Search Results (3,723)

Large-scale location-based virtual reality (LBE VR) has become an important form of immersive cultural tourism, but its role in supporting sustained value transformation remains insufficiently understood. In this study, “sustained value transformation” refers to the extension, reinterpretation, and circulation of cultural, educational, social, and engagement-related value across physical venues, embodied virtual narratives, and digital platforms. Rather than assessing economic performance, environmental impact, or long-term operational viability, this study focuses on the cultural and social circulation of experiential value. It examines how physical venues, embodied virtual narratives, and digital platforms jointly mediate visitor experience in LBE VR-based cultural tourism. It compares representative LBE VR projects in museums and heritage institutions, emerging public cultural spaces, and commercial venues in China. A total of 10,862 project-related textual items and 464 visual samples were collected from Xiaohongshu and Douyin and analyzed through comparative content and visual analyses. The findings show that visitor choices are shaped by both the spirit of place in physical venues and platform-visible experience labels. In museums and heritage institutions, institutional knowledge authority and embodied narrative depth help visitors recognize interactive educational value. In emerging public cultural spaces, the intertwining of historical narratives and commercial operations produces more ambiguous experience labels. In commercial venues, platform discussions focus more strongly on value-for-money judgment, sensory stimulation, product quality, and service experience. The study argues that sustained value transformation in LBE VR-based cultural tourism cannot rely solely on platform traffic. Instead, it depends on collaboration among cultural institutions, tourism enterprises, platform content creators, educational actors, and community stakeholders to preserve cultural distinctiveness, improve experience quality, and extend cultural and social value beyond the immediate on-site experience. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

Private Networks or Standalone Non-Public Networks (SNPNs) are essential for Industry 4.0 and enterprise connectivity. However, most existing studies rely on simulations, evaluate only a single radio access technology, or report raw key performance indicators (KPIs) without an interpretable quality assessment framework. In practical deployment, operators require measurement-driven evidence to assess the performance and feasibility of 4G LTE and 5G SNPN solutions. This study presents a controlled experimental comparison of software-defined radio (SDR)-based 4G LTE and 5G SNPNs using the same Universal Software Radio Peripheral (USRP) platform, Open5GS, srsRAN, and commercial off-the-shelf user equipment (COTS-UE). The evaluation was conducted in an indoor environment under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Experimental iPerf3 results show that the SDR-based 5G SNPN achieves higher downlink and uplink throughput than the SDR-based 4G LTE SNPN across all tested scenarios. The 5G deployment reaches up to 55 Mbps downlink and 40.5 Mbps uplink under LOS conditions, while maintaining 42 Mbps downlink and 28 Mbps uplink under NLOS conditions. Furthermore, 5G achieves lower latency than 4G LTE, with average values ranging from 21 ms to 31 ms. To provide interpretable network quality assessment, a Mamdani fuzzy logic-based Network Quality Index (NQI) with 81 inference rules is proposed to map signal-to-interference-plus-noise ratio (SINR), throughput, latency, and jitter into linguistic quality levels. The proposed approach enables nonlinear integration of heterogeneous KPIs and provides a technology-agnostic framework for practical SNPN deployment. Full article

Background: The convergence of machine learning (ML), gamification, and critical thinking assessment within adaptive educational platforms has accelerated since 2020, driven by large language models (LLMs) and graph neural networks (GNNs). No prior systematic review has jointly addressed all three dimensions, and Central Asian educational contexts remain underrepresented. Methods: Following PRISMA 2020 guidelines, we searched Scopus (n  =  4396) and OpenAlex (n  =  4152) for publications from 2016 to 2026. Quality assessment used the Mixed Methods Appraisal Tool (MMAT 2018; threshold ≥  2), yielding 82 papers. Five research questions addressed ML personalization (RQ1), gamification and engagement (RQ2), critical thinking assessment tools (RQ3), recommendation algorithms (RQ4), and regional applicability in Kazakhstan and Central Asia (RQ5). Results: Transformer-based and GNN models dominate the recent literature (52% of corpus from 2025), with an accuracy of 91–97% for dropout prediction and learning path recommendation under single-institution conditions. Gamification studies report up to 90% student satisfaction; LLM-based critical thinking assessment shows promise but faces validity concerns. Thirteen papers address Central Asian contexts. Conclusions: Significant gaps persist: no integrated gamification–critical thinking framework exists, recommendation systems lack explainability, and Kazakh-language datasets are severely underrepresented. Future research should prioritize multilingual adaptive systems, explainable algorithms, and privacy-preserving federated learning for low-resource contexts. Full article

Background: The continued evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) created ongoing challenges for molecular diagnostics and variant surveillance. Assays capable of maintaining diagnostic sensitivity across emerging variants while providing variant-related information remain essential for clinical and public health applications. This study evaluated the performance of the GXT96 X3 extraction kit in combination with the FluoroType^® SARS-CoV-2 varID Q version 1.0 assay (Hain LifeScience SA (Pty) Ltd., South Africa) for the detection, semi-quantitative assessment, and variant characterization of SARS-CoV-2 under laboratory conditions. Methods: A total of 220 samples were evaluated, including residual nasopharyngeal clinical specimens (n = 183), reference materials, and cultured SARS-CoV-2 virus dilutions. Residual specimens collected during multiple COVID-19 waves in South Africa (wild-type, Beta, Delta, and Omicron) were compared against standard-of-care (SOC) molecular assays used for routine diagnosis. RNA extraction was performed using the automated GXT96 X3 platform, followed by amplification on the FluoroCycler^® XT using the FluoroType^® SARS-CoV-2 varID Q assay targeting RdRp and N genes, with additional spike gene mutation detection for variant detection. Diagnostic accuracy, agreement (Cohen’s kappa), precision, linearity, and limit of detection (LoD) were assessed. Results: The assay demonstrated a sensitivity of 98.4% (95% CI: 94.2–99.8) and specificity of 100% (95% CI: 95.9–100.0) compared with SOC assays, with an overall agreement of κ = 0.981. Precision analysis showed acceptable reproducibility with a standard deviation of ≤1.49 and a coefficient of variation of ≤3.83%. Regression analysis demonstrated linearity across the dilution series (R² = 0.9882 for RdRp and 0.994 for N genes). The LoD was ≤100 copies/mL for the RdRp gene and 250 copies/mL for the N gene. Variant-associated spike mutations corresponded broadly with epidemiological wave patterns observed in South Africa. Conclusions: Under the evaluated laboratory conditions, the GXT96 X3 extraction platform combined with the FluoroType^® SARS-CoV-2 varID Q assay demonstrated high diagnostic accuracy and reproducibility for SARS-CoV-2 detection across a range of viral loads with additional spike gene mutation detection as an adjunct feature. Full article

Human digital twins (HDTs) are patient-specific computational models that combine medical imaging, physiological measurements and predictive algorithms. They are moving from an exciting concept to a realistic clinical opportunity. The key question is no longer whether HDTs can be built. The key question is which methods are mature enough to support clinical decisions and what is still missing for routine use. This systematic review maps the methodological landscape of HDTs and highlights practical bottlenecks that limit clinical translation. A PRISMA 2020 guided search of PubMed, Scopus, IEEE Xplore, and the Cochrane Library, covering publications from 2016 to 2026, identified 151 eligible studies. Bibliometric mapping and thematic synthesis were used to characterize research clusters, computational paradigms, and collaboration patterns. Three dominant application streams were identified: cardiovascular HDTs for hemodynamic simulation and procedural planning, musculoskeletal HDTs for biomechanics-driven orthopedic innovation, and neurological HDTs integrating neuroimaging with computational neuroscience. Across domains, the strongest technical trend is the rise in hybrid pipelines that combine physics-based simulation, including finite element and computational fluid dynamics models, with machine learning for segmentation, parameter identification, reduced-order modeling, and faster inference. However, reporting of verification, validation, uncertainty quantification, and explicit context of use remains uneven and prospective clinical evidence is still limited. Overall, the literature shows rapid progress toward clinically credible HDTs, while highlighting the need for scalable computation, standardized credibility pipelines, and workflow-integrated platforms to support safe and reproducible clinical adoption. Full article

Background: Confident chemical annotation in nontarget small-molecule mass spectrometry critically depends on the availability of high-quality tandem mass spectral (MS²) reference libraries. While community efforts have driven significant expansion of open-access repositories, technical challenges in assembling standardized, metadata-rich records continue to limit broader participation, underscoring the need for improved computational tools to assist contributors. Methods: To promote the creation and sharing of standardized reference MS² spectral records, we have developed Librarian, a free, open-access web application designed for rapid and scalable assembly of high-resolution MS² libraries. Librarian integrates automated retrieval and harmonization of chemical identifiers and metadata from PubChem, compound mixture design for high-resolution mass spectrometry (HRMS) acquisition, and assembly of curated MS² spectra into repository-ready records compatible with public spectral databases. Results: Through a simple in-browser interface, Librarian offers a flexible end-to-end workflow compatible with popular open-source pre-processing tools to lower technical barriers and facilitate broader community participation in library development. As a demonstration, we used Librarian to create and deposit a spectral library comprising over 1500 new MS² records into MassBank, which was further applied in retrospective analysis of environmental datasets. Conclusions: Librarian streamlines the creation of standardized, metadata-rich and repository-ready MS² reference records. Addressing a key bottleneck in community spectral library development and sharing, Librarian supports the continued growth of open-access resources for metabolomics, exposomics, and environmental mass spectrometry. The Librarian web application is publicly accessible via the SciLifeLab Serve platform. Full article

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings. Full article

Several countries have adopted a nationwide ban on social media access for children. Our aim was to investigate public opinion regarding the implementation of a social media ban for children, as well as the factors influencing these views. We measured agreement with the ban, information regarding its implementation, perceived need for additional measures, confidence in the effectiveness of the ban, perceived impact of the ban, and parental familiarity with digital parental control tools. The study sample included 619 participants. In our sample, 69% agreed with the implementation of the ban, while 86.5% believed that additional measures should be implemented (i.e., digital literacy courses in schools, active parental involvement in digital literacy, prohibition of inappropriate content, reasonable parental limits on social media use, and restriction of addictive platform features). Females and higher-educated participants had more positive perceptions regarding the impact of the ban. We found a positive association between age, financial status, social media use, and impact of the ban. Reduced age was associated with increased parental familiarity with digital parental control tools. Social media use was associated with parental familiarity with digital parental control tools. There is a need for holistic and evidence-informed policy frameworks that integrate regulatory measures, educational initiatives, and shared accountability among stakeholders. Full article

Against the backdrop of China’s urban modernization pathway, the core value of urban innovation systems is increasingly shifting toward an inclusive orientation. Grounded in the theoretical connotation of inclusive urban innovation, this study establishes an evaluation index system covering equal participation opportunities, procedural fairness, and outcome sharing, and applies the entropy method, kernel density analysis, and spatial autocorrelation to empirically examine the spatial distribution characteristics and formation mechanisms of inclusive innovation spaces in Changsha. The results show that (1) Changsha’s inclusive innovation level presents a gradient decline from the central urban area to the periphery; (2) high–high clusters mainly in areas with stronger innovation–resource concentration and better public service conditions, such as Yuelu District and other districts associated with major innovation platforms. Low–low agglomeration zones cluster in peripheral urban areas like certain townships in Liuyang City and remote regions of Ningxiang City; (3) the spatial differentiation of inclusive innovation is jointly shaped by multiple factors, among which Cultural Education and Industrial Structure show relatively stronger explanatory power; and (4) improving inclusive innovation requires enhancing not only innovation agglomeration, but also public service accessibility, talent support, employment inclusion, and the local sharing of innovation outcomes. This study provides a systematic framework for evaluating urban inclusive innovation space and offers policy insights for promoting balanced and inclusive innovation development in regional innovation cities. Full article

Authentication is essential to hold users accountable across online services. Conventional authentication systems rely on centralized architectures or third-party identity providers, which, however, introduce single points of failure, privacy concerns, and limited user autonomy. Conversely, fully decentralized authentication frameworks often struggle to provide reliable identity attestation mechanisms. This makes them vulnerable to Sybil attacks and self-asserted claims, while limiting their interoperability with trust-based systems. This paper presents dAuth, a hybrid blockchain-based authentication architecture based on Ethereum smart contracts to provide cryptographic tokens that enable authentication to services. These tokens, anchored to the smart contract, are derived by users from institutionally certified base credentials issued by an accredited verifying authority and enable authentication to services without further involvement of the authority. Each token is cryptographically bound to a specific service, constrained in scope and duration, and verifiable off-chain through data and cryptographic commitments provided by the user. No plaintext personal information is published on-chain: identity attributes are committed as cryptographic digests, which anchor certified identity data on-chain while keeping the underlying personal information private and auditable. This design removes the verifying authority from the authentication process, as all authentication steps are assisted by the user-controlled smart contract. The verifying authority’s role is limited to initial identity certification and exceptional update procedures. The result is a privacy-preserving and verifiable hybrid authentication framework that leverages the cryptographic security properties of the underlying blockchain infrastructure and inherits its scalability characteristics. The proposed design has been implemented and experimentally evaluated on the Ethereum platform, addressing public blockchain-specific challenges such as scalability constraints and transaction costs to ensure practical deployment. Full article

This review aims to evaluate the potential of endometriosis models, especially patient-derived iPSC models, to gain deeper insights into the disease, thereby advancing our understanding and treatment of endometriosis. This comprehensive narrative review utilized a structured search of the PubMed, Scopus, and Web of Science databases, primarily covering literature published between January 2000 and May 2025. An expansive search strategy was employed to capture the full breadth of the field using keywords such as “endometriosis,” “induced pluripotent stem cells (iPSCs),” “patient-derived organoids,” “disease modeling,” and “epigenetics” without restrictive filtering, ensuring the integration of both foundational theories and emerging biotechnological advances. In total, over 170 peer-reviewed publications were analyzed, ranging from landmark genomic meta-analyses that have identified significant risk loci to state-of-the-art 3D-culture systems for modeling patient-specific endometrial disease. By synthesizing these diverse sources, the review bridges the gap between traditional anatomical classifications and modern molecular modeling to evaluate the potential of iPSC platforms for personalized medicine and therapeutic discovery. Endometriosis is a multifactorial gynecological condition that affects 176 million women worldwide and can significantly impair quality of life. It occurs when endometrium-like tissue grows outside the uterus, responsive to ovarian hormones, causing inflammation, pain, and discomfort, and leading to fibrotic tissue. World Health Organization estimates indicate that 6–10% of women suffer from this disorder, which can cause infertility and increase the risk of developing various types of cancer and autoimmune disorders. The use of patient-derived iPSC models serves to gain deeper insights into the disease by mimicking the endometrial tissue or lesions observed in affected individuals, thereby advancing our understanding and treatment of endometriosis. Full article

Major depressive disorder (MDD) is clinically heterogeneous, and peripheral inflammatory biomarkers may help clarify early biological mechanisms before illness chronicity or pharmacologic treatment confound interpretation. This systematic review synthesized evidence on peripheral inflammatory biomarkers in first-episode, drug-naïve major depressive disorder (FEDN-MDD) compared with healthy controls and examined associations with clinical severity. Following PRISMA 2020, searches of PubMed/MEDLINE, Embase, PsycINFO, and Scopus from inception to 19 March 2026 identified 313 records; after screening, 16 publications were included in qualitative synthesis. Studies varied in age group, biological matrix, assay platform, and statistical reporting, precluding meta-analysis. The most frequently assessed biomarkers were IL-1β, TNF-α, IL-6, and CRP/hs-CRP. IL-6 showed the clearest recurrent tendency toward elevation in FEDN-MDD, whereas CRP/hs-CRP findings were partially positive but methodologically limited. TNF-α and IL-1β findings were mixed, and clinical correlations with depressive severity were sparse and inconsistent. Overall, the evidence supports heterogeneous early immune dysregulation in a subset of patients with FEDN-MDD rather than a single reproducible inflammatory signature. Peripheral inflammatory biomarkers should currently be considered research tools for biological stratification and mechanistic hypothesis generation, pending larger standardized longitudinal studies. Full article

In this experimental inquiry, I welcome readers into an unfolding undisciplined platform, expanding upon my earlier theorizing of philly soul musicking through insurgent listening to Archie Shepp’s Attica Blues in all of its radical, beautiful, and tragic public memory work of the 1971 Attica Uprising. Philly soul musicking gives regional texture to a transgenerational Black diasporic performance practice that serves to archive the complexity of Black lived experiences and articulate felt collective visions of liberated Black futures. Through these introductory comments, I improvise what I reckon to be essential to the secretive sonic histories of the album, giving shape to a fire music organizing praxis meant to call us into being-with the anticolonial worldmaking project that the men of Attica advanced with their hearts, minds, and bodies on the line. This prelude foreshadows a wider overall project speculating upon how Attica Blues and other related avenues of Black compositional practice attune us to assembling active solidarities with militants/rebels on the frontlines inventing rhythmic zones of autonomy, freedom, and liberation. I ask of the music’s fugitive archive, to draw on the words of James Baldwin, what are the contemporary use(s) of the Attica Blues? Full article

Background: Antimicrobial resistance (AMR) poses an escalating threat to global health, agriculture, and the environment, demanding urgent multisectoral action under the One Health framework. Despite global awareness efforts, understanding of AMR among the general population remains insufficient, particularly in low- and middle-income countries such as Brazil. This study aimed to evaluate the knowledge, attitudes, and perceptions (KAP) of the Brazilian population regarding AMR. Methods: An online questionnaire was distributed through social media platforms between April and August 2025, resulting in 945 valid responses after data cleaning. Quasi-Poisson models were applied to identify demographic predictors of KAP scores while logistic regression models were used to assess the association between KAP scores and antibiotic use-related practices. Results: Education level was the strongest predictor of higher KAP scores, whereas age and gender showed inconsistent influence. Only 40.3% of respondents correctly identified antibiotics among commonly used medicines, and 25.9% reported proper disposal of antibiotic packaging. More than half (54.2%) were willing to pay more for antibiotic-free products, although only 26.7% had ever noticed such labeling. Network analysis of open-ended responses indicated that concerns about potential health risks and AMR awareness were the primary motivators for purchasing antibiotic-free products. Conclusions: These findings reveal significant gaps in public understanding of antibiotic use and resistance in Brazil, emphasizing the urgent need for targeted educational initiatives, improved public communication, and behavioral interventions to support antimicrobial stewardship and sustainable antibiotic use. Full article