Search Results (15,875)

In the field of subgrade compaction quality assessment, accurate volume measurement of excavated pits is hindered by non-uniform point cloud distribution, environmental noise interference, and complex irregular boundary features. To address these challenges, this paper proposes a robust volume detection framework that integrates adaptive point cloud refinement and morphological discrimination. First, a pose normalization method employing RANSAC plane fitting and rigid body transformation corrects the spatial orientation of the raw point clouds. To balance data redundancy removal with feature preservation, a gradient adaptive simplification strategy based on local density feedback and K-nearest neighbor estimation is developed. Subsequently, a cross-sectional area calculation model utilizing piecewise-cubic polynomial fitting is proposed to mitigate boundary noise and accurately reconstruct irregular contours. Furthermore, a dynamic outlier removal mechanism based on the Median Absolute Deviation (MAD) and sliding windows is introduced to eliminate non-physical geometric fluctuations. Finally, the total volume is aggregated using a hybrid strategy of Simpson’s rule and a frustum compensation operator. Experimental results on simulated pits with typical topological defects demonstrate that the proposed algorithm outperforms traditional methods, achieving an average relative volume error of less than 0.8%. This approach significantly improves the robustness and precision of sensor-based automated subgrade compaction quality measurement. Full article

Open-vocabulary semantic segmentation (OVSS) aims to achieve pixel-level object segmentation guided by arbitrary natural language descriptions. Although pre-trained vision–language models (VLMs) have significantly advanced the development of OVSS, their reliance on the Vision Transformer (ViT) architecture imposes a fundamental constraint on dense prediction. Specifically, the absence of hierarchical downsampling in ViT-based VLM results in single-scale representations that trade spatial localization for global semantics. To address these issues, this paper proposes a hierarchical boundary-constrained decoding network for OVSS, called CLIP-HBD. Our approach leverages VLM semantic priors to reconstruct multi-scale features and introduces a boundary-constrained decoding strategy to refine edge details. Specifically, CLIP-HBD leverages a ConvNeXt-based backbone alongside a hierarchical adaptation mechanism to fuse multi-layer VLM features, generating a comprehensive multi-scale representation. To address the issue of boundary inaccuracy, we perform explicit boundary prediction based on multi-scale representations, where the resulting boundary maps are subsequently transformed into structural constraints to steer the decoder’s focus toward boundary regions. By integrating structural constraints with hierarchical features, the decoding process effectively maintains semantic consistency and restores precise object boundaries. Extensive experiments demonstrate that CLIP-HBD achieves superior performance in both segmentation precision and boundary quality across multiple benchmarks. Full article

This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2_HR_PIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0.25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales. Full article

Fusion-based hyperspectral image super-resolution (HSI-SR) on diffusion models exhibits promising performance in generating high-quality, realistic features. However, existing methods are confronted with two limitations: (1) static conditional guidance is discordant with the dynamic denoising process, and (2) modality conflicts are inadequately addressed by concatenation. To address these challenges, we propose a novel Modulated Diffusion Framework with Spatial–Spectral Disentangled Guidance (SSDG). Specifically, it introduces a Dynamic Modulated Residual Network (DMRN), which leverages a time-aware mechanism to dynamically adjust conditional feature injection, ensuring adaptive guidance throughout all denoising stages. Furthermore, we design a training-free SSDG strategy to explicitly decouple spatial and spectral guidance during sampling, allowing for flexible control over the fusion process to mitigate modality conflicts. Extensive experiments on three public datasets demonstrate that the proposed method achieves state-of-the-art performance, exhibiting superior robustness, particularly in challenging noisy scenarios. Full article

Traditional urban landscape evaluations have primarily relied on either objective spatial metrics, such as the Green View Index (GVI), or subjective human surveys, often failing to capture the complex mechanisms of human environmental perception. This study proposes a novel Explainable Artificial Intelligence (XAI) framework that integrates objective physical configuration with subjective cognitive assessment to predict human landscape preference. Utilizing 159 urban landscape images, we extracted physical features via semantic segmentation (SegFormer) and psychological perceptions via a zero-shot vision-language model (CLIP). Our hybrid Random Forest model successfully bridged these dimensions, achieving moderate yet promising predictive performance (Rsquare = 0.442). SHAP (Shapley Additive exPlanations) analysis revealed that psychological perceptions—specifically Safety (0.104), Fascination (0.096), and Tranquility (0.080)—outperformed traditional objective metrics like GVI (0.067) in determining overall preference, while sub-model interpretation linked these psychological responses to specific physical elements such as buildings, sky openness, low vegetation, and water bodies. The findings suggest that urban green space design should move beyond maximizing greenery quantity and instead prioritize spatial compositions that induce psychological security, visual interest, and restoration. The proposed framework offers a scalable and interpretable tool for human-centered landscape assessment, while acknowledging limitations related to sample size, cultural generalizability, pretrained model bias, and reliance on static two-dimensional imagery. Full article

The present work investigates the linear regime of propagation of modulated vector optical fields in isotropic dispersive media by focusing on the formation of complex vector vortex structures with amplitude-type singularities. A mathematical algorithm designed to derive novel exact analytical solutions for the linear vector amplitude equation is presented, enabling the systematic development and classification of diffraction-free vector solutions. Various types of solutions for the two orthogonal components of the vector amplitude function are obtained, resulting in non-trivial spatial amplitude structures in their cross sections. The proposed approach allows for precise analytical governance of the spatial and polarization properties of the obtained vortices via the vortex parameter n. The presented model offers a comprehensive framework for generating different types of vector vortex structures by choosing the values of the parameters n and m, depending on the initial phase of the components. The derived solutions extend the capabilities of conventional phase modulation techniques. It is demonstrated that by changing the vortex parameter n, the structural complexity of both the amplitude distributions and polarization patterns increases. A number of numerical simulations, based on the obtained analytical solutions, are performed. They validate the model and clearly illustrate the characteristic vectorial features via detailed vector diagrams. Full article

We present a modular microfluidic platform for constructing miniaturized, compartmentalized cell culture systems that support monoculture, co-culture, and organ-on-a-chip models of human tissues. The devices provide architecturally defined three-dimensional microenvironments in which heterogeneous cell populations can be cultured in close proximity while maintaining precise spatial organization and independent access to each compartment. In vivo-like perfusion into, from, and between adjacent chambers is achieved via micro-engineered porous barriers that act as perfusion microchannels, enabling controlled convective and diffusive transport and recapitulating paracrine signaling between tissue units. As a proof of concept, we implement an adipose–immune co-culture model that reproduces key features of inflamed, insulin-resistant adipose tissue, including altered cytokine secretion and glucose uptake. Together, these features establish a versatile platform for the biofabrication of customizable single-organ and multi-organ in vitro models that more faithfully recapitulate human tissue structure and function for applications in disease modeling, immunometabolic studies, and preclinical drug testing. Full article

This study estimates daily nitrogen dioxide (NO₂) concentrations at ground level across the Marmara Region of Türkiye at 0.01° resolution. The framework integrates Sentinel-5P (S5P) TROPOspheric Monitoring Instrument (TROPOMI) and GEOS Composition Forecast (GEOS-CF) tropospheric NO₂ vertical column density (VCD) data with meteorological, topographic, land-use, socioeconomic, and temporal features through four tree-based ensemble algorithms trained on 74 ground station observations. Under a temporal split (2019–2022 training, 2023 validation, 2024 testing), S5P-Categorical Boosting (CatBoost) achieved the best performance (Pearson correlation coefficient (R) = 0.706, R² = 0.498, root mean square error (RMSE) = 14.31 µg/m³). Random splitting inflated R by +0.168 due to temporal autocorrelation, while leave-one-station-out and leave-one-province-out cross-validation reduced R to ~0.50 by removing spatial dependence, together revealing the combined effect of temporal and spatial autocorrelation. SHapley Additive exPlanations (SHAP) analysis identified TROPOMI NO₂ VCD, population density, road length, and nighttime light as dominant predictors; population density was the top predictor in the GEOS-CF model, followed by VCD. Concentration maps for 2024 showed that 95.9% of the region’s 26.74 million inhabitants were exposed above the WHO annual air quality guideline of 10 µg/m³, with a population-weighted mean of 21.08 µg/m³. Full article

As an important option for energy storage projects, pumping stations can also generate electricity when the upstream has surplus water and the pump system operates as a turbine (PAT mode). When it switches from pump mode to PAT mode, the pump operation state changes significantly. This study adopts a numerical simulation to investigate the flow characteristics, time-frequency domain performance and chaotic features of pressure pulsation in a vertical mixed-flow pump device when it operates in different PAT modes. The results show that, when the pump operates in PAT mode, the flow in the straight passage remains smooth, but it deteriorates in the elbow-shaped draft tube, such as developing a spiral stream in the straight section, a disordered stream in the elbow section, and vortexes and flow separation at the beginning of the diffuser section, but it gradually becomes smooth after passing through the diffuser section. Under low-head PAT conditions, circumferential circulation cross flow occurs at the impeller inlet, reducing energy conversion efficiency. Under all PAT conditions, the flow on the blade surface near the hub is stable, but obvious vortexes happen near the shroud. As the head increases, the small-scale vortexes disappear on the mid-blade surface, and the flow becomes smoother on the blade surface near the shroud of the impeller. Except at the impeller outlet, pressure pulsation of the monitoring probes exhibits clear periodicity, with dominant frequencies corresponding to the rotational frequency, and its amplitudes decreasing from shroud to hub. Pressure pulsation under all PAT conditions is chaotic, and phase trajectories exhibit ring-shaped structures consisting of the ring circle and the ring surface. Differences in the circle spacing, size, and spatial position of the ring circle phase locus and ring surface phase locus are observed, and these variations are closely related to the PAT conditions. A correlative relationship exists between the chaotic correlation dimension and flow performance, which is of great significance for the condition monitoring and fault diagnosis of pump units. These findings not only enrich the theoretical research on the PAT mode of pumps, but also provide a reference for similar engineering applications and offer new insights into condition monitoring of hydraulic machinery. Full article

Seismic inversion is a key technique for quantitative characterization of subsurface elastic parameters and detailed reservoir description. However, due to the limited bandwidth of seismic signals and the strong heterogeneity of complex reservoirs, conventional inversion methods struggle to simultaneously achieve high vertical resolution and lateral continuity. To address these challenges, an intelligent elastic parameter inversion method based on kernel density estimation within a Bayesian framework is proposed. First, kernel density estimation is introduced to augment the training samples, thereby alleviating data scarcity. Second, a hybrid architecture integrating convolutional modules, Mamba, and cross-attention mechanisms is constructed to achieve collaborative modeling of local spatial features and long-range temporal dependencies. The cross-attention mechanism is further employed to adaptively weight and fuse multi-source features, thus enhancing the representation capability of the model. Subsequently, by designing a joint loss function, the strengths of deterministic inversion and data-driven approaches are effectively integrated, ensuring physical consistency while enhancing data adaptability, thereby improving the stability and accuracy of the inversion results. Furthermore, the neural network outputs are used as the initial model for Bayesian inversion to construct a probabilistic inversion framework for elastic parameter inversion. Finally, experimental results demonstrate that the proposed method improves the R² values of inversion results by more than 8.0% and 5.0% compared with conventional methods in thin interbedded models and real data experiments, respectively. Full article

Alzheimer’s disease (AD) is characterized by progressive neurodegeneration and prominent neuroimmune remodeling, but the contribution of macrophage and myeloid states across disease severity remains incompletely defined. We integrated bulk transcriptomic, single-cell RNA sequencing (RNA-seq), and spatial transcriptomic datasets to characterize AD-associated myeloid immune changes across Braak stage and disease status. Across datasets, M2-like macrophage and myeloid signatures showed progressive enrichment with increasing neuropathological severity and were accompanied by pathway changes related to macrophage proliferation, TGF-β signaling, and myeloid homeostasis. Immune-feature-based classifiers identified macrophage-related variables among the informative features distinguishing AD from controls. CellChat analyses further inferred that M2-like myeloid populations occupied communication-enriched positions in single-cell and spatial interaction networks, including apolipoprotein E (ApoE), CX3C chemokine signaling, and fibronectin 1 (FN1)-associated signaling contexts. Collectively, these findings indicate that M2-like myeloid programs are consistently associated with AD severity and neuroimmune network remodeling. Rather than establishing a causal disease driver, this study highlights M2-like myeloid signatures as candidate neuroimmune components that warrant experimental validation in human-relevant systems. Full article

Change detection in open-pit mining areas from remote sensing imagery is of great importance for mining supervision, ecological monitoring, and restoration planning. Nevertheless, mining-related changes usually exhibit multi-scale patterns, irregular boundaries, and fragmented spatial distributions, which make accurate detection difficult. Existing CNN- and Transformer-based methods often cannot effectively balance global context perception and local detail preservation, resulting in incomplete boundary extraction and insufficient sensitivity to subtle changes. To overcome these limitations, we propose GLMECD-Net, a Global–Local Multi-scale Cross-fusion Enhanced Change Detection Network for remote sensing image change detection in open-pit mining areas. Specifically, a Siamese encoder is used to extract hierarchical bi-temporal features, while a Global–Local Feature Mixing Embedding (GLME) module is introduced to jointly capture long-range contextual information and local spatial details. Furthermore, multi-scale feature aggregation and cross-temporal feature fusion are employed to improve change representation and boundary recovery. Experimental results on mining area datasets show that the proposed method achieves 71.66% Precision, 83.78% OA, 77.53% F1-score, and 53.82% IoU. The results demonstrate that GLMECD-Net provides effective and robust performance for detecting complex and subtle changes in open-pit mining areas. Full article

With the continuous growth of global population and increasing pressure on food security, the transformation toward precise and intelligent agricultural production has become an inevitable trend. In this context, accurate identification of field weeds is crucial for improving crop yields and reducing agricultural inputs. However, agricultural Internet of Things (IoT) edge devices are generally subject to strict constraints in terms of power consumption, storage, and real-time performance. Existing lightweight convolutional neural networks often struggle to simultaneously achieve high accuracy and low resource consumption for fine-grained weed identification tasks. To address this challenge, this paper proposes a hardware aware lightweight convolutional neural network named Weed-Aware Ghost Enhanced Network (WAGENet) for microcontroller deployment. The network synergistically integrates Ghost low-cost feature generation, Mobile Inverted Bottleneck Convolution (MBConv) for deep semantic extraction, Squeeze and Excitation (SE) and Coordinate Attention (CA) dual attention mechanisms for channel space joint calibration, and Atrous Spatial Pyramid Pooling (ASPP) for multi-scale context fusion. It constructs a progressive feature abstraction system from shallow textures to high-level semantics. On the public DeepWeeds dataset, WAGENet achieves 95.71% classification accuracy and 93.80% F1 score with only 0.163 M parameters and 2.43 × 10⁸ multiply accumulate operations (MACC), attaining a parameter efficiency of 587.19%/M and significantly outperforming existing mainstream lightweight models. The model has been successfully deployed on the STM32H7B3I microcontroller development board, achieving a single inference latency of 94.63 ms, an internal Flash footprint of only 686.95 KiB, and a single inference energy consumption of 41.45 mJ. Experimental results demonstrate that WAGENet achieves a trade off among accuracy, latency, and energy consumption under strict resource constraints, providing a reproducible microcontroller deployment paradigm for battery powered field robots, drones, and other agricultural IoT edge devices. Full article

Conventional memory machines often suffer from magnetic interference between high-coercive-force (HCF) and low-coercive-force (LCF) permanent magnets, which unintentionally alters the magnetization state and limits overload capability. To address this challenge, this paper proposes a novel axial parallel memory machine (DCB-AXMM) featuring a DC-bias-controlled variable-flux capability. Instead of a conventional structure, the proposed machine employs an axially segmented topology to spatially isolate the excitation sources, effectively shielding the LCF PMs from HCF PM interference and armature reaction. Furthermore, integrated windings are utilized to perform both armature excitation and pulse magnetization, thereby enhancing the overall space utilization. The flux-regulating mechanism is theoretically elucidated using a piecewise linear hysteresis model. To maximize electromagnetic performance, a two-step optimization framework based on a genetic algorithm (GA) is implemented. Comprehensive non-linear finite element analysis (FEA) is conducted to validate the proposed design. Quantitative results demonstrate that the DCB-AXMM achieves a wide flux regulation range, characterized by a 21.8% average torque reduction from 2.2 Nm at full magnetization to 1.72 Nm at zero magnetization, while maintaining a robust 1.5-times overload capability. These measurable outcomes confirm the topology’s effectiveness and reliability for high-performance variable-flux applications. Full article

In impedance imaging, the incompatibility and nonlinearity of the inverse problem lead to problems such as blurred boundaries and severe artifacts in the reconstructed images, making it difficult to meet the requirements for precise identification of multi-layer tissue structures in the legs. To this end, this paper proposes a post-processing algorithm for leg EIT that integrates the boundary attention mechanism, with a Wasserstein generative adversarial network as the training framework, cyclic residual U-Net as the generator, and the boundary attention module embedded in the RecurrentBlock. This leads to adaptive enhancement of the ability to extract organizational boundary features through a three-path fusion of spatial attention, channel attention, and learnable Laplacian edge enhancement. A leg anatomy prior constraint loss function was designed, integrating six constraints—pixel loss, edge loss, hierarchical tissue constraint, total variation regularization, structural similarity loss, and histogram matching—to guide the reconstruction results to conform to the multi-layered tissue structure features of the leg. A simulation dataset of leg sections containing multiple tissues such as skin, fat, muscle, bone, blood vessels, and nerves was constructed, and the pre-reconstructed images were obtained using the hybrid total variation regularization algorithm as the network input. The simulation results show that, under noise-free and different signal-to-noise ratio conditions, the proposed BAM-R2UNet algorithm achieves the best performance in RMSE, SSIM and PSNR metrics compared with HTV, DnCNN and standard U-Net algorithms, can remove artifacts, accurately restore the boundary and conductivity distribution of leg tissues, and has stronger anti-noise robustness. Full article