Search Results (4,574)

The plant mitochondrial genome has become a current research hotspot as an independent genetic model. Nevertheless, mitochondrial genome information for most Dendrobium species remains unknown. In this study, the assembly of mitochondrial genome of Dendrobium nobile Lindl.,1830 and Dendrobium denneanum Kerr., 1933 was conducted through the application of second- and third-generation sequencing technologies, with the mitochondrial genome of D. denneanum Kerr. being reported first. The results revealed that the mitochondrial genomes of the two species possessed a multi-chromosome circular structure. Their total lengths were 641,414 bp and 558,760 bp, consisting of 21 and 19 contigs, respectively. A total of 67 and 72 genes, 993 and 1491 repeat sequences, and 549 and 553 RNA editing sites were identified. Gene loss was observed. A total of 26 and 36 homologous fragments were detected between the mitochondrial and the chloroplast genome, accounting for 5.09% and 4.93% of the total lengths, respectively, indicating intracellular gene transfer. Synteny and phylogenetic analyses revealed that the two species shared extensive collinear regions and clustered together in a distinct clade of the phylogenetic tree, indicating a close sister relationship. These findings enrich the mitochondrial genome database and provide valuable insights to guide future research on species identification and molecular evolution of the genus Dendrobium. Full article

This study investigates the acoustic emission (AE) response and damage mode characteristics of ±45° glass fiber-reinforced polymer (GFRP) composites used in wind turbine blade shear webs under quasi-static tensile loading. It aims to establish the relationship between AE features and three typical damage mechanisms—matrix cracking, interfacial debonding, and fiber fracture—to support damage assessment and structural health monitoring. Quasi-static uniaxial tensile tests with synchronous AE monitoring are conducted on specimens with three orientations (0°, 45°, and 90°). AE features are selected using correlation analysis and principal component analysis, and the HAC-initialized K-means clustering method is employed for damage mode identification. The optimal number of clusters is determined to be three, according to the Davies–Bouldin index (DBI) and the Silhouette index (SI). The resulting low-, mid-, and high-frequency clusters are associated with matrix cracking, interfacial debonding, and fiber fracture, respectively. These interpretations are further supported by wavelet-based time–frequency analysis and microscopic fracture surface observations. Full article

In the context of highly interconnected global markets and the rapid dissemination of multilingual information, traditional risk prediction methods that rely on single numerical sequences or monolingual text are insufficient for achieving early perception of cross-market risks. To address this issue, a cross-market risk early warning framework based on multilingual large language models and multimodal sensing fusion is proposed. The proposed approach is centered on a unified risk semantic space, where cross-lingual semantic alignment is employed to reduce semantic discrepancies across languages. Furthermore, a semantic–volatility coupling attention mechanism is introduced to capture the dynamic relationship between textual semantic evolution and market fluctuations. In addition, cross-market knowledge transfer and low-resource enhancement strategies are incorporated to improve the model’s generalization capability across multilingual and multi-market environments, thereby establishing an intelligent perception and early warning system for complex sensing scenarios. Experimental results demonstrate that the proposed method significantly outperforms multiple baseline models in multilingual cross-market risk prediction tasks. In the main experiment, the model achieves a root mean squared error (RMSE) of $0.1127$, an mean absolute error (MAE) of $0.0846$, and an area under the curve (AUC) of $0.8879$, while the early warning gain is improved to $5.2$ days, which is substantially better than the Transformer model (RMSE $0.1365$, AUC $0.8042$) and the multilingual BERT-based fusion model (AUC $0.8395$). In terms of classification performance, higher accuracy, precision, and recall are consistently achieved, with overall accuracy exceeding $0.88$, and both precision and recall are maintained above $0.85$, indicating strong discriminative capability in risk identification tasks. Cross-lingual generalization experiments further verify the robustness of the proposed framework. When trained solely on the English market, the model achieves AUC values of $0.8624$ and $0.8471$ on the Chinese and European markets, respectively, with RMSE reduced to $0.1185$, significantly outperforming competing methods. Overall, the proposed approach achieves substantial improvements in prediction accuracy, cross-lingual generalization, and early warning performance, providing an effective solution for artificial intelligence-driven sensing and risk early warning. Full article

Acute Lymphoblastic Leukemia (ALL) is a highly prevalent hematological malignancy, especially in children, for whom precise and prompt subtype identification is essential to establish suitable treatment protocols. Current deep learning-based computer-aided diagnosis (CAD) methods for identifying ALL are hindered by numerous drawbacks, such as a dependence on solely spatial feature depictions, elevated feature dimensions, computationally extensive deep learning architectures, inadequate multi-layer feature utilization, and poor interpretability. This paper introduces LUMINA-Net, a custom, lightweight, and interpretable deep learning CAD for the automated identification and subtype diagnosis of ALL using microscopic blood smear pictures. LUMINA-Net makes four principal contributions: first, it integrates a self-attention module within a lightweight custom Convolution Neural Network (CNN) to effectively capture long-range spatial relationships across clinically pertinent cytological patterns while preserving a compact design. Second, it employs a Discrete Wavelet Transform (DWT)-based wavelet pooling layer that decreases feature dimensions by up to 96.875% while enhancing the obtained depictions with spatial-spectral information. Third, it utilizes a multi-layer feature fusion strategy that combines wavelet-pooled features from two deep layers with a third fully connected layer to create a discriminating multi-scale feature vector. Fourth, it incorporates Gradient-weighted Class Activation Mapping as a dedicated explainability process to furnish clinicians with apparent visual explanations for each classification decision. Withoit the need for image enhancement or segmentation preprocessing, LUMINA-Net outperforms the competing state-of-the-art methods on the same dataset, achieving a peak accuracy of 99.51%, specificity of 99.84%, and sensitivity of 99.51% on the publicly available Kaggle ALL dataset. This demonstrates that LUMINA-Net has the potential to be a dependable, effective, and clinically interpretable CAD tool for ALL diagnosis. Full article

The accelerated aging of the population in vulnerable urban contexts poses significant challenges for architecture, particularly with regard to the quality of life of older adults. Within this framework, the present study aimed to analyze the association between sensory architecture and the quality of life of older adults and to translate this empirical evidence into context-informed design criteria for the development of a comprehensive center for older adults. The study adopted a quantitative approach with a non-experimental, cross-sectional, and correlational design. A structured questionnaire on sensory architecture and quality of life was administered to family members and caregivers acting as proxy respondents, demonstrating high internal consistency (Cronbach’s $\mathit{\alpha }>0.90$). Given the ordinal nature of the data, inferential analysis was conducted using Spearman’s rho coefficient. Within the analyzed dataset, the results revealed a statistically significant and strong association between sensory architecture and the quality of life of older adults ($\rho$ > 0.80). At the dimensional level, visual and tactile stimuli exhibited the highest associations, followed by the social relationships dimension, while therapeutic environments showed a moderate association, allowing the identification of an empirical hierarchy among the analyzed dimensions within this dataset. These findings support the interpretation of sensory architecture as a construct statistically associated with indicators of quality of life, from a non-causal perspective. Based on this hierarchy, the results were articulated into an evidence-based architectural structure, serving as analytical input to inform context-specific criteria for spatial organization, materiality, comfort, orientation, and social interaction derived from the observed statistical associations. The study contributes a methodological approach that systematically connects correlational quantitative findings with architectural design considerations, particularly in urban contexts characterized by limited specialized infrastructure. However, a key limitation is the use of proxy respondents (family members and caregivers), which should be considered when interpreting the results. Full article

Gender-based and sexual violence are major public health concerns, and norms about consent are central to their prevention. This study examines how attitudes toward sexual consent relate to digital sexual consent and to the occasional feeling of distrust in public consent campaigns and institutions. We conducted a cross-sectional online survey embedded in the evaluation of a municipal consent campaign in Zaragoza (Spain). Adults (N = 404; 56.7% women) completed a 14-item short version of the Sexual Consent Scale–Revised, two items on digital sexual consent, and three items on institutional reluctance (perceived “sermonizing” tone, distrust in effectiveness, and lack of personal identification with the message). Correlation and multiple regression models with robust standard errors were estimated, controlling for gender, age, education, income, relationship status, and social media use. Attitudes toward sexual consent were strongly and positively associated with digital sexual consent. Gender was the most consistent sociodemographic correlate: men showed less egalitarian attitudes than women across all consent measurements. Institutional reluctance was systematically related to less supportive consent attitudes: perceiving institutional messages as exaggerated or personally irrelevant predicted lower support for sexual and digital consent norms, whereas trust in the campaign’s effectiveness was associated with more egalitarian attitudes. The findings support the continuity between sexual and digital consent and highlight gender and institutional trust as key determinants for the prevention of gender-based and sexual violence. Public health and social policies should integrate digital consent into consent education and co-design campaigns that minimize defensive reactions and rebuild trust in institutions. Full article

With the transformation of resource-based cities and the restructuring of industrial sectors, the sustainable conservation of coal mining heritage has become a global focus. In China, coal mining heritage faces challenges such as degradation and inadequate management, highlighting the urgent need for more context-sensitive and systematic conservation approaches. This study develops an integrated, landscape-oriented analytical framework for stakeholder identification to address these challenges and to better understand stakeholder differentiation in coal mining heritage conservation. The research objectives are as follows: (1) to bring together a three-dimensional framework based on material-technical, socio-cultural, and experiential dimensions; (2) to analyse the roles and interactions of stakeholders; and (3) to explore how technical knowledge, socio-cultural memory, and daily experiences influence the protection and reuse of coal mining heritage sites. The study integrates the theoretical frameworks of landscape character assessment, historic urban landscape, and experiential landscape, using data from field observations and interviews analysed via ATLAS.ti. The findings show that the proposed framework offers a more systematic understanding of the dynamic relationships between stakeholders and heritage landscapes, thereby providing practical guidance for local governments and relevant institutions in developing inclusive and context-sensitive conservation strategies. Full article

As a primary energy consumer and carbon emitter, the construction industry (CI) faces a growing conflict between traditional energy-intensive growth models and global sustainable development goals. To promote the sustainable development of the CI, this study establishes a sequential analytical framework following the logic of “coupling evaluation–driving force identification–causal inference” across 30 developed economies (DE) from 2000 to 2022. Initially, the coupling coordination degree (CCD) between the economic and environmental systems of the CI was evaluated, utilizing the Environmental Kuznets Curve (EKC) to characterize the transition from relative to absolute decoupling. The results show that the economy and the environment in the construction industry (CEECI) for DE is generally high (0.70–0.90). Subsequently, based on Green Innovation Growth (GIG) theory, Panel Data Analysis (PDA) is employed to identify the key drivers of the coupling between the economy and CEECI. The results show that for every 1% increase in per capita GDP, CEECI increases by approximately 0.035; for every 1% increase in science and technology investment (ST Inv), CEECI increases by 0.045; and for every 1 unit increase in building energy use (BEU), CEECI decreases by 0.008. Furthermore, Granger causality analysis (GCA) was used to examine the bidirectional predictive relationship. Furthermore, there is a two-way correlation between GDP and CEECI, and a one-way correlation between CEECI and ST Inv. Overall, our results show that further decoupling requires innovation, not just economic growth; therefore, the CI should optimize its industrial structure, prioritize technological innovation, strengthen lifecycle energy management, and promote coordinated global CI improvement. Full article

The integration of automated learning and video analysis enables the development of intelligent systems that can operate effectively in uncertain scenarios. These systems can autonomously identify dominant motion dynamics, depending on the theoretical framework used for representation and the learning process used for pattern identification. Current literature offers a state-based approach to describe the key temporal and spatial relationships required to understand motion dynamics. An important aspect of this approach is determining when the number of positively learned rules from a given information source is sufficient to detect dominant motion in automatic surveillance scenarios. This is crucial, as it affects both the variability of movements that monitored subjects can exhibit within the camera’s field of view and the resources needed for effective implementation. This study addresses these gaps through a grammar-based sufficiency criterion, which posits that learning is complete when production rule growth stabilizes, under the assumption of system stationarity. The stability criterion evaluates whether the most probable rules are learned over time, and whenever a high-growth rule is added, it is used to update the criterion. We outline several benefits of having a formal criterion for determining when a symbolic surveillance system has a robust model that explains the observed motion dynamics. Our hypothesis is that a correct model can consistently account for the majority of motion dynamics over time in an automated learning process. The proposed approach is evaluated by modeling motion dynamics in several scenarios using the SEQUITUR algorithm as input and computing the probability of stability along the learning curve, which indicates when the model reaches a steady state of consistent learning. Experimental validation was conducted in real-world scenarios under varying acquisition conditions. The results show that the proposed method achieves robust modeling performance, with accuracy values ranging from 83.56% to 95.92% in dynamic environments. Full article

The paper analyzes the structural transformations in tourism using the network input–output (IO) model. The study is based on IO tables for two years (2013 and 2023). This allows a comparative analysis of changes in the structure of technical coefficients and in multipliers associated with production and tax revenues. The approach enables the identification of changes in tourism’s position within the economic network. Tourism is also analyzed in terms of the degree of integration, dependence on intermediate inputs, and the capacity to spread the economic effects. The results show few upstream linkages for tourism. There is a low level of spillovers. To make it more resilient and generate more spillovers, it is important to build relationships with sectors such as agriculture, creative industries, and business services. The reliance on outsourced services could affect relationships with productive industries. Full article

Accurately identifying an urban functional zone (UFZ) is crucial for rationally allocating urban land resources and optimizing urban spatial structure. Existing research based on Points of Interest (POIs) mostly uses the relationship between the number of various types of POIs as the basis for identification. However, this approach neglects the difference of physical surface property of urban functional zones—imperviousness. Based on the FD-CR method, this study proposes the RFD-ECR identification method by combining TF-IDF and ISI. This study divides research units according to OpenStreetMap (OSM), and reclassifies POI data. It then uses the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to highlight the dominant function of study units and incorporates the impervious surface index (ISI) as a correction to recognize urban functional zones. Experiments conducted in the central urban area of Chengdu demonstrate that this method is effective in identifying urban functional zones, achieving an accuracy rate of 80.21%. Comparison with the Frequency Density-Category Ratio (FD-CR) method reveals that this method, through the TF-IDF algorithm and the impervious surface index constraint, effectively improves the classification accuracy of mixed commercial UFZs. This method broadens the scope of research on urban functional zone identification based on POI data, and also provides a valuable reference for other cities undertaking functional zone identification. Full article

This study examines the role of artificial intelligence (AI) technologies in identifying and sustaining leadership talent within the educational sector in Oman, addressing the increasing demand for evidence-based and innovative approaches to leadership development. A qualitative phenomenological research design was employed to explore how AI experts and educational leaders perceive, evaluate, and conceptualize AI-driven tools for leadership talent identification and sustainability. In-depth semi-structured interviews were conducted with 25 participants from three major Omani educational institutions. Data were analyzed using thematic analysis, allowing systematic identification of recurring patterns, conceptual relationships, and shared professional insights. The findings indicate that AI applications—including big data analytics, behavioral assessment tools, competency identification platforms, and predictive analytics—provide effective mechanisms for early detection and assessment of leadership potential. Furthermore, integrating AI into personalized professional development programs and continuous performance evaluation contributes to the long-term sustainability and strategic utilization of leadership talent. This study underscores the potential of AI to enhance strategic leadership planning within educational institutions. The results expand our empirical understanding of AI-driven leadership development and offer practical insights for implementing AI-informed strategies in Oman and the broader Gulf region. Full article

With the proliferation of edge computing in IoT and smart security, there is a growing demand for large-scale encrypted traffic anomaly detection. However, the opaque nature of encrypted traffic makes it difficult for traditional detection methods to balance efficiency and accuracy. To address this challenge, this paper proposes FMTF, a Multi-Scale Feature Fusion method based on Federated Learning for encrypted traffic anomaly detection. FMTF constructs graph structures at three scales—spatial, statistical, and content—to comprehensively characterize traffic features. At the spatial scale, communication graphs are constructed based on host-to-host IP interactions, where each node represents the IP address of a host and edges capture the communication relationships between them. The statistical scale builds traffic statistic graphs based on interactions between port numbers, with nodes representing individual ports and edge weights corresponding to the lengths of transmitted packets. At the content scale, byte-level traffic graphs are generated, where nodes represent pairs of bytes extracted from the traffic data, and edges are weighted using pointwise mutual information (PMI) to reflect the statistical association between byte occurrences. To extract and fuse these multi-scale features, FMTF employs the Graph Attention Network (GAT), enhancing the model’s traffic representation capability. Furthermore, to reduce raw-data exposure in distributed edge environments, FMTF integrates a federated learning framework. In this framework, edge devices train models locally based on their multi-scale traffic features and periodically share model parameters with a central server for aggregation, thereby optimizing the global model without exposing raw data. Experimental results demonstrate that FMTF maintains efficient and accurate anomaly detection performance even under limited computing resources, offering a practical and effective solution for encrypted traffic identification and network security protection in edge computing environments. Full article

The Fruit Weight 2.2-like (FWL) gene family, characterized by the conserved PLAC8 domain, plays important roles in plant organ development and metal ion homeostasis. However, the systematic characterization of FWL genes in rice (Oryza sativa) and their involvement in abiotic stress responses remain insufficiently understood. In this study, a genome-wide identification of the FWL gene family in rice was performed, resulting in the identification of nine OsFWL genes, including a previously unreported member, OsFWL9. Phylogenetic analysis of FWL proteins from rice, maize, soybean, and Arabidopsis thaliana classified the family into three distinct subgroups, indicating both conserved and divergent evolutionary relationships. Structural and conserved motif analyses revealed that OsFWL proteins share similar domain architectures, while promoter analysis uncovered abundant cis-acting elements associated with stress responses, phytohormone signaling, and plant growth and development. Expression profiling demonstrated that most OsFWL genes were rapidly induced by drought, high temperature, salt, and arsenic stresses at the seedling stage, suggesting their broad involvement in abiotic stress adaptation. Notably, OsFWL8 exhibited a unique expression pattern, being significantly suppressed under arsenic stress. Functional characterization using CRISPR/Cas9-generated knockout mutants and overexpression lines revealed that OsFWL8 negatively regulates arsenic tolerance in rice. Overexpression of OsFWL8 markedly increased plant sensitivity to arsenic stress. Furthermore, arsenic detoxification-related genes, including OsABCC1 and OsPCS2, were significantly upregulated in fwl8 mutants under arsenic treatment. These results indicate that OsFWL8 may modulate arsenic tolerance by influencing arsenic sequestration and detoxification pathways. Overall, this study provides a comprehensive overview of the FWL gene family in rice and identifies OsFWL8 as a key regulator of arsenic stress response, offering valuable insights for improving rice tolerance to heavy metal stress. Full article

The genus Lasiodiplodia includes numerous plant-pathogenic species whose delimitation is complicated by overlapping morphological traits and limited resolution of common genetic markers. Lasiodiplodia brasiliensis was described as a species closely related to L. theobromae; however, its taxonomic status remains controversial. In this study, we re-evaluated the species boundaries between L. theobromae and L. brasiliensis using an integrative approach that combined multilocus and genome-scale phylogenetic analyses with morphological comparisons. Multilocus phylogenetic analyses based on ITS, tef1-α, tub2, and rpb2 revealed an unresolved relationship between the two taxa. The L. theobromae clade had low bootstrap support, whereas the ancestral node connecting both species had high support. In contrast, genome-scale phylogenetic analysis using hundreds of single-copy orthologous genes strongly supported a single monophyletic clade encompassing isolates assigned to both L. theobromae and L. brasiliensis. Morphological analyses further revealed that conidial dimensions and other diagnostic characteristics largely overlapped between the two taxa, rendering them unreliable criteria for species separation. Considering the combined molecular and morphological evidence, our results support treating L. brasiliensis as a synonym of L. theobromae. Clarifying species boundaries within this group helps stabilize the taxonomy of Lasiodiplodia and provides a reliable foundation for accurate pathogen identification and disease management. Full article