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Challenges and Solutions in Implementing NEP 2020 in Engineering Education
The implementation of educational best practices in the current scenario of demand for progressive, futuristic education with employable skills expected from formal education, especially engineering is examined here. The National Education Policy 2020 (NEP 2020) introduced by the Ministry of Human Resource Development Government of India intended to align education with the changing employment landscape with demand for skill sets, and the conducive global ecosystem for emergence of startups and entrepreneurship, thus imparting the essence of lifelong learning. The NEP 2020 aims to employ more learning with increased practice through real-time problem skills with critical thinking, creative thinking capability and interdisciplinary approach to adapt to the ever-evolving technological development. The paper provides a broad overview on how an Institution has evolved innovative practices in curriculum that ticks most checkboxes of progressive engineering education and delves into a model for embodying the underlying policy ideas of NEP 2020 too, in the realm of undergraduate engineering programmes. The intertwining of the initiatives taken by the progressive introduction of the model in an engineering Institution incidentally serves to meet the aspirational objectives of NEP 2020. Further, the thrust areas at the progressive Institution over years are seen to blend synergistically with many thrust areas propounded in NEP 2020. 2025 selection and editorial matter, Kennedy Andrew Thomas, Joseph Chacko Chennattuserry and Joseph Varghese Kureethara; individual chapters, the contributors. -
Challenges and pathways in autism education in India: special educators insights from pandemic experiences
Objectives: The landscape of autism education in India before the pandemic has already been unpromising, marked by inequities in education and therapy. The study aimed to highlight the impact of inadequate resources, policies, and practices in autism training during COVID-19 and to suggest measures to address inequities in access or attainability of services within the context of the experiences of special educators. Methods: We interviewed 12 special educators, exploring their virtual teaching experiences and parental participation and inferring insights on bridging gaps in autism education using a semi-structured interview guide. Results: Pre-pandemic disparities like inadequate resources, lack of policy implementation, and non-accessibility to therapy services impacted remote learning. It was disrupted by the non-availability of devices with good connectivity and limited technological skills among educators, students with autism spectrum disorder (ASD), and parents. The special educators and parents lacked the knowledge and skills to meet their unique needs. Skill regression, health issues, and behavior challenges were observed post-COVID. Conclusions: The study revealed that the existing challenges greatly affected education, health, and social life during the pandemic. Individuals with ASD who require very substantial support were the most affected group. It calls for immediate intervention in professional skill development, parental involvement, and policy revision. 2025 The British Society of Developmental Disabilities. -
Challenges and Opportunities: Quantum Computing in Machine Learning
Many computing applications are being developed and applied in almost every aspect of life and in every discipline. With increasing number of problems and complexities, there is requirement for more computational power, faster speed and better results. To overcome these computational barriers, quantum computers, which are based on principles of quantum mechanics were introduced. Faster computation is the main reason behind the evolution of quantum computers which is achieved by using quantum bits instead of bits as quantum bits store both the values 1 and 0 together in superposition. The article focuses on basics of quantum computing in brief and the underlying phenomenon behind quantum computers. Also this article exposes recent trends and the problems that are being faced in this quantum technology. The major impact of quantum machine learning is also discussed. The quantum machine learning is providing better application in this modern field. This article analyses the different research gaps and possible solutions in quantum computing. Recent days quantum computing is implemented in different applications which is also described. 2019 IEEE. -
Challenges and opportunities in multimodal learning research
The trend of multimodal learning, which involves processing and interpreting data through multiple modes such as text, images, and audio, is one aspect that highlights a great frontier in the artificial intelligence (AI) and machine learning (ML) domains. This project explores the technical, practical, and ethical considerations of research studies on multimodality. It starts with preliminary ethical considerations that should drive progress in AI and ML technologies, which include ideas of transparency, accountability, equality, and privacy. Analysis of this paper holds prime importance for moral concerns, as it discusses the issues of bias in AI algorithms and gives strategies that may reduce the level of bias in multimodal patterns. This technology part of the research focuses on technical challenges concerning accountability and transparency in multimodal machine decision-making methodologies. Privacy concerns regarding extensive use of AI and ML have been brought forward, along with the strategy for defensive personal statistics. At each step, an opportunity for innovation and development will be sought and mapped through the complex ethical landscapes of multimodal knowledge research. Through these considerations, this observation attempts to provide a close analysis in which future recommendations and discussions in the realm of AI and multimodal learning are addressed. 2026 Elsevier Inc. All rights reserved. -
Challenges and Opportunities in Deploying Explainable AI for Financial Risk Assessment
Artificial intelligence (AI) has been used more and more in financial decision-making recently, raising questions about the accountability and transparency of these complex systems. The current study investigates the way Explained Artificial Intelligence (XAI) methods might alleviate these concerns and improve the openness of financial decision-making procedures. Nowadays machine learning algorithms are easier to use than ever before, but creating and deploying systems that facilitate real-world banking services has proved challenging. This is mostly due to the fact that algorithms for machine learning are neither transparent or explainable, two attributes that are essential to creating reliable technology. What sets this study unique is the construction of an explainable artificial intelligence (XAI) model that addresses these accessibility concerns while also serving as an instrument for the establishment of credit risk control policies. This work proposes an explainable artificial intelligence model for financing risk control to measure the risks associated with credit financing via peer-to-peer financing networks. The framework uses Shapley parameters to provide AI forecasts according to significant factors that explain. The Support Vector Machine (SVM) and gradient boosting methods had the greatest accuracy scores, 92.4 and 97.6, accordingly. The accuracy of the model was evaluated on a bigger database, and the findings demonstrated that it regularly achieved high levels of accuracy. The SVM and GBM models achieved accuracies of 94.8 and 97.6, respectively. 2025 IEEE. -
Challenges and Opportunities in Deploying Explainable AI for Financial Risk Assessment
Artificial intelligence (AI) has been used more and more in financial decision-making recently, raising questions about the accountability and transparency of these complex systems. The current study investigates the way Explained Artificial Intelligence (XAI) methods might alleviate these concerns and improve the openness of financial decision-making procedures. Nowadays machine learning algorithms are easier to use than ever before, but creating and deploying systems that facilitate real-world banking services has proved challenging. This is mostly due to the fact that algorithms for machine learning are neither transparent or explainable, two attributes that are essential to creating reliable technology. What sets this study unique is the construction of an explainable artificial intelligence (XAI) model that addresses these accessibility concerns while also serving as an instrument for the establishment of credit risk control policies. This work proposes an explainable artificial intelligence model for financing risk control to measure the risks associated with credit financing via peer-to-peer financing networks. The framework uses Shapley parameters to provide AI forecasts according to significant factors that explain. The Support Vector Machine (SVM) and gradient boosting methods had the greatest accuracy scores, 92.4 and 97.6, accordingly. The accuracy of the model was evaluated on a bigger database, and the findings demonstrated that it regularly achieved high levels of accuracy. The SVM and GBM models achieved accuracies of 94.8 and 97.6, respectively. 2025 IEEE. -
Challenges and Issues in Health Care and Clinical Studies Using Deep Learning
Deep learning is a subset of machine learning, which has more than three layers of neural networks. Neural networks resemble the functioning of human behavior in nature. These neural networks are capable of producing results with single layers, but multiple layers help in producing accurate results with increased precision rate. Deep learning supports a number of artificial intelligence (AI)-based applications and services, which helps in increased automated devices, data analysis, and many more physical tasks in various fields. Deep learning technology has become part of human day-to-day life. It is involved in every aspect of daily routine like voice-based searches, operating a device, baking transactions, and many more. Deep learning allows the healthcare industry to examine data quickly without compromising accuracy. Deep learning uses mathematical models designed to work almost like the human brain. Multiple layers of networking and technology enable unmatched computing capability and the ability to traverse and analyze through vast sets of data that would have previously been lost, forgotten, or missed. 2024 Taylor & Francis Group, LLC. -
Challenges and innovations in applying SDG and ESG frameworks
Entrepreneurial skills have evolved significantly since initiatives like the "Green Paper on Entrepreneurship" (EU Commission, 2003) and the "Small Business Act" (European Commission, 2008), which emphasized knowledge, competencies, and sustainability. Entrepreneurs today prioritize long-term sustainability over short-term profits (Elkington, 2018), leveraging frameworks like the Sustainable Development Goals (SDGs) and Environmental, Social, and Governance (ESG) standards to integrate ethical practices with financial performance.Despite these obstacles, ESG and SDG frameworks present opportunities for innovation through systems thinking, foresight, and interdisciplinary collaboration (Senge, 1990). By adopting "green thinking," entrepreneurs mitigate external threats, foster innovation, and drive sustainability. This chapter explores the role of networking, strategic thinking, and ethics in sustainable practices, offering insights into aligning business strategies with global sustainability goals for long-term success. 2025, IGI Global Scientific Publishing. All rights reserved. -
Challenges and expectations of emerging student-athletes among catholic institutions: a qualitative study
Catholic institutions in India are known for providing holistic education focusing all round development of the children. However, there are contextual dilemmas in practicing the holistic approach while meeting the gospel values amidst diversity. The study aims to understand the various challenges student-athletes face in Catholic institutions while pursuing their sporting aspirations. It includes support systems, infrastructural facilities, mental well-being, career aspirations, and policy requirements. Through semi-structured interviews, study explored the challenges and expectation of emerging student-athletes in Catholic institutions across India. Ten lead student-athletes from diverse sports categories were selected for an in-depth interview through snowball sampling technique. The narrative analysis revealed that, athletes are not satisfied with the institutional support. Furthermore, academic demands and time constraints are affecting their mental health. They frequently fall sick, face physical injuries, and lag in academic work. They are feeling unattended by their institution and are looking forward for institutional support in sport related expenses and academics. The study found that, encouragement and emotional support from family, teachers, and coaches would help them lead a healthy and purposeful life. The study recommends stakeholders to help emerging student-athletes find support for their financial, emotional, and professional goals. Future research may take an account of sports-education practices among Catholic institutions in India. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Challenges and concerns of assisted reproductive treatments: A systematic review
India, once a highly populated country with a Total fertility rate of 5.7 1(year 1960) now has one of the least fertility rates of the world around 2.3 1 (year 2015). In just one decade, with the rising economy, improving life expectancy and lifestyle, we have embraced a new disease Infertility 2. There are numerous reasons for rising infertility amongst Indians, some related to life style changes, some infections and some are occupational hazards. As a remedy to this new disease, hospitals in India were quick enough to learn Assisted Reproductive Technologies from foreign countries and practice the same in our home country. There are many ART clinics in every city however; this solution to the problem of infertility is a problem in itself. The paper uses a systematic review process to unravel the causes of infertility and highlights the concerns revolving around infertility treatments and finally presents suggestions to policy makers. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Chain funds: Transforming healthcare crowdfunding through blockchain technologies
Crowdfunding in healthcare refers to the practice of raising funds from a large number of individuals through online platform. This will eventually support the various medical expenses involving hospitalization and treatment. This method enables individuals with health problems or an urgent need for medical treatment to go out and seek financial assistance from a large number of people. Crowdfunding on health is now widely recognized as the means of catering for medical bills, surgeries, experimental treatments and other health-related expenses. The research presents a decentralized blockchain based crowdfunding platform built using ReactJS, Solidity and Thirdweb SDK. This new platform aims to change traditional crowdfunding techniques through utilization of the benefits of blockchain such as transparency, security and automation provided by smart contracts. The user interface has been designed using ReactJS; creating smart contracts on the Ethereum Blockchain was done under Solidity; Thirdweb SDK was used to create a link between the system and the blockchain. Some significant features of this platform involve start-up campaigns organization, contribution management, milestone tracking and automatic distribution of funds in order to enhance customer satisfaction and minimize operational costs. Moreover, users are awarded with certificates and crypto tokens. In addition, this policy ensures high level security that comes with immutability in blockchain technology by eliminating middlemen hence live monitoring of fund is possible through it at all times and disbursements. Rigorous testing has been conducted to assess performance, security, and scalability, highlighting the advantages of decentralization in contrast to centralized crowdfunding models. 2025 Author(s). -
Certificate Generation and Validation Using Blockchain
Verifying academic credentials is a standard procedure for employers when making job offers. After the interview procedure is complete, the employer takes a long time to supply the offer letter. The employer must have the certificate authenticated by the organization that issued it to confirm its originality. While confirming the authenticity of a certificate, the employer takes a long time. The selection procedure takes longer overall because of the long process involved in certificate verification. Blockchain offers a verified distributed ledger with a cryptography technique to combat academic certificate forgery to address this issue. The blockchain also offers a standard platform for document storage, access, and minimization of verification time. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Certain variants of domatic partitions of n-inordinate invariant intersection graphs
A class of algebraic intersection graphs, called the n-inordinate invariant intersection graphs, are introduced in the literature and several properties of these graphs are being investigated. Domination in graphs is an important structural property and partitioning the vertex set of a graph into dominating sets is called the domatic partition of a graph. In this paper, we analyze the structure of the n-inordinate invariant intersection graphs by investigating certain variants of domatic partitions of these graphs, that are defined based on different types of domination in graphs. 2026 World Scientific Publishing Company. -
Certain types of metrics on almost coKler manifolds
In this paper, we study an almost coKler manifold admitting certain metrics such as ? -Ricci solitons, satisfying the critical point equation (CPE) or Bach flat. First, we consider a coKler 3-manifold (M,g) admitting a ? -Ricci soliton (g,X) and we show in this case that either M is locally flat or X is an infinitesimal contact transformation. Next, we study non-coKler (?, ?) -almost coKler metrics as CPE metrics and prove that such a g cannot be a solution of CPE with non-trivial function f. Finally, we prove that a (?, ?) -almost coKler manifold (M,g) is coKler if either M admits a divergence free Cotton tensor or the metric g is Bach flat. In contrast to this, we show by a suitable example that there are Bach flat almost coKler manifolds which are non-coKler. 2021, Fondation Carl-Herz and Springer Nature Switzerland AG. -
Certain results on trans-paraSasakian 3-manifolds
Let M be a trans-paraSasakian 3-manifold. In this paper, the necessary and sufficient condition for the Reeb vector field of a trans-paraSasakian 3-manifold to be harmonic is obtained. Also, it is proved that the Ricci operator of M is invariant along the Reeb flow if and only if M is a paracosymplectic manifold, an ?-paraSasakian manifold or a space of negative constant sectional curvature. 2022 Walter de Gruyter GmbH, Berlin/Boston. -
Cerium-doped Co3O4 spinel structures synthesized by modified combustion route as an excellent material for electrochemical applications
This work shed light on the impact of cerium doping on the structural and electrochemical features of Co3-xCexO4(x = 0, 0.02, 0.04) synthesized via a facile and cost-effective modified combustion route. The structural, morphological and compositional investigations unveiled the formation of nanocrystalline structures with promising morphologies. BET and XPS methodologies explored the materials' porosity and electronic state of the materials. The electrochemical performance of the synthesized materials was evaluated by Cyclic Voltammetry (CV) at various scan rates, Galvanostatic Charge-Discharge (GCD) at different current densities, and Electrochemical Impedance Spectroscopic (EIS) techniques. GCD studies depicted an exquisite specific capacitance of 498 Fg-1 for Co2.98Ce0.02O4 at a current density of 1 Ag-1 and it displayed a capacitance retention of 95 % for over 2000 GCD cycles further it retains up to 90 % even after 3000 GCD cycles at a current density of 1Ag-1 juxtapose to other compositions. Our work emphasizes the importance of the material for energy storage applications. 2024 Elsevier Ltd and Techna Group S.r.l. -
Cerium-doped Co3O4 spinel structures synthesized by modified combustion route as an excellent material for electrochemical applications
This work shed light on the impact of cerium doping on the structural and electrochemical features of Co3-xCexO4(x = 0, 0.02, 0.04) synthesized via a facile and cost-effective modified combustion route. The structural, morphological and compositional investigations unveiled the formation of nanocrystalline structures with promising morphologies. BET and XPS methodologies explored the materials' porosity and electronic state of the materials. The electrochemical performance of the synthesized materials was evaluated by Cyclic Voltammetry (CV) at various scan rates, Galvanostatic Charge-Discharge (GCD) at different current densities, and Electrochemical Impedance Spectroscopic (EIS) techniques. GCD studies depicted an exquisite specific capacitance of 498 Fg-1 for Co2.98Ce0.02O4 at a current density of 1 Ag-1 and it displayed a capacitance retention of 95 % for over 2000 GCD cycles further it retains up to 90 % even after 3000 GCD cycles at a current density of 1Ag-1 juxtapose to other compositions. Our work emphasizes the importance of the material for energy storage applications. 2024 Elsevier Ltd and Techna Group S.r.l. -
Ceria doped titania nano particles: Synthesis and photocatalytic activity
Ceria (0.5, 1 and 2 mol%) doped titania nano catalysts were prepared by combustion synthesis method, using titanium isopropoxide as the starting material. The prepared catalysts were characterized by X-ray diffraction (XRD), Energy dispersive X-ray analysis (EDX), Scanning electron microscopy (SEM) and Infra red spectroscopy (FTIR). Total acidity of the prepared catalysts were determined by temperature programmed desorption of ammonia (TPD - NH3). XRD pattern of 1% ceria doped titania obtained by calcinations at 873 K indicated that the samples were crystalline with a mixture of anatase and rutile phase. No peaks corresponding to cerium oxide were observed XRD patterns indicating that the amount of cerium is negligible on the surface of titania catalyst. The photo catalytic activity was evaluated for the degradation of methylene blue (MB) under visible light irradiation. The degradation rates of MB on cerium doped TiO2 samples were higher than that of pure TiO2. The introduction of structural defects (cationic ceria dopant) into the titania crystal lattice leads to the change of band gap energy. As a result, the excitation energy is expanded from UV light of anatase TiO2 to visible light for ceria doped titania. 2016 Elsevier Ltd. -
Cerebral Stroke Classification Using Over Sampling Technique and Machine Learning Models
In recent years, cerebral stroke has ascended as a paramount concern in global public health. Proactive strategies emphasizing metabolic control over salient risk factors present a superior approach compared to relying solely on physiological indicators, which may not delineate clear preventive directives. In this research, we present the SPX-CerebroPredict modela novel machine learning framework designed to classify imbalanced cerebral stroke data for clinical diagnostics. The study delves into feature selection methodologies, employing both information gain and principal component analysis (PCA). To address the class imbalance dilemma, the Synthetic Minority Over-sampling Technique (SMOTE) was harnessed. The empirical evaluation, conducted on the cerebral stroke prediction dataset from Kagglecomprising 43,400 medical records with 783 stroke instancespitted well-established algorithms such as support vector machine, logistic regression, decision tree, random forest, XGBoost, and K-nearest neighbor against one another. The results evince that our SPX-CerebroPredict model, integrating SMOTE, PCA, and XGBoost, surpasses its contemporaries, achieving an impressive accuracy rate of 95%. This discovery underscores the models potential for clinical applicability in cerebral stroke diagnostics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Ceramic-Polymer-Carbon Composite Coating on the Truncated Octahedron-Shaped LNMO Cathode for High Capacity and Extended Cycling in High-Voltage Lithium-Ion Batteries
Long-term electrochemical cycle life of the LiNi0.5Mn1.5O4 (LNMO) cathode with liquid electrolytes (LEs) and the inadequate knowledge of the cell failure mechanism are the eloquent Achilles heel to practical applications despite their large promise to lower the cost of lithium-ion batteries (LIBs). Herein, a strategy for engineering the cathode-LE interface is presented to enhance the cycle life of LIBs. The direct contact between cathode-active particles and LE is controlled by encasing sol-gel-synthesized truncated octahedron-shaped LNMO particles by an ion-electron-conductive (ambipolar) hybrid ceramic-polymer electrolyte (IECHP) via a simple slot-die coating. The IECHP-coated LNMO cathode demonstrated negligible capacity fading in 250 cycles and a capacity retention of ?90% after 1000 charge-discharge cycles, significantly exceeding that of the uncoated LNMO cathode (a capacity retention of ?57% after 980 cycles) in 1 M LiPF6 in EC:DMC at 1 C rate. The difference in stability between the two types of cathodes after cycling is examined by focused ion beam scanning electron microscopy and time-of-flight secondary ion mass spectrometry. These studies revealed that the pristine LNMO produces an inactive layer on the cathode surface, reducing ionic transport between the cathode and the electrolyte and increasing the interface resistance. The IECHP coating successfully overcomes these limitations. Therefore, the present work underlines the adaptability of IECHP-coated LNMO as a high-voltage cathode material in a 1 M LiPF6 electrolyte for prolonged use. The proposed strategy is simple and affordable for commercial applications. 2024 The Authors. Published by American Chemical Society.