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Bt cotton and the voices of the widows in the face of farmer-suicides
This article deploys the culture-centered approach to foreground the everyday constructions of farmer-suicides amid the agrarian epidemic among the farmer-widows to attend to the everyday structures that constitute the meanings of the suicides. The depictions of the patriarchal structures of decision-making in agriculture are intertwined with the broader erasure of the interplays of inequality in farmers experiences from the discursive sites of neoliberal agriculture. Furthermore, the voices of the widows disrupt the monolithic construction of agricultural technologies as tools of modernization and progress dominant in the development communication scholarship, instead, depicting the ways in which new technologies (such as Bt cotton) are constituted within, and reproduce, the overarching inequalities. 2020 National Communication Association. -
Development and Exploratory Factor Analysis of a United States Version of the International Survey of School Counselors Activities
This manuscript details the development and exploratory factor analysis of a United States version of the International Survey of School Counselors Activities (ISSCA-US), a 42-item instrument that identifies activities of school counselors. Responses were collected from 390 US school counselors. Separate EFAs were conducted for two distinct sections of the survey involving appropriateness of role activities and their actually being undertaken, both resulting in reliable 6-factor models. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
Globalisation and carbon dioxide emissions inequality in OECD countries
Economic growth has been crucial in contributing to carbon dioxide (CO2) emissions from the Industrial Revolution, and it affects CO2 emissions heterogeneously with different income levels. Therefore, studying the role of economic growth on inequality in carbon emissions is imperative. This paper analyses the determinants of CO2 emissions inequality in the panel dataset of 37 Organisation for Economic Co-operation and Development (OECD) countries from 1990 to 2019. Age dependency, globalisation, and institutional quality reduce CO2 inequality in the OECD economies. However, gross domestic product per capita increases CO2 inequality. The results are robust to utilise different panel data estimation techniques. This paper provides the first evidence in the literature of determinants of CO2 inequality across the OECD countries. It is suggested that governments in the OECD economies offer a blueprint for a sustainable society of green economic growth. Other potential policy implications are also discussed. 2023 John Wiley & Sons Ltd. -
Indian government bonds sensitivity to macroeconomic and non-macroeconomic factors: A quantile regression approach
This paper introduces a new dataset of Clearing Corporation of India Limiteds broad total return index (BTRI) and liquid total return index (LTRI). The paper examines the impact of macroeconomic and non-macroeconomic factors on BTRI and LTRI during monthly periods from January 2010 to December 2018 using quantile regression methodology. This paper finds that the GDP has positive and significant impact on BTRI and LTRI for the upper quantiles. Further, CPI shows positive impact on both BTRI and LTRI. Moreover, both the indices are influenced by IR and there is an inverse relationship between them. ER also significantly affects both the indices. The EPUI has negative and significant impact on BTRI and LTRI for the intermediate and upper quantiles. No clear relationship is found between BTRI and Nifty, whereas Nifty has significant impact on LTRI. BTRI is not affected by VIX but LTRI is affected for the intermediate quantiles. Copyright 2021 Inderscience Enterprises Ltd. -
The behaviour of trading volume: Evidence from money market instruments
This paper analyzed the impact of macro and non-macroeconomic factors on the trading volume of the certificate of deposits and commercial paper with regard to India during the monthly period from April 2012-March 2018 using the quantile regression approach. The results revealed that gross domestic product rate, Consumer Price Index, Economic Policy Uncertainty Index, the Volatility Index, and the Nifty index had a negligible impact on the trading volume of corporate bonds. However, interest rates and exchange rates did not influence the trading volume of corporate bonds. In the other context, gross domestic product rate, Consumer Price Index, interest rates, the Volatility Index, and movements in the Nifty index showed a negligible impact on the trading volume of commercial paper. However, the variations in the trading volume of commercial papers were not explained by exchange rates and Economic Policy Uncertainty Index. 2020, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Determinants of bank profitability in India: Applications of count data models
This paper employs count data models, namely Poisson and negative binomial regression to investigate whether macroeconomic factors increase or decrease the count of number of 18 Indian public sector banks in losses. The analysis is based on quarterly data from Q3 2009 to Q4 2019. This paper also considers one and two lagged macroeconomic factors. The results provide a new perspective for understanding the determinants of bank profitability. The contemporary, one and two lagged gross domestic product (GDP) growth rate and inflation increase the count of number of banks in losses. Further, the count of number of banks in losses surges with increase in contemporary and one lagged index of industrial production (IIP). However, one and two lagged exchange rates are significant to shrink the count of number of banks in losses. This study enables banks and policy makers to deliberate on the macroeconomic determinants considered for this study. 2020 Inderscience Enterprises Ltd. -
Relationship between tea industry specific factors and tea companies share prices: empirical evidence from an emerging economy
We analyse the impact of tea industry specific macroeconomic factors on tea companies share prices listed in Bombay Stock Exchange, India using quantile regression approach. We consider monthly period from January 2003 to December 2017. We find evidence to support the relationship between tea industry and tea companies share prices. Our results reveal that the change in area of cultivation has both negative and positive impact on the share prices of tea companies. This study indicates that production of tea has a significant and only positive influence. Further, we observe a minimal impact of tea import only on three companies share prices. This paper also notes that tea companies share prices react most significantly to tea export. 2024 Inderscience Enterprises Ltd. -
Dependence between Sugar Industry Specific Factors and Sugar Companies Share Prices: Evidence from India
We assess the effects of sugar industry-specific macroeconomic factors on share prices of sugar companies in India using quantile regression approach from January 2001 to December 2017. We detect grounds to affirm the dependence between sugar industry specific macroeconomic factors and sugar companies share prices. The results indicate that the change in sugarcane cultivation area has both positive and negative effect on the share prices of sugar companies. Further, it shows that the impact of sugar production on share prices of sugar companies varies across the different quantiles except an insignificant effect on two companies for all quantiles. Moreover, most of the companies share prices are highly and positively influenced by sugar import. The study pointed out that the risk of sugar industry specific macroeconomic factors noticed in the sugar companies share prices is heterogenous. Indian Institute of Finance Vol. XXXVI No. 4, December 2022. -
Utilizing social psychology to drive financial policy solutions: Addressing female feticide and infanticide
Female feticide and infanticide, are two of the most serious problems confronting Indian society. This issue is largely caused by the identification of female fetuses through technology, which frequently results in the termination of a pregnancy. Despite the governments efforts to curb these practices, progress has been limited. There are facilities in cities for determining the gender of an unborn child. The financial difficulty of raising a girl child is a key element in the preference for male offspring. The aim of this study is to propose innovative financial solutions that the government can implement to address this longstanding and complex issue. By exploring various financial inclusion strategies, this study seeks to identify effective measures that can bring about social change and promote gender equality. 2024 by author(s). -
Forex Analysis on USD to INR Conversion: A Comparative Analysis of Multiple Statistical and Machine Learning Algorithms
Foreign Currency Exchange (FOREX) engages a major role in world economy and the international market. It is a vast study based on determining whether or not to wait, buy or sell on a trading currency pair. The main objective is to predict the future currency prices using historical data in order to make more informed and accurate investment decisions for business traders and monetary market. This work experimented and implements ten machine learning strategies namely Random Forest, Decision Tree, Support vector regressor (SVM), Linear SVM, Linear Regression, Ridge, Lasso, K-Nearest Neighbor (KNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to assess the historical data and help the traders to invest in foreign currency exchange. The dataset used to validate and verify the machine learning algorithms is available in public domain and it is the daily Foreign Currency Exchange price of United States Dollars (USD) to Indian Rupees (INR). The experimented result shows that the Long Short-Term Memory (LSTM) model performs a bit better than the other machine learning models for this particular case. This work straight away does not reject the other methods it rather needs more experimental analysis with other models that has changed architecture and different dataset. 2024 IEEE. -
Unsteady thin film flow with ohmic heating and chemical reactions
In this study, we have analyzed magnetohydrodynamic (MHD) consequences on the heat and mass transmission within unsteady dissipated liquid film flow. Flow is generated due to stretchable surface accompanied with effects of ohmic heating, chemical reaction and heat absorption. Moreover, the flow governing partial differential equations (PDEs) are further modified into equivalent ordinary differential equations (ODEs) by applying regular perturbation method to get its analytical solution after that we have applied sixth-order RungeKutta technique to get its numerical solution. These two solutions are validating each other in the simulations. Figures are plotted to study the changes in physical quantities like skin friction coefficient, concentration, velocity, temperature, Sherwood and Nusselt number with the variations of Prandtl numbers Pr, parameters of chemical reaction ?, Eckert numbers Ec, magnetic parameter Ha (also known as Hartman number) Schmidt number and coefficient of heat absorption ?. World Scientific Publishing Company. -
Predicting Stock Market Indexes with Artificial Intelligence
The forecasting of the Share market has been a popular research area, involving the analysis of input and output stock data using computer technology and algorithmic knowledge. This involves building unpredictable relationships among the data and analyzing the stock market trends to provide a reference for investors. The inception of artificial intelligence (AI) technology, blended with the web, immense data, and cloud computing has provided technical support for various industries. AI technology is employed to scrutinize and predict the equity market, exploring curvilinear associations amid stock market information, and furnishing a foundation for investors to formulate investment determinations. Predicting equity prices is a demanding undertaking due to diverse factors like governmental happenings, fiscal circumstances, business resolutions, investor mentality, and overseas currency hazards. The securities exchange is a vastly active and disordered framework, and producing precise projections of the securities exchange is of paramount significance. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
The mathematical model for heat transfer optimization of Carreau fluid conveying magnetized nanoparticles over a permeable surface with activation energy using response surface methodology
The sensitivity analysis and response surface methodology (RSM) is performed for the key parameters governed by the magneto-flow and heat transport of the Carreau nanofluids model toward a stretching/shrinking surface in the presences Arrhenius activation energy and chemical reaction. Nanofluid that displayed Brownian motion and thermophoresis was considered with the permeable condition. The effects of different physical parameters were analyzed by employing appropriate similarity transformations in nonlinear partial differential equations and converted to the dimensionless system of ordinary differential equations. The finite difference method in bvp4c code solves the equations numerically. Associated parameters are presented graphically and interpreted against local Nusselt number, Sherwood number, and skin friction coefficient. An increase in the activation energy factor leads to increased concentration in permeable flow. The higher the activation energy lower the temperature and causes the reaction rate constant to decrease. In addition, it slows down the chemical reaction and increases the concentration characteristics. The increase of radiation and Prandtl number leads to an increase in heat transfer for the permeable surface. Furthermore, the Schmidt number and the binary reaction rate parameter increase the mass transfer for suction/injection flow. As a result, the Nusselt number's highest sensitivity is the Eckert number and the lowest to the thermophoresis parameter. The Sherwood number's positive sensitivity is observed for the Eckert number and Brownian motion parameter, whereas negatively sensitive to thermophoresis. 2022 Wiley-VCH GmbH. -
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Classification of Alzheimer's Disease Stages Using Machine Learning Techniques
Alzheimer s disease (AD) is a type of mental disorder which deteriorates the normal functioning of human brain by reducing the memory capacity of an individual. Age is the most common factor for AD and this disease cannot be reversed or stopped. Doctors can only treat the symptoms of AD which include personality changes and brain structural changes. Analyzing neuro-degenerative disorders, neuroimaging plays an important role in diagnosing subjects with AD and other stages of AD. The proposed research identified this gap and using MRI and PET newlineimages for recognizing AD in its early occurrences by the professionals. This helps in tailoring an appropriate treatment procedure for treating AD. As per literature survey, many researchers have worked with convolutional methods like inbuilt skull stripping with two or more conversions and classified with different CNN architectures. The proposed research experimented advanced skull stripping method and classified using deep learning architectures. This research emphasizes the implementation of ResNet50 architecture with T1 weighted MRI and Amyloid PET images for detecting the abnormalities in the brain patterns based on the image attributes. For the proposed experiment, a total of 5000 T1 weighted MRI data and 3000 newlineAmyloid PET data were used. The collected images were pre-processed with noise removal newlinetechniques and skull stripping method. The ResNet50 is used to classify AD from the data newlineobtained from the ADNI dataset. Pre-processed images /data were fed to the tuned for three class classification on ADNI image data at 200 Epochs shows the accuracy of 97.3% for T1 weighted MRI data and 98% for Amyloid PET data. The experimental results of the proposed model prove that it classifies the images according to various stages with better accuracy than the other existing models by achieving excellent results. -
Synthesis of Chitosan Stabilised Platinum Nanoparticles and their Characterization
A simplistic green synthesis route for the platinum nanoparticles has been successfully identified by using chloroplatinic acid hexahydrate (H2 PtCl6.6H2 O) as the metal precursor and sodium borohydride (NaBH4) as the reducing agent at room temperature. Chitosan was used in minute quantities as capping and stabilizing agent. The visual observation of a black coloured colloidal suspension, the characteristic XRD peaks and the absorption peak in the range of 200-300nm confirmed the production of Pt nanoparticles. The average crystallite size calculated using Debye-Scherrer equation is about 19 2 nm and a less intense absorption peak was found at 246nm and 281nm. The FTIR spectroscopy was used to confirm the capping with chitosan molecules. Zeta-potential calculation gave a surface charge of-23.8mV, and this high negative value, then validated the stability of the nanoparticle. The synthesis of platinum nanoparticles is very significant for their catalytic activity and biomedical applications in industrial as well as healthcare sector. 2023, Books and Journals Private Ltd.. All rights reserved. -
HQA Bot: Hybrid AI Recommender Based Question Answering Chatbot
The COVID pandemic has presented a number of challenges for education, particularly when it comes to reaching and engaging students. As a result, online education has become increasingly important, and artificial intelligence (AI) has played a crucial role in supporting this shift. The proposed tutor assistance question-answering system uses AI to automatically generate responses to student questions. This system includes a feedback mechanism, known as a satisfaction index that measures the efficiency of the generated responses and suggest relevant follow-up questions. The proposed Hybrid Recommender-based Dijkstras algorithm (HRD) improves the system's accuracy. This algorithm uses a combination of techniques to group relevant questions based on context, which improves the accuracy of identifying the next relevant question. In our customized dataset, this approach achieved an accuracy of 96% and an average accuracy of 82% across benchmarked datasets. With this system, we aim to bridge the gap between students and education by providing a more engaging and personalized learning experience. 2023, Ismail Saritas. All rights reserved. -
Analyzing the Market Dynamics of Electrical Appliances with a Special Emphasis on Sustainable Electric Energy
This study looks into the market dynamics of electrical appliances with a special emphasis on sustainable electric energy. The research aims to understand how factors such as technological advancements, consumer behavior, and regulatory policies influence the electrical appliances market. By examining the trends and challenges within this sector, the study highlights the growing importance of sustainability in product development and consumer choices. The main areas of focus include the adoption of energy-efficient technologies, the impact of rising household incomes on appliance usage, and the role of government policies and initiatives in promoting sustainable energy consumption. The findings of the study would provide insights into how the industry can align its practices with environmental goals while meeting the evolving needs of consumers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Biogenic Synthesis of Zinc Oxide Nanoparticles Mediated by the Extract of Terminalia catappa Fruit Pericarp and Its Multifaceted Applications
Zinc oxide nanoparticles (ZnO-NPs) were biosynthesized by using the pericarp aqueous extract from Terminalia catappa Linn. These NPs were characterized using various analytical techniques such as X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, ultraviolet (UV) spectroscopy, dynamic light scattering (DLS), and scanning electron microscopy (SEM), and XRD studies of the nanoparticles reported mean size as 12.58 nm nanocrystals with highest purity. Further SEM analysis emphasized the nanoparticles to be spherical in shape. The functional groups responsible for capping and stabilizing the NPs were identified with FTIR studies. DLS studies of the synthesized NPs reported ? potential as ?10.1 mV and exhibited stable colloidal solution. These characterized ZnO-NPs were evaluated for various biological applications such as antibacterial, antifungal, antioxidant, genotoxic, biocompatibility, and larvicidal studies. To explore its multidimensional application in the field of medicine. NPs reported a potential antimicrobial activity at a concentration of 200 ?g/mL against bacterial strains in the decreasing order of Streptococcus pyogenes > Streptococcus aureus > Streptococcus typhi > Streptococcus aeruginosa and against the fungi Candida albicans. In vitro studies of RBC hemolysis with varying concentrations of NPs confirm their biocompatibility with IC50 value of 211.4 ?g/mL. The synthesized NPs DPPH free radical scavenging activity was examined to extend their antioxidant applications. The antiproliferation and genetic toxicity were studied with meristematic cells of Allium cepa reported with mitotic index (MI index) of 1.2% at the concentration of 1000 ?g/mL. NPs exhibited excellent Larvicidal activity against Culex quinquefasciatus larvae with the highest mortality rate as 98% at 4 mg/L. Our findings elicit the therapeutic potentials of the synthesized zinc oxide NPs. 2023 The Authors. Published by American Chemical Society -
Transforming online class recording into useful information repositories using NLP methods: An Empirical Study
Most educational institutions have adapted to the mode of online teaching which has resulted in an increase of online video recordings. Learner community can be benefited with the ability to retrieve required information from the online class recordings. In this paper, we propose a methodology for converting video transcript data into useful information repositories for the purpose of retrieving class transcripts relevant to user's information needs. We focus on the online video recording transcript data. We also discuss challenges in transcribing which are crucial to understand preliminary processing. Our dataset consists of transcripts from diverse subject domains deeper experimental insights. We use interactive transcripts obtained from ASR (automatic speech recognition) services and non-interactive human generated transcripts. State-of-the-art methods for keyword retrieval: Latent Dirichlet Topic Modelling (LDA), Term Frequency (TF.IDF) and Text Rank (graph based) are applied on the video transcript data. Further, cosine similarity metric is applied to obtain the similarity measure between the transcript documents and keywords. 2022 IEEE.