Search Results (885)

Introduction: Although anterior non-infectious uveitis affects the structures of the anterior segment of the eye, (inflammatory) disruption of the hemato–ocular barrier may lead to changes in the structures of the posterior segment of the eye. Objective: To evaluate functional retinal changes using multifocal electroretinography (mfERG) and their relationship with structural optical coherence tomography (OCT) parameters in patients with acute anterior non-infectious uveitis (AANU). Methods: This prospective study included 38 eyes of 19 patients diagnosed with unilateral AANU and age-matched healthy fellow eyes as controls. All subjects underwent comprehensive ophthalmological examination, including best-corrected visual acuity (BCVA), spectral-domain OCT, and mfERG testing at baseline, 3 months, and 6 months. mfERG parameters (amplitude and implicit times) were analyzed alongside central field thickness (CFT), macular volume (MV), and average macular thickness (AMT). Results: Eyes affected by AANU demonstrated a significant reduction in mfERG response amplitude in the central retinal region compared with control eyes, particularly during the acute phase. Although OCT parameters showed partial structural normalization during follow-up, functional recovery was less pronounced in selected retinal regions. Latency values showed minimal variation over time. These findings indicate a potential dissociation between electrophysiological function and structural morphology during disease resolution. Conclusions: Acute anterior uveitis is associated with measurable macular functional impairment detectable by mfERG, even when structural OCT parameters appear relatively stable. These results suggest that inflammatory processes in AAU may extend beyond the anterior segment and transiently affect retinal function. mfERG may therefore serve as a sensitive adjunct tool for detecting and monitoring subclinical macular dysfunction in AANU. Clinical Relevance: Functional retinal impairment may persist despite apparent structural recovery in acute anterior uveitis. Incorporating mfERG into clinical evaluation may improve the detection of subtle macular involvement and enhance understanding of disease dynamics beyond conventional imaging findings. Full article

In the existing research on information propagation modeling in social networks, hypergraphs have been widely applied to characterize the high-order interaction relationships involving multiple nodes. However, most models are still based on the assumption of undirected connections, which leads to certain limitations in depicting the information flow direction and the structural characteristics of propagation chains. To address the above problems, a social hypernetwork propagation model with directional constraints is constructed in this paper by introducing the directed hypergraph structure and combining it with the improved SEIR model. The strength of social relationships is measured by intimacy in the model, and a comprehensive characterization of the information propagation process is achieved by integrating the threshold mechanism of the directed hypergraphs with the attenuation function of information timeliness. In addition, the effectiveness of the proposed model is verified by taking the event of “imposing additional tariffs” as an example, and the evolutionary characteristics of propagation in different network structures, as well as the impacts of user confidence and information timeliness, are analyzed using simulation experiments. The results indicate that the model is applicable to characterizing the information propagation trends and dynamic characteristics in real social networks, and can provide theoretical references and methodological support for the prediction and regulation of network public opinion. Full article

This study addressed a critical gap in understanding the drivers of movie consumption during digital transformation and streaming platform proliferation. It examined the direct effects of three core dimensions—social value, functional value, and emotional value—on movie consumption behavioral intention, alongside the mediating mechanism of satisfaction. Data were collected via questionnaire surveys administered to cinema audiences in Eastern China and through Wenjuanxing online platform, yielding 1089 valid responses. Statistical analysis was conducted using SPSS 26.0, and Structural Equation Modeling (SEM) was performed employing AMOS 26.0. Findings indicate significant positive direct effects of social value and emotional value on movie consumption behavioral intention. Furthermore, these value dimensions indirectly enhance movie consumption behavioral intention through the mediating influence of satisfaction. In contrast, functional value demonstrates no significant direct effect on either movie consumption behavioral intention or satisfaction. Satisfaction serves as a significant mediator in the relationships between both social value and emotional value, and movie consumption behavioral intention. This study elaborated the distinct pathways through which varied perceived value dimensions operate and empirically validates the mediating role of satisfaction within movie consumption decision-making. For the movie industry, these insights suggest prioritizing social engagement and emotional resonance to optimize offerings, establishing dynamic satisfaction monitoring, and designing member incentives targeting these values to foster sustained behavioral activation. This provides empirically grounded guidance for refining marketing strategies and experiential enhancements. Full article

This study analyzes the long-run relationships between digitalization, investment, innovation, and economic growth in connection with the energy transition in the SPRING-F group (Spain, Poland, Romania, Italy, the Netherlands, Germany, and France) using annual data for the period of 2000–2024. The analysis starts from the premise that digitalization affects economic performance not only directly, but also through structural transmission mechanisms linked to investment and the energy transition. To capture these dynamics, this study employs three complementary panel ARDL models. The first model explains economic growth (GDP per capita) as a function of digitalization, capital accumulation, R&D expenditure, renewable energy consumption, trade openness, and foreign direct investment. The second model estimates gross capital formation (GCF) in order to assess the investment transmission channel. The third model explains renewable energy consumption (RNEC) in order to capture the sustainability dimension. The results show that trade openness and capital accumulation are the strongest long-run drivers of economic growth in the SPRING-F group. Internet use, R&D expenditure, and FDI also display positive long-run associations with GDP per capita, whereas fixed broadband subscriptions and renewable energy consumption enter the growth equation with negative coefficients, suggesting that digital infrastructure and the green transition do not automatically generate immediate growth gains. The GCF model confirms that investment acts as an important transmission mechanism, especially through the robust GDP–GCF linkage. The RNEC model indicates that the energy transition is positively associated with investment, innovation, and trade openness, while GDP and digital infrastructure remain negatively associated with the renewable energy share. Overall, the findings point to a conditional and nonlinear relationship between growth, digitalization, investment, and sustainability, with the sustainability channel remaining more specification-sensitive than the growth and investment equations. The long-run results for the GDP equation should also be interpreted with additional caution, given the comparatively weaker cointegration evidence for Model 1. Full article

Urban coastal wetlands along the Peruvian Pacific coast are increasingly affected by urban expansion, pollution, and hydrological alterations, compromising their ecological integrity. In this context, the spatiotemporal variation of the aquatic macrophyte community and its relationship with limnological conditions and drivers of change were evaluated in the Santa Rosa wetland (Chancay, Lima). The objective is to evaluate the spatiotemporal variation of the aquatic macrophyte community in the Santa Rosa wetland and analyze its relationship with physicochemical limnological variables and drivers of change. Sampling was conducted during two contrasting hydrological seasons in 2022: T1 (low-water season) and T2 (high-water season), at six sampling points (P1–P6). Physicochemical variables (water depth, temperature, pH, conductivity, total dissolved solids—TDS, total suspended solids—TSS, dissolved oxygen—DO, turbidity, nitrate—NO₃⁻, ammonium—NH₄⁺, phosphate—PO₄³⁻, and dissolved organic matter—DOM) were measured, and the relative abundance of aquatic macrophytes was evaluated. Drivers of change were identified through direct observation and a structured matrix, with phosphate a PCoA performed to summarize spatiotemporal trends. Data were analyzed using Principal Component Analysis (PCA), Co-inertia analysis, and Multi-Response Permutation Procedures (MRPP). Significant spatiotemporal variation was observed in physicochemical parameters (p < 0.05), with moderate covariation between the two matrices (RV = 0.47). A total of ten aquatic macrophyte species were recorded, with higher abundance of Pontederia crassipes and Pistia stratiotes in T1, and Hydrocotyle ranunculoides and Bacopa monnieri in T2. The most relevant drivers of change were solid waste, livestock grazing, organic contamination, and urban expansion. Spatial heterogeneity was observed in the drivers of change affecting the Santa Rosa wetland, forming a mosaic of areas with different impact profiles. Despite multiple anthropogenic pressures, the Santa Rosa wetland maintains a limnological structure and a functionally coupled macrophyte community, suggesting that essential ecological processes are maintained within the temporal scope of this study. The observed covariation between physicochemical conditions and vegetation confirms the persistence of essential ecological processes, even within an altered urban context. This study demonstrates that integrating biotic components, limnological variables, and drivers of change is fundamental to understanding and monitoring the ecological dynamics of urban wetlands along the Peruvian coast. Full article

To investigate the strength evolution of concrete structures operating in long-term service in humid environments while facing threats such as earthquakes, explosions, and impacts, this study utilized a Hopkinson pressure bar (SHPB) and an MTS testing system to conduct experiments on concrete with four different moisture contents (relative saturation of 0%, 50%, 80%, and 100%) across a strain rate range of approximately 10⁻⁵ to 2 × 10² s⁻¹. Based on these results, a relationship equation was established describing how the strength factor of wet concrete varies with strain rate. The study identified sensitive and non-sensitive regions for the strain rate effect in wet concrete. As the water content increases, the threshold for the sensitive region decreases. Specifically, the inflection strain rate for dried concrete is approximately 32 s⁻¹, whereas for saturated concrete, it drops below 5 s⁻¹. A functional equation describing the variation in the strain rate sensitivity coefficient with water content was derived, showing that the strain rate effect on strength becomes more pronounced as water content increases. The rate-wet coupling effect on concrete compressive strength was analyzed, and zones dominated by the strain rate strengthening effect and the water-weakening effect were identified. The mechanism of strength variation in wet concrete across different strain rate ranges was investigated. The analysis indicates that free water participates in the action processes of each mechanism from low to high strain rates. As the strain rate increases, the mechanisms of pore water interaction and thermal activation undergo a transition. At higher strain rates, the significant increase in the dynamic strength of wet concrete results from the combined and coupled effects of the material’s “true strain rate effect” and the stress wave effect in wet concrete, which are driven by the mutual coupling of pore water, thermal activation, and viscous drag mechanisms. This paper aims to provide a reference for the in-depth understanding of the strength evolution and control of hydraulic concrete structures. Full article

The B cell receptor (BCR) repertoire captures an individual’s immunological history and antigen-driven evolution within a vast, high-dimensional sequence space. Although bulk and single-cell adaptive immune receptor repertoire sequencing (AIRR-seq) now enables deep profiling of BCR diversity, interpreting these datasets remains challenging due to strong inter-individual heterogeneity, nonlinear sequence–structure–function relationships, dynamic clonal evolution, and the rarity of functionally relevant clones. Artificial intelligence (AI) provides a conceptual and computational framework for addressing these challenges. Here, we summarize how advanced deep learning architectures, including antibody-specific language models, graph neural networks (GNNs), and generative frameworks, uncover clonal topology, structural features, and antigen-binding semantics. We further highlight applications in cancer, infectious disease, and autoimmunity. Finally, we propose a closed-loop framework that integrates multimodal datasets, interpretable AI, and iterative experimental validation to advance predictive immunology and accelerate therapeutic antibody discovery. Full article

Background: PYR/PYL/RCAR proteins are core abscisic acid (ABA) receptors that play essential roles in ABA signal transduction, plant growth and development, and abiotic stress responses. However, the PYL gene family in watermelon (Citrullus lanatus) has not been systematically characterized, limiting our understanding of ABA-mediated stress adaptation in this economically important crop. Methods: A genome-wide analysis was performed to identify ClPYL genes in watermelon using a hidden Markov model search. Phylogenetic relationships were reconstructed using the maximum likelihood method. Segmental duplication events were analyzed using synteny analysis. Conserved motifs, gene structures, and promoter cis-acting elements were characterized using MEME and PlantCARE. Expression profiles under drought, salt, and cold stresses were examined by quantitative real-time PCR (qRT-PCR) with three biological replicates. Results: In this study, 15 ClPYL genes were identified in watermelon through genome-wide analysis. Phylogenetic reconstruction classified these genes into four subfamilies, with subfamily II being exclusively present in cucurbits—a lineage-specific feature not observed in Arabidopsis. Synteny analysis revealed eight segmental duplication events involving members of subfamilies I, III, and IV, while subfamily II members were not associated with these duplications. Members within the same subfamily share similar exon-intron structures and conserved motifs. Promoter analysis revealed that ClPYL genes are enriched with various cis-acting elements associated with hormone signaling and abiotic stress responses. Expression profiling demonstrated that ClPYL genes exhibit diverse and dynamic expression patterns under drought, high-salinity, and cold stresses. Notably, genes such as ClPYL5 under drought, ClPYL02 under salt, and ClPYL15 under cold stress displayed persistent stress-responsive expression. Conclusions: These findings reveal the evolutionary conservation and diversification of the PYL family in watermelon and provide a set of candidate genes for functional studies aimed at dissecting ABA-mediated stress adaptation. This work establishes a genomic framework for developing stress-resilient watermelon varieties through molecular breeding. Full article

Encrypted network traffic classification is an important research topic in the field of network security. Although deep learning-based methods have made progress, they still face three main challenges: first, the semantic information in encrypted traffic is inadequately represented, making it difficult for existing methods to effectively capture the hierarchical interaction relationships between packet-level and flow-level features; second, models rely on large amounts of labeled data for supervised training, resulting in high training costs and limited generalization ability in new scenarios; third, in existing self-supervised methods, the functions of the encoder and decoder are coupled, which restricts the full potential of the encoder’s representation learning. To address these issues, this paper proposes an Enhanced Traffic Classifier (ETC) based on self-supervised feature learning. The model first constructs a multi-level interactive traffic representation matrix, converting raw traffic into structured grayscale images that fuse packet-level and flow-level temporal features, thereby addressing the problem of missing semantic information. On this basis, an improved Masked Image Modeling Vision Transformer architecture is adopted. Through a three-stage decoupled design of encoder–regressor–decoder, the encoder focuses solely on feature extraction, the regressor performs masked representation prediction, and the decoder is only responsible for image reconstruction, thereby fully unleashing the encoder’s feature learning capability. Furthermore, during the fine-tuning stage, an Attentive Probing classification mechanism is introduced to replace the traditional linear classification head. By using learnable class query vectors to dynamically focus on semantic regions relevant to the classification target, the model’s recognition accuracy and robustness are further improved. Experiments are conducted on five public datasets, including USTC-TFC2016 and CICIoT2022, as well as a self-built Human-Internet dataset. The results show that ETC significantly outperforms mainstream methods such as YaTC and ET-BERT in core metrics including accuracy and F1-score, while also demonstrating strong generalization in few-shot scenarios. Full article

The synergistic integration of Hyperspectral Imaging (HSI) and Light Detection and Ranging (LiDAR) data has become a pivotal strategy in remote sensing for precise land-cover classification. However, existing multimodal deep learning frameworks frequently suffer from intrinsic limitations, including rigid feature extraction protocols, underutilization of LiDAR-derived textural information, and asymmetric fusion mechanisms that fail to balance the contribution of spectral and elevation features effectively. To address these challenges, this paper proposes a novel framework named DGC-BCAF, which integrates Dynamic Group Convolution and Bidirectional Guided Cross-Attention Fusion to achieve adaptive feature representation and robust cross-modal interaction. First, a Dynamic Group Convolution (DGConv) module embedded within a ResNet18 backbone is designed to function as the central spatial context extractor. Unlike traditional group convolution, this module learns a dynamic relationship matrix to automatically group input channels, thereby facilitating flexible and context-aware feature representation that adapts to complex spatial distributions. Second, to overcome the insufficient exploitation of elevation data, we introduce a dedicated LiDAR texture encoding branch. This branch innovatively fuses Gray-Level Co-occurrence Matrix (GLCM) statistical features with multi-scale convolutional representations, capturing both geometric height information and fine-grained surface textural details that are critical for distinguishing objects with similar elevations. Finally, central to our architecture is the Bidirectional Cross-Attention Fusion (BCAF) module. Unlike standard unidirectional fusion approaches, BCAF employs a LiDAR geometry to guide the selection of salient spectral bands, while simultaneously utilizing spectral signatures to emphasize informative LiDAR channels. This mutual guidance ensures a balanced contribution from both modalities. Extensive experiments conducted on three benchmark datasets—Houston 2013, Trento, and MUUFL—demonstrate that DGC-BCAF consistently outperforms state-of-the-art methods in terms of overall accuracy, average accuracy, and Kappa coefficient. The results confirm that the proposed adaptive grouping and bidirectional guidance strategies significantly improve classification performance, particularly in distinguishing spectrally similar materials and delineating complex urban structures. Full article

Agricultural enterprises serve as the cornerstone of food security. However, they operate under significant resource constraints and environmental risks. Adopting a systems lens, this study examines digitalization as a critical variable reshaping the input–output logic of agribusinesses. Using a longitudinal panel dataset of Chinese listed agricultural firms from 2013 to 2022 and Ordinary Least Squares regression, the study empirically identifies the mechanisms driving ESG performance. The results demonstrate that digitalization significantly enhances overall ESG performance, functioning as a governance mechanism that improves internal resource integration and transparency. Critically, the moderation analysis reveals a dynamic substitution relationship among system elements. Traditional inputs, specifically management expenses, financial slack, and intangible assets, exert significant negative moderating effects. This confirms the logic of factor substitution, suggesting that as digitalization advances, traditional governance modes relying on high administrative costs face diminishing marginal returns. In the environmental dimension, digitalization facilitates a transition from post-event remediation to whole-process control through intelligent traceability, effectively internalizing external constraints and reducing waste emissions. Additionally, heterogeneity analysis highlights significant structural variations. The ESG-enhancing effect of digitalization is more pronounced in firms characterized by high financial leverage, low long-term debt, and low industry concentration. Spatially, the marginal improvement is stronger in Western regions compared to the East, underscoring the Hu Huanyong Line as a critical structural boundary. Ultimately, digitalization serves as a core governance element that drives the structural transformation from traditional operating paradigms to digital governance architectures, thereby providing a robust pathway for corporate sustainability. Full article

Vaporized free chlorine, primarily present as hypochlorous acid (HOCl), is increasingly used for indoor microbial control; however, virus-dependent susceptibility and its molecular determinants remain unclear. We evaluated virucidal effects under controlled indoor conditions (0–9 ppb) against echovirus 30 (E30), influenza A/H1N1, and human adenovirus type 3 (HAdV3). Infectious titers were quantified by TCID₅₀ assays. Computational fluid dynamics (CFD) simulations and gas-sensor measurements assessed spatial dispersion, and structural analyses examined oxidation-sensitive amino acid residues. Significant reductions in infectivity were observed for E30 (99.0%, p = 0.00727) and influenza A/H1N1 (99.9%, p = 0.000597), whereas no significant reduction was detected for HAdV3 (p = 0.142). Analyses including all data points without outlier exclusion confirmed the robustness of these findings. CFD indicated uniform dispersion, although spatial heterogeneity within the indoor environment cannot be excluded. These findings suggest that viral susceptibility to vaporized HOCl is associated with residue-level composition and structural context; however, this relationship should be interpreted as correlative rather than causal. Moreover, integration of molecular and structural analyses provides a plausible mechanistic framework, although direct biochemical validation remains necessary. Structural analyses showed lower proportions of oxidation-sensitive residues in adenoviral proteins compared with influenza A hemagglutinin (OR = 0.34–0.40, adjusted p < 0.001) and the E30 VP1 intermediate. Residues were clustered in surface-exposed functional domains in susceptible viruses. Full article

Unlike conventional virtual idols like Hatsune Miku, which rely on pre-set voice libraries and stage scripts, AI-customized virtual idols achieve real-time interaction through generative artificial intelligence, continuously iterating their personality traits, language style, and even value expression along with fan and user interactions. AI-customized virtual idols, as pre-defined cultural commodities in the digital age, tend to focus on static, functional interpretations and have not yet fully entered the dynamic construction process as “subjects in the process of generation.” This study, based on a deep mediation perspective, employs a research method combining app roaming and semi-structured interviews to focus on the sociological examination of young fan groups’ use of AI tools to customize virtual idol companionship. It explores the reciprocal relationship between fan groups and customized virtual idols. The study finds that the AI-customized idols fan group constitutes a typical “actor group,” and its interaction practices are essentially a “fluid interaction” of human–machine intimacy. Young fan groups mainly interact with AI-customized virtual idols based on materiality, cognition, visibility, and emotional frames, thereby generating rich meaning production and symbolic imagination during the usage process. Fan groups and AI-customized virtual idols have developed different relationship paths, including mutual attachment, returning to normalcy, seeking substitutes, or direct withdrawal, revealing the inherent contradictions and tensions in digital intimacy, as well as the self-adjustment strategies of individuals under the mediation of technology. This process presents a “human-machine-idol” triadic relationship framework, becoming a new paradigm for intimacy in the digital age. Full article

Rapid digitalization and artificial intelligence are restructuring land systems by altering the functional relationship between built environments, socio-ecological processes, and territorial capital accumulation. This paper provides a conceptual and literature-based analysis of how hybrid physical–digital infrastructures are reshaping urban–rural interactions, land-use intensity, and long-term sustainability conditions. Rather than developing a fully operational measurement model, the study critically examines the limitations of aggregate productivity indicators and existing evaluation frameworks in capturing spatial reorganization processes, capital durability, and long-term dynamics. Building on insights from sustainability economics and socio-ecological systems research, the paper proposes a stock–flow interpretative perspective to better understand the interaction between physical, natural, and intangible capital within evolving land systems. The analysis focuses on three structural drivers of land system transformation: (i) the virtualization of services and the expansion of cyberspace-based infrastructures; (ii) demographic contraction and aging processes affecting land demand and settlement structures; and (iii) capital deepening in energy-intensive digital networks with implications for land–climate interactions. Within this context, particular attention is given to infrastructure life-cycle dynamics, entropy-related capital decay, and the role of artificial intelligence in reshaping labor–land relationships. The paper highlights the need for new evaluation approaches capable of distinguishing between value generated through material land transformation and value emerging from intangible and digital layers. In this sense, it aims to contribute to ongoing debates on land management and spatial planning by outlining a research agenda for the development of spatially grounded, stock–flow-based sustainability metrics. The findings suggest that future land governance and urban development strategies will need to explicitly account for hybrid spatial architectures and their long-term resource and climate implications in order to preserve territorial resilience and intergenerational equity. Full article

Surface and interface science play an important role in the tribological properties of materials. Recently, research in this field has extended from the macroscopic scale to the molecular level to elucidate energy dissipation and structural evolution mechanisms at sliding interfaces. In this work, we propose a nanolubricant strategy based on carbon nanocages (CNCs). Three types of lubricating molecules—oleylamine (amine), oleic acid (carboxyl), and stearyl alcohol (hydroxyl)—were encapsulated into a polytetrafluoroethylene (PTFE) matrix to construct a composite tribological interface model. Molecular dynamics simulations were employed to investigate the interfacial enrichment, diffusion, and interaction mechanisms of these molecules with PTFE chains and the Fe counterface. Particular emphasis was placed on how different functional groups regulate energy transfer and dissipation pathways. This study deepens the molecular–level understanding of structure–lubrication relationships and provides theoretical guidance for designing high–performance polymer–based tribological materials. Full article