A STUDY ON MINDFULNESS AND ITS IMPACT ON ETHICAL DECISION MAKING OF ACCOUNTANTS

Mindfulness, known as sati in Pali, is the practice of being fully aware and present in the moment. In Buddhism, mindfulness is essential for understanding the nature of reality, cultivating wisdom, and reducing suffering. It involves paying attention to one\u27s feelings, thoughts, bodily sensations, and the surrounding environment without judgment. Accountants often face ethical dilemmas due to the nature of their work, where they balance profitability with regulatory compliance and ethical considerations. Ethical mindfulness training aims to enhance accountants\u27 awareness of ethical issues and improve their ability to make sound decisions. The study seeks to understand how ethical mindfulness training influences the decision-making processes of accountants and to know what are the ways are available to practice mindfulness. This study is in descriptive nature and it considered both primary and secondary data. Primary data collected from accountants and accounting professionals in Bangalore through structured questionnaires and used journals, text books and websites for secondary data. The inferential statistics are used to analyse the data. This study will be going to reveals that how mindfulness will help to reduce stress, increase focus on work and that leads to increase the performance of accountants

Determining Efficient Machine Learning Techniques for Grading of Knee Osteoarthritis

Osteoarthritis (OA) of the Knee is a degenerative joint disease mainly caused due to loss of articular cartilages. The paper introduces an approach to quantify knee osteoarthritis (OA) severity using KL grades. This approach combines EDA (Exploratory Data Analysis), Pre-processing and Feature Engineering techniques. The amount of damage to the knee can be graded using KL scale (0-4). The automated detection of Knee Osteoarthritis (KOA) based on KL grades which corresponds to severity stages has been given in the paper. In the study public dataset from Osteoarthritis Initiative (OAI) has been used to evaluate the proposed approach with very promising results. Different accuracy metrices like F1 score, Receiver operating characteristic curve (ROC), Area Under Curve (AUC) and Precision were used to find the best algorithm amongst the classification models in Machine learning. Random forest and Decision trees algorithms were considered efficient giving an accuracy of 96.9% and 91.6% respectively. Our study is an economically better approach when compared to x-rays for OA detectio

Identification of main effect and epistatic quantitative trait loci for morphological and yield-related traits in peanut (Arachis hypogaea L.)

An effort was made in the present study to identify the main effect and epistatic quantitative trait locus (QTL) for the morphological and yield-related traits in peanut. A recombinant inbred line (RIL) population derived from TAG 24 × GPBD 4 was phenotyped in seven environments at two locations. QTL analysis with available genetic map identified 62 main-effect QTLs (M-QTLs) for ten morphological and yield-related traits with the phenotypic variance explained (PVE) of 3.84–15.06%. Six major QTLs (PVE > 10%) were detected for PLHT, PPP, YPP, and SLNG. Stable M-QTLs appearing in at least two environments were detected for PLHT, LLN, YPP, YKGH, and HSW. Five M-QTLs governed two traits each, and 16 genomic regions showed co-localization of two to four M-QTLs. Intriguingly, a major QTL reported to be linked to rust resistance showed pleiotropic effect for yield-attributing traits like YPP (15.06%, PVE) and SLNG (13.40%, PVE). Of the 24 epistatic interactions identified across the traits, five interactions involved six M-QTLs. Three interactions were additive × additive and remaining two involved QTL × environment (QE) interactions. Only one major M-QTL governing PLHT showed epistatic interaction. Overall, this study identified the major M-QTLs for the important productivity traits and also described the lack of epistatic interactions for majority of them so that they can be conveniently employed in peanut breeding

A QTL study on late leaf spot and rust revealed one major QTL for molecular breeding for rust resistance in groundnut (Arachis hypogaea L.)

Late leaf spot (LLS) and rust are two major foliar diseases of groundnut (Arachis hypogaea L.) that often occur together leading to 50–70% yield loss in the crop. A total of 268 recombinant inbred lines of a mapping population TAG 24 × GPBD 4 segregating for LLS and rust were used to undertake quantitative trait locus (QTL) analysis. Phenotyping of the population was carried out under artificial disease epiphytotics. Positive correlations between different stages, high to very high heritability and independent nature of inheritance between both the diseases were observed. Parental genotypes were screened with 1,089 simple sequence repeat (SSR) markers, of which 67 (6.15%) were found polymorphic. Segregation data obtained for these markers facilitated development of partial linkage map (14 linkage groups) with 56 SSR loci. Composite interval mapping (CIM) undertaken on genotyping and phenotyping data yielded 11 QTLs for LLS (explaining 1.70–6.50% phenotypic variation) in three environments and 12 QTLs for rust (explaining 1.70–55.20% phenotypic variation). Interestingly a major QTL associated with rust (QTLrust01), contributing 6.90–55.20% variation, was identified by both CIM and single marker analysis (SMA). A candidate SSR marker (IPAHM 103) linked with this QTL was validated using a wide range of resistant/susceptible breeding lines as well as progeny lines of another mapping population (TG 26 × GPBD 4). Therefore, this marker should be useful for introgressing the major QTL for rust in desired lines/varieties of groundnut through marker-assisted backcrossing