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Technology acceptance model and customer engagement: mediating role of customer satisfaction
The primary aim of this study is to examine the influence of the technological acceptance model on customer engagement. An additional aim of this study is to examine the mediating effect of customer satisfaction in the relationship between the technology acceptance model and customer engagement. The present study gathered primary data from users of retail banking services in specific metropolitan areas in India. The participants were chosen based on their considerable expertise in utilizing online, digital, and mobile banking platforms and their substantial knowledge of implementing advanced e-banking strategies. The application of confirmatory factor analysis investigated measurement validity. The studys findings indicate a significant correlation between emotional and rationalengagement, mediated via perceived usefulness. Additionally, the study revealed that the relationship between perceived usefulness and emotional engagementis mediated by customer satisfaction. Furthermore, it can be observed that this factor entirely mediates the relationship between transaction cost and emotional engagement. The studys findings suggest that customer satisfaction is a complete mediator in the association between transaction cost and rational engagement. The results of this study make a valuable contribution to the current body of literature on the technological acceptance model, customer satisfaction, and customer engagement. The Author(s), under exclusive licence to Springer Nature Limited 2023. -
Data analysis in road accidents using ann and decision tree
Road accidents have become some of the main causes for fatal death globally. A report tells that road accident is the major cause for high death rate other than wars and diseases. A study by World Health Organization (WHO), Global status report on road safety 2015 says over 1.24 million people die every year due to road accidents worldwide and it even predicts by 2020 this number can even increase by 20-50%. This can affect the GDP of the Country, for developing countries this can affect adversely. This paper shows the use of data analytics techniques to build a prediction model for road accidents, so that these models can be used in real time scenario to make some policies and avoid accidents. This paper has identified the attributes which has high impact on accident severity class label. IAEME Publication. -
Microstructure and Mechanical Behaviour of Al6061-ZrB2 In-situ Metal Matrix Composites
Aluminium matrix composites processed through in-situ molten reaction has emerged as an alternative for eliminating defects existing in ex-situ reinforced metal matrix composites. Development of composites through in-situ method using inorganic salts via liquid metallurgy route is the most widely accepted technique. In the present work, Al6061-ZrB2 in-situ composites have been developed through in-situ reaction of Al-10%Zr and Al-3%B master alloys in Al6061 alloy. Study of microstructure and mechanical properties of in-situ reinforced ZrB2 in Al6061 alloy have been carried out. Composite exhibited grain refinement and improved the mechanical properties of Al6061 alloy. Ductility of composite is reduced with increase in content of ZrB2. Published under licence by IOP Publishing Ltd. -
Chemical Reaction Effects on Nano Carreau Liquid Flow Past a Cone and a Wedge with Cattaneo-Christov Heat Flux Model
Chemical reaction aspect is utilized for heat mass transfer analysis of nano non-Newtonian liquid flow past a cone and a wedge. Flow is steady, laminar and two dimensional created due to a cone and a wedge. The Carreau liquid and Cattaneo-Christov heat flux models are utilized. The magneto-nano Carreau liquid material occupies the porous space. The relevant PDEs are rendered into coupled non-linear ODEs via appropriate transformations before treated them numerically through Runge-Kutta and Newton's method. The computed results are plotted for employing the various values of physical constraints on the profiles of velocity, temperature and nanoparticle volume fraction. Moreover, vitiation of the friction factor, Nusselt number and Sherwood number against physical parameters are presented numerically. It is figured out that convective heating and Brownian motion effects are constructive for thermal boundary layer growth. Aspect of chemical reaction is significant to control the solute layer growth and mass transfer rate. 2017 Walter de Gruyter GmbH, Berlin/Boston. -
Comprehensive study of the physicochemical properties of three-component deep eutectic solvents and their implications for microbial and anticancerous activity
Sustainable chemistry centers on substituting perilous solvents and materials with eco-conscious alternatives. Deep eutectic solvents (DES) hold substantial potential in this arena. This inquiry includes the formulation of three-component eutectic solvents and an exhaustive scrutiny of their physical and chemical attributes. These encompass solubility, boiling point, pH, density, viscosity, surface tension, refractive index, contact angle, conductivity, Fourier-transform infrared spectroscopy, polarized optical microscopy, thermogravimetric analysis, and differential scanning calorimetry. Furthermore, a biological exploration featured two bacterial strains and two fungal strains. The entire spectrum of ten three-component DES was administered to these microorganisms to discern plausible impacts. In addition, the biomedical promise of these DES was unveiled through anticancer assays employing MCF-7 and HeLa cell lines. The outcomes were favorable, underscoring robust anticancer potency, thereby hinting at future oncological utility. These interdisciplinary endeavors envelop the progression of sustainable solvent innovation, meticulous physicochemical scrutiny, microbial analysis, and anticancer appraisal. This study propels inventive resolutions with ecological and biomedical reverberations by amalgamating these distinct yet interconnected facets. 2024 Indian Chemical Society -
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) -
Elevating pyrrole derivative synthesis: a three-component revolution
Pyrrole is an essential chemical with considerable relevance as a pharmaceutical framework for many biologically necessary medications. The growing demand for biologically active compounds calls for a simple one-pot method for generating novel pyrrole derivatives. Nots surprisingly, several multicomponent reactions (MCRs) aim to synthesize pyrrole derivatives. However, this review presents the three-component synthesis of pyrrole derivatives, highlighting the significance of multicomponent reaction in synthesizing eclectic multi-functionalised pyrrole covering the selected literature on the three-component synthesis of substituted pyrrole from 2016 to late 2023. Furthermore, this article classifies the reactions based on the starting material with functional groups involved in the pyrrole ring formation. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
A Bird's-Eye View on Deep Eutectic Solvent-Mediated Multicomponent Synthesis of N-Heterocycles
Multicomponent reactions are crucial for operating organic synthesis. In today's world, chemists concentrate on greener methodologies that cater to rising environmental concerns. Most conventional organic solvents are harsh on the environment, which can be addressed by environmentally and pocket-friendly Deep eutectic solvents (DES). Organic synthesis via MCR in the presence of DES has resulted in the blend of green methodology and green solvent for organic transformations. Forasmuch as addressing the environmental concerns and developing higher heterocycles, our review focuses on the literature published on the DES-mediated Multicomponent synthesis of N-Heterocyclic compounds. 2023 Wiley-VCH GmbH. -
A Mini Review on the Multicomponent Synthesis of Pyridine Derivatives
Multicomponent reactions (MCRs) have emerged as key green tool in organic synthesis for their methodological simplicity. MCRs have made heterocycle synthesis more versatile. The most crucial molecule among the most often used heterocycles is pyridine, which is widely used in biological, industrial, and pharmaceutical sectors. In light of this, our mini-review highlights the literature on substituted pyridine synthesis published from the year 2016 to early 2022 via multicomponent approach. 2022 Wiley-VCH GmbH. -
Consumer preference towards private label brands with reference to retail apparel in India
As majority of the present day consumers are considering brands as an important element in their choice of decision making while purchasing, it is pertinent that sellers should capitalize on the type of brands they are offering to consumers. Both private labels and global brands have their own advantages and disadvantages over each other mainly in terms of pricing and quality factors. However, the main motive the consumers are looking forward is to buy a product which would effectively satisfy their requirements. If they find a product which satisfies their needs effectively, they buy it irrespective of whether it is a private label or a global brand. Even the price of the product may not be a major factor in such a case. This study focused on the preference and intention among consumers towards buying of private label products, especially retail apparel products. This study examined the causal relationships between six antecedents of customer perceived preference identified in this study as fashion consciousness, attitude, store image, price, quality, and store loyalty with regard to the purchase intention of private label brand apparels. The model was evaluated using data collected from 292 customers from different malls in Bangalore in 2016-17. The findings revealed that customers attitude played a significant role in their purchase behaviour towards private label brand apparels. 2019, Associated Management Consultants Pvt. Ltd. All Rights Reserved. -
Computational Intelligence for Solving Contemporary Problems
The special issue contains research papers elaborating advancements in computational intelligence. Computational intelligence mimics the extraordinary capacity of the human intellect to assert and understand in an environment of uncertainty and impre-cision. Computational intelligence is new-age multidisciplinary artificial intelligence. The main goal of computational intelligence is to develop intelligent systems to solve real-world problems that are not modelled or too hard to model mathematically. 2024, Bentham Science Publishers. All rights reserved. -
Limelight in Dark Times: Jyoti Kumari's 'Cyclothon'
[No abstract available] -
Forensic toxicological and analytical aspects of carbamate poisoning A review
Pesticides play a pivotal role in modern agricultural practices and effective domestic pest control. Despite their advantages, pesticides pose a great danger to humans and animals due to their toxicity. Pesticides, particularly carbamates, are extensively used all over the world in crop protection and domestic pest control, however, also causing morbidity and mortality on a larger scale, which is of great significance in both clinical and criminal justice management.Carbamates are derived from a carbamic acid (NH2COOH) that are commonly used as insecticides. Ethienocarb, Sevin, Carbaryl, Fenoxycarb, Furadan, Carbofuran, Aldicarb, and 2-(1-Methylpropyl) phenyl N-methylcarbamate are examples of insecticides that include the carbamate functional group. By reversibly inactivating the enzyme acetylcholinesterase, these insecticides can induce cholinesterase inhibition poisoning.Chromatographic methods, notably gas and liquid chromatography have traditionally been employed to analyse carbamate pesticides and their metabolites in various matrices. These approaches are employed due to their ability to separate the chemicals contained in a sample; as well as identify and quantify these compounds utilizing advanced detection systems. Aside from these GC and LC conventional methods, other detection and/or hyphenated techniques such as single-quadrupole, ion-trap, triple-quadrupole, or tandem mass spectrometry, have been used in carbamate analysis to provide quick results with excellent sensitivity, precision, and accuracy.The objective of this review is to describe various analytical techniques used to detect and determine carbamate pesticides in various matrices which include urine, blood, and tissues that are commonly encountered in emergency hospital laboratories and forensic science laboratories. 2022 Elsevier Ltd and Faculty of Forensic and Legal Medicine -
Effect of food insecurity on the cognitive problems among elderly in India
Background: Food Insecurity (FI) is a crucial social determinant of health, independent of other socioeconomic factors, as inadequate food resources create a threat to physical and mental health especially among older person. The present study explores the associations between FI and cognitive ability among the aged population in India. Methods: To measure the cognitive functioning we have used two proxies, word recall and computational problem. Descriptive analysis and multivariable logistic regression was used to understand the prevalence of word recall and computational problem by food security and some selected sociodemographic parameters. All the results were reported at 95% confidence interval. Results: We have used the data from the first wave of longitudinal ageing study of India (LASI), with a sample of 31,464 older persons 60 years and above. The study identified that 17 and 5% of the older population in India experiencing computational and word recall problem, respectively. It was found that respondents from food secure households were 14% less likely to have word recall problems [AOR:0.86, 95% CI:0.310.98], and 55% likely to have computational problems [AOR:0.45, 95% CI:0.290.70]. We also found poor cognitive functioning among those experiencing disability, severe ADL, and IADL. Further, factors such as age, education, marital status, working status, health related factors were the major contributors to the cognitive functioning in older adults. Conclusion: This study suggest that food insecurity is associated with a lower level of cognition among the elderly in India, which highlight the need of food policy and interventional strategies to address food insecurity, especially among the individuals belonging to lower wealth quintiles. Furthermore, increasing the coverage of food distribution may also help to decrease the burden of disease for the at most risk population. Also, there is a need for specific programs and policies that improve the availability of nutritious food among elderly. 2021, The Author(s). -
Skin cancer classification using machine learning for dermoscopy image
Skin cancer is highly ambiguous and difficult to identify and cure in the last stage. To increase the survival rate, it is important to recognize the stages of skin cancer for effective treatment. The main aim of the paper is to classify the various stages of skin cancer using dermoscopy images from the data repository of ISIC and PH2. The data is pre -processed with the help of median filter and wiener filter for removing the noise. Segmentation is processed using Watershed and Morphological. After the segmentation, features were extracted using Grey Level Co-occurrence Matrix (GLCM), Color, Geometrical shapes in order to improve the accuracy of dermoscopy image. Finally, the dataset is classified with some popular methods like KNN with 89%, Ensemble with 84% and SVM works better than the other two methods by giving the highest accuracy of 92%. BEIESP. -
Recent Advances in Analytical Techniques for Antidepressants Determination in Complex Biological Matrices: A Review
Depression is one of the most prevalent but severe of mental disorders, affecting thousands of individuals across the globe. Depression, in its most extreme form, may result in self-harm and an increased likelihood of suicide. Antidepressant drugs are first-line medications to treat mental disorders. Unfortunately, these medications are also prescribed for other in- and off-label conditions, such as deficit/hyperactivity disorders, attention disorders, migraine, smoking cessation, eating disorders, fibromyalgia, pain, and insomnia. This results in an increase in the use of antidepressant medications, leading to clinical and forensic overdose cases that could be either accidental or deliberate. The findings revealed that people who used antidepressants had a 33% greater chance of dying sooner than expected, compared to those who did not take the medications. Analytical techniques for precisely identifying and detecting antidepressants and their metabolic products in a variety of biological matrices are greatly needed to be developed and made available. Hence, this study attempts to discuss various analytical techniques used to identify and determine antidepressants in various biological matrices, which include urine, blood, oral fluid (saliva), and tissues, which are commonly encountered in clinical and forensic science laboratories. The Author(s) 2023. -
Message from IEEE InC4 2024 Publication Chair
[No abstract available]