Search Results (870)

Two-dimensional (2D) phase unwrapping (PU) in interferometric synthetic aperture radar (InSAR) remains a challenging ill-posed inverse problem, particularly in low-coherence regions where the Itoh condition is violated. Although deep-learning methods have shown promise, existing networks often struggle to preserve phase continuity and structural details under strong decorrelation noise. To address these limitations, a novel physics-guided conditional generative adversarial network (cGAN) is proposed in this work. Specifically, an adaptive atrous spatial pyramid pooling module mimics the multi-baseline observation mechanism to resolve multi-scale phase variations. Meanwhile, a multi-channel attention-augmented discriminator guides adversarial learning to focus on error-prone low-coherence regions. Additionally, a hybrid loss function enforces fundamental physical constraints, including phase-wrapping consistency and path continuity, explicitly embedding the prior knowledge of interferometric phase physics into the data-driven framework. Extensive experiments demonstrate that the proposed method achieves highly competitive performance in terms of root mean square error (RMSE) and structural similarity (SSIM) compared to State-of-the-Art approaches, especially in scenarios with severe noise and discontinuities. Full article

During the growth phase, pumpkin crops are susceptible to a range of diseases. However, persistent bottlenecks remain in practical field operations, particularly the difficulty of detecting lesions at multiple scales and low identification efficiency. To address this, this study proposes an improved disease detection method based on YOLO11. Specifically, data augmentation techniques—including random flipping, scaling, brightness adjustment, and color jittering—are employed to diversify the training samples and enhance the model’s generalization capability under complex field conditions. Furthermore, by integrating the C2f-RepNCSPFPN structure to strengthen global semantic representation, incorporating the CBAM attention mechanism to suppress interference from complex backgrounds, and modifying the pyramid architecture with SimSPPF to increase sensitivity to small-scale lesions, a high-performance and lightweight detection model named YOLO11n-CCS is constructed. Experimental results demonstrate that the YOLO11n-CCS model achieves significant improvements in key metrics such as mAP@0.5 and recall. It effectively handles challenging scenarios involving leaf occlusion, varying illumination, and overlapping lesions. Concurrently, the model maintains a compact size of 3.6 M parameters and 14.0 G FLOPs, making it suitable for deployment on edge devices. The findings of this research offer a practical technical solution for real-time, precise disease identification in field crops and the development of crop protection robots. Full article

Digital forensics increasingly operates under extreme data growth, where exhaustive bit-wise imaging of modern storage is constrained by time, storage, and processing cost. Selective imaging and artifact prioritization methods reduce acquisition volume, but they typically order artifacts by content, location, or offense type rather than by the relationships that make evidence interpretable. As a result, they risk discarding the contextual artifacts on which attribution and corroboration depend. This paper introduces the High-Value Evidence Area (HVEA) pyramid, a dependency-oriented abstraction that organizes Windows forensic artifacts into ten operational layers (A–J) grouped into four evidentiary tiers: attribution anchor, primary payload, behavioral contextualization, and structural corroboration, in which acquisition order follows interpretive prerequisites rather than artifact salience. The model is evaluated on nine Windows forensic images spanning Windows XP, Vista, and Windows 11, combining retrospective analysis of the M57-Patents corpus with a controlled fourteen-day behavioral experiment. Across systems and operating system generations, HVEA layers exhibit stable evidentiary function despite changing artifact implementations; behavioral execution telemetry persists even where user content is sparse or deliberately concealed; and a three-source timestamp corroboration pattern consistently converges within thirty seconds across independent OS mechanisms, providing an empirically grounded defensibility threshold for event reconstruction. The results support a dependency-ordered, NIJ-aligned selective acquisition strategy that preserves interpretive context while reducing acquisition footprint by approximately 95% (a 15–20× reduction) on the controlled Windows 11 image. The present evaluation is scoped to Windows environments; extending the tier mappings to other platforms is identified as future work. Full article

Accurate large-scale crop mapping is fundamental to agricultural management. However, in Liaoning Province, undulating terrain and fragmented fields make fine crop classification challenging. In particular, corn and soybean have overlapping phenologies, which can lead to spectral and structural confusion in conventional optical–SAR feature spaces and limit mapping accuracy. This study proposes a fine crop mapping framework integrating optical phenotypic, microwave structural, and meteorological time-series features. To overcome the curse of dimensionality caused by high-dimensional heterogeneous data, an adaptive feature truncation mechanism based on the transition pattern of the marginal-gain curve was designed. Additionally, a pyramid multi-scale sliding window algorithm was constructed to optimize meteorological features, achieving dimensionality reduction and precise identification of phenologically sensitive windows. The results indicate that: (1) The multi-scale feature selection strategy effectively eliminates redundant variables and maximizes the inter-class discriminability of core features, significantly improving computational efficiency and classification performance. (2) High-frequency meteorological features provide key physiological constraints. Specifically, mid-May shortwave radiation, early October precipitation, and early August growing degree days constitute the core environmental–physiological features for distinguishing confused crops, helping to mitigate the spectral confusion of dryland crops. (3) Driven by the multi-source features, the Support Vector Machine (SVM) exhibits the optimal generalization robustness for processing high-dimensional structured data, yielding an overall classification accuracy of 91.80% and a Kappa coefficient of 0.8905. This framework provides a reliable methodological reference for high-precision crop monitoring in large-scale complex planting areas. Full article

To address the challenges of severe optical attenuation and dynamic feature extraction for moving target depth retrieval in complex underwater remote sensing environments, this paper proposes a dynamic target depth estimation method based on multi-source data fusion. Taking optical RGB imagery and neuromorphic vision (NeuroIV) data as joint inputs, the proposed method constructs a three-channel feature extraction and fusion network. By leveraging a hypergraph structure, it establishes association weights between dynamic (temporal) and static (spatial) nodes to capture spatiotemporal correlations. To efficiently process the high-dimensional multi-modal data, the traditional dot-product attention is replaced with element-wise multiplication, significantly reducing computational complexity. Furthermore, a lightweight deformable attention pyramid (DAP) and diffusion model is introduced to refine depth image edges, effectively suppressing discontinuities and abruptness in the estimation results. Compared to single-modality optical imagery, the fused multi-modal data yields a superior signal-to-noise ratio and foreground contrast, achieving an improvement of over 20% in the MAE index. These results validate the effectiveness and superiority of the proposed multi-modal fusion strategy for dynamic target observation and depth retrieval in aquatic environments. Full article

Global climate change poses a critical challenge to sustainable urban development. The construction of low-carbon transportation systems is therefore a core strategy for enhancing the sustainability of mega-city road networks. Combining the characteristics of urban road traffic networks, this paper establishes a method for vehicle trip segmentation and carbon emission estimation based on GPS trajectory data (5699 vehicles, Beijing, September 2019) and the COPERT emission model, analyzing the spatiotemporal distribution characteristics of vehicle emissions. By incorporating the Life Cycle Assessment (LCA) emissions of electric vehicles, this study proposes carbon reduction strategies based on stochastic selection and ranking-based optimization from two dimensions: road-segment and vehicle electrification. Simulation methods are employed to evaluate the effectiveness of different strategies, as well as road network carbon emissions, under four vehicle electrification structures: Pyramid, Inverted Pyramid, Olive, and Dumbbell. Results indicate that carbon emission intensity rises significantly due to traffic congestion during peak hours. Under the LCA framework, Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) show significantly lower emissions than traditional Internal Combustion Engine Vehicles (ICEVs). Under the specified scenario assumptions, the ranking-based optimization scheme is estimated to yield carbon reductions approximately 2 times (segment control) and 3 times (electrification) those of the stochastic selection scheme, respectively. The study concludes that integrating EV promotion policies with precise carbon reduction control strategies can effectively mitigate urban road network carbon emissions. Full article

The autism spectrum disorder-associated SCN2A, ANK2, and SHANK-family genes encode molecularly distinct proteins that converge functionally on dendritic integration. Recent work established that ankyrin-B, encoded by ANK2, acts as an obligate dendritic scaffold for NaV1.2, encoded by SCN2A, in neocortical pyramidal neurons. Loss of this module mislocalizes dendritic NaV1.2, reduces dendritic Na⁺ influx, weakens backpropagating action potentials, and impairs synaptic maturation and long-term potentiation. SHANK proteins organize a complementary postsynaptic receptor scaffold within dendritic spines, coupling N-methyl-D-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), and metabotropic glutamate receptor (e.g., mGluR5) signaling to the actin cytoskeleton through layered PSD-95/GKAP/Homer interactions. Disruption of this scaffold can destabilize excitatory transmission, spine morphology, and plasticity. We propose that these dendritic shaft and spine-associated modules jointly regulate dendritic input–output gain and that their disruption may contribute to autism spectrum disorder by destabilizing, rather than uniformly shifting, excitatory integration across cortico-subcortical circuits relevant to sensory reactivity, behavioral flexibility, and social-valence processing. Here, we review the cortical evidence for this layered dendritic convergence and evaluate its potential relevance beyond the cortex. We assess the striatum, thalamus, and amygdala as subcortical sites where related dendritic scaffolding mechanisms may operate. The striatum provides the strongest current test case, with established roles for both NaV1.2 and SHANK3 in medium spiny neuron physiology and corticostriatal connectivity. Thalamic and amygdalar extensions are supported mainly by SHANK-related circuit and channelopathy data but lack direct evidence for ANK2–SCN2A involvement. The framework is experimentally testable: conditional Ank2 deletion in striatal, thalamic, and amygdalar cell types; dendritic Na⁺/Ca²⁺ imaging across Scn2a, Ank2, and Shank3 models; adult rescue experiments; and genetic-interaction designs would determine whether ankyrin-B supports dendritic excitability beyond the cortex and whether these genes converge on, rather than merely parallel, dendritic input–output gain. Validation in human subcortical tissue would then establish whether this dendritic scaffolding logic represents a shared point of convergence through which genetically distinct autism spectrum disorder-risk variants alter circuit function. Full article

Earthen archeological sites may be damaged by rain-induced erosion processes. Huaca Fortaleza (HF; 600–750 AD) is an originally four-level truncated pyramid in the semi-arid Lambayeque region of northern Peru, an area affected by seasonal intense rain due to El Niño Southern Oscillation (ENSO). We use data from a UAV-based photogrammetric survey and generate a Digital Surface Model from which we extract selected geomorphometric parameters and apply a hillslope diffusion model. The obtained data show that HF steep flanks exhibit a marked erosion expressed by a drainage network of parallel rills and gullies with architectural structures controlling pathways for concentrated flow. The southwestern flank is affected by gravity instability. Localized pits at the top of HF cause infiltration of rainwater. The erosion by ENSO rainfall is responsible for extensive architectural loss, with the HF lower platforms now entirely obliterated. We calculate vertical erosion rates of 0.28–0.38 m/century, a range of values comparable with that estimated for river incision. Erosion due to diffusion processes is estimated in the order of ~0.015 m/century. Our approach represents a transferable methodology applicable to other earthen archeological sites affected by erosion worldwide. Full article

Background: Mutations in the FBXO7 gene (PARK15) cause an autosomal recessive, early-onset neurodegenerative disorder typically presenting as Parkinsonian-Pyramidal Syndrome (PPS). Despite its recognition, the high phenotypic variability often delays diagnosis. Here, we report a novel Italian family and synthesize data from all published cases to date, offering an updated clinical and molecular overview of the disease. Methods: We performed clinical and molecular characterization of a newly identified family. Furthermore, we conducted a systematic literature review (from 2008 to 2026) to aggregate clinical, genetic, and geographic data of all reported PARK15 cases. Results: Two siblings presented with a complex phenotype including early-onset parkinsonism, cognitive decline, psychiatric symptoms, and aphasia-type speech disorders. Genetic analyses identified two novel likely pathogenic variants: a missense substitution in the UBL domain (p.Ile74Met) and a frameshift indel (p.Val233GlufsTer8). The literature review (incorporating clinical data from Europe, Asia, and South America) confirms a high prevalence of postural instability (87.5%), bradykinesia (83.3%), and pyramidal signs (~60%). We observed a distinct distribution of variants: missense mutations cluster in the N-terminal UBL and F-box domains, while truncating variants are more common in the C-terminal region. Discussion: Our findings expand the FBXO7 mutational landscape and underscore the “atypical” clinical markers, such as pyramidal signs and cognitive decline, that distinguish PARK15 from other recessive forms of parkinsonism like PARK2 and PARK6. The dual role of FBXO7 in mitochondrial quality control and proteasomal assembly suggests a broad disruption of cellular homeostasis. These observations refine genotype–phenotype correlations and may guide variant interpretation in routine diagnostic settings. Full article

Dietary exposure to microplastics (MPs) has emerged as a significant concern; therefore, its implications for exposure characterization are presented in this study. The lack of standardized testing methods currently limits effective risk management. Determining how industrial operations contribute to the presence of these xenobiotics in the food supply chain is essential, even if environmental absorption is a recognized factor. The Food Microplastics Pyramid (FOMIC-Py), a novel hierarchical structure designed to correlate MP prevalence with industrial processing intensity, is presented in this study. The investigation suggests that technogenic inputs may represent important contributors to contamination by synthesising current literature and applying the model to regional food patterns, especially an Italian North-South scenario study. The method uses sensitivity analysis (Spearman’s ρ = 0.94) for statistical validation and classifies food items from primary commodities (Level 1) to ultra-processed items (Level 5). Mechanical abrasion and packaging interactions are recognized as the main vectors by the FOMIC-Py, which reveals a consistent accumulation of MPs across all five levels of industrial transformation. While FOMIC-Py reliably assesses particles over 1 µm, current analytical constraints regarding nanoplastics lead to a significant exposure underestimation. Consequently, rather than being an established predictive model of human target-organ dosage, the FOMIC-Py framework serves as a new exploratory, hypothesis-generating tool. The absolute exposure metrics should be evaluated cautiously owing to the underlying variability of worldwide MP extraction data, even if our statistical predictions indicate a consistent relative ranking hierarchy across contaminated food categories. These first screening criteria provide a uniform basis to direct future targeted sampling procedures and regulatory prioritization. Full article

The prediction of thin-bedded, favorable sand bodies within the Middle-Lower Jurassic braided river delta–lacustrine succession of Block S (Amu Darya Right Bank) is challenging because of strong spatial heterogeneity, deep burial, and limited seismic resolution near the acoustic basement. To address this, we propose an integrated workflow that combines sedimentological characterization with geologically constrained seismic inversion. The study uses core, grain-size data, wireline logs, and 3D seismic surveys. Core–log–seismic integration first delineates three subfacies and nine numbered microfacies (MF1–MF9), with the delta front dominated by underwater distributary channels (MF1), mouth bars (MF2), and interdistributary bays (MF3). Planar microfacies distribution maps and electrofacies boundaries are then used as geological constraints for reservoir prediction. Steerable pyramid enhancement ($K=4$ scales, $N=6$ orientations) improves channel-reflection continuity, and PDF-regularized stochastic optimization inversion ($\lambda =0.8$) is performed to identify thin sand reservoirs. Sand-ratio and GR cutoffs were validated against 412 core–log contacts in five wells. Discretization sensitivity tests confirm stable inversion under 2 ms and 4 ms sampling. The results show that (1) favorable Type I and Type II reservoirs occur preferentially in MF1 and MF2 (average porosities of 12.7% and 10.1%, respectively); (2) vertically, two sand-rich progradational intervals (Lower Member and late Upper Member) are separated by a transgressive mud-prone middle–early Upper Member; and (3) inversion low-impedance anomalies delineate strip-like and lobate channel–mouth-bar sand belts with thickness up to 14 m, consistent with well control. Fault-controlled graben–horst paleotopography influenced sand fairway distribution. The workflow highlights the value of integrating sedimentary microfacies boundaries as geological constraints in seismic inversion for heterogeneous deep clastic gas reservoirs. Full article

The Cambodian genocide occurred between April 1975 and January 1979. Over one-third of Cambodia’s population perished, and many survivors suffer physical and mental health consequences. This study examines lasting influences of the Cambodian genocide on Cambodia’s population structure and on adult health and health behavior. To illustrate the legacy of decreased fertility and increased mortality during the genocide, population pyramids (1975, 1985, 2014, 2022) were generated using data from the United Nations Population Division. For comparison, population pyramids for the neighboring country of Thailand were generated. To examine the enduring health sequelae of the genocide, nationally representative Demographic and Health survey data (2014, 2021–2022) were used to compare smoking behaviors and stunted growth of women born shortly before and during the genocide (1972–1979) with women born shortly after the genocide (1980–1987). Cambodia’s population pyramids reveal a long-term paucity of individuals in the 1970s birth cohorts not observed for Thailand. Compared to women born shortly after the genocide, women with early-life exposure to the genocide were more likely to report smoking in adulthood and to have experienced stunted growth. The genocide impacted Cambodia’s population structure and affected the health and health behaviors of early childhood genocide survivors into adulthood. These findings imply life course and intergenerational impacts. Full article

Building recognition and detection in remote sensing images are of great significance for urban planning, spatial database updating, and the construction of urban geographic information systems. For remote sensing images with complex background information, variations in the size of building objects make automatic building detection and recognition challenging, thereby affecting the recognition accuracy of deep learning models. At the same time, the lack of a standardized workflow for converting detection results into vector data formats makes it difficult to directly transform building detection results into usable GIS-compatible vector data. Based on the Cascade R-CNN model, an intelligent building recognition model for remote sensing images and a vectorization workflow for the recognition results are proposed. To address the issue of building recognition accuracy in remote sensing images, an intelligent building recognition model comprising ResNet101, a Feature Pyramid Network (FPN), a Region Proposal Network (RPN), and a cascade detector is proposed, which enhances the recognition precision and localization capability of building objects in multi-scale remote sensing images. To address the efficiency issue of vectorizing detection results, a procedural conversion method for building detection results in remote sensing images is proposed, which converts raster recognition results into GIS-compatible vector files through data verification, information extraction, boundary construction, polygon generation, and format conversion. Experiments show that the intelligent recognition model achieves a recall of 0.958, a miss rate of 0.042, a precision of 0.963, and an F1-score of 0.960. In addition, mAP@0.5, mAP@0.5:0.95, and mean IoU reach 0.954, 0.793, and 0.742, respectively, indicating good performance in building detection and localization. Compared with manual vectorization, the automated workflow reduces the processing time for 57 raster files from 25.4 min to 3.1 min, corresponding to an 87.8% reduction in processing time. These results indicate that the proposed method improves building recognition accuracy while enhancing the efficiency of converting recognition results into GIS vector data, showing application potential for urban spatial information extraction. Full article

Micro-structured SAE H13 tool steel inserts for polymer injection molding require accurate replication of sub-millimeter features while retaining adequate densification and heat-treatment response. This study compared two powder-based routes on the same hemispherical insert containing pyramidal features of approximately 0.145 mm base width: hot embossing (HE) of water-atomized SAE H13 powder (supplier d50 = 5.7 µm, irregular morphology) compounded with a commercial M1 binder, and material extrusion (MEX) of a commercial gas-atomized SAE H13 filament processed on a Markforged Metal X. Rheological screening selected a 57:43 vol% powder-to-binder ratio for the in-house HE feedstock, and DSC/TGA measurements defined two-step debinding windows. The best HE conditions were 220 °C, 8 MPa, and 45 min for the in-house mixture, and 210 °C, 8 MPa, and 30 min for the granulated commercial filament; the latter showed a 0.15% linear deviation from the silicone replica diameter among the best-rated samples. Under the tested commercial MEX configuration, the pyramidal features were not resolved because the 0.40 mm deposition line width exceeded the target feature base width, causing the slicer to omit the sub-line-width geometry. The defect populations differed qualitatively: HE specimens showed porosity and local cracking associated with powder morphology and pressureless sintering, whereas MEX specimens showed build-direction-aligned inter-raster voids associated with the toolpath. Microhardness and tensile data are therefore interpreted as process-history-specific results rather than as a direct route ranking, because sintering conditions were not uniform across all specimens. The study defines an experimentally bound process-selection limit for SAE H13 micro-tooling: HE remains preferable for sub-nozzle surface features, whereas MEX remains attractive for macro-scale geometric freedom, if resolution, densification, and post-sintering consolidation are addressed. Full article

Accurate detection of field insect pests remains a significant challenge for precision agriculture due to the elongated and variable morphology of the target organisms, their frequent resemblance to complex background textures, and the long-tail distribution of species in natural datasets. While deep convolutional neural networks (CNNs) have advanced the field, they are often constrained by a limited effective receptive field and the entanglement of semantic and spatial features, which can lead to elevated false-positive rates and missed detections for low-contrast or rare targets. This paper introduces a novel detection framework that integrates state space modeling with multi-stream feature disentanglement to address these limitations. First, a visual state space module is employed as the backbone feature extractor, enabling the establishment of a global receptive field with linear computational complexity and thereby improving the perception of long-range morphological structures. Second, a Topological Feature Disentanglement Pyramid Network is proposed. This architecture explicitly separates feature representations into semantic and spatial streams and recombines them through graph convolutional interactions, which serves to suppress background interference and enhance localization precision. A meta-auxiliary detection head, active only during training, is introduced to amplify supervision signals for hard, low-contrast samples via adversarial gradient modulation. Furthermore, an implicit neural radiance field augmentation pipeline is used to generate physically consistent synthetic views of underrepresented pest classes, mitigating the negative effects of long-tail data distributions. Experimental evaluations on the public BAU-Insectv2 benchmark demonstrate that the proposed method achieves a mean average precision (mAP@0.5) of 81.8%, representing a 4.4-percentage-point improvement over a comparable baseline, while maintaining a compact parameter count of 2.33 M and an inference speed of 178.6 FPS. The framework exhibits particular efficacy in detecting elongated, minute, and rare pests, suggesting a promising technical approach for real-time, field-based pest surveillance in precision agriculture. Full article