Search Results (281)

Multi-organ medical image segmentation is essential for accurate clinical diagnosis, effective treatment planning, and reliable prognosis, yet it remains challenging due to complex backgrounds, irrelevant noise, unclear organ boundaries, and wide variations in organ size. To address these challenges, the boundary feature learning and enhancement network is proposed. This model integrates a dedicated boundary learning module combined with an auxiliary loss function to strengthen the semantic correlations between boundary pixels and regional features, thus reducing category mis-segmentation. Additionally, channel and positional compound attention mechanisms are employed to selectively filter features and minimize background interference. To further enhance multi-scale representation capabilities, the dynamic scale-aware context module dynamically selects and fuses multi-scale features, significantly improving the model’s adaptability. The model achieves average Dice similarity coefficients of 81.67% on synapse and 90.55% on ACDC datasets, outperforming state-of-the-art methods. This network significantly improves segmentation by emphasizing boundary accuracy, noise reduction, and multi-scale adaptability, enhancing clinical diagnostics and treatment planning. Full article

Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m⁻¹ spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article

This paper presents a neural network model, PINN-AeroFlow-U, for reconstructing full-field aerodynamic quantities around three-dimensional compressor blades, including regions near the wall. This model is based on structured CFD training data and physics-informed loss functions and is proposed for direct 3D compressor flow prediction. It maps flow data from the physical domain to a uniform computational domain and employs a U-Net-based neural network capable of capturing the sharp local transitions induced by fluid acceleration near the blade leading edge, as well as learning flow features associated with internal boundaries (e.g., the wall boundary). The inputs to PINN-AeroFlow-U are the flow-field coordinate data from high-fidelity multi-geometry blade solutions, the 3D blade geometry, and the first-order metric coefficients obtained via mesh transformation. Its outputs include the pressure field, temperature field, and velocity vector field within the blade passage. To enhance physical interpretability, the network’s loss function incorporates both the Euler equations and gradient constraints. PINN-AeroFlow-U achieves prediction errors of 1.063% for the pressure field and 2.02% for the velocity field, demonstrating high accuracy. Full article

In recent decades, the Brazilian Cerrado has faced rapid land conversion, resulting in the loss of approximately half of its original vegetation cover. Most existing conservation units within the biome are increasingly threatened by the expansion of land use around their boundaries. The establishment of buffer zones with land use regulations may protect biodiversity within these protected areas. In this study, we evaluated and ranked the 10 km buffer zones of 15 national parks (NPs) located in the Cerrado biome, identifying their priority for biodiversity conservation and sustainable land use interventions. The analysis considered the following data: land use and land cover change from 2012 to 2020, extent of natural vegetation fragments, presence or absence of state and municipal conservation units within the buffer zones, and drainage density. Two multicriteria analysis methods, the analytic hierarchy process and the weighted linear combination, were applied to classify the buffer zones into five levels of threat: very high, high, moderate, low, and very low. Among the 15 buffer zones analyzed, 11 were classified as having high to very high priority for conservation actions. The buffer zones surrounding the Serra da Bodoquena, Emas, Canastra, and Brasília NPs were ranked as having very high priority. Between 2012 and 2020, the most severe reductions in ecological connectivity were observed in the buffer zones of Grande Sertão Veredas (44.5%), Nascentes do Rio Parnaíba (40.4%), and Serra das Confusões (36.7%). Given the relatively high proportion of natural vegetation in the buffer zones located in the northern Cerrado, we recommend prioritizing conservation efforts in this region. In contrast, in the southern portion of the biome, where land occupation is more intense, strategies should focus on promoting environmentally sustainable land use practices. Full article

Precise extraction of rural settlements in alpine regions is critical for geographic data production, rural development, and spatial optimization. However, existing deep learning models are hindered by insufficient datasets and suboptimal algorithm structures, resulting in blurred boundaries and inadequate extraction accuracy. Therefore, this study uses high-resolution unmanned aerial vehicle (UAV) remote sensing images to construct a specialized dataset for the extraction of rural settlements in alpine mountainous areas, while introducing an innovative shadow mitigation technique that integrates multiple spectral characteristics. This methodology effectively addresses the challenges posed by intense shadows in settlements and environmental occlusions common in mountainous terrain analysis. Based on the comparative experiments with existing deep learning models, the Swin Transformer was selected as the baseline model. Building upon this, the Feature Fusion Swin Transformer (FF-SwinT) model was constructed by optimizing the data processing, loss function, and multi-view feature fusion. Finally, we rigorously evaluated it through ablation studies, generalization tests and large-scale image application experiments. The results show that the FF-SwinT has improved in many indicators compared with the traditional Swin Transformer, and the recognition results have clear edges and strong integrity. These results suggest that the FF-SwinT establishes a novel framework for rural settlement extraction in alpine mountain regions, which is of great significance for regional spatial optimization and development policy formulation. Full article

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

This study investigates supersonic flow within a nozzle under low-pressure conditions at the continuum mechanics boundary. This phenomenon is commonly encountered in applications such as the differentially pumped chamber of an Environmental Scanning Electron Microscope (ESEM), which employs an aperture to separate two regions with a great pressure gradient. The nozzle geometry and flow control in this region can significantly influence the scattering and loss of the primary electron beam traversing the differentially pumped chamber and aperture. To this end, an experimental chamber was designed to explore aspects of this low-pressure regime, characterized by a varying ratio of inertial to viscous forces. The initial experimental results obtained using pressure sensors from the fabricated experimental chamber were utilized to refine the Ansys Fluent simulation setup, and in this combined approach, initial analyses of supersonic flow and shock waves in low-pressure environments were conducted. The refined Ansys Fluent system demonstrated a very good correspondence with the experimental findings. Subsequently, an analysis of the influence of surface roughness on the resulting flow behavior in low-pressure conditions was performed on this refined model using the refined CFD model. Based on the obtained results, a comparison of the influence of nozzle roughness on the resulting electron beam scattering was conducted for selected low-pressure variants relevant to the operational conditions of the Environmental Scanning Electron Microscope (ESEM). The influence of roughness at elevated working pressures within the ESEM operating regime on reduced electron beam scattering has been demonstrated. At lower pressure values within the ESEM operating regime, this influence is significantly diminished. Full article

This study systematically investigates the effects of heat treatment at 800–1000 °C on the microstructure and mechanical properties of 10 wt.% TiB₂@Ti/AlCoCrFeNi_2.1 eutectic high-entropy alloy matrix composites (EHEAMCs) prepared by vacuum hot-pressing sintering. The results show that the materials consist of FCC, BCC, TiB₂, and Ti phases, with a preferred orientation of the (111) crystal plane of the FCC phase. As the temperature increases, the diffraction peak of the BCC phase separates from the main FCC peak and its intensity increases, while the diffraction peak positions of the FCC and BCC phases shift at small angles. This is attributed to the diffusion of TiB₂@Ti from the grain boundaries into the matrix, where the Ti solid solution increases the lattice constant of the FCC phase. Microstructural observations reveal that the eutectic region transforms from lamellar to island-like structures, and the solid solution zone narrows. With increasing temperature, the Ti concentration in the solid solution zone increases, while the contents of elements such as Ni decrease. Element diffusion is influenced by binary mixing enthalpy, with Ti and B tending to solidify in the FCC and BCC phase regions, respectively. The mechanical properties improve with increasing temperature. At 1000 °C, the average hardness is 579.2 HV, the yield strength is 1294 MPa, the fracture strength is 2385 MPa, and the fracture strain is 19.4%, representing improvements of 35.5% and 24.9% compared to the as-sintered state, respectively, without loss of plasticity. The strengthening mechanisms include enhanced solid solution strengthening due to the diffusion of Ti and TiB₂, improved grain boundary strength due to the diffusion of alloy elements to the grain boundaries, and synergistic optimization of strength and plasticity. Full article

Direct numerical simulations (DNSs) of the T106A low-pressure turbine were conducted for various turbulence intensities and length scales to investigate their effects on flow behaviour and transition. A source-term formulation of the synthetic eddy method (SEM) was implemented in the Nektar++ spectral/hp element framework to introduce anisotropic turbulence into the flow field. A single sponge layer was imposed, which covers the inflow and outflow regions just downstream and upstream of the inflow and outflow boundaries, respectively, to avoid acoustic wave reflections on the boundary conditions. Additionally, in the T106A model, mixed polynomial orders were utilized, as Nektar++ allows different polynomial orders for adjacent elements. A lower polynomial order was employed in the outflow region to further assist the sponge layer by coarsening the mesh and diffusing the turbulence near the outflow boundary. Thus, this study contributes to the development of a more robust and efficient model for high-fidelity simulations of turbine blades by enhancing stability and producing a more accurate flow field. The main findings are compared with experimental and DNS data, showing good agreement and providing new insights into the influence of turbulence length scales on flow separation, transition, wake behaviour, and loss profiles. Full article

Based on the magnetic configuration of the China Astro-Torus-1 (CAT-1) levitated dipole device, this study investigated the confinement performance of common discharge gas ions under E × B transverse transport conditions induced by electric fields. By adjusting L-coil parameters to shift the inject location, it was found that when the loss boundary is in the outer weak-field region, most particles with large Larmor radii are lost after colliding with the wall, for particles with large pitch angles, the strongly anisotropic magnetic field causes particles across a broad range of energies to be lost through the X-point into the divertor. The study demonstrates that for particle kinetic energies between 100 and 300 eV, the CAT-1 device exhibits a loss cone angle θ_loss of approximately 58°, indicating favorable confinement performance. Full article

This research evaluates the performance of six metaheuristic algorithms in the active and reactive power management of wind turbines (WTs) integrated into an AC microgrid (MG). The population-based genetic algorithm (PGA) is proposed as the primary optimization strategy and is rigorously compared against five benchmark techniques: Monte Carlo (MC), particle swarm optimization (PSO), the JAYA algorithm, the generalized normal distribution optimizer (GNDO), and the multiverse optimizer (MVO). This study aims to minimize, through independent optimization scenarios, the operating costs, power losses, or CO₂ emissions of the microgrid during both grid-connected and islanded modes. To achieve this, a coordinated control strategy for distributed generators is proposed, offering flexible adaptation to economic, technical, or environmental priorities while accounting for the variability of power generation and demand. The proposed optimization model includes active and reactive power constraints for both conventional generators and WTs, along with technical and regulatory limits imposed on the MG, such as current thresholds and nodal voltage boundaries. To validate the proposed strategy, two scenarios are considered: one involving 33 nodes and another one featuring 69. These configurations allow evaluation of the aforementioned optimization strategies under different energy conditions while incorporating the power generation and demand variability corresponding to a specific region of Colombia. The analysis covers two-time horizons (a representative day of operation and a full week) in order to capture both short-term and weekly fluctuations. The variability is modeled via an artificial neural network to forecast renewable generation and demand. Each optimization method undergoes a statistical evaluation based on multiple independent executions, allowing for a comprehensive assessment of its effectiveness in terms of solution quality, average performance, repeatability, and computation time. The proposed methodology exhibits the best performance for the three objectives, with excellent repeatability and computational efficiency across varying microgrid sizes and energy behavior scenarios. Full article

Requirements for Unmanned Aerial Vehicle (UAV) applications in low-altitude operations are escalating, which demands resilient Position, Navigation and Timing (PNT) solutions incorporating global navigation satellite system (GNSS) services. However, UAVs often operate in stringent environments with degraded GNSS performance. Practical challenges often arise from dense, dynamic, complex, and uncertain obstacles. When flying in complex environments, it is important to consider signal degradation caused by reflections (multipath) and obscuration (Non-Line of Sight (NLOS)), which can lead to positioning errors that must be minimized to ensure mission reliability. Recent works integrate GNSS reliability maps derived from pseudorange error estimations into path planning to reduce loss-of-GNSS risks with PNT degradations. To accommodate multiple constraint conditions attempting to improve flight resilience against GNSS-degraded environments, this paper proposes a reinforcement learning (RL) approach to feature GNSS signal quality awareness during path planning. The non-linear relations between GNSS signal quality in the form of dilution of precision (DoP), geographic locations, and the policy of searching sub-minima points are learned by the clipped Proximal Policy Optimization (PPO) method. Other constraints considered include static obstacle occurrence, altitude boundary, forbidden flying regions, and operational volumes. The reward and punishment functions and the training method are designed to maximize the success criteria of approaching destinations. The proposed RL approach is demonstrated using a real 3D map of Indianapolis, USA, in the Godot engine, incorporating forecasted DoP data generated by a Geospatial Augmentation system named GNSS Foresight from Spirent. Results indicate a 36% enhancement in mission success rates when GNSS performance is included in the path planning training. Additionally, the varying tensor size, representing the UAV’s DoP perception range, exhibits a positive proportion relation to a higher mission rate, despite an increment in computational complexity. Full article

Accurate visual inspection of underwater infrastructure, such as bridge piers and retaining walls, is often hindered by severe image degradation due to light attenuation and scattering. This paper introduces an unsupervised enhancement framework tailored for restoring underwater images containing structural cracks. The method combines a physical modeling of underwater light transmission with a deep image translation architecture that operates without requiring paired training samples. To address the loss of fine structural details, this paper incorporates a multi-scale feature integration module and a region-focused discriminator that jointly guide the enhancement process. Moreover, a physics-guided loss formulation is designed to promote optical consistency and texture fidelity during training. The proposed approach is validated on a real-world dataset collected from submerged structures under varying turbidity and illumination levels. Both objective evaluations and visual results show substantial improvements over baseline models, with better preservation of crack boundaries and overall visual quality. This work provides a robust solution for preprocessing underwater imagery in structural inspection tasks. Full article

To address existing issues in water extraction from SAR images based on deep learning, such as confusion between mountain shadows and water bodies and difficulty in extracting complex boundary details for continuous water bodies, the LGFUNet model is proposed. The LGFUNet model consists of three parts: the encoder–decoder, the DECASPP module, and the LGFF module. In the encoder–decoder, the Swin-Transformer module is used instead of convolution kernels for feature extraction, enhancing the learning of global information and improving the model’s ability to capture the spatial features of continuous water bodies. The DECASPP module is employed to extract and select multiscale features, focusing on complex water body boundary details. Additionally, a series of LGFF modules are inserted between the encoder and decoder to reduce the semantic gap between the encoder and decoder feature maps and the spatial information loss caused by the encoder’s downsampling process, improving the model’s ability to learn detailed information. Sentinel-1 SAR data from the Qinghai–Tibet Plateau region are selected, and the water extraction performance of the proposed LGFUNet model is compared with that of existing methods such as U-Net, Swin-UNet, and SCUNet++. The results show that the LGFUNet model achieves the best performance, respectively. Full article

Marine oil spills pose a serious and persistent threat to marine ecosystems, coastal resources, and global environmental health. These incidents not only disrupt ecological balance by damaging marine flora and fauna but also lead to long-term economic consequences for fisheries, tourism, and maritime industries. Owing to their rapid spread and often unpredictable occurrence, timely and accurate detection is essential for effective containment and mitigation. An efficient detection system can significantly enhance the responsiveness of emergency teams, enabling targeted interventions that minimize ecological damage and economic loss. This paper proposes a marine radar-based oil spill detection method that combines the Significance-to-Boundary Ratio (SBR) feature with an improved Sauvola adaptive thresholding algorithm. The raw radar data was firstly preprocessed through mean and median filtering, grayscale correction, and contrast enhancement. SBR features were then employed to extract coarse oil spill regions, which were further refined using an improved Sauvola thresholding algorithm followed by a denoising step to obtain fine-grained segmentation. Comparative experiments using different threshold values demonstrate that the proposed method achieves superior segmentation performance by better preserving oil spill boundaries and reducing background noise. Overall, the approach provides a robust and efficient solution for marine oil spill detection and monitoring. Full article