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Impact of WO3:CeO2@MXene/gC3N4 nano disk on sunlight-driven photocatalytic removal of fluoroquinolone antibiotic and high-performance supercapacitor application
This research highlights the use of a WO3:CeO2@MXene/gC3N4 (MGWC) nanodisk as a versatile material. MGWC demonstrates efficient photocatalytic degradation of moxifloxacin (MOF) in water under sunlight and also shows great promise for high-performance supercapacitor applications. MGWC was synthesized using a modified hydrothermal method and thoroughly characterized using various techniques. The MGWC showed a band gap energy of 2.79eV determined through UVVis DRS analysis and an average crystallite size of 39.6nm calculated from XRD. A promising photocatalytic activity was observed for the degradation of MOF, outperforming other photocatalysts. Additionally, preliminary studies examined variations in catalyst concentration, pH, kinetics, electrolytes, scavengers, reusability, and TOC, contributing valuable insights. Under optimal conditions, the MOF achieved almost complete degradation, reaching about 99.7% within 180min using the MGWC photocatalyst. Additionally, MGWC exhibits promising potential in supercapacitor applications. EIS and CV studies have been used to examine MGWCs exceptional charge transfer properties. CV tests confirm the pseudo-capacitive nature of MGWC electrodes. GCD studies of MGWC exhibit a high specific capacitance of 551 F/g at 1 A/g with incomparable capacitance retention of 98.1% over 10,000 cycles. This research not only aids in reducing emerging environmental pollutants but also sets the stage for sustainable energy solutions. The Author(s), under exclusive licence to Korean Carbon Society 2025. -
Impact of Work from Home During COVID-19 Scenario
In view of the recent situation, COVID-19 has spread across the world, and every country has to enforce a lockdown to prevent the virus from transmitting further. The worldwide COVID-19 outbreak has led to a large number of professionals work from their homes. Almost all the sectors like IT, academics, government, business, etc. are implementing work from home for safety of their employees and sincerely obeying the social distancing norms. Work from home can be beneficial and fruitful in terms of travel expenses, saving time commuting, working on ones own agenda, etc. But it can also be a pain and take a toll on mental well-being as you are living a quarantined life with little to no social life, which can also impact an individuals efficiency. There are so many barriers to work from home (WFH), like unavailability of resources, poor network connectivity, using digital platform and latest software for non-IT professionals, lack of proper infrastructure, etc. Our chapter focuses on every aspects of WFH during the COVID-19 lockdown period so that well-suited policies and practices can be designed to cope with the issues and hence transforming future of organizations by shifting the tradition of work from office to work from home. 2024 Apple Academic Press, Inc. All rights reserved. -
Impact ofFeature Selection Techniques forEEG-Based Seizure Classification
A neurological condition called epilepsy can result in a variety of seizures. Seizures differ from person to person. It is frequently diagnosed with fMRI, magnetic resonance imaging and electroencephalography (EEG). Visually evaluating the EEG activity requires a lot of time and effort, which is the usual way of analysis. As a result, an automated diagnosis approach based on machine learning was created. To effectively categorize epileptic seizure episodes using binary classification from brain-based EEG recordings, this study develops feature selection techniques using a machine learning (ML)-based random forest classification model. Ten (10) feature selection algorithms were utilized in this proposed work. The suggested method reduces the number of features by selecting only the relevant features needed to classify seizures. So to evaluate the effectiveness of the proposed model, random forest classifier is utilized. The Bonn Epilepsy dataset derived from UCI repository of Bonn University, Germany, the CHB-MIT dataset collected from the Childrens Hospital Boston and a real-time EEG dataset collected from EEG clinic Bangalore is accustomed to the proposed approach in order to determine the best feature selection method. In this case, the relief feature selection approach outperforms others, achieving the most remarkable accuracy of 90% for UCI data and 100% for both the CHB-MIT and real-time EEG datasets with a fast computing rate. According to the results, the reduction in the number of feature characteristics significantly impacts the classifiers performance metrics, which helps to effectively categorize epileptic seizures from the brain-based EEG signals into binary classification. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Impact on cardioprotective effect of Psidium guajava leaves extract in streptozotocin-induced Wistar mice with molecular in silico analysis
Cardiovascular disease (CVD) and its complications have been regarded as the leading cause of morbidity and mortality. The drugs available in the market are effective to treat CVD, but with many adverse reactions. Nowadays, herbal products are the attention of researchers because of their less adverse effects. In this study, the cardioprotective effects of ethanolic leaves extract of Psidium guajava Linn. (Guava) (P. guajava) were evaluated in streptozotocin (STZ)-treated animal models. Mice acquired for the study were divided into five groups, each consisting of six mice. The toxin-induced mice were treated with the ethanolic leaves extract of P. guajava (300 mg/ kg body weight [b.w.]). The results were compared to the standard drug (glibenclamide)-treated mice (3 mg/kg b.w.). The following parameters were considered for further investigations: creatine kinase-muscle brain (CK-MB), creatine kinase (CK), troponin, lysosomal, and mitochondrial enzymes. Then the docking study was accomplished. The levels of cardiac marker enzymes and lysosomal enzymes increased significantly in the toxin-induced mice, while the level of mitochondrial enzyme decreased significantly. During treatment with the ethanolic leaves extract of P. guajava, the levels of all parameters were notably reversed to normal range (P < 0.05). Further, in docking analysis, the interaction of compounds, such as alpha-terpineol, cyclopentanecarboxamide, guaiol (a sesquiterpenoid alcohol), 1H-cyclopropanaphthalene, tetracyclotridecan-9-ol, dormin/abscisic acid, and epiglobulol, with the respective protein molecules, evidenced the cardioprotective effect of P. guajava leaves. Hence, it was concluded that the ethanolic leaves extract of P. guajava leaves have a cardioprotective effect. 2023 Codon Publications. -
Impactful Micro-moments for Deep Learning in Higher Education Classrooms: A Whole-person Education Perspective
The context within which higher education operates has undergone rapid changes in the past few decades. These changes, necessitated by a brittle, anxious, non-linear and incomprehensible (BANI) world, have made the inculcation of 21st-century skills and adoption of a competency-based curriculum increasingly relevant. This chapter addresses these shifts in higher education and their challenges for faculty members and students. The authors focus on in-class pedagogical practices which can be adopted in light of the changes in what is learnt and how it is learnt. The best teacherstudent learning sessions are those that create lasting learning outcomes. This chapter draws from positive psychology concepts and highlights the importance of creating intentional macro- and micro-moments in learning spaces. As educators, crafting deliberately planned moments can make learning more experiential, quick and long-lasting. Though micro-moments can be unstructured and unscripted, the authors focus on those micro-moments that are deliberate and carefully planned to help nurture creativity, mindfulness and organisational commitment. They can be used to echo and reinforce the larger vision of the programme or the institution. By leveraging micro-moments, educators can create opportunities for continuous learning, self-reflection and personal growth beyond the traditional classroom setting. This chapter examines the role of micro-moments in enhancing cognitive, emotional, social and ethical dimensions of student development through deep learning. 2026 by John Wiley & Sons, Ltd. All rights reserved. -
Impacts of Climate Risk and Energy Consumption on Financial Markets: Commodity and Sovereign Shocks in Resource Nations
In this paper, we combined both academic and professional lenses to assess the impact of commodity price shocks and geopolitical uncertainty on sovereign country default risk, fiscal capacity, and financial instability in Commodity Exporting Countries. It uses an interdisciplinary framework with emphasis in international finance, political economy, risk management, and public policy to examines how external shocks, such as geopolitical uncertainty, reinforce sovereign risk and reduce fiscal sustainability. The chapter utilizes a PRISMA-based systematic review of the literature which draws from multiple databases to identify channels through which geopolitical uncertainty leads to price and volatility shocks, increased debt spreads, and reduced fiscal space. By bringing together supports from the disciplines of economics, finance and governance, the chapter calls attention to structural vulnerabilities to export-based economies, as well as policy reforms that reflect adaptive public policy practices. 2026 by IGI Global Scientific Publishing. -
Impacts of Cloud Computing in Digital Marketing
In modern day of digital marketing the cloud computing is proving extremely beneficial links for businesses. Moreover, it's characteristic to access the stored data from anywhere makes it more popular among the entrepreneurs. The present paper is an exploration of the cloud computing in respect of digital marketing. The paper defines and correlates the term cloud computing, digital marketing, as well as also elaborates about benefits that can be harvested by the integration of cloud computing in digital marketing strategy. 2021 IEEE. -
Impacts of imprisonment of women on the rights of their children: An Indian perspective /
International Journal of Advanced Research, Vol.3, Issue 10, pp.1297-1303, ISSN No: 2320-5407. -
Impacts of Pore Scale Gas Diffusion Layer Deformation on PEMFC Performance at Sub Zero Operation
Highlights Impact of assembly pressure on species and charge transport during cold start operation. Inhomogeneous GDL compression and intrusion is considered in the study. The intrusion effect leads to intense ice accumulation under the channels at 2 MPa. The importance of applying appropriate clamping pressure is highlighted in the study. 2023 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited -
Imparting Life Skills Through Psychoeducation Among 1st-year University Students to Cope Better with Campus Culture
Psycho-education is an interactive, skill-based programme for all first-year undergraduate students. It serves as a platform for visibility and helps facilitate changes in their preconceived notions about counselling and to end the stigma attached to mental health. Psycho-education sessions are a way to build a comfortable space in a new environment. The main objective is to empower students to handle new phases of life, create awareness of life skills and discuss youth-relevant topics such as managing time, coping with stress, relationships, self-esteem, body image, resilience and addictions. The current probe is designed to assess the awareness of life skills among first-year undergraduate students imparted through Psycho-education sessions to cope better with university life. Modules are designed in such a way that life skills are imparted in a supportive environment. Participatory methods were used. The data analysis showed an improvement in adjusting well to campus culture. The interventions after that led to a surge in the number of students approaching the counsellors for personal counselling. Additional research needs to be undertaken on life skills in higher education, which is apparent from the fact that most universities have no counsellors, and less importance is given to the student's well-being and mental health. 2025 selection and editorial matter, Kennedy Andrew Thomas, Joseph Chacko Chennattuserry and Joseph Varghese Kureethara; individual chapters, the contributors. -
Impedance and electrochemical studies of rGO/Li-ion/PANI intercalated polymer electrolyte films for energy storage application
The present manuscript describes the synthesis of reduced graphene oxide (rGO) from coke by using modified Hummers method. The synthesized emeraldine poly aniline (PANI) polymer was used as a polymer host matrix. A series of polymer electrolyte films were prepared by varying concentration of rGO, PANI and Lithium carbonate. The synthesized PANI and rGO were soluble in common polar solvent. The structural, Nyquist and cyclic voltammetry studies of polymer electrolyte were investigated. The XRD and FTIR investigation confirms the formation of rGO and PANI in view of structural and chemical compositions respectively. The electrical property of polymer electrolyte was obtained by Nyquist plot which represents the perfect semicircular pattern. It confirms the charge transport mechanism with the decreased concentration of rGO in polymer electrolyte. The cyclic voltammetry performed at different scan rate on potential window ranged between-0.5 to 0.6 V represents the oxidation and reduction peaks. The overall results describe that the present electrolyte material can be a potential candidate for energy storage application.. 2019 Elsevier Ltd. -
Impedance, Electrical and Dielectric behaviour of Tin Oxide Nanoparticle doped with Graphite, Graphene Oxide and Reduced Graphene Oxide
Nanostructured materials have attained incredible interest in recent days due to their distinctive chemical, physical, mechanical, magnetic and optoelectronic properties. In the present study, metal nano particle (SnO2) was doped with graphite, graphene oxide (GO) and reduced graphene oxide (rGO) with various composition (1:100), (1:1) and (100:1) by weight ratio. The citrate-nitrate gel combustion method was used to prepare nanocrystalline SnO2 while GO and rGO were synthesized through modified Hummer's method. The preparation of SnO2-rGO composites was done using a one-step hydrothermal process. The electrical and structural behaviour of the composites of graphite, GO and rGO mixed with SnO2 were elucidated by the impedance analyzer in the frequency range from 10Hz to 1MHz. It is observed that the composite of SnO2 with graphite and reduced graphene oxide have similar broad characteristics while SnO2 mixed with GO is exhibiting different properties which could be attributed to the presence of oxygen functionaries. 2021 The Authors. Published by ESG. All Rights Reserved. -
Impediments of product recovery in circular supply chains: Implications for sustainable development
Product recovery has fascinated the concentration of organizations and is prominent among industry practitioners and researchers due to improved environmental concerns, social awareness, and economic benefits. Circular supply chain (CSC) compounds the concept of product recovery in global supply chain management to present a sustainable perspective. Therefore, this study aims to determine impediments of product recovery and CSC toward sustainable production and consumption in the background of manufacturing organizations. This study determines potential impediments from literature and in consultation with experts. Further, a fuzzy VIKOR approach is practiced to prioritize the impediments of product recovery and CSC. Then, a sensitivity analysis is conducted to verify the robustness of the framework attained. The results from the study reflect that lack of collaboration from supply chain performers, lack of tax policies for facilitating CSC models and limited expertise, technology, information on CSC practices are the critical impediments to product recovery in CSCs. The findings of the study could assist industry managers and practitioners in developing procedures and strategies to attain sustainable development. 2022 The Authors. Sustainable Development published by ERP Environment and John Wiley & Sons Ltd. -
Imperative of revisiting the operational risk control architecture in public sector banks cyjdf (PSBs) in India: A qualitative analysis
The banking sector in India has contributed to economic growth, parity and equity while equally keeping focus on profit and social objectives. The successive prudential and regulatory reforms introduced in the banking sector have made it more robust and stronger to withstand the bubbles and external shocks. Still, the Indian banking sector in general and public-sector banks (PSBs) in particular have been suffering from the bank frauds. This study endeavors to cover the increasing incidences of banking frauds in PSBs and probes the weaknesses and chinks in the operational risk architecture at the PSBs in India. This study selects Punjab National Bank as a true representative of PSBs and treats it as a critical case study to apply the learning and findings to the PSBs in India. This qualitative analysis of the study revealed that the chinks in the operational risk control mechanism and lax corporate governance are the main reasons behind the increasing incidences of frauds at PSBs. The findings of the study showed that a strong corporate governance and compliance framework, robust risk management architecture, investment in people, technology and systems will go a long way in achieving tighter control and supervision, streamlining processes and, most of all, adhering to a culture of checks and balances. 2019 LLC CPC Business Perspectives. All Rights Reserved. -
Implantable Chip Revolutionizing Early-Stage Liver Cancer Detection with Advanced Diagnosis System
Millions of people die from cancer annually. Advanced metastatic cancers may not respond to traditional therapy. The importance for early diagnosis is highlighted by the difficulty of treating cancers in later stages. Enhancing patient outcomes using tissue-engineered cancer diagnosis and therapy is gaining popularity. Cancer and associated immune problems burden healthcare systems, making efficient, high-throughput drug development strategies essential. Thus, implanted chips may solve these issues. A revolutionary technique for early liver cancer identification is the Machine Learning-based Liver Cancer Diagnosis System (ML-LCDS). K-Nearest Neighbour (KNN) identifies liver tumors precisely in ML-LCDS. The performance evaluation reports sensitivity=97.2%, specificity=91.3%, precision=93.5%, FPR=8.7%, and accuracy=94.1%, computed from the confusion matrix derived through 10-fold cross-validation. Experimental findings validate its consistent performance, establishing ML-LCDS as an efficient and reliable diagnostic tool for early-stage liver cancer detection. The Author(s) 2025. The text of this article is open access and licensed under a Creative Commons Attribution 4.0 International License. -
Implementation and Investigation of an Optimal Full Adder Design for Low Power and Reduced Delay Conditions
Full adder is one of the important components in electronics, used for various fundamental processing algorithms such as addition and multiplication. The application of these full adders is included in but not limited to Very Large-Scale Integration (VLSI) and Digital Signal Processing (DSP). To provide scalability and reliability to the advanced algorithms for high-end applications, the designing system of full adder should be enhanced. So, in this paper, we intended to improve the efficiency of a full adder circuit to work under low power and delay conditions. The software we used in this project is MENTOR GRAPHICS using 180nm technology. The efficiency of the proposed transistor design is evaluated by analysing the power consumption, delay, PDP, capacitor load, delay w.r.t capacitance and PDP w.r.t capacitance. The parameters are compared between our proposed design and the literature schemes such as OLPFAD, DFEFA, DTLPCFA, and DPEHFA, respectively. It is evident that our proposed design outperforms the other. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Implementation challenges of Total Quality Management (TQM) in dairy sector /
Smart Journal of Business Management Studies, Vol.15, Issue 1, pp.1-9, ISSN No: 2321-2012. -
Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset. 2022 K. Karthick et al. -
Implementation of AI in manufacturing industries a case study
Artificial intelligence (AI) is getting progressively integrated into nearly every facet of our existence. Its applications are ubiquitous and ever-evolving, spanning fields such as autonomous vehicles, geology, medicine, and art. AI has, however, posed as many questions as it has answered. These include the definition and application of the technology (viz., assisted, augmented, or independent intellect), the question of whether computers are thinking machines similarly to humans, the wider implications of the impact of automation on society, and the unexpected moral and principled quandaries. This chapter provides an overview of artificial intelligence in manufacturing intended for executives in manufacturing and industrial companies who want to integrate AI into their business. Its main objective is to apply AI to the engineering, testing, and production stages of the manufacturing value chain. The goal is to discuss business applications that technology, data, and automated processes can support, and how the appropriate personnel, organizational structure, and culture can support them. This article discusses current advancements, poses problems, asks questions, and attempts to bring cutting-edge concepts and research closer to business. 2025 Mohamed Arezki Mellal. All rights reserved. -
Implementation of AI-Assisted Tools in Foreign Language Training
The article discusses didactic issues of implementing of AI-assisted tools into foreign language training of linguistics students. The purpose of the study evaluate the potential and analyze the case of integrating an AI-assisted tool into a foreign language course. Methodologically the study rests on two didactic approaches, namely a competence-based approach and a personal activity approach. The analysis of the functional scope of AI-assisted tools used for academic linguistic and practical linguistic purposes is carried out. A brief comparative analysis of the available options with an account of essential functional characteristics is given. Based on the needs-analysis the choice of a particular platforms for the training process is reasoned. A set of teaching principles was observed to master the target skills in the experiment. To estimate the efficiency of the training process and development of the target skills, a questionnaire was compiled and offered to students. The analysis of students performance as regards the focus communicative interaction skills was carried out. Challenges in combining traditional and AI-assisted tools in the educational process are analyzed; the ways to overcome them are recommended. The study suggests that the use of AI-assisted tools in foreign language training of linguistics students can be based on a combined approach aimed at improving the quality of individual work and further consolidation of target skills in contact time. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.