Search Results (474)

As urban underground spaces and infrastructure development accelerate, subsurface elements such as buried pipelines, integrated utility tunnels, subway tunnels, cavity defects, and deep-seated hidden hazards become increasingly intertwined. Consequently, urban target detection is characterized by pronounced scale discrepancies, intense environmental interference, and severely confined operational spaces. The transient electromagnetic method (TEM) is highly valuable for rapid surveys and hazard identification in urban underground spaces owing to its inherent advantages, including non-contact operation, adaptability to hardened pavements, high sensitivity to low-resistivity anomalies, and the ability to probe a broad range of depths. In recent years, research has shifted from improving isolated instrumentation to synergistically optimizing sensing platforms, transmitter–receiver systems, anti-interference methodologies, and imaging interpretation workflows. Specifically, small-loop configurations and high-frequency excitation technologies have improved shallow-sounding capabilities in confined urban spaces; anti-interference techniques have increased data reliability in complex noise environments; and apparent resistivity mapping, virtual wave-field migration, and rapid inversion methodologies have enabled profiling results to transition from qualitative identification to fine-scale interpretation. Concurrently, the evolution of ground-towed, UAV-borne, helicopter-borne, and semi-airborne platforms has progressively endowed urban TEM profiling with continuous, mobile, and scenario-specific operational capabilities. Looking to the future, further technical breakthroughs in urban TEM technology are required to improve shallow-resolution, deep-seated penetration, multi-source interference decoupling, and real-time concurrent imaging. Full article

High-resolution range profile (HRRP) reconstruction is essential for extracting range-direction scattering characteristics in wideband radar remote sensing, particularly in synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging. Coded interrupted sampling (CIS) can improve radar low probability of intercept (LPI) performance by controlling signal transmission with a binary sequence. However, the reduced number of valid echo samples may degrade HRRP reconstruction, especially under low-duty-ratio and low signal-to-noise ratio (SNR) conditions. Conventional orthogonal matching pursuit (OMP) processes each frame independently and ignores the inter-frame continuity of scattering-center positions, which may lead to false selections and missed detections. To address this problem, this paper proposes a candidate-interval-assisted orthogonal matching pursuit (CI-OMP) algorithm based on multi-frame sequential priors. Stable scattering-center positions are extracted from historical reconstruction results and expanded into candidate intervals to guide atom matching in the current frame. Simulation results show that CI-OMP outperforms standard OMP in terms of normalized mean squared error (NMSE), tolerant support recovery rate (Tol-SRR), and peak-to-sidelobe ratio (PSLR). At a duty ratio of 0.20, CI-OMP reduces the NMSE by 1.71 dB and improves the PSLR by 7.56 dB compared with OMP. In addition, the candidate-interval strategy reduces the atom-search range by approximately 54–75% under different duty ratios and by approximately 50–83% under different SNRs, demonstrating improved search efficiency. These results demonstrate that CI-OMP improves the accuracy, robustness, and search efficiency of HRRP reconstruction for CIS radar echoes, particularly under low-duty-ratio and low-to-medium-SNR conditions. Full article

Atmospheric light detection and ranging (lidar) measurements of the attenuated backscatter coefficient (ABSC) form high-dimensional vertical profiles that vary across multiple temporal scales. Directly processing the original time-domain sequence with a single prediction structure may entangle information associated with these different scales. To address this issue, we propose FFT-FDNet, a frequency-domain decomposition network based on fast Fourier transform (FFT). FFT-FDNet explicitly decomposes the input sequence along the temporal dimension into low-, mid-, and high-frequency components. Branch-specific predictors model these components separately, and a learnable fusion layer combines their outputs to forecast future ABSC profiles. Experiments were conducted using continuous single-site lidar observations from the Tokyo station at a temporal resolution of 15 min. FFT-FDNet was compared with eight representative time-series forecasting models at forecast horizons of 2, 4, and 6 h. Across the three horizons, FFT-FDNet achieved the lowest mean ${\mathrm{MAE}}_{\mathrm{std}}$ and ${\mathrm{MSE}}_{\mathrm{std}}$ and the highest mean $R^2$ among the evaluated methods. At the 2 h horizon, these metrics were 0.1221, 0.4116, and 0.7505, respectively. The ablation results showed consistent performance degradation after removing the FFT decomposition, low-frequency branch, or mid-frequency branch, whereas the high-frequency branch provided modest improvements at some forecast horizons. The frequency band sensitivity analysis supported the use of $({\nu }_1,{\nu }_2)=(0.10,0.45)$ among the evaluated cutoff combinations. These results suggest that FFT-based three-band decomposition is useful for the present Tokyo single-station short-term forecasting case. Further validation using data from more stations, seasons, and aerosol conditions is still needed. Full article

Floating photovoltaics (FPV) offer a sustainable approach to renewable energy production while enhancing water resource management by reducing land use, improving module efficiency through natural cooling, and limiting water evaporation. However, large-scale deployment requires careful site selection, operational feasibility assessment, and integration with existing water management practices. This study presents a fully cloud-native Sentinel-2-based framework to map recent water surface extents of reservoirs and reconstruct their bathymetric profiles. Developed within the Google Earth Engine (GEE) environment, the approach employs harmonized time series, automated cloud filtering, and median composites combined with adaptive NDWI/MNDWI thresholds to generate high-resolution (10 m) water masks. These dynamic surface extents are integrated with monitored water levels to estimate bathymetry through an annual multitemporal log-ratio band-switching configuration. The method has been tested across the nine main strategic reservoirs of the Basilicata region (Southern Italy). The water-surface extraction approach achieved an Overall Accuracy of 95.33% and a Kappa coefficient of 0.907 in the internal thematic assessment. The bathymetric reconstruction results led to relative volume errors for medium-to-large water bodies ranging between 13.8% and 35.1%, while larger percentage discrepancies in smaller impoundments were driven by scale-dependent normalization effects on low storage volumes. Overall, the proposed method offers a scalable, cost-effective, and Earth Observation (EO)-driven screening tool for the initial site selection, capacity assessment, and planning of FPV systems, particularly in data-scarce regions lacking updated bathymetric surveys. Full article

The Raša Fault is a prominent seismically active strike-slip fault within the Dinaric Fault System in SW Slovenia, seismotectonically estimated to be capable of producing earthquakes up to Mw = 7.4. Since the surface exposure of fault-related markers is discontinuous, and the near-surface expression of deformation is poorly constrained, there is a need to improve the detection of fault-related features in complex sedimentary environments. In such settings, signal attenuation, complex stratigraphy, and irregular fault-zone geometries often obscure subtle deformation features, limiting the interpretability of standard 2-D geophysical profiles. A pseudo 3-D Ground-Penetrating Radar (GPR) survey, along with complementary Electrical Resistivity Tomography (ERT) surveys and reprocessing of LiDAR (light detection and ranging) data to obtain high-resolution Digital Elevation Models (DEMs), was conducted in selected environments dominated by low-resistivity karstic deposits and highly heterogeneous fluvial sediments to assess and improve the capability to detect and characterize subtle shallow deformations associated with the Raša Fault. Tectonic geomorphological mapping facilitated the recognition of potentially active fault traces and the identification of Quaternary sedimentary and erosional features, where recent deformations are usually preserved and can be dated in further paleoseismological investigations. The analysis of dense GPR data and complementary ERT profiles enabled us to clearly image the fault deformation pattern and obtain quantitative information about the subsurface, showing details of faulting and related deformation structures not evident at the surface. Furthermore, it enabled the detection of fault zone complexity, revealing it as an irregular and laterally changing area with sediment infillings, rather than a single vertical discontinuity. The complexity of faulting in the near surface depends on many factors, including the competence and age of the faulted material, as well as the local geomorphology. This study has demonstrated the applicability of pseudo 3-D GPR surveying, combined with ERT profiles, for subsurface mapping of active strike-slip faults in karstic and fluvial sedimentary environments. The methodology can be recommended in particular for rapid and cost-effective investigation of sites with subtle surface evidence of active faulting in order to determine near-surface fault splaying. Full article

Background: Ultrasound-guided radiofrequency ablation (RFA) has become an established minimally invasive treatment for selected benign and autonomously functioning thyroid nodules. Although clinical outcomes are well documented, the biological mechanisms underlying tissue remodeling after ablation remain incompletely characterized. The integration of imaging, molecular, and histopathological data may provide a more comprehensive understanding of treatment response. Methods: We conducted a prospective monocentric observational translational cohort study including 60 patients undergoing ultrasound-guided RFA for benign or autonomously functioning thyroid nodules between November 2024 and December 2025. Patients underwent standardized pre-procedural assessment including high-resolution ultrasound with volumetric evaluation, three-dimensional reconstruction, and collection of peripheral blood samples for exploratory extracellular vesicle (EV)-derived microRNA (miRNA) profiling. Clinical outcomes, volumetric changes, vascular remodeling, and procedural complications were assessed during scheduled follow-up visits (mean follow-up duration: 11 months; range: 5–17 months). A subgroup of patients who subsequently required surgery because of insufficient volumetric response underwent thyroidectomy, allowing histopathological evaluation of post-ablation tissue changes. Clinical, imaging, molecular, and pathological data were integrated within a multimodal translational framework. Results: RFA resulted in a mean nodule volume reduction of 56% at early follow-up, with progressive volumetric reduction observed during longitudinal ultrasound evaluation. A clinically relevant improvement in compressive symptoms was observed, with a 44% reduction in symptom burden. Doppler ultrasound demonstrated progressive changes in vascular patterns consistent with post-ablation devascularization and tissue remodeling. Ten patients underwent thyroidectomy after RFA because of insufficient volumetric response, providing histological validation of treatment-induced tissue alterations, including coagulative necrosis, fibrosis, inflammatory changes, and architectural remodeling. Exploratory EV-derived miRNA profiling was incorporated into the translational workflow to investigate potential molecular correlates of treatment response; however, molecular findings require further validation in larger cohorts. Conclusions: This study provides a multimodal translational characterization of tissue response after thyroid RFA by integrating clinical outcomes, ultrasound imaging, histopathology, and exploratory EV-derived miRNA analysis. The proposed framework may contribute to a deeper understanding of biological changes following thermal ablation and support future investigations aimed at identifying reliable biomarkers of treatment response. Full article

Multi-label remote sensing scene classification requires identifying multiple land-cover categories from a single high-resolution aerial image. Existing methods strengthen visual features or model label dependencies, yet they apply a fixed calibration strategy regardless of the underlying label-density regime, leading to over-prediction on dense scenes or under-correction on sparse scenes. We propose Density-Adaptive CDG Calibration (DARC), a lightweight framework that explicitly conditions calibration on dataset label density. DARC comprises three modules: (1) Label-density Driven Profile Selection (DDP) automatically routes the calibration path based on training-set density statistics; (2) Label-token Correlative-Discriminative Graph Mixing (CDM) injects both co-occurrence and exclusivity relations into label semantic tokens through positive and negative graph propagation; (3) Density-aware Gated Calibration (DCM) applies cardinality-controlled gating for dense labels and EMA-stabilized graph calibration for sparse labels. Experiments on AID-ML and UCM-ML demonstrate that DARC achieves 90.12% and 89.05% sample-F1, respectively, outperforming six competitive baselines including SFIN, ASL, C-Tran, ML-Decoder, SPIN, and Two-Way Loss by 1.65–3.87%, while introducing only 1.42% additional parameters. Cross-regime routing analysis confirms that no single fixed strategy matches DARC’s adaptive approach, and sensitivity analysis shows the routing is robust across a wide threshold range. Ablation studies validate the necessity of each component, and visualization analyses demonstrate that the learned label graphs capture interpretable semantic patterns. Full article

Stratospheric airship navigation requires accurate wind field measurements at a ~20 km altitude, where low pressure and density limit the effectiveness of conventional wind sensors. To address this, we present a compact direct-detection Rayleigh Doppler wind lidar based on the molecular double-edge technique. The system utilizes a 532 nm fiber-coupled pulsed laser (0.5 W, 5 ns) and a fixed-cavity dual-channel Fabry–Perot etalon as the frequency discriminator. A liquid crystal variable retarder (LCVR) combined with a polarization beam splitter (PBS) enables non-mechanical, high-speed beam switching between two orthogonal line-of-sight (LOS) directions for horizontal wind measurement. Systematic tests are performed in controlled wind fields within Mie-dominated and Rayleigh-dominated regimes. The lidar effectively captures the sharp radial velocity profiles at wind speeds up to 7.6 m/s. Comparative experiments with a reference anemometer show that the system delivers reliable performance at 0.48 m range resolution, with measurement uncertainty below 0.34 m/s. With its compact, lightweight, and high-precision design, the developed lidar demonstrates reliable wind measurement capability under laboratory conditions, indicating its potential for future deployment on stratospheric airships. Full article

The accurate representation of frontal zones—characterized by sharp scalar gradients—remains a critical challenge in regional objective analysis and data assimilation, particularly when utilizing sparse or stochastically distributed observations. This study evaluates the efficacy of Multiquadric Radial Basis Functions (RBFs) as a high-order alternative to standard spatial mapping operators frequently used in machine learning atmospheric emulators. We contrast the performance of the regularized, $C^{\infty }$-continuous RBF approach against nearest neighbor and linear mesh interpolation schemes using both synthetic baroclinic wave profiles and an operational case study of the intense extratropical cyclone that impacted the East Coast of North America in mid-March 1993. To mitigate characteristic boundary artifacts and geometric clipping in bounded regional domains, we implement a targeted numerical stabilization framework combining localized boundary mirroring with four-corner domain anchoring. Our quantitative results demonstrate that the optimized RBF framework substantially improves gradient fidelity and reduces Root Mean Square Error across a wide range of observation densities. Furthermore, we evaluate the computational scalability of RBFs on high-performance computing architectures, demonstrating how Algebraic Multigrid solvers and Graphics Processing Unit acceleration mitigate the foundational $O\left(N^3\right)$ computational bottleneck. We conclude that RBF interpolation provides a physically consistent, analytically differentiable manifold that addresses the derivative discontinuities of traditional linear methods, offering a stable pre-processing framework for high-resolution meteorological analysis and machine learning optimization. Full article

With the increasing complexity of the electromagnetic environment, traditional radar target recognition methods face severe challenges. High-resolution range profile (HRRP) and Radar Cross Section (RCS), as two important radar features, each has its own advantages in target recognition but also exhibits limitations. To enhance radar target recognition performance in complex scenarios such as low signal-to-noise ratio (SNR), this paper proposes a recognition method based on heterogeneous multi-modal feature fusion. The proposed method constructs a three-channel parallel encoding network, which utilizes Convolutional Long Short-Term Memory (ConvLSTM), One-Dimensional Convolutional Gated Recurrent Unit (Conv1D-GRU), and Gated Recurrent Unit (GRU) to extract deep discriminative features from raw HRRP sequences, RCS sequences, and HRRP statistical features, respectively. Furthermore, it innovatively designs a dual-path cooperative fusion mechanism, achieving explicit inter-modal correlation modeling through a cross-attention module and dynamically learning the importance of each modality through an adaptive weight fusion layer, thereby realizing deep complementarity and enhancement of multi-modal information. Experimental results demonstrate that under various signal-to-noise ratios and polarization conditions, the proposed method achieves a maximum average recognition accuracy of over 99% for 6 ship targets. Compared with the traditional three-channel fixed-weight fusion method, the recognition accuracy of the proposed method increases from 90.01% to 99.42% under co-polarization, and from 81.14% to 98.15% under cross-polarization, fully validating the effectiveness and superiority of the dual-path fusion mechanism. Full article

The voltage-gated sodium channel Nav1.7 is a strongly validated target for the development of novel, non-opioid analgesics due to its genetic link to pain signaling. To accelerate the discovery of safe Nav1.7 modulators, this study outlines an integrated computational pipeline to repurpose FDA-approved drugs. A structurally complete model of the Nav1.7 central pore was generated via homology modeling from a high-resolution cryo-EM structure (PDB: 7W9K) to ensure a physically consistent model suitable for dynamic simulations. We conducted a structure-based virtual screening of 2296 FDA-approved compounds, identifying four promising candidates (DB04868, DB00941, DB01419, and DB15982) with strong predicted affinities ranging from −11.38 to −12.57 kcal/mol. Interaction fingerprinting revealed that binding is predominantly driven by hydrophobic contacts with conserved pore-lining residues, including Phe1503, Leu1010, and Ile1500. To validate these static predictions, the top protein–ligand complexes were subjected to single-replica 250 ns molecular dynamics (MD) simulations. Comprehensive trajectory analyses, including RMSD, RMSF, and principal component analysis, revealed a notable discrepancy between static docking scores and dynamic stability. The highest-scoring docking candidate, DB04868, exhibited substantial conformational flexibility and reduced stabilization under simulated physiological conditions. Conversely, DB01419, despite a lower initial docking rank, demonstrated the highest structural stability across all metrics and uniquely formed intermittent stabilizing hydrogen bonds. These findings underscore the value of post-docking MD validation in computational drug discovery and nominate DB01419 and DB15982 as candidate scaffolds that warrant subsequent experimental validation, including electrophysiological characterization and Nav-isoform selectivity profiling. We emphasize that these are computational predictions: in silico binding stability is not equivalent to functional inhibition of Nav1.7 currents, and the lead designations reported here remain hypothesis-generating until confirmed by patch-clamp and biochemical assays. Full article

This study introduces a novel empirical model to estimate the total pressure drag generated by trapped lee waves (TLW) and upward-propagating internal waves in moderate-to-strong non-hydrostatic, stratified flow over a mountain ridge, as a function of flow non-linearity. The core framework is based on a two-layer atmosphere characterized by a piecewise-constant Scorer parameter, l, where a lower layer of constant $l_1$ underlies an upper layer with $l_2<l_1$. This framework incorporates key features to extend beyond idealized assumptions, providing a reliable tool for predicting non-linear flow regimes over mountainous terrain, particularly those featuring realistic vertical profiles of the Scorer parameter. To develop the empirical formulation, a micro- to mesoscale numerical model is employed to simulate realistic, non-linear flows over steep topography. The proposed empirical model yields results that compare favorably with numerical simulations across a range of moderate-to-strong non-hydrostatic regimes, including complex cases derived from observational data and realistic vertical profiles of the Scorer parameter. The model demonstrates robust performance ranging from strongly to moderately non-hydrostatic regimes (the latter corresponding to dimensionless half-widths of approximately 5), and provides accurate drag estimates for non-linearities up to a dimensionless mountain height of approximately unity. Therefore, this empirical approach serves as a valuable foundation for improving drag parameterizations in weather prediction models, offering a computationally efficient alternative to high-resolution numerical downscaling over steep terrain. Full article

With the advancement of cognitive radar, applying deep neural networks to radar jamming recognition has become an indispensable research direction. However, as a common anti-jamming measure, the agility of radar waveform parameters degrades the effectiveness of jamming recognition, creating a trade-off between jamming recognition and anti-jamming agility. Specifically, for the same type of jamming, radar agility in frequency, pulse width, and bandwidth alters the profile and scale features of the jamming, posing challenges to conventional CNN-based jamming recognition. To address this challenge, this paper proposes an Adaptive Feature-Focusing CNN (AFF-CNN). A pre-trained AFF module is designed to establish a mapping between agile parameters and adaptive feature scales. Operating on time-domain high-resolution range profiles (HRRP) and time–frequency domain short-time Fourier transform (STFT) data, this module calibrates deviations induced by radar inter-pulse parameter agility and enhances the capability of salient signal feature-focusing. Furthermore, a lightweight 1D-2D feature fusion CNN is designed to process these adaptive features and recognize jamming using single-pulse signals, thereby enhancing the network’s adaptability to inter-pulse parameter agility in radar systems. Simulation results demonstrate superior recognition accuracy and generalization capability compared to five comparative approaches, confirming effective adaptation to inter-pulse agility scenarios. Full article

The sodium-coupled citrate transporter NaCT (SLC13A5) imports extracellular citrate into cells. In the CNS, SLC13A5 is described to be expressed predominantly in neurons. Cytosolic citrate levels rely on citrate generated in mitochondria and imported from other CNS cells, regulating intermediary metabolism and supplying acetyl-CoA for lipid synthesis and histone acetylation. Despite evidence for NaCT’s role in neurometabolic homeostasis, its transcriptional behavior across Alzheimer’s disease (AD) progression and across astrocyte subtypes remains uncharacterized at single-cell resolution. We analyzed single-nucleus RNA sequencing data from 1,378,211 nuclei across 84 donors in the Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) Middle Temporal Gyrus dataset to profile SLC13A5 and seven citrate metabolism genes across a continuous AD pseudoprogression score. SLC13A5 expression was restricted to astrocytes (~20% prevalence) and concentrated in the Astro 2 supertype (24.0%), a homeostatic subtype characterized by low C3 (1.6%) and CD44 (5.5%), which expanded with pseudoprogression (Spearman rho = +0.345, FDR < 0.001). The A1-reactive Astro 3 supertype, where SLC13A5 prevalence was 0.87%, declined concordantly (rho = −0.393). Opposing compositional and transcriptional forces produced apparent stability in overall SLC13A5 prevalence. SLC13A3 and ACO1 showed progressive donor-level declines correlating with Braak stage and Thal phase (rho range: −0.307 to −0.349, FDR < 0.01). APOE4 carriers exhibited lower SLC13A5 prevalence specifically within Astro 2 nuclei (median 17.6% vs. 25.9%; Wilcoxon p = 0.025), though this association did not survive multivariate regression. No difference in Astro 2 SLC13A5 expression was detected between cognitively resilient and expected-AD donors with equivalent high Braak burden (p = 0.888). Contrary to the prevailing description of NaCT as a neuronal transporter, SLC13A5 transcript in the SEA-AD MTG dataset was detected almost exclusively in astrocyte nuclei, concentrated in the homeostatic Astro 2 subtype, and maintained as this subtype expanded with advancing AD pathology. Because these are nuclear transcript measurements, they delimit where SLC13A5 mRNA is detectable rather than establishing the cellular site of NaCT protein or activity, which requires in situ validation. Full article

Background: Traditional genotype-guided dosing often fails to predict real-time variability in the metabolic phenotype during complex polypharmacy. This secondary analysis of a retrospective cohort aims to elucidate mechanisms underlying real-time phenoconversion during antipsychotic therapy, focusing on homozygous loss-of-function CYP3A5*3/*3 non-expressors. Methods: Using an additive phenoconversion model that integrates a genotype-derived baseline with environmental modifiers for drug–drug interactions (DDI), systemic inflammation (CRP), and renal function (eGFR), we demonstrate that the expressed metabolic phenotype is a dynamic, context-dependent construct that can markedly diverge from the genotype-predicted state. Objectives: Our data show that patients with CYP3A5*3/*3 and CYP3A inhibitors (e.g., ritonavir) had a quetiapine plasma concentrations reached 1850 ng/mL, corresponding to 3.7-fold above the internationally accepted therapeutic reference range of 100–500 ng/mL. Acute systemic inflammation (CRP > 50 mg/L) induced a functional poor metabolizer phenotype (P_act < −0.9) in individuals with a genotypic normal metabolizer status. In contrast, strong inducers such as carbamazepine, phenytoin, and heavy smoking promoted an ultra-rapid metabolizer state (CL_ind > 4.0 L/h, quetiapine < 30 ng/mL), consistent with treatment failure. In this cohort, the additive P_act model showed a strong association with observed clearance and identified clinically relevant phenoconversion mechanisms not predicted from genotype alone. Conclusions: These results support a dynamic, multi-parametric approach that integrates pharmacogenomics, therapeutic drug monitoring, biomarker profiling (CRP, eGFR), and structured DDI assessment to enable higher-resolution, real-time phenotype tracking and more informed dose individualization in high-risk psychiatric polypharmacy. Full article