bluuebunny/crossref_metadata_2025 huggingface dataset:
Detección y Manejo de Pacientes con Estratificación de Muy Alto Riesgo Cardiovascular en la Unidad de Medicina Familiar No 9, del Imss
Authors: Romero Ruiz Carolina , Vargas Aragón Ricardo , Aguilar Hernández Guadalupe
Published: February 2024

El riesgo cardiovascular muy alto es la probabilidad de más del 10% de desarrollar un evento cardiovascular fatal a 10 años. El control del nivel de colesterol LDL con estatinas a dosis adecuadas es indispensable. Objetivo: Identificar la detección y manejo en pacientes con estratificación de muy alto riesgo cardiovascular en la Unidad de Medicina Familiar No 9, del IMSS. Material y métodos: Estudio transversal, analítico y retrospectivo en 358 pacientes con riesgo cardiovascular muy alto, del 1° de julio al 31 de diciembre del año 2022. Se utilizó el programa CIETmap 2.1 para el análisis de resultados, con estadística descriptiva, análisis bivariado y multivariado para obtener OR e intervalos de confianza al 95%. Resultados: Predominó el sexo femenino (57.8%), promedio de edad 53 años. El riesgo cardiovascular no se encontró plasmado en la nota médica en el 51.1% de los expedientes clínicos, el 92.2% tenía niveles de colesterol LDL fuera de metas terapéuticas, el tratamiento hipolipemiante más frecuente fue atorvastatina 20 mg/día con un 42.2%, sólo el 33.8% de los pacientes tenían tratamiento adecuado de acuerdo con los algoritmos diagnósticos y terapéuticos del IMSS. Conclusiones: No plasmar el riesgo cardiovascular en la nota médica aumenta el riesgo de no establecer un tratamiento farmacológico adecuado y tener niveles de colesterol LDL fuera de metas terapéuticas.
Estado del Arte en Relación del Uso de la Madera para Intervención en el Patrimonio Edificado
Authors: Córdova Cruz Byron René , Arias Salazar Daicy Paola , Guevara Altamirano Erick Fernando
Published: February 2024

Este proyecto de investigación está relacionado con el uso de la madera para desarrollar intervenciones en edificaciones de valor patrimonial, la madera ha sido un material de construcción fundamental a lo largo de la historia. En muchas edificaciones históricas, la madera desempeña un papel clave en la autenticidad del diseño original. Comprender cómo se utilizó la madera en diferentes períodos arquitectónicos es esencial para preservar la historicidad de las estructuras, es un material renovable y sostenible si se gestiona de manera responsable. Muchas estructuras históricas están construidas principalmente con madera, y su conservación requiere un conocimiento profundo de las propiedades de este material. Comprender cómo la madera envejece, se deteriora y responde a factores ambientales es esencial para aplicar métodos de restauración que preserven la integridad original de la estructura, su estudio es esencial para preservar el patrimonio cultural, garantizar la sostenibilidad, abordar los desafíos técnicos específicos y aplicar enfoques innovadores en la conservación del patrimonio edificado.
Brazo Robótico de Tres Articulaciones Mediante Servomotores con Tecnología Arduino
Authors: Ortega Ordoñez Roberto Carlos
Published: February 2024

En este estudio se expone el proceso de construcción de un brazo robótico con una longitud de 0.40 m y tres articulaciones. Este brazo está conectado a una garra mecánica de cierre angular, diseñada para llevar a cabo movimientos y manipulación de objetos. El sistema utiliza servomotores en corriente continua, distribuidos en función de cada articulación dentro del brazo. Estos servomotores tienen una abertura de funcionamiento de 270°, según sus especificaciones técnicas, y son controlados mediante una placa electrónica Arduino Uno, junto con un probador digital multicanal de servomotores. El brazo robótico se ha desarrollado con fines didácticos, destinados a facilitar la enseñanza y el aprendizaje de tecnologías. El controlador utilizado permite realizar movimientos básicos, aunque no de precisión para aplicaciones especiales.
DETERMINING CONCESSIONARY ITEMS FOR “AVAILABILITY PAYMENT ONLY” PPP PROJECTS: A HOLISTIC FRAMEWORK INTEGRATING VALUE-FOR-MONEY AND SOCIAL VALUES
Authors: Guo Yubo , Chen Chuan , Luo Xiaowei , Martek Igor
Published: February 2024

Public-private partnerships (PPPs) have been widely applied in infrastructure development around the world. However, reasonable concessionary items are critical to compromise interest conflicts between government agencies and sponsors to ensure project success. A broad literature review centering on PPP transaction structuring revealed two significant research gaps: (1) a lack of attention to the ‘availability payment only’ (APO) funding method and (2) negligence of the public side’s perspective in determining concessionary items. The research objective was to develop a methodological framework for determining concessionary items in APO PPP projects while considering the interests of the public side. This study proposed a value-for-money (VFM) and social values integrated framework which accommodates discounted cash flow (DCF) analysis, bargaining game modeling, and multi-objectives decision-making (MODM). This framework enables a decision-making process based on both an indifferent feasible interval of concessionary items under a discount rate agreed upon by both parties and an optimal set of concessionary items. Additionally, results of a sensitivity analysis indicated that project construction profit can significantly affect feasible and optimal concession items, and the optimal concession period is less sensitive to changes in risk allocation. The application of proposed model indicated that this paper successfully provides a methodology for determining a feasible interval and an optimal concession items group tailored to APO PPP projects. This study paves the way towards a platform for the public and private partners to jointly and quickly come up with sound PPP concessional items in light of the win-win principle, particularly under the APO funding mechanism.
True Pair-instability Supernova Descendant: Implications for the First Stars’ Mass Distribution
Authors: Koutsouridou Ioanna , Salvadori Stefania , Skúladóttir Ása
Published: February 2024

The initial mass function (IMF) of the first Population III (Pop III) stars remains a persistent mystery. Their predicted massive nature implies the existence of stars exploding as pair-instability supernovae (PISNe), but no observational evidence had been found. Now, the LAMOST survey claims to have discovered a pure PISN descendant, J1010+2358, at [Fe/H] = − 2.4. Here we confirm that a massive 250–260 M
⊙ PISN is needed to reproduce the abundance pattern of J1010+2358. However, the PISN contribution can be as low as 10%, since key elements are missing to discriminate between scenarios. We investigate the implications of this discovery for the Pop III IMF, by statistical comparison with the predictions of our cosmological galaxy formation model, NEFERTITI. First, we show that the nondetection of mono-enriched PISN descendants at [Fe/H] < − 2.5 allows us to exclude (i) a flat IMF at a 90% confidence level; and (ii) a Larson-type IMF with characteristic mass m
ch/M
⊙ > 191.16x − 132.44, where x is the slope, at a 75% confidence level. Second, we show that if J1010+2358 has only inherited <70% of its metals from a massive PISN, no further constraints can be put on the Pop III IMF. If, instead, J1010+2358 will be confirmed to be a nearly pure (>90%) PISN descendant, it will offer strong and complementary constraints on the Pop III IMF, excluding the steepest and bottom-heaviest IMFs: m
ch/M
⊙ < 143.21x − 225.94. Our work shows that even a single detection of a pure PISN descendant can be crucial to our understanding of the mass distribution of the first stars.
Health Emergency Preparedness and Response Authority’s (HERA) role in dealing with the monkeypox emergency in the European Union
Authors: Toshev Atanas , Petkova-Gueorguieva Elina , Mihaylova Anna , Pavlova Galinka , Parahuleva Nikoleta , Balkanski Stefan , Peikova Lily , Getova Violeta , Madzharov Vasil , Gueorguiev Stanislav
Published: February 2024

The current article examines and analyzes the actions taken by the European Commission, specifically through the Directorate-General Health Emergency Preparedness and Response Authority (HERA) – an organization that anticipates threats and potential health crises, through intelligence gathering and building the necessary response capacities), aimed at supporting member states in limiting the spread of MPOX (monkeypox – an infectious disease caused by the monkeypox virus). It explores specific pharmaceutical products and vaccines procured by HERA and how they have been distributed among member states. The article raises questions about the compliance in purchasing pharmaceutical products and vaccines lacking approval for use within the European Union, highlighting the potential new regulatory challenges for Bulgaria if it needs to secure medications for treatment of human smallpox disease such as Jynneos and TPOXX (Tecovirimat) for its citizens. In conclusion, the article notes the swift response of HERA through the procurement of the Jynneos vaccine and TPOXX medicinal product. This swift response may have contributed to the decline of MPOX cases in the European region, potentially due to collaborative efforts among health authorities at both European and national levels. This success underscores the importance of cooperation among health authorities at various levels in combating infectious diseases.
Recherches sur la Sustainability
Authors: Caillaud Emmanuel
Published: February 2024

Le greenwashing montre l’absence de prise en compte des réels enjeux de soutenabilité et d’éthique dans les organisations. Un état de l’art des concepts à prendre en compte (soutenabilité forte, approches éthiques, parties prenantes) sert de base à des propositions managériales.
Recherches sur la Sustainability
Authors: Bourbon Sébastien
Published: February 2024

Les idées de soutenabilité et de développement durable se traduisent dans le secteur immobilier à travers une notion encore neuve : la « valeur verte ». L’essor de ce concept symbolise un processus d’écologisation du marché de l’immobilier. Il s’agit alors d’un défi pour tous les acteurs du secteur, notamment les agents immobiliers. Ce chapitre cherche ainsi à proposer un début de réponse stratégique et managériale pour s’adapter à cette réalité de marché nouvelle et structurelle. Une étude quantitative menée auprès d’agents immobiliers met en évidence trois lectures distinctes des enjeux stratégiques autour de la « valeur verte » et des normes qui l’accompagnent. Pour organiser une stratégie face à cette logique d’écologisation du marché immobilier, il semble pertinent de mener un travail managérial sur la rente cognitive des agents immobiliers.
Que sait‐on du travail ?
Authors: Mias Arnaud
Published: February 2024

Le mouvement de contestation de la réforme des retraites au début de l’année 2023 a rappelé l’impossible dissociation des enjeux de protection sociale et d’emploi des problématiques de travail et de conditions de travail. Il a fait ressortir ce que d’aucuns nomment une « crise du travail » en France (voir la contribution de Maelezig Bigi et Dominique Méda ). En parallèle, les Assises du travail, dont le rapport des garants a été remis en avril 2023, soulignent la nécessaire évolution des pratiques managériales pour faire face aux risques accrus pour la santé au travail, dans un contexte de transitions écologique et numérique.
Que sait‐on du travail ?
Authors: Baghioni Liza , Moncel Nathalie
Published: February 2024

En juillet 2022, un Secrétariat général à la planification écologique ( SGPE ) est mis en place auprès des services de la Première ministre, sous la bannière « France Nation verte ». Il a pour objectif une « mobilisation collective pour réaliser ensemble une transition écologique juste et efficace ». Quelle est la place accordée au travail au sein de ce processus ? Au-delà des effets de la « transition écologique » sur l’emploi mais aussi les métiers et les activités, quelles seraient les conditions d’une écologisation du travail, visant une intégration systémique et systématique des préoccupations environnementales ?
Que sait‐on du travail ?
Authors: Gautié Jérôme
Published: February 2024

Quand on les compare à leurs homologues des pays européens, il est frappant de constater que les Français expriment une bien moindre satisfaction générale quant à leurs conditions de travail et d’emploi, et une forte interrogation sur le sens de leur travail. Ils sont même les champions de l’insatisfaction salariale. Pourquoi cette spécificité française ? La façon dont le travail est organisé et « managé » en France y joue un rôle sans doute essentiel.
Que sait‐on du travail ?
Authors: Delahaie Noélie , Fretel Anne , Petit Héloïse
Published: February 2024

Face au manque de reconnaissance des salariés de la « seconde ligne » mis en évidence durant la crise sanitaire, le gouvernement a fait le choix de s’en remettre aux négociations de branche pour améliorer les conditions de travail et d’emploi des salariés concernés. Plus récemment, il a encouragé l’activité conventionnelle pour limiter les effets de l’inflation (en incitant à la négociation de minima de branche supérieurs au niveau du Smic) ou pour répondre aux difficultés de recrutement. Ce faisant, il réitère une pratique déjà ancienne par laquelle les pouvoirs publics utilisent la négociation collective de branche comme intermédiaire de l’action publique, voire comme vecteur d’une « action publique négociée » ( Groux, 2005 ). La négociation devient un relais clé dans la généralisation et la mise en place des mesures prises par le législateur. Elle en devient même la condition d’application.
Que sait‐on du travail ?
Authors: Gautié Jérôme , Perez Coralie
Published: February 2024

Les ouvriers n’ont pas disparu, même si, avec l’érosion des bastions ouvriers traditionnels, ils tendent à s’effacer des représentations collectives. Leur invisibilisation résulte en partie du fait qu’un grand nombre assure des activités de type tertiaire, notamment dans la logistique, qui a connu un fort développement marqué par l’essor des technologies numériques au cours des dernières décennies (Benvegnù et Gaborieau, 2020).
Que sait‐on du travail ?
Authors: Devetter François-Xavier , Dussuet Annie , Nirello Laura , Puissant Emmanuelle
Published: February 2024

Dans une société vieillissante, l’accompagnement des personnes âgées se révèle crucial et les métiers du care deviennent essentiels. Or, que ce soit à domicile ou en institution, les emplois dans ce secteur sont à la fois mal rémunérés et peu reconnus, et les conditions de travail sont difficiles, entraînant à la longue une usure et une perte de sens pour des travailleuses et travailleurs pourtant fortement impliqués. Paradoxalement, les politiques publiques ne semblent qu’alimenter ce phénomène, en dégradant la situation au travail par une demande de rendement accrue. Il est aujourd’hui urgent de reconnaître et revaloriser ces métiers, insoutenables, mais essentiels au bon fonctionnement de notre société.
Histopathological Image Classification Using Convolutional Neural Networks for Detection of Metastatic Breast Cancer in Lymph Nodes
Authors: Cadillo-Laurentt Diego Alberto , Paiva-Peredo Ernesto Alonso
Published: February 2024

Breast cancer is currently one of the most diagnosed oncological diseases worldwide, with thousands of new cases per year. Early detection and identifying its progression are key to overcoming the mortality rate. A recurrent test, to determine how far the disease has spread throughout the patient’s body, is the histological analysis of the sentinel lymph node near the breast. Although an expert pathologist performs this, it is usually an exhausting and time-consuming task, with a high possibility of error. This work presents a method to detect breast cancer metastasis through histological imaging of sentinel lymph nodes using convolutional neural networks. In this study, the performance of three models DenseNet-121, DenseNet-169 and DenseNet-201 are tested and compared. Experimental results indicated that the accuracy, precision, sensitivity and specificity (97.93%, 97.4%, 97.48% and 98.24%) of DenseNet-201 could reduce pathologist errors during the diagnostic process or serve as a second opinion tool.
Static, Dynamic, and High Cycle Fatigue Analysis of Crossed Spherical Gearing for Robotic Arm Ball Joint: A Finite Element Analysis Approach
Authors: Serna-Landivar José L. , Risco Sernaqué Madelaine Violeta , Rivas Moreano Ana Beatriz , Algoner William C. , Anticona-Valderrama Daniela M. , Zúñiga Porras Walter Enrique , Oliva Guevara Carlos
Published: February 2024

Crossed spherical gearing is used in the joints of robotic arm prostheses and allows mobility in 3 degrees of freedom. This paper aims to evaluate the design of a cross-spherical gear with three different materials, PEEK, AISI 304L, and Ti-6Al-4V, for a robotic arm prosthesis by finite element analysis. ANSYS mechanical software (version 2021 R1) was used to perform the static analysis and evaluate the deformations and stresses, modal analysis of natural frequencies and vibration modes, and high cycle fatigue analysis to determine fatigue resistance. The results obtained in the static analysis show that the maximum stresses are in the same zones for the three materials and have similar values. However, the Ti-6Al-4V and ASI 304L materials have a higher safety factor than PEEK, with a value of 5.17. In conclusion, the crossed spherical gearing is numerically validated using the finite element analysis so that the prototype can be later manufactured at an experimental level, and the values obtained for the crossed spherical gearing of the robotic arm prosthesis can be verified.
TOWARD EFFECTIVE BREAST CANCER DETECTION IN THERMAL IMAGES USING EFFICIENT FEATURE SELECTION ALGORITHM AND FEATURE EXTRACTION METHODS
Authors: Moayedi Seyedeh Maryam Zareh , Rezai Abdalhossein , Hamidpour Seyedeh Shahrbanoo Fallaheye
Published: February 2024

In this paper, an intelligent method is developed for improving the performance of the Computer-Aided Detection (CAD) system. The research objective is to improve the performance of the CAD system in Breast Cancer (BC) detection with high accuracy using thermal images. The research strategy is efficient using feature extraction, feature selection, classification and artificial intelligence methods. In the developed method, the features in the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are extracted from images. The features are selected using the firefly feature selection algorithm. These selected features are observed to be relevant for the abnormality detection in healthy and unhealthy breasts. The [Formula: see text]-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision-Tree (D-Tree) classifiers are then applied to these features for the detection of malignancy in the breast. The breast thermograms of 200 subjects available at the Database for Mastology Research for breast research using InfraRed images, DMR-IR database, are considered for evaluation of our intelligent method. The results demonstrate that the accuracy is 98.8%, 81.5%, and 94.5%, the sensitivity is 99%, 83.15%, and 94%, and the specificity is 98.2%, 80%, and 93% when using SVM, kNN, and D-Tree classifier algorithm, respectively. This reveals the effectiveness of our intelligent method to improve the accuracy of the CAD system in the BC detection.
A Distributed Model for IoT Anomaly Detection Using Federated Learning
Authors: Tahir Sidra , Zaheer Anam
Published: February 2024

Anomaly detection in IoT-based sleep patterns is crucial for early identification of health issues. This chapter presents a distributed model using federated learning for privacy and data security. The proposed approach involves data collection, preprocessing, model initialization, federated learning server, model distribution, and anomaly detection. Sleep pattern data is preprocessed to extract features, initializing the global anomaly detection model. A federated learning server enables collaborative learning with devices, distributing the updated global model iteratively for synchronized anomaly detection. Precision and accuracy metrics yielded 0.67% precision and 0.84% accuracy, showcasing the effectiveness of the distributed model. Leveraging federated learning ensures privacy, data security, and synchronized anomaly detection across devices, supporting early detection of sleep-related anomalies and health interventions.
The Internet of Things (IoT) Applications in Inventory Management Through Supply Chain
Authors: Ozkan-Ozen Yesim Deniz
Published: February 2024

Supply chains are affected by globalization and digitalization, and organizations face a great challenge in order to stay up-to date and competitive. As a very significant element of the supply chains, inventory management plays a crucial role in supply chain success. Therefore, traditional approaches in inventory management should be altered according to new trends. From this point of view, this study focuses on Internet of Things (IoT) applications in inventory management with a scope of supply chains. The aim of this chapter is to analyze the current studies related to IoT impacts in inventory management by conducting a literature review and a bibliometric analysis, and propose the future research directions that are interrelated with the current trends.
Cybersecurity
Authors: Srisakthi S. , Babu C. V. Suresh
Published: February 2024

The chapter thoroughly explores the multifaceted cybersecurity landscape's significance in safeguarding business intelligence amid rapid technological evolution. It starts with an overview of cybersecurity, defining its scope, exploring its historical context, and highlighting ongoing research. The chapter emphasizes cryptography's foundational role, addressing challenges and practical applications. Real-world scenarios illustrate the diverse cybersecurity landscape. The discussion extends to global, national, and local perspectives on cyber attacks, emphasizing the imperative of cybersecurity. The chapter advocates for cybersecurity education, addressing challenges and proposing solutions. It concludes by summarizing key takeaways, stressing ongoing cybersecurity.
Industry 4.0 and Beyond
Authors: Azizi Neda , Jan Tony , Miao Yuan , Haass Omid , Sidnal Nandini
Published: February 2024

As we enter the era of Industry 4.0, the fusion of advanced technologies with industrial processes has unlocked unprecedented opportunities for growth and innovation. The seamless integration of cyber-physical systems, internet of things (IoT) devices, and artificial intelligence has elevated manufacturing efficiency and productivity to new heights. However, this digital transformation has also brought forth complex challenges, with cybersecurity emerging as a critical concern. As industries continue to embrace the potential of Industry 4.0 and look beyond, fortifying industrial cybersecurity becomes paramount for sustaining growth and ensuring the safe and secure operation of critical infrastructures. This chapter delves into the importance of robust cybersecurity measures in the Industry 4.0 landscape and explores strategies for safeguarding assets, preserving business intelligence, and fostering sustainable growth in the face of evolving cyber threats.
Digital Transformation in Healthcare and Medical Practices
Authors: Martin Mario Sierra , Alarcón-Urbistondo Pilar
Published: February 2024

The evolution of technologies in the healthcare sector has revolutionized the health landscape, from the expansion of telemedicine to the vital role of social networks. The authors delve into how technology redefines healthcare, promoting efficient communication and empowering both patients and healthcare professionals. They offer a comprehensive and captivating insight into the interaction between health and the digital era. They analyze the challenges faced by the healthcare sector and the opportunities presented by the digital age. Through a systematic review of the literature in recent years, the authors obtain results that clarify the current landscape of the healthcare sector and how both professionals and patients make use of these tools that technologies provide. These findings offer vital insights into how technology is reshaping the healthcare landscape, presenting both challenges and opportunities that can guide future innovations and policymaking in this rapidly evolving sector.
Particle Swarm Optimization in Biomedical Technologies
Authors: Suriyan Kannadhasan , Nagarajan R.
Published: February 2024

This chapter explores particle swarm optimization (PSO) in the rapidly evolving landscape of biomedical technologies. The study begins by introducing the fundamental principles of PSO, emphasizing its advantages in addressing complex optimization problems common in biomedical applications. The authors delve into innovative uses of PSO in various biomedical fields, including image enhancement, data clustering, and drug development, highlighting how PSO contributes to more accurate diagnoses, efficient treatment plans, and streamlined research methodologies. Significantly, this chapter identifies emerging opportunities where PSO can be further leveraged, particularly in personalized medicine and predictive health analytics, suggesting a roadmap for future research and development. By combining theoretical insights with practical examples, this work aims to provide a comprehensive overview of PSO's role in advancing biomedical technologies, offering valuable perspectives for researchers, practitioners, and policymakers in the field.
Emerging Trends in Ultrasound Education and Healthcare Clinical Applications
Authors: Lobo Manuel Duarte , Miravent Sérgio , de Almeida Rui Pedro Pereira
Published: February 2024

In this chapter, the authors explore the transformation of ultrasound training in the digital era of higher education. As the digital landscape redefines access to information and learning modalities, this chapter critically examines the integration of innovative digital tools in ultrasound education. The authors focus on leveraging technologies like extended realities and simulations, alongside the practicality of mobile applications, to enhance the learning experience. The chapter underscores the importance of evolving educational systems to actively engage students in these advanced learning frameworks. It aims to stimulate a comprehensive discussion on effectively incorporating these technologies at the undergraduate level, evaluating their impact on student learning outcomes, and preparing future healthcare professionals for a technology-driven medical landscape. This review offers a forward-looking perspective on integrating cutting-edge digital tools in ultrasound education, signifying a shift towards more interactive, immersive, and effective learning experiences.
Textile Products in Healthcare
Authors: Roy Rupayan , Ashmika R.
Published: February 2024

Healthcare textiles have seen a considerable transition throughout time, becoming an increasingly important part of the healthcare ecosystem. A thorough introduction to healthcare textiles is provided in this chapter, along with an analysis of their use in patient care, infection control, and the larger healthcare sector. It explores the numerous kinds of healthcare textiles, how crucial they are to maintaining hygiene, and the difficulties in using them. It also looks at cutting-edge advancements that have the potential to change the way healthcare textiles are used in the future, such as smart textiles and sustainable practices. Healthcare textiles are positioned to become increasingly important to patient safety and environmental sustainability as the healthcare industry develops. This chapter is significant as it provides an in-depth exploration of the evolving role of healthcare textiles in patient safety and sustainability, highlighting cutting-edge developments and future trends in the industry.
Social Media and Health Communication
Authors: Thapliyal Kavita , Thapliyal Manjul , Thapliyal Diya
Published: February 2024

The extensive impact of social media on modern society has transformed the landscape of health communication, by introducing novel techniques, strategies, and best practices. This chapter investigates the connection between social media and health communication, researching successful techniques that promote public health and well-being globally. Such an exploration is significant as it investigates the intricate relationship between social media and health communication, exploring successful techniques that promote public health and well-being globally. With enormous users worldwide, digital platforms like Facebook, Instagram, and Twitter serve as powerful tools for circulating health information and encouraging community engagement. Social media health communication requires adapting and reshaping content to various audiences, incorporating multimedia formats for convenience, and leveraging influencers to enhance messages. The ability to reach large and diverse demographics in real time enhances the dissemination of preventive measures, health education, and crisis communication.
Fundamentals of Deep Learning
Authors: Chandrika P. V. , Kelkar Sandeep Madhusudhan
Published: February 2024

This chapter deals with the basic understanding of the deep learning model. It starts with a view of artificial intelligence, machine learning, and deep learning. It also represents how these three concepts are related to each other. The entire chapter is divided into three sections where Section 1 represents the evolution of deep learning models, Section 2 talks about the components of neural network, and Section 3 captures working of neural network models and different types. The chapter starts with introduction to Neurodegenerative Diseases and lastly the chapter gives a thorough implementation of neural network model by considering a Parkinson's case study along with the Python code. It also clearly interprets the metrics of evaluation based on the classification neural network model.
Deep Learning Techniques for Alzheimer's Disease Detection
Authors: Farooq Bazila , Ganie Shahid Mohammad
Published: February 2024

Alzheimer's disease (AD) is a common chronic disorder with a high incidence rate that disproportionately affects elderly people. Deep learning (DL) has been increasingly popular in recent years, resulting in notable developments and innovations in medical imaging. Consequently, deep learning has emerged as the preferred approach for analyzing medical visuals, particularly in the realm of Alzheimer's disease detection. In this chapter, the authors performed a comparative analysis of various DL models such as convolutional neural networks, DenseNet, ResNet, EfficientNet, etc., which have shown some groundbreaking results for AD disease detection. Also, they focused on investigating data collection and feature extraction techniques pertinent to AD. In addition, they further discussed briefly the deep learning models for AD detection. This not only increases hope for the advancement of AD research and therapy but also highlights how deep learning can revolutionize the field of medical image analysis and illness identification.
Mobile Text Misinformation Identification Using Machine Learning
Authors: Pillai Sanjaikanth E. Vadakkethil Somanathan , Hu Wen-Chen
Published: February 2024

More than eighty percent of U.S. adults receive news from digital devices like smartphones, computers, or tablets. Unlike the traditional news dominated by organizations, this new kind of news could be created by anyone. It is quick and engaging. At the same time, misinformation may be easily generated or spread intentionally or unintentionally. Misinformation is a serious problem for the general public, and there is no method to solve the problem satisfactorily so far. Instead of covering general misinformation, this research tries to identify mobile health text misinformation by proposing a self-reconfigurable system. The system includes the preprocessing functions (involving lexical analysis, stopword removal, stemming, and synonym discovery), a dataflow graph from TensorFlow, and a reconfiguration method for self-improvement. Experiment results show the proposed method significantly improves the accuracy of the mobile health text misinformation detection compared to the one without using self-reconfiguration.
Maximizing Employee Satisfaction and Productivity Through Karma Yoga Practices
Authors: Singh Anu Raj , Mishra Pratima , Singh Priyesh Kumar
Published: February 2024

Karma Yoga, which has its roots in the Bhagavad Gita, gives useful guidelines that help improve employee productivity and well-being at work. In order to increase job happiness and effectiveness, this study investigates the incorporation of Karma Yoga practises. Employee stress may be reduced and performance can be at its best by concentrating on the process rather than the results. Karma Yoga's mindfulness practise has been related to increased workplace wellbeing. This strategy encourages altruistic action for the benefit of the organisation as a whole, promoting emotional intelligence and pleasure. Karma Yoga promotes mindfulness of the present moment, which lowers stress levels and fosters a sense of community and cooperation. These factors promote mental health by fostering a work-life balance and providing social support. Employees who do Karma Yoga can flourish professionally and emotionally while remaining aloof from other influences, thereby improving overall wellbeing and productivity.
Key Challenges Faced When Preserving Records in Traditional Councils During the 4th Industrial Revolution
Authors: Kgomoeswana Kabelo Bruce , Makgahlela Lefose
Published: February 2024

The 4th industrial revolution (4IR) has brought significant technological advancements that have impacted various sectors, including governance and record-keeping. This chapter examines the challenges faced by traditional councils when preserving records during the 4IR. The chapter adopted a desktop research methodology. The data were subjected to substantive and extensive analysis through the instrumentality of content validity, content analysis, and textual criticism to establish facts that defend or refute the hypothesis. The study revealed that traditional councils lack the necessary infrastructure and resources to digitise records, leading to concerns about data loss, system failures, and cybersecurity threats. In essence, the councils are still struggling to protect and secure the records preserved in their custody; as such, council members need digital literacy training. The study recommends strategies that traditional councils should use to preserve records during the 4IR.
Toward a Convergence of Memory Institutions in the Indonesian Presidential Library
Authors: Hendrawan Muhammad Rosyihan , Shobaruddin Muhammad
Published: February 2024

Libraries, archives, and museums (LAMs) as memory institutions share valuable existence to preserve advanced national cultural heritage. Various causes drove LAM convergence in various cases. The Bung Karno Library in Blitar City, one of the Presidential Libraries in the Republic of Indonesia, helps explain the memory institution's convergence. The case studies indicate that collaboration and convergence face shared challenges. The authors propose that collaboration between these organizations is complicated. In the Bung Karno Library, cohesive procedure and accessibility were essential to service the user. However, removing boundaries between memory institutions in Bung Karno Library was universally accepted as necessary to improve user service and promote cultural heritage. These actions might lead to future research and boost the convergence's worth. To balance professional capability and integrate multiple systems, the researched institutions are making significant efforts. A fine balance appears needed to achieve and maintain successful convergence initiatives.
From Collaboration to Adaptation
Authors: Anoir Lamya , Khaldi Mohamed
Published: February 2024

The evolution of information technologies has created new perspectives in the field of education, enabling the creation of online learning systems. Among these systems, collaborative learning and adaptive learning are emerging as powerful approaches for optimizing the effectiveness of teaching and learning. The chapter explores the transition from collaborative to adaptive learning, highlighting the design of an adaptive collaborative system. By demystifying the key concepts of each approach, the chapter identifies their respective benefits and explores how they can complement each other. Collaborative learning promotes peer interaction, knowledge sharing and the development of social skills, while adaptive learning focuses on personalized content, continuous feedback and dynamic adjustment based on learning data.
E-Learning From Virtual Reality to Augmented Reality
Authors: Abdennour Omar , Kemouss Hassane , Erradi Mohammed , Khaldi Mohammed
Published: February 2024

E-learning has evolved to incorporate innovative technologies, from virtual reality (VR) to augmented reality (AR). VR immerses learners in immersive virtual worlds, ideal for simulations and hands-on training. In contrast, AR superimposes digital elements onto the real world via devices such as smartphones and AR glasses, offering contextual information and interactive simulations. These technologies improve engagement by capturing attention, enabling hands-on experiences and deeper understanding, while personalizing learning. VR's 3D environments and AR's overlays enhance memorization. The integration of these approaches depends on pedagogical objectives and available resources, while their combination can create even richer and more varied learning experiences.
E-Learning and Course Design in Higher Education
Authors: AbdElhalim Randa Muhammed , Gull Humera
Published: February 2024

This chapter describes e-learning that can be defined as the use of computer and internet technologies to deliver a broad array of solutions to enable learning and improve performance. E-learning can be used to develop cognitive, interpersonal, and psychomotor skills. E-learning reaches a wider target audience by engaging learners who have difficulty attending conventional classroom training due to different reasons. E-learning approaches can combine different types of e-learning components. This chapter also reports the experience of a course design phase in higher education department. Effective course design begins with understanding who your students are, deciding what you want them to learn, determining how you will measure student's learning, and planning activities, assignments, and materials that support student learning.
Harnessing Advanced Logical Models for Pedagogical Innovation in Educational Recommender Systems
Authors: Aammou Souhaib , Jdidou Youssef
Published: February 2024

This chapter presents a groundbreaking approach to educational recommender systems, underpinned by the integration of advanced logical models. The authors delve into how situational logic can be ingeniously applied to revolutionize pedagogical strategies within educational recommender systems. By transcending traditional rule-based adaptation models, the approach offers a nuanced and dynamic method for personalizing learning paths, catering to diverse learner profiles and styles. They explore the application of these logical models in formulating sophisticated algorithms that not only adapt to individual learning trajectories but also predict and respond to evolving educational needs. The chapter demonstrates how such an integration leads to a significant leap in pedagogical innovation, offering educators and learners a more engaging, effective, and responsive educational experience. The ensuing discussion highlights the transformative potential of these logical models in reshaping the educational landscape, providing a roadmap for future advancements in educational technology.
Enhancing Innovation Through Bayesian Networks in Recommender Learning Paths
Authors: Tagdimi Zakaria , Amzil Ikram , Jdidou Youssef , Aammou Souhaib
Published: February 2024

In an era characterized by rapidly evolving educational paradigms, the imperative to foster innovation and facilitate personalized learning experiences cannot be overstated. Traditional recommendation algorithms, such as collaborative filtering and content-based approaches, have played a pivotal role in tailoring educational content to individual needs. However, these methods, though effective in many respects, tend to perpetuate established preferences, inadvertently stifling innovative thinking. This chapter explores the transformative potential of Bayesian networks within recommender systems to address these limitations. It begins by examining the restrictions inherent in conventional recommendation algorithms, emphasizing the significance of fostering innovation in education. Bayesian networks are introduced as a revolutionary solution capable of bridging these gaps. This chapter offers a comprehensive overview of how Bayesian networks can reshape the landscape of personalized learning.
E-Governance, E-Participation, and E-Service Delivery in the Parliament of Zimbabwe Amid COVID-19 Exigencies
Authors: Gwakwara Cleophas , Niyitunga Eric Blanco Blanco
Published: February 2024

This article explored how the Parliament of Zimbabwe (PoZ) leveraged e-governance, e-participation, and e-service delivery within COVID-19 pressures. The study used the qualitative research methodology. An assortment of parliamentary reports, hansards, press statements, newspaper articles, documents, and other relevant literature on e-governance, e-participation, e-service delivery and COVID-19 were also used. The findings indicated that following the lockdowns in Zimbabwe which restricted the physical conduct in the transaction of business, the PoZ was quick to suspend business temporarily, so as to come up with measures to cope with the situation. It was found that parliamentarians adopted a hybrid strategy in their meetings, either physical or virtual. The adoption of e-governance closed the gap resulting from the restrictive measures brought about by COVID-19, thus enabling its members to continue with their constitutionally mandated functions. The findings suggest that e-governance, e-participation, and e-service delivery can help organisations cope with disruptions like COVID-19.
Application of Recommendation Algorithms Based on Social Relationships and Behavioral Characteristics in Music Online Teaching
Authors: Yin Chunjing
Published: February 2024

This research designed an improved collaborative filtering algorithm to be responsible for music recommendation tasks in the online music teaching platform. This algorithm integrates the user's social trust into the similarity calculation formula. Then, the algorithm uses behavioral feature data driven by preferences, music tags, and popularity as the basis for recommendation calculation. It adopts user data testing on an online music teaching platform. The results showed that when the number of recommended music was eight, the recommended recall rates of XCF, CTR, TSR, and UB-CF recommendation models reached their maximum, reaching 97.82%, 95.26%, 93.95%, and 88.72%, respectively. The AUC and average computational time of the ROC curves for XCF, CTR, TSR, and UB-CF recommended models are 0.7, 0.68, 0.64, 0.57, and 160ms, 136ms, 114ms, and 88ms, respectively. The experimental data shows that the recommendation accuracy of the music recommendation model designed in this study is significantly higher than that of traditional recommendation models.
Intrusion Detection System
Authors: Singh Amit , Prakash Jay , Kumar Gaurav , Jain Praphula Kumar , Ambati Loknath Sai
Published: February 2024

The use of encrypted data, the diversity of new protocols, and the surge in the number of malicious activities worldwide have posed new challenges for intrusion detection systems (IDS). In this scenario, existing signature-based IDS are not performing well. Various researchers have proposed machine learning-based IDS to detect unknown malicious activities based on behaviour patterns. Results have shown that machine learning-based IDS perform better than signature-based IDS (SIDS) in identifying new malicious activities in the communication network. In this paper, the authors have analyzed the IDS dataset that contains the most current common attacks and evaluated the performance of network intrusion detection systems by adopting two data resampling techniques and 10 machine learning classifiers. It has been observed that the top three IDS models—KNeighbors, XGBoost, and AdaBoost—outperform binary-class classification with 99.49%, 99.14%, and 98.75% accuracy, and XGBoost, KNneighbors, and GaussianNB outperform in multi-class classification with 99.30%, 98.88%, and 96.66% accuracy.
Cutting Edge: Phagosome-associated Autophagosomes Containing Antigens and Proteasomes Drive TAP-Independent Cross-Presentation
Authors: Sengupta Debrup , Galicia-Pereyra Rodrigo , Han Patrick , Graham Morven , Liu Xinran , Arshad Najla , Cresswell Peter
Published: February 2024

Activation of naive CD8-positive T lymphocytes is mediated by dendritic cells that cross-present MHC class I (MHC-I)-associated peptides derived from exogenous Ags. The most accepted mechanism involves the translocation of Ags from phagosomes or endolysosomes into the cytosol, where antigenic peptides generated by cytosolic proteasomes are delivered by the transporter associated with Ag processing (TAP) to the endoplasmic reticulum, or an endocytic Ag-loading compartment, where binding to MHC-I occurs. We have described an alternative pathway where cross-presentation is independent of TAP but remains dependent on proteasomes. We provided evidence that active proteasomes found within the lumen of phagosomes and endolysosomal vesicles locally generate antigenic peptides that can be directly loaded onto trafficking MHC-I molecules. However, the mechanism of active proteasome delivery to the endocytic compartments remained unknown. In this study, we demonstrate that phagosome-associated LC3A/B structures deliver proteasomes into subcellular compartments containing exogenous Ags and that autophagy drives TAP-independent, proteasome-dependent cross-presentation.
Failed Downregulation of PI3K Signaling Makes Autoreactive B Cells Receptive to Bystander T Cell Help
Authors: Fiske Brigita E , Getahun Andrew
Published: February 2024

The role of T cell help in autoantibody responses is not well understood. Because tolerance mechanisms govern both T and B cell responses, one might predict that both T cell tolerance and B cell tolerance must be defeated in autoantibody responses requiring T cell help. To define whether autoreactive B cells depend on T cells to generate autoantibody responses, we studied the role of T cells in murine autoantibody responses resulting from acute B cell–specific deletion of regulatory phosphatases. Ars/A1 B cells are DNA reactive and require continuous inhibitory signaling by the tyrosine phosphatase SHP-1 and the inositol phosphatases SHIP-1 and PTEN to maintain unresponsiveness. Acute B cell–restricted deletion of any of these phosphatases results in an autoantibody response. In this study, we show that CD40–CD40L interactions are required to support autoantibody responses of B cells whose anergy has been compromised. If the B cell–intrinsic driver of loss of tolerance is failed negative regulation of PI3K signaling, bystander T cells provide sufficient CD40-mediated signal 2 to support an autoantibody response. However, although autoantibody responses driven by acute B cell–targeted deletion of SHP-1 also require T cells, bystander T cell help does not suffice. These results demonstrate that upregulation of PI3K signaling in autoreactive B cells, recapitulating the effect of multiple autoimmunity risk alleles, promotes autoantibody responses both by increasing B cells’ cooperation with noncognate T cell help and by altering BCR signaling. Receptiveness to bystander T cell help enables autoreactive B cells to circumvent the fail-safe of T cell tolerance.