bluuebunny/crossref_metadata_2025 huggingface dataset:
Inferring the CO2 Abundance in Comet 45P/Honda-Mrkos-Pajdušáková from [O i] Observations: Implications for the Source of Icy Grains in Cometary Comae
Authors: Huffman Mikayla R. , McKay Adam J. , Cochran Anita L.
Published: February 2024

The study of cometary composition is important for understanding our solar system's early evolutionary processes. Carbon dioxide (CO2) is a common hypervolatile in comets that can drive activity but is more difficult to study than other hypervolatiles owing to severe telluric absorption. CO2 can only be directly observed from space-borne assets. Therefore, a proxy is needed to measure CO2 abundances in comets using ground-based observations. The flux ratio of the [O i] λ5577 line to the sum of the [O i] λ6300 and [O i] λ6364 lines (hereafter referred to as the [O i] line ratio) has, with some success, been used in the past as such a proxy. We present an [O i] line ratio analysis of comet 45P/Honda-Mrkos-Pajdušáková (HMP), using data obtained with the Tull Coudé Spectrograph on the 2.7 m Harlan J. Smith Telescope at McDonald Observatory, taken from UT 2017 February 21–23, when the comet was at heliocentric distances of 1.12–1.15 au. HMP is a hyperactive Jupiter-family comet (JFC). Icy grains driven out by CO2 sublimation have been proposed as a driver of hyperactivity, but the CO2 abundance of HMP has not been measured. From our [O i] line ratio measurements, we find a CO2/H2O ratio for HMP of 22.9% ± 1.4%. We compare the CO2/H2O ratios to the active fractions of the nine comets (including HMP) in the literature that have data for both values. We find no correlation. These findings imply that CO2 sublimation driving out icy grains is not the only factor influencing active fractions for cometary nuclei.
The Use of Vasopressor and Tourniquet to Reduce Hemorrhage during Myomectomy for Uterine Fibroid Removal: A Systematic Review
Authors: Djusad Suskhan , Bimo Ario , Futihandayani Annisa
Published: February 2024

AIM: This review aims to evaluate the efficacy of vasopressin and tourniquet to reduce blood loss during myomectomy for uterine fibroids removal.
BACKGROUND: Uterine fibroids and uterine leiomyomata, the most common benign tumors in women, can be difficult for women trying to conceive. Surgical therapy, such as hysterectomy or myomectomy, is one of the main treatment options. Excessive blood loss may occur during the procedure. Clamps or tourniquets around the uterine vessels are commonly used as anti-bleeding precautions during myomectomy. Intra-myometrial injection of vasopressin at the base of the largest fibroids and during hysteroscopic myomectomy is effective in reducing blood loss during laparoscopic myomectomy.
METHODS: A literature search and computer-aided comprehensive electronic bibliographic search was performed using PubMed, Science Direct, SCOPUS, and Wiley for studies published from January 1997 to November 2017.
RESULTS: Ten studies met the eligibility criteria and were included in this review. Each study assessed and compared the effects of vasopressin to the control group that exerts a hormonal hemorrhagic effect during myomectomy procedures. About 50% of vasopressin hemostatic agents have better outcomes in reducing blood loss. However, the use of both vasopressin and tourniquet simultaneously failed to reduce blood loss.
DISCUSSION: Intramyometrial vasopressin injection resulted in a higher efficacy to decrease blood loss during myomectomy. Despite deciding which methods provide the most effective way to decrease blood loss, the two techniques showed excellent outcomes for the quality of the myomectomy procedure itself.
CONCLUSION : Hemostatic medications have shown to have positive effects on myomectomy patients. The predicted volume of blood loss significantly decreased by both the mechanical tourniquet approach and the injection of the hormone vasopressin.
Epigenetics and Cellular Memory
Authors: Heard Edith
Published: February 2024

How is the information contained in our genes read, memorized and interpreted? What mechanisms control gene expression in an individual or across generations? The understanding of these mechanisms is crucial to knowledge on life forms.

Epigenetics studies the influence of a genome’s cellular history on the way in which it is read. Since the sequencing of the complete human genome in the early twenty-first century, epigenetics has also given rise to the hope that we are “more” than the sequencing of our genes. This is probably why there is currently a huge upsurge of interest in this discipline.
Water in International Law: Between Singularity and Plurality
Authors: Boisson de Chazournes Laurence
Published: February 2024

Essential to human life and activity, freshwater is increasingly becoming a concern of international law. Its regulation, initially focused on rivers shared by several States, has gradually shifted to other sources of water. Today, its scarcity, the need for sustainable distribution of its uses, its use as a weapon of war, its vulnerability to environmental degradation and the prevention of disputes are all issues that call for the mobilization and reinforcement of international law.
La compensation écologique permet-elle vraiment de tendre vers l’absence de perte nette de biodiversité ?
Authors: Padilla Brian , Gelot Salomée , Guette Adrien , Carruthers-Jones Jonathan
Published: February 2024

Depuis 2016, le droit français poursuit un objectif de non perte nette de biodiversité en imposant aux projets ayant des incidences sur la biodiversité d’éviter, de réduire, puis de compenser ces incidences. Les mesures compensatoires doivent générer des gains écologiques suffisants, ce qui suppose qu’elles soient réalisées sur des sites en mauvais état écologique. Cet article analyse le contexte écologique dans lequel ont été réalisées 1153 mesures compensatoires, entre 2017 et 2021, sur le territoire hexagonal français. En utilisant des données de la "naturalité potentielle de France métropolitaine", nous comparons d’une part les scores d’intégrité biophysique des sites choisis et d’autre part les scores de qualité écologique incluant un indice de connectivité du paysage autour des sites, par rapport aux scores nationaux. Nos résultats montrent que 64 % de la surface des sites de compensation se situe sur des espaces où le score d’intégrité biophysique est supérieur à la médiane française, et que 40 % se situe sur des espaces où il est supérieur au dernier quartile. En revanche, la majorité des sites de compensation se trouve dans des paysages présentant une qualité écologique inférieure à celle du territoire hexagonal. Ces résultats suggèrent que la stratégie de localisation des mesures compensatoires ne vise pas prioritairement à générer un gain écologique important, mais répond à d’autres contraintes économiques et foncières. Cela questionne l’efficacité de la compensation à tendre vers l’absence de perte nette de biodiversité.
Trois pistes d’exploration de l’écoformation : retour sur un trajet de recherche
Authors: Galvani Pascal
Published: February 2024

Cet article présente un trajet de recherche qui témoigne de la pertinence toute particulière des recherches et des pratiques en écoformation au regard des crises écologiques actuelles. Trois grandes pistes d’exploration se dégagent. La première est celle du dialogue avec les cultures amérindiennes. J’analyserai d’abord comment mes expériences dans des contextes amérindiens aux États-Unis et au Québec ont transformé ma relation au monde et m’ont amené à développer une démarche de co-formation interculturelle. La deuxième piste est celle d’une recherche-action pour expérimenter la transdisciplinarité et l’épistémologie de la complexité dans les pratiques universitaires. Cette expérience a contribué à une écologisation de la formation universitaire en reliant les savoirs et la vie. La troisième piste est celle de la réflexion sur les nouvelles pratiques d’auto-éco-formation. De nombreuses pratiques deviennent des voies de connexion au monde vivant : marche itinérante, pistage, école en forêt, aquarelle en plein air, etc. Ces différentes approches montrent le potentiel heuristique du concept d’écoformation pour accompagner les mutations vitales qui s’opèrent tant au niveau personnel, social, que culturel.
Business Resilience in a Cyber World
Authors: Burton Sharon L.
Published: February 2024

Examined is the shift from business impact analysis to the business resilience required to safeguard organizations from cyber-attacks and endure such attacks for Sigma Point with the application of a qualitative intrinsic exploratory case study. Skilled business resilience experts support accepted practices that enhance efficiencies and quality of business continuity strategy and planning programs, plus guard against cyber terrorism. Explored is evidence of the ubiquitous reliance on technology in business strategies. Shown is how business resilience procedures provide an array of advantages. Readers will learn about resilience as the final critical planning, preparative and related action recommended to substantiate that organizations' significant business functions should either persist to function despite serious cataclysms or events of cyber terrorism that otherwise might interrupt services or production, or will be recovered to an operational state within a reasonably short period.
Security and Privacy Challenges in IoT Applications
Authors: Iqbal Shaheen , Nazish Areesha
Published: February 2024

As the digital landscape continues to embrace IoT technologies for their transformative potential, the security and privacy of these networks become paramount. This chapter delves into the critical realm of securing long-range-wide-area-network (LoRaWAN) within the context of IoT applications. Focusing on LoRaWAN, this chapter comprehensively examines the challenges that arise in safeguarding sensitive data. A thorough analysis of the diverse range of devices within these networks and potential vulnerabilities is shown. It also sheds light on the nuanced cybersecurity considerations stemming from LoRaWAN's unique characteristics, such as long-range communication and low-power requirements. By surveying existing research and frameworks, the chapter offers valuable insights into mitigation strategies and best practices. With the overarching goal of enhancing industrial cybersecurity and promoting responsible IoT deployment, this chapter serves as a guide for stakeholders, industry professionals, and policymakers alike.
A Comprehensive Study of Cyber Threats in the Banking Industry
Authors: Shankar Sahana P. , Gudadinni Samarth Manjunath , Mohta Ronit
Published: February 2024

In the contemporary digitally connected world, the banking sector places a significant reliance on technology to provide its customers with seamless online services. This chapter presents a comprehensive analysis of the multifaceted risks that confront the banking sector, with a primary focus on three key assets: the devices employed for online banking access, the network infrastructure, and the centralized data center/servers. Furthermore, the study explores the area of internal attacks, recognizing the threat posed by insiders and providing knowledge into possible scenarios. The chapter presents a practical scenario that demonstrates the use of identified threats and vulnerabilities in banking. The scenario explains how compromised devices, network breaches, and exploited data center vulnerabilities could lead to a breach of sensitive financial information. The chapter enhances our understanding of the complex security landscape in the banking sector by examining various security risks.
Integrative Approaches for Understanding and Characterizing ADHD
Authors: Balani Nisha , Mulchandani Mona
Published: February 2024

This chapter explores innovative methodologies at the intersection of neuroscience, psychology, and technology to advance our understanding of ADHD. By combining multimodal data integration, virtual reality (VR), longitudinal data analysis, and deep phenotyping, researchers gain comprehensive insights. The integration of diverse data sources is facilitated by advanced machine learning and artificial intelligence. VR environments provide controlled simulations for assessing attention and impulse control in real-life scenarios. Longitudinal studies track individuals over time, mapping symptom progression and treatment responses. Deep phenotyping enhances ADHD subtype characterization, encompassing cognitive profiles, sensory processing, emotional regulation, and executive functions. This integrated approach offers a nuanced understanding of ADHD heterogeneity, potentially informing precise diagnostics and personalized interventions. Ethical considerations and careful validation are emphasized as researchers navigate this integrative approach's uncharted territories.
Neurogenerative Disorder Identification From Speech and Hand Drawn Inputs Using Machine Learning Techniques
Authors: Jansi K. R. , Vidhya S. , Sandhia G. K.
Published: February 2024

Parkinson's disease symptoms mainly occur at the age of 60 years or older. There is a need for more accurate methods and objectives for early detection, which doctors can use in all cases to predict Parkinson's disease. The proposed hybrid approach analyses the patient's voice and hand-drawing data. Combining both results, the doctor may conclude the normal or abnormal conditions in patients and prescribe medication based on the category. The dimensions from the spiral drawings are obtained and analyzed using a machine learning algorithm; then, they are compared with trained data sets to get the required results. If both the voice and spiral drawing data state the presence of Parkinson's disease, then the disease is confirmed. If both the voice and spiral drawing state the absence of Parkinson's disease; the patient is without this disease. Finally, a linear regression machine learning algorithm is used to identify the presence of Parkinson's disease.
Shifting Paradigm in Early Detection and Prediction of Alzheimer's Disease
Authors: Bashir Tabassum , Akhouri Deoshree , Hamza
Published: February 2024

Alzheimer's disease (AD) is a progressive, irreversible neurodegenerative disease impacting cognition, function abilities, behavior, and social interactions. Alzheimer's disease progresses along a continuum from preclinical disease to mild cognitive and/or behavioral impairment, and then Alzheimer's disease dementia. AD is a chronic, nonlinearly progressive, multifactorial neurodegenerative disease that affects multiple domains of an affected individual's life during the advanced stage of progression. After the pandemic of COVID-19 and its adverse impact on physical and mental health, current statistics of prevalence are now classified as a silent pandemic, despite these major healthcare challenges, no effective treatment or accurate diagnosis currently exists. To address these challenges, we need a paradigm shift, with focus on the pre-dementia stages of AD. In this perspective, early detection and assessment are crucial and critical for the effective management of AD. Early diagnosis of AD will become important as treatments that alter the underlying disease pathology.
Emergent Applications of Organ-on-a-Chip (OOAC) Technologies With Artificial Vascular Networks in the 21st Century
Authors: Barua Ranjit , Biswas Nirmalendu , Das Deepanjan
Published: February 2024

The organ-on-a-chip (OOAC) technology stands at the forefront of emergent technologies, representing a biomimetic configuration of functional organs on a microfluidic chip. This technology synergizes biomedical engineering, cell biology, and biomaterial technology to mimic the microenvironment of specific organs. It effectively replicates the biomechanical and biological soft tissue interfaces, enabling the simulation of organ functionality and responses to various stimuli, including drug reactions and environmental effects. OOAC has vast implications for precision medicine and biological defense strategies. In this chapter, the authors delve into the principles of OOAC, exploring its role in creating physiological models and discussing its advantages, current challenges, and prospects. This examination is significant as it highlights the transformative potential of OOAC technologies in the 21st century and contributes to a deeper understanding of OOAC's applications in advancing medical research.
Effects of Genetic Counseling on Reducing Prenatal Stress and Autism Rates in the Asia-Pacific Region
Authors: Bi Yanhua , Uludag Kadir
Published: February 2024

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social interaction, repetitive behaviors, and narrow interests. People with ASD often experience additional mental health issues such as depression and anxiety. While genetics have long been considered a significant factor in the development of ASD, recent research indicates that the interplay between genes and the environment is crucial in understanding its underlying causes. This chapter aims to discuss the relationship between prenatal stress and the characteristics of ASD in countries within the Asia-Pacific region. The findings indicate a connection between prenatal stress and the traits of ASD in China, South Korea, and Japan. Further investigation is required to fully comprehend the specific mechanisms involved in this relationship. Genetic consultation can provide insights into potential risk factors, genetic counseling, and guidance on personalized interventions.
Human Brain Imaging for Cognitive Neuroscience
Authors: Shaikh Affaan
Published: February 2024

In this chapter, a brief background of neuroimaging a human brain by data acquisition and preprocessing is provided. Neuroimaging is a medical imaging process that uses various cutting-edge technologies with artificial intelligence and machine learning to produce a clear and specific image of the brain in a non-invasive manner. Neuroimaging methods such as EEG, CT, and MRI allow researchers to directly observe brain activities from different perspectives. Data acquisition and preprocessing are essential steps in the data analysis and machine learning pipeline. They involve collecting, cleaning, and preparing raw data for further analysis or modeling. These steps are used in noise reduction, sharpening, or brightening an image, and contrast enhancement, color correction, makes it easier to identify the key features. By combining functional brain imaging with sophisticated experimental designs, data analysis methods and machine learning algorithms, functions of brain regions and their interactions can be examined and further how the neurodegenerative diseases are diagnosed.
A Comprehensive Survey of Deep Learning Approaches in Neurodegenerative Disease Diagnosis and Prediction
Authors: Boda Pruthvi , Munari Sumanth , Prasanth K. Sai Rama , Ganie Shahid Mohammad
Published: February 2024

In recent years, there has been an adverse rise in neurodegenerative disorders which are commonly found in older people. There has been a lot of research conducted to characterize and diagnose these diseases. Many computational methods, particularly deep learning (DL) models, are being used to diagnose these diseases. In this chapter, the authors conduct an extensive literature survey on neurodegenerative diseases including Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis using deep learning models. Also, they performed a comparative analysis of DL models such as CNN, RNN, ResNet50, DenseNet, etc., which have shown some groundbreaking results for neurodegenerative disease detection and classification using medical image computing including MRI and PET scans. In addition, they have discussed how these DL models are used for feature extraction and to identify disease-relevant biomarkers from high-dimensional data. Furthermore, this study emphasizes the potential of deep learning techniques to revolutionize the diagnosis of neurodegenerative diseases to collaborate in clinical practice.
Deep Learning Approaches in the Early Diagnosis of Parkinson's Disease
Authors: El-Mansoury Bilal , Hamdan Youssef Ait , Smimih Kamal , El Khiat Abdelaati , Draoui Ahmed , Hammani Mohamed , Jayakumar Arumugam Radhakrishnan , El Hiba Omar
Published: February 2024

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. PD is characterized by motor and non-motor symptoms. It is highly established that PD is mainly caused by the degeneration of dopamine (DA) producing neurons in the substantia nigra pars compacta of the midbrain leading to nigro-striatal pathway dysregulation. The diagnosis of PD is difficult since its symptoms are quite similar to those of other disorders and current assessments of symptoms have many limitations. Moreover, there are currently no effective biomarkers for diagnosing this condition or tracking its progression. Recently, digital technologies including artificial intelligence (AI) methods have emerged. Indeed, machine learning and deep learning models can help in the diagnosis and management of PD. Deep learning models have shown promising results in the diagnosis of PD even at the early stages of the disease. This chapter will discuss the potential role of deep learning methods in the early diagnosis of PD.
Automatic Diagnosis of Parkinson's Disease Based on Deep Learning Models and Multimodal Data
Authors: Li Ling , Dai Fangyu , He Songbin , Yu Hao , Liu Haipeng
Published: February 2024

Parkinson's disease (PD) is a common age-related neurodegenerative disorder in the aging society. Early diagnosis of PD is particularly important for efficient intervention. Currently, the diagnosis of PD is mainly made by neurologists who assess the abnormalities of the patient's motor system and evaluate the severity according to established criteria, which is highly dependent on the neurologists' expertise and often unsatisfactory. Artificial intelligence provides new potential for automatic and reliable diagnosis of PD based on multimodal data analysis. Some deep learning models have been developed for automatic detection of PD based on diverse biomarkers such as brain imaging images, electroencephalograms, walking postures, speech, handwriting, etc., with promising accuracy. This chapter summarizes the state-of-the-art, technical advancements, unmet research gaps, and future directions of deep learning models for PD detection. It provides a reference for biomedical engineers, data scientists, and health professionals.
Future Directions and Emerging Trends
Authors: Vemireddy Revanth , Kakaraparthi Harish , Challakolusu Naveen Kumar
Published: February 2024

In this chapter, the authors talk about how AI and deep learning are rapidly transforming the study of neurodegenerative diseases. This chapter highlights the significant advances in neuro-imaging technology and motor function study, thanks to the integration of ML methods in research. The authors also discuss the “label-free identification of neurodegenerative-disease-associated aggregates” technique which is a method used in DL. It is focused on studying diseases like Huntington's disease. The authors also continue to talk about a few more ML methods, like support vector machines, random forests, and CNNs. These techniques can help predict these diseases and also treat conditions such as Alzheimer' s disease or ALS. The pressing need to add more AI and ML technologies in this difficult research area is clearly shown by the authors. While the techniques discussed are promising, there's still a long way to go. The authors talk about future directions of this technology, the challenges, overall impact on diagnostics, treatment, patient care, and the future of this technology itself.
6G-Enabled Internet of Things-Artificial Intelligence-Based Digital Twins
Authors: Kumari Shabnam , Thompson Aderonke , Tiwari Shrikant
Published: February 2024

The convergence of sixth-generation (6G) wireless networks, internet of things (IoT), and artificial intelligence (AI) has changed the way for the development of 6G-enabled IoT-AI based digital twins. These digital twins, virtual representations of physical objects or systems, offer enhanced capabilities for real-time monitoring, optimization, and control. However, as these systems become more interconnected and critical to various domains, cybersecurity and resilience become important issues. This work explores the cyber-security challenges and resilience requirements associated with 6G-enabled IoT-AI based digital twins. It examines potential vulnerabilities, threats, and attacks that could compromise the integrity, confidentiality, and availability of digital twin ecosystems. Moreover, it discusses the measures and strategies that can be employed to ensure cybersecurity and resilience, including secure design principles, authentication and access control mechanisms, anomaly detection, data encryption, and secure communication protocols.
Industry 4.0
Authors: Vijay K. , Gnanavel S. , Sowmia K. R , Vijayakumar R. , Elsisi Mahmoud
Published: February 2024

As the present, fourth generation of production, “Industry 4.0” describes the state of the art. Under this general term, you'll find a number of manufacturing, data-sharing, and automation technologies. Industry 4.0 is driving significant transformation across many different business sectors by focusing on improving process, resource utilisation, and efficiency. The internet revolution has had a dramatic impact on several B2C industries, including media, retail, and finance. The industrial sector, which includes manufacturing, energy, agriculture, transportation, and others, accounts for over two-thirds of global GDP. These sectors will be profoundly impacted by digital transformation initiatives during the next decade. The World Economic Forum predicts that the digital revolution, often known as the fourth industrial revolution, will have far-reaching consequences for our personal lives, professional life, and social connections.
Transformative Effects of Blockchain and IoT in Shaping Industry 5.0 Environments
Authors: Kumari Shabnam , Ragab Mahmoud
Published: February 2024

In the era of Industry 5.0, characterized by the convergence of physical and digital worlds, the integration of blockchain technology and the internet of things (IoT) has emerged as a pivotal paradigm shift. This chapter explores the perspectives, issues, and challenges associated with the amalgamation of Blockchain and IoT to foster a transformative environment for industries. This chapter begins by providing an overview of Industry 5.0 and its key principles, emphasizing the need for robust and secure data handling in this context. It then explains the application of blockchain as a distributed ledger technology to enhance the IoT ecosystem by enabling trust, security, and transparency in data transactions. Various use cases and real-world applications of this fusion (of IoTs and blockchain) are discussed, ranging from supply chain management to smart cities and healthcare. Despite its promising potential, the integration of blockchain and IoT also presents several critical issues and challenges.
Edge-Cloud Collaborative Inference Expending Federated Learning in Task Migration
Authors: Kaliappan Vishnu Kumar , Velusamy Sakthivel , Dhanasekaran P. , Gnanamurthy S. , Illavarasi S.
Published: February 2024

The unique cloud-edge collaboration architecture has been put out in recent years to make up for standard cloud computing's deficiency in data processing capability. Due to the public's increased need for confidential information, federated data mining has been promoted as a solution to traditional centralized machine learning's security flaws. Although federated learning and cloud-edge collaboration are both popular research topics right now, little study has been done on the problem of implementing federated learning in cloud-edge collaborative design. The edge-cloud interactive inference technique necessitates partial task migration in circumstances where several edge devices are deployed, whereas conventional scheduling methods solely migrate tasks at the assignment level. The chapter makes two recommendations for work scheduling methods that can deal with the problem of partial job migration in multi-edge settings. The challenging findings demonstrate that this suggested scheduling strategy can reduce the delay by up to 7.48 xs when compared to the baseline approach.
Privacy-Centric Approach in Leveraging Federated Learning for Improved Parkinson's Disease Diagnosis
Authors: Joshi Rujuta Amit , Mangle Shlok Rahul , Katre Neha , Dalvi Harshal
Published: February 2024

Parkinson's disease (PD), a neurodegenerative disorder that affects millions of people worldwide, requires an accurate and early diagnosis for opportune interventions and individualized treatment. However, the process of diagnosis frequently necessitates access to extensive patient data, which raises significant privacy concerns. Federated machine learning (FedML) techniques provide an enticing solution by permitting model training across multiple data sources without sharing raw data, thereby mitigating privacy risks. In the context of Parkinson's disease diagnosis, this chapter provides a comprehensive review in the field of medical diagnosis by highlighting the potential of FedML strategies in improving PD diagnosis with the use of various FedML techniques. Also, the chapter offers a novel approach which utilizes the strengths of FedML techniques. By distributing the training process and leveraging FedML techniques, the authors propose an approach which harnesses a diverse range of patient data and enables accurate PD diagnosis without compromising patient privacy.
Biometric Identification Using Face Mask DL and Open CV
Authors: Rastogi Rohit , Varshney Yati , Jaiswal Sonali , Sharma Markandey , Gupta Mayank
Published: February 2024

COVID-19 is a disease which spreads with human-to-human interaction. In this situation, humans need to be distanced from each other. To prevent the pandemic, people need to wear masks. Wearing a mask is mandatory for all people. That's why it is important to detect whether a person is wearing it or not. This chapter aims to provide a biometric approach for COVID 19 prevention. In this research the machine is able to check whether a person is wearing a mask or not from an image or a live stream. This research is also a smart approach for smart cities. In this research, the authors generate the artificial dataset by adding the mask on the faces of the persons in the images. And that can be done by the automation of the unmasked people's images dataset. The dataset is trained using tensor flow/keras for providing the classifier which classifies the image. This research is valid on image and on live streams.
Upanishads, Bhagavad Gita, and Yoga
Authors: Uttarkar Kalika Sachin
Published: February 2024

This essay explores the Upanishads' unique contributions to Indian psychology. It delves into the philosophical concepts that differentiate Indian psychology from contemporary psychology. The Upanishadic model of transformation and transcendence, which can foster various therapeutic models for mental health, is discussed in detail. This process begins with understanding self-realization, the nature of Karma and rebirth, life goals, and the path to achievement. The essay emphasizes the model's power to help individuals transcend life's limitations and miseries. It argues that mental health issues cannot be addressed merely at the physical and mental level; spiritual progression is crucial for resolving deeper issues. The essay concludes by suggesting the development of sub-models as therapies and highlights the model's social benefits, such as promoting peace, love, and inclusivity.
Ushering in the Subjective Age of Sri Aurobindo
Authors: Balasubramanian Gomathy Nambiur
Published: February 2024

Sri Aurobindo theorises that in the future, human civilisations and cultures worldwide will turn inwards for knowledge, ushering in the Subjective Age following the current Individualist Rational Age. This new age will integrate the objective epistemologies of recent rational enquiry with subjective epistemologies of the subliminal worlds. Against this vast theoretical canvas, this chapter focuses on the potential of process work as a discipline of applied behavioural sciences to contribute to this civilisational turn through its experiential knowledge of individuals, groups, and societies. Such understanding transcends and integrates the rational mind to arrive at greater and greater planes of truth, consciousness, and bliss. To illustrate this possibility, it draws correspondences between the author's experiences in labs, particularly Suprarational ones, with the theories of Sri Aurobindo on the personality structure and subjective epistemologies.
Mindfulness for Child and Adolescent Well-Being
Authors: Dhamodharan M.
Published: February 2024

The chapter examines the significant impact of mindfulness on children and adolescents. Mindfulness is the mental state defined by greater concentration, consciousness, and focus on the here and now, free from judgment. This chapter starts with the theoretical background of mindfulness and the current developments of mindfulness related to children's and adolescents' wellbeing. There are three main domains focused on mindfulness. Begin with emotional domains—that explains how mindfulness helps to regulate the emotions. Secondly, cognitive domains explain how mindfulness practices help cognitive development and academic performance in educational settings. Thirdly, social and interpersonal domains explain the uses of mindfulness to improve social skills and interpersonal relationships with peer groups, family, and society. The chapter concludes with the role of parental and educational institutions in promoting mindfulness among children and adolescents to create a positive impact on their lives.
Applications of Gene Expression Programming for Estimating CFRP Wrapping Effects on the Bond Strength After Elevated Temperature Exposure
Authors: Ghatte Hamid Farrokh
Published: February 2024

The study reports on the proposed gene expression programming (GEP) to predict the ability of CFRP wrapping to enhance the post-heated bond strength between steel rebar and concrete. For this purpose, the results of 80 pullout specimens (150×150×250 mm) were selected in two categories of compressive strength of concrete: C30 and C40. In the experiments, a 20 mm rebar was placed in the corner of each specimen with a 25- or 35-mm clear cover and 200 mm of bond length. The specimens were subjected to different levels of heat before using CFRP jackets. The exposure to heat reduced the concrete-rebar bond strength; however, confinement with CFRP jackets significantly improved the bond strength between steel rebar and concrete. The estimated bond strength could be predicted by the accuracy and high prediction ability of the proposed model. Based on the findings, machine learning can bring significant improvements and benefits to the civil engineering industry in terms of the estimation of the mechanical properties of the materials in various conditions.
Open Access, Open Doors
Authors: Morgan Emily
Published: February 2024

The open access (OA) movement, a paradigm shift in scholarly communication, has emerged as a cornerstone of modern higher education and academic libraries. By removing financial barriers, OA ensures unrestricted access to academic resources, democratizing knowledge and fostering a more equitable learning environment. Furthermore, OA promotes interdisciplinary collaboration, enabling scholars to engage in expansive knowledge exchange across diverse fields, fueling innovation. Academic libraries have become instrumental in curating vast repositories of openly accessible content, enhancing their role as facilitators of information literacy. Embracing open access principles enhances the visibility of institutions and researchers but also promotes inclusivity, ensuring that diverse voices find resonance in academic discourse. Explored in this chapter are the multifaceted advantages of the OA movement, underscoring its pivotal role in shaping a more accessible, collaborative, and egalitarian landscape within higher education and academic libraries.
Electric Vehicles in India and Customer Perception
Authors: Sagar Sephalika , Upadhyay Devesh K.
Published: February 2024

The authors propose a model for examining the moderating effect of government regulations on electric vehicles and their infrastructure in India. The model was tested empirically using data collected from 101 respondents. Principal component analysis and mediation analysis were employed to analyze the data using JASP 0.17.2.1 (Apple Silicon) software package. Findings based on interaction support the hypothesis that government regulations concerning the manufacturing of electric vehicles, standards and specification of EVs and EV batteries, subsidies, and incentives by central and state governments support. In other words, the potentially negative aftermaths of the lumbering infrastructure of EVs can be controlled and reduced when government regulations concerning infrastructure are implemented meticulously. The research study highlights the theoretical and practical implications of the findings, as well as recommendations for future studies, are suggested.
From a Face-to-Face Educational Scenario to a Remote Educational Scenario
Authors: Khaldi Maha
Published: February 2024

The transition from a face-to-face educational scenario to a remote educational scenario represents an important change in the field of education. This chapter explores the multifaceted journey of educational institutions and educators as they adapt their teaching methodologies to accommodate distance learning environments. By delving deeper into the challenges, opportunities, and strategies inherent in this transition, the chapter aims to provide a comprehensive overview of the evolving landscape. It addresses the technological, pedagogical, and socio-cultural dimensions that influence this change, highlighting the importance of digital tools, learner engagement, and the role of educators as facilitators in the distance learning paradigm. The chapter provides an overview of effective instructional design, assessment methods, and creating inclusive virtual learning communities. By addressing the nuances of this transformation, this chapter contributes to the ongoing discourse on reimagining education in the digital age.
From Educational Innovation to Educational Design
Authors: Khaldi Mohamed
Published: February 2024

Teaching today in the digital age requires being able to adapt to the multiple upheavals affecting individuals and societies. The idea that education systems should equip learners with the skills and competencies to cope with an ever-changing landscape. We often refer to skills such as critical thinking, problem solving, collaborative skills, innovation, digital literacy, and adaptability. What is negotiable is how best to achieve the development of these skills, particularly which teaching and learning approaches are suitable to facilitate or enable the development of complex skills. However, educational innovation must be perceived as a proposed novelty, a change, a creation, a transformation, or even an invention in the fields of pedagogy and teaching. Note that the term “innovation” refers to the act of introducing something new in a particular field.
Exploring Communication and Communication Tools for E-Learning
Authors: Ouariach Fatima Zahra , Zine El Abidine Mohammed , Nejjari Amel
Published: February 2024

In the realm of e-learning, educators are presented with the choice between synchronous and asynchronous methods. Communication assumes a paramount role in the fabric of daily life and the interactions that transpire between individuals. This chapter delves deep into the captivating realm of e-learning, meticulously examining the plethora of tools available in the digital sphere. Communication and its accompanying tools assume an indispensable role within the e-learning environment, serving as virtual bridges that connect learners, educators, and educational content. Moreover, the chapter accentuates the potential of communication and its associated tools in the realm of e-learning, transcending the mere transfer of information. Instead, they foster an interactive, collaborative, and flexible milieu that optimizes learning opportunities, engenders learner engagement, and cultivates the development of crucial skills for the modern era.
The Impact of Data Processing by Neural Networks on Academic Failure
Authors: Admeur Smail , Haddani Outman , Saleh Mohammed Ahmed Moqbel , Attariuas Hicham
Published: February 2024

Academic failure has become a shocking phenomenon, affecting large numbers of students at all stages of education, particularly in the early years, and students and their families experience academic failure in a dramatic way. For a long time, academic failure among university students has been the subject of heated debate. Many educational psychologists have tried to understand and explain it. Statisticians have tried to predict it. This research (chapter) aims to classify students into several categories, as well as using artificial neural networks to classify first-year students and identify variables likely to explain the problem (students failing at the end of their first year).
Pitfalls in Online Digital Content Creation for Undergraduate Dental Education
Authors: Rao Gururajaprasad Kaggal Lakshmana
Published: February 2024

Undergraduate dental education, which is at the forefront of utilizing innovative pedagogies and instructional methods to deliver learning content, saw an unprecedented disruption globally due to the pandemic. This disruption in dental education resulted in limited access to traditional forms of learning content, which relies heavily on face-to-face teaching. An asynchronous mode of delivery was used to support and maintain the continuity of lectures and knowledge exchange. The mainstream method used was through pre-recorded video lectures, using PowerPoint lecture slides hosted on YouTube. This approach worked for maintaining the knowledge transfer continuum; though the creation and distribution of online content was in itself a challenge to dental educators. Thus, the chapter aimed to identify the pitfalls in digital content creation in undergraduate dental education. Furthermore, the chapter highlights the pathways needed to overcome the hindrances of digital content creation both from a pedagogical and technological perspective.
Teaching the Scientific Discourse at the Secondary Level Schools
Authors: Alexandropoulos Georgios
Published: February 2024

After the pandemic with the coronavirus, many teachers and students were trained in the use of digital tools. Indeed, many of these tools are being used more and more frequently and facilitate learning in combination with other methods. In this study, the authors present the teaching approach of scientific discourse to high school students, aged 17-18 (secondary level education in Greece). Scientific discourse is characterized by specific conventions, standards, and practices that are recognized and accepted within the scientific community. It typically involves various forms of communication, including scientific papers, research articles, conference presentations, and scientific discussions. Through this didactic scenario, students will acquire the knowledge required for communication, discourse, persuasion techniques, and means.
The Place of E-Learning in Language Learning
Authors: Ait-Hroch Aicha , Ibrahimi Ahmed
Published: February 2024

The way languages are learned in today's educational environment has changed dramatically due to the incorporation of technology and the introduction of new methods and approaches, such as e-learning. This chapter breaks down aspects that relate e-learning to language acquisition. It explains e-learning, its emergence, its history throughout the years, and its most efficient platforms and systems. Then, it discusses language learning, its importance, and the basic language skills that provide an effective basis for language learning. Next, it discusses the relationship between e-learning and the development of language skills, how to learn languages through e-learning, and its contribution to language acquisition. Further on, it focuses on instructional design, which plays a major role in the success of training programs; discovers some of the most used instructional design models; and digs into the ADDIE model and how to design educational devices through it. Afterwards, it illustrates all of that with an example of an e-learning educational scenario designed using the ADDIE model.
Machine Learning in Adaptive Online Learning for Enhanced Learner Engagement
Authors: Er-radi Hicham , Touis Badr , Aammou Souhaib
Published: February 2024

In this chapter, the authors delve into the intricate world of online learning, emphasizing the paramount importance of adaptability in adaptive online learning systems. Beginning with an overview of the dynamic nature of today's digital learners, the chapter underscores the need for learning platforms to be flexible and responsive to individual learner needs. A significant portion of the chapter is dedicated to the potential of machine learning as a method to enhance adaptability. The authors elucidate how machine learning, with its ability to represent knowledge in a structured and interconnected manner, can be harnessed to create more personalized and contextually relevant learning experiences.
Decoding Diverse Learning Needs
Authors: Touis Badr , Er-radi Hicham , Aammou Souhaib
Published: February 2024

In the evolving landscape of pedagogical innovation, addressing the diverse learning needs of students remains a paramount challenge. This chapter delves into the intricacies of knowledge heterogeneity and its implications in educational settings. Drawing from the broader research on semantic networks, the authors explore their potential as a tool for decoding and managing the multifaceted learning needs of students. Semantic networks, with their ability to represent complex knowledge structures and relationships, offer a promising avenue for instructional designers to create adaptive and personalized learning experiences. By integrating semantic network methodologies into pedagogical strategies, educators can better understand learners' prior knowledge, misconceptions, and learning trajectories. This chapter provides a comprehensive overview of the theoretical underpinnings of semantic networks, their application in addressing learner diversity, and practical insights for instructional design.
Integration of Information and Communication Technologies in Education for the Deaf
Authors: Izquierdo-Álvarez Vanessa
Published: February 2024

The educational revolution that has been generated by events such as the information society, the digital transformation of organizations, or the pandemic caused by COVID-19 has also had an impact on the educational world. Faced with this scenario, teachers have continued working to offer educational responses adapted to the real world. This work focuses its attention on deaf students, on the educational needs they present, and on being able to solve these needs with the help of technology. Specifically, a pedagogical innovation carried out with the help of an instructional design model for deaf students attending primary and secondary education is presented. A training itinerary organized into three learning modules is proposed to work on concepts related to location, time, and quantity.