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Catalytic Activity and Reusability of Mesoporous Iron Aluminophosphate Catalyst in Pharmacologically Important Organic Transformations
Journal Atoms and Molecules an International Online Journal, Vol-4 (1), pp. 675-681. ISSN-2277-1247 -
Catalytic potential of fluorescein under visible light irradiation: Enabling single-pot open flask synthesis of novel pyrazolyl methanesulfonamides
This groundbreaking study introduces a novel and efficient method for synthesizing a range of substituted pyrazolyl methanesulfonamides through a five-component cyclocondensation reaction. This reaction incorporates five different components, such as ethyl acetoacetate, hydrazine, dimedone, benzaldehydes, substituted phenyl acetonitriles, and methyl sulfonyl chloride was made to react under visible light irradiation, with fluorescein serving as an effective catalyst and ethanol as solvent for 30 mintues. This method offers significant advantages, including simplified handling, higher yields of target products with shorter reaction times, and easier purification processes. We successfully synthesized around 15 novel pyrazolyl methanesulfonamide derivatives with high efficiency. Comprehensive spectral characterization confirmed the structural integrity and purity of these derivatives, demonstrating the robustness and versatility of this approach. Facilitated by visible light and utilizing fluorescein as a bio-friendly catalyst, this methodology is both green and sustainable. This innovative approach not only streamlines the synthesis of pyrazolyl methanesulfonamides but also holds considerable promise for advancing research and applications in fields such as medicinal chemistry and materials science. 2024 The Author(s) -
Catalyzing Green Mobility: Consumer Preferences for Green Energy Vehicles
Due to growing urbanization and the increase of vehicles, most Indian cities endure traffic congestion and significant air pollution. As a result, alternate technology in autos, such as electric vehicles, may become necessary (EV). This study aims to identify consumer preferences toward electric vehicles in the Indian market. This research conducted a survey and analyzed the opinions of people regarding their preferences for electric vehicles, demographics, and some of the demotivation which might be stopping them to switch to electric vehicles altogether. This research will help in determining different factors influencing the perception of consumers toward electric vehicles and what they expect when they think about purchasing a new electric vehicle. It is important to understand that electric vehicles are really getting popular now because of the rising fuel prices and environmental concerns. People are thinking about electric vehicles and replacing them with their regular petrol or diesel vehicles. In this research there might be some challenges or roadblocks in switching to electric vehicles. This research found out that despite a favorable attitude toward electric vehicles, individuals are hesitant to transition to electric vehicles due to different hurdles connected with them. This research found out that mostly the preferences of the consumers are good charging infrastructure, a good range of the electric vehicle, pocket-friendly vehicles are the most common preferences of consumers buying an electric vehicle. 2023 EDP Sciences. All rights reserved. -
Catalyzing Security and Efficiency: Blockchains Integration with IoT and Cloud Computing
Blockchain technology is a system that combines a number of computer technologies, encryption, shared storage, namely intelligent contracts, consensus processes, and peer-to-peer (P2P) networks. This research project begins with a description of the architecture of blockchains, followed by a comparison of the various consensus techniques used across various blockchain implementations. This studys scope includes a thorough analysis of the entire blockchain ecosystem. Our investigation also explores the complexity of the consensus models built into different blockchain platforms. This research painstakingly dissects these elements to pinpoint crucial elements that are essential for propelling the adoption and development of blockchain technology. In conclusion, our research corrects misconceptions about blockchains expansive potential and helps to direct the development of the technology across a wide range of industries. These results are significant for determining the future direction of blockchains enduring influence. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Catalyzing the Affordability of Perovskite Solar Cells with Aluminum-Modified Cubic Titania
As a key component of perovskite solar cells (PSCs), the electron transport layer (ETL) extracts charges efficiently. While TiO2 is widely recognized as a superior electron transport material (ETM) for its numerous advantages, the morphological limitations of spherical TiO2 nanoparticles (NPs) lead to significant electron losses. Therefore, as an alternative to nanospheres, TiO2 nanocubes are synthesized through a solvothermal route and employed as ETM in the low-cost carbon electrode-based perovskite solar cells (CPSCs). The structural, morphological, and optical properties of the TiO2 nanocubes (NCs) are studied and compared with TiO2 nanospheres (NSs) in detail. The device possessing cubic TiO2 achieved a power conversion efficiency (PCE) of 10.6% with a current density (Jsc) of 21.79 mA/cm2. Recognizing that the oxygen vacancies in cubic TiO2 are lower than in spherical TiO2, it is inferred that further reduction of oxygen vacancies in cubic TiO2 could enhance the current collection. Hence, to get rid of the oxygen vacancy (which acts as an electron trap) in the cubical TiO2, aluminum (Al3+) is incorporated into its matrix. A comprehensive analysis of its impact on structural and optical behavior follows. In addition to its cost-effectiveness and conductive nature, it has been observed that the stable form of Al3+ replaces the unstable Ti3+ (which acts as a trap state), thereby reducing the recombination rate. With the highest current collection of 22.85 mA/cm2, a PCE of 11.3% has been recorded for the solar cell that possessed 1% Al-doped TNC. Furthermore, the ambient stability of the respective device shows ?85% of its initial PCE. The effect of the TiO2 nanostructure and Al3+ doping in TiO2 nanocubes is discussed elaborately in this work. 2024 American Chemical Society -
Catchment-specific approaches in human resource management: Enhancing recruitment practices
In today's dynamic business outdoors, identifying the most skilled employees has become a challenging and captivating challenge. This chapter explores the catchment-specific approach in human resource management in the information technology (IT) industry. This conceptual chapter analyzed peer-reviewed academic literature, the business press, and other media outlets. This conceptual chapter outlines the key issues for catchment-specific approaches in human resource management in the area of recruitment with the changing trends of the recruitment process. Certain emergent practices include analyzing the catchment area, tailoring recruitment strategies, and evaluating and refining catchment-specific in recruitment. This chapter helps raise awareness and understanding of this new and emerging aspect of catchment-specific approach in human resource management. 2024, IGI Global. All rights reserved. -
Categorization of artwork images based on painters using CNN
Artworks and paintings has been an integral part of human civilization since the dawn of the Stone Age. Paintings gives more insight about any subject compared to the scriptures and documents. Archiving of digital form of paintings helps to preserve the artworks of different painters. The anticipated work is aimed for the classification of painters' artworks. The artworks of Foreign & Indian painters are considered for the proposed work. The foreign painters' artworks are obtained from [14]. At present, the Indian painters' artwork dataset is not readily available. The images were downloaded from the specific genuine website [13]. Conventional Neural Network is used for Feature learning and classification. Around 20k images of artworks is used for the experiment and got an average accuracy of 85.05%. Published under licence by IOP Publishing Ltd. -
Categorizing Disaster Tweets Using Learning Based Models for Emergency Crisis Management
Social media communication is essential to the crisis response aftermath of a massive tragedy. Facebook, Twitter, and other social media network platforms are effective instruments for connecting and fostering collaboration among catastrophe victims and other groups. As a result, numerous research publications on tweet analysis have been released. Tweet analysis during a crisis helps in understanding the nuances of the incident. Existing works primarily focused on tweet sentiment analysis and binary categorization of tweets into catastrophe relevant or not. Our work mainly categorizes catastrophe tweets into seven categories: Blizzard, earthquake, flood, hurricane, tornado, wildfire, and not-relevant tweets. Deep learning and machine learning methods were employed to categorize the tweets. The annotated data is subjected to classification using Support Vector Machine (SVM) utilizing Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer and Word2Vec Vectorizer and compares the accuracy of different kernel functions. Bidirectional Long Short Term Memory (Bi-LSTM) is used on the labeled data as a deep learning technique. SVM exhibited 88% accuracy compared to 87% for Bi-LSTM. Empirical evidence shows that our methodology is more productive and efficient than previous approaches. From this knowledge of the incident, emergency aid organizations may draw conclusions and act immediately. 2023 IEEE. -
Cation-controlled wetting properties of vermiculite membranes and its promise for fouling resistant oilwater separation
Manipulating the surface energy, and thereby the wetting properties of solids, has promise for various physical, chemical, biological and industrial processes. Typically, this is achieved by either chemical modification or by controlling the hierarchical structures of surfaces. Here we report a phenomenon whereby the wetting properties of vermiculite laminates are controlled by the hydrated cations on the surface and in the interlamellar space. We find that vermiculite laminates can be tuned from superhydrophilic to hydrophobic simply by exchanging the cations; hydrophilicity decreases with increasing cation hydration free energy, except for lithium. The lithium-exchanged vermiculite laminate is found to provide a superhydrophilic surface due to its anomalous hydrated structure at the vermiculite surface. Building on these findings, we demonstrate the potential application of superhydrophilic lithium exchanged vermiculite as a thin coating layer on microfiltration membranes to resist fouling, and thus, we address a major challenge for oilwater separation technology. 2020, The Author(s). -
Cattaneo-Christov heat flux on UCM nanofluid flow across a melting surface with double stratification and exponential space dependent internal heat source
Melting and exponential space dependent internal heat source effects on magnetohydrodynamic of upper convected Maxwell liquid towards a horizontal flat surface are addressed. The combined effect of Brownian motion and thermophoresis in nanofluid modeling are retained. The Cattaneo-Christov heat flux model is imposed. Impacts of thermal and solutal stratifications are also accounted. A set of similarity variables are utilized to form ordinary differential system from the prevailing partial differential equations. The problem of ordinary differential system is analyzed numerically through Runge-Kutta-Fehlberg based shooting method. Graphical results of pertinent parameters on the velocity, temperature and nanoparticle concentration are studied. Skin friction coefficient, local Nusselt number and Sherwood number are also addressed. 2017 -
Cattaneo-Christov Theory to model heat flux effect on nanoliquid slip flow over a spinning disk with nanoparticle aggregation and Hall current
The heat transport of a nanoliquid on a spinning disk with velocity slip and thermal jump boundary conditions is modeled. The effects of external magnetism and the aggregation of nanoparticles are analyzed. The Cattaneo-Christov heat flux model and the Joule heating phenomenon are incorporated in the thermal analysis. The central composite design (CCD) of the response surface methodology is implemented to optimize heat transfer in the nanoliquid. The sensitivity of the heat transport is analyzed. The partial differential governing model is converted into a system of ordinary differential equations using a novel von Karmans transformation, the consequent system is solved numerically. The significance of physical operating parameters is analyzed through a detailed parametric study. Optimal levels of Hall parameter, Hartmann number, and Eckert number, that optimize the heat transport are determined. The Lorentz force expands the structure of the thermal layer and subsequently reduces the heat transport of the system. The Hall current improves the thickness of the velocity layer in the radial direction, while the thickness of the thermal layer is reduced. Viscous dissipation improves the thickness of the thermal boundary layer. The isothermal boundary condition causes less heat transport in the system than the temperature jump condition. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Causal relationship among various development indices: A panel study
The concept of development has been regarded as a broader phenomenon encompassing various interrelated factors leading to improvement in the overall human wellbeing. So, it is important to understand the interlinkages between various dimensions of development. The present study was an attempt to analyze the causal relationship between the four aspects of development measured by the indices, namely the Economic Development Index (EDI), Social Development Index (SDI), Environment Development Index (ENDI), and Institutional Development Index (IDI) for a panel of 102 counties from 1996 to 2015. The long?run relationship between these indices through the panel ARDL model were also examined. The results indicated that there existed a bidirectional causal relationship between EDI and SDI, IDI and SDI, ENDI and SDI, and between IDI and ENDI. The one-way causality runs from IDI to EDI and ENDI to EDI. Further, given the nature of the variables considered here, panel autoregressive distributed lag models were used to examine the long?run relationship between the indices of development. The results showed that the impact of development indices with one another was statistically significant in the long run. 2021 The Society of Economics and Development, except certain content provided by third parties. -
Causal relationship between leverage and performance: Exploring Dhaka Stock Exchange
To magnify shareholders' returns, managers employ the use of debt in the firms' capital structure. However, excessive debt financing can often cause financial distress for the firms. In fact, various debt equity ratio levels may lead to different financial performance when compared for high levered and low levered firms. Thus, the aim of this paper is to examine the cause and effect relationship between financial leverage and financial performance of firms. To pursue the purpose, a purposive sample of 163 non-financial firms listed on the Dhaka Stock Exchange (DSE) was selected to conduct this study. Findings indicate that there was no significant difference in the financial performance between high levered and low levered firms, neither in terms of their size nor growth rates. A negative relationship therefore persists between leverage and performance of such firms. Implications of these findings can provide policy guidelines for managers and directions for any further work in this context. Copyright 2018 Inderscience Enterprises Ltd. -
Causal relationship between leverage and performance: Exploring Dhaka Stock Exchange
To magnify shareholders' returns, managers employ the use of debt in the firms' capital structure. However, excessive debt financing can often cause financial distress for the firms. In fact, various debt equity ratio levels may lead to different financial performance when compared for high levered and low levered firms. Thus, the aim of this paper is to examine the cause and effect relationship between financial leverage and financial performance of firms. To pursue the purpose, a purposive sample of 163 non-financial firms listed on the Dhaka Stock Exchange (DSE) was selected to conduct this study. Findings indicate that there was no significant difference in the financial performance between high levered and low levered firms, neither in terms of their size nor growth rates. A negative relationship therefore persists between leverage and performance of such firms. Implications of these findings can provide policy guidelines for managers and directions for any further work in this context. Copyright 2018 Inderscience Enterprises Ltd. -
Causality among Energy Consumption, CO2 Emission, Economic Growth and Trade: A Case of India
The present study attempts to examine the causal nexus between energy consumption, CO2 emissions, economic growth and trade in India using the Perron (1989) unit root test, Gregory and Hansen (1996) cointegration test and vector error-correction model (VECM). The study results exhibit a long-run relationship between energy consumption, CO2 emissions, economic growth and trade in India. The empirical results confirm that energy consumption influences the economic activity in the short run, implying that higher rate of economic growth is driven by consumption demand for energy in the economy. This is also well in consistence with the findings of Paul and Bhattacharya (2004) in the Indian context. Further, the study detects one-way causation that exists from energy use to CO2 emission and trade, and CO2 emissions to economic growth in the short run. 2015 Indian Institute of Foreign Trade. -
Causality and volatility spillovers of banks' stock price returns on BSE Bankex returns
This paper investigates the causal relationships and volatility spillovers between the BSE Bankex index and the stock prices of five major Indian banks (Axis Bank, HDFC Bank, ICICI Bank, Kotak Bank, and SBI). Daily data from January 2, 2018 to March 8, 2023 are used, and statistical techniques such as descriptive statistics, Unit Root test, Cointegration test, Ganger Causality test, OLS regression, and GARCH model are employed. The study finds bidirectional causal relationships between the bank stocks and BSE Bankex returns, suggesting that the movement of the bank stocks significantly affects the overall market returns and vice versa. The study also finds significant volatility spillovers between the bank stocks and BSE Bankex returns, implying that the shocks in the bank stocks affect the market returns and vice versa. The study's outcomes have practical implications for investors and policymakers. Investors can use the results to make informed investment decisions in the Indian stock market, while policymakers can use the findings to monitor the financial stability of the banking sector and design appropriate policy interventions to address any potential financial crises. Overall, the study's findings suggest that policymakers should proactively monitor and manage market risks to safeguard overall financial stability. 2023 Wiley Periodicals LLC. -
Causality between public expenditure and economic growth: The indian case
This study investigates the causal nexus between public expenditure and economic growth in India using cointegration approach and error correction model. The analysis was carried out over the period 1973 to 2012. The Cointegration test result confirms the existence of long-run equilibrium relationship between public expenditure and economic growth in India. The empirical results based on the error-correction model estimate indicates one-way causality runs from economic growth to public expenditure in the short-run and long-run, supporting the Wagner's law of public expenditure. -
CBMIR: Content Based Medical Image Retrieval Using Hybrid Texture Feature Extraction Method
Due to the revolution of digital era in the medical domain at various hospitals across the world, the online users on the internet access have been increased. So the amount of collections of digitized medical images has grown rapidly and continuously. As well it is ratting significant to mention that the images are globally used by radiologists, professors in medical colleges and Lab technicians, etc. These Images are increasingly applied to communicate information about patient history. In this context, there is a necessity to develop appropriate systems to manage these medical images in storage and retrieval for diagnosis of the patient information. Another big issue is the convolution of image data and that can be interpreted in different ways. In order to manipulate these data and establish policies to its content is very tedious job. This will raise another big question. These issues motivated the researchers to give more focus on the image retrieval area whose goal is trying to solve those problems to provide an efficient retrieval system to the user community. In this perspective, this work has been proposed to facilitate radiologists, professors in medical colleges, lab technicians, and all other medical image user communities for their purpose for easy access from the remote location. 2022 IEEE. -
CDADITagger: An Approach Towards Content Based Annotations Driven Image Tagging Integrating Hybrid Semantics
Considering the rapid growth of multimedia data, especially images, image tagging is considered the most efficient way to organize or retrieve images. The significance of image tagging is growing extensively but the frameworks employed for tagging these images aren't sophisticated. These images aren't properly tagged because of a lack of resources for tagging or manual tagging is a challenging task considering such voluminous data. Already existing frameworks take both the image data and tag-related textual data but ultimately resulted in mediocre or unpalatable performance as they are dataset centered. To overcome these limitations in existing frameworks we proposed an image tagging mechanism, CDADITagger capable of automatically tagging images efficiently and much more reliable compared to existing frameworks. This framework can tackle real-world applications like tagging a new unknown image as the framework isn't powered by dataset alone but is designed to inculcate images from search engines like Google, Bing, etc. to have comprehensive knowledge of real-time data. These images are classified using CNN and tag-related textual data is classified using decision trees for enhanced performance. While tagging images from the classified tags, are sorted based on the semantic computation values, only the top 50% of the instances classified are selected. The tags which are more correlated to the image are ranked and finalized. The proposed semantically inclined framework CDADITagger outshined the well-established frameworks with an accuracy of 96.60% and a precision of 95.84% making it a more reliable approach. 2022 IEEE.