Encyclopedia

The incorporation of graphene into cellulose matrices has emerged as a promising strategy for enhancing the structural and functional properties of composite materials. This comprehensive review provides a critical analysis of recent advances in optimizing graphene content in cellulose matrices and its impact on composite performance. Various optimization techniques, including response surface methodology, particle swarm optimization, and artificial neural networks, have been employed to identify optimal graphene concentrations and processing conditions. Quantitative analyses demonstrate significant improvements in mechanical properties, with notable increases in tensile strength and Young’s modulus reported for graphene/microfibrillated cellulose composites. Substantial enhancements in thermal stability have been observed in lysozyme-modified graphene nanoplatelet–cellulose composites. Electrical conductivity has been achieved at low graphene loading levels. Additionally, barrier properties, biocompatibility, and functionality for applications such as energy storage and environmental remediation have been substantially improved. This review explores case studies encompassing the optimization of thermal conductivity, viscosity, durability behaviors, pollutant removal, and various other properties. Despite promising results, challenges remain, including uniform dispersion, scalability, cost-effectiveness, and long-term stability. Strategies such as surface functionalization, solvent selection, and protective coatings are discussed. Future research directions, including novel processing techniques like 3D printing and electrospinning, as well as the incorporation of additional functional materials, are outlined. This review synthesizes current knowledge, identifies emerging trends, and provides a roadmap for future research in the rapidly evolving field of graphene–cellulose composites. Full article

This study aims to develop a multifaceted conceptual basis for employee collaboration with regard to promoting organizational sustainability, which encompasses environmental, social, and economic dimensions. Employing a mixed-methods framework, the study integrates a thorough literature review with a qualitative content analysis. A distinctive feature of this investigation is its emphasis on incorporating collaborative methodologies into sustainability strategies across various organizational frameworks, illustrating how collaboration can be refined through adaptive leadership, interdisciplinary teams, and digital technologies. The results indicate that organizations characterized by a robust collaborative culture demonstrate greater success in fostering sustainable innovations, minimizing environmental repercussions, and enhancing employee engagement. Furthermore, the study introduces a novel model that correlates collaboration with operational sustainability, taking into account diverse levels of resource sharing, leadership engagement, and employee empowerment. By focusing on actionable strategies, this research provides novel insights into how adaptive leadership, digital tools, and shared responsibility can transform collaboration into a driver of sustainability. This research enriches the existing body of literature by presenting an evidence-based framework for cultivating sustainable organizational cultures and provides valuable insights for prospective research on harnessing collaboration to attain long-term sustainability goals. Full article

The increasing complexity of social science data and phenomena necessitates using advanced analytical techniques to capture nonlinear relationships that traditional linear models often overlook. This chapter explores the application of machine learning (ML) models in social science research, focusing on their ability to manage nonlinear interactions in multidimensional datasets. Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. The chapter begins by examining the limitations of linear models and introduces essential machine learning techniques suited for nonlinear modeling. A discussion follows on how these models automatically detect interactions and threshold effects, offering superior predictive power and robustness against noise compared to traditional methods. The chapter also covers the practical challenges of model evaluation, validation, and handling imbalanced data, emphasizing cross-validation and performance metrics tailored to the nuances of social science datasets. Practical recommendations are offered to researchers, highlighting the balance between predictive accuracy and model interpretability, ethical considerations, and best practices for communicating results to diverse stakeholders. This chapter demonstrates that while machine learning models provide robust solutions for modeling nonlinear relationships, their successful application in social sciences requires careful attention to data quality, model selection, validation, and ethical considerations. Machine learning holds transformative potential for understanding complex social phenomena and informing data-driven psychology, sociology, and political science policy-making. Full article

Interleukin-33 (IL-33) is a cytokine belonging to the IL-1 family. It is primarily associated with type 2 immune responses. It interacts with a receptor complex on immune cells in reaction to tissue damage or cellular injury. IL-33 is crucial in immune responses and is involved in various autoimmune and inflammatory diseases. Obesity is marked by chronic inflammation and is a known risk factor for several types of cancer. Recent studies have shown that IL-33 and its receptor complex are expressed in adipose (fat) tissue, suggesting they may play a role in obesity. While inflammation connects obesity and cancer, it is not yet clear whether IL-33 contributes to cancer associated with obesity. Depending on the cellular context, inflammatory environment, expression levels, and bioactivity, IL-33 can exhibit both protumorigenic and antitumorigenic effects. This review will explore the various functions of IL-33 in the inflammation linked to obesity and its relationship with cancer. Full article

Non-destructive testing (NDT) is essential for evaluating the integrity and safety of structures without causing damage. The integration of artificial intelligence (AI) into traditional NDT methods can revolutionize the field by automating data analysis, enhancing defect detection accuracy, enabling predictive maintenance, and facilitating data-driven decision-making. This paper provides a comprehensive overview of AI-enhanced NDT, detailing AI models and their applications in techniques like ultrasonic testing and ground-penetrating radar. Case studies demonstrate that AI can improve defect detection accuracy and reduce inspection times. Challenges related to data quality, ethical considerations, and regulatory standards were discussed as well. By summarizing established knowledge and highlighting advancements, this paper serves as a valuable reference for engineers and researchers, contributing to the development of safer and more efficient infrastructure management practices. Full article

Coaching is a dynamic, evolving field that seeks to unlock individuals’ potential to achieve their personal and professional best. It has gained widespread recognition across various sectors as an effective means of leadership skills development, including in the vital healthcare sector, which requires innovative strategies to build leadership skills that can sustain high-quality services and maintain patient safety. Many healthcare organizations have thus embraced coaching as a valuable tool for leadership development. This review presents the definitions, concepts, methodology, and applications of coaching, and identifies its salient characteristics in relation to other professions. The second section of this study focuses on a literature review, specifically examining coaching in the healthcare industry and its role as a method for leadership development. The outcome from the review offers insights and results from a review of related research, specifically identifying the potential for coaching within healthcare and its effectiveness in leadership development. The information gathered from this study provides valuable guidance to healthcare leaders and organizations seeking to implement coaching practices in their leadership development programs. Full article

Life satisfaction is a concept related to an individual’s evaluation of their life, which is determined by taking into account all of the aspects that could affect it. Such an evaluation usually takes into consideration more than just economic conditions. In this paper, we present the relationship of life satisfaction with two of its main determinants, namely, self-reported health and leisure satisfaction. To exemplify this relationship, we utilize data from Mexico. Full article

This entry reviews the health condition known as Roemheld syndrome, or gastrocardiac syndrome in modern medicine. A pathology of gastrointestinal origin, the syndrome relies on a gut–brain–heart triad, interconnected by the vagus nerve. Pressure from the intestines pushes the stomach into the diaphragm and activates the vagus nerve, which affects the heart rate and gives the perception of cardiac issues. This distressing sensation, which usually comes after meals or with other digestive problems, causes anxiety or panic. Although events not arising from anatomic abnormalities are relatively harmless, hypersensitivity to these uncomfortable sensations may precipitate psychiatric problems (anxiety or depression) that cause repeated gastrocardiac events through sympathetic activation and the disruption of peristalsis. Treatment is usually symptomatic and may include diet, lifestyle changes, probiotics, or prescriptions that increase gut motility, but this specific set of reproducible symptoms may also be caused by hiatal hernia or side effects from medication/surgery and treated with respect to those mechanical causes. This review highlights details from the most current knowledge of the condition and offers suggestions for clinical management based on the literature. Full article

The evolving landscape of Corporate Social Responsibility (CSR) has transcended its traditional boundaries, transitioning into Environmental, Social, and Governance (ESG) principles and their more advanced iteration, ESG 2.0. Unlike traditional CSR, which primarily emphasizes voluntary ethical practices, ESG integrates sustainability into the core business strategy, transforming how corporations address environmental and societal challenges while enhancing shareholder value. This entry focuses specifically on the European and North American contexts, where regulatory pressures, investor demands, and societal expectations have played pivotal roles in accelerating this transition. Understanding the evolution from CSR to ESG practices is crucial, given the increasing complexity of global challenges such as climate change, inequality, and governance scandals. The emphasis on ESG 2.0 highlights a proactive, strategic approach to embedding sustainability into corporate DNA, ensuring relevance in a rapidly changing world. Full article

The widespread use of social media among children has raised concerns about its impact on their dietary habits and health. This systematic review investigates the negative effects of social media on children’s diets to inform evidence-based interventions and policies. A search of peer-reviewed studies from 2020 to 2024 was conducted using PubMed, Web of Science, and Scopus. Studies involving children aged 5–18 and examining social media’s influence on diet were included. Two independent reviewers screened the studies, and data extraction and quality assessment were done using standardized methods. Of 945 identified studies, 25 met the inclusion criteria. The key themes included (1) exposure to unhealthy food advertisements, (2) peer influence promoting energy-dense, nutrient-poor foods, (3) distorted body image perceptions leading to unhealthy eating, and (4) reduced mealtime quality due to social media distractions. Stronger associations were observed for marketing exposure and peer influence on food choices. The review highlights social media’s negative effects on children’s diets, emphasizing the need for interventions, stricter food marketing regulations, and educational programs to enhance media literacy. Future research should explore the long-term impacts and protective factors to guide policies for creating healthier digital environments for children. Full article

The central nervous system (CNS) relies on precise regulation of potassium ion (K⁺) concentrations to maintain physiology. This regulation involves complex cellular and molecular mechanisms that work in concert to regulate both intracellular and extracellular K⁺ levels. Inflammation, a key physiological response, encompasses a series of cell-specific events leading to inflammasome activation. Perturbations in K⁺-sensitive processes can result in either chronic or uncontrolled inflammation, highlighting the intricate relationship between K⁺ homeostasis and inflammatory signalling. This review explores molecular targets that influence K⁺ homeostasis and have been implicated in inflammatory cascades, offering potential therapeutic avenues for managing inflammation. We examine both cell-specific and common molecular targets across different cell types, providing a comprehensive overview of the interplay between K⁺ regulation and inflammation in the CNS. By elucidating these mechanisms, we identify leads for drug discovery programmes aimed at modulating inflammatory responses. Additionally, we highlight potential consequences of targeting individual molecular entities for therapeutic purposes, emphasizing the need for a nuanced approach in developing anti-inflammatory strategies. This review considers current knowledge on K⁺-sensitive inflammatory processes within the CNS, offering critical insights into the molecular underpinnings of inflammation and potential therapeutic interventions. Our findings underscore the importance of considering K⁺ homeostasis in the development of targeted therapies for inflammatory conditions within the CNS. Full article

The advent of the internet has fundamentally transformed how people access and interact with information. The digital landscape offers easy access to educational materials and healthcare and can support informed decision making. It also presents significant challenges in effectively using this information. This entry discusses the multifaceted barriers that can impede individuals from fully benefiting from the internet’s information resources. Key challenges include discerning between accuracy and misinformation, the uneven distribution of digital literacy, disparities in access to technology, and motivational barriers in seeking reliable information. Additionally, the growing involvement of artificial intelligence (AI) in content curation, regulation, and moderation introduces opportunities and ethical dilemmas. These factors complicate efforts to support the accuracy and trustworthiness of the information shared through digital platforms. Full article

Educational data mining (EDM) is a novel scientific area that focuses on developing and applying methods to analyze datasets generated within educational settings. This paper outlines the evolution, significance, and applications of EDM. With the increasing popularity of e-learning in web-based educational systems, EDM has expanded to include a variety of analytical methods and data sources. Some key methodologies addressed include classification, regression analysis, clustering techniques, association rule mining, and Natural Language Processing, among others. Additionally, this paper looks at how EDM can facilitate data-driven decision-making among other areas such as curriculum development and customization of learners’ experiences. It also touches on issues related to the challenges of the scientific field. Finally, some projections about EDM’s future trends are made, especially concerning its integration into AI technologies and development trends like augmented reality or virtual reality, which imply greater possibilities for changes than any other series witnessed before within this sphere. Full article

The salt fractions of milk consist of cations (e.g., Ca, Mg, and Na) and anions (e.g., phosphate, citrate, and chloride). These salts are present as free ions or in complexes with other ions or proteins, primarily the caseins. Furthermore, significant levels of Ca and phosphate are also found in insoluble form, inside the casein micelles. The distribution of salts between this micellar phase and the soluble phase is important for the stability and properties of milk and dairy products. Various processes, such as (ultra-)centrifugation, (ultra-)filtration, dialysis, and selective precipitation have been used to separate the micellar and soluble phases in milk and dairy products to allow for studying the salts’ distribution between these phases. These different methods can lead to different levels of soluble salts because the salts in the supernatant from centrifugation, the permeate from ultrafiltration, and the diffusate from dialysis can differ notably. Hence, understanding which components are fractionated with these techniques and how this affects the levels of the soluble salts determined is critical for milk and dairy products. Applying the aforementioned methods to cheese products is further challenging because these methods are primarily developed for fractionating the soluble and micellar phases of milk. Instead, methods that analyze salts in water-soluble extracts, or soluble phases expressed from cheese by pressing or centrifugation are typically used. This review focuses on the significance of salt distribution and variations in salt fractions obtained using different methodologies for both milk and cheese. Full article

¹H High-Resolution Nuclear Magnetic Resonance (¹H HR-NMR) spectroscopy is a powerful analytical methodology used in various fields, including food science. In the food science field, NMR combined with the principles of metabolomics can provide detailed information about a food’s molecular composition, structure, dynamics, and interactions within food matrices, making it invaluable for assessing changes during storage, processing, and shelf life. This entry aims to list the main applications of one-dimensional ¹H HR-NMR methods in the field of food science, such as their use in the assessment of the stability, quality, authenticity, and shelf life of food samples. Several kinds of foods are taken into consideration to give a huge overview of the potentiality of the methods. Full article

Up until recently, research on loyalty and Islam—here considered as the discourses and practices of Muslims in regard to the Muslim tradition—had ignored the topic of loyalty. Interest in loyalty in Islam has just lately grown sufficiently and extensively. This entry is both bibliographical and thematic. It seeks to identify the principal themes that have dominated Muslim loyalty studies for the past thirty years or so. Additionally, it provides a thorough synopsis of over 100 studies on loyalty that were published during the same time span in Arabic and European languages. Allegiance in Muslim political ethics always had more than one connotation and the religious component of loyalty, while not strictly defined, allowed and even justified the overlap of multiple loyalties. A person’s or a family’s, ruler’s, or dynasty’s power to govern an area or defend religious institutions and symbols was intimately linked to the loyalty they could claim. Most studies agree on the diversity of political allegiance in modern Islam with regard to perceptions of religion, ethnicity, self-interest, etc.; it is also co-relative in the sense that it is mixed with other allegiances, such as those of family, tribe, leadership, or country. Full article

Social media sentiment analysis is the computational detection and extraction of human subjective evaluation of objects embedded on social media. Previous sentiment analysis was conducted on isolated written texts, and typically classified sentiment into positive, negative, and neutral states. Social media sentiment analysis has included multi-modal texts, temporal dynamics, interactions, network relationships, and sentiment propagation. Specific emotions and sentiment intensity are also detected. Full article

Geomasking is a set of techniques that introduces noise or intentional errors into geospatial data to minimize the risk of identifying exact location information related to individuals while preserving the utility of the data to a controlled extent. It protects the geoprivacy of the data contributor and mitigates potential harm from data breaches while promoting safer data sharing. The development of digital health technologies and the extensive use of individual geospatial data in health studies have raised concerns about geoprivacy. The individual tracking data and health information, if accessed by unauthorized parties, may lead to privacy invasions, criminal activities, and discrimination. These risks underscore the importance of robust protective measures in the collection, management, and sharing of sensitive data. Geomasking techniques have been developed to safeguard geoprivacy in geospatial health data, addressing the risks and challenges associated with data sharing. This entry paper discusses the importance of geoprivacy in geospatial health data and introduces various kinds of geomasking methods and their applications in balancing the protection of individual privacy with the need for data sharing to ensure scientific reproducibility, highlighting the urgent need for more effective geomasking techniques and their applications. Full article

This review explores the current applications of artificial intelligence (AI) in nuclear magnetic resonance (NMR) spectroscopy, with a particular emphasis on small molecule chemistry. Applications of AI techniques, especially machine learning (ML) and deep learning (DL) in the areas of shift prediction, spectral simulations, spectral processing, structure elucidation, mixture analysis, and metabolomics, are demonstrated. The review also shows where progress is limited. Full article