Search Results (626)

This paper explores the possibility of counteracting the crisis of culture and institutions by investing in the identity values of the user-actor within digital spaces built for the purpose. The strategy is applied to the analysis of three Catalan cloisters (Spain), with a focus on the representation of the cloister of Sant Cugat (Barcelona). Heuristic picklocks are found in the semantic richness proposed by Marius Schneider exclusively on the verbal level. The authors interpret the contents and transcribe them into graphic signs and digital denotations of sounds and colors. They organize proprietary ontologies, or syntagmatic lines, to be entrusted to the management of computer algorithms. The syncretic culture that characterized the medieval era allowed the ability to mediate science and faith to be entrusted to the mind of the praying monk alone in every canonical hour. The hypothesis that a careful direction has programmed the ways in which to orient souls to “navigate by sight” urges the authors to find the criteria that advanced statistics imitates to make automatic data processing “Intelligent”. In step with the times and in line with the most recent directions for the Safeguarding of Heritage, the musical, astral, and narrative rhythms feared by Schneider are used to inform representative models, to increase not only the visual perception of the user (XR Extended Reality) but also to solicit new analogies and illuminating associations. The results return a vision of the culture of the time suitable for shortening the distances between present and past, attracting the visitor and, with him, the resources necessary to protect and enhance the spaces of the Romanesque era. The methodology goes beyond the contingent aspect by encouraging the ‘remediation’ of contents with the help of machine learning. Full article

This study develops a sustainable evaluation framework for Yixing Zisha pottery design schemes generated by Artificial Intelligence Generated Content (AIGC) technology, emphasizing the integration of cultural heritage preservation with innovation. As a traditional Chinese craft and a recognized element of intangible cultural heritage (ICH), Yixing Zisha pottery is celebrated for its cultural depth and unique design techniques. Guided by emotional design theory, the framework assesses aesthetic, functional, and emotional dimensions through hierarchical analysis. Using the Delphi method and Analytic Hierarchy Process (AHP), primary and secondary indicators were identified and weighted based on expert consensus. AIGC technology, underpinned by advanced AI algorithms, generates culturally authentic yet innovative design solutions, striking a balance between tradition and modernity. The findings reveal that this approach enhances design diversity, functionality, and efficiency while fostering global cultural awareness. By providing practical guidance for integrating AIGC technology into traditional craftsmanship, the research offers a replicable model for other traditional crafts and contributes to the theoretical advancement of sustainable cultural heritage practices. By bridging the gap between digital innovation and heritage preservation, this study addresses the critical need for sustainable strategies in the creative industries. Full article

Pediatric tuberculosis (TB) is still challenged by several diagnostic bottlenecks, imposing a high TB burden in low- and middle-income countries (LMICs). Diagnostic turnaround time (TAT) and ease of operation to suit resource-limited settings are critical aspects that determine early treatment and influence morbidity and mortality. Based on TAT and ease of operation, this article reviews the evolving landscape of TB diagnostics, from traditional methods like microscopy and culture to cutting-edge molecular techniques and biomarker-based approaches. We examined the benefits of efficient rapid results against potential trade-offs in accuracy and clinical utility. The review highlights emerging molecular methods and artificial intelligence-based detection methods, which offer promising improvements in both speed and sensitivity. The review also addresses the challenges of implementing these technologies in resource-limited settings, where most pediatric TB cases occur. Gaps in the existing diagnostic methods, algorithms, and operational costs were also reviewed. Developing optimal diagnostic strategies that balance speed, performance, cost, and feasibility in diverse healthcare settings can provide valuable insights for clinicians, researchers, and policymakers. Full article

This study introduces a design transformation model for AI-generated Chinese traditional architectural images (SD Lora&Canny) based on Stable Diffusion (SD). By integrating parameterization techniques such as Low-Rank Adaptation (Lora) and edge detection algorithms (Canny), the model achieves precise restoration of the architectural form, color elements, and decorative symbols in Chinese traditional architecture. Using the Beijing Drum Tower as the experimental subject, statistical analysis software (SPSS V28.0) was employed to conduct a quantitative evaluation and comparative analysis of architectural images generated by the DALL-E, MidJourney, SD, and SD Lora&Canny models. The results demonstrate that the SD Lora&Canny model significantly outperforms traditional generation tools in restoration accuracy and visual fidelity. Finally, this study applied the SD Lora&Canny model to create the digital cultural product AR Drum and Bell Tower Fridge Magnet, showcasing its practical application in digital cultural creation and verifying its innovative potential in the digital preservation and transmission of Chinese traditional architecture. Full article

This article engages in a philosophical dialogue with Nietzsche’s views on the discourse of power, examining the rising concerns surrounding the digitization and algorithmization of society in the context of advancements in robotics and AI. It highlights human agency through Nietzsche’s perspective on creative culture as a space for individuals to actively engage in free thought and action, with responsibility as the key foundation of social resilience. By approaching metaphysical systems through the discourse of power, Nietzsche emphasizes that humanity can overcome system-driven delusions through reason, which he understands as the form of critical reflection existing solely in the domain of creative culture. We assert that Nietzsche’s arguments offer alternative perspectives on the ethics of technology, particularly through the dialectics of “weak and strong types of behavior”. It allows us to explore how resistance—existing in creative culture—can serve as a vital counterbalance to the mechanization of social life. Such dialectics provide a strong foundation for supporting algorithmic resistance by inspiring ethical frameworks rooted in individuality and emotional depth, challenging the homogenizing tendencies of digitization and algorithmization. It emphasizes the importance of subjective stories, emotions, and compassion, forming human-centered ethical principles that preserve the richness of individual experiences and protect against system-driven delusions. Full article

We analyze the Google matrix of directed networks of Wikipedia articles related to eight recent Wikipedia language editions representing different cultures (English, Arabic, German, Spanish, French, Italian, Russian, Chinese). Using the reduced Google matrix algorithm, we determine relations and interactions of 23 society concepts and 17 religions represented by their respective articles for each of the eight editions. The effective Markov transitions are found to be more intense inside the two blocks of society concepts and religions while transitions between the blocks are significantly reduced. We establish five poles of influence for society concepts (Law, Society, Communism, Liberalism, Capitalism) as well as five poles for religions (Christianity, Islam, Buddhism, Hinduism, Chinese folk religion) and determine how they affect other entries. We compute inter-edition correlations for different key quantities providing a quantitative analysis of the differences or the proximity of views of the eight cultures with respect to the selected society concepts and religions. Full article

Objective: In the present study, we used phenotypic and molecular methods to determine susceptibility to oxacillin in coagulase-negative staphylococci (CoNS) and estimate the prevalence of strains with low-level resistance to oxacillin, mecA-positive oxacillin-susceptible methicillin-resistant (OS-MRCoNS), and borderline oxacillin-resistant (BORCoNS). Methods: One hundred one CoNS strains were screened for oxacillin and cefoxitin susceptibility using phenotypic (disk diffusion, agar dilution, latex agglutination, and chromagar) and molecular (detection of mecA, mecB, and mecC) methods. Staphylococcal cassette chromosome mec (SCCmec) typing was performed. Results: Sixteen (15.8%) CoNS strains were mecA-positive, and 85 (84.2%) were mec-negative. Seven (6.9%) were classified as OS-MRCoNS, accounting for 43.8% of all mecA-positive strains. Twelve (11.9%) mec-negative strains were classified as borderline oxacillin resistant (BORCoNS). Compared with MRCoNS and BORCoNS, OS-MRCoNS strains demonstrated lower resistance to non-beta-lactams. SCCmec type I cassette was predominant. The disc-diffusion method with oxacillin accurately predicted OS-MRCoNS strains but did not provide reliable results for BORCoNS strains. Meanwhile, the latex agglutination test and CHROMagar culture accurately identified BORCoNS but not OS-MRCoNS. Conclusions: Finally, our findings imply that the recognition of methicillin resistance in CoNS requires a meticulous approach and that further research is needed to develop unified laboratory diagnostic algorithms to prevent the misreporting of borderline CoNS. Full article

This study examines change detection techniques in dense point clouds for the purpose of cultural heritage preservation, with a particular focus on the San Pietro Barisano Bell Tower in Matera, Italy. Dense point clouds, obtained via laser scanning, offer detailed 3D representations of heritage structures, facilitating the precise monitoring of changes over time. The investigation uses a variety of change detection algorithms, including the Iterative Closest Point (ICP) algorithm, which is renowned for its robust registration capabilities in aligning point clouds with high accuracy. The combination of ICP with deviation analysis and feature-based methods allows for the effective identification of alterations, including deformations, material loss, and surface degradation. This methodology establishes a comprehensive framework for the monitoring of cultural heritage, thereby enabling timely and targeted preservation efforts. The results emphasise the substantial contribution of dense point cloud analysis to the enhancement of heritage management and the safeguarding of vulnerable architectural sites. Full article

Talent cultivation is the fundamental mission of higher education institutions, and the key to improving the quality of talent cultivation lies in enhancing the quality of teaching. In this regard, the Joint Committee recommends that the United Nations Educational, Scientific and Cultural Organization (UNESCO) should be invited to participate in this conference, in accordance with their respective mandates. However, in China, research on course evaluation systems and mechanisms in application-oriented universities is relatively scarce, and the evaluation dimensions are often limited; therefore, the evaluation of graduate courses in universities faces challenges such as a lack of specialized assessment systems, limitation of evaluation methods, and an imbalance between emphasis on outcomes and neglect of the teaching process. In this study, a comprehensive evaluation system for the Advanced Structural Dynamics (ASD) course is constructed based on the context-input-process-product (CIPP) evaluation model. The evaluation was conducted from four perspectives: teaching objectives, teaching inputs, teaching processes, and teaching outcomes. The fuzzy analytic hierarchy process (AHP) and simulated annealing particle swarm algorithm (SAPSO) are employed to study evaluation indicators and weights at various levels for the ASD course, and the proposed method is validated through practical examples. This study combines qualitative and quantitative evaluation indicators to achieve comprehensive assessment and adopts more scientifically rational algorithms for weight calculation, aiming to improve the accuracy and efficiency of weight calculation. The research findings of this study can further enhance the evaluation level of teaching quality and talent cultivation in graduate courses at application-oriented universities. Full article

Ethnic minority architecture is a vital carrier of the cultural heritage of ethnic minorities in China, and its quick and accurate extraction from remote sensing images is highly important for promoting the application of remote sensing information in urban management and architectural heritage protection. Taking Buyi architecture in China as an example, this paper proposes a minority architectural heritage identification method that combines low-altitude unmanned aerial vehicle (UAV) remote sensing technology and an improved deep learning algorithm. First, UAV images are used as the data source to provide high-resolution images for research on ethnic architecture recognition and to solve the problems associated with the high costs, time consumption, and destructiveness of traditional methods for ethnic architecture recognition. Second, to address the lack of edge pixel features in the sample images and reduce repeated labeling of the same sample, the ethnic architecture in entire remote sensing images is labeled on the Arcgis platform, and the sliding window method is used to cut the image data and the corresponding label file with a 10% overlap rate. Finally, an attention mechanism SE module is introduced to improve the DeepLabV3+ network model structure and achieve superior ethnic building recognition results. The experimental data fully show that the model’s accuracy reaches as high as 0.9831, with an excellent recall rate of 0.9743. Moreover, the F1 score is stable at a high level of 0.9787, which highlights the excellent performance of the model in terms of comprehensive evaluation indicators. Additionally, the intersection/union ratio (IoU) of the model is 0.9582, which further verifies its high precision in pixel-level recognition tasks. According to an in-depth comparative analysis, the innovative method proposed in this paper solves the problem of insufficient feature extraction of sample edge pixels and substantially reduces interference from complex environmental factors such as roads, building shadows, and vegetation with the recognition results for ethnic architecture. This breakthrough greatly improves the accuracy and robustness of the identification of architecture in low-altitude remote sensing images and provides strong technical support for the protection and intelligent analysis of architectural heritage. Full article

Cardiovascular disease is a leading cause of death worldwide. The differentiation of human pluripotent stem cells (hPSCs) into functional cardiomyocytes offers significant potential for disease modeling and cell-based cardiac therapies. However, hPSC-derived cardiomyocytes (hPSC-CMs) remain largely immature, limiting their experimental and clinical applications. A critical challenge in current in vitro culture systems is the absence of standardized metrics to quantify maturity. This study presents a data-driven pipeline to quantify hPSC-CM maturity using gene expression data across various stages of cardiac development. We determined that culture time serves as a feasible proxy for maturity. To improve prediction accuracy, machine learning algorithms were employed to identify heart-related genes whose expression strongly correlates with culture time. Our results reduced the average discrepancy between predicted and observed culture time to 4.461 days and CASQ2 (Calsequestrin 2), a gene involved in calcium ion storage and transport, was identified as the most critical cardiac gene associated with culture duration. This novel framework for maturity assessment moves beyond traditional qualitative methods, providing deeper insights into hPSC-CM maturation dynamics. It establishes a foundation for developing advanced lab-on-chip devices capable of real-time maturity monitoring and adaptive stimulus selection, paving the way for improved maturation strategies and broader experimental/clinical applications. Full article

To effectively separate strong cultural noise in Magnetotelluric (MT) signals under strong interference conditions and restore the true forms of apparent resistivity and phase curves, this paper proposes an improved method for suppressing strong cultural noise based on Particle Swarm Optimization (PSO) and Variational Mode Decomposition (VMD). First, the effects of two initial parameters, the decomposition scale K and penalty factor α, on the performance of variational mode decomposition are studied. Subsequently, using the PSO algorithm, the optimal combination of influential parameters in the VMD is determined. This optimal parameter set is applied to decompose electromagnetic signals, and Intrinsic Mode Functions (IMFs) are selected for signal reconstruction based on correlation coefficients, resulting in denoised electromagnetic signals. The simulation results show that, compared to traditional algorithms such as Empirical Mode Decomposition (EMD), Intrinsic Time Decomposition (ITD), and VMD, the Normalized Cross-Correlation (NCC) and signal-to-noise ratio (SNR) of the PSO-optimized VMD method for suppressing strong cultural noise increased by 0.024, 0.035, 0.019, and 2.225, 2.446, 1.964, respectively. The processing of field data confirms that this method effectively suppresses strong cultural noise in strongly interfering environments, leading to significant improvements in the apparent resistivity and phase curve data, thereby enhancing the authenticity and reliability of underground electrical structure interpretations. Full article

Tang Dynasty female terracotta figurines, as important relics of ceramics art, have commonly suffered from natural and man-made damages, among which facial damage is severe. Image inpainting is widely used in cultural heritage fields such as murals and paintings, where rich datasets are available. However, its application in the restoration of Tang Dynasty terracotta figurines remains limited. This study first evaluates the extent of facial damage in Tang Dynasty female terracotta figurines, and then uses the Global and Local Consistency Image Completion (GLCIC) algorithm to restore the original appearance of female terracotta figurines, ensuring that the restored area is globally and locally consistent with the original image. To address the issues of scarce data and blurred facial features of the figurines, the study optimized the algorithm through data augmentation, guided filtering, and local enhancement techniques. The experimental results show that the improved algorithm has higher accuracy in restoring the shape features of the female figurines’ faces, but there is still room for improvement in terms of color and texture features. This study provides a new technical path for the protection and inpainting of Tang Dynasty terracotta figurines, and proposes an effective strategy for image inpainting with data scarcity. Full article

The scientific literature on residential segregation in large metropolitan areas highlights various explanatory factors, including economic, social, political, landscape, and cultural elements related to both migrant and local populations. This paper contrasts the impact of these factors individually, such as the immigrant rate and neighborhood segregation. To achieve this, a machine learning analysis was conducted on a sample of neighborhoods in the main Spanish metropolitan areas (Madrid and Barcelona), using a database created from a combination of official statistical sources and textual sources, such as Wikipedia. These texts were transformed into indexes using Natural Language Processing (NLP) and other artificial intelligence algorithms capable of interpreting images and converting them into indexes. The results indicate that the factors influencing immigrant concentration and segregation differ significantly, with crucial roles played by the urban landscape, population size, and geographic origin. While land prices showed a relationship with immigrant concentration, their effect on segregation was mediated by factors such as overcrowding, social support networks, and landscape degradation. The novel application of AI and big data, particularly through ChatGPT and Google Street View, has enhanced model predictability, contributing to the scientific literature on segregated spaces. Full article

This study explores the Potaxie, Fifes, and Tilinx subcultures on TikTok, examining their origins, characteristics, and cultural significance. Originating from a viral video in 2020, the Potaxie subculture emerged within the Spanish-speaking LGBTQ+ community and evolved to symbolise inclusivity and gender equality. Potaxies use vibrant aesthetics influenced by Japanese and Korean pop culture to express their identities and resistance. In contrast, Fifes, associated with cisgender heterosexual men, embody traditional patriarchal values, often sexist and homophobic, creating a narrative of resistance between the groups. The Tilinx, symbolic descendants of the Potaxies, are inspired by ballroom culture and drag houses, with “Potaxie mothers” continuing the fight for inclusion and diversity. Using a mixed-methods approach, including quantitative analysis through the TikTok API and qualitative content analysis via MAXQDA and Python, this study provides a comprehensive understanding of the subculture that accumulates over 2.3 billion interactions. The findings highlight how TikTok serves as a platform for identity construction, cultural resistance, and the redefinition of social norms. Additionally, the study examines how digital platforms mediate intersectional experiences, favouring certain types of content through algorithms, and how participants navigate these opportunities and constraints to express their intersecting identities. The implications for communication strategies, youth policies, educational plans, and research on the commercialization of these subcultures are profound, offering insights into the transformative potential of social media in shaping contemporary cultural and social narratives. Full article