Search Results (394)

In the context of the increasing digitalization of urban cultural heritage communication, public acceptance, identification, and dissemination of symbolic cultural heritage content exhibit pronounced structural differences across sustainability levels. Taking Xuzhou—a national historical and cultural city in China—as the empirical context, this study conceptualizes cultural heritage as symbolic carriers of cultural meaning and constructs a sustainability-stratified analytical framework. By integrating the Theory of Planned Behavior (TPB) and Cultural Identity (CI) theory, and incorporating Perceived Sustainability of Cultural Heritage (PSC) and Digital Interactive Media Participation (DMP), the study develops a comprehensive model of public communication acceptance mechanisms. Based on 931 valid questionnaires collected from local residents and visitors, exploratory and confirmatory factor analyses, structural equation modeling (SEM), and permutation-based multi-group analysis (MGA) are employed to examine both overall behavioral pathways and cross-group structural heterogeneity across symbolic heritage contexts with different sustainability tiers. The results indicate that: (1) PSC significantly influences communication intention through attitude, subjective norms, and perceived behavioral control, with cultural identity playing a central mediating role; (2) digital interactive media participation primarily functions as a contextual enabler, significantly moderating the relationship between perceived behavioral control and communication intention; and (3) substantial structural differences exist across sustainability tiers, with medium-sustainability symbolic contexts demonstrating the strongest psychological activation effects in attitude formation, identity internalization, and intention conversion. Theoretically, this study extends the integrative application of TPB and cultural identity theory by embedding sustainability perception as an upstream cognitive trigger and repositioning cultural identity as a mediating mechanism within symbolic heritage communication processes. Methodologically, it establishes a systematic “sustainability evaluation–stratified modeling–multi-group comparison” analytical framework. Practically, the findings provide empirical guidance for differentiated communication strategies and digital media interventions tailored to symbolic cultural heritage systems. Full article

Cities exhibit sound patterns that vary across locations and time, while transmitting raw audio introduces communication and privacy concerns. We present a federated TinyML architecture for real-time urban sound classification on microcontroller-class edge devices. A compact audio embedding network is deployed as a frozen feature extractor, while a lightweight classifier head is trained on-device and shared via MQTT, enabling communication-efficient collaborative learning. The system is evaluated on ESP32 (Espressif Systems, Shanghai, China) hardware under cross-dataset transfer from UrbanSound8K to SONYC. Domain shift reduces baseline accuracy from 90.39% to 78.27%, while local adaptation and federated aggregation improve accuracy to approximately 85%, recovering most of the performance loss. Repeated aggregation further improves macro-F1 and class balance across heterogeneous data. Embedded measurements confirm real-time inference (~250 ms per window) with negligible overhead, while each update exchanges only a compact classifier head (~1.2 kB). These results demonstrate that adaptive classification can be achieved on resource-constrained nodes in distributed smart-city networks. Full article

The scientific data of the Earth surface system is characterized by multi-source heterogeneity and dynamic correlation, so constructing an efficient data association network and enabling intelligent knowledge services is a hot topic. Nevertheless, confronted with the existing challenges of onerous data acquisition, inadequate precision of data recommendation, excessive time and labor consumption, as well as insufficient semantic reasoning in intelligent question-and-answer (Q&A) systems, we propose an intelligent framework that integrates dynamic optimization and retrieval-augmented generation (RAG) technology to address the problems of strong subjectivity in the setting of edge weight thresholds in association networks and insufficient semantic inference in intelligent Q&A. First, a multidimensional association network is constructed based on metadata features, redundant edge pruning is achieved through dynamic threshold analysis, and key nodes are identified by combining complex network centrality theory to optimize network structure and storage efficiency. Secondly, the RAG-based intelligent Q&A model is designed to transform the association triples into a paragraph-based knowledge base, generate a domain Q&A dataset using a large language model GPT-4o, and fine-tune the word embedding model to improve the semantic representation accuracy. Experiments show that the number of network edges is reduced by about 70% after optimization, and the node importance analysis accurately identifies key data nodes; the fine-tuned model improves each index by 6% on average in the retrieval task, and the Q&A system significantly outperforms the traditional method in terms of indexes such as relevance and completeness. This study provides innovative solutions for the intelligent service of scientific data in Earth surface systems and promotes the deep integration of association networks and generative AI. Full article

The rising incidence of skin cancer necessitates scalable and accurate diagnostic tools. While dermoscopy-based systems have achieved expert-level performance, clinical smartphone images pose challenges due to variability in lighting, resolution, and artifacts. Recent advances in multimodal deep learning have shown promise, yet most approaches rely on simple feature concatenation or single-model classifiers, limiting their ability to capture complex cross-modal interactions. This study aims to bridge the diagnostic gap in resource-limited settings by developing a robust multimodal framework that synergizes clinical smartphone images with structured patient metadata for automated skin cancer classification. We propose a novel hybrid architecture integrating a Swin Transformer V2 (SwinV2-Tiny) for hierarchical visual feature extraction and a Denoising Autoencoder (DAE) with PCA for robust metadata embedding. These heterogeneous modalities are fused via a Gated Attention Mechanism that dynamically weighs feature importance across streams. Classification is performed by a Heterogeneous Meta-Stack Ensemble comprising CatBoost, LightGBM, XGBoost, and Logistic Regression, designed to maximize calibration and generalization across imbalanced classes. Evaluated on the PAD-UFES-20 dataset (2298 clinical smartphone images, six diagnostic classes), the proposed framework achieves state-of-the-art performance with a macro-averaged F1-score of 0.977, accuracy of 0.978, and an AUC of 0.990. It significantly outperforms unimodal baselines and existing multimodal methods, demonstrating superior sensitivity (0.974) and precision (0.981), particularly for underrepresented malignant classes like Melanoma (F1: 0.995) and Squamous Cell Carcinoma (F1: 0.960). The integration of clinical metadata with advanced visual embeddings via gated attention significantly enhances diagnostic reliability. Comprehensive ablation studies confirm the contribution of each architectural component. This framework offers a viable pathway for deploying high-precision, AI-driven dermatological screening tools on standard smartphone devices. Full article

Interleukin-5 (IL-5) has long been positioned as a lineage-restricted cytokine primarily responsible for eosinophil differentiation and survival. However, emerging mechanistic and clinical evidence supports a broader conceptual shift: IL-5 should no longer be viewed solely as an eosinophil growth factor, but as a context-dependent regulator embedded within dynamic airway immune networks. Drawing on advances in eosinophil subset biology, receptor signaling, and tissue-level immune crosstalk, this review reframes IL-5 biology through the lens of systems-level inflammatory regulation across airway and systemic eosinophilic diseases. Recent data reveal functional heterogeneity between resident and inflammatory eosinophil subsets, challenging the assumption that blood eosinophilia uniformly reflects pathogenic activity. In parallel, functional IL-5 receptor expression has been identified on multiple structural and immune cell populations—including epithelial cells, mast cells, plasma cells, basophils, neutrophils, and fibroblasts—supporting a broader tissue-signaling paradigm. Experimental and translational studies further link IL-5 to epithelial integrity, airway remodeling, and mucus pathology, suggesting structural and network-level effects beyond simple eosinophil depletion. Comparative analyses across asthma, chronic rhinosinusitis with nasal polyps, and COPD demonstrate that eosinophilic inflammation is biologically heterogeneous and context-dependent. While IL-5-targeted therapies yield consistent benefit in severe asthma, therapeutic responses in other airway diseases appear to be shaped by local tissue architecture and mixed inflammatory programs. Together, these observations illustrate a paradigm shift from pathway-specific inhibition toward network-informed disease control and highlight key areas for future mechanistic investigation. Full article

Tourism development in ecologically fragile areas faces the dual challenge of promoting economic growth while safeguarding environmental sustainability. Taking Inner Mongolia as a case study, this paper examines the spatiotemporal evolution, spatial resilience, and driving factors of A-level tourist attractions from 2005 to 2023. By integrating the Average Nearest Neighbor (ANN), Kernel Density Estimation (KDE), Standard Deviational Ellipse (SDE), and Geodetector methods, the study reveals three main findings. First, the tourism system has shifted from extensive quantitative expansion to intensive quality improvement, with the grade structure evolving from a pyramid-shaped distribution toward a more olive-shaped pattern. During 2019–2023, the spatial structure exhibited resilient stability and maintained a relatively mature polycentric pattern characterized by “one core and two sub-centers”. Second, the overall distribution of tourist attractions consistently followed a northeast–southwest orientation shaped by the regional geographical framework, while also showing increasing lateral diffusion into surrounding hinterland areas. Third, the driving factors displayed marked regional heterogeneity. Western Inner Mongolia followed an oasis-based development pattern, mainly associated with tertiary-industry GDP and public cultural facilities; eastern Inner Mongolia exhibited a resource-based, accessibility-constrained pattern, shaped primarily by topography and transport conditions; and central Inner Mongolia showed a coordinated support pattern supported by infrastructure, service capacity, and population concentration. Interaction analysis further suggests a landscape-embedded culture–nature coupling effect, in which the combined influence of topography and cultural facilities is more strongly associated with the spatial differentiation of tourist attractions than single factors alone. These findings provide a useful reference for differentiated spatial governance and sustainable tourism planning in ecologically fragile border regions. Full article

Rapid metropolitan expansion reshapes not only land-use patterns and infrastructure networks but also the spatial organization of micro-commercial systems embedded in everyday urban life. While large-scale retail restructuring has been extensively examined, the mechanisms underlying micro-commercial spatial transformation remain insufficiently theorized, particularly in rapidly urbanizing contexts. This study investigates the spatio-temporal restructuring of a representative low-threshold urban food economy in Xi’an between 2014 and 2024. Using multi-temporal point-of-interest (POI) data, kernel density estimation, and spatial Shannon entropy, we model changes in intensity gradients, distributional complexity, and zonal differentiation across morphologically distinct urban belts. The results reveal a systematic transition from centralized concentration toward polycentric embedding, characterized by the relocation of clustered micro-commercial activities along metro corridors and within emerging residential zones. Unlike classical decentralization, which implies outward diffusion, polycentric embedding reflects the infrastructural and demographic re-anchoring of clustered economic activities within newly stabilized urban territories. Entropy analysis further indicates increasing structural heterogeneity in metropolitan expansion zones, while historic cores retain symbolic concentration but exhibit declining structural dominance. These findings demonstrate that micro-commercial systems reorganize not through random dispersion, but through infrastructure-mediated embedding processes driven by metro expansion, residential aggregation, and institutional anchoring. By integrating longitudinal POI data with spatial complexity metrics, this study advances a replicable analytical framework for linking micro-scale commercial dynamics with metropolitan structural transformation. The study contributes to urban theory by reframing low-threshold economic systems as embedded infrastructures of everyday urban reproduction and provides planning insights for fostering resilient and spatially balanced commercial ecosystems under rapid metropolitan growth. Full article

Background/Objectives: The COVID-19 pandemic intensified concerns regarding the resilience and financing architecture of mental health services, yet it remains unclear whether crisis-induced adjustments fundamentally altered hospital case-mix complexity or merely exposed pre-existing structural configurations. This study examines the relationship between financing regimes and case-mix complexity in psychiatric hospitals in Romania, a Central and Eastern European health system characterized by mixed financing arrangements and pronounced interregional heterogeneity. Methods: Using administrative data comprising 752 hospital section–year observations (2019–2024), we identify structural financing–organization regimes through a two-step clustering procedure (hierarchical Ward method followed by K-means refinement) based on revenue composition, expenditure allocation, workforce structure, and operational pressure indicators. Results: Three distinct regimes emerge, reflecting persistent institutional configurations rather than temporary crisis-induced groupings. Chi-square tests confirm that regime membership is statistically independent of pandemic timing. A multivariate regression model controlling for financing composition and expenditure structure shows that structural variables (particularly the share of contract-based revenues and the allocation of expenditures) exert systematic and economically meaningful effects on the case-mix index (CMI). Pandemic and post-pandemic indicators do not retain robust explanatory power once structural determinants are accounted for. Regional robustness analyses further demonstrate that financing architecture consistently outweighs temporal shock effects in explaining territorial variation in clinical complexity. Conclusions: The findings suggest that psychiatric hospital case-mix dynamics are structurally embedded within differentiated financing regimes whose influence persists beyond crisis periods. By integrating regime identification with outcome modeling in a Central and Eastern European context, this study contributes to the international literature on health system resilience and highlights the primacy of institutional financing architecture over episodic shock effects in shaping hospital complexity. Full article

Predicting the concrete breakout strength of an expansion anchor embedded in short carbon fiber-reinforced concrete (SCFRC) is challenging due to the nonlinear and heterogeneous nature of fiber–matrix interaction. This study develops an Artificial Neural Network (ANN) model to estimate the breakout capacity of a single expansion anchor installed in SCFRC. Experimental data from 48 cases covering variations in compressive strength, tensile strength, fiber volume fraction, and fiber length were used to train and validate multiple ANN architectures in MATLAB’s Regression Learner. A 4-4-1 trilayered ANN with Rectified Linear Unit (ReLU) activation and 5-fold cross-validation achieved the most reliable performance, yielding R² values of 0.6726 (validation) and 0.9376 (test), with correspondingly low RMSE, MAE, and scatter index (SI < 0.1). SHAP-based sensitivity analysis identified tensile strength as the dominant predictor, contributing 70.78% to model output influence. ANN predictions were compared with the Concrete Capacity Design (CCD) model adopted by ACI and the National Structural Code of the Philippines (NSCP) and a multiple linear regression (MLR) model, showing that while the ANN is not the most precise model, it provides acceptable accuracy and captures nonlinear concrete breakout behavior more effectively than linear approaches. Results demonstrate that the ANN framework offers a viable data-driven tool for estimating concrete breakout strength in SCFRC anchorage systems. Full article

Introduction: Soft tissue sarcomas (STS) exhibit profound molecular heterogeneity. While recurrent gene fusions hold significant diagnostic and therapeutic value—guiding treatment selection and identifying novel molecular targets—our understanding of their broader clinical implications remains limited. Materials and Methods: We performed next-generation sequencing (NGS; FusionPlex Sarcoma v2, Archer™) and bioinformatic analysis (STAR v.2.7, Arriba) on formalin-fixed paraffin-embedded (FFPE) core needle biopsy specimens. The cohort consisted of patients enrolled in a phase II clinical trial (NCT03651375) who received preoperative chemoradiotherapy according to the UNRESARC protocol. Results: The analysed cohort comprised nine adult patients (median age 66 years; range 44–73) diagnosed with undifferentiated pleomorphic sarcoma (UPS; n = 3), malignant peripheral nerve sheath tumour (MPNST; n = 3), myxofibrosarcoma (MFS; n = 2), and leiomyosarcoma (LMS; n = 1), predominantly high-grade (G3; 5/9) and extremity-localised (6/9). Gene fusions were detected in one-third of patients (3/9), exclusively in G3 tumours. Specifically, we identified an SGSH-PRKCA fusion in MFS (thigh), a LINC01133-OGA fusion in MPNST (thorax), and a concurrent JAZF1-MYH7B (chr7:27995037 intronic-chr20:33563203 exon/splice-site, out-of-frame but preserving myosin domains) with a PRKCA-associated intergenic rearrangement (chr1, retaining C1/kinase domains) in UPS (upper back). Notably, the SGSH-PRKCA and JAZF1-MYH7B pairs have not been previously described in the literature for these STS subtypes. Fusion-positive (F1) cases showed stable radiological disease (RECIST 1.1 SD) and EORTC C/D pathological responses with 5–20% residual viable tumour, whereas fusion-negative (F0) cases showed a wider range of radiological and pathological outcomes, including partial response, progression, and stable disease. Conclusions: Our analysis suggests that broad genomic profiling may provide complementary molecular information in diagnostically challenging cases managed at specialised sarcoma centres, particularly when morphology and immunohistochemistry are insufficient. In the present series, however, the detected rearrangements did not alter systemic treatment, and the data do not support claims of prognostic, predictive, or therapeutic actionability. Full article

Climate change has emerged as a defining global crisis, with the frequency and intensity of climate-induced disasters rising sharply and imposing disproportionate costs on developing economies and small island states. This article examines the role of climate-resilient infrastructure as a central pillar of climate-smart growth, integrating mitigation, adaptation, and long-term development objectives. It situates climate-resilient infrastructure within a planetary health setting, emphasizing the interdependence between human well-being, ecological systems, and infrastructure resilience. Climate-resilient infrastructure, not merely seen as an engineering solution but as a public good that generates significant positive externalities, reduces systemic macroeconomic risk and delivers welfare gains that exceed private financial returns. It discusses the cross-country heterogeneities in resilience outcomes, driven by differences in geographic exposure, economic capacity, institutional quality, and political economy constraints. Building on this, the study advances a welfare-based approach to infrastructure prioritization that incorporates service disruptions, distributional impacts, and fiscal risk, rather than asset values alone. It further outlines policy and financing strategies to bridge the gap between social and private returns, including public investment, concessional finance, blended instruments, and nature-based solutions. By embedding infrastructure within a planetary health lens, the paper argues that resilient systems are critical not only for safeguarding lives and livelihoods, but also for sustaining ecological stability, reducing health risks, and enabling inclusive, sustainable, and climate-smart economic growth. Full article

Breast cancer stem-like cells (bCSCs) fundamentally represent a highly dynamic “immune-adaptive functional state” rather than a fixed cellular lineage, serving as the core engine driving tumor recurrence, metastasis, and therapeutic resistance. Despite rapid advances, the heterogeneity of bCSC states and their intricate interactions with the immune microenvironment lack systematic integration. This review centers on the dynamic evolution and niche adaptation of bCSCs. First, we systematically dissect the multilayered regulatory network maintaining stemness, encompassing core transcription factors, epigenetic–metabolic coupling, and the synergistic mechanisms of critical signaling pathways such as Wnt and Notch. Second, we propose a trinary “stemness–immune–spatial” feedback model, elucidating how bCSCs achieve active immune evasion by downregulating antigen presentation, secreting immunosuppressive factors, and embedding within perivascular “immune-cold niches.” Finally, leveraging a multi-omics integration perspective, we reconstruct precision intervention strategies, exploring the synergistic potential of targeting stemness pathways in conjunction with immunotherapies like PD-1/PD-L1 blockade and STING agonists. Furthermore, we highlight the pivotal role of integrating organoids, PDX models, and AI-assisted decision systems in overcoming heterogeneity and enabling personalized treatment. By establishing a closed-loop framework spanning mechanistic insight to spatially precise intervention, this review aims to provide novel theoretical foundations and translational pathways to surmount the bottleneck of therapeutic resistance in breast cancer. Full article

System logs have been critical for analyzing the operational status and abnormal behavior of highly distributed and heterogeneous edge computing nodes. In edge environments, logs exhibit cross-event and cross-field structural interactions, making it difficult to uncover potential anomaly patterns from isolated events. Moreover, sparse annotations and varying log formats limit the effectiveness of existing methods. To address these challenges, we propose a graph neural network (GNN) anomaly detection framework with prompt learning. It leverages few-shot prompt learning to automatically extract key fields and constructs a weighted directed graph that jointly models semantic embeddings and temporal dependencies, fully representing the structural interactions and semantic associations across events and fields. Furthermore, the framework performs graph-level anomaly detection by jointly optimizing graph representation learning and classification objective within an enhanced one-class directed graph convolutional network, enabling effective identification of global structural anomaly patterns in log graphs. Experimental results demonstrate that the proposed method achieves an average F1-score of 93.3%, surpassing the current state-of-the-art (SOTA) methods by 6.93%. Full article

In the hepatobiliary system, the majority of neoplasms grow within the hepatic parenchyma; however, some arise, grow, and/or spread within the lumen of the intrahepatic large bile ducts and the perihilar/distal bile ducts (collectively referred to as large bile ducts), representing specialized ductal organs associated with unique peribiliary glands and being distinct from the intrahepatic small bile ducts and bile ductules embedded within the hepatic parenchyma. Precursors of cholangiocarcinoma (CCA) arising within the lumen of large bile ducts have recently been proposed. Neoplasms growing and spreading within the lumen of large bile ducts have been categorized into four groups and are discussed here in light of updated pathological findings. (i) Precursor(s) of CCA arising in the large bile ducts (large-duct-type intrahepatic CCA and perihilar/distal CCA): These precursors include high-grade biliary intraepithelial neoplasia (BilIN), intraductal papillary neoplasm of the bile duct (IPNB), and intraductal oncocytic papillary neoplasm (IOPN). High-grade BilIN presents as a flat, microscopic lesion with dysplastic cytoarchitectural alterations and grows along the luminal surface of large bile ducts, whereas the latter two present as grossly visible polypoid or tumorous lesions composed of papillary, villous, or tubular proliferation of neoplastic epithelium with delicate fibrovascular cores. These lesions may eventually progress to invasive CCA. Intraductal tubulopapillary neoplasm of the bile duct (ITPN), previously categorized as another precursor of CCA arising in large bile ducts, appears to represent a heterogeneous group of neoplasms with respect to progression and presumed cell of origin. Some ITPNs are frequently associated with nodular invasive carcinoma resembling small-duct-type intrahepatic CCA (SD-iCCA) and share genetic alterations with SD-iCCA; such cases may arise in association with small bile ducts or bile ductules. In contrast, other ITPNs exhibit cystic changes with tubulopapillary features and may arise in association with peribiliary glands or cysts. (ii) Secondary growth and spread of biliary neoplasms: This category comprises several patterns. First, intraepithelial neoplastic spread directly and continuously from the primary neoplastic lesion is observed in almost all cases of high-grade BilIN, IPNB, and IOPN; it spreads laterally along the luminal surface of the proximal and distal bile ducts and extends vertically into the adjacent peribiliary glands. Intraluminal cast-like spread in the bile ducts adjacent to the primary neoplastic lesion also occurs in some precursor lesions, particularly in ITPN. Implantation of a biliary neoplasm from one part of the biliary tract to another results in discontinuous, multifocal biliary neoplasms, particularly in IPNB, and occurs mainly in the distal bile ducts relative to the main tumor. Multicentric tumorigenesis may contribute to the multifocal development of precursors and CCA in the bile ducts. The accumulation of additional genetic alterations, beyond the common mutations detected in primary tumors, may contribute to metachronous recurrence of CCA after curative resection of the primary biliary tumor. Cancerization of the duct (COD) by CCA may also contribute to secondary growth and spread within the bile duct lumen. Specifically, flat-type cancerization of pre-existing non-neoplastic bile ducts, resembling high-grade BilIN, occurs in approximately one-third of hilar CCA cases. Intraductal polypoid, cast-like cancerization within the lumen of adjacent bile ducts, resembling polypoid precursors of CCA, can also occur in approximately one-tenth of SD-iCCA. (iii) Prominent intraductal polypoid growth of invasive CCA: Invasive CCA rarely presents with predominant intraductal polypoid carcinoma that is continuous with periductal infiltrating CCA; this pattern can be referred to as polypoid invasive CCA. (iv) Nonbiliary neoplasms presenting bile duct tumor thrombus (BDTT): BDTT associated with hepatocellular carcinoma and with extrahepatic malignancies extending into the bile duct lumen can mimic the intraluminal growth and spread patterns of the above-mentioned biliary neoplasms. In conclusion, intraluminally growing biliary neoplasms in the large bile ducts comprise a heterogeneous group that can be reasonably classified into four categories. This categorization may facilitate understanding of these intrabiliary growing neoplasms. Full article