bluuebunny/crossref_metadata_2025 huggingface dataset:
Impacto de la Implementación de Ecosistemas Virtuales en la Enseñanza de las Matemáticas: Un Estudio de Caso en la Institución Educativa Municipal José Antonio Galán
Authors: Torres Burbano Álvaro Camilo
Published: July 2024

Este estudio investiga el impacto de la implementación de ecosistemas virtuales en la enseñanza de las matemáticas, centrándose en la Institución Educativa Municipal José Antonio Galán. A través de un enfoque metodológico mixto, se analizó la percepción y experiencia de estudiantes y docentes frente a la enseñanza híbrida y el uso de tecnología en el aula. Los resultados revelan que la integración de ecosistemas virtuales ha generado beneficios significativos, como una mayor participación y motivación de los estudiantes que tienen acceso a tecnología, así como un cambio positivo en las prácticas pedagógicas de los docentes. Sin embargo, también se identifican desafíos, especialmente relacionados con la brecha digital y la capacitación docente en el uso de herramientas tecnológicas. La implementación del modelo teórico ha demostrado correlacionarse positivamente con el rendimiento académico de los estudiantes, evidenciando un aumento promedio en las calificaciones en matemáticas. Además, se destaca el desarrollo de habilidades cognitivas y socioemocionales en los estudiantes, como la resolución de problemas y el trabajo en equipo. En resumen, este estudio proporciona evidencia sólida sobre la efectividad de los ecosistemas virtuales en la enseñanza de las matemáticas y sugiere la necesidad de continuar explorando enfoques innovadores para mejorar la calidad de la educación en este campo.
Uso de bioinsumos no algodoeiro e seus efeitos sobre o bicudo-do-algodoeiro e na produtividade
Authors: Damasceno Neves Benedito Charlles , Aquino de Albuquerque Fábio , Pereira de Araújo Gildo , Roberto de Azevedo Francisco
Published: July 2024

Excesso de fertilizantes nitrogenados interfere na fisiologia das plantas acumulando aminoácidos livres e açúcares redutores, tornando-as vulneráveis ao ataque de pragas. Objetivou-se avaliar a influência da aplicação de bioinsumos na indução de resistência do algodoeiro ao ataque do bicudo e o seu efeito na produtividade da cultura. As pesquisas foram conduzidas na Embrapa Algodão, na Estação Experimental em Missão Velha e, no Sítio Agroecológico, em Barbalha, no Ceará. Foi utilizada a cultivar BRS 433FL B2RF e os tratamentos consistiram do fungo Beauveria bassiana, bactéria Azospirilium brasilense, Fert Bokashi Premium®, Vorax® Biofertilizante, Nuvem® e a ureia como testemunha. Os tratamentos foram aplicados via solo exceto o Nuvem® e o monitoramento do bicudo foi realizado semanalmente ao longo da fase reprodutiva da cultura. Os dados foram submetidos à análise de variância e as médias comparadas pelo teste Scott-Knott. Na Estação Experimental o Beauveria bassiana induz resistência ao algodoeiro contra o bicudo devido às melhores condições de fertilidade do solo e o Fert Bokashi Premium®. Constatou-se que a Azospirillum brasilense promove aumento da produtividade mesmo as plantas sendo atacadas severamente. No sítio agroecológico embora com baixa fertilidade do solo, o Vorax® induz resistência ao algodoeiro e aumenta a produtividade das plantas tratadas.
Texto para Discussão 2959
Authors: Tatsch Ana Lúcia , Botelho Marisa dos Reis Azevedo , Koeller Priscila
Published: July 2024

Este trabalho analisou o papel dos dois laboratórios oficiais – Fundação Oswaldo Cruz (Fiocruz) e Instituto Butantan – no enfrentamento da pandemia de covid-19 no Brasil, a partir da análise do processo de construção de suas capacidades produtivas e inovativas e das políticas públicas que os apoiaram. Os procedimentos metodológicos envolveram revisão bibliográfica, pesquisa documental e coleta de dados. Verificou-se que, a partir da identificação de uma missão (garantir a suficiência no fornecimento de imunizantes) e de políticas que utilizaram sobretudo o poder de compra do Estado, desenvolveu-se grande capacidade de produção de vacinas. Essa, no entanto, não se assentou em uma capacidade de inovação endógena. Fiocruz e Butantan, laboratórios públicos centenários, provedores do sistema universal de saúde, cujas capacitações são fruto de investimentos de longo prazo, foram protagonistas nas iniciativas de enfrentamento da crise sanitária recente. Entende-se, assim, que o Estado teve um papel-chave na construção do Sistema de Inovação em Saúde Brasileiro (Sisb), assim como o terá no futuro, na busca pela superação de suas fragilidades.
Texto para Discussão 2964
Authors: Melo Valdir
Published: July 2024

A história do extinto Dasp e da reforma do funcionalismo nos anos 1930 mostra que de fato não vigorou um sistema de mérito. Isto é, grande parte dos funcionários continuou a ingressar e ficar nos postos de trabalho do Estado por meio de favoritismo. Em seguida, apesar de diversas mudanças legislativas pertinentes, o favoritismo quase sempre prevaleceu em todo o período até antes de 1988. O texto tece considerações sobre o tema, envolvendo vocábulos e conceitos que auxiliam o entendimento; apresenta alguns costumes e instituições importantes do favoritismo; e relata a continuidade do favoritismo em vários estágios da história política brasileira. Este conhecimento pode ser útil para que se repensem modificações institucionais que fortaleçam e aperfeiçoem o sistema de mérito.
Texto para Discussão 2965
Authors: Santos Gesmar Rosa dos , Mendes Alesi Teixeira
Published: July 2024

Este artigo tem como objetivo principal apresentar um panorama do financiamento dos serviços públicos de saneamento básico no Brasil, com destaque para as emissões de debêntures incentivadas como complemento aos mecanismos tradicionais de crédito. Utiliza-se a base de dados de debêntures emitidas no Brasil, descrevendo-se categorias e variáveis de destaque por setor de infraestrutura e foco em água e esgotamento. Destaca-se a importância das debêntures no atual conjunto de fontes de financiamento do setor no Brasil, apesar de a concessão de incentivos ser recente no país, pois se iniciou em 2013. Os projetos e as empresas emissoras de títulos aumentaram consideravelmente desde 2015, de modo que a carteira do saneamento apresentou a maior expansão de volume captado. Contudo, restam dúvidas quanto à contribuição do foco nas debêntures para a universalização do saneamento, uma vez que tais títulos financeiros são limitados a grandes empresas consolidadas no mercado e têm foco em projetos de água e esgotamento em localidades com boa viabilidade financeira. Assim, o financiamento para as demais concessionárias e os titulares dos serviços continua dependente de outras fontes. Nesse cenário, o Estado deve equilibrar os esforços entre o foco atual, que é o de renúncia de tributos às debêntures, e a alocação de recursos públicos com foco no déficit dos municípios, nos quatro componentes do saneamento e na universalização.
Writing exercises: Grammar and comprehension
Authors: Van Niekerk Angelique
Published: July 2024

Text and test with memorandum on Dutch Grammar, comprehension and vocabulary
BEHAVIOURAL BIASES INFLUENCING INVESTMENT DECISIONS OF INDIVIDUAL INVESTORS
Authors: Kumar Rahul , Kumar Jha Nagendra
Published: July 2024

The aim of this present study is to ratify the scale of behavioural biases such as overconfidence, herding effect, loss aversion and investment decisions relating to individual investors who invest in Indian stock market. The present study assesses the influence of aforesaid behavioural biases on investment decisions made by individual investors. The study is based on a questionnaire investigation by including the individual investors of Indian stock market residing in Bihar (India) using a convenient sampling technique. 500 hundred questionnaires have been distributed among the individual investors and 428 responses has been taken into consideration for the present study. The collected data were analysed through multiple regression using SPSS. The present study found that the scale used to measure behavioural biases and investment decisions of individual investors were valid and overconfidence biases have a significant influence on investment decisions of individual investors. In contrary, herding effect and loss aversion does not significantly influence the investment decision of individual investor. The present study found that the individual investors are behaviourally biased while making investment decisions. The present study backs to the academia of behavioural aspects and individual investors who invest in Indian stock market. The present study supports in understanding the concept of behavioural biases of individual investors investing in Indian stock market.
STUDYING CONSUMER BEHAVIOR RELATED TO DAIRY PRODUCTS, INCLUDING PREFERENCES FOR ORGANIC AND PLANT-BASED ALTERNATIVES, AND THE IMPACT ON DAIRY FARMING
Authors: Singh Manisha Jayprakash , Khatib Prof Noaman
Published: July 2024

This study looks at consumer behavior related to dairy products, specifically focusing on preferences for plant-based and organic alternatives and how that affects dairy production. Health, environmental, and ethical considerations are influencing consumer preferences in the dairy industry more and more. Dairy products that are organic are thought to be healthier and more environmentally friendly than their conventional counterparts, while plant-based substitutes are becoming more and more well-liked for moral and perceived health reasons. Changes in consumer preferences have a big impact on market strategies and sustainability initiatives related to dairy farming techniques. Comprehending these dynamics is vital for dairy sector players to proficiently modify production techniques and marketing strategies. Using a mixed-methods approach, this study investigates consumer behavior and motivations, offering insights into the trajectory of dairy product consumption going forward and its effects on the agricultural sector.
Checklist of marine macroalgae in two contiguous Marine Protected Areas in the south-western Atlantic
Authors: Karez Claudia , Bahia Ricardo , Nunes José , Santos Gabriel , Moura Rodrigo , Salomon Paulo , Ribeiro Clarice , Silva Carolina , Cardial Pedro , Leal Gabriella , Lyra Manoela , Salgado Leonardo
Published: July 2024

The Costa das Algas Environmental Protection Area (EPA) and the Santa Cruz Wildlife Refuge (WR), located in the Espírito Santo Continental Shelf, Brazil, are outstanding marine protected areas due to their high biodiversity, particularly of macroalgae. Together, these two relatively small protected areas (1,150 and 177 km2, respectively) harbour about a quarter of all macroalgal species recorded in Brazil.

The checklist presented herein updates the algal flora of these two protected areas with data obtained until 2019. Two hundred and sixty-five macroalgal taxa were recorded, most of which with vouchers. Checklists based on the collections of each protected area were published on: "Catálogo de Plantas das Unidades de Conservação do Brasil" (https://catalogo-ucs-brasil.jbrj.gov.br/).

Besides specimens collected between 2018 and 2019, the algal flora presented herein includes previous records from different Brazilian herbaria (e.g., SP, SPF, ALCB). Herbaria records may include species that do not occur in intertidal reefs (e.g., Laminaria). Overall, 249 macroalgal taxa and one marine angiosperm were recorded in the Costa das Algas EPA (87 new records) and 136 macroalgal taxa and one marine angiosperm in the Santa Cruz WR (46 new records). All taxa are native to Brazil and nine are endemic to Brazil. Our results provide a taxonomic foundation to support management, long-term monitoring and conservation in these protected areas.
A Hybrid Study for Epileptic Seizure Detection Based on Deep Learning using EEG Data
Authors: Buldu Abdulkadir , Kaplan Kaplan , Kuncan Melih
Published: July 2024

Epilepsy, a neurological disease characterized by recurrent seizures, can be diagnosed using Electroencephalogram (EEG) signals. Traditional diagnostic methods often face limitations, leading to delays and potential misdiagnoses. In response, researchers have been developing low-cost assistive systems to enhance diagnostic accuracy and reduce life-threatening risks for epilepsy patients. In this study, a hybrid approach is proposed to diagnose epilepsy disease. To validate the success of the proposed algorithm, Hauz Khas and Bonn data sets were used. AlexNet, GoogleNet, VGG19, ResNet50, and ResNet101 classifiers were employed in this study along with the Continuous Wavelet Transform (CWT) and Short Time Fourier Transform (STFT). To increase the generalization capability, 10-fold cross-validation method was used in the classification process. Firstly, the preictal and ictal moments in the Hauz Khas dataset was classified with 99.5% success rate by CWT method and Resnet101. Similarly, 99.8% accuracy was achieved in the binary classification of the Bonn dataset using the CWT method with Resnet101. Finally, for the classification with the AB-CD-E group, 99.33% classification success rate was achieved by using the CWT method with the Resnet-101 model. These findings underscore the potential of the proposed assistive system to significantly improve the diagnosis and management of epilepsy, demonstrating high accuracy and reliability across different datasets and classification techniques. 
LESCA: Scaffolding and its impact on the higher cognitive levels and emotions of the student
Authors: Alulema Darwin , Paredes-Velasco Maximiliano , de Arriba Lasso Ricardo
Published: July 2024

Teaching technical content in science and engineering requires the development of high-level competencies such as analytical and critical thinking skills, and is perceived by students as a difficult subject to understand. One way to help students learn this type of content is through the use of scaffolding, which dynamically regulates and adjusts learning according to the student’s needs. Although the use of scaffolding has already been applied in different educational contexts, so far there are no studies analysing its impact on students’ emotions and perception. In this paper we propose the LESCA system, which performs adaptive content feedback through scaffolding. The main hypothesis of this article is that the use of this tool together with teacher scaffolding improves the acquisition of content at higher cognitive levels and improves the student’s emotional state during learning. An experience has been carried out with 36 students of Industrial Electronics and Robotics Associate Degree with a pre-post design, where one group of students did not use the tool and another one did. The findings indicate that knowledge acquisition at the higher levels of Bloom’s taxonomy improved after the use of technological scaffolding and that this acquisition improved significantly when incorporating teacher scaffolding. On the other hand, students who performed the tasks with the system experienced significantly less anxiety and despair than students who did not use it. In addition, it has been found that students perceive teacher scaffolding to be significantly more useful than technological scaffolding.
An Empirical Study on the Correctness and Effort to Integrate Feature Models
Authors: Bischoff Vinicius , Farias Kleinner
Published: July 2024

Feature model integration is pivotal in software development, particularly in evolving software product lines through new feature accommodations. Despite its significance, the influence of developers’ experience on integration efforts and correctness still needs to be more adequately understood. This study conducted a controlled experiment with 25 participants (18 students and seven professionals) following well-known guidelines to run empirical studies. Each participant addressed ten experimental tasks, encompassing 250 integration scenarios, to explore two research questions. The effort and correctness rate in integrating feature models were quantified, revealing that students exerted higher effort (29.23%) and achieved a higher number of correct integrations (39.53%) than professionals. Notably, this superiority lacked statistical significance. Additionally, this article highlights practical implications and noteworthy challenges for the scientific community, providing valuable insights for software development practices. The findings lay a foundation for future studies, delving into software development tasks where students and professionals may achieve comparable results. Finally, this study marks an initial step towards an ambitious agenda, empirically advancing the feature model integration field.
Longitudinal PLS-SEM analysis of the performance and participation of students in mathematics
Authors: Alzahrani Ali Rashash R.
Published: July 2024

The mathematics performance of Australian high-school students in Year 9 and their participation in mathematics subjects in Year 12 has plummeted in the last 20 years. In this paper, a retrospective cohort study was conducted to understand how non-cognitive variables controlled or explained the correlation between mathematics performance in Year 9 and mathematics participation in Year 12. The sample consisted of a cohort of Australian students (N = 6653; n = 3115, 46.8% male) who participated in the Longitudinal Survey of Australian Youth (LSAY) in 2007 to 2017. Partial least squares structural equation modelling (PLS-SEM) was conducted to explore the degree to which one moderating variable and five mediating variables intervened in the relationship between mathematics performance in Year 9 and mathematics participation in Year 12. Mathematics performance in Year 9 predicted mathematics participation in Year 12 (R2 = 18.4%). The positive correlation was moderated by gender and mediated by five non-cognitive factors associated with self-related beliefs: mathematics self-concept, self-efficacy, interest, motivation, and anxiety (R2 = 28.9%). At any given level of mathematics performance in Year 9, a male student with a higher level of mathematics self-concept, self-efficacy, interest, and motivation, and a lower level of anxiety, was more likely to participate in mathematics subjects in Year 12 than a female student with a lower level of mathematics self-concept, self-efficacy, interest, and motivation, and a higher level of anxiety. These finding have implications for ameliorating the declining levels of mathematics performance and participation.
Virtual Professional Learning Communities for Educators
Authors: Martin Sarah
Published: July 2024

A virtual professional learning community (PLC) has the potential to impact positive change in a variety of work environments. The PLC model fosters collaboration and community among educators while achieving learning goals and shaping professional practice. As educators, we are often presented with professional learning opportunities that feel irrelevant to our daily classroom practice. Professional development opportunities often miss their mark because the stakeholders do not feel vested in the learning. Professional development delivered through a PLC model leverages the collective experiences of the participants with the powerful reflection that comes through collaboration. Well-designed PLCs engage stakeholders because the PLC model takes into account adult learning styles and hinges on the meaningful contribution of each participant. Thus, PLCs are a powerful tool for delivering professional development and shaping positive outcomes. This chapter will examine the definition of a PLC, describe the logistics of designing and implementing a meaningful community of virtual learners, and offer a first-hand account of how a virtual PLC was implemented in a middle school to impact professional practice. While the anecdotal accounts are specific to a middle school environment, the fundamental aspects of PLC design and implementation can be applied across a wide array of professional environments. Thus, much of the chapter content will be applicable beyond the scope of a school.
Collaborative Research Data Management Using ResearchGate
Authors: Khan Nadim Akhtar , Asmi Nowsheeba Ashraf , Bhat Aimen Nazir
Published: July 2024

Research data management benefits the scientific community through data sharing and collaborations, and its importance is much more owing to concerns from publishers, funding agencies, the public, and research organizations focussed on the quality and impact of research. Academic social networking sites facilitate the achievement of this goal through sharing and collaborating on research and ideas. ResearchGate has emerged as a platform for research data management, revolutionizing the ways to store, share, and access research data sets. The chapter highlights the significance of research data management and explores the importance of academic social networking sites in this area. It further explores ResearchGate features, which research scholars use to reflect successful collaborations, research growth, and better communication with peers. It attempts to assess the impact of RG research matrics for retaining the quality and visibility of research, which significantly impacts RG's success through openness, effectiveness, and interconnectivity in the scientific domain.
A Review of Capsule Network Limitations, Modifications, and Applications in Object Recognition
Authors: Haq Mahmood Ul , Sethi Muhammad Athar Javed , Rehman Atiq Ur
Published: July 2024

Modern computer vision and machine learning technologies have enabled numerous advances in a variety of domains, including pattern recognition and image classification. One of the most powerful machine learning methods is the capsule network, which encodes features based on their hierarchical relationships. A capsule network is a sort of neural network that uses inverted graphics to represent an item in distinct sections and see the existing link between these pieces, as opposed to CNNs, which lose most of the evidence relating to spatial placement and require a large amount of training data. As a result, the authors give a comparison of various capsule network designs utilized in diverse applications. The fundamental contribution of this study is that it summarizes and discusses the major current published capsule network topologies, including their advantages, limits, modifications, and applications.
Advancements in Machine Learning and Deep Learning
Authors: Darwish Dina
Published: July 2024

Among the most important methodologies in the field of modern intelligent technology is data-driven advanced machine learning methodology. In order to find rules, it makes use of data samples that have been observed, and it makes use of regular patterns in order to forecast unknown data in the future. In tandem with the development of artificial intelligence, the field of machine learning is making further strides forward. Due to this, there is a need for increased requirements for the training and applications of models, as well as the enhancement of the algorithm and the improvement of technological capabilities. This chapter discusses the recent technologies and trends in the artificial intelligence field, while giving examples and conclusions at the end of the chapter.
The Development, Applications, Challenges, and Analysis of a Cricket Player Face Recognition Dataset
Authors: Haq Mahmood Ul , Sethi Muhammad Athar Javed , Ullah Subhan , Ullah Abd
Published: July 2024

Over the last decade, face recognition technology has played a critical role in various circumstances, such as airport boarding, security applications, biometric verification, and smart homes. Along with the major role of face recognition in the areas above, we must recognize the important role of face recognition in various sports (i.e., cricket and football). The importance of proper player surveillance and identification in sports, particularly cricket, cannot be overstated. Articles are saturated with many deep-face evaluation systems; however, they are not up to mark due to the lack of significant face posture data. To address the black box in facial expression datasets, this chapter presents a comprehensive cricket player facial recognition dataset. The authors have a wide selection of cricket player images from various teams, playing styles, and backgrounds. It includes images taken during games, practices, and official team photos, providing a diverse range of facial changes and challenges for facial recognition systems. Furthermore, they evaluate the efficacy of cutting-edge facial recognition algorithms on our dataset, providing insights into the effectiveness of current methodologies as well as potential areas for development. Eventually, the extensive experimental analyses demonstrate that the current work is significant in addressing the black box in facial expression datasets.
A Systematic Review of Security Issues in 6G Networks and Communication
Authors: Pandey Digvijay , Goyal Sumeet , Bhaumik Kallol , Suneja Sonia , Sharma Manvinder , Dadheech Pankaj Dadheech
Published: July 2024

6G networks are the next frontier in wireless communication, promising unprecedented speeds, lower latencies and advanced capabilities. However, with greater connectivity comes increased security risks. This chapter provides a systematic review of security issues in 6G networks and communication systems. A comprehensive search of academic databases is conducted for studies published between 2020-2023 that examined security related issues of 6G. The studies indicate authentication, privacy and trust will be major concerns due to new technologies like reconfigurable intelligent surfaces and cell free architectures. Specific vulnerabilities include impersonation attacks, side channel leaks, and insider threats from rogue base stations. End to end encryption, blockchain, and machine learning are emerging security mechanisms to address these issues. This review synthesizes the current work on 6G security, highlights critical challenges and opportunities, and provides a framework for the future.
Distributed Ledger Technology in 6G Management Strategies for Threat Mitigation
Authors: Thakar Chetan , Saxena Rashi , Himabindu Modi , C. Rakesh , Dutt Amit , Arockia Dhanraj Joshuva
Published: July 2024

Establishing strong security protocols is crucial in the quickly changing internet of things (IoT) environment to reduce potential risks and weaknesses. It is possible to manage security risks more effectively, but there are also challenges because of the interconnected nature of IoT devices, the introduction of 6G networks, and the incorporation of distributed ledger technology (DLT). The focus of this note is on proactive methods of protecting infrastructure and sensitive data. It explores different management strategies that are intended to mitigate threats in IoT environments. Using a security-by-design methodology is a fundamental tactic for threat mitigation in internet of things settings. Every phase of the lifecycle of an IoT device, from design and development to deployment and operation, must incorporate security measures.
Defending the Digital Twin Machine Learning Strategies for 6G Protection
Authors: Ochoa-Tataje Freddy , Alanya-Beltran Joel , Ruiz-Salazar Jenny , Paucar-Elera Juan , Mendez-Escobar Michel , Alvarez-Huertas Frank
Published: July 2024

The idea of the digital twin has become a game-changer in today's quickly developing technological environment, revolutionizing everything from manufacturing to urban planning. A digital twin replicates the behavior and qualities of real-world physical objects, processes, services, or environments in a virtual space. This essay explores the nuances of the digital twin idea, its uses, and its importance in influencing technology in the future. A digital twin is a dynamic simulation that uses real-world data to predict behavior and performance, going beyond a static representation.
Advancements and Challenges in System on Chip Antennas (SoC) for 6G Communication
Authors: Sharma Manvinder , Talwar Rajneesh , Anand Satyajit , Bhola Jyoti , Pandey Digvijay
Published: July 2024

The upcoming 6G wireless networks aims to provide substantial improvements over existing 5G networks. These include improvements in data rate, latency, reliability, and connectivity. To enable these goals, significant advancements in all aspects of wireless communication systems are required. The major factor of communication hardware is antenna design. System on chip (SoC) antennas can be directly integrated with transceiver circuitry. These designs can meet the demanding performance requirements of 6G networks. In this chapter, recent advancements in SoC antenna technology for 6G communication systems are discussed. The key challenges that need to be addressed are also discussed. Literature related to the development of wideband millimeter wave antennas with beamforming capabilities is presented. Research directions related to modeling, design and implementation are identified to guide future work in this area.
Navigating Through Education 5.0 Era
Authors: Singh Ram
Published: July 2024

As we stand on the precipice of Education 5.0, characterized by unprecedented technological advancements and societal changes, the need for a transformative approach to education becomes paramount. This chapter delves into the imperative competencies that individuals must cultivate to thrive in this dynamic era. The chapter underscores the shift from conventional academic achievements to holistic competencies that encompass critical thinking, adaptability, and a collaborative mindset. The chapter provides a roadmap for navigating the Education 5.0 era, offering practical insights and actionable strategies for both learners and educators. By recognizing and developing these imperative competencies, individuals can not only succeed but also contribute meaningfully to a rapidly evolving global society.
STEMvolution
Authors: Ghosh Soumya Sankar
Published: July 2024

This research assesses the effectiveness of Blended Learning (BL) in reducing English-speaking anxiety among STEM students. Language anxiety hinders engagement and knowledge acquisition in STEM, where English proficiency is crucial. The BL program combines traditional methods with digital resources to enhance language proficiency and confidence. The study uses a mixed-methods approach, measuring anxiety and performance, followed by a tailored behavior change intervention. Results show a significant reduction in anxiety levels, and a strong link between enhanced linguistic confidence and academic success in STEM. BL is identified as a valuable tool connecting language competency to academic excellence in STEM, with implications for educators, institutions, and policymakers. Further research is needed for a comprehensive understanding of its lasting impact.
The Project-Based Learning Approach (PBL)
Authors: Nayak Arpita , Satpathy Ipseeta , Jain Vishal
Published: July 2024

Education is an essential component of human growth, equipping individuals with the skills and information required to tackle life's problems. Raising educational understanding is vital to providing equitable access for all. Education 5.0 aims to use modern technology to provide a more compassionate kind of education, emphasizing social and emotional development with creative solutions that improve societal well-being. Project-based learning (PBL) is a teaching strategy in which students investigate real-world challenges and topics via hands-on projects. This technique encourages the development of reasoning, problem-solving, and communication skills, all of which are crucial for success in today's professional world. PBL is an essential part of Education 5.0, a framework for personalized, collaborative, and technology-driven learning experiences. In conclusion, project-based learning systems in Education 5.0 are transforming education for the better, fostering meaningful learning experiences and practical skills that students can apply throughout their lives.
Teacher Professional Development in Education 5.0
Authors: Minh Nhat Truong Tran
Published: July 2024

In the rapidly evolving landscape of education, the advent of Education 5.0 brings with it new challenges and opportunities for teachers. This chapter focuses on teacher professional development in the context of Education 5.0. It explores how teacher professional development has evolved with the integration of technology and new pedagogical approaches in education. The chapter discusses the importance of continuous professional development for teachers to keep up with the changing landscape of education and implement effective teaching practices. It also highlights the role of collaboration, personalized learning, and mentorship in enhancing teacher professional development in Education 5.0. Additionally, the chapter examines the challenges and barriers that teachers may face in adapting to the new demands of Education 5.0 and offers strategies for overcoming these challenges.
Mindless Eating
Authors: Bolisetti Santha Kumari
Published: July 2024

Digital world is bestowed with loads of distractions causing infinite challenges to concentrate (Kukreja, 2019). The need to take fast decisions is more dominating than taking right and mindful decisions. Food which is the basic ingredient for survival, its choice is threatened amidst rapid emails and various app notifications putting nutrition outside the window. This chapter unveils the challenges that prohibit mindful eating and the solutions that can solve the labyrinth issues of making careful food decisions.
Neuroinflammation and Binge Eating
Authors: Asuku Abraham Olufemi , Ogungbangbe Gbonjubola Oyinlola
Published: July 2024

The relationship between neuroinflammation and binge eating is a complex interplay involving the central nervous system. Neuroinflammatory processes, often triggered by various factors, can influence neural circuits related to appetite regulation. Elevated levels of inflammatory markers in the brain have been observed in individuals with binge eating tendencies, suggesting a potential connection between inflammation and dysregulated eating behaviors. Exploring these mechanisms can contribute to a deeper understanding of the neural basis of binge eating, offering avenues for targeted interventions and therapeutic strategies. This review delves into the connection between neuroinflammation and binge eating, its neurobiological implications, insights, and therapeutic interventions. Also, insights into potential personalized treatment approaches based on neurinflammatory markers were considered. The review concludes by stating future directions and suggesting recommendations.
Neuromarketing for Healthy Eating and Disease Prevention
Authors: Anthuvan Thamburaj , Salis Sheryl S. , Maheshwari Kajal
Published: July 2024

This chapter explores the application of neuromarketing techniques to encourage healthier eating habits, aiming to counteract the global rise in lifestyle diseases linked to poor dietary choices. As the prevalence of non-communicable diseases escalates due to unhealthy eating patterns, identifying innovative strategies to influence consumer behavior towards more nutritious food consumption becomes crucial. Employing a comprehensive methodological approach that includes review of existing neuromarketing campaigns, detailed analysis of consumer behavior studies, and evaluation of health outcomes. Exploring the ethical use of neuromarketing within the food industry, highlighted through a series of case studies are the successes of health-oriented marketing efforts, showcasing the potential of neuromarketing to subtly influence consumer preferences. This chapter advocates for a balanced approach where ethical considerations guide the deployment of neuromarketing strategies, underlining its significance in the prevention of lifestyle diseases and fostering a healthier population.
Eating Disorders
Authors: Kadyan Jagbir Singh , Verma Rahul R. , Kulshrestha Shilpi , Kataria Abhinav , Pathak Purnima
Published: July 2024

“Eating Disorders: A Dialogue Between Neuroscience and Therapy” serves as a guiding light for individuals in the research, clinical, and mental health fields looking to enhance their understanding of eating disorders. It discusses the current issues surrounding binge eating and provides a plan for upcoming research and clinical uses. This extensive guide combines the most recent discoveries in neuroscience with cutting edge treatment methods, leading to better results in the end. Episodes of overeating and lack of control require a detailed understanding of the neuroscientific mechanisms for successful prevention and intervention strategies. The current situation is defined by the urgent need to connect advanced neuroscience research with the changing landscape of therapy. The complex connection between the brain and eating disorders necessitates a thorough resource that delves into the neurobiological origins and offers insights into new treatment methods. This chapter is specifically designed for academic scholars in the field of neuroscience.
Unmasking the Binge-Purge Cycle
Authors: Ashtikar Sunitha Purushottam , Manoharan Geetha , Kamalanathan M. , Kumar Billa Ronith
Published: July 2024

Binge-eating (BE), a severe life-threatening eating disorder characterised by consuming enormous amounts of food quickly and feeling out of control, is discussed in the chapter. A binge eater struggles with relationships, work, social life, stress, and depression. After eating, remorse, disgust, and embarrassment often follow—binge-eating harms those who do it. Early detection and evidence-based treatment benefit binge eaters. Most binge-eaters do it privately, without medical assistance, and rarely with evidence-based therapies. Patients and doctors must realise that many quick-fix binge-eating treatments work. The study used qualitative research to understand. The chapter discusses binge-eating history, symptoms, problems, and treatment that affect binge-eating disorder potentialities and cognition.
The Role of the Gut-Brain Axis in Eating Disorders
Authors: Kukreja Jyoti , Tewari Reena , Morande Swapnil
Published: July 2024

This chapter seeks to discuss the metabolic and internal processes that intricately takes place in an individual. The food choices and eating decisions are based on gut and brain requirements and command. The study presented is inclusive of the survey on the undergraduate students about their food choices which they have more liberty from moving strict and rigid school hours to college time. Reports of food sneaking and regret after eating had been reported. Implications are that food marketers are doing great jobs and falling prey to the target of temptations of tasty food is a trap as is corroborated in the survey. Food is consumed for pleasure and beverages are consumed as a matter of not nutrition requirement but socialize and shoot off boredom.
The Future of Eating Disorder (ED) Treatments
Authors: Verma Rahul R. , Dash Biswajit , Mahat Roshan Kumar , Jaiswal Aniket , Singh Abhishek K.
Published: July 2024

Neuroscientific experiences into eating disorder (ED) are revolutionizing our understanding of these Disorders, highlighting the exchange between brain work, organic forms, and natural components. This information is pivotal in creating focused on and viable restorative approaches. Whereas solutions and psychotherapy stay basic, long - term holds exciting conceivable outcomes with rising medicines. By bridging the hole between brain and body, we will empower individuals with eating disorders to recover their wellbeing and well - being. This approach can offer assistance people with ED address maladaptive contemplations and practices related to nourishment, body picture, and self - regard, eventually advancing recuperation and a more beneficial relationship with nourishment and body.
AI-Infused Quantum Machine Learning for Enhanced Supply Chain Forecasting
Authors: Gutta Leela Manush , Dhamodharan Balaji , Dutta Pushan Kumar , Whig Pawan
Published: July 2024

In the rapidly evolving landscape of supply chain management, the fusion of artificial intelligence (AI) and quantum machine learning (QML) holds immense promise for optimizing forecasting processes. This chapter delves into the synergistic potential of AI and QML techniques, presenting a comprehensive framework for harnessing their combined power in supply chain forecasting. By leveraging AI's ability to analyze vast datasets and extract meaningful insights alongside QML's capacity to process complex probabilistic distributions, organizations can achieve unprecedented accuracy in demand forecasting, inventory optimization, and risk mitigation. Through case studies and practical applications, this chapter elucidates how AI-infused QML models can revolutionize supply chain forecasting, driving efficiency, resilience, and competitiveness in the global marketplace.
Theoretical Overview of Quantum Machine Learning Applications in Enhancing Demand Forecasting for Supply Chain Management
Authors: Mandal Kamaluddin , Chatterjee Tathagata , Sarkar Abhijit , Ghosh Joyita , Gupta Subir
Published: July 2024

This chapter explores the potential of quantum machine learning (QML) to enhance demand forecasting in supply chain management. With the increasing complexity and data volumes in modern supply chains, traditional forecasting methods often fall short. QML leverages the principles of quantum mechanics, such as superposition and entanglement, to process data at unprecedented speeds and complexity. The authors discuss theoretical frameworks, focusing on quantum algorithms like quantum support vector machines and quantum neural networks, which can handle high-dimensional data more efficiently than classical counterparts. The chapter evaluates the feasibility, challenges, and prospects of implementing QML in real-world supply chain scenarios, suggesting that QML could significantly improve forecasting accuracy and operational efficiency, ultimately transforming supply chain management practices.
Quantum-Inspired Data-Driven Decision Making for Supply Chain Logistics
Authors: Whig Pawan , Mudunuru Krishnamurty Raju , Remala Rajesh
Published: July 2024

This chapter explores the paradigm shift in supply chain logistics driven by the convergence of quantum computing and data-driven decision making. With the exponential growth of data and the increasing complexity of supply chain networks, traditional decision-making approaches are becoming inadequate in meeting the demands of modern logistics operations. By integrating quantum-inspired methodologies with data analytics, organizations can unlock new insights and optimize their decision-making processes across the supply chain. This chapter examines the principles of quantum computing and how they can be applied to enhance decision making in areas such as demand forecasting, inventory management, transportation optimization, and risk analysis. Through case studies and practical examples, the chapter illustrates the transformative impact of adopting a quantum perspective on data-driven decision making in supply chain logistics.
Quantum Computing in Inventory Management and Dynamic Allocation
Authors: R. Ramya
Published: July 2024

The technology known as quantum computing has shown great promise and the ability to completely transform a number of industries, including dynamic allocation and inventory management. The use of quantum computing in these fields is examined in this work, with an emphasis on how well it can solve challenging optimisation problems in comparison to traditional computer techniques. It goes over how supply chain effectiveness, cost reduction, and inventory optimisation can all be achieved with quantum algorithms. The constraints and difficulties of integrating quantum computing into dynamic allocation and inventory management are also covered in the chapter, along with the requirement for specialised hardware and knowledge. The chapter's overall thesis is that, while quantum computing has great promise for improving inventory management and dynamic allocation procedures, further study and development are required to fully reap these advantages.
Quantum Computing Applications in Real-Time Route Optimization for Supply Chains
Authors: Vaddy Rama Krishna , Dhamodharan Balaji , Jain Anupriya
Published: July 2024

This chapter explores the transformative potential of quantum computing in the context of real-time route optimization for supply chains. In today's dynamic business environment, efficient logistics operations are essential for meeting customer demands while minimizing costs and maximizing efficiency. Traditional route optimization techniques often struggle to adapt to rapidly changing variables and dynamic environments. However, quantum computing offers a paradigm shift by harnessing the principles of superposition and entanglement to explore multiple route possibilities simultaneously and identify the most optimal solutions in real-time. This chapter examines the principles of quantum computing and their applications in route optimization algorithms tailored for supply chain logistics. Through case studies and practical examples, the chapter demonstrates how quantum computing can revolutionize route planning and scheduling, leading to reduced transportation costs, improved delivery times, and enhanced overall supply chain performance.
AI-Driven Inventory Management for Optimizing Operations With Quantum Computing
Authors: Mittal Sumit , Koushik Priyanka , Batra Iti , Whig Pawan
Published: July 2024

This chapter explores the transformative potential of integrating artificial intelligence (AI) with quantum computing to revolutionize inventory management practices. In today's dynamic business environment, efficient inventory management is paramount for organizations seeking to meet customer demand while minimizing costs and maximizing profitability. By harnessing AI's cognitive capabilities and quantum computing's computational prowess, this chapter elucidates how organizations can achieve unprecedented levels of optimization in their inventory operations. From demand forecasting and order fulfillment to supply chain optimization and risk mitigation, AI-driven quantum computing empowers organizations to make data-driven decisions with unparalleled accuracy and agility. Through real-world examples and case studies, this chapter showcases the tangible benefits of leveraging AI and quantum computing in inventory management, paving the way for enhanced operational efficiency, improved customer satisfaction, and sustainable competitive advantage in the marketplace.
Enhancing Predictive Maintenance in Supply Chain Systems With Deep Learning Strategies
Authors: Koushik Priyanka , Mittal Sumit , Jain Seema Nath , Whig Pawan
Published: July 2024

This chapter investigates the application of deep learning strategies to advance predictive maintenance within supply chain systems. Predictive maintenance plays a critical role in ensuring the reliability and efficiency of equipment and machinery across supply chain operations. Traditional approaches often struggle to effectively capture complex patterns and anticipate impending failures. However, deep learning techniques offer a promising solution by leveraging neural networks to analyze vast amounts of sensor data and identify early indicators of potential equipment malfunctions. This chapter explores various deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), tailored for predictive maintenance applications. Through case studies and real-world examples, the chapter illustrates how deep learning strategies can empower organizations to proactively manage asset health, minimize downtime, and optimize maintenance schedules, thereby enhancing overall supply chain resilience and performance.