Search Results (4,353)

This prospective pilot study aimed to develop and evaluate a VR–based screening tool for assessing distance fusional vergence amplitude in healthy orthotropic young adults aged 18 to 30 years. A VR–based balloon-hitting game was used to measure hitting deviation angles and total vergence amplitudes under five conditions: control (0 prism diopter [PD]), inward image rotation for 10 and 20 PD (negative fusional vergence [NFV] 10/20 groups), and outward image rotation for 10 and 20 PD (positive fusional vergence [PFV] 10/20 groups). Of the 20 subjects recruited, one was excluded due to esotropia, leaving 19 participants (mean age: 22.2 ± 2.2 years; 13 wore glasses and 3 were female). In the control group, the mean hitting deviation was 0.65 ± 0.25 PD. The PFV 10 PD group showed similar deviation (0.67 ± 0.25 PD, p = 0.67), while the PFV 20 PD group had a significant increase (1.71 ± 2.0 PD, p = 0.04). NFV groups demonstrated greater deviations (NFV 10 PD: 3.40 ± 2.05 PD; NFV 20 PD: 9.9 ± 2.40 PD, both p < 0.01). Total vergence amplitudes were 8.65, 16.48, 6.60, and 10.05 PD for PFV 10, PFV 20, NFV 10, and NFV 20 PD, respectively. The VR–based tool enables standardized, efficient assessment of fusional vergence and shows promise for large-scale screening. Full article

In Chinese character learning, distinguishing similar characters is challenging for learners regardless of their proficiency. This is due to the complex orthography (visual word form) linking symbol, pronunciation, and meaning. Multimedia learning is a promising approach to implement learning strategies for Chinese characters. However, the availability of multimodal resources specifically designed for distinguishing similar Chinese characters is limited. With the advanced development of generative artificial intelligence (GenAI), we propose a practical framework for constructing multimodal resources, enabling flexible and semi-automated resource generation for Chinese character learning. The framework first constructs image illustrations due to their broad applicability across various learning contexts. After that, other four types of multimodal resources implementing learning strategies for similar character learning can be developed in the future, including summary slide, micro-video, self-test question, and basic information. An experiment was conducted with one group receiving the constructed multimodal resources and the other receiving the traditional text-based resources for similar character learning. We explored the participants’ learning performance, motivation, satisfaction, and attitudes. The results showed that the multimodal resources significantly improved performance on distinguishing simple characters, but were not suitable for non-homophones, i.e., visually similar characters with different pronunciations. Micro-videos introducing character formation knowledge significantly increased students’ learning motivation for character evolution and calligraphy. Overall, the resources received high satisfaction, especially for micro-videos and image illustrations. The findings regarding the effective design of multimodal resources for implementing learning strategies (e.g., using visual mnemonics, character formation knowledge, and group reviews) and implications for different Chinese character types are also discussed. Full article

Reversible data hiding in encrypted point clouds presents unique challenges due to their unstructured geometry, absence of mesh connectivity, and high sensitivity to spatial perturbations. In this paper, we propose an efficient and secure reversible data hiding framework for encrypted point clouds, incorporating KD tree-based path planning, adaptive multi-MSB prediction, and a dual-model design. To establish a consistent spatial traversal order, a Hamiltonian path is constructed using a KD tree-accelerated nearest-neighbor algorithm. Guided by this path, a prediction-driven embedding strategy dynamically adjusts the number of most significant bits (MSBs) embedded per point, balancing capacity and reversibility while generating a label map that reflects local predictability. The label map is then compressed using Huffman coding to reduce the auxiliary overhead. For enhanced security and lossless recovery, the encrypted point cloud is divided into two complementary shares through a lightweight XOR-based (2, 2) secret sharing scheme. The Huffman tree and compressed label map are distributed across both encrypted shares, ensuring that neither share alone can reveal the original point cloud or the embedded message. Experimental evaluations on diverse 3D models demonstrate that the proposed method achieves near-optimal embedding rates, perfect reconstruction of the original model, and significant obfuscation of the geometric structure. These results confirm the practicality and robustness of the proposed framework for scenarios involving secure 3D point cloud transmission, storage, and sharing. Full article

In this paper, a novel Tunneling-Drift-Diffusion Field-Effect Transistor (TDDFET) based on the combination of the quantum tunneling and conventional drift-diffusion mechanisms is proposed for the design of ternary logic circuits. The working principle of the TDDFET is analyzed in detail. Then, the device is packaged as a “black box” based on the table lookup method and further embedded into the HSPICE platform using the Verilog-A language. The basic unit circuits, such as the Standard Ternary Inverter (STI), Negative Ternary Inverter (NTI), Positive Ternary Inverter (PTI), Ternary NAND gate (T-NAND), and Ternary NOR gate (T-NOR), are designed. In addition, based on the designed unit circuits, the combinational logic circuits, such as the Ternary Encoder (T-Encoder), Ternary Decoder (T-Decoder), and Ternary Half Adder (T-HA), and the sequential logic circuits, such as the Ternary D-Latch and edge-triggered Ternary D Flip-Flop (T-DFF), are built, which has important significance for the subsequent investigation of ternary logic circuits. Full article

Target-oriented opinion words extraction (TOWE) is a novel subtask of aspect-based sentiment analysis (ABSA), which aims to extract opinion words corresponding to a given opinion target within a sentence. In recent years, neural networks have been widely used to solve this problem and have achieved competitive results. However, when faced with complex and long sentences, the existing methods struggle to accurately identify the semantic relationships between distant opinion targets and opinion words. This is primarily because they rely on literal distance, rather than semantic distance, to model the local context or opinion span of the opinion target. To address this issue, we propose a neural network model called DTOWE, which comprises (1) a global module using Inward-LSTM and Outward-LSTM to capture general sentence-level context, and (2) a local module that employs BiLSTM combined with DT-LCF to focus on target-specific opinion spans. DT-LCF is implemented in two ways: DT-LCF-Mask, which uses a binary mask to zero out non-local context beyond a dependency tree distance threshold, α, and DT-LCF-weight, which applies a dynamic weighted decay to downweigh distant context based on semantic distance. These mechanisms leverage dependency tree structures to measure semantic proximity, reducing the impact of irrelevant words and enhancing the accuracy of opinion span detection. Extensive experiments on four benchmark datasets demonstrate that DTOWE outperforms state-of-the-art models. Specifically, DT-LCF-Weight achieves F1-scores of 73.62% (14lap), 82.24% (14res), 75.35% (15res), and 83.83% (16res), with improvements of 2.63% to 3.44% over the previous state-of-the-art (SOTA) model, IOG. Ablation studies confirm that the dependency tree-based distance measurement and DT-LCF mechanism are critical to the model’s effectiveness, validating their ability to handle complex sentences and capture semantic dependencies between targets and opinion words. Full article

This study investigates sedimentation dynamics and microstructural evolution of silty clay and mucky sediments from the immersed tube tunnel trench of the Shunde Tanzhou Waterway. Experiments examined different initial unit weights (11.5–12.6 kN/m³) and heights (10–60 cm) through sedimentation tests (N = 30, representing five heights × three unit weights × two soil types) and scanning electron microscopy (SEM) imaging. Results identified two sedimentation patterns: consolidation (inverse “S” curve) and hindered (three-stage) types. Key findings reveal that silty clay exhibits height-dependent transition between patterns (critical height = 30 cm at γ = 12.6 kN/m³). Mucky soil demonstrates stable hindered settlement across conditions (rate = 0.09 ± 0.01 cm/min at γ = 12.0 kN/m³). Moisture distribution analysis reveals that unstable structures in low-unit-weight slurries exhibit slow drainage and steady moisture content changes. Microstructural analysis uncovered height-dependent porosity increases and pore complexity in mucky soils, alongside reduced honeycomb-like cavities and enhanced particle aggregation in silty clay under lower unit weights. These results provide novel insights into the interplay between initial slurry conditions and sedimentation behavior, offering a theoretical foundation for optimizing dredging strategies and ensuring long-term sediment stability in immersed tube tunnel projects. Full article

In landslide susceptibility evaluation, scientific sampling minimizes potential societal losses and enhances the efficiency of disaster prevention and mitigation. However, traditional sampling methods, such as selecting landslide and non-landslide samples based on equal proportions or area proportions, overlook the different societal losses resulting from landslide omission and misreporting, and the potential societal losses faced by their evaluation results are often not minimized. Therefore, this study proposes a sampling method that takes potential societal losses into account and uses the Landslide Misjudgment Potential Societal Loss Evaluation Index (LMPSLEI) to quantify the total potential social losses in the area due to landslide omission and misreporting. The LMPSLEI is minimized by optimizing the sample ratio, thus minimizing the potential societal losses faced by the evaluation results and enhancing the scientific basis of disaster prevention and mitigation efforts. This study takes the Wenchuan earthquake area as the research region, selects 13 conditional factors and employs two models—Random Forest (RF) and Convolutional Neural Network (CNN)—to conduct case studies. We derive the recommended sample ratio based on the formula, hypothesizing that the LMPSLEI will be minimized under this ratio. The results show that the sample ratio for LMPSLEI minimization in the RF model is similar to the recommended sample ratio, while the sample ratio for LMPSLEI minimization in the CNN model is slightly higher than the recommended sample ratio. The recommended sample ratio can achieve the minimum of LMPSLEI or reach a lower value under different societal losses weights of landslide omission/misreporting, and thus it can be used as a preliminary choice of sampling for landslide susceptibility evaluation considering the potential societal losses. Full article

This study investigated the anti-obesity effects of a homogenized, viscous vegetable (VV) mixture prepared from mucilaginous vegetables, with a focus on modulating hepatic lipid metabolism and gut microbiota composition in mice fed with a high-fat (HF) diet. The VV mixture was formulated by blending freeze-dried powders of ten mucilaginous vegetables, classified as moderately thick using a line-spread test and extremely thick according to the IDDSI framework in a 1:9 ratio (VV mixture: water, w/w). Six-week-old male C57BL/6 mice were fed control or HF diets, with or without 10% VV mixture for 8 weeks (n = 7 per group). The HF diet induced significant weight gain, adipose tissue accumulation, hepatic steatosis, and inflammation. The HF diet also significantly reduced hepatic ACO1, CPT1 mRNA expression, and α-diversity with distinct fecal microbiota profiles. On the other hand, VV mixture supplementation reduced serum TC, LDL-C levels and NAFLD scores. VV mixture supplementation also increased hepatic ACO1 and CPT1 mRNA expression, enhanced α-diversity, and enriched SCFA-producing bacteria, particularly the Lachnospiraceae NK4A136 group. In conclusion, the VV mixture attenuated HF diet-induced obesity, possibly through its high viscosity–mediated effects on hepatic fatty acid oxidation and gut microbiota modulation. Full article

The remarkable success of autoregressive Large Language Models (LLMs) is predicated on the causal attention mechanism, which enforces a static and rigid form of informational asymmetry by permitting each token to attend only to its predecessors. While effective for sequential generation, this hard-coded unidirectional constraint fails to capture the more complex, dynamic, and nonlinear dependencies inherent in sophisticated reasoning, logical inference, and discourse. In this paper, we challenge this paradigm by introducing Dynamic Asymmetric Attention (DAA), a novel mechanism that replaces the static causal mask with a learnable context-aware guidance module. DAA dynamically generates a continuous-valued attention bias for each query–key pair, effectively learning a “soft” information flow policy that guides rather than merely restricts the model’s focus. Trained end-to-end, our DAA-augmented models demonstrate significant performance gains on a suite of benchmarks, including improvements in perplexity on language modeling and notable accuracy boosts on complex reasoning tasks such as code generation (HumanEval) and mathematical problem-solving (GSM8k). Crucially, DAA provides a new lens for model interpretability. By visualizing the learned asymmetric attention patterns, it is possible to uncover the implicit information flow graphs that the model constructs during inference. These visualizations reveal how the model dynamically prioritizes evidence and forges directed logical links in chain-of-thought reasoning, making its decision-making process more transparent. Our work demonstrates that transitioning from a static hard-wired asymmetry to a learned and dynamic one not only enhances model performance but also paves the way for a new class of more capable and profoundly more explainable LLMs. Full article

Background: Glaesserella parasuis (GPS) is a conditional pathogen that colonizes the upper respiratory tract in pigs and causes Glässer’s disease, resulting in high morbidity and mortality in piglets. GPS infection increases the vascular endothelial permeability, but the mechanism has not been fully elucidated. Luteolin (Lut) is a naturally occurring flavonoid found in plants such as vegetables, herbs, and fruits, but its potential to treat the increased vascular endothelial permeability caused by GPS infection has not been evaluated. Results: This study revealed that GPS infection induces increased vascular endothelial permeability in porcine iliac artery endothelial cells (PIECs) by increasing the gene expressions of tumor necrosis factor (TNF), interleukin 6 (IL-6), IL-8, and IL-1β, and by regulating F-actin cytoskeleton reorganization. Mechanistically, GPS infection or Cluster of differentiation 44 (CD44) overexpression significantly increased the expressions of vascular-endothelial-permeability-related proteins (CD44; vascular endothelial growth factor (VEGFA); matrixmetalloProteinase-3 (MMP-3); MMP-9; and SRC proto-oncogene, non-receptor tyrosine kinase (c-Src)) and increased the vascular endothelial permeability; these changes were alleviated by a Lut treatment or CD44 silencing in the PIECs. Conclusions: This study comprehensively illustrates the potential targets and molecular mechanism of Lut in alleviating the GPS-induced increase in vascular endothelial permeability. The CD44 pathway and Lut may be an effective target and antibiotic alternative, respectively, to prevent the increased vascular endothelial permeability caused by GPS. Full article

Fusarium oxysporum f. sp. cucumerium, a resilient saprophytic fungus, poses a significant risk to cucumber crops. The research investigated the suppressive impact of Bacillus velezensis dhm2 on this pathogen and the synergistic performance of its crude lipopeptide extract with synthetic fungicides. Strain dhm2 inhibited the pathogen by 52.27% in confrontation culture. Its fermentation supernatant showed peak activity at 4 h bacterial age and 60 h fermentation duration, while the crude lipopeptide extract had an EC₅₀ of 9.99 g L⁻¹. Among the six chemical fungicides, prochloraz exhibited the highest toxicity, with an EC₅₀ value of 0.03 μg mL⁻¹. In all mixed combinations of the crude lipopeptide extract and chemical fungicides, there existed synergistic mixing ratios, particularly with difenoconazole (volume ratio 7:3, synergistic ratio 5.88) and propiconazole (7:3, 3.41), as confirmed by Wadley tests. Pot experiments revealed that the combined use of the crude lipopeptide extract and difenoconazole controlled cucumber Fusarium wilt by 80.95%. The mixture showed the highest SOD (315.76 U g⁻¹ FW min⁻¹), POD (281.63 U g⁻¹ FW min⁻¹), and CAT (23.39 U g⁻¹ FW min⁻¹), with increases over single treatments. This study provides an eco-friendly strategy for managing cucumber wilt, advocating reduced fungicide use via synergistic formulations. Full article

Fermented wheat protein (FWP) has a high protein content and is beneficial to intestinal health, making it an ideal alternative to fishmeal (FM). This study evaluated the effects of replacing FM with FWP on growth performance, nutrient digestibility, and gut health in weaned piglets. A total of 144 weaned piglets (28 days old) were randomly divided into three dietary treatments with 48 piglets per treatment for a duration of 28 days. The treatments included a control diet containing 3% FM (CON group), a diet with 1.5% FM and 1.5% FWP (50% FWP replacement), and a diet with 3% FWP (100% FWP replacement). The results showed that replacing FM with FWP did not significantly affect average daily gain, average daily feed intake, or the feed conversion ratio but significantly reduced diarrhea rates (p < 0.01). The 50% and 100% FWP replacement significantly improved the digestibility of crude protein and acid detergent fiber, respectively (p < 0.05). The 100% FWP replacement significantly improved the activity of trypsin and chymotrypsin in the duodenum and jejunum (p < 0.05). Additionally, the 100% FWP replacement significantly enhanced serum total antioxidant capacity, glutathione peroxidase, and immunoglobulin A contents (p < 0.05). The 100% FWP replacement significantly increased villus height in the jejunum (p < 0.01). Moreover, the 100% FWP replacement significantly increased the expression of intestinal-barrier-related genes Zonula Occludens-1 and occludin (p < 0.01) and decreased the expression of inflammation-related genes (p < 0.01). Overall, FWP effectively reduces diarrhea rates, improves gut structure, enhances digestion, and boosts antioxidant and immune functions, particularly at a high replacement ratio (100%). Therefore, FWP can be considered an effective alternative to FM. Full article

Subway train operators face the risk of cumulative cognitive stress due to factors such as visual fatigue from prolonged high-speed tunnel driving, irregular shift patterns, and the monotony of automated operations. This can lead to cognitive decline and human error accidents. Current monitoring of cognitive stress risk predominantly relies on single-modal methods, which are susceptible to environmental interference and offer limited accuracy. This study proposes an intelligent multimodal framework for cognitive stress monitoring by leveraging the symmetry principles in physiological and behavioral manifestations. The symmetry of photoplethysmography (PPG) waveforms and the bilateral symmetry of head movements serve as critical indicators reflecting autonomic nervous system homeostasis and cognitive load. By integrating these symmetry-based features, this study constructs a spatiotemporal dynamic feature set through fusing physiological signals such as PPG and galvanic skin response (GSR) with head and facial behavioral features. Furthermore, leveraging deep learning techniques, a hybrid PSO-CNN-GRU-Attention model is developed. Within this model, the Particle Swarm Optimization (PSO) algorithm dynamically adjusts hyperparameters, and an attention mechanism is introduced to weight multimodal features, enabling precise assessment of cognitive stress states. Experiments were conducted using a full-scale subway driving simulator, collecting data from 50 operators to validate the model’s feasibility. Results demonstrate that the complementary nature of multimodal physiological signals and behavioral features effectively overcomes the limitations of single-modal data, yielding significantly superior model performance. The PSO-CNN-GRU-Attention model achieved a predictive coefficient of determination (R²) of 0.89029 and a mean squared error (MSE) of 0.00461, outperforming the traditional BiLSTM model by approximately 22%. This research provides a high-accuracy, non-invasive solution for detecting cognitive stress in subway operators, offering a scientific basis for occupational health management and the formulation of safe driving intervention strategies. Full article

The Amazon River Basin (ARB) experienced an extreme drought from summer 2023 to spring 2024, driven by complex interactions among multiple climatic and environmental factors. A detailed investigation into this drought is crucial in understanding the entire process of the drought. Here, we employ drought indices derived from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GFO), and Swarm missions to reconstruct the drought’s progression, combined with reanalysis datasets and extreme-climate indices to analyze atmospheric and hydrological mechanisms. Our findings reveal a six-month drought from September 2023, reaching a drought peak of −1.29 and a drought severity of −5.62, with its epicenter migrating systematically from the northwestern to southeastern basin, spatially mirroring the 2015–2016 extreme drought pattern. Reduced precipitation and abnormal warming were the direct causes, which were closely linked to the 2023 El Niño event. This event disrupted atmospheric vertical movements. These changes led to abnormally strong sinking motions over the basin, which interacted synergistically with anomalies in land cover types caused by deforestation, triggering this extreme drought. This study provides spatiotemporal drought diagnostics valuable for hydrological forecasting and climate adaptation planning. Full article

Grassland ecosystems in arid regions increasingly experience resilience loss due to intensifying climatic variability. However, the limited interpretability of conventional machine learning models constrains our understanding of underlying ecological drivers. This study constructs an integrative framework that combines temporal autocorrelation (TAC) metrics with explainable machine learning, employing Random Forest and SHAP (SHapley Additive exPlanations) analysis. Time series of satellite-derived vegetation indices from MODIS (2001–2023), particularly the kernel Normalized Difference Vegetation Index (KNDVI), support the generation of TAC and its trend-based derivative δTAC. The framework assesses ecosystem resilience across seven representative grassland types in Xinjiang, capturing diverse responses to climate variability and vegetation dynamics. Results reveal pronounced spatial heterogeneity: resilience declines in radiation-stressed arid zones, while hydrothermally stable regions maintain stronger recovery capacity. Key drivers include temperature variability and vegetation dynamics, with divergent effects among grassland types. Meadow and Typical Steppe exhibit higher resilience under stable hydrothermal regimes, whereas desert and alpine systems show greater sensitivity to warming and climatic fluctuations. This framework enhances diagnostic transparency and ecological insight, offering a spatially explicit, data-driven tool for resilience monitoring. The findings support the formulation of targeted adaptation strategies and sustainable grassland management in response to ongoing climate change. Full article