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Poly(aniline) decorated with nanocactus platinum on carbon fiber paper and its electrocatalytic behavior toward toluene oxidation
Electrochemically deposited polymeric film of polyaniline (PANI) is used as a substrate for electrochemical deposition of platinum by using cyclic voltammetry. The electrochemical properties of multi-layered films were analyzed by cyclic voltammetry and AC impedance spectroscopy. The structural properties of multi-layered polymer films were analyzed by SEM, TEM, XRD, XPS, Raman and FTIR. The modified electrode showed strong electrocatalytic activity toward oxidation of toluene in presence of NaNO 2 /H 2 SO 4 in actetonitrile medium. Toluene gave a sensitive anodic peak at 1.318 V (vs. SCE). Cyclic voltammetry studies suggest that the electrochemical activity of Pt-PANI/CFP electrode is higher than that of Pt/CFP electrode toward toluene oxidation. 2018 The Electrochemical Society. -
Platinum decorated polythiophene modified stainless steel for electrocatalytic oxidation of benzyl alcohol
Abstract: Platinum nanoparticles were electrochemically deposited on conducting polymer polythiophene (PTh)-coated stainless steel (SS) substrate. A thin layer of PTh on the steel substrate facilitates uniform deposition of Pt nanoparticles on the substrate, thereby improving the surface area to a great extent. The electrochemical properties of the modified electrodes were analyzed by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The physicochemical properties of the modified electrodes were investigated by Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction spectroscopy (XRD), Raman spectroscopy, and Fourier transform infrared spectroscopy (FTIR). The proposed method has been applied for the electrocatalytic oxidation of benzyl alcohol in the presence of a mediator, 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO). Cyclic voltammetric studies reveal that the electrocatalytic activity of PtPTh/SS electrode is higher than that of PTh/SS electrode toward the conversion of benzyl alcohol to benzaldehyde. Graphic abstract: [Figure not available: see fulltext.]. 2019, Springer Nature B.V. -
Smart Tracker Device for Women Safety
Internet of Things (IoT) technologies assists by which machines, circuits, and many types of devices and interfaces communicate with one another. This IoT technology is useful for several purposes, especially in the field of Networking and Run-time data storage. Considering Women safety as our primary objective, we have used this technology and some other hardwares, including Raspberry Pi, to help the user in case of any emergency. Here also, with the help of IoT we are trying to make a device which can track the runtime location and the live, exact and efficient coordinates of the system which is in track. In the above context, in case of emergencies, it is very important to know the right place for the person to perform several important and critical actions whatsoever present at the right time.The GPS coordinates can be used to solve and analyse this problem. Additionally, we intend to add a voice recorder in case the women want to record any suspicious activity or information that can be helpful in the future for evidence purposes. In this paper, IoT is acting directly by receiving a person's GPS links from his server. Furthermore, we are combining the web interface with Google Maps on a single server so that the user's location can be tracked immediately using real-time coordinates.Our application can be used for wildlife, school-aged children, parent safety, and transportation services where location is a key factor. Although there are several direct and indirect usages of this project, the main use to which the project is concentrated is the use of this device to help our loved ones in the time of need. 2021 IEEE. -
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Predictive Analytics for Train Timeliness Using Long Short Term Memory and Machine Learning Techniques
Train delays are one of the most persistent issue faced by Indian Railways and it has still been the issue with all the modern infrastructure and increasing travel demands. Traditional methods mainly depend on historical averages and simple modelling which fail to capture complex patterns in delays caused. This research aims to build machine learning and neural network models to analyse historical data from past train journeys and make predictions for future train journeys. Machine learning models include Decision Trees, XGBoost, Random Forest, Extra Trees and a neural network model LSTM to predict the delay for a particular train on a given day based on the previous running status. The highest accuracy of 94.02% was found using LSTM model and a lowest of 72.65% for Decision Tree Regressor algorithm. 2025 IEEE. -
Classification and correlational analysis on lower spine parameters using data mining techniques
The application of data mining in the field of medical science is slowly gaining popularity. This is due to the fact that enormous statistical inferences from data related to the human body and medicine was a possible with high accuracy rates which was a tedious task in the past. This had led to discoveries and breakthroughs which has saved thousands of lives. Lower back pain is one of the most common issues faced by majority of the population throughout the world. The early detection and treatment of LBP can avoid life threatening issues in the body. Objective: This study aims to create a classification model which can be used to detect an unhealthy spine using the lumbar and sacral parameters. Correlational analysis was performed between different attributes to find distinguishing factors between healthy and unhealthy spine. Method: Classification methods were used such as decision tree and SVM. Correlational analysis was performed using pearson method between each attribute. Results: After creating the model using the different classification methods it was found that Ctree produced the highest accuracy with 92.80% on average. It was also found that there were 6 attribute pairs that had high correlation coefficient to distinguish unhealthy and healthy spine observations. BEIESP. -
Effect of nickel uptake on selected growth parameters of Amaranthus viridis L. /
Journal of Applied And Natural Science, Vol.10, Issue 3, pp.1011-1017, ISSN No: 0974-9411. -
Economics of Farming in Mahatwar, Uttar Pradesh
Recent policy efforts have focussed on transforming eastern Uttar Pradesh, an acknowledgement of the relative backwardness of the regions agricultural development. Despite this, there has been little discussion in the literature of agrarian relations and their implications for the economics of farming. Taking Mahatwar village in eastern Uttar Pradesh as a case study, this article examines disparities across socio-economic classes in incomes and the costs of cultivation. We found substantial inequality, with landlord and big capitalist farmer households earning nearly 30 times the annual income of lower peasant and manual worker households. These disparities arise primarily from differences in costs: poor peasant and manual worker households bear a disproportionate rental burden, rely excessively on family labour, and use much of their produce for self-consumption. Our findings highlight the need for rent reduction and yield enhancement, along with support measures such as minimum support prices (MSPs), to provide meaningful incomes to low-income farmers. 2025, Tulika Books. All rights reserved. -
A Univariate and Multivariate Time Series Analysis for the Prediction of Maize Production in India
This study examines the use of time series analysis for predicting maize production in India. The objective is to analyze the relationship between maize productions, domestic consumption, exports, and to forecast maize production using various time series models. The study employs cointegration techniques such as Johansen's test, Engle-Granger test, and Granger causality test to determine the long-term relationship between the variables. The findings exhibit that there is a bidirectional causal relationship between domestic consumption and export series and between domestic consumption and production series and that all three variables are co-integrated. To forecast maize production, the study employs both multivariate and univariate time series models. The multivariate models used are vector auto regressive and vector error correction models, while the univariate models used are ARIMA (auto regressive integrated moving averages), Holts exponential smoothing, NNAR (neural network auto regression), K-nearest neighbors (KNN), and LSTM (long short-term memory). The best forecast model is selected on the basis of a comparison of three evaluation metrics: mean absolute square error, mean absolute percentage error, and root mean square log error. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Modelling bivariate vector autoregressive model using copula approach
In this study, we propose a novel approach to model the relationship between bivariate time series by introducing a bivariate vector autoregressive model with Ali-Mikhail-Haq(AMH) copula, incorporating non-normal errors. The utilization of the Ali-Mikhail-Haq copula will allow for flexible modeling of the dependence structure between the two time series. This copula framework enables us to model the joint distribution of the errors with greater accuracy. Our approach provides a way to capture the relationships between the two time series, making it more suitable for complex data structures where traditional methods based on normal error assumptions may fall short. The Inference Functions for Margins (IFM) technique is employed to estimate both the model parameters and the dependency structure in our proposed model. To evaluate the accuracy of the proposed model, we conduct an extensive simulation study. The results demonstrate that the suggested model performs robustly across different scenarios, effectively capturing the dependence structure and delivering precise parameter estimates. The AMH copula efficiently models moderate levels of both negative and positive dependence. To enhance forecasting performance, we introduce a hybrid extension in which an artificial neural network(ANN) is applied to the residuals of the copula-based AMHVAR model. This hybrid approach captures remaining nonlinear patterns not explained by the linear VAR dynamics and the copula-based dependence structure, leading to improved predictive accuracy. Finally, we apply the proposed models to real-world data, further validating its practical applicability. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026. -
What is beautiful is good: An evalutation of effectiveness of attractiveness in celebrity endorsements
The studies in the field of marketing have shown that characteristics of the source will influence persuasiveness of an advertisement. Attractiveness is one such celebrity characteristic that is widely studied by researchers in the field of marketing. However, still, literature failed to explain how attractiveness of celebrity endorsers influenced purchase intentions. This study tried to fill this gap by modelling the influence of celebrity attractiveness on purchase intention. It also evaluated the effect of respondents' gender on the model. The data collection for the study were carried out during March - June 2017. The study found that the effect of celebrity attractiveness on purchase intention was mediated by celebrity brand fit, attitude towards the advertisement, and attitude towards the brand. The study also evaluated the moderating effect of respondents' gender using chi- square analysis, which found no significant model difference among male and female respondents. These findings indicated that celebrity attractiveness created purchase intention in a mediated manner among the respondents, irrespective of their gender. 2018, Associated Management Consultants Private Limited. -
Non-Antibacterial Carbon Nanoparticles and Its Fluorescence Properties
Highly fluorescent carbon nanoparticles are synthesized from corn starch via one-pot hydrothermal method. Upon treatment with the lime juice as the catalyst, carbon nanoparticles are functionalized with potassium, and an improvement in the luminescence behavior is also observed. The synthesized nanoparticles did not exhibit any antibacterial activity against gram-positive (Staphylococcus aureus, Bacillus subtilis) and gram-negative (Pseudomonas fluorescence, E.coli) bacteria. The excellent photoluminescence coupled with non-toxic behaviour of the carbon nanoparticles would be best suited for biomedical applications. The Electrochemical Society -
Work motivation of teachers: Role of self-leadership and self-directed professional development
A developing body of literature endorses that the sustainment of quality is a vital predictor for the success of educational processes and systems. Teachers who are the important stakeholders in the education system should strive to improve their quality for better student outcomes. Teachers' motivation at work is a basic condition for professional growth and development. This chapter specifically focuses on the need to understand work motivation of teachers in the context of self-determination theory. The chapter also focusses on the role of self-leadership and self-directed professional development in enhancing motivation. In the era of constant changes, it is essential that teachers should have the capability to devise new strategies to lead effectively. To establish a better workplace wellbeing, it is vital to understand the role of self-leadership and self-directed professional development in influencing intrinsic and extrinsic motivation. 2025, IGI Global Scientific Publishing. -
A Comparative Study of Gender and Age-Based Differences in Organisational Culture: Evidence from an Empirical Analysis
Organisational culture (OC) plays a significant role in shaping employee attitudes, engagement, and overall effectiveness. However, limited empirical evidence explores how demographic factors, such as age and gender, influence employees perceptions of organisational culture. This study reveals age and gender-based differences in organisational culture among employees from leading Indian-origin IT services companies in Bengaluru. Grounded in the Denison Organizational Culture Model, the study examines four key dimensions: involvement, consistency, adaptability, and mission. Data were collected from employees using a structured questionnaire, and statistical analyses, including ANOVA and Z-tests, were applied to examine differences in cultural perceptions. The results indicated that overall organisational culture scores did not differ significantly among age or gender groups. Specific dimensions, such as capability development, core values, agreement, and vision, exhibited significant age-related differences, with younger employees (2030 years) perceiving a stronger culture than those in the 3140 age group. No significant gender-based differences were observed across any dimension. These findings demonstrate the importance of demographic responsiveness in shaping inclusive organisational practices. The study contributes to organisational behaviour literature and offers practical implications for HR managers and leaders seeking to develop employee engagement and cultural alignment in the IT services sector. Keywords:. 2026, Iquz Galaxy Publisher. All rights reserved. -
Impact of organizational culture traits on employee intention to stay in the IT services sector: An empirical analysis
Employee retention poses a significant challenge in the Indian IT services sector, where frequent turnover leads to the loss of organizational knowledge and reduced productivity. This study assesses the impact of organizational culture on employees intention to stay in Indian-origin IT services companies in Bengaluru. We used Denisons Organizational Culture Model to measure culture across four dimensions, namely involvement, consistency, adaptability, and mission, and the Michigan Organizational Assessment Questionnaire to measure intention to stay. Using purposive sampling, we collected data from 384 employees of major Indian-origin IT firms between July 2023 and March 2024. Data were analyzed using factor analysis, linear regression, and Hayess PROCESS macro in SPSS 20.0. The results indicate that organizational culture has a significant and positive impact on employees intention to stay (? = 0.286, p < .001), accounting for 8.2% of the variance (R2 = 0.082). Path analysis confirms a strong positive effect (effect size = 0.486, 95% CI [0.322, 0.650], p < .001). The factor analysis demonstrates that stronger cultural dimensions especially empowerment, coordination and integration, organizational learning, and goals and objectives enhance employee retention in IT services firms. The study recommends that HR policies integrate cultural development to strengthen employee commitment and retention. Future studies should explore additional job and organizational factors that influence employee loyalty. Silpa Mary John, Smita Kavatekar, 2025. -
Influence of e-service quality on customer retention in commercial banks
Keeping in mind the dynamic changes that have been taking place in the Indian banking scenario, since the introduction of technology and the advancement of the internet even into the remote regions of the country, the proposed study is designed to determine the impact of e-service quality newlineon the retention of banking customers in the city of Bangalore. Most of the existing studies are based on conceptual understanding and are not based on model testing. The proposed study is relevant to Bangalore as it is a tier one city with a dynamic and fast-paced lifestyle having the best newlinetechnological facilities among the tier one cities in India. The literature review initiates an exhaustive discussion of various newlineconstructs that are outcomes of e-service quality and determinants of customer retention. Based on references from the literature review,constructs identified for e-service quality are customer satisfaction, newlinecustomer commitment and customer retention. The constructs that are outcomes of customer satisfaction were identified as customer trust, commitment and loyalty, which in turn influences customer retention. Thus, commitment, loyalty, commitment and e-service quality were concluded to be influencers of customer retention, with commitment being newlinea construct that mediates the relationship between e-service quality and customer retention. Through extensive literature review, hypotheses were derived and the proposed conceptual model is developed. newlineObjectives of the proposed study are to empirically validate a model to establish the relationship between e-service quality and customer retention, linking customer satisfaction, commitment, trust and loyalty with select antecedents. Research methodology gives an explanation of the population newlinefrom which the samples are collected, the justification for using the particular sampling technique and also the tool employed for data collection. Detailed explanations have also been given for checking the newlinereliability and validity of the tool and pilot data. -
Leadership quality analyzing device in employees /
Patent Number: 356428-001, Applicant: Dr.Nishi Tyagi. -
Internet of things based financial data managing device in bank /
Patent Number: 359269-001, Applicant: Sapna Bisht. -
A critical study on role of social media in Delhi state elections 2013 & 2015 /
Social media is often hailed as an instrument of digital democracy and social change. This perception is deeply rooted in the well acknowledged potential of ICT’s as ‘agents for development and empowerment .It has liberated people from the tyranny of the free flow of information and ideas. The Delhi elections held in 2013 and 2015 made a revolutionary change in India’s political equations. It shows the emergence of a new party Aam AadmiPart, Aam Aadmi Party and its victory. -
Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques
Mental stress, a critical concern worldwide, necessitates precise and nuanced characterization. This study introduces a novel approach to effectively characterize mental stress through a multi-label, multi-class classification framework through natural language processing techniques. Building on existing literature, discussions with psychologists and other mental health practitioners, we developed a taxonomy of 27 distinctive markers spread across 4 label categories; aiming to create a preliminary screening tool leveraging textual data. The core objective is to identify the most suitable model for this complex task, encompassing comprehensive evaluation of various machine learning and deep learning algorithms. we experimented with support vector machines (SVM), random forest (RF) and long short-term memory (LSTM) algorithms incorporating various feature combinations involving Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA). The best performer of this comparative study was further evaluated against an LLM. The potential of large language models (LLMs), including their language understanding and prediction capabilities, is another key focus. We explore how these models could augment and advance mental health research, offering new perspectives and insights into the characterization of mental stress. Our findings show that the top model, an LSTM with TF-IDF and LDA (class weights assigned) outperformed the PaLM model with a coefficient of variation as low as 0.87% across all labels. Despite the PaLM model's superior average performance, it exhibited higher variability among different labels. 2025 The Author(s)