromandel Coast of Peninsular India

A new variety, Gymnosporia emarginata (Willd.) Thwaites var. coromandelica N.Balach. & P. Umamaheswari (Celastraceae) has been described from Tamil Nadu, India. The diagnostic characters of this variety are: long stamens, ovary immersed in the disc, style sessile and stigma lobes converged. Detailed descriptions, differences in characters between the 2 varieties, ITS based phylogenetic analysis and related images are provided for easy identification

Optimized Preprocessing using Time Variant Particle Swarm Optimization (TVPSO) and Deep Learning on Rainfall Data

In the recent past, rainfall prediction has played a significant role in the meteorology department. Changes in rainfall might affect the world's manufacturing and service sectors. Rainfall prediction is a substantial progression in giving input data for weather information and hydrological development applications. In machine learning, accurate and efficient rainfall predictionis used to support strategy for watershed management. The prediction of rain is a problematic occurrence and endures to be a challenging task. This paper implements a novel algorithm for preprocessing and optimization using historical weather from a collection of various weather parameters. The Moving Average-Probabilistic Regression Filtering (MV-PRF) method eliminates unwanted samples with less amplitude from the database. The Time Variant Particle Swarm Optimization (TVPSO) model optimizes the preprocessing rainfall data. Then this optimized data is used for the different classification processes. The preprocessing methods emphasize the recent rainfall data of the time series to improve the rainfall forecast using classification methods. Machine Learning (ML) technique classifies the weather parameters to predict rainfall daily or monthly. These experimental results show that the proposed methods are efficient and accurate for rainfall analysis

BIOACTIVE POTENTIAL OF ENDOPHYTIC FUNGI ASPERGILLUS FLAVUS (SS03) AGAINST CLINICAL ISOLATES

Objective: This study was to evaluate the antimicrobial potential of endophytic fungi isolated from the medicinal plant Moringa oleifera Lam. collected from the Omalur region, Salem district.Methods: The endophytic fungi were isolated from stem, leaves, flowers and calyx of Moringa oleifera by surface sterilization method. The samples were surface sterilized by immersing it in 70% ethanol for 5 seconds followed by 4% sodium hypochlorite for 90 seconds and then a final rinsing in sterile distilled water. Then fungal biomass was extracted for intracellular metabolites by using ethyl acetate as solvent. The crude extract was filtered, and the filtrate was dried under vacuum at 40 °C. The filtrate was analyzed for antimicrobial activity. The fungi which showed the maximum activity was identified and the metabolite present in the ethyl acetate extract was characterized and identified by GC-MS and NMR analysis.Results: The predominant endophytic fungi isolated belongs to the genera of Aspergillus spp, Aspergillus flavus, Aspergillus versicolor, Aspergillus niger, Aspergillus ochraceus, Aspergillus terreus and dematiaceous fungi namely Bipolaris spp. From this Aspergillus flavus showed the highest zone of inhibition was observed against Staphylococcus aureus and Bacillus 22 mm and strain of Candida tropicalis 19 mm. The efficiency of the bioactive compound was identified by GC-MS and NMR analysis and found to be Fenaclon, (R)(-) 14 methyl-8-hexadecyn-1-ol, Trans-β-farnesene (E)-β-farnesene, 9-Octcadecene,1,1, DimethoxyConclusion: This study results indicate that the bioactive metabolites produce the endophytic fungi Aspergillus flavus could be promising source as antimicrobial agents

Should shrimp farmers pay paddy farmers? : the challenges of examining salinisation externalities in South India

This study calculates the externality costs of salinization of land by comparing rice paddy yields in two similar villages in southern India. Shrimp farming causes two kinds of externality costs due to salinization: (i) An externality borne by the current generation due to decline in crop yields; (ii) An inter-generational externality borne by future generations because of environmental damage to land and groundwater resources. Findings show that if soil salinity is reduced to safe levels crop gains are estimated in the range of Rs 1,000 to Rs 5,000 per hectare. A regulatory framework for taxing externalities is recommended