bluuebunny/crossref_metadata_2025 huggingface dataset:
Recognising and Supporting Student Achievement
Authors: Whewell Emma , Tiplady Helen , Waters Lee
Published: August 2024

This chapter seeks to discuss the ways in which practitioners can work with students to support their achievements. Using the Universal Design for Learning (UDL) (CAST, 2018), which enables learning to be designed or modified for the greatest diversity of learners possible, this chapter offers suggestions for designing and teaching inclusive activities. It also considers wider strategies of inclusion and inclusive approaches to assessment and feedback. It exemplifies three case study pieces of research conducted with students at a higher education institute to consider the range of challenges, opportunities, and needs presented by learners. By using the principles of UDL, educators can create learning environments that are accessible and engaging for all students, regardless of their individual learning styles, abilities, or backgrounds. This chapter provides a comprehensive framework for understanding how UDL can be practically applied in diverse educational settings, ensuring that all students have equal opportunities to succeed. The chapter explores specific inclusive teaching practices, such as the use of multiple means of representation, expression, and engagement, which are core components of UDL. Additionally, the chapter analyes inclusive assessment strategies that move beyond traditional testing methods to embrace a variety of assessment forms that accommodate different learning needs and preferences. Feedback mechanisms are also examined, highlighting the importance of constructive, timely, and personalized feedback in fostering student growth and achievement. The case studies presented illustrate real-world applications of UDL and inclusive practices, providing insights into the successes and challenges encountered by both students and educators. These case studies shed light on the practicalities of implementing inclusive strategies and offer evidence-based recommendations for enhancing educational inclusivity. Through these examples, the chapter emphasises the importance of a holistic approach to education that considers the diverse experiences and needs of all students. Thus, this chapter not only outlines the theoretical underpinnings of UDL but also provides actionable strategies for creating inclusive learning environments. It highlights the critical role of inclusivity in education and offers practical guidance for educators seeking to support the diverse learning needs of their students.
Obstacles in Science Teaching in Colleges in Bangladesh
Authors: Roy Goutam , Riad Sheikh Shahbaz
Published: August 2024

College science teachers face several challenges worldwide, and Bangladesh is no exception. Several studies addressed various challenges in the context of Bangladeshi college education; however, a gap in terms of addressing the obstacles in college science teaching is identified. This chapter addresses science teachers' perspectives and discusses the impediments to science teaching in Bangladeshi colleges. A total of 12 college science teachers were interviewed using the qualitative data collection approach, and a thematic analysis was conducted. Four specific themes, such as lack of laboratory facilities, insufficient teaching resources, lack of motivation and unplanned study management, were identified as challenges, and the participants provided three ways to overcome this situation. This chapter could help college teachers and administrators understand the challenges and provide a direction for compelling science teaching. The chapter also provides some insights to policymakers regarding the development of science teaching in college.
Bioethics Enhancement Sessions at the Bedford Sixth Form
Authors: Redwan Afsan , Amin Md Sojal , Carss Jenna
Published: August 2024

This chapter explores further education (FE) enrichment sessions, focusing on The Bedford Sixth Form's enhancement sessions as The Bioethics Club. Drawing from literature, it highlights the tension of tailored enrichment between addressing academic gaps and fostering holistic student development. The Bioethics Club exemplifies the latter's principles by blending scientific exploration with ethical deliberation. Three themes are found to be integrated across the Bioethics sessions: interdisciplinary learning, practical application, and inclusive design. These themes promote specific soft and life skills such as ethical analysis, critical reflection, inclusive awareness, collaborative decision-making and interpersonal skills. This chapter also recommends that future enhancement sessions include interdisciplinary modules, practical application frameworks, and inclusive design strategies. As a limitation, future research needs to be conducted to assess long-term impacts and refine pedagogical approaches to these enhancement sessions. This inquiry enhances understanding of FE enrichment, providing insights for students' personal and professional advancement in diverse educational contexts.
Revolutionizing Healthcare
Authors: Singhal Neha , Goyal S. B. , Sinha Somnath , Sharma Annu , Kalaimani Suresh , K. N. Bhanu
Published: August 2024

Digitization and automation are emerging trends in today's tech era. Digitization and robotization essentially always had a significant influence on real-time medical applications. The chapter presents a proposed architecture in the healthcare domain using the metaverse virtual platform for patients' and doctors' interactions. In the proposed architecture, doctors, nurses, and patients are represented using the avatars. Interaction and consultation-related data like health parameters and doctor recommendations are recorded using recent technologies like blockchain technology, digital twins, NLP, etc. Artificial intelligence (AI) based technologies are used for logic interpretation, transparency, and result calculations. This chapter presents the conceptual framework for metaverse technologies, and further various technological aspects are discussed that are essential for metaverse collaborations. Several case studies, future trends, and recommendations based on supervised learning are discussed in more detail.
Impact of Generative AI on Metaverse-Enabled Healthcare Industries
Authors: Vashishth Tarun Kumar , Sharma Vikas , Sharma Kewal Krishan , Kumar Bhupendra , Chaudhary Sachin , Panwar Rajneesh
Published: August 2024

The integration of generative artificial intelligence (AI) into metaverse-enabled healthcare industries is catalyzing a profound transformation in the way healthcare is delivered, experienced, and optimized. This chapter explores the multifaceted impact of generative AI within the immersive and interconnected realm of the Metaverse on healthcare systems, services, and patient outcomes. Generative AI, driven by powerful deep learning algorithms and neural networks, is revolutionizing medical data generation, diagnostics, and personalized treatment. This abstract discusses how generative AI-driven avatars and virtual healthcare environments are enhancing patient engagement and facilitating remote consultations, transcending geographical barriers. Moreover, generative AI is pivotal in the creation of lifelike medical simulations and predictive modeling for disease prevention. However, this transformation also raises ethical concerns surrounding data privacy, security, and the potential biases embedded in AI algorithms.
Biological Control Agents for Pest Management and Advancements in Crop Development
Authors: Khalid Sadia , Qadeer Sundas , Rahman Attiqa , Zamir Muhammad Shahid Ibni , Shafiq Saira , Iftikhar Amna
Published: August 2024

Changing agro-climatic conditions are exacerbating the incidence of various insect pests, posing significant threats to crop productivity for human consumption. Due to the drawbacks associated with conventional chemical approaches, there is a growing interest in employing biological control methods as a secure and economical remedy for pest management challenges. The maximum utilization of biological control in recent years can be attributed due to its long-term ability against and specially for target species. Unlike chemical methods that harm non-target species, pose health risks to humans, and pollute the environment, biological control offers a more targeted and sustainable approach. This chapter aims to explore the major aspects of biological pest control, focusing on various strategies employed for successful pest management in farms, greenhouses, gardens, and forests. These strategies include conservation biological control, classical bio-control and control by augmentation the further unleashing of natural enemies via inundation or by inoculation.
The Intelligent Urban Nexus
Authors: Manivannan R. , Rajakumaran M. , Ananth A. Dennis , Markkandeyan S. , Venkatesan R. , Ghalib Muhammad Rukunuddin
Published: August 2024

Artificial intelligence (AI) in smart urban solutions offers benefits like efficient energy/water management, and reduced pollution, noise, and traffic. Challenges include data reliability, third-party dependence, and skill shortages. AI operates via data-driven processes and intelligent algorithms, enabling machines to learn, adapt, and perform human-like tasks. This chapter explores AI's role, applications, and challenges in smart cities, focusing on gathering and disseminating intelligence. It provides insights into AI's significance in shaping smart cities for enhanced social awareness and connectivity. This chapter aims to explore the role of AI, its applications, and the challenges inherent in the concepts and technologies associated with AI in smart cities. Specifically, it focuses on AI's contribution to gathering and disseminating intelligence in smart urban environments. Through this analysis, the paper seeks to provide insights into AI's significance in shaping smart cities for enhanced social awareness and connectivity.
A Comprehensive Framework on Emotion-Aware Stress Detection Using IoT and Machine Learning
Authors: Pasupuleti Venkat Rao , Ayeesha Shaik , Sai Gopi Venkata , Kadhana Penumaka
Published: August 2024

In today's society, the chapter thrives on performance, competition, and perfection, which leads to an insidious increase in stress. Stress is a condition of mental pressure for particular individuals facing problems from environmental and social well-being, which leads to so many diseases. Stress causes damage that is often underestimated, and it is a social phenomenon that should be closely examined and evaluated. Nowadays, stress leads to suicide attempts, depression, mental conditions, etc. Using IoT sensors like GSR MAX30105 and IoT components such as Node MCU, Arduino UNO, and LCD can detect stress. Stress can also be measured through parameters like Spo2, HR/PR, and temp levels. The internet of things (IoT) and machine learning (ML) alarm the situation when the person is at real risk. ML is used to predict the condition of the patient by using techniques like SVM (support vector machines), CNN (convolutional neural networks), k-nearest neighbours, and logistic regression, etc. The user will get feedback through the GSM module and the internet based on the above information. According to the latest findings in the literature, an IoT-based system is designed to monitor the well-being of people during their daily activities. By capturing human facial expressions through the camera, emotion is detected. Finally, by combining stress and emotion detection through an efficient ML algorithm, the stress levels as low, medium, or high stress are predicted.
An Efficient Optimization Technique for Classification of Multi-Crop Leaf Diseases Using Hybrid Deep Learning Model
Authors: Priya S. Anu , Khanaa V.
Published: August 2024

Plant diseases are undesirable factors that drastically lower crop quality and production. The risk that a plant will sustain additional damage may be reduced by early and precise detection of plant diseases. This research recommends a new hybrid model called BOA-CNN-LSTM that can autonomously identify and categorize multi-class leaf diseases with the highest detection rate. The provided approach pre-processes the data to extract features and boost the classification accuracy readily. The CNN technique is used first to extract the features from the plant leaf images. The LSTM technique is then applied to classify each image. Additionally, the CNN-LSTM model's hyper-parameter optimization utilizes the butterfly optimization algorithm (BOA), which improves detection performance. The plant village database is utilized to evaluate the functionality of the proposed approach, and the results are examined in terms of multiple assessment criteria. Four distinct plant types—apples, corn, tomatoes, and rice—are employed in the test. The proposed work achieves an accuracy of 92%, sensitivity of 0.91, and specificity of 0.89, according to the experimental findings of the study.
Data-Driven Strategies on Growth Through AI and Machine Learning
Authors: Perumal P. , S. Sivagnana Bharathi , Senthilkumar K. , Thirunavukkarasu T. , Mishra Biswo Ranjan
Published: August 2024

The fast-paced market of today is being helped by data-centric approaches that are enabled by artificial intelligence and machine learning. By utilizing enormous data sets to get practical insights and make informed decisions, this chapter analyzes how artificial intelligence and machine learning have the potential to alter company strategies significantly. Through the utilization of advanced analytics and predictive models, businesses can refine their operations, enhance their interactions with customers, and discover new chances for expansion. In addition, it discusses the difficulties and factors to take into account when putting AI and ML efforts into action, with a particular emphasis on data protection, ethics, and the employment of talented individuals. Within the context of the digital age, this chapter explores how data-driven initiatives that make use of artificial intelligence and machine learning have the potential to transform the business environment and promote sustainable growth.
Comprehensive Prediction of Retinopathy in Preterm Infants Using Deep Learning Approaches
Authors: Hubert G. , Priscila S. Silvia
Published: August 2024

Preterm newborns' visual condition is greatly endangered by retinopathy, which makes prompt and precise identification essential for successful treatment. Using the capabilities of ML (machine learning) computations, the current investigation suggests a combined strategy for retinopathy forecasting in preterm infants. To improve prediction accuracy, specificity, and sensitivity, the investigation also includes a gray-level co-occurrence matrix (GLCM) for collecting features and a median filter to reduce noise. The work uses the RF, SVM, and MLP algorithms for modeling predictions, using their different learning capacities. Together, the non-linear mapping of MLP strengthens the retinopathy forecasting framework. Experiments on a dataset consisting of retinal pictures from preterm infants with different levels of retinopathy show how effective the suggested combined strategy is; the tool used is Jupyter Notebook, and the language used is Python. The findings confirm the usefulness of the suggested method in healthcare settings, guaranteeing prompt and precise identification to avert vision-threatening problems. From the results obtained, the proposed MLP produces an accuracy of 90%, a sensitivity of 0.91, and a specificity of 0.86.
Exploring the Relationship Between ESG Performance and Profitability in Indian Power Companies
Authors: Viswanath Antara , Narayana Sudha Hari , Bellamkonda Vimala Shruti , M. P. Sunil
Published: August 2024

This study aims to assess the relationship and impact of Indian power companies' environment, social, governance (ESG) scores on firm performance. Since 2022, CRISIL Ratings Ltd publishes yearly ESG scores for 225 companies in India. The final ‘ESG' score is a composite of individual scores for the three metrics: ‘Environment', ‘Social', and ‘Governance'. This study intends to examine the influence of ESG scores on financial profitability measured by both net profit ratio and return on investment. The existence and degree of association between the variables is measured using correlation and simple linear regression analyses, respectively. The findings suggest that the overall ESG composite score is positively and significantly associated with firm profitability and moderately associated with returns. The ‘Governance' metric stands out which has a strong and positive relationship with profitability in particular. These findings advocate that by investing in ESG initiatives, the chances of better financial performance are improved.
Reinventing the Business Model to Navigate the Evolving Business Landscape
Authors: Tantia Veerta , Sukanya R.
Published: August 2024

In today's rapidly evolving business landscape, technological advancements, shifting consumer behaviours, and global economic fluctuations increasingly challenge traditional business models. Organisations must reinvent their business models to remain competitive, embracing innovation, agility, and sustainability. This chapter explores the critical components of reinventing business models, including leveraging digital transformation, adopting customer-centric approaches, and integrating sustainable practices through a comprehensive view of their development. This study thoroughly understands the existing literature on business models, focusing on the features. Integrating sustainability into a business model is also a challenge for many practitioners. The business model innovation agenda is the topic of discussion among most companies. This chapter will explore adopting a business model towards sustainability by integrating environmental, social, and governance factors. The findings underscore the necessity for continuous adaptation and strategic foresight to drive long-term success.
Ethical Implications in Money Laundering Detection
Authors: White McKay , Lyn Alexandra
Published: August 2024

The obligation of businesses to submit suspicious transaction reports (STRs) in anti-money laundering efforts is a critical component of regulatory frameworks worldwide. Businesses increasingly look to technology, including artificial intelligence (AI) tools, to satisfy these obligations. While STRs remain a key component of compliance, they raise complex ethical issues involving client privacy and human rights. Employing AI technology in transaction monitoring and reporting processes exacerbates these issues. This chapter explores ethical dilemmas surrounding STR compliance, including the tensions inherent in balancing regulatory imperatives with individual rights. The role of AI in enhancing the efficiency and effectiveness of suspicious transaction monitoring is considered, alongside the ethical risks associated with algorithmic bias, lack of interpretability, and the potential erosion of human oversight. By synthesizing insights from theory and research, this chapter offers a comprehensive framework for navigating the ethical complexities of STR compliance.
Corporate Governance Dilemma
Authors: Minhas Rajinder , Dwivedi Ashutosh
Published: August 2024

The governance of corporate has to be exemplary. Corporate, as a separate legal entity with limited liability in most cases, derives its corpus from varied shareholders and in consequence, it is obligated that the affairs of such a person are to be commanded with upkept trust. But it is alarmingly strange that there are testimonial facts to prove that corporate as a person failed, when the custodian of the trust themselves betrayed it. Yes, it cannot be denied that the fraudulent acts are belligerent and shakes the conscience of one and make the scene susceptible for a thorough proof. It is also understandable that in a duty- fiduciary, if there is done any negative to the interest of anyone in the stake, it is criminal and not acceptable at all! To target such mentalities of the vested, an alarm has been raised in this paper.
Framework for Corporate Governance
Authors: Chandra Saurabh , Sharma Simran
Published: August 2024

Corporate governance refers to the controls, procedures, and relationships that govern corporations. It consists of the policies and procedures for making business decisions as well as the methods used to create and pursue an organization's goals in the context of the social, regulatory, and market environments. Monitoring corporate policies, practices, and choices, as well as those of their agents and other stakeholders, is a function of governance structures. The rights and obligations of the various members of the corporation are defined by the governance structures and principles. Corporate governance in India is governed by various laws, regulations, and guidelines that aim to ensure transparency, accountability, and responsible management of companies. The framework for corporate governance in India includes both mandatory requirements as well as voluntary guidelines. In recent years, there has been a growing recognition worldwide of the importance of gender-inclusive governance in corporate structures. This recognition stems from the understanding that diverse perspectives, including those of women, are essential for effective decision-making, risk management, and sustainable business practices. In the Indian context, where gender disparities have historically been prevalent, there has been a push to introduce frameworks and guidelines to promote gender-inclusive corporate governance. Globally, the call for more gender diversity on corporate boards has been gaining momentum. The Companies Act, 2013 in India also recognizes the importance of gender diversity and provides for mandatory appointment of atleast one women director in the listed and other specified class of companies. In the past years, there have been a variety of instances when a corporate crisis has been caused by systematic fraud against investors that involves deception and suppression of crucial information that would-be investors should be aware of. This paper discusses legislative perspectives on the framework for corporate governance in India, specifically focusing on gender inclusion.
Understanding Ethical Attitudes
Authors: Ng Hee Song , Kee Daisy Mui Hung
Published: August 2024

The prevalence of ethics-related crises in organisations has drawn significant public attention. In today's morally confusing and polarised world, characterised by rapid technological advancements and a growing emphasis on sustainability, the role of business ethics has become crucial and relevant. It is important to understand ethical behaviour in the business context, as it is not only integral to organisational success but also a fundamental component of environmental, social, and governance (ESG) principles. Despite the traditional focus on profit maximisation, businesses are increasingly expected to align with sustainable development principles and adapt to the challenges in the business world. Understanding the concepts of personal ethics, ethical attitudes, and ethical climate is important for managers to react to and handle major ethical issues in today's fiercely competitive world. This chapter aims to discuss the issues and solutions of the three concepts and also analyse the relationship between personal ethics and ethical attitudes under the moderating influence of ethical climate in organisations. Through an examination of these factors, organisations can foster a culture of ethical awareness and responsiveness, thereby mitigating risks and promoting sustainable business practices.
Unraveling Quantum Dots
Authors: Das Niharika , Panda Subhraraj , Das Dipan Kumar , Nayak Santanu Kumar , Parthasarathy Siddharth , Patnaik Padmaja , Behera Saubhagyalaxmi
Published: August 2024

Quantum dots represent a class of semiconductor nanocrystals with exceptional optical and electronic properties, stemming from their quantum confinement effects and size-tunable energy levels. This chapter provides a comprehensive exploration of quantum dots, beginning with a historical overview of their discovery and development. We delve into the synthesis methods employed to fabricate quantum dots, highlighting their influence on size, shape, and composition. Structural properties such as quantum confinement effects and surface chemistry considerations are elucidated to underscore their impact on quantum dot behavior.Furthermore, the chapter comprehensively examines different properties of quantum dots, including size-dependent emission spectra, high quantum yields, and applications in optoelectronic devices.Through this chapter, readers will gain a deep understanding of the underlying principles governing quantum dots and their transformative potential across scientific disciplines.
AI-Infused Strategies for Customer Retention
Authors: Semwal Rajeev , Tripathi Nandita , Tyagi Pankaj Kumar , Nadda Vipin
Published: August 2024

The integration of artificial intelligence (AI) into customer retention strategies is revolutionising service marketing by leveraging advanced technologies to enhance customer engagement and loyalty. This abstract explores how AI, including machine learning, natural language processing (NLP), and predictive analytics enables businesses to gain deep insights into customer behaviours, predict preferences, and deliver personalised experiences. By analysing vast datasets from diverse sources, AI facilitates effective customer segmentation and targeted marketing campaigns tailored to individual needs and preferences.
Artificial Intelligence in Banking
Authors: Karageyim Meral Ahu
Published: August 2024

Artificial intelligence is considered a multi-layered and multidimensional disruptive innovation. Many studies have been conducted to understand the multidimensional impacts of AI, particularly its social, legal, economic, environmental, and business impacts. The use of artificial intelligence is increasing in almost every industry today, and it is expected to change the service industry, in particular. This book chapter examines the development of artificial intelligence in financial services and discusses the customer adoption criteria for social robots. The characteristics and advantages of chat bots and robo advisors were analyzed in detail. This chapter argues that chatbots and robo-advisors are new actors in banking and discusses their role in the service ecosystem and their impact on banking strategies. The book chapter treats the long-term effects of AI in the banking industry and provides a framework for understanding customer perceptions and customer adoption criteria in financial services.
A Bibliometric Analysis of Artificial Intelligence in Service Marketing
Authors: Paatlan Sandeep , Ranga Jayati
Published: August 2024

The present study is a bibliometric analysis of artificial intelligence (AI) and service marketing. The purpose of the study is to map the evolving intellectual space by examining the bibliometric characteristics of this fast-developing research field. The publication trend in the past decade signifies the growth of research in AI. Key journals and conferences represent the primary publishing platforms for research in the area. Citation and co-citation analysis reveal the most influential authors and institutional contributors along with the growth of collaborative networks. The keyword co-occurrence analysis helps to map the evolution in theme among AI and service marketing scholars. The scope of the present study characterises the current state, reveals critical knowledge gaps, and recommends potential research directions to explore in AI and service marketing. Specifically, the findings will be useful to academic scholars and practitioners, and will also inform about the changing landscape of service marketing due to AI technologies.
Improving Efficiency Through AI-Powered Customer Engagement by Providing Personalized Solutions in the Banking Industry
Authors: Kaluarachchi Buddhika Nishadi , Sedera Darshana
Published: August 2024

Artificial intelligence (AI) is revolutionizing banking by improving client engagement and operational efficiency with personalized solutions. This chapter analyses how AI-powered customer engagement enhances operations and customizes solutions. AI tools help banks learn client preferences and behaviors by analyzing massive volumes of data, supporting a customer-centric strategy that promotes happiness and loyalty. The chapter reviews prominent banks' AI deployments and case studies, addresses data protection, ethics, and regulatory compliance, and offers advice for banks seeking competitive advantage. The chapter also discusses AI-powered banking trends like better credit evaluation, personalized services, and fraud protection. Banks can improve operational efficiency and provide personalized client experiences by using AI-driven service marketing. For banking professionals interested in using AI to create a competitive edge, this chapter provides practical tactics, insights, and recommendations for successful AI adoption in financial services.
Integrating AI-Driven Technologies Into Service Marketing
Authors: Freeda Adline R. , Anju A. , Kanthavel R. , Dhaya R. , Vijay Frank
Published: August 2024

Artificial intelligence (AI) powered solutions are transforming client interactions, experience optimization, and strategy optimization in the service marketing industry. This chapter explores the revolutionary effects of artificial intelligence (AI) on contemporary marketing strategies, emphasizing the critical role AI plays in delivering personalized customer experiences, sifting through large datasets to extract useful information, and streamlining and optimizing marketing procedures. Businesses may provide customized solutions, anticipate consumer demands, and engage more deeply across multiple touch points by utilizing AI-powered technologies like chat bots, customer relationship management (CRM) systems, and predictive analytics. The chapter also looks at real-world implementations and success stories from various industries to show the observable advantages and competitive advantages of using AI in service marketing. It also discusses the difficulties and moral issues that come with implementing AI technology, such as algorithmic transparency and data privacy. This chapter offers practical advice for companies looking to take use of AI's potential as the digital landscape develops, helping them to stay ahead of the competition. Businesses may improve their marketing results and build stronger, more individualized relationships with their customers by adopting AI-driven technologies. This will set them up for long-term success and growth.
Strategies for Organisational Learning
Authors: Kılınç Sebahattin
Published: August 2024

Adaptation to the environment of the organization happens through questioning, interpretation, internalization and institutionalization of knowledge with organizational learning. Organizational learning is explained with explanatory and interpretive approaches at the individual or organizational level. Organizations that can adapt to their environment can achieve sustainable competitive advantage by bringing about change when necessary. In this study, the importance of organizational learning and learning organizations and the role of leaders are explained by literature review method.
Ethical Use of Artificial Intelligence in Organizations
Authors: Uçkun Seher , Uçkun Gazi , Kilinç Sebahattin , Yener Serdar , Ceyhan Sinan
Published: August 2024

The expectation of high performance, efficiency and innovation brought by the desire for sustainable competition advantage fuels continuous change and development. However, technologies such as AI facilitate many functions of organizations. While AI technologies increase efficiency and innovation in organizational processes, they also generate some threats. With the use of AI, some factors that increase the anxiety of employees/customers come to the fore. With the widespread use of AI, consequences such as the fear of losing the job, privacy violations, and obtaining erroneous and ineffective information due to faulty algorithms may occur. However, the negative situations that may be encountered with the use of AI may differ. The fact that the use of AI is still very new increases the responsibilities of leaders in this regard. The situations that can be encountered with the use of AI may differ. In this respect, the leader's observations and research can enable them to develop proactive strategies. In this study, the discussion of strategies for the use of AI by leaders and the management of ethical responsibilities was made according to the literature review method. Scientific publications, articles, books, and reports obtained with the keywords determined in the research were systematically examined.
Beyond the Desk
Authors: Bhattacharyya Sreeparna
Published: August 2024

The study conducted in Kolkata found that emotional well-being is crucial for management education. The researcher conducted a cross-sectional quantitative survey among 200 management graduates at 10 institutions. They found a significant inverse relationship between good emotional well-being and perceived stress and a slight positive link between negative emotional well-being and personal suffering. The study also found that 61.3% of differences in perceived stress ratings could be reliably predicted. This innovative approach aims to improve the sustainability of management education in the business sector. The findings can be interpreted using facial feedback and cognitive appraisal theories of emotional well-being. The study highlights the importance of incorporating emotional well-being programs in management education.
Advancing Architectural Design Through Generative Adversarial Network Deep Learning Technology
Authors: Huang Yueming , He Jianhua
Published: August 2024

Recent advancements in deep learning have popularized Generative Adversarial Networks for image generation. This study investigates integrating Generative Adversarial Networks technology into architectural design to empower architects in creating diverse, innovative, and practical designs. By analyzing architectural research, deep learning theory, and practical Generative Adversarial Networks applications, we substantiate the feasibility of using Generative Adversarial Networks for architectural design optimization. The generated architectural images exhibit significant diversity, innovation, and practicality, inspiring architects with numerous design possibilities. Overall, Generative Adversarial Networks technology not only expands design methodologies but also stimulates groundbreaking innovation in architectural practice. As technology progresses, Generative Adversarial Networks-based architectural design optimization shows promising potential for widespread adoption, heralding a new era of creativity and efficiency in architecture.
Design and Application of Art Design Information Teaching System Based on Federated Learning
Authors: Zhao Yawen , Gao Licheng
Published: August 2024

This paper presents a federation learning visualization framework using machine learning to tackle the data island issue and enhance information-based teaching systems. Leveraging machine learning, teachers understand classroom limitations and enact corresponding enhancements. The framework aims for resource sharing, enriching teaching methods, providing diverse online learning opportunities, and overcoming temporal and geographical constraints. The integrated database enables access to a wide range of materials. Results indicate a 30.26% improvement over traditional methods, demonstrating enhanced learning efficiency. Integrating online self-study and offline teaching promotes comprehensive learning outcomes and practical applications in art design education, fostering diverse teaching modes and enriching learning experiences.
Research on Music Teaching Systems Assisted by Artificial Intelligence
Authors: Yuan Kuixing
Published: August 2024

In music teaching, the application of artificial intelligence has brought revolutionary changes to teaching and greatly improved the teaching quality and learning effect. The purpose of this study is to explore the application and effectiveness of artificial intelligence in auxiliary music teaching system. With the rapid development of information technology, the application of artificial intelligence in the field of education is more and more extensive, especially in the field of music teaching, and its potential and value are gradually emerging. In this study, by constructing a music teaching system based on artificial intelligence, the influence of learners on music cognition, skill mastery and innovation ability was deeply studied. In terms of research methods, this study uses empirical research methods, combined with questionnaires, teaching experiments, data analysis and other means to comprehensively evaluate the effectiveness of artificial intelligence-assisted music teaching system.
Mobile Application System Efficiency and the Degree of User Satisfaction in Accessing Information
Authors: Alduaij Manal Y. , Alahmad Fahad A. , AlHammad Faisal
Published: August 2024

The purpose of this study was to explore the degree of user satisfaction with the use of mobile application systems to access information, specifically, the Moodle learning management system. Using quantitative methods, the authors explored user satisfaction in relation to system efficiency in accessing information and examined variables related to important demographic variables, such as local user location. The results showed that students were not afraid to use the Moodle learning management system but at the same time did not think Moodle was the best option for online learning because it continuously froze during exams, leading to student frustration. The results also showed that students were satisfied with information technology support when they needed it; however, they did not believe they received enough training to use Moodle. In addition, students did not find Moodle useful for online lectures because it does not have a live lecture feature.
Enhancing Car Segmentation for Thailand's Expressway Industry With an Automated Hybrid Machine Learning Framework
Authors: Kanokngamwitroj Kulkatechol , Srisa-An Chetneti
Published: August 2024

Car segmentation on Thailand's expressways poses challenges for traditional models due to unique characteristics, often resulting in predictive inaccuracies. Manual data analytics in this field is time-consuming and human centric. This research introduces an Automated Hybrid Machine Learning (AHML) framework leveraging advancements in AutoML, tailored for personalized customer segmentation in Thailand's expressway industry. The framework streamlines and automates the machine learning process, aiming to expedite model construction while enhancing performance. By employing clustering as an initial step followed by the Random Forest classifier as a hybrid classification approach, significant performance improvements are achieved compared to existing methods. Specifically, the model outperforms by 9.15% and 12.84% in both clusters, respectively. This research highlights the potential of the framework to address complex segmentation challenges and advance personalized customer targeting.
Maintenance Resources Optimization Using Pareto Analysis
Authors: Egbe Gregory Omozuhiomwen , Arinze Stella N. , Ebenuwa Solomon H. , Obi Emenike Raymond , Nwajana Augustine O.
Published: August 2024

Managing resource allocation for optimum effectiveness at various levels of maintenance activities is always a challenging task. Optimizing maintenance resources enables an organization to set priorities towards achieving certain goals which are availability and reliability of the equipment for operational excellence. The purpose of this analysis is to determine the optimum resources allocation proportions among the failure modes and to identify the failure modes that have the greatest cumulative effect on the equipment's downtime. This paper presents a methodology using the Pareto analysis in conjunction with failure mode effect and criticality Analysis in maintenance resources optimization. The approach is based on ensuring all failure mode criticality number are considered to obtain the significant failures mode that you should focus on as a priority. The analysis shows that failure mode; FM5, FM 3, FM 2, FM 12, FM 7 and FM 13 are confirmation to the Pareto principle, identifying that most of the downtime of the Instrumentation Air Compressors originated from these failure modes.
Organizing Students' Online Teamwork for Sustainable Development
Authors: Balyshev Pavel , Darinskaia Larisa , Molodtsova Galina , Oskina Anna
Published: August 2024

The article tackles the problem of developing students' teamwork competence in e-learning environments. The aim of the study is to substantiate the methodological foundations for successful organization of students' online teamwork. The article contains the results of the pilot study that was conducted to investigate the challenges of online teamwork and formulate the correspondent recommendations for instructors on how to successfully develop students' team competence online. The study resulted in clarifying the component composition of the team competence, reasoning the psychological basis for organizing students' distance teamwork, and formulating pedagogical approaches to the successful organization of online teamwork. Also, several digital tools and instructions techniques are mentioned as recommended for avoiding the difficulties and challenges that were mentioned by the respondents in the survey. The obtained data can be used for theoretical substantiation of pedagogical models for organizing educational teamwork in distance and blended learning for sustainable development.
Technology of Manufacturing Hollow Soil-Cement Blocks
Authors: Mykhailovska Olena
Published: August 2024

The author analyzed technological solutions for the production of blocks from soil cement. It is proposed to make soil cement blocks from portland cement brand 400 in the amount of 20 % of the mass of soil and water. Experimental addition of fly ash to the solution in different amounts depending on the cement content. It is proposed to add ash removed from the Darnytsya thermal power plant, which was sifted through a 4 mm sieve, because the ash contains a significant amount of impurities. The content of inclusions from 1 to 4 mm was up to 40 %. It is proposed to make blocks from soil cement with the addition of fly ash with the aim of improving the thermotechnical properties. It is proposed to make soil-cement blocks with two voids. To ensure high-quality compaction of products in forms, the moisture content of the content for most soils should be within 14–23 %. The maximum force that the sample can withstand was taken as a destructive load. It was decided that the use of fly ash as part of the mixture in the production of soil cement gives a positive effect. With an increase in the period of exposure of samples in water to 90 days, the average compressive strength of soil cement samples without additives and with the addition of the appropriate percentage of fly ash increases.
Polymer Concretes Based on Thermosetting Polymers: Technological, Physical, Mechanical Properties and Use in Construction
Authors: Danchenko Yuliya , Andronov Vladimir , Olijnyk Halina , Mirus Oleksandr , Evtushenko Valentina
Published: August 2024

The article provides an analytical review of the main problems and prospects for the use and introduction of polymer concrete in modern construction industries. It was found that due to high plasticity, low porosity and the ability to quickly gain strength, polymer concretes are used for the manufacture of decorative products of small architecture, structural load-bearing and decorative overhead parts, decorative paving tiles and paving stones, products for hydrotechnical purposes, etc. by methods of vibration molding and casting. The classification of polymer concretes used in modern construction industries is provided, as well as an idea of the properties of the most popular polymer concretes based on thermosetting polymers – furan, epoxy and polyester. The advantages and disadvantages of known polymer concretes and the main promising directions of implementation for the manufacture of building products and structures are given. Attention is focused on the influence of the qualitative and quantitative composition of polymer concrete, the nature of the thermosetting polymer binder, the type of fillers and aggregates, the terms of hardening, the degree of polymerization on the most important physical, mechanical and technological properties of the finished materials.
A Holistic Isotopic and Mineralogical Investigation of Ancient Thasos Theatre: Technology and Provenance of Thasos Marbles
Authors: Kyropoulou Dafni , Karalis Petros , Dotsika Elissavet , Diamantopoulos Giorgos
Published: August 2024

The Stable Isotope Unit of the National Centre of Scientific Research and the Ephorate of Kavala and Thasos collaborated on a project to investigate construction materials from the Ancient Theatre on Thasos Island, which was funded by NSRF European funds (2007-2013 and 2014-2020). Bulk samples of marble were collected to determine their provenance, technology, degradation features, and mechanical and physicochemical properties, with the ultimate goal of developing a conservation plan. By utilizing novel scientific techniques, the research team was able to date constructions discovered within excavations and establish remedial and preventive conservation plans using marble technology. The samples were characterized in terms of their isotopic, chemical, and mineralogical composition. Additionally, extended physicochemical and mechanical testing was applied to estimate the current condition of the Ancient Thasos Theatre. The 13C and 18O isotopic composition of the calcitic marbles was analysed to determine the environmental conditions during calcite formation, making it possible to define the marble provenance. Compositional and morphological analyses were achieved using energy-dispersive X-ray analysis by scanning electron microscopy, while the mineralogical phases were detected using X-ray diffraction and petrographic (polarized optical microscopy) analysis. Mechanical testing according to European Standards was achieved through the examination of compressive tensile strength and grain size distribution.
A Robust Statistical Analysis of Factors Affecting Interface Bonding between Asphalt Pavement Layers
Authors: Al-Jarazi Rabea , Rahman Ali , Ai Chang Fa
Published: August 2024

Interlayer bonding within a multilayered pavement system plays an essential role in the overall performance of the pavement structure. Therefore, studying the interlayer shear strength (ISS) as a major index of the bonding strength and its accurate evaluation is of great importance. The main objective of this research is to assess the ISS of pavement using an experimental and statistically rigorous approach. The results showed that the ISS is highly temperature-dependent, experienced a rapid decline with increasing temperature. As tack coat rate increased, the ISS initially increased to reach a pick at 0.8kg/m2 rate and then started to decline. The ISS demonstrated an almost linear correlation with vertical pressure at all temperature levels. Two-way factorial analysis of variance (FAV) underscored the significant impact of any two, namely temperature (T), tack coat rate (TC), and vertical pressure (VP), on ISS results. However, three-way FAV results indicated that the combined effect of T, TC and VP did not hold statistically significant influence on ISS. Moreover, all ISS models developed in this study were statistically significant at a 0.05 significance level, with a good coefficient of determination (R2 =0.73) for multiple linear regression (MLR) and an excellent R2 of 0.976 for polynomial regression (PR).