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An Aqueous Phase TEMPO-Mediated Electrooxidation of Benzyl Alcohol at ?-CD-PPy-Modified Carbon Fibre Paper Electrode
A green and facile electrocatalytic method for the oxidation of benzyl alcohol in an acidic aqueous medium was developed using an anionic micellar system. ?-cyclodextrin-polypyrrole-modified carbon fibre paper (?-CD-PPy/CFP) electrode was successfully used in the oxidation of benzyl alcohol with TEMPO as the mediator. The modified electrode was characterized by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy. The modified electrode exhibited a strong electrocatalytic activity towards TEMPO-mediated oxidation of benzyl alcohol. [Figure not available: see fulltext.]. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
An Architecture for Risk-Based Authentication System in a Multi-Server Environment
Identity authentication, a vital part of any application access, is also one way for imposters to gain access to an application using various fingerprint authentication technologies. Therefore, because of the lack of security in the authentication architecture, this paper proposes an architecture for a risk-based authentication system using a machine learning model in a multi-server environment. Since the recent study mainly focuses on the multi-server environment and adaptive authentication independently, very little work has been proposed using a multi-server environment for adaptive authentication. The study aims to estimate risk for the user during the initial login process and when the user's data is extracted enough for prediction in a multi-server environment. 2023 IEEE. -
An ARDL approach: case study of COVID-19 death and insurance stock returns
The movement of investment vehicles helps investors communicate as a stock market shield via a diverse portfolio. The COVID-19 outbreak had impacted the India along with other countries across the globe. The diseases progression and economic impact are highly uncertain. The current study is a novel effort to untangle the dynamic relationship between COVID-19 death and returns of NSE-listed life insurance stocks. We look at COVID-19 death data as well as returns from SBI Life, HDFC Life, and ICICI PRU from April 2020 to July 2021, when pandemic was the leading cause of death. Using the autoregressive distributed lag (ARDL) model technique, we discover that COVID-19 death has no dynamic relationship with the returns of selected life insurance stocks. In conclusion, our findings will provide stockholders, investment advisors, and policy experts with significant foresight into guaranteeing returns on life insurance stocks free from uncertain calamities such as COVID-19. Copyright 2025 Inderscience Enterprises Ltd. -
An Area-Efficient Unique 4:1 Multiplexer Using Nano-electronic-Based Architecture
Quantum dot cellular automata computing methodology is a new way to develop systems with less power consumption. Nanotechnology-based computing technology has enabled the QCA principles to be more relevant with respect to the critical limitations of current VLSI-based design. In this paper, a novel 4:1 multiplexer design based on the QCA concept is presented. As compared to the previous designs of a multiplexer, this novel design is area efficient and power efficient. A five-input majority voter is used for the design of the multiplexer. The 4:1 multiplexer is constructed by making use of three 2:1 multiplexer. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Assessment of Farmers' Perception and Adaptive Capacity for Climate Change
In the past decades, various regions in U.P. had experienced severe floods. The effects of climate change also affected agricultural production. This study investigated the farmers' perception of climate change and suggested strategies for mitigating its effects using a primary survey with the help of a pre-structured schedule. Change in rainfall pattern, problems in seed quality, the emergence of new pests and diseases, changes in the crop cycle were the few effects that farmers' perceived due to climate change. Even the most mitigation efforts by the farmers cannot prevent some of the impacts of climate change within the following decades. It makes adaptation a must-have for addressing these impacts. 2022, The Society of Economics and Development. All rights reserved. -
An asymmetric analysis of overall globalization on financial inclusion
Purpose: Financial inclusion is acknowledged as a critical facilitator of the United Nations Sustainable Development Goals agenda for 2030. Therefore, this study aims to examine the asymmetric role of overall globalization on financial inclusion by controlling economic growth, urbanization and population for the selected South Asian countries. Design/methodology/approach: Applying the nonlinear autoregressive distributed lag approach to cointegration explores the impact of overall globalization on financial inclusion in the presence of additional variables like economic growth, urbanization and population in the designed financial inclusion function. Findings: The estimated econometric outcomes show that increasing overall globalization fosters financial inclusion while decreasing overall globalization reduces financial inclusion. Furthermore, a positive (negative) change in economic growth leads to an increase (decrease) in financial inclusion while varying short-run findings. Moreover, both positive and negative changes increase financial inclusion in the long run in connection with urbanization. Although the short-run results are not significant, the study finds that an increase (decrease) in population leads to a decrease (increase) in financial inclusion. Finally, to support the promotion of financial inclusivity throughout South Asia, several policies pertaining to financial inclusion are suggested. Originality/value: To the best of the authors knowledge, this is the first study to examine the asymmetries related to overall globalization on financial inclusion by controlling economic growth, urbanization and population. 2024, Emerald Publishing Limited. -
An asymmetric analysis of overall globalization on financial inclusion
Purpose: Financial inclusion is acknowledged as a critical facilitator of the United Nations Sustainable Development Goals agenda for 2030. Therefore, this study aims to examine the asymmetric role of overall globalization on financial inclusion by controlling economic growth, urbanization and population for the selected South Asian countries. Design/methodology/approach: Applying the nonlinear autoregressive distributed lag approach to cointegration explores the impact of overall globalization on financial inclusion in the presence of additional variables like economic growth, urbanization and population in the designed financial inclusion function. Findings: The estimated econometric outcomes show that increasing overall globalization fosters financial inclusion while decreasing overall globalization reduces financial inclusion. Furthermore, a positive (negative) change in economic growth leads to an increase (decrease) in financial inclusion while varying short-run findings. Moreover, both positive and negative changes increase financial inclusion in the long run in connection with urbanization. Although the short-run results are not significant, the study finds that an increase (decrease) in population leads to a decrease (increase) in financial inclusion. Finally, to support the promotion of financial inclusivity throughout South Asia, several policies pertaining to financial inclusion are suggested. Originality/value: To the best of the authors knowledge, this is the first study to examine the asymmetries related to overall globalization on financial inclusion by controlling economic growth, urbanization and population. 2024, Emerald Publishing Limited. -
An attention-based loss function and synthetic minority oversampling technique for alleviating class imbalance in predicting diabetes
Diabetes is a chronic disease due to higher blood sugar (or Glucose) levels in the blood. This study proposes a novel attention-based loss function and a lightweight artificial neural network (ANN) called Diabetic Lite (DB-Lite) for diabetes prediction in the Pima Indian Diabetes Dataset (PIDD). We show that the Pima dataset has many challenges. It is a small and imbalanced dataset; moreover, many features are non-linearly correlated in this dataset. The novelties of this research work are as follows: (i) A novel loss function of attention-based binary cross entropy (ABCE) is proposed for the first time to alleviate the statistical imbalance present within the Pima dataset. This ABCE loss function is incorporated in the DB-Lite model, which is trained from scratch. (ii) A Swish activation function is deployed in the hidden layer of DB-Lite instead of Rectified Linear Unit (ReLU) to deal with the non-linear dependency of features with the final outcome. (iii) The synthetic minority oversampling technique (SMOTE) is used as a pre-processing technique to mitigate the class imbalance problem from the Pima dataset. (iv) An adaptive learning rate is utilized while training the model to speed up the convergence of the DB-Lite model. Our final proposed framework has achieved 99.7% accuracy, 99.4% precision, 99.8% recall, and 99.6% F1 score in testing, which is the best result on this Pima dataset. The Welch t-testing (as a statistical hypothesis testing) and 10-fold cross-validation are utilized to prove the validity of the proposed loss function. 2025 -
An augmented artificial bee colony algorithm for data aggregation in wireless sensor networks
As wireless sensor networks comprise of a vast number of resource constrained tiny sensor nodes which are designed to operate for a long period of time, it is inevitable to efficiently utilize the available resources. Even though energy harvesting approaches exist, energy efficiency in these networks remains the primary concern. Innovative data collection methods help in the optimal utilization of the confined resources like energy, memory and processing capabilities. Majority of the energy is consumed for data transmission in contrast to sensing and processing. Adopting self-organizing system intelligence of the nature for modern advancements is effective and efficient. This paper provides a gist of the existing bio-inspired routing algorithms and describes a new energy efficient data collection strategy with mobile sinks in wireless sensor networks. IAEME Publication. -
An Automated Deep Learning Model for Detecting Sarcastic Comments
The concept of Natural Language Processing is immensely vast with a wide range of fields in which ideas can be explored and innovations can be developed. An algorithm based on deep learning is used to detect sarcasm in text in this paper. It is usually only possible to detect sarcasm through speech and very rarely through text. 1.3 million comments from Reddit were analyzed, of which half were sarcastic and half were not, and then various deep learning models were applied, such as standard neural networks, CNNs, and LSTM RNNs. The best performing model was LSTM-RNNs, followed by CNNs, and standard neural networks came last. With textual data, it is much harder to understand whether the other person is being sarcastic or not, it can only be understood by listening to their tone of voice or looking at their behaviour. The purpose of this paper is to demonstrate how to detect sarcasm in textual data using deep learning models. 2021 IEEE. -
An Automated Path-Focused Test Case Generation with Dynamic Parameterization Using Adaptive Genetic Algorithm (AGA) for Structural Program Testing
Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 4060% of the total effort and time involved in project development. Generating test cases is the most important process in software testing. There are many techniques involved in the automatic generation of these test cases which aim to find a smaller group of cases that could allow for an adequacy level to be achieved which will hence reduce the effort and cost involved in software testing. In the structural testing of a product, the auto-generation of test cases that are path focused in an efficient manner is a challenging process. These are often considered optimization problems and hence search-based methods such as genetic algorithm (GA) and swarm optimizations have been proposed to handle this issue. The significance of the study is to address the optimization problem of automatic test case generation in search-based software engineering. The proposed methodology aims to close the gap of genetic algorithms acquiring local minimum due to poor diversity. Here, dynamic adjustment of cross-over and mutation rate is achieved by calculating the individual measure of similarity and fitness and searching for the more global optimum. The proposed method is applied and experimented on a benchmark of five industrial projects. The results of the experiments have confirmed the efficiency of generating test cases that have optimum path coverage. 2023 by the authors. -
An autonomic computing architecture for business applications
Though the vision of autonomic computing (AC) is highly ambitious, an objective analysis of autonomic computing and its growth in the last decade throw more incisive and decisive insights on its birth deformities and growth pains. Predominantly software-based solutions are being preferred to make IT infrastructures and platforms, adaptive and autonomic in their offerings, outputs, and outlooks. However the autonomic journey has not been as promising as originally envisaged by industry leaders and luminaries, and there are several reasons being quoted by professionals and pundits for that gap. Precisely speaking, there is a kind of slackness in articulating its unique characteristics, and the enormous potentials in business and IT acceleration. There are not many real-world applications to popularize the autonomic concept among the development community. Though, some inroads has been made into infrastructure areas like networking, load balancing etc., very few attempts has been exercised in application areas like ERP, SCM, or CRM. In this paper, we would like to dig and dive deeper to extract and explain where the pioneering and path-breaking autonomic computing stands today, and the varied opportunities and possibilities, which insists hot pursuit of the autonomic idea. A simplistic architecture for deployment of autonomic business applications is introduced and a sample implementation in an existing CRM system is described. This should form the basis of new start and ubiquitous application of AC concepts for business applications. 2012 IEEE. -
An Early Detection of Autism Spectrum Disorder Using PDNN and ABIDE I&II Dataset
The current study's objective was to use deep learning methods to separate valetudinarians amidst autism spectrum disorders (ASDs) from controls employing just the patients brain activation patterns from a dataset of large brain images. We examined brain imaging data from ASD patients from the global, multi-site ABIDE dataset (Autism Brain Imaging Data Exchange). Social impairments and repetitive behaviors are hallmarks of the brain condition known as autism spectrum disorder (ASD). ASD affects one in every 68 kids in the USA, as of the most recent data from the Disease Control Centers. To understand the neurological patterns that arose from the categorization, we looked into functional connectivity patterns that can be used to diagnose ASD participants precisely. The outcomes raised the state of the art by correctly identifying 72.10% of ASD patients in the sample vs. control patients. The classification patterns revealed an anti-correlation between the function of the brain's anterior and posterior regions; this anti-correlation supports the empirical data currently showing achingly ASD impedes communication between the livid brain's anterior and posterior areas. We found and pinpointed brain regions damn frolic, distinguishing ASD among typically developing reign according to our deep learning model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
An Early-Stage Diabetes Symptoms Detection Prototype using Ensemble Learning
Diabetes is one of the most increasing health issues that the whole world is facing. Recent research has shown that diabetes is spreading quickly in India. Having more than 77 million sufferers, India is actually regarded as the diabetes capital of the world. The lifestyle and eating patterns of people who move from rural to urban settings alter, which raises the prevalence of diabetes. Diabetes has been linked to consequences like vision loss, renal failure, nerve damage, cardiovascular disease, foot ulcers, and digestive issues. Diabetes can harm the blood arteries and neurons in a variety of organs. FPG (Flaccid Plasma Glucose) is a popular test that is done to find out whether a person is a diabetic patient or not. However, not all people consistently take medication and neither monitor their blood sugar levels on a regular basis. Early detection of this disease is also an important thing that people usually don't do. Technology these days has emerged a lot in the healthcare zone. Many prototypes have already been made for the detection of diabetes. The prototype discussed in this paper is an ensemble learning approach for the detection of diabetes in a very early stage. Ensemble learning which includes the use of multiple model prediction has been used to make the outcome stronger and more trustworthy. The overall accuracy achieved by the model is 96.54%. XGBoost also records the minimal execution time of 2.77 seconds only. 2023 IEEE. -
An Eco-Friendly Antenna for 6G Communication Enabling Sustainable Infrastructure
This research presents the development of an antenna on an organic substrate for 6G infrastructure utilizing sustainable materials. The substrate is developed from 75% used tea powder and 25% carbon sourced from used batteries, combined with polyvinyl alcohol (PVA), molded, and thermally processed to create a 5mm thick organic substrate. The developed patch antenna operates within the frequency range of 14.8 to 17.3GHz, encompassing the new lower 6G candidate band (14.8 to 15.3GHz). The antenna exhibits 2.5GHz bandwidth with a resonant frequency of 15.7GHz. This antenna highlights the potential for integrating eco-friendly materials into modern telecommunications technology, promoting sustainability, and supporting the circular economy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
An ecology intervention in an English studies programme: Contexts, Complexities and Choices
Over the past few decades, there has been a critical mass gained regarding the need to engage purposefully with Ecology. Unfortunately, this has not provoked any stimulating work within the Humanities and Social Sciences academia. In fact, alongside growing realisations about the necessity to address Ecology, there is a glaring absence of any significant engagement. In response to such a vexing reality, the Department of English at Christ University chose to initiate an Ecological venture within its Honours programme. This paper captures - the vigorous debates it lit up, the tough choices that had to be made, and the promise it offers - that complex journey. 2014 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (Dharmaram Vidya Kshetram, Bangalore). -
An Econometric Approach Towards Exploring the Impact of Workers Remittances on Inflation: Empirical Evidence from India
This paper attempts to study short and long run impact of increased workers remittances on general price level. It uses the real GDP growth, real effective exchange rate (REER), M3 (broad money), fiscal deficit to gauge the impact of foreign remittances on inflation. The study makes use of VAR/VECM framework to gauge the impact of workers remittances on inflation. Inflation is measured in terms of CPI and WPI, real income or GDP at constant prices is taken as a measure of GDP growth, REER is used for exchange rates and M3 is taken as a proxy for money supply. Monthly data of all these variables has been taken from Bloomberg and World Bank data base. The findings provide important insights into the nature of association between remittances and inflation suggesting causality between inflation, remittances, real GDP, real effective exchange rates and money supply due to increased workers remittances. The findings have policy implications for decisions to channelize workers remittances in a way to increase real GDP growth and money supply while at the same time not causing the general price levels to soar. The present study focuses on how increased (decreased) workers remittances is leading to increase (decrease) in general price levels in India. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
AN ECONOMIC RELIABILITY TEST PLAN BASED ON TRUNCATED LIFE TESTS FOR MARSHALL-OLKIN POWER LOMAX DISTRIBUTION WITH APPLICATIONS
In every competitive enterprise, there has been a resurgence of interest in increasing the quality of products. In this paper, we create new acceptance sampling plans based on truncated life tests for the Marshall-Olkin power Lomax distribution. The minimum sample sizes needed to declare the specified mean life with respect to the newly developed sampling plans are obtained for different values of the model parameters. Besides, the operating characteristic function values, minimum ratios of the true value and the required value of the parameter with a given producer risk are discussed. Moreover, the results are illustrated using numerical examples, and a real data set is considered to illustrate the functioning of the recommended acceptance sampling plans. The result shows that the proposed plan is more adequate compared with other acceptance sampling plans available in the open literature. So, it can be used for industry applications. 2010 Mathematics Subject Classification. 60E05, 62E15, 62F10. 2022, Asia Pacific Academic. All rights reserved. -
An educational animation intervention to enhance HPV awareness and practices among transgender individuals associated with NGOs in North India: Preliminary findings
Background: Transgender individuals in India face an elevated risk of Human Papillomavirus (HPV) infection due to limited knowledge, awareness, and access to preventive care. However, targeted research and interventions for this population are scarce. Addressing this gap is critical for improving HPV-related health outcomes among transgender individuals. Aim: This pilot study aimed to develop and evaluate the effectiveness of an animation-based intervention designed to enhance knowledge, attitudes, and practices (KAP) related to HPV among transgender individuals in India. Methods: The study was conducted in four phases among sixteen participants from different NGOs in North India. Phase 1 involved a needs analysis through a focus group discussion (FGD) with ten transgender participants, which led to co-designing the intervention. Phase 2 focused on developing the animation intervention based on the identified needs and existing literature. Phase 3 tested the intervention with six participants using a pre-post assessment with the Knowledge, Attitude, and Practice (KAP) Questionnaires and Human Papillomavirus Knowledge Questionnaire (HPV-KQ) questionnaires. Phase 4 finalized the module by incorporating feedback from the transgender community, with minor modifications made following validation. Results: Preliminary analysis showed significant improvement in KAP scores (Knowledge: 0.167 to 1.000, p = 0.073; Attitude: 0.583 to 0.833, p = 0.132; Practice: 0.250 to 0.500, p = 0.200) post-intervention. Participants demonstrated increased knowledge and more positive attitudes toward HPV prevention. Feedback highlighted the animations effectiveness in simplifying complex health information. Conclusion: The animation-based intervention effectively improved HPV-related knowledge, attitudes, and practices among transgender individuals, demonstrating the potential of culturally sensitive, visual interventions to enhance public health outcomes in underserved populations. 2025 Taylor & Francis Group, LLC. -
An Effecient Approach to Detect Fraud Instagram Accounts Using Supervised ML Algorithms
Nowadays social media plays a vital role in different fields including business, economic communication and personal. Many person get profit from the different origins of availability of data from these social media, but cyber-crimes are increasing day by day. A person can generate many fake accounts and hence pretenders can easily be made. Instagram, as one of the popular types of online social media site, carries big information and messages through the posts. Most of the person use Instagram as a digital life marketing place because it is a one of the big social media site. The goal of the research paper is to recognize and stop fake IDs and pages. Because through the professional pages of Instagram, many fake cases and things are occurring present days. So the main thing is to recognize fake pages and fake accounts also. In this paper, we work on various IDs of Instagram. We want to observe an ID is real or not using Machine Learning techniques namely Logistic Regression, Naive Bayes, Support vector machine, Decision tree, Random Forest. 2022 IEEE.