STUDY ON EFFECT OF SELF-COMPACTING CONCRETE WITH PARTIAL REPLACEMENT OF MINERAL ADMIXTURES

This project is experimental investigation on self compacting concrete by using mineral additive such as Fly ash, Micro silica & Metakaolin. Self Compacting concrete is a concrete that exhibit the high flow ability and avoid the segregation and bleeding. The industrial waste such as fly ash use in this project as a partial replacement of cement to produce concrete, thus minimizes the amount of cement and reducing the cost.  Self−compacting concrete is one of "the most revolutionary developments" in concrete research; this concrete is able to flow and to fill the most restacked places of the form work without vibration. There are several methods for testing its properties in the fresh state: the most frequently used are Slump−flow test, L−box, U-box and V−funnel. This work presents properties of self−compacting concrete, mixed with different type’s additives: fly ash, micro silica, metakaolin. So we added admixture ac-hypercrete and ac-viscocrete about 0.5% and 0.2% of total cementatious content in every mix thereafter. The compressive strength carried in the compressive testing machine. The additions of fly ash were 20%, 25%, 30% and 35% of concrete. It was seen that increase the percentage of fly ash resulted in the decrease of compressive strength

A Method to Assess Linkage Disequilibrium between CNVs and SNPs Inside Copy Number Variable Regions

Since the discovery of the ubiquitous contribution of copy number variation to genetic variability, researchers have commonly used metrics such as r2 to quantify linkage disequilibrium (LD) between copy number variants (CNVs) and single nucleotide polymorphisms (SNPs). However, these reports have been restricted to SNPs outside copy number variable regions (CNVR) as current methods have not been adapted to account for SNPs displaying variable copy number. We show that traditional LD metrics inappropriately quantify SNP/CNV covariance when SNPs lie within CNVR. We derive a new method for measuring LD that solves this issue, and defaults to traditional metrics otherwise. Finally, we present a procedure to estimate CNV–SNP allele frequencies from unphased CNV–SNP genotypes. Our method allows researchers to include all SNPs in SNP/CNV LD measurements, regardless of copy number

Unveiling the nexus between fuel consumption, vehicle registration, population and GDP of Nepal

Vehicle numbers soar with the increase in travel demand, thus increasing petroleum consumption, one of the extensive non-renewable resources. The increased demand for travel is also linked to Gross Domestic Product (GDP). However, due to the rise in fuel standards and higher fuel efficiency vehicles, the fuel consumption per vehicle is following the decreasing patterns. Thus, this study is about the relationship between petroleum consumption, vehicle registration and GDP of Nepal using regression analysis. Data for analysis were between 1994 and 2022 for registered vehicles, petroleum consumption and GDP whereas population data were collected from 1930 to 2021. The linear regression model came to be statistically significant between variables, (a) vehicles registered and petroleum consumption (diesel and petrol sales); (b) operating vehicles and petroleum consumption; and (c) operating light vehicles and petrol consumption. Similarly, significant exponential regression models were observed between (a) GDP and operating vehicles; and (b) GDP and petroleum consumption. Additionally, the study presented a logistic population growth model and vehicle growth model as significant models to put forth predicted population and vehicles in 2030 and 2040. These models were used to estimate the possible petroleum consumption in 2030 and 2040. Alongside, a situation, where high electric vehicle penetration might be observed, was also taken to predict the possible petroleum consumption. Rising petroleum consumption can be curbed to a certain limit with proper policy interventions and research and development in electric vehicles

Effect of Drip Irrigation and Polythene Mulch on the Fruit Yield and Quality Parameters of Mango (Mangifera indica L.)

A field experiment was carried out at Horticultural Research Farm, Precision Farming Development Centre, Department of Horticulture, Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh during the year 2009- 2010 in Randomized Block Design with three replications and ten treatment combinations ( 100%, 80%, 60%, and 40% water through drip irrigation system with and without polythene mulch + Basin irrigation with and without mulch). Fruits characters, yield and yield attributing parameter were higher under drip irrigation with 0.6 V volume of water + polythene mulch (T8) and the same characters were lowest under control (Basin irrigation with V- volume of water). Application of black plastic mulch with drip irrigation system can conserve moisture, check the growth of weeds and improve the fruit yield and quality. Water use efficiency was higher under drip irrigation with 0.6 V volume of water + polythene mulch and low under basin irrigation with V volume of water. The net income and benefit cost ratio was also higher under the treatment T8 as compared to surface method of irrigation

COGA phenotypes and linkages on chromosome 2

An initial linkage analysis of the alcoholism phenotype as defined by DSM-III-R criteria and alcoholism defined by DSM-IV criteria showed many, sometimes striking, inconsistencies. These inconsistencies are greatly reduced by making the definition of alcoholism more specific. We defined new phenotypes combining the alcoholism definitions and the latent variables, defining an individual as affected if that individual is alcoholic under one of the definitions (either DSM-III-R or DSM-IV), and indicated having a symptom defined by one of the latent variables. This was done for each of the two alcoholism definitions and five latent variables, selected from a canonical discriminant analyses indicating they formed significant groupings using the electrophysiological variables. We found that linkage analyses utilizing these latent variables were much more robust and consistent than the linkage results based on DSM-III-R or DSM-IV criteria for definition of alcoholism. We also performed linkage analyses on two first prinicipal components derived phenotypes, one derived from the electrophysiolocical variables, and the other derived from the latent variables. A region on chromosome 2 at 250 cM was found to be linked to both of these derived phenotypes. Further examination of the SNPs in this region identified several haplotypes strongly associated with these derived phenotypes

Multiple Imputation to Correct for Measurement Error in Admixture Estimates in Genetic Structured Association Testing

Objectives: Structured association tests ( SAT), like any statistical model, assumes that all variables are measured without error. Measurement error can bias parameter estimates and confound residual variance in linear models. It has been shown that admixture estimates can be contaminated with measurement error causing SAT models to suffer from the same afflictions. Multiple imputation (MI) is presented as a viable tool for correcting measurement error problems in SAT linear models with emphasis on correcting measurement error contaminated admixture estimates. Methods: Several MI methods are presented and compared, via simulation, in terms of controlling Type I error rates for both non-additive and additive genotype coding. Results: Results indicate that MI using the Rubin or Cole method can be used to correct for measurement error in admixture estimates in SAT linear models. Conclusion: Although MI can be used to correct for admixture measurement error in SAT linear models, the data should be of reasonable quality, in terms of marker informativeness, because the method uses the existing data to borrow information in which to make the measurement error corrections. If the data are of poor quality there is little information to borrow to make measurement error corrections. Copyright © 2009 S. Karger AG, Base

Structural equation model-based genome scan for the metabolic syndrome

BACKGROUND: The metabolic syndrome is characterized by the clustering of several traits, including obesity, hypertension, decreased levels of HDL cholesterol, and increased levels of glucose and triglycerides. Because these traits cluster, there are likely common genetic factors involved. RESULTS: We used a multivariate structural equation model (SEM) approach to scan the genome for loci involved in the metabolic syndrome. We found moderate evidence for linkage on chromosomes 2, 3, 11, 13, and 15, and these loci appear to have different relative effects on the component traits of the metabolic syndrome. CONCLUSION: Our results suggest that the metabolic syndrome components, diabetes, obesity, and hypertension, are under the pleiotropic control of several loci

Serosurveillance among COVID-19 Cases in Ahmedabad Using SARS-COV2 IgG Antibodies

Background: Serosurveillance study focusing on antibodies against SARS-CoV2 among the Covid19 cases can add value in the scientific knowledge & help in formulating valid predictions regarding immunity status in the post-covid period. Objectives: To estimate seropositivity among covid19 cases and to identify various factors affecting seropositivity. Methods: During second half of October 2020, a population based serosurvey on Covid19 cases was carried out in Ahmedabad. Covid-Kavach test kits were used and estimated seroprevalence was compared with available demographic and covid19 case related parameters to identify factors affecting seropositivity in the post-covid period. Simple proportions and Z-test were used as appropriate. Results: As on October 2020, the sero-positivity among Covid19 cases in Ahmedabad was 54.51% [95% Confidence Interval (CI) 52.14-56.86%]. Females have higher positivity (54.78%) as compared to males (54.30%) but the difference was statistically not significant (Z=0.19, P=0.84). Among children and elderly, the positivity is high and from young adults to elderly the seropositivity has an increasing trend. Severity of clinical illness and longer duration of hospitalization are associated with higher seropositivity. Conclusion: With 54.51% seropositivity among covid19 cases, it is clear that all the covid19 cases may not have developed IgG antibodies, have undetectable level or might have disappeared during the post-covid period. Comparison of seropositivity with age group and clinical case details clearly suggest close correlation with the severity of clinical symptoms. The seronegative cases indicate the need for further in-depth scientific research to identify the factors affecting immunity and to uncover the reasons behind the same