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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. -
Advances in Surface-Enhanced Raman Spectroscopy for the Detection of Synthetic Dyes in the Food Matrices
Food adulteration is a global concern that is a significant public health risk. Such issues demand sophisticated analytical devices with high-speed sensitivity toward detecting adulterants. The surface enhanced Raman spectroscopic (SERS) technique is an advanced tool that gains wide appreciation due to its very high sensitivity and selectivity. Being an excellent ultra-trace contaminant-detecting tool even within complex food matrices, this resource becomes fundamental in food safety protection. SERS is found to detect trace concentrations of synthetic dyes. Recent advancements have been made on new substrates that enhance signal stability among noble metals, hybrid nanostructures, and metal-organic frameworks (MOFs). These substrates are further functionalized with specific chemical moieties to improve the selectivity and reduce the matrix interference. SERS, combined with advanced computing tools like machine learning and chemometric algorithms, has completely changed the data analysis scenario; nowadays, high-throughput simultaneous detection and accurate adulterant quantification are possible. With rapid outputs and less sample preparation, portable SERS devices revealed promise in on-site food safety monitoring. Subsequent developments that address several fundamental concerns regarding cost, reusability, and scalability ultimately make SERS devices more widely adaptable. This chapter covers the emerging aspects of SERS substrates, enhancement strategies, and computational improvements concerning synthetic dye detection. It speaks of the potential of SERS as a flexible tool for ensuring public health and food safety worldwide while discussing the challenges and opportunities in developing inexpensive, reusable substrates. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Impact of plastic contaminants on marine ecosystems and advancement in the detection of micro/nano plastics: A review
Micro/nanoplastics pollute all levels of the food web, beginning from aquatic algae, invertebrates, and other fish, through bioaccumulation or even physical and chemical damages augmenting degradation of the marine ecosystem. Besides plastic litter, other toxic chemicals employed in the manufacture of plastics also destroy stable ecosystems. Micro/nanoplastics are toxic to marine organisms through induction of blockage of ingestion, oxidative stress, and reproductive effects. Bivalves such as oysters accumulate microplastics in tissues, which decreases filtration rates. Polystyrene nanoplastics induce endocrine disturbance and neurotoxicity in fish. Seabirds suffer from gut inflammation ("plasticosis"), and zooplankton suffers from decreased feeding rates, which impacts trophic transfer. This review identifies some of the recent developments in electrochemical detection techniques, with a focus on electrochemical sensors and surface-enhanced Raman spectroscopy (SERS). Electrochemical sensors like CdS/CeO? heterojunction-based sensors have been able to detect 0.38 ng/mL of polystyrene nanoplastics. Biochar-modified electrodes and nanoporous gold sensors have also become more sensitive to trace detection levels (?0.44 nM) for microplastics. SERS-based techniques, for instance, membranes with Ag nanoparticles on anodic aluminium oxide (AAO) and metalphenolic networks with luminescence, have facilitated detection of polystyrene, polyethylene, and polypropylene nanoplastics in environmental matrices, with detection limits of 0.1 ?g/mL for 500 nm polystyrene and 1 ?g/mL for smaller plastic mimics. Although portable Raman spectrometers are sufficient for larger particulates, they need SERS enhancement for detecting oceanic matrix-bound nanoparticles. This article presents a critical overview of recent progress in the application of electrochemical sensors, Raman spectroscopy, and commercially available hardware to investigate their extended applications. Challenges and future directions for improved real-time monitoring with improved sensitivity and selectivity are also presented along with interference mitigation. 2025 The Author(s) -
Multivariate optimization of electrochemical sensing parameters for bisphenol a detection using an AgNPs/g-C3N4/IL@GCE via box-behnken design
We developed an electrochemical sensor for detecting BPA using AgNPs/g-C3N4/IL@GCE. Graphitic carbon nitride was synthesized by the calcination of melamine at 550 C. In contrast AgNPs were synthesized via a green tea extract method, and the nanocomposite was dropcast onto the GCE surface along with 1-butyl-3-methylimidazolium methyl sulfate (BMIM-MeSO4) ionic liquid as a binder. Morphology was characterized, and response surface methodology (RSM) using Box-Behnken Design (BBD) was used as a concurrent strategy to optimize pH, scan rate, and deposition time. Independent validation experiments conducted at multiple interior points within the design space showed good agreement between predicted and experimental responses, with prediction errors of 6.29%-10.11% for oxidation peak current and 1.63%-5.24% for oxidation potential. Applying the optimized conditions, the sensor linearized BPA detection over the concentration range of 1-10 ?M, with a limit of detection of 0.66 ?M, and a limit of quantification of 2.20 ?M. The sensor showed excellent recovery (99.16%-102.26%) of BPA present in real water samples. The improved electrocatalytic activity of the sensor interfaces was due to the synergistic effects of AgNPs and g-C3N4. The novelty of this research was the use of an RSM-BBD approach to systematically optimize a green synthesized AgNPs/g-C3N4 nanocomposite electrode, enabling predictive modelling to show reasonable electrochemical sensitivity towards BPA detection. 2026 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited. All rights. -
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). -
An Extensive Analysis of Artificial Intelligence Integration in Management Approaches
Artificial Intelligence (AI) is now a strategic enabler across a large number of management domains in the digital transformation era. We conducted this review which analyzes AI and its integration into management by 990 peer reviewed publications from 2015 to 2025 from the Scopus database. It filtered studies on AI's role in strategy, HR, finance, operations and decision-making in a systematic manner. Latent Dirichlet Allocation (LDA) formed five key themes of predictive analytics, AI in HR, financial planning, intelligent decision systems, and explainable AI. The findings suggest that digital resilience needs drive the surge of AI related management research after 2019. This review points out emerging trends, difficulties in integrations, as well as critical insights which can orient the future research over such specific studies. 2025 IEEE. -
Optimizing Base Station Placement toMinimize Interference forSatellite Terrestrial Networks (STN)
The rapid advancement of 5G and 6G technologies has spurred the development of Satellite-Terrestrial Networks (STNs), integrating terrestrial infrastructure with Low Earth Orbit (LEO) satellites to enable seamless global connectivity. Efficient spectrum allocation and interference management remain major challenges due to limited resources and the dynamic behavior of satellites. This study addresses these challenges by optimizing base station (BS) deployment to enhance spectral efficiency and reduce interference in STN environments. Delaunay Triangulation (DT) is employed to establish initial spatial separation between BSs, followed by gradient descent (GD) for fine-tuned optimization. Simulation results demonstrate that the optimized scenario substantially reduces interference and improves key performance metrics, including SINR, INR, CI Ratio, and received power, with gains ranging from 30% to 400%. These findings, derived from small-scale simulations, indicate the frameworks potential for enhancing STN performance in dense and interference-prone environments and provide a foundation for future research on interference-resilient STN architectures. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
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. -
Enhancing Personalization in Search Engines Through Behavioral Profiling
With the development of search engines, people demand more contextual, relevant, and important results according to their needs and preferences. The current paper will examine the enhancement of search engine personalization through behavioral profiling, which involves capturing user interaction data, such as search histories, clicks, and other similar data, to understand user interests and intentions. The behavioral profiling promotes the ability to adjust the results to the requirements of mutual changes in user behavior and apply machine learning algorithms and advanced data mining techniques. We describe the key aspects of the successful behavioral profiling systems, such as user modeling, data collection frameworks, and privacy boundaries of the data protection. The paper will address the points mentioned by providing behavioral profiling to enhance user satisfaction and effective search and engagement. It will discuss the predictive relevance ranking's triple impacts on socioeconomic gains: time, energy costs, and attention time. We also discuss the ethical issues of user data collection, and the invitation implies achieving the appropriate compromise between individualization and privacy. By the case studies and comparisons, we affirm that the behavioral personalization greatly improves the accuracy of the search when the methods are either static or generic. This study enhances the design of a smart, convenient search engine by cultivating actionable, individual-sensitive recency search. It aims to smoothly aid personalized interactions in real time, inspiring advancement in context-sensitive retrieval systems. The Research Publication,. -
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. -
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. -
Chitosan stabilized platinum nanoparticles: In vitro and in vivo screening for analgesic and anti-inflammatory applications
In this interdisciplinary research work, the chitosan stabilized platinum nanoparticles are synthesized through the wet chemical method, and the structural, surface morphological, and optical characterizations are done using X-ray crystallography, Raman spectroscopy, transmission electron microscopy, etc. The samples were tested in in vitro trials namely egg albumin denaturation assay and DPPH radical scavenging assays and showed significantly lower effective concentrations (EC50) such as 5.44 ?g/ml and 8.068 ?g/ml respectively. The in vitro experiments were followed by in vivo animal model for analgesic and anti-inflammatory behaviour at two doses of 25 mg/kg and 50 mg/kg utilizing the hot plate method and the carrageenan-induced paw edema model respectively. The in vivo hot plate model for analgesic effect demonstrated that the chitosan stabilized platinum nanoparticles perform exceptionally well and show >90 % analgesia (p < 0.01) by extending the reaction time in the hot plate methodindicating better analgesia. Carrageenan-induced paw edema model demonstrated the exceptional anti-inflammatory ability of chitosan-stabilized platinum nanoparticles. Despite being given at a comparatively lower dosage, chitosan stabilized platinum nanoparticles showed a considerable decrease in paw volume (4045 % edema inhibition) by the third hour of the anti-inflammatory experimentation (p < 0.01) outperforming the standard drug aspirin given at 100 mg/kg. 2025 Elsevier B.V. -
Synthesis and third-order nonlinear optical properties of PEGylated platinum nanoparticles
PEGylated platinum nanoparticles, which are capped with polyethylene glycol-400, are synthesized through the chemical reduction technique. The sample was comprehensively characterised through UVvisible spectroscopy, X-ray diffraction (XRD) and transmission electron microscopy (TEM). XRD pattern for the sample revealed a face-centered cubic crystalline phase of platinum, with a lattice constant of 3.939 The average particle size, obtained from high-resolution electron microscopy analysis, is 3.73 nm. The third order NLO features were explored through the Z-scan technique, employing a continuous wave regime. The observed phenomena of nonlinear absorption (NLA) and nonlinear refraction (NLR) are attributed to reverse saturable absorption and thermal lens models. NLR index was measured to be in the range of 5.72 10?10 cm2/W, while NLA coefficient was found to be in the range of 1.86 10?5 cm/W, highlighting the potential of PEGylated Pt NPs for NLO applications. 2025 Elsevier B.V.