74 research outputs found
IMPACT OF CORRUPTION ON FOREIGN DIRECT INVESTMENTS (FDI) IN MALAYSIA
No full textAbstract: his paper discusses the impact of corruption on foreign direct investment (FDI) in Malaysia. Corruption is an abuse of power to obtain personal benefits while foreign direct investment (FDI) refers to a growing investment relationship between nation, people and economic activities. In this regard, this study examines the relationship between corruption and FDI in Malaysia. Since this study need the latest information, this study uses quantitative secondary data. The data were obtained from 1995 to 2016. This study covers time series data, then the statistical test such as Unit Root Test, Vector Autoregression Estimates (VAR) Test, ARDL Method Test, Breusch-Godfrey Test, White Test Heteroskedasticity and the Ramsey Reset Test are used. The results of the study shows that when foreign direct investment in a country increases, the level of corruption in the country will increases as well. The corruption can be reduced by certain actions and strict laws.
Keywords: Corruption, Foreign Direct Investment, Malaysia.
Title: IMPACT OF CORRUPTION ON FOREIGN DIRECT INVESTMENTS (FDI) IN MALAYSIA
Author: KAVITHA CHANDRAN
International Journal of Recent Research in Commerce Economics and Management (IJRRCEM)
ISSN 2349-7807
Vol. 10, Issue 1, January 2023 - March 2023
Page No: 89-97
Paper Publications
Website: www.paperpublications.org
Published Date: 14-March-2023
DOI: https://doi.org/10.5281/zenodo.7732593
Paper Download Link (Source)
https://www.paperpublications.org/upload/book/IMPACT%20OF%20CORRUPTION-14032023-5.pdfInternational Journal of Recent Research in Commerce Economics and Management (IJRRCEM), ISSN 2349-7807, Paper Publications, Website: www.paperpublications.or
South Asians and the problem of the color line: migration, race, and identity in South Africa and the United States
No full textModern migration has resulted in the unsettlement of the identities of migrants who live, work, and struggle – for rights, opportunities, and recognition – with other populations in new national contexts. This dissertation considers the identities of South Asian migrants to South Africa and the United States, two nation-states that have been involved deeply in the creation of ideologies of race as well as regimes of racial practice. Focusing on the late 19th century to the late 20th century, I show how South Asian migrants were historically positioned within the racial hierarchies of these two societies, and how they constructed their identities in relation to racial others. I analyze contact, conflict, and cooperation between South Asians and other racial subjects in a comparative, cross-national perspective, and consider the transnational exchange of ideas that led to particular strategies of resistance. I argue that South Asian struggles in South Africa and the United States for rights and recognition resulted in a transnational articulation of modern social movements for national liberation, civil rights, and democracy.Ph. D.Includes bibliographical referencesIncludes vitaby Kavitha Ramsam
A Word Embeddings based Approach for Author Profiling: Gender and Age Prediction
Get PDFAuthor Profiling (AP) is a method of identifying the demographic profiles such as age, gender, location, native language and personality traits of an author by processing their written texts. The AP techniques are used in multiple applications such as literary research, marketing, forensics and security. The researchers identified various differences in the authors writing styles by analysing various datasets. The differences in writing styles are represented as stylistic features. The researchers extracted several style based features like structural, content, word, character, syntactic, readability and semantic features to recognize the profiles of the authors. Traditionally, the researchers extracted various feature combinations for differentiating the profiles of authors. Several existing works are used Machine Learning (ML) methods for predicting the author characteristics of a new author. The existing works achieved good accuracies for predicting the author characteristics by considering the both stylistic features and ML algorithms combination. Recently, in advent of Deep Learning (DL) techniques the researchers are proposed approaches to author profiling by using these techniques. Few researchers identified that the deep learning techniques performance is good for author profiles prediction than the results of style based features. In this work, a word embeddings based approach is proposed for gender and age prediction. In this approach, the experiment conducted with different word embedding models such as Word2Vec, GloVe, FastText and BERT for generating word vectors for words. The documents are converted as vectors by using the document representation technique which uses the word embeddings of words. The document vectors are transferred to three different ML algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Logistic Regression (LR) for generating the trained model. This model is used for predicating the accuracy of age and gender prediction. The XGBoost classifier with word embeddings of BERT achieved good accuracies for age and gender prediction than other word embeddings and ML algorithms. The experiment implemented on PAN 2014 competition Reviews dataset for age and gender prediction. The proposed approach attained best accuracies for predicting age and gender than the performances of various existing approaches proposed for AP
Studies on Functional Properties of Whey Protein and Development of High Protein Product
No full textThis Dissertation / Report is the outcome of investigation carried out by the creator(s) / author(s) at the department/division of Central Food Technological Research Institute (CFTRI), Mysore mentioned below in this page
Genome-scale analysis of in vivo spatiotemporal promoter activity in Caenorhabditis elegans
No full textDifferential regulation of gene expression is essential for cell fate specification in metazoans. Characterizing the transcriptional activity of gene promoters, in time and in space, is therefore a critical step toward understanding complex biological systems. Here we present an in vivo spatiotemporal analysis for ∼900 predicted C. elegans promoters (∼5% of the predicted protein-coding genes), each driving the expression of green fluorescent protein (GFP). Using a flow-cytometer adapted for nematode profiling, we generated 'chronograms', two-dimensional representations of fluorescence intensity along the body axis and throughout development from early larvae to adults. Automated comparison and clustering of the obtained in vivo expression patterns show that genes coexpressed in space and time tend to belong to common functional categories. Moreover, integration of this data set with C. elegans protein-protein interactome data sets enables prediction of anatomical and temporal interaction territories between protein partners. [ABSTRACT FROM AUTHOR]Peer reviewedfinal article publishe
Growth and characterization of organic 2, 4, 6-triaminopyrimidinium 4-nitrophenolate single crystal
No full textPaediatric asthma prevalence and environmental factors: A community-based cross-sectional study.
Get PDFBackground
Pediatric asthma is a critical public health issue with its origins in a complex array of environmental, socio-economic, and potentially genetic factors. Understanding these can aid in crafting targeted preventive measures and management strategies.
Aim and Objectives: This study aims to determine the prevalence of pediatric asthma within a representative sample and investigate its associations with environmental conditions, socio-economic status, and familial health history, aiming to unravel the condition's multifaceted causes.
Materials and methods
This cross-sectional analysis involved 100 children and adolescents aged 0-18 years. Through interviews and medical record reviews, data were gathered on asthma diagnoses, environmental exposures, socio-economic status, physical activity, and family health history. Chi-square tests and logistic regression analyses identified key asthma predictors.
Results
The study found a 22% prevalence of asthma, highest among 6-12-year-olds (54.5%), and more common in males (59%). Critical environmental risk factors included poor indoor air quality (affecting 73% of asthmatic children), environmental tobacco smoke exposure (40%), and high outdoor air pollution (50%). Additionally, 68% of affected children came from lower socio-economic backgrounds. The most potent asthma predictor was poor indoor air quality (odds ratio = 4.5), alongside significant influences from tobacco smoke, outdoor pollution, sedentary lifestyles, and family asthma history.
Conclusions
The study found a prevalence of pediatric asthma at 22%, with the highest burden among school-aged children and males. Environmental and socio-economic factors such as poor indoor air quality, exposure to tobacco smoke, and lower socio-economic status were significantly associated with asthma. These findings underscore the need for targeted interventions to reduce environmental exposures and improve health outcomes in children.
Recommendations
To reduce pediatric asthma prevalence, prioritize interventions targeting indoor air quality improvement, reduce exposure to tobacco smoke, address outdoor pollution, and promote physical activity, especially in socio-economically disadvantaged areas
Associations of mammographic dense and nondense areas and body mass index with risk of breast cancer
No full textMammographic density measurements are associated with risk of breast cancer. Few studies have investigated the concurrent associations of mammographic dense and nondense areas, body mass index (weight (kg)/height (m)2), and ages at mammogram and diagnosis with breast cancer risk. We conducted a matched, case-control study nested within the Melbourne Collaborative Cohort Study (cohort recruitment in 1990-1994 and follow-up until 2007) to estimate the associations between these factors and breast cancer risk under alternative causal models. Mammographic dense area was positively associated with risk, and the strength of this association was only slightly influenced by the choice of the causal model (relative risk per 1 standard deviation = 1.50, 95% confidence interval: 1.32, 1.70). Mammographic nondense area was inversely associated with risk under the assumption that fat in the body and fat in the breast cause breast cancer through independent mechanisms (relative risk per 1 standard deviation = 0.75, 95% confidence interval: 0.65, 0.86), whereas it was not associated with risk under the assumption that they are both proxies of adiposity. Knowledge about the biological mechanisms regulating the role played by mammographic nondense area and body fat on breast cancer risk is essential to better estimate their impacts on individual risk. © The Author 2013
On the class of analytic functions defined by Robertson associated with nephroid domain
Get PDFThe primary focus of this article is to explore a novel subclass, denoted as GNGN, of analytic functions. These functions exhibit starlike properties concerning a boundary point within a nephroid domain. The author obtains representation theorems, establishes growth and distortion theorems, and investigates various implications related to differential subordination. In addition to the investigation of coefficient estimates, the study also explores specific consequences of differential subordination.
Mathematics Subject Classification (2010): 30C45, 33C50, 30C80.
Received 12 September 2023; Accepted 29 March 2024
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