bluuebunny/crossref_metadata_2025 huggingface dataset:
Nutritional habits as risk factors for dental caries in children from rural and urban areas in Skopje
Authors: Lazarova Aneta , Kokoceva-Ivanovska Olga
Published: February 2024

Proper nutrition is a long-term investment in one’s own health. Oral health as an integral part of general health is in bidirectional relationship with diet and nutrition. Globalization and urbanization have led to nutrition transition from a traditional agriculturally-based diet to consumption of processed foods that are high in sugar. Children have been particularly affected by this change. The aim of this study was to investigate the association between specific nutritional and hygiene habits and the development of caries in children from urban and rural areas. The study was conducted in December 2019 and comprised children aged 6 years. A survey was made and children’s dental status was tested. The results obtained showed a higher percentage of healthy deciduous (50%) and permanent (35%) teeth in children from rural areas than in children from urban area. Both groups had similar habits in terms of cariogenic and cariostatic food intake, but there were differences in children's hygiene habits.
Insulin resistance and metabolic syndrome in hepatitis C virus seronegative heroin dependents
Authors: Pereska Zanina , Janicevic-Ivanovska Danijela , Simonovska Natasa , Babulovska Aleksandra , Trajanovska-Spasovska Aneta , Naumoski Kiril , Kostadinoski Kristin
Published: February 2024

Initial studies on impaired glucose-insulin homeostasis in heroin dependents have not defined the impact of concomitant hepatitis C infection (HCV), which has been  strongly associated with the development of insulin resistanceand metabolic syndrome (MS). The aim of our study was to evaluate the association of heroin dependence with glucose-insulin homeostasis and MS in heroin dependents with HCV seronegativity. Materials and methods: The study was prospective and cross-sectional, including 160 heroin dependents compared to a control group of 60 participants.MS was diagnosed using International Diabetes Federation criteria. The homeostatic model assessment for insulin resistance (HOMA-IR) and pancreatic β-cell function (HOMA-%B) were used for assessing insulin resistance and β-cell function of pancreas. Results: MS was detected in 9.32% of heroin addicts. Heroin dependents with MS compared to dependents without MS were older, had higher BMI, waist circumference and significantly higher systolic and diastolic blood pressure, increased triglycerides (F=8.233, df=2, p<0.001), apoB (F=8.154, df=2, p=0.001), and reduced HDL-C (F=25.926, df=2, p<0.001) and apoA-I (F=16.406, df=2, p<0.001), significantly increased inuslinemia (F=4.928, df=2, p<0.05), insulin resistance-HOMA-IR (F=4,928, df=2, p<0,05) and insignificantly increased pancreatic β-cell function (194.66 ±224.05) (F=2.461, df=2, p>0.05). Conclusions: Insulin resistance and МS, independent of HCV, was also registered in heroin dependence. Timely recognition will enable more successful treatment of comorbidities and illicit drug dependence.
Geometric morphometric methods for identification of oyster species based on morphology
Authors: Liu Qian , Guo Yuepeng , Yang Yanzhuo , Mao Junxia , Wang Xubo , Liu Haijiao , Tian Ying , Hao Zhenlin
Published: February 2024

Both genetic and environmental factors affect the morphology of oysters. Molecular identification is currently the primary means of species identification, but it is inconvenient and costly. In this research, we evaluated the effectiveness of geometric morphometric (GM) techniques in distinguishing between two oyster species, Crassostrea gigas and C. ariakensis. We used traditional morphometric and GM methods, including principal component analysis (PCA), thin-plate spline analysis (TPS) and canonical variable analysis (CVA), to identify specific features that distinguish the two species. We found that differences in shape can be visualised using GM methods. The Procrustes analysis revealed significant differences in shell morphology between C. gigas and C. ariakensis. The shells of C. ariakensis are more prominent at the widest point and are more scattered and have a greater variety of shapes. The shells of C. gigas are more oval in shape. PCA results indicated that PC1 explained 45.22%, PC2 explained 22.09% and PC3 explained 10.98% of the variation between the two species, which suggests that the main morphological differences are concentrated in these three principal components. Combining the TPS analysis function plots showed that the shell shape of C. ariakensis is mainly elongated and spindle-shaped, whereas the shell shape of C. gigas is more oval. The CVA results showed that the classification rate for the two species reached 100% which means that C. ariakensis and C. gigas have distinct differences in shell morphology and can be completely separated, based on morphological characteristics. Through these methods, a more comprehensive understanding of the morphological characteristics of different oyster populations can be obtained, providing a reference for oyster classification and identification.
Multimodal Imaging for Subacute Sclerosing Panencephalitis: Including Anterior Segment
Authors: Aygün Figen Bezci , Ulutas Celal , Dikmetas Ozlem , Kocabeyoglu Sibel , Kadayıfcılar Sibel
Published: February 2024

Subacute sclerosing panencephalitis (SSPE) is an irreversible and progressive neurological disorder. A 20-year-old woman with SSPE presented with a decline in visual acuity. Anterior segment was evaluated with slit lamp and in vivo confocal microscopy, revealing the presence of dendritiform keratic precipitates and +2 cells in the anterior chamber. In fundus examination, white-yellowish lesions involving the macula and periphery were observed, which gradually progressed into atrophy over time. Optical coherence tomography (OCT) imaging showed progression to atrophy of moth-eaten shaped cavities including all retinal layers due to necrotizing retinitis. OCT angiography (OCTA) further revealed reduced vessel densities and flow void areas. Notably, to the best of our knowledge, this is the first case documenting anterior segment findings in SSPE in detail.

[
Ophthalmic Surg Lasers Imaging Retina
2024;55:344–348.]
Solutions to a discrete resonance problem with eigenparameter-dependent boundary conditions
Authors: Gao Chenghua , Yang Enming , Li Huijuan
Published: February 2024

In this paper, we considered the existence of solutions to a discrete second-order resonance problem with eigenparameter-dependent boundary conditions. We first transformed the resonance problem into its corresponding equivalent system using the Lyapunov-Schmidt method. In addition, using Schauder's fixed-point theorem and the connectivity theories of the solution set of compact vector fields, we obtained the existence and multiplicity of solutions to the second-order discrete resonance problem with eigenparameter-dependent boundary conditions.
Random dynamics for a stochastic nonlocal reaction-diffusion equation with an energy functional
Authors: Liu Ruonan , Caraballo Tomás
Published: February 2024

In this paper, the asymptotic behavior of solutions to a fractional stochastic nonlocal reaction-diffusion equation with polynomial drift terms of arbitrary order in an unbounded domain was analysed. First, the stochastic equation was transformed into a random one by using a stationary change of variable. Then, we proved the existence and uniqueness of solutions for the random problem based on pathwise uniform estimates as well as the energy method. Finally, the existence of a unique pullback attractor for the random dynamical system generated by the transformed equation is shown.
Global robust stability of fuzzy cellular neural networks with parameter uncertainties
Authors: Zhang Tiecheng , He Wei
Published: February 2024

The global robust stability of uncertain delayed fuzzy cellular neural networks (UDFCNNs) was analyzed in this paper. The major results of this paper provided some new criteria for the existence and uniqueness of the equilibrium point of UDFCNN. Furthermore, suitable Lyapunov-Krasovskii functionals was designed for obtaining the adequate conditions for the global asymptotic robust stability and global exponential robust stability of UDFCNN. Finally, several numerical examples was provided to verify the validity of the results.
Existence theory and stability analysis of neutral $ \psi $–Hilfer fractional stochastic differential system with fractional noises and non-instantaneous impulses
Authors: Ma Yanli , Khalil Hamza , Zada Akbar , Popa Ioan-Lucian
Published: February 2024

In this article, with the help of Laplace transform, the existence of solution was established in a finite dimensional setting for nonlinear $ \psi $-Hilfer fractional stochastic equation with both retarded and advanced arguments driven by multiplicative and fractional noises, with Hurst index $ H \in (\frac{1}{2}, 1) $. At first, we obtained the existence and uniqueness results by using the Banach fixed point theorem (FPT). Second, the existence result was also obtained by applying Schaefer's fixed point theorem with less conservative conditions. Furthermore, we investigated the Hyers Ulam Rasisas stability for the aforementioned system. At the end, an example was illustrated to validate the obtained theoretical results.
Transformer-based cascade networks with spatial and channel reconstruction convolution for deepfake detection
Authors: Li Xue , Zhou Huibo , Zhao Ming
Published: February 2024

The threat posed by forged video technology has gradually grown to include individuals, society, and the nation. The technology behind fake videos is getting more advanced and modern. Fake videos are appearing everywhere on the internet. Consequently, addressing the challenge posed by frequent updates in various deepfake detection models is imperative. The substantial volume of data essential for their training adds to this urgency. For the deepfake detection problem, we suggest a cascade network based on spatial and channel reconstruction convolution (SCConv) and vision transformer. Our network model's front portion, which uses SCConv and regular convolution to detect fake videos in conjunction with vision transformer, comprises these two types of convolution. We enhance the feed-forward layer of the vision transformer, which can increase detection accuracy while lowering the model's computing burden. We processed the dataset by splitting frames and extracting faces to obtain many images of real and fake faces. Examinations conducted on the DFDC, FaceForensics++, and Celeb-DF datasets resulted in accuracies of 87.92, 99.23 and 99.98%, respectively. Finally, the video was tested for authenticity and good results were obtained, including excellent visualization results. Numerous studies also confirm the efficacy of the model presented in this study.
COVID-19, Satirical Activism, and Chinese Youth Culture: An Introduction
Authors: Yu Haiqing
Published: February 2024

This article introduces the key theme of the special issue on “satirical activism and youth culture in and beyond COVID-19 China.” It contextualizes the case studies offered by the five articles in the special issue in three key terms: COVID-19, satirical activism, and Chinese youth culture. It asks: What are the new and evolving forms and genres of satirical activism in China, and what is the implication of satirical activism on Chinese youth culture? The article points out the significance of the study of dynamics and politics of the relationship between humor and satire, youth, popular culture, and citizen activism in the era of digital connectivity and vernacular creativity; it calls for more research on the roles of humor and satire in manufacturing and managing consent and opinion.
From Diaosi to Sang to Tangping: The Chinese DST Youth Subculture Online
Authors: Zhu Ying , Peng Junqi
Published: February 2024

Diaosi, Chinese slang for “underdogs,” first appeared on the Chinese Internet in 2010, capturing youth discontent amid the cutthroat competition in China. The term became the most popular word by 2012 but would soon be overtaken by sang (“bereavement”), a new youth phenomenon akin to beatnik/hippie/punk culture in the West, which manifested itself in a variety of catchphrases and memes that amplified the weary and suffering human body, eventually leading to the rise in 2021 of the tangping (“lying flat”) movement that called for a life free from societal pressures. This article traces the origin, evolution, and impact of this youth cyber culture, what we call DST. It explores the motivations, perspectives, and actions of participants as well as reactions and responses of the Chinese government and the media.
IoT Devices in Drones
Authors: Shah Imdad Ali , Jhanjhi Noor Zaman , Ray Sayan Kumar
Published: February 2024

Machine learning is a branch of AI, and it gives computers the capacity to learn automatically from data and prior experiences to recognize patterns and make predictions with the least amount of human intervention. Large data sets can yield useful information through machine learning, which uses algorithms to find patterns and learn through iterations. ML algorithms do not rely on any fixed equation that may be used as a model; instead, they use computation techniques to learn directly from data. As the number of samples accessible increases during the “learning” processes, the performance of machine learning algorithms improves adaptively. For instance, one kind of machine learning called deep learning teaches computers to mimic human behaviors, like learning from examples.
Drone Technology
Authors: Shah Imdad Ali , Laraib Areeba , Ashraf Humaira , Hussain Fida
Published: February 2024

Unmanned aerial vehicles are becoming more common among business users and enthusiasts. The integration of technology is having a huge impact on globalization. There is a growing need for forensic examination of these devices due to the popularity of drones and their potential use for criminal activity. One notable example of a technical innovation is drone technology. The usage of drones in various fields, such as military, medical, and agriculture, has increased dramatically. Drones in agriculture give farmers access to real-time data, enabling them to make educated decisions about how best to use farm inputs. Drones are also significant to the healthcare industry because they allow flying vital medical supplies—like blood, vaccines, medications, and lab samples—to remote places in impoverished countries during medical severe situations. Drones monitor enemy movements and enable targeted attacks, which improve security and surveillance operations in the military. Drones have many advantages, but it's essential to recognize that they also have risks.
Student and Teacher Assessment of Digital Education During the COVID-19 Pandemic and After
Authors: Agliz Rachid
Published: February 2024

COVID-19 spread through the entire world and has automatically changed student and teacher perceptions of the entire educational system. This serious pandemic has totally affected students and teachers in their diverse pedagogical tasks. It was therefore important to cope with the far-reaching implications of the virus at the educational level. Obviously, those tremendous implications and changes have given us a glimpse at how the whole system of education could actually change for the better or worse. Most governments around the world have taken swift decisions in order to mitigate the impact of this dangerous and full blown pandemic. Like the rest of the world, the Moroccan government opted for the online education as an alternative to secure a good education for thousands and thousands of students in urban and rural areas. Distance education has completely revolutionized the Moroccan society. Surely, after the pandemic, the students are free to opt for the kind of learning that suits their own interests.
Education With Passion
Authors: Maiorana Francesco , Csizmadia Andrew , Cristaldii Giusy , Riedesel Charles
Published: February 2024

The fourth UNESCO goal for sustainable development addresses issues related to quality education for all. A worldwide effort strives to introduce computing from early studies. In this chapter, the authors will share and analyze approaches for embedding computational thinking (CT) in STEAM teaching, approaches drawn upon their experience as computing subject experts spanning their teaching careers. They will share experiences and best practices on the impact of CT on 1) Creativity supporting all the educational activities; 2) Challenges for educators and their students, with a particular emphasis on how to deliver an inclusive and accessible education addressing the expectation of all the students in the same class from the gifted one to those requiring a special education; 3) Convenience and how computing and CT promote special abilities within STEAM; 4) Custom and ethical principles supporting the teaching and learning process of all; and 5) Citizenship with an emphasis on digital citizenship and wisdom on how CT and computing can be taught, learned, and applied for the social good.
Teachers' and Learners' Acceptance of the Use of Robotics in the Intermediate Phase
Authors: Mihai Maryke Anneke , Mapheto Doctor
Published: February 2024

This study explored teachers' and learners' acceptance of the use of robotics based on their attitudes and experiences in two primary schools, which integrate robotics as a learning tool. Robotics is the current digital technology in the educational sector and offers new possibilities for modeling teaching and learning. The study used the technological acceptance model (TAM) as the theoretical framework and a qualitative approach. The researcher purposively sampled six learners and six teachers from the two identified schools in Pretoria, Gauteng. The study discovered that the integration of robotics in education is demanding, costly, and requires adequate resources. It necessitates additional time to design educational programs, requires more time for workshops and solving technical glitches, and puts more pressure on teachers. Teachers need support with the resolving of hardware and software issues as well as technical maintenance. Learners perceive robotics as a positive and exciting technological learning approach, which promotes teamwork and hands-on learning.
Implementation of Coding and Robotics in South African Public Schools, Fostering Teachers' Self-Directed Learning
Authors: Gorrah Averil , Papers Francois
Published: February 2024

This study utilizes a scoping review of literature to explore the implementation of coding and robotics and its potential to foster and enhance teachers' self- directed learning skills through the implementation of coding and robotics within the South African educational context. This chapter presents a comprehensive synthesis of scholarly articles, reports, and studies published from 2013 to 2023. The study further aims to provide an extensive examination of the current status quo of coding and robotics implementation, as well as the possibilities that arise for professional development as well as the development of self-directed learning skills, also identifying gaps that will guide further studies in this field and later produce a systematic literature review. This scoping review utilizes a methodical and rigorous methodology to identify the current body of literature, discern significant themes, and provide a concise summary of the results.
AI and Computer Science Education
Authors: Laubscher Michael Casparus
Published: February 2024

AI plays a significant role in education in general and very definitely in computer science education. AI offers various benefits, but there are also well-grounded concerns and potential dangers present when AI is used in education. It is therefore essential that a proper investigation and analysis of the need for improved regulation for the use of AI in computer science education is conducted. An ethic of care coupled with ethical guidelines for the use of AI in education should be the minimum requirement, but an approach that enhances human rights in the use of AI in education, might be the safest and most effective approach to ensure that the use of AI does not infringe about the human rights of all stakeholders.
An Introduction to Reinforcement Learning and Its Application in Various Domains
Authors: Amin Samina
Published: February 2024

Reinforcement learning (RL) is a dynamic and evolving subfield of machine learning that focuses on training intelligent agents to learn and adapt through interactions with their environment. This introductory article provides an overview of the fundamental concepts and principles of RL, elucidating its core components, such as the agent, environment, actions, and rewards. This study aims to give readers an in-depth introduction to RL and show examples of its different uses in various domains. RL can allow agents to learn through interaction with an environment, which has led to its enormous interest. The core ideas of RL and its essential elements will be covered in this study, after which it will go into applications in industries including robotics, gaming, finance, healthcare, and more. The fundamental ideas of RL will become clearer to readers, and they will recognize how transformative it can be when used to address challenging decision-making issues. These applications demonstrate the versatility and significance of RL in shaping the future of technology and automation.
Real-Time Billiard Shot Stability Detection Based on YOLOv8
Authors: Yang Boning , Yan Wei Qi
Published: February 2024

This book chapter presents a real-time system for detecting the stability of a player's billiard shot, based on the YOLOv8 neural network. The system comprises a real-time object detection model and a real-time slope monitoring system. The model focuses on detecting four classes: The cue ball, hand, cue stick tip, and the bridge hand (hand support point). The project involved iterative model training on a custom dataset, eventually achieving a YOLOv8 model with 95% accuracy. The stability of a player's shot is detected by simulating slope change of cue stick during aiming, using the cue stick tip and bridge hand. Overall, the project highlights the immense potential of YOLOv8 in sports applications.
Introduction to Emotion Detection and Predictive Psychology in the Age of Technology
Authors: Vasani Vaibhav Prakash , Chandra Umesh , Sahu Gayatri , Boyineni Srinivasulu , Dhamodaran S. , Kumbhkar Makhan , Rai Mritunjay , Gupta Swati
Published: February 2024

A performance study of a deep unified model for a facial expression system using hybrid models like ResNet and DenseNet in a CNN-based image classification framework is crucial. Assessing the effectiveness of this complex system that interprets facial expressions to identify emotions is crucial. Data is carefully prepared and separated to produce a balanced and representative dataset. The hybrid ResNet-DenseNet model architecture is crucial. It must be customised for face expression recognition. Data augmentation, hyper parameter adjustment, and regularisation are used to optimise model performance during training. Accuracy, precision, recall, F1-score, and confusion matrix are used to evaluate the model's performance. A different dataset may be used to evaluate its generalisation skills. Comparing the model to others might reveal its performance.
Pattern Analysis for Feature Extraction in Complex Images
Authors: Muniandi Balakumar , Nassa Vinay Kumar , Pandey Digvijay , Pandey Binay Kumar , Dadheech Pankaj , George A. Shaji
Published: February 2024

A concept that has been shown to be valuable in one circumstance and is likely to be useful in others is known as a pattern. A pattern can be interpreted in a variety of ways, and each interpretation has its own particularizations that are suited to the particular form of the pattern it represents. The term “pattern” can be used to describe anything, like a group of items that function in tandem with one another. The analysis of these patterns is important in order to improve recognition. Finding patterns in data is the primary emphasis of the field of pattern analysis, which is a subfield of artificial intelligence and computer science that employs the usage of algorithms. In the context of a data stream, the term “pattern” refers to any underlying correlations, regularities, or structures. If it finds significant patterns in the data already stored, a system may be able to anticipate generating predictions based on fresh information arriving from a source that is analogous to the one it is currently using.
Strategies for Developing Entrepreneurial Start-Up Scalability and Sustainability
Authors: Ramakrishna Yanamandra , Agarwal Ruchi , Indiran Logaiswari
Published: February 2024

Entrepreneurship is a system of economic players, government, and other mediators who take initiatives to start new businesses, ventures, and start-ups to produce goods and services to meet the need of society in several ways. Entrepreneurship ecosystem (EE) is a system that nurture a proper path for businessmen to start new business and promote entrepreneurial activities. This chapter proposes strategies to develop entrepreneurial ecosystems for achieving startup scalability and sustainability using systematic literature review. The implications of this research will add value to the budding startup owners, government, academicians, researchers, and entrepreneurs. The outcome of this research will enable all stakeholders to understand strategies to manage the resources of startups in an optimal manner for scalability and sustainability.
Implementation of Machine Translation (MT) in University Curricula
Authors: Sañudo Belén Lozano
Published: February 2024

In spite of the fact that in the last few years the widespread implementation of MT has completely reshaped translators' workbench, university curricula are not reacting quickly enough, which widens the gap between skills and competences required in the labor market and those acquired by students during their university education. As a result, the employability of graduates and the value of the degree are being affected negatively. In this chapter, a didactic unit is presented which is based on socio-constructivist methodology and aims at developing students' self-concept as future professionals, who shall assume a central role in the translation process, by interacting with MT and taking on control in the different stages and responsibility for the final product. Contrary to the prevalent idea that the implementation of MT in early stages of translator training may interfere with the development of translation competence, this didactic unit highlights its benefits.
Digital Progress and Information Society
Authors: Domazet Ivana S. , Marjanović Darko
Published: February 2024

The chapter aims to show the development of the information society in the EU countries, its digital progress, and its repercussions on the economy of Serbia as a country that is a candidate for EU membership. The methodological concept and the analysis are based on the Digital Economy and Society Index (DESI), an official report of the European Commission. The DESI 2022 reports track the advancements made in the digital space in EU Member States and are primarily based on 2021 data. The findings of the study revealed that although Member States made progress in their efforts to go digital during the COVID-19 timeframe, they are still unable to address the gaps in digital skills, transform SMEs into digital businesses, or put out cutting-edge 5G networks. Also, the results show moderate progress in the development of the information society in Serbia in terms of the functioning of the internal market for electronic communications, the availability of modern electronic services, the development of information security, and the application of eGovernment and eCommerce.
What Do We Know About Artificial Intelligence and Blockchain Technology Integration in the Healthcare Industry?
Authors: Oberoi Sumit , Arora Sugandh , Verma Balraj , Roy Krishna Kanta
Published: February 2024

This study aims to identify artificial intelligence and blockchain technology's publication productivity and intellectual structure in the healthcare industry. This study employs a bibliometric-content analysis technique to determine intellectual structure and publication productivity. The Scopus database analyses identified research articles from 2018 to 2023. The findings of the thematic mapping show that AI and blockchain are emerging techniques and topics such as “smart healthcare”, “patient-centric”, “healthcare management”, “virtual & augmented reality”, “decentralization”, etc. are the potential and new dimensions that can be looked upon in future themes. This study advances knowledge by providing a current and future overview of AI and blockchain integration in the healthcare industry that would create new and enhance existing research streams.
Cyber Security in the Cloud
Authors: Wani Nahida Majeed , Verma Ajay
Published: February 2024

In an era defined by digital interconnectivity, securing information in the cloud is paramount. By harnessing the power of present advanced technologies, organizations can fortify their defenses against evolving cyber threats while simultaneously embracing environmentally conscious practices. The model begins by integrating machine learning (ML) algorithms into fabric of cyber security. Anomaly detection, threat prediction, and adaptive response mechanisms enable a proactive defense, continually evolving to thwart emerging threats. Beyond the realm of cyber security efficacy, ML optimizes resource utilization, contributing to the sustainability of cloud operations. Complementing this adaptive intelligence, cloud cryptography emerges as a cornerstone for securing data at rest and in transit. From traditional encryption to quantum-resistant cryptographic techniques, the model ensures confidentiality and integrity of information. Sustainable cryptographic practices, coupled with efficient key management, further mitigate the environmental impact associated with cryptographic operations.
Reinventing the Global Order
Authors: Modgil Anuj , Marshall B.
Published: February 2024

In an ever-evolving global landscape, the need for strategic foresight and innovative approaches to address contemporary challenges has never been more essential. This chapter delves into the profound transformations taking place in today's international relations and geopolitical dynamics. This comprehensive analysis examines the driving factors and practical implications of this reinvention, encompassing technological advancements such as Industry 4.0, blockchain, and cybersecurity. Furthermore, the exploration of governance models, including collaborative and participatory systems, highlights the means for states and non-state actors to engage in proactive dialogue and collective problem-solving. By dissecting the intricate links between digital sovereignty and data privacy, this chapter underscores the pivotal role of equitable access to resources and the protection of individual rights in a hyperconnected world.
Building Capabilities and Workforce for Metaverse-Driven Retail Formats
Authors: Singha Ranjit , Singha Surjit
Published: February 2024

The metaverse, a breakthrough virtual reality environment, offers boundless retail potential. Metaverse-driven retail needs a good strategy to succeed in a time of changing consumer expectations and the digital revolution. This chapter covers metaverse-driven retail preparation tactics. The metaverse allows retail innovation and adaptation during e-commerce and COVID-19 pandemic upheavals. Understanding metaverse dynamics and developing the abilities is crucial. Determine metaverse applicability to retail, define requisite capabilities, analyze staff competencies, and establish practical training and development programs. Examples include understanding metaverse technology, immersive shopping, data-driven personalization, and strong cybersecurity. Digital fluency, collaboration, design, and cybersecurity awareness are workforce competencies. This chapter stresses metaverse readiness through training, growth, and strategic alignment. It emphasizes that the metaverse transforms reality and opens up new possibilities.
Incorporating the Metaverse Into the Green Banking Revolution
Authors: Devesh Sonal , Mahale Prasad , Subburayan Baranidharan
Published: February 2024

This study aims to explore the awareness and perceptions of green banking among bankers and customers in rural and semi-urban areas of India. A structured questionnaire was employed to gather information from 807 customers and 200 officials of selected commercial banks, utilizing the snowball sampling method. The study utilized chi-square and factor analysis techniques. The chi-square test results revealed an association between educational status and the customer's opinion regarding green banks. Factor analysis derived three key factors influencing the adoption of green banking: convenience and environmental sustainability, financial and technological advantages, and customer retention and prestige. The findings indicate that green banking services provide more benefits to its customers than traditional banking.
Climate Change on Our Plates
Authors: Rawat Harish , Sinha Keshav
Published: February 2024

Climate change poses a significant threat to global food safety and security. Rising temperatures, extreme weather events, and changes in precipitation patterns create favorable conditions for foodborne pathogens, disrupt pest distributions, and alter crop yields and nutritional composition. This chapter examines the intricate interplay between these factors and emphasizes the urgency of adopting innovative agricultural practices, implementing adaptation strategies, and nurturing collaborative efforts to ensure resilient food systems.
Promoting Health Equity and Social Resilience in Black and Brown Communities
Authors: Richardson Kevin
Published: February 2024

In recent years, the intersectionality of environmental, social, and economic factors has become increasingly evident in the realm of public health. One crucial aspect that demands our attention is the pervasive health disparities faced by Black and Brown communities, which often result from systemic inequalities in access to nutritious food, economic resources, and healthcare. For instance, studies have consistently shown that Black and Brown communities are disproportionately affected by food deserts, limiting their access to fresh and healthy food options. Furthermore, the economic challenges faced by individuals in these communities can exacerbate the problem, creating a cycle of limited resources, increased crime rate, drug abuse, and compromised well-being in general.
Leveraging LinkedIn to Build Your Online Persona
Authors: Graf Danielle Gervacio
Published: February 2024

LinkedIn is the world's most popular job-oriented social networking site. It has helped individuals obtain jobs or connect with associates in their wanted career fields. By leveraging their LinkedIn profiles, individuals can highlight skills and tasks, change their job titles, add certifications to their name, and ask for endorsements from connections. Participants in this study found value in making these alterations. They believed it helped them reach their goals, whether obtaining a new role, growing their professional network, or passive job searching. This study also explores the use of artificial intelligence tools to help individuals build their online personas. Although this study has several limitations, it provides a baseline for understanding the importance of an individual's online professional persona when reaching their professional goals.
Enhancing Prediction Precision and Reliability in Cervical, Lung, and Breast Cancer Diagnosis
Authors: Ghosh Sayani , Dutta Arnab , Ghosh Shivnath , Chaudhuri Avijit Kumar
Published: February 2024

This study covers a wide range of cancers, focusing on cervical, lung, and breast cancer. Developing fast, accurate, and interpretable machine learning models for early diagnosis is critical to reducing the multifactorial mortality associated with these cancer types. Using a two-stage hybrid feature selection method, this study evaluates classification models using specific cervical, lung, and breast cancer data obtained from the UCI Machine Learning Repository. The cervical cancer dataset contains 36 features, the lung cancer dataset contains 16 features, and the breast cancer dataset contains 31 features. In the first stage, a random forest architecture is used for feature selection to identify features 5,7, and 7 that show a strong correlation with their cancer while reducing the difference between them. In Stage 2. Logistic regression (LR), naive bayes (NB), support vector machine (SVM), random forest (RF), and decision making (DT) were used to identify cervical cancer, lung, and breast cancer patients for five selections.
Bone Fracture Detection and Classification Using Deep Learning Techniques
Authors: Rao B. Narendra Kumar , Pranitha B. , Reddy B. Kushal , Varsha D. , Reddy N. Nithin
Published: February 2024

The accurate classification of bone fractures plays a pivotal role in orthopaedic diagnosis and treatment planning. This study investigates the integration of deep learning techniques in fracture classification, specifically focusing on the analysis of bone fractures using X-ray images. Leveraging convolutional neural networks (CNNs), a subset of deep learning algorithms, our research aims to develop an automated system for precise fracture classification based on X-ray imaging data. The inherent capabilities of CNNs to extract nuanced features from medical images are harnessed to discern subtle details in fracture patterns, thereby enhancing diagnostic accuracy. The study addresses challenges associated with diverse fracture types and variations in X-ray image quality, employing deep learning methodologies to overcome these obstacles. The proposed model seeks to streamline the fracture classification process, offering a standardized and efficient approach in orthopaedic diagnostics.
Internet of Things-Combined Deep Learning for Electroencephalography-Based E-Healthcare
Authors: Das Sima , Ghosh Ahona , Saha Sriparna
Published: February 2024

Deep Learning (DL) is the most popular subset of machine learning, with many applications in healthcare and other areas. On the other hand, electroencephalography has effectively solved different brain activity-related healthcare applications. This non-invasive data collection method can monitor and diagnose brain-based health conditions as it is painless and safe. This chapter discusses different smart healthcare applications of DL. Different DL models applied in the existing literature have been described also, from which future researchers will benefit by having a clear insight into the concerned area. Then a smart health framework has been proposed where step by step-by-step process of designing the system has been presented using the Internet of Things (IoT) combined with DL.
Adopting of Smartphone Technologies Amongst Older Adults in Windhoek, Namibia
Authors: Semente Efigenia Madalena Mario , Haragaes Ricartha B.
Published: February 2024

Recent technological advancements show that mobile phones are becoming an increasingly significant part of our daily lives. Older adults (OAs) [60+] constitute a key demographic for this study. This study aims to determine the features of smartphone technologies used by OAs in Windhoek and to analyse the possible factors that may influence their adoption as well as to assess OAs perceptions of smartphones. A quantitative research method was adopted. A structured questionnaire was used to collect data from 99 OAs in Windhoek through convenience sampling method. Data were analysed employing SPSS. The study revealed that OAs do not fully adopt smartphones. However, they believe that smartphones provide benefits such as giving entertainment and curbing loneliness. The findings suggest there is a relationship between the smart technology (ST) features and perceptions towards the adoption of STs by OAs in Windhoek. This study can educate smartphone manufacturers and developers about elements that should be considered when designing communication devices and applications for OAs.
Factors Affecting the Adoption and Use of ChatGPT in Higher Education
Authors: Alshammari Sultan Hammad , Alshammari Mohammed Habib
Published: February 2024

The current study aims at assessing the factors which could affect students' use of ChatGPT. The study proposed a theoretical model that included five factors. Data were collected from 136 students using a questionnaire. The data were analyzed using two steps: CFA for measuring the model and SEM for analyzing the relationships and testing hypothesis. The findings revealed that both performance expectancy and facilitating conditions significantly influenced students' intentions to use ChatGPT. Contrary to expectations, both social influence and effort expectancy had insignificant effects. By elucidating the core factors influencing the utilization of ChatGPT, this study can provide valuable insights for policymakers. Furthermore, this study contributes to the existing literature and lays the foundation for future research seeking a deeper understanding of the factors influencing the use of other AI technologies in teaching and learning.
Constructing an Interpretive Structural Model to Unravel the Interconnected Drivers of Teaching Quality in Higher Education
Authors: Almerino Jr. Porferio , Martinez Marilou , Sala Jr. Rogelio , Maningo Kent , Garciano Lourdes , Catyong Christine , Guinocor Marvin , Alcantara Gerly , de Vera John , Calasang Veronica , Mangubat Randy , Peconcillo Jr. Larry , Peteros Emerson , Wenceslao Charldy , Villarosa Rica , Ocampo Lanndon
Published: February 2024

Identifying the primary factors of teaching quality remains a pivotal agenda for informed decision making, strategic planning, and resource allocation. This study builds upon ten key factors derived from previous research and recognizes the inherent complexity within their relationships. Emphasizing the necessity for a structured model, this work employs an interpretive structural modelling (ISM) approach and Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis for constructing a hierarchical model that delineates the interrelationships among the factors influencing teaching quality. The findings indicate the substantial impact of intrinsic factors, particularly teachers' individual and psychological characteristics, on other factors. Additionally, our analysis highlights the critical role of student composition in enhancing overall teaching quality. These insights significantly contribute to the literature by offering valuable guidance to decision makers for maintaining teaching quality within higher education institutions.
Induction of MTHFD2 in Macrophages Inhibits Reactive Oxygen Species–mediated NF-κB Activation and Protects against Inflammatory Responses
Authors: Cui Yan , Li Zihan , Ni Lina , Yu Sujun , Shan Xiao , Hu Penghui , Ji Zemin , Jing Weijia , Zhou Yanzhao , Wang Baochen , Dong Hongyuan , Zhou Jinxue , Xie Keliang , Yu Qiujing
Published: February 2024

The one-carbon metabolism enzyme methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is critical for cancer cell proliferation and immune cell phenotypes, but whether it can contribute to macrophage inflammatory responses remains unclear. In this study, we show that MTHFD2 was upregulated by LPS in murine macrophages upon activation of the TLR4–MyD88–IKKα/β–NF-κB signaling pathway. MTHFD2 significantly attenuated LPS-induced macrophage proinflammatory cytokine production through its enzymatic activity. Notably, ablation of myeloid MTHFD2 rendered mice more sensitive to septic shock and CCl4-induced acute hepatitis. Mechanistically, MTHFD2 restrained IKKα/β–NF-κB activation and macrophage inflammatory phenotype by scavenging reactive oxygen species through the generation of NADPH. Our study reveals MTHFD2 as a “self-control” mechanism in macrophage-mediated inflammatory responses.
Risk of Cardiovascular Disease according to Baseline Low-Density Lipoprotein Cholesterol Level in Different Age Groups in Korean Diabetes Population: A Cohort Study
Authors: Yoo Tae Kyung , Han Kyung-Do , Rhee Eun-Jung , Lee Won-Young
Published: February 2024

Background: The association between low-density lipoprotein (LDL-C) levels and cardiovascular disease (CVD) risk in different age groups within the diabetes mellitus (DM) population remains unclear. The cohort study was conducted to investigate this relationship.Methods: We assessed the 2009 to 2012 Korean National Health Screening and National Health Insurance Service records, with follow-up to the primary outcome (myocardial infarction [MI] or stroke) or December 2018. After excluding the participants with a history of MI or stroke, 2,227,394 participants with DM were included and categorized according to baseline LDL-C levels and age. Cox proportional hazards modeling was conducted. The CVD risk of age <40 years and LDL-C <70 mg/dL was set as the reference. In each age group, LDL-C <70 mg/dL was used as a reference for the subgroup analysis.Results: The cut-off LDL-C value for increased MI risk in each age group varied (<40 years old, LDL-C ≥160 mg/dL: hazard ratios [HR], 2.03; 95% confidence interval [CI], 1.644 to 2.506) (40–49-year-old, LDL-C <115 mg/dL: HR, 1.245; 95% CI, 1.04 to 1.489) (50–59-year-old, LDL-C <115 mg/dL: HR, 1.21; 95% CI, 1.014 to 1.445) (60-69-year-old, LDL-C <145 mg/dL: HR, 1.229; 95% CI, 1.022 to 1.479) (≥70 years old group, LDL-C <100 mg/dL: HR, 1.238; 95% CI, 1.018 to 1.504). The cut-off LDL-C values for increased stroke risk varied in each age subgroup (<40 years old, LDL-C ≥160 mg/dL: HR, 1.395; 95% CI, 1.094 to 1.779) (40–49-year-old, LDL-C <145 mg/dL: HR, 1.13; 95% CI, 1.019 to 1.253) (50–59-year-old, LDL-C <160 mg/dL: HR, 1.079; 95% CI, 1.008 to 1.154) (60–69-year-old, LDL-C <130 mg/dL: HR, 1.07; 95% CI, 1.022 to 1.119) (≥70 years old, LDL-C <115 mg/dL: HR, 1.064; 95% CI, 1.019 to 1.112).Conclusion: The effect of LDL-C on the risk of CVD differs depending on the age of the population with DM.
Quality of Life in Patients with Phenylketonuria: A Systematic Review
Authors: Jahangiri Zahra , Rostampour Noushin , Hovsepian Silva , Chegini Rojin , Hashemipour Mahin
Published: February 2024

The impact of phenylketonuria (PKU) on Quality of life (QoL) has been a topic of interest in recent research. This article reviews current researches on the impact of PKU on QoL. The review examines factors that may influence QoL, such as age, metabolic control, and treatment adherence. In this systematic review study, relevant articles were identified using a search strategy built with the keywords phenylketonuria, PKU, or hyperphenylalaninemia (or their synonyms) and QoL in Web of Science, Scopus, and PubMed databases. After identifying the articles, duplicates, reviews, scientific abstracts, articles published in languages other than English, and non relevant studies were excluded. The search strategy identified 951 records from databases, and after excluding duplicates, irrelevant studies, and those published in non English languages, 26 records were left that contained data on 1816 patients with PKU/hyperphenylalaninemia. The studies included both children/adolescents and adults. Overall, the studies found that the QoL of PKU patients was comparable to normative data, but some aspects such as emotional health and school functioning were lower. Metabolic control was found to significantly correlate with QoL. Younger patients and men had better QoL in several studies, while late treated patients and those with lower education had worse outcomes. It is concluded that QOL in patients with PKU is similar to the general population. However, given the chronic nature of the condition, it is important to pay special attention to their QoL. Poor QOL is associated with female gender, lower education, older age, and poor metabolic control.