Search Results (553)

This study introduces the Human-AI Symbiotic Theory (HAIST), designed to guide authentic collaboration between human researchers and artificial intelligence in academic contexts, while pioneering a novel AI-assisted approach to theory validation that transforms educational research methodology. Addressing critical gaps in educational theory and advancing validation practices, this research employed a sequential three-phase mixed-methods approach: (1) systematic theoretical synthesis integrating five paradigmatic perspectives across learning theory, cognition, information processing, ethics, and AI domains; (2) development of an innovative validation framework combining three established theory-building approaches with groundbreaking AI-assisted content assessment protocols; and (3) comprehensive theory validation through both traditional multi-framework evaluation and novel AI-based content analysis demonstrating unprecedented convergent validity. This research contributes both a theoretically grounded framework for human-AI research collaboration and a transformative methodological innovation demonstrating how AI tools can systematically augment traditional expert-driven theory validation. HAIST provides the first comprehensive theoretical foundation designed explicitly for human-AI partnerships in scholarly research with applicability across disciplines, while the AI-assisted validation methodology offers a scalable, reliable model for theory development. Future research directions include empirical testing of HAIST principles in live research settings and broader application of the AI-assisted validation methodology to accelerate theory development across educational research and related disciplines. Full article

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article

Thiosemicarbazones and thiocarbohydrazones are key sulfur-containing organic compounds known for their diverse biological, pharmaceutical, and industrial applications. Beyond their well-established therapeutic potential, their strong chelating ability allows them to form stable complexes with transition metals, enabling uses in catalysis, corrosion inhibition, and dyeing processes. Their structural characteristics and dynamic conformations critically influence both biological activity and industrial performance, making nuclear magnetic resonance (NMR) spectroscopy an indispensable tool for their analysis. This review provides a comprehensive overview of the conformational and functional properties of bioactive thiosemicarbazones and thiocarbohydrazones, with a focus on how experimental NMR techniques are used to investigate their conformational behavior. In addition to experimental findings, available computational data are discussed, offering complementary insights into their structural dynamics. The integration of experimental and theoretical approaches offers a robust framework for predicting the behavior and interactions of these compounds, thereby informing the rational design of novel derivatives with improved functionality. By highlighting key structural features and application contexts, this work addresses a critical gap in the current understanding of these promising agents across both biomedical and industrial domains. Full article

As brain–computer interface (BCI) technology continues to advance, research on human brain function has gradually transitioned from theoretical investigation to practical engineering applications. To support EEG signal acquisition in a variety of real-world scenarios, BCI electrode systems must demonstrate a balanced combination of electrical performance, wearing comfort, and portability. Dry electrodes have emerged as a promising alternative for EEG acquisition due to their ability to operate without conductive gel or complex skin preparation. This paper reviews the latest progress in dry electrode EEG systems, summarizing key achievements in hardware design with a focus on structural innovation and material development. It also examines application advances in several representative BCI domains, including emotion recognition, fatigue and drowsiness detection, motor imagery, and steady-state visual evoked potentials, while analyzing system-level performance. Finally, the paper critically assesses existing challenges and identifies critical future research priorities. Key recommendations include developing a standardized evaluation framework to bolster research reliability, enhancing generalization performance, and fostering coordinated hardware-algorithm optimization. These steps are crucial for advancing the practical implementation of these technologies across diverse scenarios. With this survey, we aim to offer a comprehensive reference and roadmap for researchers engaged in the development and implementation of next-generation dry electrode EEG-based BCI systems. Full article

This study investigates the underwater transient vibro-acoustic response of double-layered stiffened cylindrical shells through an integrated experimental-numerical approach. Initially, vibration and noise responses under transient impact loads were experimentally characterized in an anechoic water tank, establishing benchmark datasets. Subsequently, based on the theory of transient structural dynamics, a numerical framework was developed by extending the time-domain finite element/boundary element (FEM/BEM) method, enabling comprehensive analysis of the transient vibration and acoustic radiation characteristics of submerged structures. Validation through experimental-simulation comparisons confirmed the method’s accuracy and effectiveness. Key findings reveal broadband features with distinct discrete spectral peaks in both structural vibration and acoustic pressure responses under transient excitation. Systematic parametric investigations demonstrate that: (1) Reducing the load pulse width significantly amplifies vibration acceleration and sound pressure levels, while shifting acoustic energy spectra toward higher frequencies; (2) Loading position alters both vibration patterns and noise radiation characteristics. The established numerical methodology provides theoretical support for transient impact noise prediction and low-noise structural optimization in underwater vehicle design. Full article

The integration of gamification into digital learning environments is reshaping educational models, advancing towards more adaptive and personalized teaching evolution. However, within large Chinese corpora, the transition mechanism from passive participation to adaptive gamified learning remains underexplored in a systematic manner. This study fills this gap by utilizing LDA topic modeling and sentiment analysis techniques to delve into user comment data on the Bilibili platform. The results extract five major themes, which include multilingual task-driven learning, early-age programming thinking cultivation, modular English competency certification, cross-domain cognitive integration and psychological safety, as well as ubiquitous intelligent educational environments. The analysis reveals that most themes exhibit highly positive emotions, particularly in applications for early childhood education, while learning models that involve certification mechanisms and technological dependencies tend to provoke emotional fluctuations. Nevertheless, learners still experience certain challenges and pressures when faced with frequent cognitive tasks. In an innovative manner, this study proposes a theoretical framework based on Self-Determination Theory and Connectivism to analyze how motivation satisfaction drives cognitive restructuring, thereby facilitating the process of adaptive learning. This model demonstrates the evolutionary logic of learners’ cross-disciplinary knowledge integration and metacognitive strategy optimization, providing empirical support for the gamification learning transformation mechanism in China’s digital education sector and extending the research framework for personalized teaching and self-regulation in educational technology. Full article

This article discusses contemporary strategies to deal with immersed boundary (IB) frameworks useful for analyzing flow–structure interaction in complex settings. It focuses on immense advancements in various fields: biology, oscillation of structures due to fluid flow, deformable materials, thermal processes, settling particles, multiphase systems, and sound propagation. The discussion also involves a review of techniques addressing moving boundary conditions at complex interfaces. Evaluating practical examples and theoretical challenges that have been addressed by these frameworks are another focus of the article. Important results highlight the integration of IB methods with adaptive mesh refinement and high-order accuracy techniques, which enormously improve computational efficiency and precision in modeling complex solid–fluid interactions. The article also describes the evolution of IB methodologies in tackling problems of energy harvesting, bio-inspiration propulsion, and thermal-fluid coupling, which extends IB methodologies broadly in many scientific and industrial areas. More importantly, by bringing together different insights and paradigms from across disciplines, the study highlights the emerging trends in IB methodologies towards solving some of the most intricate challenges within the technical and scientific domains. Full article

Sustainable housing is increasingly recognized as a crucial social determinant of health, intersecting environmental sustainability with affordability, safety, and inclusivity to shape population health and equity. This paper reviews the existing literature and presents that integrating sustainable housing into public health frameworks can mitigate health risks, reduce inequities, and promote resilient urban futures. This review paper reframes sustainable housing through a holistic lens, emphasizing its potential to improve health through inclusive design, energy efficiency, green infrastructure, and affordability. Theoretically grounded in the Social Determinants of Health framework, Ecological Systems Theory, Environmental Health Theory, and Life Course Perspective, sustainable housing is shown to influence health outcomes across multiple levels and life stages. Empirical studies further validate these connections, demonstrating improved physical and mental health, particularly among vulnerable populations, when sustainable housing features are implemented. While these benefits span multiple health domains, persistent implementation challenges related to equity, financing, and policy coherence can limit their reach. Equity-centered approaches and cross-sector collaboration are essential to ensure the health benefits of sustainable housing are distributed fairly. Climate-resilient design strategies further underscore the role of housing in protecting communities against growing environmental threats. Furthermore, research priorities are required to strengthen the evidence base, including studies utilizing longitudinal study designs and participatory approaches. The findings of this review call for policy innovations that embed sustainable housing within broader public health and urban development agendas. Full article

Effective proppant transport is critical to the success of hydraulic fracturing, particularly when using a non-Newtonian fluid. However, accurately predicting the proppant settling behavior under complex rheological conditions is still a significant challenge. This study proposes a new method for estimating the velocity of proppant settling in the power-law non-Newtonian fluid by accounting for spatial variations in viscosity within the fracture domain. The local shear rate field is first obtained using an analytical expression derived from the velocity gradient, and then used to approximate spatially varying viscosity based on the power-law rheological model. This allows the modification of Stokes’ law, which was initially developed for Newtonian fluid, to be used for the power-law non-Newtonian fluid. The results indicate that the model achieved high accuracy in the fracture center region, with an average relative error of 8.2%. The proposed approach bridges the gap between traditional settling models and the non-Newtonian behavior of the fracturing fluid, offering a practical and physically grounded framework for predicting the velocity of proppant settling within a hydraulic fracture. By considering the distribution of the shear rate and viscosity of the fracturing fluid, this method enables an accurate prediction of proppant settling velocity, which further provides theoretical support to the optimization of pumping schedules and operation parameters for hydraulic fracturing. Full article

Background: Vanishing twin syndrome (VTS) represents a complex and under-recognized phenomenon in multifetal pregnancies, associated with both clinical uncertainty and significant psychosocial impact. Despite its frequency, gaps remain in diagnostic clarity, international guidelines, and communication strategies with patients and families. Materials and Methods: This hybrid review integrates narrative and systematic elements to assess the diagnostic, clinical, and psychosocial gaps in VTS. A systematic literature search was conducted across Medline/PubMed, CINAHL, PsycINFO, EBM Reviews, and Scopus using terms such as “vanishing twin syndrome,” “patient-provider communicat*,” and “bereave* care.” Sources included systematic reviews, randomized controlled trials, cohort studies, and qualitative studies. Exclusion criteria were outdated publications (>10 years old). Results: Evidence revealed multiple domains of concern. Clinical risks and diagnostics remain poorly defined, with inconsistent recognition of maternal and neonatal complications. Psychosocial impacts were prominent, encompassing grief, identity disruption, and unmet support needs. Patient–provider communication was frequently inadequate, with insufficient training and lack of standardized language. International guidelines varied widely in scope, with only a few of them providing clear recommendations for bereavement care in multifetal loss contexts. Discussion: Emerging discourse highlights the limitations of the traditional fission model and alternative conceptual frameworks, such as Herranz’s model, for understanding VTS. These theoretical differences underscore the need for precise terminology and consistent diagnostic practices. Clinical implications extend to prenatal screening, obstetric management, and the integration of psychosocial support. Patient-centered communication and structured support initiatives (e.g., the Butterfly Project) demonstrate the potential to bridge communication gaps and improve care experiences. Conclusions: VTS requires recognition as both a medical and psychosocial condition. Improved clinical definitions, harmonized international guidelines, and emphasis on empathetic communication are essential to address the current gaps. Integrating these elements into practice may enhance patient outcomes and provide families with validation and support following multifetal loss. Full article

This paper explores the application of transform-domain sparsification and compressed sensing (CS) techniques to improve the efficiency and quality of magnetic resonance imaging (MRI). We implement and evaluate three sparsifying methods—discrete wavelet transform (DWT), fast Fourier transform (FFT), and discrete cosine transform (DCT)—which are used to simulate subsampled reconstruction via inverse transforms. Additionally, one accurate CS reconstruction algorithm, basis pursuit (BP), using the L₁-MAGIC toolbox, is implemented as a benchmark based on convex optimization with L₁-norm minimization. Emphasis is placed on basis pursuit (BP), which satisfies the formal requirements of CS theory, including incoherent sampling and sparse recovery via nonlinear reconstruction. Each method is assessed in MATLAB R2024b using standardized DICOM images and varying sampling rates. The evaluation metrics include peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index measure (SSIM), execution time, memory usage, and compression efficiency. The results show that although discrete cosine transform (DCT) outperforms the others under simulation in terms of PSNR and SSIM, it is inconsistent with the physics of MRI acquisition. Conversely, basis pursuit (BP) offers a theoretically grounded reconstruction approach with acceptable accuracy and clinical relevance. Despite the limitations of a controlled experimental setup, this study establishes a reproducible benchmarking framework and highlights the trade-offs between the quality of transform-based reconstruction and computational complexity. Future work will extend this study by incorporating clinically validated CS algorithms with L₀ and nonconvex Lp (0 < p < 1) regularization to align with state-of-the-art MRI reconstruction practices. Full article

Industry 4.0 has intensified the skills gap in industrial automation education, with graduates requiring extended on boarding periods and supplementary training investments averaging USD 11,500 per engineer. This paper introduces VisFactory, a multimedia learning system that extends the cognitive theory of multimedia learning by incorporating haptic feedback as a third processing channel alongside visual and auditory modalities. The system integrates a digital twin architecture with ultra-low latency synchronization (12.3 ms) across all sensory channels, a dynamic feedback orchestration algorithm that distributes information optimally across modalities, and a tripartite student model that continuously calibrates instruction parameters. We evaluated the system through a controlled experiment with 127 engineering students randomly assigned to experimental and control groups, with assessments conducted immediately and at three-month and six-month intervals. VisFactory significantly enhanced learning outcomes across multiple dimensions: 37% reduction in time to mastery (t(125) = 11.83, p < 0.001, d = 2.11), skill acquisition increased from 28% to 85% (${\eta }_p^2=0.54$), and 28% higher knowledge retention after six months. The multimodal approach demonstrated differential effectiveness across learning tasks, with haptic feedback providing the most significant benefit for procedural skills (52% error reduction) and visual–auditory integration proving most effective for conceptual understanding (49% improvement). The adaptive modality orchestration reduced cognitive load by 43% compared to unimodal interfaces. This research advances multimedia learning theory by validating tri-modal integration effectiveness and establishing quantitative benchmarks for sensory channel synchronization. The findings provide a theoretical framework and implementation guidelines for optimizing multimedia learning environments for complex skill development in technical domains. Full article

Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework that integrates Bayesian inference with symmetric deep neural architectures to enable adaptive, efficient knowledge assessment. Our method models student knowledge as latent representations within a graph-structured concept dependency network, where probabilistic mastery states, updated through variational inference, are encoded by symmetric graph properties and symmetric concept representations that preserve structural equivalences across similar knowledge configurations. The system employs a symmetric dual-network architecture: a concept embedding network that learns scale-invariant hierarchical knowledge representations from assessment data and a question selection network that optimizes symmetric information gain through deep reinforcement learning with symmetric reward structures. We introduce a novel uncertainty-aware objective function that leverages symmetric uncertainty measures to balance exploration of uncertain knowledge regions with exploitation of informative question patterns. The hierarchical structure captures both fine-grained concept mastery and broader domain understanding through multi-scale graph convolutions that preserve local graph symmetries and global structural invariances. Our symmetric information-theoretic method ensures balanced assessment strategies that maintain diagnostic equivalence across isomorphic concept subgraphs. Experimental validation on large-scale educational datasets demonstrates that our method achieves 76.3% diagnostic accuracy while reducing the question count by 35.1% compared to traditional assessments. The learned concept embeddings reveal interpretable knowledge structures with symmetric dependency patterns that align with pedagogical theory. Our work generalizes across domains and student populations through symmetric transfer learning mechanisms, providing a principled framework for intelligent tutoring systems and adaptive testing platforms. The integration of probabilistic reasoning with symmetric neural pattern recognition offers a robust solution to the fundamental trade-off between assessment efficiency and diagnostic precision in educational technology. Full article

The digital economy is fundamentally transforming urban landscapes by disrupting traditional relationships between land use and land value. This scoping review aims to examine how digital transformations alter urban activities, human behaviours, and mobility patterns, and to assess the subsequent impacts on land use planning and land valuation frameworks. Following PRISMA guidelines, Scopus, Web of Science, Google Scholar, and ProQuest databases were systematically searched for peer-reviewed articles published between 2019 and 2024. Inclusion criteria comprised empirical studies, theoretical papers, and case studies examining digital economy impacts on urban land use or land value. Grey literature, non-English publications, and studies without clear urban spatial implications were excluded. The data were synthesised using bibliometric analysis and thematic analysis to identify patterns of disruption across three domains: urban activities, behaviours, and mobility. Of the 512 initially identified articles, 66 studies met the inclusion criteria. The evidence demonstrates significant geographic bias and methodological limitations, including the scarcity of longitudinal studies tracking actual land value changes and inconsistent metrics for measuring disruption intensity. Despite these limitations, findings indicate that the digital economy is decoupling land value from traditional determinants, such as physical proximity to services and employment centres. These transformations necessitate fundamental revisions to urban planning frameworks, land valuation models, and regulatory approaches to ensure equitable and sustainable urban development in the digital age. Full article

This review systematically examines intelligent event perception in distributed acoustic sensing (DAS) systems. Beginning with the elucidation of the DAS principles, system architectures, and core performance metrics, it establishes a comprehensive theoretical framework for evaluation. This study subsequently delineates methodological innovations in both traditional machine learning and deep learning approaches for event perception, accompanied by performance optimization strategies. Particular emphasis was placed on advances in hybrid architectures and intelligent sensing strategies that achieve an optimal balance between computational efficiency and detection accuracy. Representative applications spanning traffic monitoring, perimeter security, infrastructure inspection, and seismic early warning systems demonstrate the cross-domain adaptability of the technology. Finally, this review addresses critical challenges, including data scarcity and environmental noise interference, while outlining future research directions. This work provides a systematic reference for advancing both the theoretical and applied aspects of DAS technology, while highlighting its transformative potential in the development of smart cities. Full article