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Scientific basis of dietary inflammatory index (DII): A dietary tool to metabolic syndrome risk
No full textBackground and Aims: The dietary inflammatory index (DII) is a tool that quantifies the inflammatory potential of an individual\u27s diet, offering a scientific basis for personalized nutrition. The DII scores of foods and nutrients are based on their pro- or anti-inflammatory potential. DII is associated with metabolic diseases and health status but the results are inconsistent. Therefore this review was conducted to highlight the scientific basis of DII and its association with metabolic diseases. Methods: We conducted independent literature research for this review between January 2006 and January 2025 utilizing scholarly databases such as PubMed, ScienceDirect, Google Scholar, and Web of Science. Results: This review highlights the scientific basis of DII, focusing on its ability to capture the complex interactions between dietary patterns and health outcomes in various inflammatory and metabolic diseases. Moreover, the current review discusses the modification and interpretation of multiple types of DII over time. By calculating an individual\u27s DII score, healthcare professionals can identify pro-inflammatory dietary patterns that may exacerbate chronic diseases with an inflammatory predisposing background, such as obesity, cardiovascular disease, diabetes, and cancer. This review also highlight the association of DII score with various inflammation-associated diseases and strengthen nutrition guidelines to promote anti-inflammatory dietary patterns. Conclusion: The DII offers a valuable tool for healthcare professionals to integrate nutrition into patient care, promoting a more comprehensive disease prevention approach. Further research and validation will continue to refine and optimize the DII, enhancing its potential to transform the practices of clinical nutrition and improve patient outcomes
Stimulating Environmental and Health Protection Through Utilizing Statistical Methods for Climate Resilience and Policy Integration
Full text linkClimate change, a critical global challenge, is evident in rising global temperatures, shifting precipitation trends, and extreme weather events, including floods, heatwaves, and rising sea levels. The impacts of climate change not only endanger physical health but also affect mental well-being, particularly among populations experiencing frequent or severe climate-related events. Understanding individual perceptions of climate risks and adaptive capacities is crucial for developing strategies that promote health resilience and environmental protection. This paper examines how risk perceptions, direct experiences with extreme weather, and perceived adaptive capacities influence climate change protection measures and support for relevant policies. Data were gathered from 291 respondents in the United Arab Emirates using structured questionnaires. The data were analyzed using descriptive statistics, reliability analysis, Cronbach’s alpha, Spearman correlation analysis, and multiple regression analysis to determine key predictors of policy support. The results indicate that age is positively correlated with policy support (ρ = 0.16, p = 0.001), while gender also plays a role, with women showing greater risk perception and stronger policy support than men. In contrast, formal education and employment status do not significantly impact policy endorsement or climate adaptation behaviors. These findings suggest that awareness-based interventions alone may be insufficient to drive climate action. Instead, policies should leverage older individuals’ experiences, enhance workplace and community-based climate engagement, and prioritize hands-on, action-oriented education to bridge the gap between climate knowledge and adaptive behavior
Spot Bitcoin ETFs: The Effect of Fund Flows on Bitcoin Price Formation
No full textInflows to the newly established bitcoin exchange-traded funds (ETFs) surpassed 500 million or roughly 10,000 bitcoins, and surpass bitcoin mining production by the factor of 5; 2) net flows to ETFs are a strong positive predictor of bitcoin price levels with the R-squared of 95%; 3) most bitcoin price changes occur outside ETF trading hours; 4) an increase in bitcoin price leads to abnormal ETF trading volume; 5) inflows to bitcoin ETFs correlate with outflows from gold ETFs. Overall, during the period studied, capital flows to spot-bitcoin-ETFs emerge as a dominant single factor predicting bitcoin valuation effects
The synergy of artificial intelligence and education: New perspectives of an innovative artificial tutoring in school settings
No full textThe integration of Artificial Intelligence (AI) in education has ushered in a new era of adaptive and personalized learning, improving instructional methodologies and supporting diverse learner needs. AI-powered Intelligent Tutoring Systems (ITS), such as MetaTutor and nStudy, exemplify this change. MetaTutor utilizes hypermedia and real-time data tracking, including facial recognition, physiological sensors, and eye-tracking technology, to adapt instruction in complex STEM subjects (Azevedo et al., 2012). Similarly, nStudy (Winne, 2019) facilitates self-regulated learning through personalized digital tools for tasks such as online research, collaborative projects, and academic writing. These systems demonstrate the potential of AI to optimize engagement, further autonomy, and improve learning outcomes. Through tailored instruction to individual learning styles, cognitive abilities, and linguistic backgrounds, ITS can bridge learning gaps and support students with disabilities, increasing accessibility in education. However, ethical considerations regarding algorithmic bias, data privacy, and equitable access need to be addressed. This chapter explores how AI-driven tutoring technologies redefine modern education, balancing innovation with ethical responsibility to create more inclusive and successful learning experiences
From Unrealistic to Functional Optimism in Illness Perception: A Psychometric Comparison Across 10 Countries
Full text linkPeople\u27s perceptions of illness and its risks influence health behaviors, including risk management and precautionary measures. Illness perception often involves unrealistic optimism, reducing infection risk perception. However, crises disrupt self-regulation and optimism due to uncontrollable situations. This study examines optimism\u27s link to risk and illness perception during the first COVID-19 wave in 10 countries, with 7254 participants (48.1% women, mean age = 40, SD = 14.8). We used Bayesian structural equation modeling for psychometric stability and one-way ANOVAs for country comparisons. Multiple regression analyses examined the impact of optimism and demographic variables on illness perception. Significant cross-country variations emerged in illness perception and optimism. In terms of the relationship between variables, optimism correlated with increased COVID-19 risk perception, especially for negative outcomes, concern, and consistency. During crises, optimism shifted from unrealistic to functional, promoting treatment adherence, personal control, and coherence. These dimensions represent individuals\u27 beliefs in managing illness, highlighting optimism\u27s adaptive role in crises
Teaching gender equality: shifting the focus from aspiration to practice
Full text linkPurpose: This study aims to inform management educators and students about how to identify, analyze, and sustain gender equality as a business practice in organizational settings. Design, Methodology, and Approach: Guided by practice theory and social learning theory we: a) offer a theoretical framework to help students recognize gender equality practices in real-world contexts, and b) provide educators with an applied example and pedagogical tool for teaching students how to identify and analyze gender equality practices in written and audio texts. We draw from two theoretical templates to analyze excerpts related to gender equality practices taken from an interview with an MNE business leader based in Latin America conducted by Harvard Business School as part of the Creating Emerging Markets–Oral History Collection Project. Findings: Results apply practice theory to the teaching domain and suggest strategies for equipping educators with a tool for informing and sensitizing students regarding gender equality as practice. Originality: Unless put into practice, gender equality will continue to lag in implementation in today’s organizations. This paper addresses gender equality as practice and thus equips future business leaders with a necessary understanding of gender equality as practice in written and audio texts in real-world settings. This will help them create and maintain gender equality in their future workplaces and thus become better equipped to manage grand challenges and other complexities in today’s organizations
Angelman Syndrome Neurocognitive and Linguistic Profile, Overlaps, Interventions, and Quality of Life
No full textAngelman Syndrome (AS) is a rare neurogenetic disorder caused by a mutation or deletion of the UBE3A gene on chromosome 15, affecting the brain\u27s development and function. Neurocognitively, individuals with AS often exhibit significant developmental delays, with limited cognitive abilities, impaired motor coordination (ataxia), and epilepsy being common. While cognitive impairment is a hallmark of the syndrome, memory and problem- solving abilities are also significantly affected. Linguistically, AS is characterized by profound speech impairments, with most individuals developing little to no functional speech. Early diagnosis, often through genetic testing, is crucial for the implementation of early intervention programs that focus on speech therapy, physical therapy, and behavioral management. Specialized interventions that address motor deficits, language development, and behavioral concerns can enhance cognitive and social functioning, although the overall prognosis remains one of lifelong disability
The Object-Oriented Approach to Problem Solving and Machine Learning with Python
No full textThis book is a comprehensive guide suitable for beginners and experienced developers alike. It teaches readers how to master object-oriented programming (OOP) with Python and use it in real-world applications. Start by solidifying your OOP foundation with clear explanations of core concepts such as use cases and class diagrams. This book goes beyond theory as you get practical examples with well-documented source code available in the book and on GitHub. This book doesn’t stop at the basics. Explore how OOP empowers fields such as data persistence, graphical user interfaces (GUIs), machine learning, and data science, including social media analysis. Learn about machine learning algorithms for classification, regression, and unsupervised learning, putting you at the forefront of AI innovation. Each chapter is designed for hands-on learning. You’ll solidify your understanding with case studies, exercises, and projects that apply your newfound knowledge to real-world scenarios. The progressive structure ensures mastery, with each chapter building on the previous one, reinforced by exercises and projects. Numerous code examples and access to the source code enhance your learning experience. This book is your one-stop shop for mastering OOP with Python and venturing into the exciting world of machine learning and data science
Real-time active-learning method for audio-based anomalous event identification and rare events classification for audio events detection
Full text linkIntroduction: Audio event detection, the application of scientific methods to analyze audio recordings, can be helpful in examining and analyzing audio recordings to preserve, analyze, and interpret sound evidence. Furthermore, it can be helpful in safety and compliance, security, surveillance, maintenance, and predictive analysis. Audio event detection aims to recover meaningful information from audio recordings, such as determining the authenticity of the recording, identifying the speakers, and reconstructing conversations. However, filtering out noise for better accuracy in audio event detection is a major challenge. A greater sense of public security can be achieved by developing automated event detection systems that are both cost-effective and real-time. Methods: In response to these challenges, this study presented a method for identifying anomalous events based on noisy audio evidence and a real-time scenario to help the audio event detection investigator during the investigation. This study created a large audio dataset containing both noisy and original audio. The dataset includes diverse environmental background settings (e.g., office, restaurant, and park) and some abnormal events (e.g., explosion, car crash, and human attack). This study used an ensemble learning model to conduct experiments in an active learning environment. Nine methods are employed to create the feature vector. Results: The experiments show that the proposed ensemble learning model using the active learning settings obtained an accuracy score of 99.26%, while the deep learning model obtained an accuracy of 95.35%. The proposed model was tested using noisy audio evidence and a real-time scenario. Discussion: The experiment results show that the proposed approach can efficiently detect abnormal events from noisy audio evidence and a real-time scenario in real-time
UAV-supported communication: Current and prospective solutions
No full textThe advancement in wireless communication has significantly resulted in unprecedented new applications and services. This, coupled with Next-Generation Networking (NGN) and the recent advances in cellular communication and networking, predominantly the Fifth-Generation (5G) network, has resulted in the rise of new technological solutions. Unmanned Aerial Vehicles (UAVs) is one such solution that has evolved from its traditional usage in military and civilian applications, towards new and innovative solutions that provide support to wireless communication and networking. With advances in their processing and communication capabilities, UAVs are now supporting both core and edge networks to deliver services to end-users in a reliable and fast manner. The synergy and collective collaboration between UAVs and continuously evolving and progressing technologies such as Artificial Intelligence (AI) and blockchain are reshaping the landscape of wireless communication and networking, enabling more robust, secure and adaptable systems that transcend traditional limitations. In this article, we explore various collaborative solutions that leverage UAV communication and networks that not only bolster communication between mobile nodes and the core network but also reinforce the edge computing infrastructure. This reinforcement enables scalable data storage and intelligent processing to elevate end-user services and applications. Additionally, we address the obstacles, concerns, and future pathways concerning UAV-supported NGNs