Search Results (5,526)

Multi-channel common-aperture optical systems, which excel at simultaneous multi-spectral information acquisition, are widely used for image fusion. However, complex systems for long-distance multi-band detection suffer from difficulties in assembly and adjustment and light vignetting. To resolve this, the paper proposes a modular design method that splits the optical path into independent modules: the common-aperture optical path adopts an off-axis reflective beam-shrinking structure to extend the focal length and ensure 100% light input, compared with coaxial multi-channel common-aperture systems. The relay optical path of each spectral channel uses a continuous zoom design for smooth detection–recognition switching. Based on the method, a three-channel common-aperture system is developed integrating visible light (VIS), short-wave infrared (SWIR), and mid-wave infrared (MWIR). The modulation transfer function (MTF) and wavefront distribution of the common-aperture optical path approach the diffraction limit. After integration with the relay optical paths, the system, without global optimization, can achieve the following performance: the root mean square (RMS) across the full field of view (FOV) at different focal lengths for each channel is smaller than the detector pixel size (3.45 μm for VIS, 15 μm for SWIR/MWIR); the MTF exceeds 0.2 at the cutoff frequency. Subsequently, the results of the tolerance analysis verify the feasibility of the design for each module and the advantage of the modular layout in the assembly and adjustment of the system. Finally, the paper discusses the influence of parallel plates on the wavefront distortion of the system and proposes optimization thinking using freeform surfaces. The design results of the study validate the feasibility of the modular layout in simplifying the design and assembly of multi-channel common-aperture optical systems. Full article

The Lower North River (LNR) exhibits a distinctive anabranching pattern in the Pearl River Basin, China. However, research has predominantly focused on vertical channel adjustments relying on in situ measurements, while the large-scale spatiotemporal dynamics of the anabranching planform have received limited attention. To address this gap, this study quantified the evolution of the anabranching planform from 1990 to 2022 using remote sensing images, focusing on anabranching intensity and island morphology, and analyzed driving factors using hydrological observations. Results revealed three evolutionary phases driven by shifting dominance of human interventions. During the first phase (1990–2004), the LNR experienced a moderate decline in anabranching intensity and widespread shrinkage of river islands, primarily attributed to sediment starvation induced by upstream dams. In the second phase (2004–2013), the decline in anabranching intensity accelerated and the proportion of expanding islands increased, driven by unregulated sand mining and channel regulation. In the third phase (2013–2022), the rapid decline in anabranching intensity decelerated and the islands shifted from a shrinkage-dominated to a stable-dominated state following the implementation of strict mining management and the physical confinement imposed by engineering structures. These findings reveal distinct morphological responses of the LNR to flow–sediment regimes and anthropogenic physical interventions, offering insights into the sustainable management of large anabranching rivers worldwide in the Anthropocene. Full article

The exceptional adjustability and ambipolar behavior of graphene offer significant potential for next-generation optoelectronics, where the conductivity of graphene is primarily modulated by the interface field of heterojunction. However, interface defects, which are inevitably introduced during fabrication, severely limit the effectiveness of gate voltage modulation. Although the layer-by-layer transfer method can effectively enhance conductivity, it also raises the carrier concentration and impairs the symmetry of ambipolar characteristics. This work presents a stacked multi-gate graphene transistor in which synergistic modulation enables efficient regulation of channel conductivity while maintaining low carrier concentration. Simulations are carried out to analyze how mobility, doping concentration, and the number of stacking layers influence the modulation of conductivity. Experimentally, a three-layer stacked graphene structure with distributed source and drain electrodes is fabricated. The device exhibits pronounced ambipolar transfer characteristics and demonstrates a clear improvement in transconductance compared to its conventional one-layer graphene counterpart. This research offers a feasible design strategy for high-performance, vertically integrated graphene-based electronic devices. Full article

The stochastic fluctuations of AI computational loads inject harmonic currents into the DC bus, amplifying bus voltage ripples and weakening the power quality. Existing strategies typically rely on high-gain control strategies to minimize harmonic output impedance, aiming at full absorption of harmonic currents. However, such designs rarely consider engineering constraints such as capacity and current boundaries, which impose inherent limits on harmonic absorption. To address these issues, this paper proposes an adaptive harmonic impedance control and flexible compensation method for AI data centers. By integrating DC bus voltage feedforward with output current feedback, a virtual harmonic impedance control channel is constructed to enable real-time impedance shaping. Then, an adaptive gain regulation mechanism is developed to adjust harmonic impedance according to the available capacity and current margin. Compared with traditional strategies relying on fixed high gains or resonant links, the proposed method allows for the continuous regulation of harmonic impedance over a wide range. This enables the dynamic matching of harmonic absorption capability with the available capacity, effectively suppressing the risks of overcurrent, saturation, and stability degradation. Simulation and 8 kW experimental results verify the correctness and effectiveness of the proposed analysis and control strategy. Full article

Introduction: Resting-state EEG (rsEEG) is a scalable window onto trait-like “executive readiness,” but findings have been fragmented by task impurity on the executive-function (EF) side and heterogeneous EEG pipelines. This review synthesizes rsEEG features that reliably track EF in healthy samples across development and aging and evaluates moderators such as cognitive reserve. Materials and methods: Following PRISMA 2020, we defined PECOS-based eligibility (human participants; eyes-closed/eyes-open rsEEG; spectral, aperiodic, connectivity, topology, microstate, and LRTC features; behavioral EF outcomes) and searched MEDLINE/PubMed, Embase, PsycINFO, Web of Science, Scopus, and IEEE Xplore from inception to 30 August 2025. Two reviewers were screened/double-extracted; the risk of bias in non-randomized studies was assessed using the ROBINS-I tool. Sixty-three studies met criteria (plus citation tracking), spanning from childhood to old age. Results: Across domains, tempo, noise, and wiring jointly explained EF differences. Faster individual/peak alpha frequency (IAF/PAF) related most consistently to manipulation-heavy working may and interference control/vigilance in aging; alpha power was less informative once periodic and aperiodic components were separated. Aperiodic 1/f parameters (slope/offset) indexed domain-general efficiency (processing speed, executive composites) with education-dependent sign flips in later life. Connectivity/topology outperformed local power: efficient, small-world-like alpha networks predicted faster, more consistent decisions and higher WM accuracy, whereas globally heightened alpha/gamma synchrony—and rigid high-beta organization—were behaviorally sluggish. Within-frontal beta/gamma coherence supported span maintenance/sequencing, but excessive fronto-posterior theta coherence selectively undermined WM manipulation/updating. A higher frontal theta/beta ratio forecasts riskier, less adaptive choices and poorer reversal learning for decision policy. Age and reserve consistently moderated effects (e.g., child frontal theta supportive for WM; older-adult slow power often detrimental; stronger EO ↔ EC connectivity modulation and faster alpha with higher reserve). Boundary conditions were common: low-load tasks and homogeneous young samples usually yielded nulls. Conclusions: RsEEG does not diagnose EF independently; single-band metrics or simple ratios lack specificity and can be confounded by age/reserve. Instead, a multi-feature signature—faster alpha pace, steeper 1/f slope with appropriate offset, efficient/flexible alpha-band topology with limited global over-synchrony (especially avoiding long-range theta lock), and supportive within-frontal fast-band coherence—best captures individual differences in executive speed, interference control, stability, and WM manipulation. For reproducible applications, recordings should include ≥5–6 min eyes-closed (plus eyes-open), ≥32 channels, vigilant artifact/drowsiness control, periodic–aperiodic decomposition, lag-insensitive connectivity, and graph metrics; analyses must separate speed from accuracy and distinguish WM maintenance vs. manipulation. Clinical translation should prioritize stratification and monitoring (not diagnosis), interpreted through the lenses of development, aging, and cognitive reserve. Full article

Rapid urbanization has led to increasing structural modification of river catchments through dam construction and concrete-lining of natural channels as flood management measures. These interventions can alter the natural hydrology. This necessitates assessment of their influence on hydrology at a catchment scale. However, such evaluations are particularly challenging in data-scarce regions such as the Chongwe River Catchment, where hydrometric records capturing conditions before and after structural modifications are limited. Therefore, we applied a 2D rain-on-grid approach in HEC-RAS to evaluate changes in high-flow responses to short-duration, high-intensity rainfall events in the Chongwe River Catchment in Zambia, where structural interventions have been implemented. The terrain was modified in HEC-RAS to represent 21 km of concrete drains and ten dams. Sensitivity analysis conducted on five key model parameters showed that parameters controlling surface runoff generation, particularly curve number, exerted the strongest influence on simulated peak flows, while routing-related parameters had a secondary effect. Model calibration and validation showed strong performance with R² = 0.99, NSE = 0.75 and PBIAS = −0.68% during calibration and R² = 0.95, NSE = 0.75, PBIAS = −2.49% during validation. Four scenarios were simulated to determine the hydrological effects of channel concrete-lining and dams. The results showed that concrete-lining of natural channels in the urban area increased high flows at the main outlet by approximately 4.6%, generated localized instantaneous maximum channel velocities of up to 20 m/s, increased flood depths by up to 11%, decreased lag times and expanded flood inundation widths by up to 15%. The existing dams reduced peak flows by about 28%, increased lag times, reduced flood depths by about 11%, and reduced flood inundation widths by up to 8% across the catchment. The findings demonstrate that enhancing stormwater conveyance through concrete-lining must be complemented by storage to manage high flows, while future work should explore nature-based solutions to reduce channel velocities and improve sustainable flood mitigation. Therefore, the study provides event-scale insights to support flood-risk management and infrastructure planning in rapidly urbanizing, data-scarce catchments. Full article

In modern marine seismic exploration, ocean bottom node (OBN) acquisition systems are increasingly valued for their flexibility in deep-water complex structural surveys. However, the high operational costs associated with OBN systems often lead to spatially sparse sampling, which adversely affects the fidelity of wavefield reconstruction. To overcome these limitations, hybrid deep learning frameworks that integrate physics-driven and data-driven approaches show significant potential for interpolating OBN four-component (4C) seismic data. The proposed frequency-domain residual-attention U-Net (ResAtt-Unet) architecture systematically exploits the inherent physical correlations among 4C data to improve interpolation performance. Specifically, an innovative dual-branch dual-channel network topology is designed to process OBN 4C data by grouping them into complementary P–Z (hydrophone–vertical geophone) and X–Y (horizontal geophone) pairs. A synchronized joint training strategy is employed to optimize parameters across both branches. Comprehensive evaluations demonstrate that the ResAtt-Unet achieves superior performance in component-wise interpolation, particularly in preserving signal fidelity and maintaining frequency-domain characteristics across all seismic components. Future work should focus on expanding the training dataset to include diverse geological scenarios and incorporating domain-specific physical constraints to improve model generalizability. These advancements will support robust seismic interpretation in challenging ocean-bottom environments characterized by complex velocity variations and irregular illumination. Full article

The proliferation of data-intensive IoT applications has created unprecedented demand for wireless spectrum, necessitating more efficient bandwidth management. Spectrum sensing allows unlicensed secondary users to dynamically access idle channels assigned to primary users. However, traditional sensing techniques are hindered by their sensitivity to noise and reliance on prior knowledge of primary user signals. This limitation has propelled research into machine learning (ML) and deep learning (DL) solutions, which operate without such constraints. This study presents a comprehensive performance assessment of prominent ML models: random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) against DL architectures, namely a convolutional neural network (CNN) and an Autoencoder. Evaluated using a robust suite of metrics (probability of detection, false alarm, missed detection, accuracy, and F1-score), the results reveal the clear and consistent superiority of RF. Notably, RF achieved a probability of detection of 95.7%, accuracy of 97.17%, and an F1-score of 96.93%, while maintaining excellent performance in low signal-to-noise ratio (SNR) conditions, even surpassing existing hybrid DL models. These findings underscore RF’s exceptional noise resilience and establish it as an ideal, high-performance candidate for practical spectrum sensing in wireless networks. Full article

Background: Inherited retinal dystrophies are a heterogeneous group of progressive disorders impacting photoreceptor function, often limiting the usefulness of standard electroretinography in advanced cases. Full-field stimulus test (FST) testing has become a sensitive psychophysical technique for detecting residual visual function when traditional electrophysiology is non-recordable. This study evaluated the ability of chromatic FST to differentiate rod–cone from cone photoreceptor dysfunction in patients with genetically confirmed inherited retinal dystrophies. Methods: Cross-sectional FST data were analyzed from 39 patients (mean age 45.7 ± 20.0 years) with genetically confirmed inherited retinal dystrophies at a tertiary academic center. All participants underwent standardized FST testing using white, red, and blue stimuli. Patients were classified into rod–cone dystrophy (n = 27) or cone dystrophy (n = 12) groups based on genetic and clinical criteria. Group comparisons focused on FST thresholds and especially blue–red threshold differences as markers of photoreceptor-mediated function. Bonferroni correction was applied to adjust for multiple comparisons across four primary FST parameters. Additional analyses by genotype were performed with nonparametric tests. Results: Eight different genetic mutations were represented, including Phosphodiesterase 6A (PDE6A) (n = 10), Rhodopsin (RHO) (n = 7), Phosphodiesterase 6B (PDE6B) (n = 6), Cyclic Nucleotide-Gated Channel Beta 1 (CNGB1) (n = 4), Cyclic Nucleotide-Gated Channel Alpha 3 (CNGA3) (n = 4), Nuclear Receptor Subfamily 2 Group E Member 3 (NR2E3) (n = 4), Guanylate Cyclase 2D (GUCY2D) (n = 2), and Cyclic Nucleotide-Gated Channel Beta 3 (CNGB3) (n = 2). Blue–red FST threshold differences exhibited moderate group discrimination in uncorrected analysis, with rod–cone dystrophies averaging −8.35 ± 10.37 dB and cone dystrophies −11.20 ± 14.60 dB. The area under the receiver operating characteristic curve for blue–red difference was 0.74 (95% CI: 0.59–0.90), with 75% sensitivity and 70.4% specificity at a −10 dB cutoff. However, no chromatic FST parameter maintained statistical significance between groups after Bonferroni correction. Inter-eye FST correlation was high (r = 0.758, p < 0.001), supporting test reliability. Conclusions: Chromatic FST testing provides a practical and sensitive means to assess photoreceptor function in advanced inherited retinal dystrophies, particularly when standard electrophysiologic methods are uninformative. Although the blue–red threshold difference offers moderate discrimination between rod–cone and cone dystrophies in uncorrected analysis, no chromatic parameter reached statistical significance after adjustment for multiple testing. Chromatic FST should be considered a supplementary approach for clinical monitoring and therapeutic studies in advanced retinal dystrophies, with further validation needed in larger cohorts. Full article

Conventional multi-channel ultrasonic motor (USM) drive systems commonly adopt a one-motor–one-driver architecture, in which each drive channel requires an independent isolated power supply and inverter stage. As the number of motors increases, the system volume and structural complexity grow significantly. To address this issue, this paper proposes a shared DC-bus multi-channel drive architecture for traveling-wave USM. In the proposed scheme, multiple half-bridge power stages are connected in parallel to a common high-voltage DC-bus to achieve centralized energy supply and distributed driving. A DC-side midpoint reference network is introduced to establish an AC voltage reference under a unipolar DC supply, while an independent series matching inductor is employed in each channel to shape the half-bridge output into a quasi-sinusoidal motor-terminal voltage through resonant filtering. Based on the equivalent electrical model of the USM, a unified analytical model is established to analyze the voltage formation mechanism under shared DC-bus conditions. Time-domain simulations and experimental tests are carried out on a two-channel prototype operating at a 150 V DC-bus and a 40 kHz switching frequency. The results demonstrate stable quasi-sinusoidal output voltages, preserved phase consistency, and limited inter-channel coupling during parallel operation. Compared with conventional independent-supply solutions, the proposed architecture achieves an approximately 27% reduction in overall system volume for a three-motor configuration, demonstrating good scalability for compact multi-channel USM drive systems. Full article

Frequent drilling fluid lost circulation in the Kuqa foreland area of the Tarim Oilfield severely constrains drilling efficiency and safety. The complex formation structures and diverse lost circulation types in this region are compounded by a lack of systematic classification in existing studies and weak correlation between mechanism analysis and field plugging measures, leading to a deficiency in quantitative decision-making for lost circulation prevention and control. Based on lithology analysis, loss zone pressure differential calculation, well log interpretation, and core observations, this study establishes an integrated “formation–lithology–pressure” diagnostic and classification method for lost circulation. A systematic classification framework comprising five types of lost circulation channels and mechanisms was developed. Based on this, the dominant lost circulation types and characteristics of three typical vertical formations in the Kuqa foreland were clarified: ① The supra-salt sandy conglomerate formations (e.g., Q1x, N2k) are dominated by permeability loss, where the loss rate (V) and bottomhole pressure differential (ΔP) exhibit a strong positive correlation (V ∝ ΔP). On-site application of graded bridging plugging formulations achieved a first-attempt success rate of ≥90%. ② The salt–gypsum formations (E1-2km) are primarily characterized by induced fracture loss, with a weak correlation between V and ΔP and dynamic fracture opening/closing behavior. Conventional rigid plugging materials showed limited effectiveness, resulting in a first-attempt success rate of <50%. ③ The K1bs formation is dominated by vertically developed natural fracture loss, where V and ΔP also demonstrate a strong positive correlation. In a specific Keshen block, a power-law relationship between the fracture aperture (W) and loss rate was established (W = 0.26·V^0.62, R² = 0.98), providing a basis for predicting fracture aperture and optimizing plugging formulations, with a plugging success rate of ≥80%. The classification system and quantitative criteria developed in this study effectively link lost circulation mechanisms, dynamic characteristics, and engineering countermeasures, offering theoretical support and a decision-making framework for optimizing lost circulation prevention and control measures and improving success rates in the Kuqa foreland area. Full article

Precise medical image segmentation plays a vital role in disease diagnosis and clinical treatment. Although U-Net-based architectures and their Transformer-enhanced variants have achieved remarkable progress in automatic segmentation tasks, they still face challenges in complex medical imaging scenarios, particularly around simultaneously modeling fine-grained local details and capturing long-range global contextual information, which limits segmentation accuracy and structural consistency. To address these challenges, this paper proposes a novel medical image segmentation framework termed DA-TransResUNet. Built upon a ResUNet backbone, the proposed network integrates residual learning, Transformer-based encoding, and a dual-attention (DA) mechanism in a unified manner. Residual blocks facilitate stable optimization and progressive feature refinement in deep networks, while the Transformer module effectively models long-range dependencies to enhance global context representation. Meanwhile, the proposed DA-Block jointly exploits local and global features as well as spatial and channel-wise dependencies, leading to more discriminative feature representations. Furthermore, embedding DA-Blocks into both the feature embedding stage and skip connections strengthens information interaction between the encoder and decoder, thereby improving overall segmentation performance. Experimental results on the LiTS2017 dataset and Sliver07 dataset demonstrate that the proposed method achieves incremental improvement in liver segmentation. In particular, on the LiTS2017 dataset, DA-TransResUNet achieves a Dice score of 97.39%, a VOE of 5.08%, and an RVD of −0.74%, validating its effectiveness for liver segmentation. Full article

Traditional steel–wood composite formwork systems often exhibit mechanical imbalances, such as high strength with insufficient stiffness or high stiffness with low toughness, under both ultimate and serviceability limit states. To address the deficiency, this paper proposes a novel reinforced steel–wood composite formwork system (RSWC-FS). The system features a multi-layer plywood panel, ribbed cold-formed thin-walled Q235 steel secondary wales, and double-channel steel primary wales, interconnected by high-strength bolts to create a surface-to-surface bonded interface. This design enhances load transfer efficiency and mitigates stress concentration. Field testing was conducted on cast-in-place shear walls and frame columns, and corresponding finite element models were established in ANSYS for numerical analysis. The results demonstrate that the RSWC-FS delivers stable mechanical performance. The maximum stress of shear walls reaches 42.57 MPa and that of columns 49.98 MPa, while the corresponding displacements are 4.719 mm and 1.541 mm, all of which remain well within the allowable limits. Through an inverse analysis calibration process, optimal load partial factors of 1.26 for shear walls and 1.31 for columns are recommended, significantly reducing the deviation between calculated and measured values. The proposed RSWC-FS effectively resolves the mechanical imbalance inherent in traditional steel–wood composite formwork systems and demonstrates considerable potential for practical engineering application. Full article

Remote sensing solutions for avalanche segmentation and mapping are key to supporting risk forecasting and mitigation in mountain regions. Synthetic Aperture Radar (SAR) imagery from Sentinel-1 can be effectively used for this task, but training an effective detection model requires gathering a large dataset with high-quality annotations from domain experts, which is prohibitively time-consuming. In this work, we aim to facilitate and accelerate the annotation of SAR images for avalanche mapping. We build on the Segment Anything Model (SAM), a segmentation foundation model trained on natural images, and tailor it to Sentinel-1 SAR data. Adapting SAM to our use case requires addressing several domain-specific challenges: (1) domain mismatch, since SAM was not trained on satellite or SAR imagery; (2) input adaptation, because SAR products typically provide more than three channels while the SAM is constrained to RGB images; (3) robustness to imprecise prompts that can affect target identification and degrade the segmentation quality, an issue exacerbated in small, low-contrast avalanches; and (4) training efficiency, since standard fine-tuning is computationally demanding for the SAM. We tackle these challenges through a combination of adapters to mitigate the domain gap, multiple encoders to handle multi-channel SAR inputs, prompt-engineering strategies to improve avalanche localization accuracy, and a training algorithm that limits the training time of the encoder, which is recognized as the major bottleneck. We integrate the resulting model into a segmentation tool and show experimentally that it speeds up the annotation of SAR images. Full article

Accurate crop type mapping remains challenging in regions where persistent cloud cover limits the availability of optical imagery. Multi-temporal dual-polarization Sentinel-1 SAR data offer an all-weather alternative, yet existing approaches often underutilize polarization information and rely on single-scale temporal aggregation. This study proposes PTU-Net, a polarization–temporal U-Net designed specifically for pixel-wise crop segmentation from SAR time series. The model introduces a Polarization Channel Attention module to construct physically meaningful VV/VH combinations and adaptively enhance their contributions. It also incorporates a Multi-Scale Temporal Self-Attention mechanism to model pixel-level backscatter trajectories across multiple spatial resolutions. Using a 12-date Sentinel-1 stack over Kings County, California, and high-quality crop-type reference labels, the model was trained and evaluated under a spatially independent split. Results show that PTU-Net outperforms GRU, ConvLSTM, 3D U-Net, and U-Net–ConvLSTM baselines, achieving the highest overall accuracy and mean IoU among all tested models. Ablation studies confirm that both polarization enhancement and multi-scale temporal modeling contribute substantially to performance gains. These findings demonstrate that integrating polarization-aware feature construction with scale-adaptive temporal reasoning can substantially improve the effectiveness of SAR-based crop mapping, offering a promising direction for operational agricultural monitoring. Full article