Search Results (2,950)

The avalanche speed is an important indicator for measuring the intensity of avalanches, and its measurement method is relatively complex. In practical engineering, empirical formulas based on statistics are usually adopted. However, research on the applicability of existing calculation methods in different regions is still insufficient, and further verification and improvement are urgently needed. Based on the integrated space–air–ground field survey data, this study uses RAMMS::AVALANCHE to conduct dynamic numerical simulations of 78 avalanche events in the Qiet’ akesu Gully of the Wenquan to Horgos transportation corridor in the Western Tianshan Mountains during the winter of 2023–2024, analyses the avalanche movement process, and compares the calculation results of the numerical tests of avalanche movement speed with empirical formulas. The results indicate that the velocities calculated using Formula A and Formula B are generally overestimated, approaching approximately 1.5 times the reference value. The mean absolute percentage error of Formula A (19.46%) is lower than that of Formula B (48.27%). In contrast, Formula C exhibits a significantly lower mean absolute percentage error (8.42%) compared with the other two methods, and its results remain stably around one-half of the reference value. Based on these findings, a comprehensive estimation strategy is proposed: twice the value calculated by Formula C is adopted as the primary reference, while two-thirds of the value from Formula A is taken into consideration, and the larger of the two is selected as the final estimated velocity. This strategy ensures the robustness of the results while effectively avoiding the potential overestimation or underestimation associated with reliance on a single empirical formula. This study provides a scientific basis for highway route selection and the placement of avalanche mitigation measures in high-altitude mountainous areas, and offers technical support for the construction and operational safety of infrastructure along the G219 Wenquan–Horgos transportation corridor. Full article

Achieving comprehensive improvements in the drying rate (DR) and the quality after drying of agricultural products is a major goal in the field of drying. To further shorten the drying time while improving product quality, this study introduced a Convolutional Neural Network (CNN) and MultiHead Attention (MHA) to enhance the prediction accuracy of the Long Short-Term Memory (LSTM) network regarding the properties of dried samples. These properties included DR, shrinkage rate (SR), and total color difference (ΔE). The CNN-LSTM-MHA network was proposed, developing a novel hot-air drying (HAD) scenario utilizing an intelligent temperature control system based on the real dynamics of material properties. The results of drying experiments with temperature-sensitive yuba showed that the CNN-LSTM-MHA network’s predictive accuracy was better than that of other networks, as evidenced by its coefficient of determination (R²: 0.9855–0.9999), root mean square error (RMSE: 0.0001–0.0099), and mean absolute error (MAE: 0.0001–0.0120). Comparative analysis with fixed-temperature drying indicated that CNN-LSTM-MHA-controlled drying significantly reduced drying time and enhanced the SR, color, rehydration ratio (RR), texture, protein content, fat content, and microstructure of yuba. Overall, the findings highlight the potential of CNN-LSTM-MHA-based intelligent drying as a viable strategy for yuba stick processing, providing insights for other food drying applications. Full article

Subsurface deposition determines whether soils, aquifers, or ocean sediment represent a sink or temporary reservoir for microplastics. Deposition is generally studied by applying the Smoluchowski–Levich equation to determine a particle’s sticking efficiency, which relates the number of particles filtered by sediment to the probability of attachment occurring from an interaction between particles and sediment. Sticking efficiency is typically measured using column experiments or estimated from theory using the Interaction Force Boundary Layer (IFBL) model. However, there is generally a large discrepancy (orders of magnitude) between the values predicted from IFBL theory and the experimental column measurements. One way to bridge this gap is to directly measure a microparticle’s interaction forces using Atomic Force Microscopy (AFM). Herein, an AFM method is presented to measure sticking efficiency for a model polystyrene microparticle (2 μm) on a model geomaterial surface (glass or quartz) in environmentally relevant, synthetic freshwaters of varying ionic strength (de-ionized water, soft water, hard water). These data, collected over nanometer length scales, are compared to sticking efficiencies determined through traditional approaches. Force measurement results show that AFM can detect extremely low sticking efficiencies, surpassing the sensitivity of column studies. These data also demonstrate that the 75th to 95th percentile, rather than the mean or median force values, provides a better approximation to values measured in model column experiments or field settings. This variability of the methods provides insight into the fundamental mechanics of microplastic deposition and suggests AFM is isolating the physicochemical interactions, while column experiments also include physical interactions like straining. Advantages of AFM over traditional column/field experiments include high throughput, small volumes, and speed of data collection. For example, at a ramp rate of 1 Hz, 60 sticking efficiency measurements could be made in only a minute. Compared to column or field experiments, the AFM requires much less liquid (μL volume) making it effortless to examine the impact of solution chemistry (temperature, pH, ionic strength, valency of dissolved ions, presence of organics, etc.). Potential limitations of this AFM approach are presented alongside possible solutions (e.g., baseline correction, numerical integration). If these challenges are successfully addressed, then AFM would provide a completely new approach to help elucidate which subsurface minerals represent a sink or temporary storage site for microparticles on their journey from terrestrial to oceanic environments. Full article

Direction of arrival (DOA) estimation is a fundamental problem in array signal processing, with extensive applications in radar, communications, sonar, and other fields. Traditional DOA estimation methods, such as MUSIC and ESPRIT, rely on eigenvalue decomposition or spectral peak search, which suffer from high computational complexity and performance degradation under conditions of low signal-to-noise ratio (SNR), coherent signals, and array imperfections. Cylindrical arrays offer unique advantages for omnidirectional sensing due to their circular structure and three-dimensional coverage capability; however, their nonlinear array manifold increases the difficulty of estimation. This paper proposes a circular-attention residual network (CA-ResNet) for DOA estimation using uniform cylindrical arrays. The proposed approach achieves high accuracy and robust angle estimation through phase difference feature extraction, a multi-scale residual network, an attention mechanism, and a joint output module. Simulation results demonstrate that the proposed CA-ResNet method delivers superior performance under challenging scenarios, including low SNR (−10 dB), a small number of snapshots (L = 5), and multiple sources (1 to 4 signal sources). The corresponding root mean square errors (RMSE) are 0.21°, 0.45°, and below 1.5°, respectively, significantly outperforming traditional methods like MUSIC and ESPRIT, as well as existing deep learning models (e.g., ResNet, CNN, MLP). Furthermore, the algorithm exhibits low computational complexity and a small parameter size, highlighting its strong potential for practical engineering applications and robustness. Full article

This study addresses the challenge of automating cocoa pod ripeness classification from drone imagery through a comprehensive and statistically rigorous investigation conducted on data collected from Ghanaian cocoa fields. We perform a direct comparison by subjecting a curated set of seven deep learning models to an identical, advanced algorithmic framework. This pipeline incorporates high-resolution ($384\times 384$) imagery, aggressive TrivialAugmentWide data augmentation, a weighted loss function with label smoothing, a unified two-stage fine-tuning strategy, and validation with Test Time Augmentation (TTA). To ensure statistical robustness, all experiments were repeated three times using different random seeds. Under these demanding experimental conditions, modern architectures demonstrated strong and consistent performance on this dataset: the Swin Transformer achieved the highest mean accuracy ($79.27\%\pm 0.56\%$), followed closely by ConvNeXt-Base ($79.21\%\pm 0.13\%$). In contrast, classic architectures such as ResNet-101 ($55.86\%\pm 4.01\%$) and ResNet-50 ($64.32\%\pm 0.94\%$) showed substantially reduced performance. A paired t-test confirmed that these differences are statistically significant ($p<0.05$). These results suggest that, within the evaluated setting, modern CNN- and transformer-based architectures exhibit greater robustness under challenging, statistically validated conditions, indicating their potential suitability for drone-based agricultural monitoring tasks. Full article

Cucumber downy mildew, angular leaf spot, and powdery mildew represent three predominant fungal diseases that substantially compromise cucumber yield and quality. To address the challenges posed by the irregular morphology, prominent multi-scale characteristics, and ambiguous lesion boundaries of cucumber foliar diseases in complex field environments—which often lead to insufficient detection accuracy—along with the existing models’ difficulty in balancing high precision with lightweight deployment, this study presents YOLOv11n-DSU (a lightweight hierarchical detection model engineered using the YOLOv11n architecture). The proposed model integrates three key enhancements: deformable convolution (DEConv) for optimized feature extraction from irregular lesions, a spatial and channel-wise attention (SCSA) mechanism for adaptive feature refinement, and a Unified Intersection over Union (Unified-IoU) loss function to improve localization accuracy. Experimental evaluations demonstrate substantial performance gains, with mean Average Precision at 50% IoU threshold (mAP₅₀) and mAP_50–95 increasing by 7.9 and 10.9 percentage points, respectively, and precision and recall improving by 6.1 and 10.0 percentage points. Moreover, the computational complexity is markedly reduced to 5.8 Giga Floating Point Operations (GFLOPs). Successful deployment on an embedded platform confirms the model’s practical viability, exhibiting robust real-time inference capabilities and portability. This work provides an accurate and efficient solution for automated disease grading in field conditions, enabling real-time and precise severity classification, and offers significant potential for advancing precision plant protection and smart agricultural systems. Full article

Landslides are among the most prevalent geological hazards worldwide, posing severe threats to public safety due to their sudden onset and destructive potential. The rapid and accurate automated segmentation of landslide areas is a critical task for enhancing capabilities in disaster risk assessment, emergency response, and post-disaster management. However, existing deep learning models for landslide segmentation predominantly rely on unimodal remote sensing imagery. In complex Karst landscapes characterized by dense vegetation and severe shadow interference, the optical features of landslides are difficult to extract effectively, thereby significantly limiting recognition accuracy. Therefore, synergistically utilizing multimodal data while mitigating information redundancy and noise interference has emerged as a core challenge in this field. To address this challenge, this paper proposes a Triple-Stream Guided Enhancement and Fusion Network (TriGEFNet), designed to efficiently fuse three data sources: RGB imagery, Vegetation Indices (VI), and Slope. The model incorporates an adaptive guidance mechanism within the encoder. This mechanism leverages the terrain constraints provided by slope to compensate for the information loss within optical imagery under shadowing conditions. Simultaneously, it integrates the sensitivity of VIs to surface destruction to collectively calibrate and enhance RGB features, thereby extracting fused features that are highly responsive to landslides. Subsequently, gated skip connections in the decoder refine these features, ensuring the optimal combination of deep semantic information with critical boundary details, thus achieving deep synergy among multimodal features. A systematic performance evaluation of the proposed model was conducted on the self-constructed Zunyi dataset and two publicly available datasets. Experimental results demonstrate that TriGEFNet achieved mean Intersection over Union (mIoU) scores of 86.27% on the Zunyi dataset, 80.26% on the L4S dataset, and 89.53% on the Bijie dataset, respectively. Compared to the multimodal baseline model, TriGEFNet achieved significant improvements, with maximum gains of 7.68% in Recall and 4.37% in F1-score across the three datasets. This study not only presents a novel and effective paradigm for multimodal remote sensing data fusion but also provides a forward-looking solution for constructing more robust and precise intelligent systems for landslide monitoring and assessment. Full article

Vitamin D deficiency is increasingly recognized as a systemic factor influencing retinal health through inflammatory, neuroprotective, and vasculotropic pathways. Evidence regarding early retinal alterations in otherwise healthy adults remains limited. This cross-sectional study evaluated 120 eyes from 60 healthy adults stratified by serum 25(OH)D levels into <30 ng/mL (n = 60) and ≥30 ng/mL (n = 60). All subjects underwent optical coherence tomography (OCT), OCT angiography (OCTA), visual field testing, and contrast sensitivity assessment. Central macular thickness (CMT), ganglion cell complex (GCC) thickness, and perfusion density in the superficial and deep capillary plexuses (SCP, DCP) were compared between groups. Vitamin-D-insufficient eyes showed significantly reduced CMT (267.66 ± 13.31 µm vs. 274.69 ± 14.96 µm; p = 0.035). GCC thinning was significant only in the inner inferior nasal sector (70.7 ± 13.14 µm vs. 76.45 ± 12.12 µm; p = 0.030), whereas other GCC sectors were comparable between groups. Perfusion density was lower in the DCP across whole, inner, and outer regions (all p < 0.001) and in the SCP inner (p = 0.027) and outer (p = 0.009) regions, while whole SCP did not differ (p = 0.065). FAZ area was numerically larger in vitamin-D-insufficient eyes but was not statistically different (p = 0.168). Functionally, retinal sensitivity decline was greater in vitamin-D-insufficient eyes (−2.89 ± 1.29 dB vs. −2.16 ± 1.04 dB; p = 0.003), and mean central sensitivity was lower (p = 0.010), whereas contrast sensitivity did not differ between groups. Serum vitamin D levels < 30 ng/mL are associated with early, subclinical, structural and microvascular retinal alterations in healthy adults, supporting a potential role of hypovitaminosis D as a modifier of retinal integrity. Full article

Accurate detection and counting of oat ears are essential for yield estimation but remain challenging in complex field environments due to occlusion, significant scale variation, and fluctuating lighting. The aim of this study is to develop a high-precision detection and counting model to address these challenges. The adopted methodology was an improved YOLOv8n model, named YOLOv8n-DSP. To address significant scale variation, a Diverse Branch Block (DBB) was introduced into the backbone to enhance multi-scale feature representation. For improved detection of small, dense oat ears, the neck was augmented with a Spatial and Channel Synergistic Attention (SCSA) mechanism to strengthen discriminative feature extraction. Furthermore, to refine localization among overlapping oat ears, the PIoUv2 loss function was employed for bounding box regression. The main results revealed that the proposed model achieved a mean average precision (mAP) of 94.0% and an F1-score of 87.1% on the oat ear detection task, representing gains of 3.2 and 1.8 percentage points over the baseline YOLOv8n, respectively. For counting, it reached an accuracy of 82.5%, a 9.2-point improvement. In conclusion, the YOLOv8n-DSP model provides an effective and practical approach for in-field oat ear detection and counting, suggesting considerable potential as a reliable tool for future yield prediction systems and advanced intelligent agricultural equipment. Full article

Agrivoltaics integrates photovoltaic (PV) power generation with agricultural practices, enabling dual land-use and mitigating land-use competition between agriculture and energy production. China has 3.43 million hectares of tea fields, offering significant potential for PV-integrated tea plantations (PVtea) to address land scarcity in clean energy development. This study aimed to investigate the impact of PV modules above tea bushes in PVtea on the yield and quality of tea, as well as tea plant resistance to environmental stresses. The PV system uses a single-axis tracking system with a horizontal north–south axis and ±45° tilt. It includes 70 UL-270P-60 polycrystalline solar panels (270 Wp each), arranged in 5 columns of 14 panels, spaced 4500 mm apart, covering 280 m². The panels are mounted 2400 mm above the ground, with a total capacity of 18.90 kWp (656 kWp/ha). Tea yield, quality-related components, leaf photosystem II (PSII) activity, and plant resistance to environmental stresses were investigated in comparison to an adjacent open-field tea plantation (control). The mean photosynthetic active radiation (PAR) reaching the plucking table of PVtea was 52.9% of the control, with 32.0% of the control on a sunny day and 49.0% on a cloudy day, accompanied by an increase in ambient relative humidity. These changes alleviated the midday depression of leaf PSII activity caused by high light, resulting in a 9.3–15.3% increase in leaf yield. Moreover, PVtea summer tea exhibited higher levels of amino acids and total catechins, resulting in tea quality improvement. Additionally, PVtea enhanced the resistance of tea plants to frost damage in spring and heat stress in summer. PVtea integrates photovoltaic power generation with tea cultivation practices, which not only facilitates clean energy production—an average annual generation of 697,878.5 kWh per hectare—but also increases tea productivity by 9.3–15.3% and the land-use equivalence ratio (LER) by 70%. Full article

Rare species at the edge of their range often persist after range contractions, yet basic information is typically lacking. We created species distribution models (SDMs) to guide field surveys for a disjunct population of Sessileflower Indian parsley (Cymopterus sessiliflorus; Apiaceae). We used historical observations to produce an initial model that guided field surveys in 2023. We refined the model using new observations from these surveys and the best predictors were shrubs, rock outcrops, mean monthly precipitation of the warmest quarter and rock type (area under the curve = 0.97). Suitable habitat (moderate-high and high classes) was predicted in <2% of Wyoming. We discovered 11 new populations over 2 summers. We collected 17 bee genera (n = 272 individuals) during C. sessiliflorus flowering suggesting diverse potential pollinators may transport pollen. Our model highlighted other areas predicted suitable and surveys in these areas may reveal new populations of this rare plant. The SDMs demonstrated how sparse historical data on rare species can be used to direct surveys in an efficient and effective manner. The information we gathered provided basic data for a rare plant at the periphery of its range where the most robust populations may occur making them critical for conservation efforts. Full article

Electrokinetics has established itself as a central pillar in microfluidic research, offering a powerful, non-mechanical means to manipulate fluids and analytes. Mechanisms such as electroosmotic flow (EOF), electrophoresis (EP), and dielectrophoresis (DEP) re-main central to the field, once more layers of complexity emerge heterogeneous interfaces, viscoelastic liquids, or anisotropic droplets are introduced. Five research directions have become prominent. Field-driven manipulation of droplets and emulsions—most strikingly Janus droplets—demonstrates how asymmetric interfacial structures generate unconventional transport modes. Electrokinetic injection techniques follow as a second focus, because sharply defined sample plugs are essential for high-resolution separations and for maintaining analytical accuracy. Control of EOF is then framed as an integrated design challenge that involves tuning surface chemistry, engineering zeta potential, implementing nanoscale patterning, and navigating non-Newtonian flow behavior. Next, electrokinetic instabilities and electrically driven micromixing are examined through the lens of vortex-mediated perturbations that break diffusion limits in low-Reynolds-number flows. Finally, electrokinetic enrichment strategies—ranging from ion concentration polarization focusing to stacking-based preconcentration—demonstrate how trace analytes can be selectively accumulated to achieve detection sensitivity. Ultimately, electrokinetics is converging towards sophisticated integrated platforms and hybrid powering schemes, promising to expand microfluidic capabilities into previously inaccessible domains for analytical chemistry and diagnostics. Full article

Knowledge of the reduction in tree stem diameter with increasing height is considered significant for reliable tree taper prediction. Tree taper modeling offers a comprehensive framework that connects tree form to growth processes, enabling precise estimates of volume and biomass. In this context, machine learning modeling approaches offer strong potential for predicting difficult-to-measure field biometric variables, such as tree stem diameters. Two promising machine learning approaches, temporal convolutional networks (TCNs) and extreme gradient boosting (XGBoost), were evaluated for their ability to accurately predict trees’ stem profiles, suggesting a powerful and safe strategy for predicting tree stem sectional volume with minimal ground-truth measurements. The comparative analysis of TCN- and XGBoost-constructed models showed their strong ability to capture the taper trend of the trees. XGBoost proved particularly well adapted to the stem profile of black pine (Pinus nigra) trees in the Karya forest of Mount Olympus, Greece, by summarizing its spatial structure, substantially improving the accuracy of total stem volume up to RMSE% equal to 3.71% and 7.94% of all ranges of the observed stem volume for the fitting and test data sets. The same trend was followed for the 1 m sectional mean stem-volume predictions. The tested machine learning methodologies provide a stable basis for robust tree stem volume predictions, utilizing easily obtained field measurements. Full article

Background: Quantum Machine Learning (QML) has attracted significant attention in recent years. With quantum computing achievements in computationally costly domains, discovering its potential in improving the performance and efficiency of deep learning models in medical imaging has become a promising field of research. Methods: We investigate QML in healthcare by developing a novel quantum-enhanced U-Net (QU-Net). We experiment with six configurations of parameterized quantum circuits, varying the encoding technique (amplitude vs. angle), depth and entanglement. Using the ISIC-2017 skin cancer dataset, we compare QU-Net with classical U-Net on self-supervised image reconstruction and binary classification of benign and malignant skin cancer, where we combine bottleneck embeddings with patient metadata. Results: Our findings show that amplitude encoding stabilizes training, whereas angle encoding introduces fluctuations. The best performance is obtained with amplitude encoding and one layer. For reconstruction, QU-Net with entanglement converges faster (25 epochs vs. 44) with a lower Mean Squared Error per image (0.00015 vs. 0.00017) on unseen data. For classification, QU-Net with no entanglement embeddings reaches 79.03% F1-score compared with 74.14% for U-Net, despite compressing images to a smaller latent space (7 vs. 128). Conclusions: These results demonstrate that the quantum layer enhances U-Net’s expressive power with efficient data embedding. Full article

In recent years, e-bikes have rapidly gained popularity in China. However, riders frequently engage in aberrant behaviors, posing significant traffic safety concerns. Field observation combined with traffic conflict techniques offer an effective approach for identifying risky riding behaviors that significantly affect traffic safety. This study aims to address two major limitations in existing research that can lead to estimation biases: the unsystematic and incomplete inclusion of potential risky riding behaviors, and the insufficient consideration of unobserved heterogeneity in conflict data. Data on 437 e-bike–motor vehicle conflicts were collected at four signalized intersections in Hefei, covering 21 variables including illegal, negligent, and error-prone riding behaviors, as well as sociodemographic factors. Appropriate conflict risk indicators were selected for straight-line and angle conflicts, respectively. A random parameters binary logit model with heterogeneity in means and variances (RPBL-HMV) was developed and compared against binary logistic and mixed logit models. The results indicate that the RPBL-HMV model provides a significantly better goodness-of-fit than the other two models. Six factors with fixed parameters are positively associated with high-risk conflicts, while two factors exhibit random parameters—one of which decreases in mean when riders fail to slow down before turning. The identified risky behaviors and the corresponding targeted countermeasures offer practical insights for regulating unsafe e-bike riding and improving intersection safety. Full article