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Utilization of CO2 for Electrocarboxylation of Benzophenone Using MXene-Based Electrodes: A Sustainable Approach
The significant rise in atmospheric carbon dioxide (CO2) levels has prompted the need to develop efficient methods for CO2 conversion and fixation methods. Electrocarboxylation reaction is a highly efficient and sustainable method for activating and utilizing CO2, yielding essential carboxylic acids and their analogues, which are important intermediates in the pharmaceutical and fuel industries. This research demonstrates the efficiency of 2D Ti3C2Tx and Ta2CTx MXene-modified carbon fiber paper electrodes (Ti3C2Tx/CFP and Ta2CTx/CFP) for CO2 fixation with benzophenone in a tetrabutylammonium bromide/acetonitrile (TBABr/CH3CN) medium, yielding benzilic acid. Ti3C2Tx/CFP exhibited superior electrocatalytic activity with a lower reduction potential for benzophenone at ?1.0 V and achieved a 72% yield of benzilic acid at an optimum current density of 50 mA cm-2. In comparison, Ta2CTx/CFP exhibited a cathodic peak at ?1.08 V, producing a 66% yield at 70 mA cm-2. The electron paramagnetic resonance spectrum substantiates the generation of reactive radical intermediates during the reaction. Ti3C2Tx/CFP showed robust structural stability with ?88% Faradaic efficiency and a turnover frequency of 1.90444 10-5 s-1, indicating its potential for CO2 fixation. 2024 American Chemical Society. -
Utilization of aluminum dross: Refractories from industrial waste
Aluminum oxide (Al2O3) and Magnesium-Aluminum oxides (MgAl2O4) are well known refractory materials used in engineering industries. They are built to withstand high temperatures and possess low thermal conductivities for greater energy efficiency. Dross, a product/byproduct of slag generated in aluminum metal production process is normally comprised of these two oxides in addition to aluminum nitride (AlN). Worldwide, thousands of tons of aluminum dross are generated as industrial wastes and are disposed of in landfills causing serious environmental hazard. This paper explores the potential to synergize the characteristics of the favourable contents of aluminum dross and its availability (in tons) via synthesis of refractories and thereby develop a value added product useful for the modern industries. In this work, Al-dross as-received from an aluminum industry which comprised of predominantly Al2O3, MgAl2O4 and AlN, was used to develop the refractories. AlN possesses high thermal conductivity values and therefore was leached out of the dross to protect the performance of the developed refractory. The washed dross was calcined at 700 and 1000C to facilitate gradual elimination of the undesired phases and finally sintered at 1500C. The dross refractory pellets were subjected to thermo-physical and structural properties analysis: XRD (structural phase), SEM (Microstructure), EDS (chemical constituents) and thermal shock cycling test by dipping in molten aluminum and exposing to ambient (laboratory). The findings include the favourable prospects of using aluminum dross as refractories in metal casting industries. Published under licence by IOP Publishing Ltd. -
Utilisation of Virtual Assistant and Its Impact on Retail Industry
Virtual assistant is nothing but an independent contractor, who offers administrative services to the clients of a particular organisation while operating outside of the office of the client. Generally, a virtual assistant operates from a home-based office. This virtual assistant application has the ability to access the required planning documents, such as shared calendars. The contemporary retail organisations like e-commerce companies in this competitive global business environment are using virtual assistant to enhance omnichannel experience, 24/7 customer service, order tracking, and product recommendations. Overall, virtual assistant helps the organisations in enhancing social media management activities. This concept of the use of virtual assistant has been significantly emerged after the increase in demands for e-commerce business activities in this decade. Research objectives related to the title of this research are developed and listed. Relevant theories on virtual assistant are applied in the literature review section of this study. The researcher has decided to adopt qualitative research methodology to achieve the objectives of the research. Moreover, the researcher has considered secondary data analysis approach to conduct this research. In terms of findings, it has been identified that virtual assistant has a positive impact on the business operation activities of retail organisations. Authentic secondary sources are considered to collect and analyse the data. Some challenges associated with the utilisation of virtual assistant also have been identified in the findings section. Some valuable recommendations are suggested for the future researchers to overcome those identified associated challenges. 2022 IEEE. -
Using Time series analysis, analyze the impact of the wholesale price index on the price escalation in the automotive industry
The automobile industry is a crucial sector of the economy, contributing significantly to employment and economic growth. One of the major challenges faced by this industry is the problem of price escalation, which can affect both consumers and manufacturers. In this project, we explore the impact of wholesale price index (WPI) on the price escalation of automobiles using time series analysis. We analyze the historical data of WPI and automobile prices in India from 2010 to 2022. We use statistical techniques like stationarity tests, autocorrelation analysis, and Granger causality tests to understand the relationship between WPI and automobile prices. Furthermore, we employ a SARIMA model in predicting WPI value and Vector Auto regression (VAR) model to analyze the dynamic interactions between WPI and CPI value. Our findings suggest that WPI has a significant impact on the price escalation of automobiles in India. The VAR model shows that there is a positive feedback loop between WPI, CPI and automobile prices, implying that an increase in WPI leads to a corresponding increase in automobile prices and vice versa. This feedback loop can create an inflationary spiral in the automobile industry, which can be detrimental to the economy. Our project highlights the importance of monitoring WPI and its impact on the automobile industry. Policymakers and industry experts can use our findings to develop effective strategies to manage price escalation in the automobile industry and mitigate its negative impact on the economy. 2023 ACM. -
Using service learning to fuel multi-disciplinary research in Indian HEIs: A novel approach
The current work proposes a novel approach that can allow Indian HEIs to offer service-learning-based curricula while enhancing the institutional research output. The proposed model suggests a unique linkage between existing volunteer and academic departments at institutions of higher education such that data already being generated through existing outreach programs can be utilised for meaningful social science research. The proposed model thus utilizes existing resources already available with an institution to bolster research output, enhances the institutional capacity to include a pedagogical approach with proven benefits, and facilitates institutional compliance with regulatory directives mandating the inclusion of Service-learning-based courses at UG and PG levels. 2024, IGI Global. All rights reserved. -
Using Sentiment Analysis to Identify Consumers Emotions in the Hotel Industry
This research attempted to present a more comprehensive overview of online user-generated data by extending far beyond quantitative analysis. We gathered a distinctive and substantial database of online user ratings for the hotel industry from numerous websites over a significant amount of time. To gauge the quality of hotel service, we divided customer reviews into two categories using the sentiment analysis technique. The impact of those factors in influencing users overall evaluation and content creation behavior is then investigated. The findings imply that different aspects of user evaluations have considerably diverse effects on how users evaluate products and what motivates them to create content. 2025 by Apple Academic Press, Inc. -
Using machine learning in an industrial control network to improve cybersecurity operations /
Patent Number: 202241052879, Applicant: Abhijit Das. A machine-learning service, which receives data related to a plurality of features related to internet traffic metrics, processes said data by performing operations selected from among an operation of ranking at least one feature, an operation of classifying at least one feature, an operation of predicting at least one feature, and an operation of clustering at least one feature, and as a result, the method monitors online security threats. -
Using machine learning architecture to optimize and model the treatment process for saline water level analysis
Water is a vital resource that makes it possible for human life forms to exist. The need for freshwater consumption has significantly increased in recent years. Seawater treatment facilities are less dependable and efficient. Deep learning systems have the potential to increase the efficiency as well as the accuracy of salt particle analysis in saltwater, which will benefit water treatment plant performance. This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. Here, the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization. Then the modelled water saline particle analysis has been carried out using back propagation with Kernelized support swarm machine. Experimental analysis is carried out based on water salinity data in terms of accuracy, precision, recall, and specificity, computational cost, and Kappa coefficient. The proposed technique attained an accuracy of 92%, precision of 83%, recall of 78%, specificity of 81%, computational cost of 59%, and Kappa coefficient of 78%. 2023, IWA Publishing. All rights reserved. -
Using Ensemble Model to Reduce Downtime in Manufacturing Industry: An Advanced Diagnostic Framework for Early Failure Detection
The fourth industrial resolution marks a significant shift that uses emerging technologies such as intelligent automation, extensive machine-to-machine communication, and the internet of things (IoT) to modernize conventional manufacturing and industrial methods. The examination of vast data gathered in modern industrial facilities has not only greatly leveraged artificial intelligence (AI) tools but has also driven the development of innovative technologies. In this context, a novel framework for predictive maintenance in the production sector is introduced in this research, which depends on an ensemble model. First, a set of input features are collected from sensors. Then, data normalization technique is applied to standardize and prepare data for further analysis. These normalized input features are then used to train an ensemble classifier. In the ensemble model, multilayer perceptron (MLP), K-nearest neighbors (KNN), and support vector machine (SVM) are serve as base classifiers. Efficacy of the designed framework is validated using predictive maintenance dataset. Results demonstrated that the proposed ensemble model exhibited improved accuracy compared to individual base classifiers. The results further demonstrated that the implemented model had superior efficiency compared to the other benchmark models. 2025 selection and editorial matter, Amit Kumar Tyagi, Shrikant Tiwari, and Gulshan Soni; individual chapters, the contributors. -
Using Behavioural Economics to Analyse and Enhance Contraception Usage Decisions
Existent literature helped narrow down variables influencing modern contraceptive adoption (usage), a behaviour carrying enormous positive externality. Using finite population sample size formula and probability proportional to size method of sample selection, primary data was collected from participants using inclusion and exclusion criterions. Binary logistic regression model was used to predict probability of occurrence of dependent variable usage of modern method of contraception being treated at a dichotomous outcome level. Predictor variables after confirming association by cross tabulation were introduced stepwise to build model subject to elimination of those variables adding insignificantly to the overall predictability of the model. Variables such as gender, education level, spousal influence, extended family influence, financial well-being and contraceptive information were found to significantly predict the probability of occurrence of the dependent variable. Except for financial well-being with three sub-categories, other independent variables were treated at dichotomous level. Income level was found to be an important predictor although found statistically insignificant. Non-contributory factors such as age, occupation and years of marriage were dropped. Post-model construction, borrowing nudges from behavioural economics (BE) domain, strategies to nurture the significant context specific influencing variables, were articulated. BE was particularly preferred for its openness to the paradigm of non-rational behavioural choices. 2022 SAGE Publications. -
Using Analytics to Measure the Impact of Pollution Parameters in Major Cities of India
Coronavirus is airborne and can spread easily. Air pollution may have an impact on breathing and also keep the virus airborne. The levels of air pollution were impacted by the lockdown measures, restricting the vehicular and industrial pollutants. Therefore, there is a need to understand the relation between air pollution levels and the Coronavirus infection rate. The study aims to find the effect of various pollutants across major cities of India on the R-value. The pollution data was collected from the Governments official portal. The major pollutants on which the data was collected are PM2.5, PM10, NO, NO2, NOx, SO2, CO, and Ozone. The data on air pollution levels were also collected for the selected cities from April 2020 to April 2021. The spread is measured as the reproduction number at time t (Rt), which is an estimate of infectious disease transmissibility throughout an outbreak, or it is the rating of Coronavirus or any diseases ability to spread. The data is analysed using MS Excel and R Programming. Descriptive statistics and regularisation are performed on the data. The study results reveal that some pollutants positively and negatively affect the infection rate. However, the effect is very low, and it concluded that the pollution might not directly affect infection rates. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Using Academic Performance Indicator to Evaluate the Cost to Company of Management Graduates
As the placement season hits CBS Business School, India, the pressure to get placed is at its peak. As the placement season draws to a close, the unplaced students storm the Directors office complaining about unfair treatment in the process. They lay blame on the random shortlisting followed by the Placement co-ordinator. Concerned with these allegations, the Director calls on faculty to investigate the situation. During the conversation one of the students, Rachit, expresses regret in not focusing solely on academics and instead on developing a more well-rounded profile. He feels that that is the reason for his failure to get placed. A fundamental question arises of how closely academic performance and Cost to Company (CTC) are related. Data is collected to examine the validity of the long-held belief that higher academic performance leads to higher paying job placement. 2022 NeilsonJournals Publishing. -
Users Perception and Barriers to Using Self-Driven Rental Bikes
The research study has two objectives. The first objective of this paper was to find users' perception towards self-drive rental bikes. The second objective was to identify the factors that act as barriers to users using self-drive rental bikes. The research was a formal and structured conclusive research type and used quantitative data analysis techniques. The study had a representative sample of 350 respondents. The population selected for this study were people of various demographics in Bangalore. We used judgemental sampling to decide on the right sample. In achieving both objectives, factor analysis was used to arrive at a minimum number of factors or dimensions. The major perception factors are: Economical Choice, Environmental Consciousness, Alternative Source of Transport, Rationality, and Convenience. The major barriers to using self-drive rental bikes are Safety Issues, Conservative Nature of Users, the Expensive Nature of Service, and the Difficulty in Using Mobile Applications. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
User Sentiment Analysis of Blockchain-Enabled Peer-to-Peer Energy Trading
A new way for the general public to consume and trade green energy has emerged with the introduction of peer-to-peer (P2P) energy trading platforms. Thus, how the peer-to-peer energy trading platform is designed is crucial to facilitating the trading experience for users. The data mining method will be used in this study to assess the elements affecting the P2P energy trading experience. The Natural Language Processing (NLP) approach will also be used in this study to evaluate the variables that affect the P2P energy trading experience and look at the role of topic modeling in the topic extraction using LDA. The findings show that the general public was more interested in the new technology and how the energy coin payment system operated during the trade process. This explanation of energy as a CC is an outlier that fits well with the conventional literature. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
USER SATISFACTION OF CHRIST UNIVERSITY WEB PORTAL
Apart from the various factors contributing to the use and adoption of technologys newness, system usability has gained immense relevance. Here the usability is often identified as the satisfaction of the user from the particular system in use. The researcher intends to analyse and study the user-satisfaction of Christ University students, primarily, from the Christ University web portal. By drawing definitions from the International Standard Organisation, the meaning of user satisfaction is understood as the extent to which a specific user can use the product effectively to achieve specific goals. The researcher will search for answers among students of Christ University by confronting them with them questionnaires on its information quality, user-friendly nature and the availability of information and details. -
User request scheduling for multimedia resource using improved fuzzy logic with hybrid lyapunov based algorithm in hybrid cloud
The hybrid cloud provides vast opportunity to access the varied resources for effective provisioning of services to its users. The proposed scheduling algorithm uses the K-Nearest Neighbor(KNN) to locate the current location of the user and the nearest available computing resource. The Improved Fuzzy Logic (IFL) is applied for improving the resource balancing so that the resources are better utilized for the scheduling process. The wastage of resource usage and ideal resource are reduced considerably. The HLA scheduling is applied with the IFL, and based on the waiting of the jobs; the slots are allocated with jobs for execution. All the jobs are executed successfully with minimized execution time and makespan of the workflow application request. The performances of three algorithms are measured with parameters such as execution time, makespan time, in millisecond (ms). The execution speed is measured as throughput in MIPS (Millions of Instruction per Second). The resource utilization and usage of VMs are increased in the proposed scheduling algorithm resulting in a less number of ideal resources and reduced application cost. BEIESP. -
User profiling based on keyword clusters for improved recommendations
Recommender Systems (RS) have risen in popularity over the years, and their ability to ease decision-making for the user in various domains has made them ubiquitous. However, the sparsity of data continues to be one of the biggest shortcomings of the suggestions offered. Recommendation algorithms typically model user preferences in the form of a profile, which is then used to match user preferences to items of their interest. Consequently, the quality of recommendations is directly related to the level of detail contained in these profiles. Several attempts at enriching the user profiles leveraging both user preference data and item content details have been explored in the past. We propose a method of constructing a user profile, specifically for the movie domain, based on user preference for keyword clusters, which indirectly captures preferences for various narrative styles. These profiles are then utilized to perform both content-based (CB) filtering as well as collaborative filtering (CF). The proposed approach scores over the direct keyword-matching, genre-based user profiling and the traditional CF methods under sparse data scenarios as established by various experiments. It has the advantage of a compact user model representation, while at the same time capturing the essence of the styles or genres preferred by the user. The identification of implicit genres is captured effectively through clustering without requiring labeled data for training. 2014 Springer International Publishing Switzerland. -
User Perception of Mobile Banking: Application of Sentiment Analysis and Topic Modelling Approaches to Online Reviews
The digital revolution has led to significant changes in the global as well as Indian banking sector. The introduction of mobile banking apps has provided increased convenience to customers, who can now avail various banking services remotely. Thus, it is imperative to study the customers' sentiments regarding these applications and find scope for improvement, so that customers can seamlessly operate their bank accounts without having to visit bank branches. Thus, the primary purpose of this research is to study the perceptions of customers towards mobile applications of six major banks in India. A sample of 3000 reviews left by users of these apps was scraped from Google Play Store and sentiment analysis was conducted using RoBERTa-base model from the Transformers library. This was followed by topic modeling using Latent Dirichlet Allocation to find the aspects that are most important to the users. Results revealed that user experience is majorly driven by customer support service, features and functionality of apps, and app performance. Our findings shall help banks identify key areas of improvement so that they can work on enhancing overall customer experience. Despite the growing popularity of mobile banking, this study is the first of its kind in Indian context. 2024 IEEE. -
USER EXPERIENCES OF CHATGPT AMONG ENGINEERING STUDENTS, TEACHERS, AND WORKING PROFESSIONALS IN INDIA
The introduction of Chat Generative Pre-Trained Transformer (ChatGPT) in November 2022 brought about rapid changes in the workplace and academia. Its usage ranged from student assignments to workplace targets in the engineering field. Although it has brought novel ideas to its application in various fields and task efficiency in the workplace, its perceived application varies among students, teachers, and professionals. This study employed the snowball sampling technique and interviews with eight students, eight faculty members, and eight working professionals from computer science engineering who used ChatGPT regularly. The study adopted a qualitative research design and employed the narrative data analysis technique. Researchers conducted in-depth, semi-structured interviews to elicit user experiences from the recruited samples. The findings brought out six main and twelve subordinate themes regarding ChatGPT user experiences: adapt, adopt, embrace, ease, speed, engage, and automate. The inclusion criteria involved ChatGPT users from the computer science engineering domain only. Future research may focus on developing ChatGPT user policies for various fields of their applications. 2024, Grand Canyon University. All rights reserved. -
User Authentication with Graphical Passwords using Hybrid Images and Hash Function
As per human psychology, people remember visual objects more than texts. Although many user authentication mechanisms are based on text passwords, biometric characteristics, tokens, etc., image passwords have proven to be a substitute due to its ease of use and reliability. The technological advancements and evolutions in authentication mechanisms brought greater convenience but increased the probability of exposing passwords through various attacks like shoulder-surfing, dictionary, key-logger, and social engineering attacks. The proposed methodology addresses these vulnerabilities and ensures to keep up the usability of graphical passwords. The system displays hybrid images that users need to recognize and type the randomly generated alphanumeric or special character values associated with each of them. A mechanism to generate One Time Password (OTP) is included for additional security. As a result, it is difficult for an attacker to capture and misuse the password. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.