Search Results (22,609)

In recent years, with the rapid advancement of science and technology, generative artificial intelligence has increasingly entered the public eye. Primarily through intelligent algorithms that simulate human logic and integrate vast amounts of network data, it provides designers with solutions that transcend traditional thinking, enhancing both design efficiency and quality. Compared to traditional design methods reliant on human experience, generative design possesses robust data processing capabilities and the ability to refine design proposals, significantly reducing preliminary design time. This study employs the CiteSpace visualization tool to systematically organize and conduct knowledge map analysis of research literature related to generative AI in architectural design within the Web of Science database from 2005 to 2025. Findings reveal the following: (1) International research exhibits a trend toward interdisciplinary convergence. In recent years, research in this field has grown rapidly across nations, with continuously increasing academic influence; (2) Research primarily focuses on technological applications within architectural design, aiming to drive innovation and development by providing superior, more efficient technical support; (3) Generative AI in architectural design has emerged as a prominent international research focus, reflecting a shift from isolated design to industry-wide integration; (4) Generative AI has become a core global architectural design topic, with future research advancing toward full-process intelligent collaboration. High-quality knowledge graphs tailored for the architecture industry should be constructed to overcome data silos. Concurrently, a multidimensional evaluation system for generative quality must be established to deepen the symbiotic design paradigm of human–machine collaboration. This significantly enhances efficiency while reducing the iterative nature of traditional methods. This study aims to provide empirical support for theoretical and practical advancements, offering crucial references for practitioners to identify business opportunities and policymakers to optimize relevant strategies. Full article

Digital technology is profoundly transforming the experiential landscape of tourism. However, its application does not necessarily produce cultural experiences, creating a critical bottleneck that constrains the sustainable development of the cultural tourism industry and broader societal culture. To address this gap, this study introduces psychological ownership theory as an overarching explanatory framework. It constructs and validates an integrated model that examines how digital technology characteristics (interactivity and innovativeness) influence cultural experience through three parallel mediating pathways: cognitive evaluation (perceived usefulness and ease of use), scenario construction, and flow experience. Based on 540 visitor questionnaires, structural equation modeling validated the theoretical model. Findings reveal that the interactivity and innovation of digital technology jointly stimulate visitors’ psychological ownership through three parallel pathways. Specifically, technological innovativeness exhibited the strongest effect on perceived ease of use (β = 0.387, p < 0.001), while the indirect effect via the flow experience path was also significant (effect size = 0.036). This process stimulates visitors’ psychological ownership, ultimately leading to cultural experiences. The study systematically reveals the pathways through which digital technology empowers cultural experiences across three dimensions: as a rational tool, an emotional narrative medium, and an intrinsic psychological catalyst. It highlights that strategically allocating technological resources to cultivate visitors’ psychological ownership is crucial for driving high-quality industrial development. Furthermore, the research offers significant implications for cultural sustainability, suggesting that such internally motivated identification provides a more effective foundation for the living transmission of culture and socio-cultural sustainability than external regulations or imposed norms. Full article

While 3D Gaussian Splatting (3DGS) has significantly advanced large-scale 3D reconstruction and novel view synthesis, it still suffers from high memory consumption and slow training speed. To address these issues without compromising reconstruction quality, we propose a novel 3DGS-based framework tailored for large-scale scenes. Specifically, we introduce a visibility-aware camera selection strategy within a divide-and-conquer training approach to dynamically adjust the number of input views for each sub-region. During training, a spatially aware densification strategy is employed to improve the reconstruction of distant objects, complemented by depth regularization to refine geometric details. Moreover, we apply an enhanced Gaussian pruning method to re-evaluate the importance of each Gaussian, prune redundant Gaussians with low contributions, and improve efficiency while reducing memory usage. Experiments on multiple large-scale scene datasets demonstrate that our approach achieves superior performance in both quality and efficiency. With its robustness and scalability, our method shows great potential for real-world applications such as autonomous driving, digital twins, urban mapping, and virtual reality content creation. Full article

Graphs are widely used to model complex interactions among entities, yet they struggle to capture higher-order and multi-typed relationships. Hypergraphs overcome this limitation by allowing for edges to connect arbitrary sets of nodes, enabling richer modelling of higher-order semantics. Real-world systems, however, often exhibit heterogeneity in both entities and relations, motivating the need for heterogeneous hypergraphs as a more expressive structure. In this study, we address the problem of local clustering on heterogeneous hypergraphs, where the goal is to identify a semantically meaningful cluster around a given seed node while accounting for type diversity. Existing methods typically ignore node-type information, resulting in clusters with poor semantic coherence. To overcome this, we propose HHLC, a heuristic heterogeneous hyperedge-based local clustering algorithm, guided by a heterogeneity-aware conductance measure that integrates structural connectivity and node-type consistency. HHLC employs type-filtered expansion, cross-type penalties, and low-quality hyperedge pruning to produce interpretable and compact clusters. Comprehensive experiments on synthetic and real-world heterogeneous datasets demonstrate that HHLC consistently outperforms strong baselines across metrics such as conductance, semantic purity, and type diversity. These results highlight the importance of incorporating heterogeneity into hypergraph algorithms and position HHLC as a robust framework for semantically grounded local analysis in complex multi-relational networks. Full article

Objectives: We aimed to develop expert-informed recommendations for colleges and universities to support students with celiac disease (CeD) managing a gluten-free (GF) diet. Methods: A multidisciplinary panel of 40 stakeholders, including physicians, dietitians, a disability rights attorney, university staff, and students, was convened by the Celiac Disease Foundation to create expert-based and experience-informed recommendations. Over a 6-month period, the group conducted literature reviews, stakeholder interviews, and expert consensus discussions to identify common barriers and accommodations aligned with federal disability law. The expert panel collaboratively developed and revised an initial set of recommendations. Two rounds of structured voting were held during which panelists provided feedback to refine content and ensure clarity. All final recommendations were adopted with at least 90% of panelists voting in support. Results: The panel identified 24 accommodations across four domains: academics, housing, dining, and campus life. Academic recommendations include flexibility for illness-related absences, support for remote learning, and classroom modifications. Housing recommendations emphasize access to priority placement, appropriate appliances, and proximity to safe dining. Dining accommodations address GF food availability, ingredient transparency, staff training, and meal plan flexibility. Campus life recommendations ensure full participation in athletics, study abroad, social events, and internships, with supports for psychosocial well-being. Conclusions: This manuscript presents the first expert-informed recommendations focused specifically on the needs of college students with CeD. These recommendations are intended to support institutions as they develop strategies to enhance access to GF food, quality of life, educational supports, and student experience for those living with this chronic autoimmune condition. Full article

Due to complex underwater environments characterized by severe scattering, absorption, and color distortion, accurate restoration remains challenging. This paper proposes a hybrid approach combining dual transmission estimation, adaptive ambient light estimation with color correction, and a U-Net Transformer (Uformer) for underwater image enhancement. Our method estimates transmission maps by integrating boundary constraints and local contrast, which effectively address visibility degradation. An adaptive ambient light estimation and color correction strategy are further developed to correct color distortion robustly. Subsequently, a Uformer network enhances the restored image by capturing global and local contextual features effectively. Experiments conducted on publicly available underwater image datasets validate our approach. Performance is quantitatively evaluated using widely adopted non-reference image quality metrics, especially Underwater Image Quality Measure (UIQM) and Underwater Color Image Quality Evaluation (UCIQE). The results demonstrate that our proposed method achieves superior enhancement performance over several state-of-the-art methods. Full article

Digital inclusion has become an essential component in ensuring the autonomy, social participation, and well-being of older adults. However, their learning of digital skills depends to a large extent on the quality of support provided by the facilitator, whose age, training, and experience directly influence teaching processes and how older adults relate to technology. This study compares the digital competences, and ICT skills of 107 facilitators of digital literacy programs, classified into three groups: peer educators (PEERS), young students without gerontological training (YOS), and young gerontology specialists (YGS). A quantitative design was used. Statistical analyses included non-parametric tests (Kruskal–Wallis, Mann–Whitney, Kendall’s Tau) and parametric tests (ANOVA, t-tests), to examine associations between socio-demographic variables, the level of digital competence, and ICT skills for teachers (technological and pedagogical). The results show clear differences between profiles. YOS achieved the highest scores in digital competence, especially in problem-solving and tool handling. The YGS achieved a balanced profile, combining competent levels of digital skills with pedagogical strengths linked to their gerontological training. In contrast, PEERS recorded the lowest levels of digital competence, particularly in security and information management; nevertheless, their role remains relevant for fostering trust and closeness in training processes among people of the same age. It was also found that educational level is positively associated with digital competence in all three profiles, while age showed a negative relationship only among PEERS. The findings highlight the importance of creating targeted training courses focusing on digital, technological, and pedagogical skills to ensure effective, tailored teaching methods for older adults. Full article

Tool wear degrades sharpness and durability, causing poor surface quality, dimensional errors, and high costs. Precise RUL prediction optimizes production, reduces rework, and prevents downtime. Conventional replacement relies on experience and risks inaccuracy. Real-time monitoring enables optimal intervals. Predictive maintenance cuts tooling costs and ensures quality. Industry 4.0 integrates sensors for intelligent wear management. This study applies GRNN to predict RUL with minimal TMD. A C#-based system with intuitive HMI was validated in real machining. Full article

Speed-of-sound (SoS) heterogeneities introduce pronounced artifacts in full-ring photoacoustic tomography (PAT), degrading imaging accuracy and constraining its practical use. We introduce a transfer learning-based deep neural framework that couples an ImageNet-pretrained ResNet-50 encoder with a tailored deconvolutional decoder to perform end-to-end artifact correction on photoacoustic tomography reconstructions. We propose a two-phase curriculum learning protocol, initial pretraining on simulations with uniform SoS mismatches, followed by fine-tuning on spatially heterogeneous SoS fields, to improve generalization to complex aberrations. Evaluated on numerical models, physical phantom experiments and in vivo experiments, the framework provides substantial gains over conventional back-projection and U-Net baselines in mean squared error, structural similarity index measure, and Pearson correlation coefficient, while achieving an average inference time of 17 ms per frame. These results indicate that the proposed approach can reduce the sensitivity of full-ring PAT to SoS inhomogeneity and improve full-view reconstruction quality. Full article

The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies. Full article

Maintaining color constancy in polymer extrusion processes is a key difficulty in manufacturing applications, as fluctuations in processing parameters greatly influence pigment dispersion and the quality of the finished product. Preliminary historical data mining analysis was conducted in 2009. This work concentrates on Opaque PC Grade 5, which constituted 2.43% of the pigment; it contained 10 PPH of resin2 with a Melt Flow Index (MFI) of 6.5 g/10 min and 90 PPH of resin1. It also employs a fixed resin composition with an MFI of 25 g/10 min. This research identified the significant processing parameters (PPs) contributing to the lowest color deviation. Interactions between processing parameters, for the same color formulation, were analyzed using statistical methods under various processing conditions. A principle-driven General Trends (GT) diagnostic procedure was applied, wherein each parameter was individually varied across five levels while holding others constant. Particle size distribution (PSD) and colorimetric data (CIE Lab*) were systematically measured and analyzed. To complete this, correlations for the impact of temperature (Temp) on viscosity, particle characteristics, and color quality were studied by characterizing viscosity, Digital Optical Microscopy (DOM), and particle size distribution at various speeds. The samples were characterized for viscosity at three temperatures (230, 255, 280 °C) and particle size distribution at three speeds: 700, 750, 800 rpm. This study investigates particle processing features, such as screw speed and pigment size distribution. The average pigment diameter and the fraction of small particles were influenced by the speed of 700–775 rpm. At 700 rpm, the mean particle size was 2.4 µm, with 61.3% constituting particle numbers. The mean particle size diminished to 2 µm at 775 rpm; however, the particle count proportion escalated to 66% at 800 rpm. This research ultimately quantifies the relative influence of particle size on the reaction, resulting in a color value of 1.36. The mean particle size and particle counts are positively correlated; thus, reduced pigment size at increased speed influences color response and quality. The weighted contributions of the particles, 51.4% at 700 rpm and 48.6% at 800 rpm, substantiate the hypothesis. Further studies will broaden the GT analysis to encompass multi-parameter interactions through design experiments and will test the diagnostic assessment procedure across various polymer grades and colorants to create robust models of prediction for industrial growth. The global quality of mixing polycarbonate compounding constituents ensured consistent and smooth pigment dispersion, minimizing color streaks and resulting in a significant improvement in color matching for opaque grades. Full article

Any industrial application that uses convolutional neural networks (CNNs) requires initial data and resources in order to train the models. However, the selection of models must be appropriate to the quality and quantity of the available data and computational resources. This study analyses the influence of data quantity and quality on the performance of CNN models of different complexity. Image preprocessing and image transformation data augmentation techniques are applied to generate different amounts of synthetic data with which to train the aforementioned models, shedding light on the following question: does the quality and quantity of the data or the depth of the model have more influence? Different experiments are performed using the Northeastern University (NEU) Steel Surface Defects Database, which contains surface defects found in hot-rolled steel. After analyzing the results, the authors conclude that data quality and quantity have a much greater influence than model choice. As resources and time are often limited in industry and the ultimate goal is to maximize profit by increasing efficiency, the authors encourage researchers to carefully consider the industrial application at hand and analyze the available data and resources before selecting CNN models. Full article

Background: Considering the significant impact on quality of life and the chronic nature of temporomandibular dysfunction (TMD), seeking healthcare is also part of the reality of individuals with this disorder. However, cultural differences and similarities in the experiences of individuals with TMD have not yet been investigated. This study aimed to describe and compare the experiences, beliefs, and sociocultural factors of Brazilian and Spanish individuals with TMD, focusing on their perceptions of the disorder, diagnostic pathways, information-seeking behaviors, and treatment expectations. Methods: A descriptive qualitative study was conducted. A purposive sample of 50 participants (25 Brazilian, 25 Spanish), aged 18–50 and diagnosed with TMD according to DC/TMD criteria, was recruited. Data were obtained through semi-structured interviews and analyzed using thematic analysis. Results: Six themes emerged, revealing both similarities and differences between the groups. Brazilian participants reported uncertainty about which professional to consult and difficulty accessing specialized care. In contrast, Spanish participants frequently sought physical therapists as their first option and identified them as primary sources of information. Beliefs about TMD etiology varied across samples. Treatment expectations also differed. Brazilians emphasized the difficulty of obtaining effective care, while Spanish participants perceived physiotherapy as being limited to muscular disorders. Perceptions of occlusal splint effectiveness showed variation between the groups. Conclusions: These findings underscore the necessity of culturally sensitive approaches to patient care that address not only clinical aspects, but also the sociocultural context that influences health behaviors. Full article

Background/Objectives: Sleep stage classification is crucial for assessing sleep quality and diagnosing related disorders. Electroencephalography (EEG) is currently recognized as a primary method for sleep stage classification. High-performance automatic sleep staging methods based on EEG leverage the powerful contextual modeling capabilities of Transformer Encoder architectures. However, the global self-attention mechanism in Transformers incurs significant computational overhead, substantially hindering the training and inference efficiency of automatic sleep staging algorithms. Methods: To address these issues, we introduce an end-to-end framework for automatic sleep stage classification using single-channel EEG: SleepMFormer. At the algorithmic level, SleepMFormer adopts a task-driven simplification of the Transformer encoder to improve attention efficiency while preserving sequence modeling capability. At the training level, supervised contrastive learning is incorporated as an auxiliary strategy to enhance representation robustness. From an engineering perspective, these design choices enable efficient training and inference under resource-constrained settings. Results: When integrated with the SleePyCo backbone, the proposed framework achieves competitive performance on three widely used public datasets: Sleep-EDF, PhysioNet, and SHHS. Notably, SleepMFormer reduces training and inference time by up to 33% compared to conventional self-attention-based models. To further validate the generalizability of MaxFormer, we conduct additional experiments using DeepSleepNet and TinySleepNet as alternative feature extractors. Experimental results demonstrate that MaxFormer consistently maintains performance across different model architectures. Conclusions: Overall, SleepMFormer introduces an efficient and practical framework for automatic sleep staging, demonstrating strong potential for related clinical applications. Full article

Background and Objectives: Chronic low back pain (CLBP) is a prevalent condition that impairs quality of life, functionality, and work productivity. While most acute episodes of back pain resolve, 4–25% become chronic due to factors such as high pain intensity, psychological distress, and maladaptive behaviors. Nonspecific CLBP is best understood through the biopsychosocial model, encompassing biological, psychological, and social influences, including kinesiophobia. Management relies on physical activity, pain education, and psychological interventions, with therapist knowledge and attitudes affecting outcomes. This study aimed to assess the prevalence of CLBP among healthcare workers, examine their knowledge of pain neurophysiology, evaluate kinesiophobia, and explore how personal experience with CLBP influences their beliefs, attitudes, and interactions with patients. Materials and Methods: A cross-sectional observational study was conducted from January to May 2025 among healthcare professionals. A total of 50 participants completed an online questionnaire, of which 42 were valid and included in the analysis. The questionnaire collected demographic and professional data, determined the presence of CLBP, and included three standardized instruments: the Revised Neurophysiology of Pain Questionnaire (rNPQ) to assess knowledge of pain mechanisms, the Health Care Providers’ Pain and Impairment Relationship Scale (HC-PAIRS) to evaluate beliefs about pain and disability, and the Tampa Scale of Kinesiophobia (TSK-11) to measure fear of movement. Data were analyzed using SPSS and Microsoft Excel. Results: Among the 42 participants, 11 demonstrated low, 28 moderate, and 3 high knowledge of pain neurophysiology (rNPQ), with a mean score of 5.66. On the HC-PAIRS, the majority (30 participants) scored above 60, indicating beliefs that pain leads to disability, while 12 scored below 60, reflecting a biopsychosocial perspective; gender did not significantly affect HC-PAIRS scores (p = 0.213). As for kinesiophobia (TSK-11), 24 participants had low, 17 moderate, and 1 clinically significant fear of movement. Correlation analysis revealed that younger participants had higher rNPQ scores (r = −0.358, p = 0.020) and lower TSK-11 scores (r = −0.389, p = 0.011). TSK-11 scores increased with age (r = 0.432, p = 0.004), while HC-PAIRS scores showed no significant correlations. Conclusions: Healthcare professionals, particularly physiotherapists, show gaps in knowledge of pain neurophysiology and a tendency toward biomedical beliefs regarding chronic low back pain. This cross-sectional study indicates that a greater understanding of pain mechanisms is associated with lower kinesiophobia, emphasizing the importance of education. Integrating the biopsychosocial model into undergraduate and continuing professional training, through interdisciplinary and practical modules, may improve knowledge, reduce maladaptive fear-avoidance behaviors, and enhance patient care. Future studies should include larger, more diverse samples and assess the long-term impact of educational interventions on clinical practice. Full article