Search Results (746)

Introduction: Migraine is featured by altered nociceptive processing and the presence of cognitive impairments. No study has previously investigated the influence of neurocognitive performance and executive functions in descending pain processing in this population. Aim: To assess the influence of neurocognitive processes and executive functions in conditioned pain modulation (CPM) activation in women with migraine. Methods: A cross-sectional case–control study including 140 women with migraine (50% chronic) and 70 control women was conducted. Clinical migraine features, neurocognitive processes (e.g., attention), and executive functions (memory, mental inhibition, speed of processing) were evaluated. Pressure pain thresholds (PPTs) were bilaterally assessed at the temporalis muscle, lateral epicondyle, and tibialis anterior muscle. Heat (HPT) and cold (CPT) pain thresholds were assessed at the frontalis (trigeminal area) muscle. Thus, CPM was evaluated with the cold pressor test paradigm by analyzing changes in mechanical/thermal stimuli after a conditioned stimulus. Results: Significant group*time interactions not associated with neurocognitive process/executive function, educational level, and employment status were found for PPTs at the temporalis muscle (Wilk’s λ = 0.588, F[2,199] = 69.756, p < 0.001, n²p = 0.412, 1 − β = 0.999), lateral epicondyle (Wilk’s λ = 0.674, F[2,200] = 48.331, p < 0.001, n²p = 0.326, 1 − β = 0.999), and tibialis anterior (Wilk’s λ = 0.751, F[2,200] = 33.110, p < 0.001, n²p= 0.249, 1 − β = 0.999): PPTs were higher after the conditioned stimulus in all points in control women (increases ranging from 11% to 17%), whereas PPTs were lower after the conditioned stimulus in women with migraine (decrease from −7.5% to −0.1%) when compared with PPTs at baseline. Changes in HPT and CPT were small and not significant, ranging from 0.1% to 0.5%. Conclusion: This study revealed that women with episodic or chronic migraine showed CPM deficits particularly against mechanical stimuli when compared with pain-free women. Neurocognitive (e.g., attention) processes or executive functions (e.g., working memory, mental inhibition) did not modulate CPM activity in women with migraine. Full article

This study addresses the challenge of digitally modeling Indigenous Traditional Ecological Knowledge (TEK) in a manner that respects and preserves its epistemic integrity. Grounded in ethnographic inquiry and system design, the research introduces a four-tier knowledge typology that conceptualizes how tacit, explicit, tribal and cultural knowledge circulate within Indigenous communities. This cyclical model highlights recursive and embodied processes of knowledge internalization, transmission, and integration, offering a dynamic alternative to linear knowledge flow frameworks. Building upon this epistemological foundation, this study traces the transition from traditional data practices, which are centered on oral histories, ritual performances, and ecological observation, to a contemporary AI-assisted architecture that operationalizes these forms through structured semantic enrichment, modular knowledge storage, and culturally aligned reasoning systems. The proposed system integrates layered components, from data acquisition to multi-agent inference models, while embedding ethical protocols that affirm community sovereignty and relational authority. The findings suggest that TEK systems can be effectively encoded into modern digital infrastructures without erasing their socio-cultural contexts. By foregrounding Indigenous epistemologies within system design, the research advances a critical paradigm for culturally responsive knowledge technologies in sustainability, education, and heritage preservation. Full article

Digital twins (DTs) powered by artificial intelligence (AI) are becoming important transformational tools in healthcare, enabling real-time simulation and personalized decision support at the patient level. The aim of this review is to critically examine the evolution, current applications, and future potential of AI-based DTs in healthcare, with a particular focus on their role in enabling real-time simulation and personalized patient-level decision support. Specifically, the review aims to provide a comprehensive overview of how AI-based DTs are being developed and implemented in various clinical domains, identifying existing scientific and technical gaps and highlighting methodological, regulatory, and ethical issues. Taking a “local to global” perspective, the review aims to explore how individual patient-level models can be scaled and integrated to inform population health strategies, global data networks, and collaborative research ecosystems. This will provide a structured foundation for future research, clinical applications, and policy development in this rapidly evolving field. Locally, DTs allow medical professionals to model individual patient physiology, predict disease progression, and optimize treatment strategies. Hospitals are implementing AI-based DT platforms to simulate workflows, efficiently allocate resources, and improve patient safety. Generative AI further enhances these applications by creating synthetic patient data for training, filling gaps in incomplete records, and enabling privacy-respecting research. On a broader scale, regional health systems can use connected DTs to model population health trends and predict responses to public health interventions. On a national scale, governments and policymakers can use these insights for strategic planning, resource allocation, and increasing resilience to health crises. Internationally and globally, AI-based DTs can integrate diverse datasets across borders to support research collaboration and improve early pandemic detection. Generative AI contributes to global efforts by harmonizing heterogeneous data, creating standardized virtual patient cohorts, and supporting cross-cultural medical education. Combining local precision with global insights highlights DTs’ role as a bridge between personalized and global health. Despite the efforts of medical and technical specialists, ethical, regulatory, and data governance challenges remain crucial to ensuring responsible and equitable implementation worldwide. In conclusion, AI-based DTs represent a transformative paradigm, combining individual patient care with systemic and global health management. These perspectives highlight the potential of AI-based DTs to bridge precision medicine and public health, provided ethical, regulatory, and governance challenges are addressed responsibly. Full article

This study focuses on Quanti Xinlun (1851), translated and compiled by the Protestant missionary Benjamin Hobson in the late Qing dynasty, examining how the Western anatomical knowledge it introduced challenged and reshaped the traditional Chinese conception of the body grounded in Visceral Manifestation theory. The research finds that the book’s influence was achieved through multiple mechanisms, including conceptual innovation, visual representation, and the creation of terminology. Although Hobson’s original motivation for translation was rooted in Natural Theology, its scientific core was selectively appropriated by the late Qing intellectual community, becoming a tool to address indigenous academic and ideological predicaments. This study argues that Quanti Xinlun facilitated a significant paradigm shift: on the level of the conception of the body, it used the physical body to challenge the traditional perception of a functional, qi-based organism; on the epistemological level, it invoked the authority of empirical positivism to challenge the scholarly method of text-based classicism. This fundamental shift in epistemology not only spurred the medical trend of thought known as “Convergence of Chinese and Western Medicine” but also laid the cornerstone for the modernization of modern Chinese medical education and even the entire knowledge system. Full article

The integration of biomimetics and artificial intelligence (AI) in architecture is reshaping the foundations of computational design. This paper provides a comprehensive review of the current research trends and applications that combine AI-driven modeling with biologically inspired principles to optimize architectural forms, material efficiency, and fabrication processes. By examining recent studies from Q1–Q2 journals (2019–2025), the paper identifies five primary “interfaces” through which AI expands the field of biomimetic design: biological pattern recognition, structural optimization, generative morphogenesis, resource management, and adaptive fabrication. The paper highlights the transition from conventional simulation-based design toward iterative, data-driven workflows integrating machine learning (ML), deep generative models, and reinforcement learning. The findings demonstrate that AI not only serves as a generative tool but also as a learning mechanism capable of translating biological intelligence into architectural logic. The paper concludes by proposing a methodological and educational framework for AI-driven biomimetic optimization, emphasizing the emergence of Artificial Intelligence in Architectural Design (AIAD) as a paradigm shift in architectural education and research. This convergence of biology, algorithms, and material systems is defining a new, adaptive approach to sustainable and intelligent architecture. Full article

Today, digital transformation is a major factor that determines the renewal and strategic expansion of higher education institutions. It is not only about the use of technology in classrooms but the whole paradigm of universities organizing learning, research, and societal interaction. This study explores how universities perceive and implement digital change, what factors influence these changes, and what barriers prevent them from moving forward. This research explores both the theory and the real-world application of digital transformation in higher education with an emphasis on creating a national model of a digital university in Kazakhstan. This research adopts a qualitative design and is grounded in well-founded theory principles. A wide range of academic publications from 2020 to 2025 have been reviewed, and key studies were deeply analyzed with the help of Atlas.ti25, which facilitated the identification of recurring themes and conceptual relationships in the higher education sector. The results reveal that successful digital transformation depends on the balancing of the four interrelated factors that are articulated in this study. Among those are empathetic leadership, a trustworthy digital infrastructure, innovative teaching and learning methods, and the continuous development of employee qualifications. The universities that were able to implement digital transformation more thoroughly are usually the ones showing strategic consistency, internal collaboration, and the sustained investment in human and technological resources. However, at the same time, numerous institutions confront financial constraints, weak infrastructure, and faculty members’ varying levels of digital preparedness. To the problems mentioned above, this research offers a Digital University Model which sees the university as a changing ecosystem that merges institutional, technological, pedagogical, and cultural processes into one structure of ongoing innovation. This study continues the discussions on digital transformation in higher education in a clear and accessible manner. The authors explain how digitalization influences the development of universities and colleges, offering valuable guidance for institutional leaders and decision makers. In addition, this article presents concrete and practical recommendations that can support educational institutions in advancing their digital maturity. Full article

Using survey data from five countries in the Southeast Europe (SEE) region, we examine the factors that contribute to the green career intention. As expected, the prior pro-environmental orientation, as measured by the New Ecological Paradigm scale, is associated with green career intention. In addition, there is also a significant association between prior volunteering and the observed career plans. Other factors, including gender, age, study level, social background, and work-related experiences, did not prove to be empirically significant predictors in this context. There is an interesting, but insignificant, tendency among SEE business students at higher study levels to choose green careers; however, seemingly important career determinants, such as work experience, managerial experience, and entrepreneurial experience, do not appear to matter at all. The examined model explains only a small portion of the variation in career intentions, indicating that a wealth of factors remain to be accounted for in future research. We conclude the study with a discussion of implications for business education in the SEE region and offer recommendations for fostering ESG talent in emerging economies. Full article

To address the issue of “studying agriculture but not engaging in agricultural work” in Chinese agricultural universities, this study developed a practical teaching quality framework based on the CIPP model. The framework includes four dimensions: goal identity, resource support, process experience, and ability and emotional gain. A questionnaire survey was conducted among 433 students majoring in agricultural economics and management. The study combined structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to explore how practical teaching quality influences behavioral intention to serve “agriculture, rural areas, and farmers”. The findings are as follows: (1) All four dimensions significantly positively influence behavioral intention, but the pathways differ. Process experience has the strongest direct effect (β = 0.51). Resource support (β = 0.125) and ability-emotional gain (β = 0.155) are partially mediated by student satisfaction, while goal identity is completely indirectly driven through satisfaction. (2) Three configurations for high behavioral intention are identified: the all-dimensional driven type (coverage 52.8%), the emotion-experience driven type (coverage 7.5%), and the experience-ability compensation type (coverage 12.5%), with an overall consistency of 93.46%; (3) The study confirms the applicability of the CIPP model and customer satisfaction theory in agricultural education. It reveals a multiple transmission mechanism of “quality–satisfaction–behavioral intention”. The study provides a theoretical basis and practical paradigm for constructing a precision practical teaching system and strengthening talent support for rural revitalization. Full article

Business Economics lacks coherent theoretical foundations despite its prominence in business education. This paper critiques conventional equilibrium-based curricula that begin with ceteris paribus assumptions, proposing instead a systems-based evolutionary framework integrating macro–meso–micro perspectives. Through conceptual analysis, we demonstrate how traditional approaches fail to capture dynamic business realities. Our evolutionary framework incorporates seven pillars: variation–selection–retention dynamics, multi-level integration, dynamic capabilities, institutional networks, complexity theory, organizational form evolution, and behavioral insights. The paper provides curriculum guidelines (12-week structure) that maintain economic literacy while teaching students to reason through feedback loops, uncertainty, and systemic change. This repositioning represents the need for a paradigm shift from static optimization toward understanding businesses as adaptive systems, better preparing students for navigating continuous change in complex environments. Full article

Background: This study examines how the Yörük community in Türkiye balances the preservation of traditional childrearing practices with modernization and compulsory schooling. Context: The Yörüks, a historically nomadic Turkic people, possess a distinct cultural identity that centres around oral knowledge transmission, family-based education, and pastoral living. Methods: A qualitative phenomenological approach was used. Data Collection: In-depth semi-structured interviews were conducted with 11 Yörük participants selected through purposive sampling. Results: Four core themes were identified: Yörük culture, expectations of children, childrearing practices, and perceptions of schooling. Tensions emerge between traditional responsibilities, gender roles, and modern schooling structures. Conclusions: The study shows that cultural dissonance with the national education system contributes to educational exclusion and identity tension among Yörük children. Theoretical Implications: This study challenges dominant educational paradigms that overlook minority epistemologies and underscores the need to incorporate pastoralist worldviews into inclusive education theory. Practical Implications: It calls for flexible, culturally responsive policies, such as mobile or seasonal schooling and culturally inclusive curricula. Research Implications: Future studies could focus on the longitudinal impacts of modernization on the educational outcomes of pastoral communities or conduct comparative studies with other nomadic groups. Full article

This paper explores the history of evolving teaching techniques in Digital Signal and Image Processing (DSIP) education with a focus on integrating Artificial Intelligence (AI) tools to close the continuing gap between theory and practice. Since DSIP has been at the center of Telecommunication, Medical Imaging, Robotics and AI, this paper examines active and student-centered learning paradigms, like collaborative, situation-based, and project-based learning (PBL) as viable pedagogical methods. The research methodology includes analyzing a survey using Data Visualization Tools in Python-3.12, 2023. This paper overviews the application of digital tools including MATLAB-R2024a, Python, Cloud-based systems, and AI-based learning analytics to promote experiential and adaptive learning to enable students to test complex signal and image processing systems. The findings emphasize the fact that these practices contribute to developing conceptual knowledge, critical thinking, and solving problems through engaging learners in real-life and data-driven scenarios. The results also indicate how the teachers can upgrade their instructional approach to technological innovations in teaching. Finally, this paper highlights the nature of AI-enriched pedagogies and practical experience to build the skills needed to operate in a more data-intensive, technologically advanced and sustainable engineering environment. Full article

This study critically examines how the deep institutionalization of Islamic thought in Indonesia’s religious educational establishments has fallen short in addressing contemporary challenges for Indonesian Muslims. The central argument is that a significant gap remains between the educational ideals of the Qur’an and Hadith and the lived realities of Muslim life. Employing Muhammad Abed al-Jabiri’s critical-analytical approach, the article advances three main claims: First, Islamic education in Indonesia is still dominated by logocentrism and school-centrism. Second, these paradigms act as obstacles to developing a critically engaged Muslim society capable of responding to global issues. Third, logocentrism encourages dogmatism by promoting a singular truth, while school-centrism entrenches fanaticism, formalism, and, at times, radicalism. The study concludes that only a critical reassessment of these paradigms can drive meaningful educational reform and enable Indonesian Muslims to better confront contemporary realities. Full article

The main purpose of this study is to analyze the effects of environmental (New Environmental Paradigm) and eco-recreational attitudes on environmental behavior using structural equation modeling (SEM). In the context of increasing environmental problems, individuals’ attitudinal and experiential relationships with the environment play a critical role in shaping sustainable environmental behaviors. In this context, a multidimensional model incorporating cognitive, affective, and behavioral orientations toward nature has been developed. In the study, environmental attitude was addressed in terms of environmentalist and human approaches, while eco-recreational attitude was addressed in terms of its cognitive, affective, and behavioral sub-dimensions. Environmental behavior was modeled as recycling efforts and responsible citizenship. Data were collected using validated scales and tested using SEM. The findings have revealed that both environmental and eco-recreational attitudes have direct effects on environmental behavior. The obtained results suggest that supporting individuals with nature-based experiences is considered to be an effective strategy for the development of environmentally conscious behaviors regarding environmental education and sustainability policies. Nevertheless, it bears several limitations, particularly in terms of its generalizability for diverse socio-demographic groups and cultural contexts. The contribution of this study is that it empirically demonstrates the mutually supportive and descriptive potential of environmental attitudes and eco-recreational attitudes in the process of behavioral transformation. The study makes a significant contribution to the theoretical discussions in the literature by revealing the integrated effect of environmental attitudes and eco-recreational attitudes in shaping environmental behavior. Full article

As AI systems increasingly structure educational processes, they shape not only what is learned, but also how epistemic authority is distributed and whose knowledge is recognized. This article explores the normative and technopolitical implications of this development by examining two prominent paradigms in AI ethics: Ethical AI and Responsible AI. Although often treated as synonymous, these frameworks reflect distinct tensions between formal universalism and contextual responsiveness, between rule-based evaluation and governance-oriented design. Drawing on deontology, utilitarianism, responsibility ethics, contract theory, and the capability approach, the article analyzes the frictions that emerge when these frameworks are applied to algorithmically mediated education. The argument situates these tensions within broader philosophical debates on technological mediation, normative infrastructures, and the ethics of sociotechnical design. Through empirical examples such as algorithmic grading and AI-mediated admissions, the article shows how predictive systems embed values into optimization routines, thereby reshaping educational space and interpretive agency. In response, it develops the concept of situated ethics, emphasizing epistemic justice, learner autonomy, and democratic judgment as central criteria for evaluating educational AI. To clarify what is at stake, the article distinguishes adaptive learning optimization from education as a process of subject formation and democratic teaching objectives. Rather than viewing AI as an external tool, the article conceptualizes it as a co-constitutive actor within pedagogical practice. Ethical reflection must therefore be integrated into design, implementation, and institutional contexts from the outset. Accordingly, the article offers (1) a conceptual map of ethical paradigms, (2) a criteria-based evaluative lens, and (3) a practice-oriented diagnostic framework to guide situated ethics in educational AI. The paper ultimately argues for an approach that attends to the relational, political, and epistemic dimensions of AI systems in education. Full article

This study proposes a novel structure-preserving algorithm for the Ginzburg–Landau equation (GLE) by combining the Fourier pseudospectral method with the Exponential Average Vector Field (EAVF) scheme. The proposed numerical framework strictly preserves the energy dissipation property of GLE systems, as validated through theoretical analysis and numerical experiments on solitary wave dynamics. Compared to conventional methods such as the average vector field approach, the EAVF-based scheme demonstrates superior computational efficiency, including faster convergence and enhanced stability under larger time steps, enabling accurate long-term simulation of strongly nonlinear GLE systems. Furthermore, this research incorporates a pedagogical innovation through its implementation within an undergraduate innovation project. By adopting a “problem decomposition–code verification–modular development” training model, students engage in the full cycle of algorithm design, implementation, and validation. This practice-oriented approach significantly enhances students’ competencies in scientific programming, complex problem-solving, and research-oriented thinking, providing an effective paradigm for synergizing advanced computational research with talent cultivation in STEM education. Full article