bluuebunny/crossref_metadata_2025 huggingface dataset:
The Blockchain Technology to Protect Data Access Using Machine Learning
Authors: Rao M. Chalapathi , Goswami Chandrshekhar , Jadhav Rohini , Chaudhari Ashvini , Naganjaneyulu K. Venkata , Tiwari Mohit
Published: December 2024

All cryptocurrencies are built on the blockchain, and machine learning (ML) represents one of the most well-liked techniques with an array of potential applications. ML could be used to enhance and increase the effectiveness of blockchain. Blockchain uses confidentiality to protect data, but it is not entirely dependable. It depends on a number of factors, such as the specific use scenario, the sort of data, and any applicable legal restrictions, whether it is appropriate to maintain sensitive and confidential information. Even while there can be advantages, it's critical to consider potential risks and comply by privacy and security rules.
Evaluating the Role of Blockchain to Secure Identity in Big Data for Industry 5.0
Authors: Saxena Akash , Verma Neha , Kaur Sukhpreet , Kaur Harsimran , Tiwari Mohit , Tiwari Tripti
Published: December 2024

Blockchain is one of the future technological innovations that Industry 5.0 would advance. For both small and large enterprises, blockchain technology offers an opportunity to improve data transparency, safety, and secrecy. The confluence of several cutting-edge production methodologies known as “Industry 5.0” accelerates the rate at which organizations may achieve their objectives. Several among the technological components Industry 5.0, such as artificial intelligence (AI), blockchain, and the internet of things (IoT), have been the subject of research during the past few years. Industry 5.0 is supported by a number of cutting-edge technologies, including cloud computing, corporate integration, cyber physical frameworks, the IoT, adaptive robotics, AI, ML, and service-focused computing.
Scalability and Performance of Blockchain-Based Solutions for IoT Applications
Authors: Bhargavi V. Sesha , Sawai Nandkishor Marotrao , Chakravarthi M. Kalyan , Ibrahim Shaik Hussain Shaik , Castro Pérez Luis Alvaro , Tiwari Mohit
Published: December 2024

The importance of software design in the scalability and performance of blockchain solutions for IoT applications is revealed in this study. This paper highlights software-driven trade-offs between security and scalability while analyzing consensus techniques, sharing, as well as off-chain processing. A qualitative examination of case studies emphasises the importance of software selection in real-world scenarios. The findings highlight the importance of precise software-driven techniques for addressing IoT-specific issues. As blockchain and IoT become more intertwined, this study provides critical insights that assist software architects in efficiently leveraging the promise of blockchain technology within dynamic IoT environments.
Mapping the Knowledge Domain of Machine Learning Methods in Performance Evaluation
Authors: Babu Tadipigari Mahesh , Nallasivam Anitha , Mahalakshmi S. , Lyer K. N. Anantha Subramanya , Thakar Chetan M. , Selvaraj C.
Published: December 2024

Today, businesses are interested in innovation by way of technological exploration to increase reach and efficiency. This study aims to visualize the research done in business management and performance, with an implication of machine learning techniques. The Scopus database was used to collect the source documents relating to this study area. A bibliometric analysis, including a network link analysis study, has been conducted to understand the relatedness of the various descriptive characteristics of the literature. The analysis is done using a threefold approach; co-citation map, bibliographic coupling map and co-word analysis. The results show increased application of machine learning tools in analyzing the operational and financial performance, predicting and knowledge management systems. Machine learning techniques like deep learning, neural network and long short-term memory are spotted in very few recent studies which enhances the opportunity for future researchers to use the same in various performance analysis and forecasting models for organizations.
Using Natural Language Processing and the Internet of Things for Employee Performance Evaluation
Authors: Ravisankar A. , Kulkarni Akshay , Takore Tamirat Tagesse , Hasan Dler Salih , Dharani Kamepalli , Naganjaneyulu K. Venkata
Published: December 2024

The performance evaluations of employees in firms are carried out manually. This is the situation that exists at the moment. The possibility of discriminating is therefore made possible by this. Using the pervasive detecting capabilities of Internet of Things (IoT) technologies, it has been discovered that traditional employee evaluation methods can be effectively removed by monitoring commercial workers. This was discovered through surveillance of commercial workers. The discovery that was made is incredibly important. A small portion of the previous research, on the other hand, made use of information obtained from the Internet of Things in order to simplify the processes involved in evaluating the performance of employees. According to the findings of this research, a novel method for evaluating the performance of employees is offered. In order to assess service inquiry messages, the approach makes use of fuzzy logic and the Internet of Things (IoT). Following this, natural language processing (NLP) is utilized in order to autonomously allocate workers and prioritize service requests. According to the conclusions of the empirical research, a solution that is based on the Internet of Things performs better than earlier solutions in studies for all instructional courses and decision-making supervisors. The findings of this study present a natural language processing (NLP) technique that not only offers a method for evaluating personnel but also makes a contribution to the automated data systems that are utilized in enterprises.
Smart Healthcare Systems Using IoE and AI
Authors: Lanjewar Pritam , Bansal Mani , Sayeed Nayela , Palav Manesh R. , Priya L. , Vasuki S. , George A. S. Hovan
Published: December 2024

Artificial intelligence (AI) has emerged as a game-changing result in the field of ultramodern healthcare. The Internet of Everything makes it possible to integrate various biases and data sources in a seamless manner, which in turn enables real-time monitoring and analysis of patient health considerations. Through the provision of timely perceptivity into case situations, the optimization of treatment plans, and the perfection of general healthcare difficulties, this integration improves the efficiency with which healthcare is delivered. In a coincidental turn of events, artificial intelligence techniques such as machine literacy and prophetic analytics enable these systems to recognize patterns within enormous datasets, anticipate potential health risks, and provide the best possible care for patients. Within the context of intelligent healthcare systems, investigates the synergistic operation of the Internet of Everything (IoE) and artificial intelligence (AI), examining their technological foundations, barriers to perpetration, and prospective operations in clinical contexts.
Machine Vision and Image Processing for Drones
Authors: Chui Kwok Tai , Arya Varsha , Gaurav Akshat , Bansal Shavi , Bansal Ritika
Published: December 2024

This chapter delves into the pivotal role of machine vision and image processing in the realm of artificial intelligence for industrial robotics and intelligent drones. It begins by introducing the fundamental concepts and techniques of machine vision, including image capture, analysis, and interpretation methods that enable machines to 'see' and make sense of their surroundings. The discussion extends to advanced image processing strategies, such as edge detection, feature extraction, and object recognition, highlighting their critical applications in enabling autonomous operation of robots and drones. The chapter emphasizes the importance of 3D mapping and scene reconstruction in creating detailed environmental models for navigation and task execution. Furthermore, it explores the emerging use of augmented reality (AR) for industrial applications, offering insights into how AR enhances human-machine interaction by overlaying digital information onto the physical world.
Drones and Unmanned Aerial Vehicles Automation Using Reinforcement Learning
Authors: Kaur Jaskirat , Kumar Sudhakar , Singh Sunil K. , Thakur Ruchika , Bansal Shavi , Arya Varsha
Published: December 2024

This chapter provides an in-depth study at the latest developments in deep reinforcement learning (DRL) as applied to drones and unmanned aerial vehicles (UAVs), with a focus on safety standards and overcoming challenges like object detection and cybersecurity. Real-world instances across sectors demonstrate how DRL significantly improves efficiency and adaptability in autonomous systems. By contrasting various DRL techniques, the chapter highlights their effectiveness and potential to advance drone and UAV capabilities. The practical implications and advantages of DRL applications are emphasized, showcasing their transformative influence on industries. Moreover, the chapter explores future research paths to stimulate innovation and enhance self-piloting vehicle technology. In essence, it provides a thorough overview of DRL's role in drone and UAV operations, underscoring the importance of safety standards, DRL's capacity to boost efficiency and safety in autonomous systems, and its contribution to the evolving autonomous technology landscape.
Multi-Modal Sensor Fusion With CRNNs for Robust Object Detection and Simultaneous Localization and Mapping (SLAM) in Agile Industrial Drones
Authors: Thakur Ujjwal , Singh Sunil K. , Kumar Sudhakar , Singh Anubhav , Arya Varsha , Chui Kwok Tai
Published: December 2024

The integration of multi-modal sensor fusion with CRNNs in agile industrial drones addresses the need for improved object detection and SLAM capabilities. This technology enhances the drone's ability to navigate and map complex industrial environments with greater accuracy and reliability. It is crucial for tasks such as inspection, monitoring, and autonomous navigation in dynamic and challenging industrial settings. By integrating visual, LiDAR, and inertial measurement unit (IMU) sensor information, the approach enhances situational awareness, facilitating safer navigation and intelligent interaction amidst challenging conditions. The integration of LiDAR, IMU, and optical sensors provides awareness of the environment, enabling the drone to adjust to changing circumstances instantly. The suggested method meets the needs of industrial applications, which demand dependable and durable solutions. It achieves this by offering improved precision, dependability, and flexibility that eventually expands the potential of industrial drones in a variety of operational contexts.
Climbing Human-Machine Interaction and Wireless Tele-Operation in Smart Autonomous Robots
Authors: Singh Bhupinder , Kaunert Christian
Published: December 2024

The future of space exploration is defined by a close partnership between intelligent autonomous robots and humans. These robots serve as extensions in space which do tasks in dangerous or harsh environments where direct human participation is impossible. In recent years, researchers, commercial firms and space organizations have made considerable advances in the creation of autonomous and remotely controlled space robots. These robots serve an important role in space exploration, assisting people on the International Space Station (ISS) and exploring distant celestial bodies. These robots require highly developed tele-operation interfaces and HMI designs to bridge the knowledge gap between human competency and machine execution. This chapter provides a thorough analysis of the dynamic interactions between intelligent autonomous robots and humans. It also offers insights for space agencies, academics, engineers and everyone else interested in the fascinating field of space technology and exploration which lays out a vision for the future of autonomous systems and space robots.
Effective Information Technology Education in Grade 10-12 Classrooms, South Africa
Authors: Goosen Leila
Published: December 2024

Against the background of effective Computer Science Education (CSE) in Kindergarten/Grade R-12 classrooms, the purpose of this chapter is to specifically investigate effective Information Technology (IT) education in grade 10-12 classrooms in South Africa, where the subject IT is offered in the Further Education and Training (FET) phase. As such, the chapter will e.g., provide an introduction to Computer Science in R-12 education, as well as effective teaching approaches and strategies in the South African context.
Mapping the Anatomy of Novice-Generated Mistakes in Learning Programming
Authors: Erdoğdu Fatih , Gökoğlu Seyfullah
Published: December 2024

Programming has become very important in modern education. Research shows that exposing students to coding at a young age positively contributes to their academic, cognitive and professional development. However, students may make some mistakes during the programming process, which are considered a natural part of programming. In this context, the aim of this study is to classify various mistakes encountered by novice programmers while learning programming. For this purpose, a literature review was conducted, and it was found that there are basically two types of mistakes: strategic mistakes and knowledge-based mistakes. It was found that these mistakes are divided into groups within themselves. This book chapter recommends that researchers and practitioners conduct practical studies of the mistakes uncovered and conduct qualitative research in which the mistakes can be examined in greater depth.
Hybrid MCDM (Multi-Criteria Decision-Making) Approaches for Sustainable Energy Management
Authors: Rana Rachna , Bhambri Pankaj
Published: December 2024

Energy management judgment is regularly difficult measures comprising many information supports such as physical, social, political, technological, and financial. Energy management judgment creator regularly use a variety of processor illustrations, investigational tests, and tools to examine human fitness, the hazard associated with environmental troubles and ecological forces, and the special effects of sure solutions on risk reduction. The use of these computational models, experiments and tools is difficult for us for a reason. First, new situations arise (such as climate change, life cycle assessment, cost of living analysis, environmental risk issues and human consumption risk health). There is a lack of information to mitigate these threats. Second, decisions must be made carefully in situations where there are many normal pressures. This section discusses the hybrid MCDM (Multi Criteria Decision Making) approach to sustainable energy management.
Quantum Computing in Healthcare Using AI-Driven Medical Technologies
Authors: Anbazhagu U. V. , Priyadharshini K. , Deepajothi S. , M. P. Haripriya , Banu E. Afreen
Published: December 2024

Quantum computing with AI-driven medical technologies represents an absolute frontier of transformation in precision medicine, diagnostics, and drug discovery. This chapter explores the intersection of quantum computing and artificial intelligence in transforming complex medical challenges, namely in big data analysis, acceleration in molecular simulations, and personalizing treatment plans. Quantum algorithms can scale exponentially beyond AI's capabilities in the breakthrough of genomic analysis, radiological interpretation, and predictive modeling for patient outcomes. The focus of the chapter will be on state-of-the-art achievements in quantum machine learning, possible applications in healthcare areas, and their future prospects for the revolutionizing of medical imaging, optimization of clinical trials, and real-time data processing on wearable devices. They confront the computational limits of classical systems and offer new ground for significant progress in both medical research and health care delivery by solving problems that have until now been intractable.
AI-Enhanced Real-Time Economic Data Analysis Using IoE
Authors: Kumar M. Sunil , Nagalakshmi M. V. N. , Reddy P. Chandrika , Charan Y. V. N. Sai Sri , Kataria Abhinav , George A. Shaji
Published: December 2024

The management of current economic data has been entirely revolutionised by the integration of artificial intelligence (AI) and the Internet of Everything (IoE). This research is dedicated to the improvement of the collection, processing, and interpretation of economic data through the application of AI algorithms and IoE technology. This method swiftly and reliably reveals economic trends, consumer behaviour, and market dynamics by combining the advanced analytical capabilities of artificial intelligence with the interconnected sensors, devices, and systems of the Internet of Things. The integration of AI and the Internet of Everything (IoE) facilitates immediate analysis, which can be employed by governments, corporations, and researchers to improve decision-making processes, thereby contributing to the economy's growth. This paper investigates the potential, constraints, and future ramifications of the paradigm transition towards AI-enhanced Internet of Everything (IoE) technologies. It illustrates the potential for these systems to transform the processing of economic data.
Utilizing the Internet of Everything and Artificial Intelligence for Real-Time Workforce Management
Authors: Kumar M. Sunil , Prasanna P. Lakshmi , Lalitha M. , Joy Mishma , Jha Srijib Shankar , Pandey Binay Kumar
Published: December 2024

The real-time pool operation through the combination of the Internet of Everything (IoE) and Artificial Intelligence (AI). The purpose of this study is to evaluate how the Internet of Everything (IoE) technologies, which connect people, processes, data, and effects, may best optimize pool effectiveness and production when paired with the predictive and logical skills of artificial intelligence (AI). The utilization of IoE detectors and bias allows for the continuous collection and analysis of data, which provides insight into hand performance, workload allocation, and functional backups. Artificial intelligence systems also take advantage of this data to provide real-time decision assistance, which enables dynamic work allocation and envisions solutions to problems. Among the significant advantages that are brought to light by the investigation are improved resource application, increased functional translucency, and enhanced hand engagement. The practical operations and problems that are associated with this integrated approach are demonstrated by case studies coming from diligence.
IoE and AI in Real-Time E-Commerce Personalization
Authors: Kumar M. Sunil , Manju G. , Pandey Neha , Nagalakshmi M. , Reddy P. Chandrika , George A. Shaji
Published: December 2024

By offering clients real-time customisation, the e-commerce industry is being transformed by the incorporation of Artificial Intelligence (AI) and the Internet of Everything (IoE). By working together, we can offer users more personalised information, services, and suggestions, which will enhance their experience, sustain their interest, and ultimately increase their income. Artificial intelligence (AI) employs the vast amount of data collected by the Internet of Everything (IoE) from interconnected devices to evaluate and derive insights, as well as to make predictions. This article investigates the structures, tools, and techniques that utilise artificial intelligence (AI) and the Internet of Everything (IoE) to customise online purchases in an instant. We evaluate the benefits and drawbacks of integrating these technologies and offer case studies to demonstrate their impact on business outcomes and customer satisfaction.
IoE-Enabled Real-Time Customer Experience Enhancement
Authors: Sethi Jayaashish , Anand J. , Raju Shathaboina , Khan Azeem Ahmad , Javed Sarfaraz , Palav Manesh R.
Published: December 2024

By connecting individuals, systems, data, and devices, the Internet of Everything (IoE) can enhance real-time customer experiences. Enterprises can acquire a comprehensive comprehension of customer behaviours and preferences by collecting vast quantities of data from numerous client touchpoints through the Internet of Everything (IoE). This article investigates the potential for the Internet of Everything (IoE) to enhance the customer experience (CX) by means of adaptive services, predictive analytics, and personalised interactions. We improve the customer experience (CX) by implementing Internet of Everything (IoE) solutions, which are substantiated by our research of case studies. For the protection of consumer information, it is imperative to implement robust cybersecurity measures. The study underscores the benefits of real-time data processing. The Internet of Everything (IoE) has the potential to significantly improve the customer experience (CX) by fostering business growth and increasing consumer engagement, as indicated by the results.
IoE and AI in Real-Time Customer Behavior Analysis
Authors: Kumar M. Sunil , Srivastava Vishal , Behera Bibhuti Bhusana , Savariapitchai Michael , Sahu Sunita , Mahajan Rashmi , George A. Shaji
Published: December 2024

This article investigates how different technologies work together to provide a profound insight into the behavior and preferences of target consumers. The connected network of bias created by the Internet of Everything (IoE) gathers enormous amounts of data, which artificial intelligence systems then analyze to discover patterns and trends in real time. Through the utilization of artificial intelligence's capabilities in machine literacy and predictive analytics, businesses can acquire a full understanding of customer behavior, which ultimately results in the development of more substantiated marketing plans and improved customer behavior. The research focuses on the pragmatic applications of the Internet of Everything (IoE) and artificial intelligence (AI) in diligence, illustrating the influence these technologies have on improving decision-making processes, optimizing operations, and driving competitive advantage. Challenges that are comparable to those that are talked about include data sequestration, security, and the requirement for a robust structure.
AI in Real-Time Student Performance Monitoring Using IoE
Authors: Marouf Maha Alaa Aldeen , Alchilibi Haider , Sakr Dalal Kadim , Alkhayyat Ahmad , Pateriya Neelesh
Published: December 2024

The potential to revolutionise the real-time surveillance of students' academic progress within educational environments is present through the integration of artificial intelligence (AI) and the Internet of Everything (IoE). This study investigates the utilisation of AI-powered solutions within IoE frameworks to accurately and consistently evaluate student progress, identify areas for improvement, and establish personalised learning paths. Educational institutions will be able to establish classrooms that are capable of promptly adapting to the unique requirements of each student by combining the advanced analytics of Artificial Intelligence (AI) with the networked devices of the Internet of Things (IoT). This approach fosters an inclusive learning environment, personalised instruction, and active engagement among all students. The investigation investigates the technological and pedagogical repercussions of integrating AI and IoE into classrooms, as well as the potential for scalable education-related data-driven decision-making, ethical concerns, and data security.
Real-Time Signal Analysis for Remote Patient Monitoring
Authors: Mahalle Amruta , Priyadarshini S. Hema , Moorthy Hari Krishna , Pandit Shraddha V. , Satyanarayana K. N. V. , Mahender Kommabatla , Shahul Aakifa
Published: December 2024

When it comes to the development of improvement systems for remote healthcare coverage, signal processing is a crucial component. In this work, we will analyze recent approaches and algorithms that are aimed at recycling real-time physiological signals gathered from cases that have ever been recorded. The goal of this work is to examine these methods and algorithms. This study places a strong emphasis on the integration of these technologies to enable continuous and reliable monitoring of patient health indicators. These indices include the variability of the patient's heart rate, trends in blood pressure, and patterns of respiration. The enhancement of signal processing abilities is one of the ways this inquiry contributes to the production of reliable telemedicine findings. These findings have the potential to successfully support healthcare staff in delivering timely treatments and resolving patient difficulties in settings that are located in remote locations.
IoE Systems for Real-Time Medication Adherence Monitoring
Authors: Adsul Alpana Prashant , Jeevidha S. , Barani D. , Chavan M. , Choudary Surendra Kumar , Shaik Nazeer
Published: December 2024

In the management of chronic illnesses and the resolution of patient difficulties, effective drug adherence is critically important. This integration makes it possible to monitor medicine adherence in real-time. Connected detectors, mobile bias, and pall platforms are utilized by these systems to acquire and analyze data regarding the actions of patients involved in drug input. Through the utilization of Internet of Everything technology, healthcare providers can continuously monitor and evaluate patterns of drug adherence, immediately identify issues of non-compliance, and intervene as required. The provision of substantiated monuments and real-time feedback through the Internet of Everything bias helps to improve patient involvement and encourages adherence to treatment regulations that have been recommended. The purpose of this study is to examine the existing Internet of Everything-based systems for monitoring drug adherence, focusing on their technological foundations, the obstacles they face in terms of perpetration, and the implicit benefits they offer in healthcare contexts.
Neuromarketing for Decision Making in the Digital Era
Authors: Thakur Vaishali , Pasha Shaikh Ayman
Published: December 2024

This chapter reviews neuromarketing and AI interaction, especially related to sophisticated decision-making techniques that provide better insights into the phenomenon of consumer behavior. Neuromarketing incorporates neuroscience into marketing strategies to analyze subconscious reactions to achieve a level of understanding beyond traditional means such as surveys. Neuromarketing in the world of digital era lies in the complexity of the consumer structure where fast decisions are made unconsciously. Amongst the three key benchmark decision-making methods-neural networks, fuzzy decision-making and evolutionary algorithms-the three represent processing complex data, incorporating subtle consumer preferences, and continuously optimizing marketing strategies within iterative feedback loops. An AI-driven decision making conceptualizes a holistic approach to data gathering using neuromarketing tools, feature extraction with neural networks, and decision optimization through fuzzy logic and evolutionary algorithms.
Quick Commerce in the Quantum Age
Authors: Haneefa K. V.
Published: December 2024

The integration of quick commerce with advanced technologies like quantum computing and artificial intelligence represents a significant shift in retailing. This paper explores how quantum AI could transform supply chain management and operational frameworks in the quick-commerce domain. The study employs a conceptual research design based on a typology approach and synthesises existing literature to identify key opportunities, challenges, and implications of quantum AI adoption. The findings suggest that businesses must leverage these technologies to stay competitive, unlocking operational efficiencies and enhancing real-time consumer engagement. However, successful adoption also requires addressing ethical, social, and environmental dimensions to ensure long-term sustainability. As quantum technologies drive innovation, they offer the potential for a more sustainable and efficient quick-commerce ecosystem. This paper contributes to the growing discourse by providing a framework for integrating quantum AI into business strategies.
INICIAÇÃO À DOCÊNCIA NA PANDEMIA
Authors: Batista Patrícia Barros Soares , Pereira Letícia Gomes , Souto Kely Cristina Nogueira
Published: December 2024

O presente artigo tem como objetivo tematizar os significados da formação inicial docente durante o Ensino Remoto Emergencial (ERE) em uma escola federal de Educação Básica localizada na cidade de Belo Horizonte, Minas Gerais.  Tem-se como foco duas questões centrais: Como este espaço de formação contribui para que futuros/as docentes construam suas referências acerca do que é ser professor/a? Como se constituiu e se configurou a docência para uma futura professora ao inserir-se num dado tempo/espaço escolar totalmente atípico em função da pandemia? Metodologicamente optamos pela pesquisa (auto)biográfica e, a partir das análises, argumentamos que, embora o Ensino Remoto tenha ocasionado profundos impactos em todos os sujeitos envolvidos no processo educacional, a experiência de formação inicial por meio da imersão docente revela a potencialidade da educação como experiência e encontro, abrindo caminhos para um posicionamento profissional mais sensível e crítico em relação aos diferentes modos de existir no mundo. Ser professor/a é um devir, tornar-se docente é um processo permanente processo de (re)construção que se dá num continuum a partir de uma travessia rizomática, entrelaçada por várias experiências.
HOW AN AUTHENTIC SCIENCE COURSE BECAME DIGITAL AND ONLINE DURING THE COVID-19 PANDEMIC
Authors: Macdonald Maritza Bentham , Aquino-Gerard Adriana
Published: December 2024

This paper describes the conditions for teaching students and teacher-residents in the autumn of 2020 when professors in many countries needed to implement remote teaching. Also, the teaching and cultural institutions were closed and only functioning online. The context is a science and culture museum in NYC with a graduate college (Richard Gilder Graduate School). The program offers a Master Level Residency Program for secondary science teachers. All courses are co-taught by teams of a teacher educator and a museum scientist. In this case, the teaching team (authors) included a teacher educator specialized in curriculum design and pedagogy for informal and multilingual settings; the scientist is an evolutionary biologist specializing in Ichthyology at the museum.
O INÍCIO NA DOCÊNCIA NO CONTEXTO DE PANDEMIA
Authors: Cavalcante Maria Mikaele da Silva , Castro Íris Martins de Souza , Farias Isabel Maria Sabino de
Published: December 2024

Com a Covid-19, instaurou-se um novo modo de se relacionar e viver em sociedade. Na Educação, as aulas assumiram um caráter remoto, com uma rotina em ambiente virtual, constituindo um desafio novo para a comunidade escolar. Nesta análise, voltamo-nos especificamente para a realidade vivenciada por professores cearenses que estão iniciando na docência. O objetivo é compreender os desafios e as formas de reagir de professores iniciantes na carreira durante a pandemia, assim como o modo como esses docentes têm lidado com as adversidades desse cenário. Desenvolvemos uma pesquisa qualitativa, realizada com a participação de professores iniciantes que apresentam, por meio de narrativas escritas, os desafios e experiências durante o seu ingresso na docência, em meio à pandemia. Os estudos de Garcia (2010), Stake (2011), Cruz, Hobold e Farias (2020), Contreras (2012), Cardoso e Mendonça (2020), Nono e Mizukami (2002), serviram de base para as análises em foco. Os resultados indicam que os desafios vivenciados no início da docência foram intensificados nesse contexto e que os professores buscam superá-los de diversas formas. Espera-se contribuir para a reflexão sobre a relevância social do apoio e acompanhamento desses professores, como problematizar o contexto pandêmico, tornando a realidade escolar ainda mais complexa e desafiadora.
MAL-ESTAR, SOFRIMENTO E ADOECIMENTO DOS PROFESSORES QUE ATUAM NA PÓS-GRADUAÇÃO STRICTO SENSU
Authors: Yaegashi Solange Franci Raimundo , Caetano Luciana Maria , Bianchini Luciane Guimarães Batistella , Glatz Emanoela Thereza Marques de Mendonça
Published: December 2024

A saúde do trabalhador é considerada uma temática pertinente no mundo do trabalho, uma vez que o ser humano busca realização, superação e prazer em sua atividade laboral. A partir da concepção de que o trabalho é essencial para o desenvolvimento dos seres humanos, considera-se que o ato de trabalhar relaciona-se diretamente com a identidade social. Nesse sentido, por meio de uma revisão de literatura, tem-se como objetivo refletir acerca das condições organizacionais de trabalho (fatores psicossociais, estruturais e relacionais) que comprometem a saúde física e mental de docentes de ensino superior que atuam na pós-graduação stricto sensu. A coleta de dados foi realizada em três bases de dados: 1) Periódicos da Capes; 2) Scientific Electronic Library Online (SciELO); e 3) Google Scholar.  Foram selecionados artigos publicados entre os anos de 2014 e 2023. Após busca nas bases de dados e aplicação dos critérios de elegibilidade, foram selecionados 18 artigos para compor o corpus de análise. A revisão apontou diversas abordagens metodológicas nas pesquisas sobre a temática, revelando que o adoecimento dos professores universitários está relacionado à exaustão ocasionada pela sobrecarga de atividades e à exigência por produtividade, deixando-os em situação de vulnerabilidade. Nesse sentido, considera-se importante a realização de estudos voltados para a elaboração de estratégias de intervenção que possam ser implantadas e realizadas tanto em nível individual quanto organizacional, a fim melhorar as condições de trabalho e a qualidade de vida dos docentes.
Study on the Mechanical Properties of High-Toughness Concrete Hollow Wall Panels Prepared with POM Fiber
Authors: Shi Bo Yuan , Cheng Bao Jun , Xiong Ming Fei , Wang Fu Tao , Ao Fang
Published: December 2024

With the acceleration of urbanization and the expansion of densely populated areas, the safety and durability of building structures in metropolitan areas have become increasingly significant issues. This trend has raised the requirements for building materials, particularly in the production of prefabricated building components, where the use of high-strength, high-toughness concrete has become the norm. Using high-toughness concrete reinforced with organic fibers can enhance the mechanical properties of concrete while ensuring good workability, and POM fibers are among the most widely used organic fibers. This study primarily investigates the mechanical properties of prefabricated hollow wall panels made from high-toughness concrete reinforced with POM (polyoxymethylene) fibers. The mechanical behavior of POM fiber-reinforced concrete was analyzed through laboratory tests, including assessments of compressive and tensile properties. The results indicate that the inclusion of POM fibers significantly improves the maximum elastic compressive strength and ultimate compressive strength of the concrete, as well as enhancing its tensile capabilities. Using the CDP model theory in finite element analysis, this study further calculated the structural response of high-toughness concrete prefabricated hollow wall panels under wind and seismic loads, demonstrating their practical feasibility in engineering applications. This research not only provides a scientific basis for the application of POM fiber-reinforced high-toughness concrete but also offers new directions for future research and application in building materials, particularly in urban constructions requiring high safety and durability.
Post COVID-19 Symptoms and their Associations: A Cross-Sectional Study at One Month After Acute COVID-19 Infection
Authors: Kaushalya L. , Bowatte G. , Yasaratne D.
Published: December 2024

Introduction: Post-COVID-19 symptoms have been identified among a considerable proportion of individuals following acute COVID-19. This study cross-sectionally analyzed the post-COVID-19 symptoms of confirmed COVID-19 patients, between April – September 2021 who visited the post-COVID clinic, Teaching Hospital, Peradeniya, Sri Lanka.Methods: Patients’ clinical course of COVID-19 was classified as mild, moderate, and severe based on the peripheral oxygen saturation. Chi-square tests were performed to test the differences in the prevalence of persistent symptoms, and logistic regression to investigate the association between persistent symptoms and the variables examined.Results: The sample comprised 212 patients (55.17% females) with a mean age of 52.8 (SD±14.75) years. At baseline, 74.40%, 16.91%, and 8.70%, respectively, had mild, moderate, and severe COVID-19. One month after acute COVID-19, 98.11% of patients still had at least one persistent symptom with fatigue (65.57%), dyspnoea when climbing stairs (61.79%), and dyspnoea when walking a short distance (56.13%) being the most common. Females were more likely to develop persistent feverishness (aOR = 9.01; 95%CI: 1.13-71.98) and joint pain (aOR = 2.06; 95%CI: 1.15-3.69). Age (≥60) was associated with an increased risk of persistent depressed mood (aOR = 3.00; 95%CI: 1.06-8.45). Among comorbidities, chronic wheezers were more likely to experience fatigue (aOR = 3.81; 95%CI: 1.07-13.49), dyspnoea when climbing stairs (aOR = 6.71; 95%CI: 1.51-29.77), dyspnoea when walking a short distance/30m (aOR = 3.96; 95%CI: 1.27-12.33), and cold (aOR = 3.05; 95%CI: 1.14-8.21).Conclusion: Post COVID-19 symptoms had a high prevalence in this population, where fatigue, dyspnoea, feverishness and arthralgia were the most prevalent. Depressive symptoms were common among females. These findings may empower clinicians to counsel and investigate patients appropriately and suggest the need for further research to raise understanding and public awareness.
ANCA-associated Leucocytoclastic Vasculitis presenting with Bronchiectasis, GI Involvement and Hypertensive Urgency: A Complex Case without Renal Involvement
Authors: Risly N. M. M. , Jayasinghe I. K. , Athauda N. , Ekanayake E. M. C. B. , Hettige S. , Sahana J. F.
Published: December 2024

Introduction: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV) are autoimmune diseases characterized by inflammation of small to medium blood vessels. This case explores AAV pathophysiology, its link to bronchiectasis and leucocytoclasia, ANCA positivity, and current treatment guidelines.Case Presentation: A 48-year-old with abdominal pain, rash, hypertensive urgency, and respiratory symptoms was diagnosed with AAV, confirmed by skin biopsy and positive C-ANCA, stool occult blood with negative abdominal imaging along with bronchiectasis on HRCT. The patient initially failed to respond to broad-spectrum antibiotics but improved with high-dose corticosteroids.Conclusion: We highlight an atypical presentation of AAV, showing the importance of timely immunosuppressive therapy for favorable outcomes.
Retrieving the information stored in the donated organ may cause the patient’s personality to change after the transplant operation
Authors: Al-Karaki Jamal , Al-Zafar Khan Muhammad , Rathebe Phoka , Valverde Raul , Sepehri Alireza , Fioranelli Massimo
Published: December 2024

The results of some scientific research show that organ transplantation, especially heart transplantation, causes changes in the patient’s personality and even memory. These changes can be due to the recovery of information stored in the donor’s molecules and cells. In fact, as in the orchestrated objective reduction model, information in the form of polarization or spin of molecules, electrons, and photons is stored in microtubules due to the connection between the brain and the heart through blood vessels and nerves, and waves. A copy of the information is stored in molecules and heart cells and possibly through the induction of polarization and spin in some hexagonal or pentagonal molecules of DNA structures.
Where does it all start? Gender-differences in athletes’ intention to pursue a coaching career
Authors: Majake Refiloe , Kubayi Alliance
Published: December 2024

Background: While studies have addressed the reasons why coaches choose or leave the coaching profession, an initial inquiry into coaching should focus on the level of interest among athletes in becoming coaches.Objectives: The study investigated differences between male and female athletes’ intention to pursue a coaching career in South Africa.Methods: This study adopted a cross-sectional research design involving 200 (152 male and 48 female; Mage = 29.21, standard deviation [s.d.] = 4.75) athletes. Participation in the study was voluntary. Data were collected through various scales assessing athletes’ self-efficacy, outcome expectations, barriers and supports, choice goals and intention to coach.Results: The findings showed that male athletes recorded significantly (p  0.05) higher mean values for self-efficacy, outcome expectations, barriers, choice goals and intention to coach than female athletes. Two factors were shown to be independently predictive of the intention to coach: self-efficacy (β = -0.21, p  0.01) and barriers (β = 0.19, p  0.01).Conclusion: This study provides novel information to sports federations on the factors influencing athletes’ career aspirations.Contribution: This study adds to the body of knowledge on interest in coaching after an athlete’s playing career is over and offers baseline data for encouraging and enlisting athletes as coaches.
A programme of support for care assistants of children admitted with cerebral palsy
Authors: Molefe Lebogang L. , Sehularo Leepile A. , Koen Magdalena P.
Published: December 2024

Background: Cerebral palsy affects children’s movement and posture because of damage to the brain’s development. Care assistants in healthcare facilities provide care to children. Caring for the children is overwhelming, hence support is required. Such support is absent, causing frustration among care assistants, which leads to poor quality care for children.Objectives: To explore and describe the experiences of care assistants of children admitted with cerebral palsy in healthcare facilities of the Gauteng province, and to develop a support programme for care assistants.Method: A qualitative, exploratory, descriptive, and contextual research design was used. Participants were selected from healthcare facilities in Gauteng province. Semi-structured interviews were used to collect data. Content data analysis was used to analyse data. The results were used to develop a support programme for care assistants.Results: Three themes emerged, namely, a lack of training opportunities, a lack of resources, and a lack of support. The results were used to develop a support programme, using the three steps of the Donabedian model for care: structure, process and outcome.Conclusion: Care assistants are not given training opportunities, work with limited resources and are not supported, hence the development of a support programme. If effectively utilised, the programme can lead to staff satisfaction and improvement of quality care for children.Contribution: The study enabled managers in healthcare facilities to see the need for policy and the need for support strategies for care assistants. A support programme was further developed.
A grammar of trinitarian experience? On Sarah Coakley’s théologie totale
Authors: Delport Khegan M.
Published: December 2024

The Anglican theologian Sarah Coakley has attempted to think trinitarian doctrine in explicitly experiential terms. Her erotically-charged, pneumatically-centred account of salvific incorporation attempts to articulate, on the one hand, an account of the purgation of desire and the senses through ascetic practice, contemplative prayer and liturgical habituation, while also maintaining, on the other hand, the priority of spiritual appetition as being itself the route through which believers are, gradually, drawn into the trinitarian life.Intradisciplinary and/or interdisciplinary implications: The study makes a contribution by critically articulating an aspect of Coakley’s ‘théologie totale’, a new proposal for the method of systematic and dogmatic theology, one that is centrally focused on bringing in a wider interdisciplinary focus to the practice of systematic theology.
Comparison of the Efficacy of Intradermal Tranexamic Acid and Topical Triple Combination Modification in Decreasing the Severity Score of Melasma: A Meta-analysis
Authors: Indraswari Anindia , Indramaya Diah Mira , Sari Maylita , Utomo Budi , Ervianti Evy , Setyaningrum Trisniartami , Astindari Astindari , Umborowati Menul Ayu
Published: December 2024

Melasma is a symmetrical chronic hypermelanosis that affects the skin of the face, characterised by brownish patches with uneven margins, particularly in areas regularly exposed to sunlight. Although topical triple combination modifications are good first-line therapy for melasma, they have a wide range of negative effects. Consequently, new effective treatments with fewer side effects are required. It has been observed that intradermal tranexamic acid (TA) has low adverse effects and a good therapeutic response in treating melasma. This study aimed to assess the degree of the melasma area and severity index in patients treated with topical triple combination modifications and intradermal tranexamic acid therapy. Electronic databases were searched for relevant articles based on predetermined eligibility requirements. The statistical analysis was conducted using the Review Manager (RevMan) v5.4 software. Randomized controlled trials were used in designing three investigations with a total of 184 patients. After intervention, there was a significant difference in the MASI score between the intradermal tranexamic acid group and the modified topical triple combination group, with a mean difference (MD) of -0.81 (95% CI [-1.05, -0.57]), a combined result of 6.54 with P < 0.00001 and low heterogeneity (I2 = 0%). The intradermal tranexamic acid group also demonstrated a lower MASI score, and the funnel plot revealed no publication bias. This study found that intradermal tranexamic acid is more effective than topical triple combination modifications and has fewer side effects. Therefore, it can serve as an alternative primary therapy option for melasma.
Resurgence of sexually transmitted infections in India
Authors: Narain Jai Prakash , Mohan Nithya , Vedhanaygam Mohankumar , Swamiappan Murugan , Rajagopalan Rajesh
Published: December 2024

Sexually transmitted infections (STIs) are increasing throughout the world including India at present. In 1960s and 70s, the bacterial STIs were predominant than the viral STIs. The discovery of human immunodeficiency virus (HIV) and its rampant spread changed the situation with the increase of viral STIs in 1980s and 90s. There was a declining trend of viral and bacterial STIs in late 1990s to 2010. During the past two decades, HIV infection is on the declining trend, but other viral STIs are widely prevalent with insufficient decline. Currently, there is resurgence of syphilis and emergence of drug-resistant Neisseria gonorrhea. The interplay of various factors may have a role in this resurgence and this may act as an alarming sign of an impending epidemic. Hence, stringent monitoring of the trend of STIs including antibiotic resistance, appropriate management of STIs, and proper implementation of STI control program is needed. This will tackle the current situation and prevent the further spread of STIs.
Relapse of secondary syphilis in a PLHIV – A new entity
Authors: Sankaranantham Murugan
Published: December 2024

Syphilis and other bacterial sexually transmitted infections (STIs) are on the verge of resurge throughout the world. Coinfection of syphilis and HIV is quite common as they both share a common mode of transmission. The behavior of syphilis is quite altered in the presence of HIV. Relapse of secondary syphilis after the recommended treatment is quite unusual. One such case has been reported, and possible pathogenesis was discussed.