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Research of Prospects and Challenges in Artificial Intelligence Technology Teaching and Learning
Recently, knowledge in the field of artificial intelligence in order to modernize various aspects of human activity has played a significant role. The exploration of the opportunities and difficulties associated with the development of artificial intelligence technologies is becoming an important area of research, as it profoundly affects our perception of work, education, medicine and other spheres of existence. New methods of machine learning, deep learning and reinforcement learning are being developed. These technologies are changing our understanding of how machines can learn and adapt to the world around them. The application of artificial intelligence covers many areas, including healthcare, finance, education and industry. In medicine, for example, AI can improve diagnostic accuracy and develop customized treatments. In education, it is possible to create personalized learning plans for each student. While in industry, artificial intelligence technologies are able to optimize production processes and increase business efficiency. However, despite the potential benefits associated with learning artificial intelligence technologies, there are serious challenges that require careful analysis. These challenges include ethical dilemmas, such as issues of algorithm transparency and responsibility for making principled decisions. Data security and privacy are also among the key aspects that require innovative approaches to AI technology training. The main purpose of the research is to deeply analyze the prospects and challenges in the field of artificial intelligence technology training, provide a comprehensive understanding of the current state of this field, identify key areas of development and propose practical strategies for effectively overcoming challenges. Taking into account both positive and negative aspects, it is necessary to have a meaningful look at the future of artificial intelligence technology education, taking into account social, ethical and technical aspects. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research on Big Data for Industry 4.0 Cyber-Physical Systems
The objective of the revolution known as Industry 4.0 seeks to optimize goods creation based on consumer requirements, specifications for quality, and financial viability. Big data collected by the Internet of Things (IoT)-based commercial Cyber-Physical Systems (CPS) plays an essential part in boosting platform operation efficiency to promote throughput with improved consumer encounters in Industry 4.0. This study shows big databases derived from IoT-based Optical-Wireless CPS (OWCPSs) for optimizing the functioning of maintenance networks in the electronics-manufacturing Industry 4.0. This research collected and analyzed big databases including five parameters: data delivery, delay, overload, throughput, and package error percentage in OWCPSs. The information gathered is important for optimizing the functioning of service systems in the production of electronic goods Industry 4.0. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Research on Effectiveness and Assistants in the Educational Process for Student Learning
The article discusses the topical issue of the use of AI assistants in the educational process of universities, which is becoming especially important in the context of the rapid development of digital technologies. Artificial intelligence, as one of the key tools of our time, is being actively introduced into the educational sphere, allowing us to optimize routine tasks and increase the effectiveness of the educational process. AI assistants are able not only to automate tasks such as checking assignments, drawing up curricula and analyzing student performance, but also to provide personalized recommendations for students, which significantly improves the quality of learning and creates an immersive environment for students. This article examines the role of AI assistants as a tool to support teachers in the educational process of universities. The main advantages of using them are considered, including improving the effectiveness of teachers, reducing the burden on teaching staff and the possibility of a more flexible approach to student learning. Attention is paid to the difficulties and limitations associated with the introduction of AI technologies, such as the need to adapt existing educational programs and the ethical aspects of using artificial intelligence. The purpose of the study is to analyze the effectiveness of using AI assistants in various educational environments, as well as to identify key factors for the successful integration of these technologies into the educational process. Based on the analysis, recommendations are offered for teachers on the optimal use of AI assistants, taking into account the specifics of educational tasks. The article will be useful both for researchers in the field of educational technologies using IT tools, and for practitioners interested in optimizing and improving the quality of the educational process. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research on secure workload execution scheme in heterogeneous cloud environment
The increasing demand for the hardware, software and infrastructure is playing a big role in the information technology domain towards the need of customers specific requirements. Cloud computing is a major backbone for providing such services over the internet. It includes the services such as applications, storage, network, scalability, sharing, virtualization, confidentiality, security, authentication, and integrity. A large number of data intensive workflow applications uses heterogeneous cloud environment for communication and computation operation. An intruder/attacker will utilize these environments for their benefit by flooding malicious links, unwanted information and others. In cloud environment, detecting a malicious device/packet during workflow execution is a critical and challenging task. The various workflow method with security, service level agreement (SLA) and quality of service (QoS) have been modelled in recent time; However, these models are not efficient in detecting malicious users and maintaining high level of QoS or workflow applications. This article focus is on addressing research future direction, issues and challenges of work in meeting secure and efficient workflow execution model for heterogeneous cloud environment. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Research on Unmanned Artificial intelligence Based Financial Volatility Prediction in International Stock Market
This study digs into the area of unmanned artificial intelligence (AI) for financial volatility prediction in the worldwide stock market, delivering unique insights into the deployment of cutting-edge technology to handle the multifarious issues of market dynamics. Our research uses Long Short-Term Memory (LSTM) networks as the AI model of choice, showing its usefulness in capturing temporal relationships in financial data by analyzing past stock price data, trading volumes, and a variety of technical indicators. Our findings suggest a potential capacity to reliably predict financial market volatility after extensive data pretreatment, feature engineering, and model training. A powerful instrument for investors, fund managers, and financial institutions to make better informed and accurate investment choices, the model's low Root Mean Squared Error (RMSE) and high (R2) values highlight its practical usefulness. Beyond the purely technical, our study considers the ethical, regulatory, risk reduction, and optimization implications for the financial sector. Financial decision-making and risk management are being transformed by the increasingly globalized market environment, and the results given here provide a concrete roadmap towards the appropriate integration of unmanned AI systems. 2024 IEEE. -
Research Perspectives on Load Balancing Strategies in Serverless Computing
Serverless computing, a groundbreaking trend in cloud computing, has transformed how applications are deployed and managed by abstracting the infrastructure layer. Serverless computing enables developers to concentrate exclusively on their code while cloud providers care for server provisioning, maintenance, and scaling. Services like AWS Lambda, Google Cloud Functions, and Azure Functions exemplify this model, offering s ubstantial advantages in terms of reduced operational complexity and cost. However, one persistent challenge in this domain is load balancing. Effective load balancing in serverless computing ensures efficient resource utilization, optimal performance, and cost-effectiveness. Unlike traditional load balancing, which typically relies on long-lived server instances, load balancing in serverless environments must accommodate the stateless and ephemeral nature of serverless functions. Traditional techniques are not directly applicable because serverless architectures functions that are instantiated on-demand in response to incoming requests. This paper surveys various strategies and approaches developed to address the unique load balancing challenges in serverless computing, providing a comprehensive overview of the current state of research and practice. The paper extends further research on serverless computing by analyzing the survey papers. The paper highly focuses the research areas in the field of edge computing, hybrid cloud models and distributed load balancing for the future usage. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research Potentials and Future Trends of Digital Sustainability
A once-in-a-century pandemic has left scars on countries already roiled by the increasingly erratic weather patterns induced by the climate crisis, wreaking havoc on sectors as diverse as food security, industrial production, and defence. Countries are torn between ameliorating COVID-19's devastating impact on education, health, and livelihoods of citizens, and finding their footing in a new global order. But from this ferment are emerging technologies, ideas, and solutions that will drive the world of the future; innovation and big ideas are building a vision that is bold and transformative. As the digital technologies evolve, its comprehensive impact on the environment needs to be considered to harness its full potential. Technology is transforming our world, but at the same time it brings new opportunities as well as challenges for sustainability. The unintended negative environmental impacts emerging from technologies are likely to be outweighed by potential of technology to solving it. Advances in technology, coupled with artificial intelligence, innovation in analytics, and data generation, is likely to have positive sustainability impacts. This chapter highlights the research potential and future trends of digital technologies for sustainability purposes. We intend to evaluate the implications of digital technology such as cloud computing, blockchain, Internet of Things, big data analytics, and artificial intelligence on pollution reduction, sustainable farming practices, conservation of biodiversity, and natural disaster management. Using real-life cases, we will investigate how digital technologies can be both an obstacle and enabler to global sustainability, which will enable devising appropriate digitalization strategies geared towards the achievement of sustainability. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
Research progress of MXenes and MXenes-based catalysts for photocatalytic water splitting: A systematic review
The field of two-dimensional (2D) materials has witnessed remarkable growth over the years, especially on a class of materials known as MXenes. MXenes have garnered significant attention for their exceptional physicochemical properties, which include high electrical and thermal conductivity, large surface area, adjustable bandgap, and hydrophilicity. These characteristics have paved the way for a diverse range of applications, including photocatalysis, electrocatalysis, supercapacitors, sensing, and biomedicine. MXenes have been recognized to be particularly effective in applications such as photocatalytic hydrogen production through water splitting reactions. This involves using MXenes as cocatalysts to enhance the efficiency of the photocatalytic process. In this review, the various synthetic methods for producing MXenes and MXenes-based catalysts are summarized, shedding light on the versatility of their fabrication techniques. The underlying mechanisms of photocatalytic H2 evolution are explored, providing insights into how MXenes function as cocatalysts in these reactions. These mechanisms are crucial for understanding the enhancement of H2 production and improving the overall efficiency of the water splitting process. Furthermore, the review delves into the challenges that researchers face when utilizing MXenes and MXene-based materials for electrocatalytic water splitting. These challenges serve as motivation for further exploration and innovation in the field, driving the development of more efficient and sustainable electrocatalytic systems. In this discussion, the potential future applications of MXenes and their composites in electrocatalytic water splitting and other fields are explored. This suggests that ongoing research and advancements in MXene-based materials have the potential to revolutionize various technological areas, contributing to the development of cleaner energy sources and more efficient catalytic processes. 2024 Elsevier Ltd -
Research Trends on Workplace Criminal Behaviour: A Bibliometric Analysis
This study presents a comprehensive bibliometric analysis of the research landscape surrounding Workplace Criminal Behaviour (WCB), examining its evolution over time. By focusing on thematic areas, research trends, and patterns of scholarly output, the study offers a systematic overview of scientific contributions in this field. A total of 767 peer-reviewed publications were retrieved from the scientific database and analyzed using bibliometric techniques. The findings indicate that scholarly interest in WCB began to gain momentum in 1989, marking a significant turning point in the field. The analysis also highlights the most prominent institutions, journals, and influential scholars contributing to the field. Keyword mapping revealed closely related areas of inquiry, including white-collar crime, workplace theft, and corporate crime, reflecting the multidimensional nature of WCB research. This study offers a valuable resource for emerging scholars, outlining key areas of focus, frequently used methodologies, high-impact publication outlets, and potential collaborators. By mapping the intellectual structure of the field, the findings contribute to shaping future research directions and fostering more targeted and impactful scholarly efforts in workplace criminal behaviour. (2026), (South-West University "Neofit Rilski"). All rights reserved. -
ResFruitGrader: Leveraging Residual Networks for Advanced Fruit Quality Grading Systems
The rising agricultural industrys requirement for effective sorting and grading procedures has increased the demand for automated and precise fruit quality assessment in recent years. This study aims to attain high classification accuracy by investigating the use of Convolutional Neural Networks for fruit quality identification. As customers place a higher value on fresh and wholesome options, the agriculture and food industries must meet rising demands for premium produce. Fruit quality must be guaranteed since it directly affects consumer happiness and the profitability of the sector. Preprocessing methods, CNN model creation, training, and evaluation utilizing cutting-edge deep learning techniques comprise the methodology applied in our study. The research demonstrates the CNN-based methods stability and dependability in identifying a range of quality attributes, such as fruit imperfections, size, color, and maturity. The suggested CNN architecture performs remarkably well, recognizing fruit quality parameters with a 99.5% accuracy rate by utilizing a collection of various fruit photos. A promising path for improving efficiency and accuracy in fruit quality assessment within the agricultural industry is provided by the researchs insights into the transferability and scalability of the developed model for practical applications in automated fruit sorting systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Reshaping the Education Sector of Manipur Through Blockchain
The use of technology in education has been over a century, yet blockchain is in its nascent stage in education. Over the years, technology has enhanced the teaching-learning method, and blockchain can improve even in the administrative section of education. The states of North East, India, in general, lag behind the rest of Indian states in almost all sectors, and the lack of transparency in the administrative sector significantly contributed to it. If blockchain is incorporated into the education department at the administrative level, it could pave the way for a faster, more transparent, and smoother administration. Given the harsh reality that transportation is hard and expensive, a standardised blockchain can alleviate the need to be physically present for any academic-related activity. The attempt of this study would be to show how blockchain can be beneficially used even at the institutional level, where unabated printing could be reduced and adopting to e-paper be maximised. Besides the educational institutions, the administrative sector in education could profitably use them in offices, thereby avoiding red tape for the common people. The chapter points out how blockchain can be a trailblazer in reshaping the education sector in Manipur. Educational institutions must take the lead towards a sustainable future, and blockchain can aid in bringing some visible change in the educational sector. This chapter uses an interdisciplinary approach to substantiate the importance and need for blockchain in the context of Manipur to change for a sustainable future. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Residual stress analysis on functionally graded 8% Y2O3-ZrO2 and NiCrAlY thermal barrier coatings
Thermal Barrier Coatings (TBCs) protect metallic components that operate in high temperature environments and enhance their service life. The conventional two-layered TBC system consists of a duplex ceramic top coat (TC) fabricated from 8 wt% yttria stabilized zirconia (8-YSZ) and an underlying bond coat (BC) comprised of intermetallic layers such as NiAl or MCrAlY (M = Co, Ni) etc. In the present study, functionally graded material (FGM) TBCs were fabricated by introducing a third blend layer of 8-YSZ and NiCrAlY, in between the BC and TC in order to enhance the thermal fatigue life of the TBC. The blend layer in FGM TBCs provides a smoother transition in thermal expansion properties between the metallic substrate and the top ceramic coat (8YSZ) which have widely different thermal expansion characteristics compared with each other. In service, thermal fatigue introduces severe tensile stresses between the coated layers and the substrates leading to ultimate detachment of the coatings from the substrates. In this work, residual stress analysis by Cos ? method was carried out as a non-destructive assessment tool to foresee the likelihood of onset of failure in the TBCs, well before the damage was visible. The two-layered (conventional) and three-layered (FGM) TBCs were synthesized on Inconel 718 substrates by atmospheric plasma spray (APS) technique. The TBCs were subjected to thermal fatigue tests between 1200? (by using gas flame) and ambient temperature and evaluated for residual stress analysis at different stages of thermal fatigue testing. The goal was to assess if residual stress analysis could be used to determine if the TBC was about to fail well before the delamination occurred and the catastrophic failure could be avoided. The tests conducted and results obtained are presented. 2022 -
Residual stresses analysis on thermal barrier coatingsndt tool for condition assessment
Improvement in the engine efficiency follows reduction in fuel consumption which is possible by increasing the engine combustion temperature. Coating the piston of diesel engine with a high temperature-resistant material, known as thermal barrier coating, generally 68% Y2O3 stabilized ZrO2, is a popular method to reduce the temperature it experiences in service and to increase engine efficiency. Whether bare or coated component, fabrication and different thermal expansion coefficients of the ceramic coating and piston metal cause generation of residual stresses in them. These hidden residual stresses (tensile or compressive) play a significant role in governing the failure mechanism of the different sections of the components and their important role (also developed in service) is mostly neglected in engineering practices. Residual stresses analysis of components in service may throw light on the condition of the components without destroying them. In this work, portable X-ray residual stress analyzer was used to evaluate the condition of AlSi alloys piston flat plates that were coated with 250-m-thick 68% Y2O3 stabilized ZrO2 and subjected to thermal treatments. The analysis revealed (a) residual stress-free pattern for uncoated AlSi substrate, (b) compressive residual stress at the substrate (AlSi)coating (TBC) interface and (c) tensile residual stress at the substrate (AlSi)coating (TBC) interface of a thermal shocked coated substrate. The analysis method exhibited good possibility for using this as a tool for non-destructive testing for predicting the onset of failure at the coating substrate interface, without destroying the component in service. Springer Nature Singapore Pte Ltd 2020. -
Residual-based MEWMA control charts in the presence of multicollinearity
Statistical Process Control has been performing a critical role in attaining quality assurance from historic times to the modern era. Examining and governing the process variables involves rigorous stages and several control charts. The multivariate process is considered for a more comprehensive understanding of handling multiple correlated variables of the process. The study here focuses on the unique creation and deployment of residual-based Multivariate Exponentially Weighted Moving Average control charts in the presence of multicollinearity, specially constructed and evaluated for Phase I and Phase II. The chart offers a reliable framework for understanding shifts in multivariate processes across time from minor to moderate changes in process parameters. Agro-Economy data of Indian States for the years 2019 and 2020 are utilized in an application example. The proposed residual-based MEWMA control charts detect out-of-control circumstances with few false alarms and this is critical for rapid interventions, resulting in optimal crop management and production. 2025, Prince of Songkla University. All rights reserved. -
Residual-Based Statistical Process Control Charts in the Presence of Multicollinearity: an EWMA Framework with (RK) Estimator
Reliability monitoring of financial health requires strong control mechanisms, and the residual chart is an invaluable instrument to perform it. One of the key problems statisticians face while modeling is the problem of multicollinearity which arises when there is a strong correlation between independent variables leading to imprecise coefficient estimates and poor outcomes. To solve this problem and to make sure that the control chart works even with correlated data, we integrated a Weighted Moving Average Exponential smoothing chart within the modeling technique. The theoretical approach assures long-term variability and consistency of the residual control chart. These control charts are used to understand the process and the performances in various sectors. The charts can be used as analytical instruments to help recognize patterns, variations, or anomalies in economic indicators specifically in budget deficit data and facilitate rapid identification of any changes or inconsistencies in the fiscal deficit by policymakers. Further advances in statistical process control are rendered feasible by this study, which deepens the understanding and awareness of the potential uses and implications of the Weighted Moving Average Exponential smoothing chart for fiscal deficit data in the Economic realm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Resilience Amidst Neglect: Analyzing Government Failures and Community Strengths in the Wayanad Crisis
Disaster-prone areas are geographically, environmentally, or climatically vulnerable to natural disasters and require strong preparedness and adaptation. Collaboration, indigenous knowledge, and resource mobilization help communities recover and rebuild after crises, reducing vulnerability. This study investigates the catastrophic landslide in Wayanad, Kerala, in July 2024, to analyze governmental inadequacies in disaster preparedness and the notable resilience strategies exhibited by the local community. The landslide, intensified by unregulated development in ecologically vulnerable areas, underscores the failure to execute essential environmental recommendations, including those specified in the Madhav Gadgil and Kasturirangan reports. Notwithstanding these challenges, the community's swift response via local organization, resource mobilization, and local leadership was instrumental in alleviating additional losses and facilitating recovery. The research highlights the relationship among crisis management, community empowerment, and environmental sustainability in areas susceptible to disasters. This research analyzes the Wayanad disaster and proposes a model that integrates proactive policy measures with community-driven strategies to bolster resilience and diminish vulnerability in ecologically sensitive areas. The RESTORE Model Framework is a comprehensive methodology for disaster management, highlighting Resilience, Early Warning, Sustainability, and Strategic Collaboration as fundamental components. The results underscore the imperative for collaborative frameworks that incorporate government entities, local stakeholders, and environmental specialists to enhance disaster management systems. 2026, IDRiM Society. All rights reserved. -
Resilience and Human-Centric Perspectives for Organizations in Industry 5.0
In the emerging world researchers put forward the idea of industry 5.0, focusing on the concept of human centricity, sustainability and resilience approaches in the organizations. Industry 5.0 includes technological advancement along with giving importance to the human capital. During the past, the industrial revolution emphasized the productivity, production quality and growth of business. There was less importance for the human capital and employees well being in the changing industrial style giving importance to the human capital, by integrating technological advancements. It is recognised for the societal values that are embedded, the whole industry 5.0 revolves around sustainability, human centric and resilience in the organization. In this book chapter we explore the concept and applicability of these topics. The book chapter used literature review method and case study analysis for thorough understanding of the subject. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Resilience in Children from Different Socioeconomic Backgrounds: An Exploratory Study
Poverty, violence, substance abuse, family dissonance and illness represent a few potential vulnerabilities in the lives of children who are at risk of failing in their future prospects. It is thus essential to explore resilience in children, owing to the excess or deficit of exposure and access in a childs life. This study aims at exploring the resilience of children of the age group 710years, from different socioeconomic backgrounds. The socioeconomic status was determined using the Kuppuswamy socioeconomic scale and these children had parents with authoritarian and permissive parenting styles which were screened through the Parenting Styles and Dimensions Questionnaire which act as risk factors for the children. Data was collected through individual semi-structured interviews with the participants and was analysed using thematic analysis. For the lower socioeconomic status group, the main themes identified were social interaction and competence, overcoming distress and future focus, and for the upper socioeconomic status group, the main themes identified were social interaction and competence and emotional management. The study paves the way for further exploration of the impact of economic status on childrens wellbeing and might inform changes at a clinical, research and policy level. 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Resilience in Children from Different Socioeconomic Backgrounds: An Exploratory Study
Poverty, violence, substance abuse, family dissonance and illness represent a few potential vulnerabilities in the lives of children who are at risk of failing in their future prospects. It is thus essential to explore resilience in children, owing to the excess or deficit of exposure and access in a childs life. This study aims at exploring the resilience of children of the age group 710years, from different socioeconomic backgrounds. The socioeconomic status was determined using the Kuppuswamy socioeconomic scale and these children had parents with authoritarian and permissive parenting styles which were screened through the Parenting Styles and Dimensions Questionnaire which act as risk factors for the children. Data was collected through individual semi-structured interviews with the participants and was analysed using thematic analysis. For the lower socioeconomic status group, the main themes identified were social interaction and competence, overcoming distress and future focus, and for the upper socioeconomic status group, the main themes identified were social interaction and competence and emotional management. The study paves the way for further exploration of the impact of economic status on childrens wellbeing and might inform changes at a clinical, research and policy level. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. -
Resilience in Education: Addressing Addiction and Cultivating Mindfulness for Well-Being
The critical significance of addressing precarity within the neoliberal education system is the implementation of comprehensive interventions and support systems. The text identifies addiction, resilience, and mindfulness as crucial elements in addressing the intricate challenges that individuals encounter within educational environments. Substance abuse frequently manifests as a means of coping with tension, highlighting the criticality of robust support networks. Mindfulness is a potentially effective strategy for navigating uncertainty through cultivating resilience and emotional regulation. Support systems for mental health and mentoring programs, examples of initiatives that foster resilience, are crucial in enabling individuals to confront precarious circumstances effectively. Adopting a comprehensive strategy incorporating mindfulness, addiction treatment, and resilience development is imperative to advancing educational equity and well-being. 2026, IGI Global Scientific Publishing.