Search Results (8,050)

The spectral variation hypothesis (SVH) posits that the complexity of spectral information in remote sensing imagery can serve as a proxy for regional biodiversity. However, the relationship between spectral diversity (SD) and biodiversity differs for different environmental conditions. Previous SVH studies often overlooked these differences. We utilized species data from field surveys in Inner Mongolia and drone-derived multispectral imagery to establish a quantitative relationship between SD and biodiversity. A geographically weighted regression (GWR) model was used to describe the SD–biodiversity relationship and map the biodiversity indices in different experimental areas in Inner Mongolia, China. Spatial autocorrelation analysis revealed that both SD and biodiversity indices exhibited strong and statistically significant spatial autocorrelation in their distribution patterns. Among all spectral diversity indices, the convex hull area exhibited the best model fit with the Margalef richness index (Margalef), the coefficient of variation showed the strongest predictive performance for species richness (Richness), and the convex hull volume provided the highest explanatory power for Shannon diversity (Shannon). Predictions for Shannon achieved the lowest relative root mean square error (RRMSE = 0.17), indicating the highest predictive accuracy, whereas Richness exhibited systematic underestimation with a higher RRMSE (0.23). Compared to the commonly used linear regression model in SVH studies, the GWR model exhibited a 4.7- to 26.5-fold improvement in goodness-of-fit. Despite the relatively low R² value (≤0.59), the model yields biodiversity predictions that are broadly aligned with field observations. Our approach explicitly considers the spatial heterogeneity of the SD–biodiversity relationship. The GWR model had significantly higher fitting accuracy than the linear regression model, indicating its potential for remote sensing-based biodiversity assessments. Full article

Wild edible fruit plants are integral to the cultural, nutritional, medicinal, and economic practices of Indigenous Isan communities in Roi Et Province, northeastern Thailand, a region characterized by plateau and lowland topography and a tropical monsoon climate. This study aimed to document the diversity, traditional uses, phenology, and conservation status of these species to inform sustainable management and conservation efforts. Field surveys and ethnobotanical interviews with 200 informants (100 men, 100 women; random ages) were conducted across 20 local communities to identify species diversity and usage patterns, while phenological observations and conservation assessments were performed to understand reproductive cycles and species vulnerability between January and December 2023. A total of 68 species from 32 families were recorded, with peak flowering in March–April and fruiting in May–June. Analyses of Species Use Value (0.19–0.48) and Relative Frequency of Citation (0.15–0.44) identified key species with significant roles in food security and traditional medicine. Uvaria rufa had the highest SUV (0.48) and RFC (0.44). Informant consensus on medicinal applications was strong for ailments such as gastrointestinal and lymphatic disorders. Economically important species were also identified, with some contributing notable income through local trade. Conservation proposed one species as Critically Endangered and several others as Vulnerable. The results highlight the need for integrated conservation strategies, including community-based initiatives and recognition of Other Effective area-based Conservation Measures (OECMs), to ensure the preservation of biodiversity, traditional knowledge, and local livelihoods. Full article

The cecal microbiota is essential for intestinal health and performance. This study describes the succession patterns of the cecal microbiota in Japanese quail (Coturnix japonica) until 42 days of age. Sixty quails were raised using standard conditions and fed corn–soybean meal diets. Cecal contents were sampled from five birds weekly from 7 to 42 days of age and submitted to Illumina 16S rRNA sequencing for metabarcoding analysis. Diversity and functional prediction were carried out with QIIME2, PICRUSt2, STAMP and MicrobiomeAnalyst 2.0. Firmicutes increased from 50% at 7 days to more than 80% at 42 days, whereas Bacteroidota decreased from 45% to 12% in the same period. Alpha diversity progressively increased with age, indicating a richer and more balanced microbiota at later ages. Genera such as Bacteroides were predominant in the beginning and later were replaced by Lachnospiraceae, Ruminococcus and Faecalibacterium. These developmental taxonomic features aligned with significant shifts in ten metabolic pathways identified by prediction, revealing a transition from biosynthetic functions to complex carbohydrate metabolism and cell wall biosynthesis. The first seven days are considered a critical window for probiotics intervention, which may favor the establishment of a microbiota that is more stable and beneficial to quail performance. Full article

Disparities in the functional classification of secretory genes among aphid taxa may be attributed to variations in coding sequences and gene expression profiles. However, the driving factors that regulate sequence evolution remain unclear. This study aimed to investigate the differences in coding sequences and expression patterns of secretory genes between the rose grain aphid (Metopolophium dirhodum) and the pea aphid (Acrythosiphon pisum), with a particular focus on their roles in evolutionary adaptations and functional diversity. The study involved the rearing of aphids, RNA extraction, de novo transcriptome assembly, functional annotation, secretory protein prediction, and comparative analysis of coding sequences and expression patterns across various functional categories using bioinformatics tools. The results revealed that metabolic genes exhibited greater coding sequence divergence, indicating the influence of positive selection. Moreover, significant expression divergence was noted among functional categories, particularly in metabolic and genetic information processing genes, which exhibited higher variability. This study enhances our understanding of the molecular mechanisms that contribute to phenotypic and genetic diversity among aphid species. This study elucidates the relationship between variations in coding sequences and differences in gene expression among functional categories, thereby establishing a foundation for future studies on gene evolution in response to environmental pressures. Full article

Grounded in health behavior theory, this study examined patterns of preventive health behavior in a culturally diverse, multilingual region of northern Italy using data from a representative population survey (n = 2090). Preventive behaviors were assessed using the 16-item Good Health Practices (GHP-16) scale. Latent profile analysis (LPA) identified five behavioral profiles, ranging from ‘Globally Low Engagers’ to ‘Comprehensive High Engagers’. Binary logistic regression compared ‘Globally Low Engagers’ to ‘Broadly Moderate Preventers’, examining predictors including gender, age, education, language, chronic disease status, health literacy (HLS-EU-Q16), patient activation (PAM-10), mistrust of health information, living situation, and healthcare employment. The results showed that men, younger adults, individuals with low patient activation, those living alone, and respondents with high mistrust of health information had higher odds of belonging to the low engagement group. Health literacy and language group membership were not significantly associated with the profile membership. Item-level comparisons revealed gender differences in information-seeking, oral hygiene, and dietary behaviors, with men reporting lower engagement. These findings support a segmentation-based understanding of preventive health behavior and highlight the need to address personal capacities and contextual barriers in interventions while challenging assumptions of uniformly higher female health vigilance. Full article

Medical education often emphasizes theoretical knowledge, limiting students’ opportunities to practice history taking, a structured interview that elicits relevant patient information before clinical decision making. Large language models (LLMs) offer novel solutions by generating simulated patient interviews. This study evaluated the educational potential of LLM-generated history-taking dialogues, focusing on clinical validity and diagnostic diversity. Chest pain was chosen as a representative case given its frequent presentation and importance for differential diagnosis. A fine-tuned Gemma-3-27B, specialized for medical interviews, was compared with GPT-4o-mini, a freely accessible LLM, in generating multi-branching history-taking dialogues, with Claude-3.5 Sonnet inferring diagnoses from these dialogues. The dialogues were assessed using a Chest Pain Checklist (CPC) and entropy-based metrics. Gemma-3-27B outperformed GPT-4o-mini, generating significantly more high-quality dialogues (90.7% vs. 76.5%). Gemma-3-27B produced diverse and focused diagnoses, whereas GPT-4o-mini generated broader but less specific patterns. For demographic information, such as age and sex, Gemma-3-27B showed significant shifts in dialogue patterns and diagnoses aligned with real-world epidemiological trends. These findings suggest that LLMs, particularly those fine-tuned for medical tasks, are promising educational tools for generating diverse, clinically valid interview scenarios that enhance clinical reasoning in history taking. Full article

Radio Frequency Identification (RFID) technology is widely applied in various scenarios, including logistics tracking, supply chain management, and target monitoring. In these contexts, the malicious cloning of legitimate tag information can lead to sensitive data leakage and disrupt the normal acquisition of tag information by readers, thereby threatening personal privacy and corporate security and incurring significant economic losses. Although some efforts have been made to detect cloning attacks, the presence of missing tags in RFID systems can obscure cloned ones, resulting in a significant reduction in identification efficiency and accuracy. To address these problems, we propose the block-based cloned tag identification (BCTI) protocol for identifying cloning attacks in the presence of missing tags. First, we introduce a block indicator to sort all tags systematically and design a block mechanism that enables tags to respond repeatedly within a block with minimal time overhead. Then, we design a superposition strategy to further reduce the number of verification times, thereby decreasing the execution overhead. Through an in-depth analysis of potential tag response patterns, we develop a precise method to identify cloning attacks and mitigate interference from missing tags in probabilistic cloning attack scenarios. Moreover, we perform parameter optimization of the BCTI protocol and validate its performance across diverse operational scenarios. Extensive simulation results demonstrate that the BCTI protocol meets the required identification reliability threshold and achieves an average improvement of $24.01\%$ in identification efficiency compared to state-of-the-art solutions. Full article

The goal of phylogeography is to explain how microevolutionary forces shape the gene pool of a lineage into the geography. In this study we have evaluated the amount of genetic variation in 13 populations of Dorymyrmex bicolor distributed in a mountainous region in Central Veracruz, Mexico. To do so, we sequenced fragments from the mitochondrial COI, COII, and nuclear LWRh genes. Segregated sites were found only at the mitochondrial markers, recovering a total of 21 different haplotypes. The nucleotide diversity ranged from 0 to 0.5% at the different sampling sites. Phylogenetic and spatial analyses of molecular variance revealed a weak but significant phylogeographic structure associated with lowland and mountainous zones. Molecular clock analysis suggests that radiation in the mountain area started 7500 years ago, whereas lineage radiation in the lowland started more recently, around 2700 years ago. The phylogeographic structure is incipient, with nests from lowlands more closely related to mountain nests than to other lowland nests, and vice versa. This seems to be consistent with a model of incomplete lineage sorting. The obtained patterns appear to be the result of restricted gene flow mediated by a complex topographic landscape that has been shaped by a dynamic geologic history. Full article

The reliable and economical operation of modern power systems is increasingly complex due to the integration of diverse energy sources and dynamic load patterns. A critical challenge is maintaining the balance between electricity supply and demand within various operational constraints. This study addresses the economic scheduling of generation units using a Mixed Integer Programming (MIP) optimization model. Key constraints considered include reserve requirements, ramp rate limits, and minimum up/down time. Simulations are performed across multiple scenarios, including systems with spinning reserves, responsive demand, renewable energy integration, and energy storage systems. For each scenario, the optimal mix of generation resources is determined to meet a 24 h load forecast while minimizing operating costs. The results show that incorporating demand responsiveness and renewable resources enhances the economic efficiency, reliability, and flexibility of the power system. Full article

News recommendation systems must simultaneously address multiple challenges, including dynamic user interest modeling, nonlinear popularity patterns, and diversity recommendation in cold-start scenarios. We present a Popularity-Aware Dynamic Multi-Perspective Fusion Network (PAD-MPFN) that innovatively integrates three key components: adaptive subspace projection for multi-source interest fusion, logarithmic time-decay factors for popularity bias mitigation, and dynamic gating mechanisms for personalized recommendation weighting. The framework uniquely combines sequential behavior analysis, social graph propagation, and temporal popularity modeling through a unified architecture. Experimental results on the MIND dataset, an open-source version of MSN News, demonstrate that PAD-MPFN outperforms existing methods in terms of recommendation performance and cold-start scenarios while effectively alleviating information overload. This study offers a new solution for dynamic interest modeling and diverse recommendation. Full article

Urban blue-green infrastructure (UBGI) is a comprehensive solution that balances environmental, social, and economic development objectives and has emerged as a critical approach for fostering urban resilience and sustainable development. This paper conducts a systematic bibliometric analysis of 975 academic articles published between 2000 and 2023 in the Web of Science Core Collection, focusing specifically on the ecosystem services associated with UBGI. Employing CiteSpace visualization technology, this study elucidates the major research trends, thematic clusters, and international collaboration patterns shaping this field. The research delves into the diverse range of ecosystem services provided by blue-green infrastructure and analyzes their contributions to urban well-being. Findings indicate that regulatory services—particularly climate regulation, biodiversity enhancement, and water resource management—have become central research foci within the contexts of urban green infrastructure (UGI), urban blue infrastructure (UBI), and UBGI. Co-citation and keyword analyses reveal that nature-based solutions, hybrid green–gray infrastructure, and the application of urban resilience frameworks are gaining increasing scholarly attention. By summarizing the evolutionary trajectory and priority directions of UBGI research, this study provides significant insights for future interdisciplinary research aimed at enhancing the supply of urban environmental ecosystem services. Full article

Accurate classification of breast cancer histopathology images is critical for early diagnosis and treatment planning. Yet, conventional deep learning models face significant challenges under limited annotation scenarios due to their reliance on large-scale labeled datasets. To address this, we propose Dynamic Cross-Scale Swin Transformer (DCS-ST), a robust and efficient framework tailored for histopathology image classification with scarce annotations. Specifically, DCS-ST integrates a dynamic window predictor and a cross-scale attention module to enhance multi-scale feature representation and interaction while employing a semi-supervised learning strategy based on pseudo-labeling and denoising to exploit unlabeled data effectively. This design enables the model to adaptively attend to diverse tissue structures and pathological patterns while maintaining classification stability. Extensive experiments on three public datasets—BreakHis, Mini-DDSM, and ICIAR2018—demonstrate that DCS-ST consistently outperforms existing state-of-the-art methods across various magnifications and classification tasks, achieving superior quantitative results and reliable visual classification. Furthermore, empirical evaluations validate its strong generalization capability and practical potential for real-world weakly-supervised medical image analysis. Full article

As cyberattacks continue to rise in frequency and sophistication, extracting actionable Cyber Threat Intelligence (CTI) from diverse online sources has become critical for proactive threat detection and defense. However, accurately identifying complex entities from lengthy and heterogeneous threat reports remains challenging due to long-range dependencies and domain-specific terminology. To address this, we propose XLNet-CRF, a hybrid framework that combines permutation-based language modeling with structured prediction using Conditional Random Fields (CRF) to enhance Named Entity Recognition (NER) in cybersecurity contexts. XLNet-CRF directly addresses key challenges in CTI-NER by modeling bidirectional dependencies and capturing non-contiguous semantic patterns more effectively than traditional approaches. Comprehensive evaluations on two benchmark cybersecurity corpora validate the efficacy of our approach. On the CTI-Reports dataset, XLNet-CRF achieves a precision of 97.41% and an F1-score of 97.43%; on MalwareTextDB, it attains a precision of 85.33% and an F1-score of 88.65%—significantly surpassing strong BERT-based baselines in both accuracy and robustness. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

Ice accumulation on exposed surfaces presents substantial economic and safety challenges across various industries. To overcome limitations associated with traditional anti-icing methods, such as the use of nanoparticles, this study introduces a novel and facile approach for fabricating superhydrophobic and anti-icing microstructures using cost-effective LCD 3D printing technology. The influence of diverse pillar geometries, including square, cylindrical, hexagonal, and truncated conical forms, was analyzed to assess their effects on the hydrophobic and anti-icing/icephobic performance in terms of wettability, ice adhesion strength, and icing delay time. The role of microstructure topography was further investigated through cylindrical patterns with varying geometric parameters to identify optimal designs for enhancing hydrophobic and icephobic characteristics. Furthermore, the effectiveness of surface functionalization using a low surface energy material was evaluated. Our findings demonstrate that the synergistic combination of tailored microscale geometries and surface functionalization significantly enhances anti-icing performance with reliable repeatability, achieving ice adhesion of 13.9 and 17.9 kPa for square and cylindrical pillars, respectively. Critically, this nanoparticle-free 3D printing and low surface energy treatment method offers a scalable and efficient route for producing high-performance hydrophobic/icephobic surfaces, opening promising avenues for applications in sectors where robust anti-icing capabilities are crucial, such as renewable energy and transportation. Full article