Search Results (125)

Problematic smartphone use in adolescence has been linked cross-sectionally to poorer academic and emotional functioning, but the longitudinal mechanisms remain unclear. Using three waves of the Panel Study on Korean Children (W14–W16; ages 14–16), this study tested whether problematic smartphone use at age 14 predicts four developmental outcomes at age 16—school adjustment, self-regulated learning, ego resilience, and subjective happiness—through four age-15 mediators: gaming, video-watching, self-study hours, and academic stress. Hierarchical regressions with autoregressive controls, bootstrap a-paths, and 16 indirect effects tested with 20,000-draw Monte Carlo confidence intervals were estimated. Problematic smartphone use predicted three outcomes but not subjective happiness, for which the association was indirect only. Seven indirect effects were significant, and six survived Benjamini–Hochberg false discovery rate correction. The findings indicated two pathways: a time-displacement pathway (gaming and reduced self-study) specific to self-regulated learning and an academic-stress pathway linked to school adjustment, ego resilience, and happiness. Crucially, in baseline-controlled sensitivity analyses, the time-use pathways remained significant, whereas the stress pathway was fully attenuated, indicating a concurrent rather than a prospective association. Results were robust across path-model SEM, full-information maximum likelihood, and inverse-probability weighting, supporting a dual-pathway account of adolescent digital development. Full article

Flexible metamaterial absorbers have exhibited tremendous potential for applications in intelligent wearable devices and radar stealth protection due to their remarkable electromagnetic response characteristics and mechanical conformal adaptability. However, conventional metamaterial development relies heavily on iterative full-wave simulations, which not only incurs prohibitive computational costs but also hinders the efficient identification of global optima within high-dimensional geometric parameter spaces. To address these challenges, this paper proposes an inverse design framework based on a deep learning-powered adversarial diffusion model. By integrating residual blocks and self-attention mechanisms within the U-Net architecture, the model’s capacity to capture global spectral features is significantly enhanced. Furthermore, the introduction of a discriminator for adversarial fine-tuning optimizes generation quality, resulting in a 22.46% reduction in the target loss function compared with conventional approaches. This method effectively resolves the “one-to-many” inverse mapping challenge between spectral requirements and geometric structures. Experimental results demonstrate that the designed absorber exhibits excellent polarization insensitivity and maintains efficient, stable absorption performance even under large-angle conformal bending. Moreover, a multi-sample collaborative validation strategy is employed to cross-verify measured samples across different frequency bands, establishing the model’s high precision and engineering reliability. Full article

Electromagnetic wave absorbing materials are important for electromagnetic protection, radar stealth, wireless communication, and advanced electronic systems. However, traditional design methods mainly rely on repeated experiments and full-wave simulations, which are time-consuming and inefficient when dealing with complex compositions, microstructures, and multilayer structures. Machine learning provides a new route to accelerate the design of high-performance absorbers by learning the relationship among material composition, structure, electromagnetic parameters, and absorption performance. This review summarizes recent progress in machine-learning-empowered electromagnetic wave absorbing materials. First, the basic physical principles of electromagnetic wave absorption are introduced, including reflection loss, impedance matching, attenuation, and physical limits such as the Rozanov and Snoek limits. Then, typical machine learning models are discussed, including classical machine learning, deep learning, generative models, physics-informed models, large language models, and artificial-intelligence (AI) Agents. Their applications are further summarized from forward property prediction, high-throughput screening, inverse design, electromagnetic parameter decoupling, physics-informed modeling, explainability, multi-objective optimization, and data augmentation. Finally, the main challenges and future directions are discussed, including data standardization, physics-guided learning, foundation models, autonomous laboratories, and engineering-scale validation. This review shows that machine learning is changing absorber research from experience-driven trial-and-error to data-driven and knowledge-driven design, and provides a useful reference for developing next-generation electromagnetic wave absorbing materials. Full article

Objective: This study examined whether the cholesterol–high-density lipoprotein–glucose (CHG) index was associated with subsequent urinary incontinence (UI) among middle-aged and older adults and evaluated its contribution to 4-year UI risk prediction. Methods: A total of 2059 participants without UI at baseline were included and followed from Wave 2 to Wave 7 in this prospective cohort study. The relationship between the CHG index and subsequent UI was examined using Cox proportional hazards models, supplemented by restricted cubic spline and subgroup analyses. A 4-year prediction model for UI was developed using LASSO and Cox regression. Results: UI occurred in 308 participants during follow-up, yielding an incidence of 14.96%. An independent inverse association was observed between CHG levels and the risk of subsequent UI. After full adjustment, each 1-standard deviation increase in CHG corresponded to an 18% lower risk of UI (HR of 0.82, 95% CI from 0.72 to 0.93). Relative to the lowest CHG tertile, the hazard ratios were 0.68 (95% CI from 0.51 to 0.90) for T2 and 0.63 (95% CI from 0.47 to 0.85) for T3. The relationship was predominantly linear, and no significant effect modification was detected in subgroup analyses. The 4-year prediction model showed moderate performance (AUC 0.663), while incorporation of CHG led to modest improvements in discrimination and reclassification. Conclusions: Among middle-aged and older adults, higher CHG levels were independently related to a reduced risk of UI. The CHG index may provide complementary information for early risk assessment in this population, but should not be regarded as a stand-alone predictor. Full article

Artificial intelligence (AI), particularly deep learning (DL), is revolutionizing optical metamaterial design by overcoming the fundamental challenges of multidimensional parameter spaces, nonlinear structure–property relationships, and the intrinsic non-uniqueness of inverse problems. By learning complex mappings between geometric structures and electromagnetic responses, DL enables rapid forward prediction and on-demand inverse design without computationally intensive full-wave simulations. This review provides a comprehensive survey of AI-driven design methodologies across four key metamaterial platforms: localized resonant nanostructures, metasurfaces, periodic and guided-wave photonic structures, and complex scattering systems. For each platform, we systematically examine the neural network architectures employed, the specific design challenges addressed, and the representative achievements attained. These data-driven approaches not only significantly accelerate the discovery of high-performance structures but also offer new opportunities for extracting physical insights into light–matter interactions. We assess the critical challenges of data efficiency, model interpretability, and experimental feasibility, and outline emerging research directions that may address these barriers. This review aims to provide both a comprehensive summary of the current state of the art and forward-looking perspectives for this rapidly evolving interdisciplinary field. Full article

This study presents a reusable adjoint-octree multilevel fast multipole algorithm (MLFMA) for full-wave radar scattering analysis of multi-state unmanned aerial vehicle (UAV) formations. The method is motivated by remote-sensing applications in which dense angular sampling or long motion sequences are required for physically reliable signature generation. Instead of rebuilding a global octree for the full formation at every motion state, the proposed approach assigns each sub-target an independent target-attached local octree that translates and rotates with the rigid body. This preserves mesh–cell affiliation in the body-fixed frame and separates the system operator into a state-invariant intra-target near-field component and a state-dependent inter-target far-field component. Consequently, near-field matrices and sparse approximate inverse preconditioners are assembled once and reused throughout the state sequence, while only inter-target far-field coupling terms are updated. The method is evaluated for six representative UAV formations at 3.5 GHz using monostatic radar cross section (RCS) over a full azimuth sweep. Across all tested formations, the proposed solver reproduces the RCS behavior of conventional MLFMA while substantially reducing computational cost. For Formation A, the center-state total time decreases from 251.4 s to 66.06 s; for Formation C, it decreases from 470.95 s to 76.06 s. Over 100-state sequences, the resulting acceleration reaches approximately 11.8-fold and 15.2-fold, respectively. Jitter-envelope analysis further shows that orientation perturbation produces stronger signature uncertainty than planar displacement. The proposed framework therefore provides an efficient and physically consistent forward solver for radar remote-sensing studies of cooperative UAV formations. Full article

High-resolution soil moisture monitoring in a lysimeter requires precise Ground-Penetrating Radar (GPR) systems that can provide clean time-domain data for a Full-Waveform Inversion (FWI) algorithm. Using high-speed Radio Frequency System-on-Module (RFSoM) devices provides flexibility in signal generation. To optimize such a system, an appropriate transmit waveform and processing pipeline need to be selected. This paper presents a performance evaluation of three GPR waveforms—impulse, Stepped-Frequency Continuous Wave (SFCW) and non-linear Frequency-Modulated Continuous Wave (FMCW/chirp)—on the same hardware setup. To ensure a fair comparison, all waveforms were tested under an identical total measurement time. Numerical simulations were performed using an electromagnetic model of the system. Physical validation was conducted in an anechoic chamber using a 4 GS/s RFSoM setup and planar elliptical dipole antennas. Simulations showed that both sinewave-based methods provide better signal-to-noise ratios (SNRs) than the impulse GPR, with the non-linear chirp achieving the best results (20.7 dB improvement compared to impulse). Experimental measurements supported these results, showing better SNR across the frequency band for the SFCW and chirp waveforms. Because of its high SNR and simple hardware implementation, the non-linear chirp was identified as the most suitable waveform for this RFSoM-based GPR system. Full article

Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature vector, implicitly assuming a single dominant driver such as precipitation, even though atmospheric moisture demand, radiation and wind-mediated evapotranspiration co-determine drought onset; (ii) wavelet preprocessing is typically applied to the full series, introducing future-information leakage that violates the operational causality requirement of forecasting; and (iii) most architectures predict a single horizon and provide no causal attribution explaining when, where and which climatic variables initiated the event. This study proposes WaveDroughtNet, a multi-modal, multi-horizon deep-learning framework that addresses these limitations through five integrated components: (a) a strictly causal Daubechies-4 wavelet decomposition computed in a rolling fashion; (b) six modality-specific encoders with stochastic modality dropout (p = 0.15); (c) cross-modal multi-head attention with four heads; (d) a four-layer temporal convolutional network (TCN) backbone with dilation factors yielding a 240-step receptive field; and (e) a post hoc DroughtOriginTracer that combines temporal attention, modal-attribution and inter-district propagation scans. The Standardised Precipitation Evapotranspiration Index (SPEI), used as the supervisory target, is computed following the canonical Vicente-Serrano formulation. water balance $D=P-\mathrm{P}\mathrm{E}\mathrm{T}$ (Hargreaves PET) at a 4-week (≈1-month) timescale, fitted with a three-parameter log-logistic distribution via L-moments, validated by Kolmogorov–Smirnov goodness-of-fit testing ($\alpha =0.05$) per district, and standardised through the inverse-normal cumulative distribution function. Trained on 18,304 weekly district records from NASA POWER reanalysis (2014–2025) covering all 32 districts of Tamil Nadu, India, WaveDroughtNet uses only 256,869 parameters and produces, in a single forward pass, four forecasts (1 week, 1 month, 3 months, 1 year). On the held-out 2024 test partition ($N=1728$), the model attains weighted $F1=0.9221$ and $R^2=0.8512$ at the 1-week horizon, and weighted $F1=0.8498$ and $R^2=0.6812$ at the 1-year horizon. Diebold–Mariano tests confirm that WaveDroughtNet significantly outperforms naive persistence, seasonal naive, LSTM, ConvLSTM and a vanilla Transformer at the 3-month and 1-year horizons (p < 0.001). The DroughtOriginTracer successfully back-projects 15 Coimbatore events to causal origins 29–41 weeks prior to onset. We explicitly acknowledge three limitations that constrain operational deployment in its current form—zero severe events in the 2024 test partition ($F{1}_{severe}$ = 0.000), static inter-district modelling, and absence of vegetation-index supervision—and propose concrete mitigation pathways in the Discussion. Full article

With the advancement of aerospace technology, full-scale wind tunnel testing has become a crucial approach to overcoming bottlenecks in hypersonic technology. The design of ultra-large, high-performance nozzles stands out as one of the core challenges. This paper focuses on a profiling design method for supersonic/hypersonic nozzles with interchangeable throats at the 6 m outlet scale, addressing issues such as significant boundary layer effects and difficulties in achieving variable Mach numbers due to the large dimensions. An empirical boundary layer correction method is proposed to efficiently compensate for viscous effects. By parameterizing and controlling the Mach number distribution along the nozzle axis using cubic B-spline curves and applying the method of characteristics for accurate inviscid supersonic flow field computation, the nozzle profile is optimized. To enable multi-Mach-number operation, a design strategy is adopted, where the high-Mach-number profile serves as the baseline, and the low-Mach-number throat section is inversely designed to ensure a smooth transition between multi-Mach nozzles and a shared expansion section. Using this approach, nozzle profiles for Mach numbers 4, 5, and 6 were successfully designed and validated through fully viscous CFD simulations. Results demonstrate that under all design conditions, a wide and uniform core flow region forms at the nozzle exit, with no strong shock waves present in the flow field. This study confirms the effectiveness and reliability of the integrated design method for large-scale interchangeable-throat nozzles, providing important theoretical foundation and technical support for the future development of advanced large-scale hypersonic wind tunnels. Full article

Objective: Traditional Full Waveform Inversion (FWI) methods for Ultrasound Computed Tomography (UCT) are computationally expensive and can be sensitive to strong acoustic contrasts. In this work, we propose the Multi-Channel Transducer Network (CUCT-Net), a deep learning framework that directly maps received ultrasound signals to image-space outputs for quantized speed-of-sound (SoS) estimation and for direct tissue-level segmentation over both low- and high-contrast regions, enabling end-to-end recovery of both contrast-driven and anatomically meaningful structures from raw measurements. Method: CUCT-Net uses a multi-input encoder–decoder architecture that maps raw multi-static UCT measurements to quantized SoS (or tissue-class) maps without requiring an initial guess or iterative optimization. Parallel per-transducer encoders extract view-specific features that are fused and refined by a decoder, with Shift Units (SU) used to enhance fine-scale feature modeling under sparse sensing. Experiments are performed on k-Wave simulations using (i) Shepp–Logan-inspired disc phantoms with Original/Distorted/Mixed variants and (ii) DBB-derived anatomical brain phantoms, under clean and noisy measurement conditions. Results: The proposed network achieves accurate quantized SoS estimation and direct tissue-level segmentation across synthetic and anatomically derived phantom experiments. Strong robustness to noise is demonstrated through transfer learning. Compared with FWI, CUCT-Net significantly reduces computational cost while maintaining stable performance under reduced-sensor conditions for quantized SoS estimation and complex tissue heterogeneity for segmentation. Conclusions: CUCT-Net formulates UCT as a direct signal-to-image learning problem that supports both quantized SoS estimation and tissue-level segmentation. By learning an end-to-end mapping from raw ultrasound measurements to quantized SoS or tissue representations, the proposed framework bypasses iterative inversion and achieves efficient and robust performance under reduced-sensor and strong-contrast conditions. The multi-input architecture enables effective integration of information from multiple transducers, demonstrating the feasibility and potential of data-driven end-to-end quantized SoS estimation and tissue segmentation for UCT. Full article

For electromagnetic (EM)-driven design, we summarize surrogate modeling and inverse design based on full-wave simulations and, when available, measurements, use adjacent-domain EM exemplars only as methodological templates where direct closed-loop RF design-to-recognition evidence remains limited, and explain why reported speedups depend on the sampled design domain, sample density near feasibility boundaries, and how constraint-boundary checks and final verification are implemented. For RF sensing and recognition, we discuss how the learned “signature” is shaped by the measurement chain (hardware, placement, synchronization, calibration, and preprocessing). We then outline an author-synthesized set of checkable evaluation and reporting items, including explicit domains/constraints, complete sensing metadata, cross-condition tests, and edge deployment evidence, where deployability is defined at the full-pipeline level and requires measured latency, throughput, peak memory, energy per inference, preprocessing overhead, runtime, numerical precision, and an explicit statement of whether communication or offloading is included. Full article

This work presents the application of a Time-Domain Reflectometry (TDR) inversion algorithm for localizing water along a bi-wire cable acting as a distributed sensing element (SE), and for evaluating the uncertainty of the water position measurement. The TDR inversion relies on a simplified yet effective gray-box circuital model of the measurement system that, without attempting a full-wave electromagnetic (EM) simulation, reproduces with good accuracy any actually observed reflectograms. The model parameters are estimated from a single acquired reflectogram so as to reproduce the measured signal, without a prior EM characterization of the system components. The model provides the water localization and enables extensive simulation campaigns under realistic variations in water position, stimulus pulse duration, and disturbance effects. A specific measurement setup, designed to perform repeated measurements in controlled laboratory conditions, is analyzed in detail as a case study. The water localization error of the measurement system is statistically evaluated in terms of confidence intervals, bias, and standard deviation, by means of simulated measurements of the model, with different water positions and TDR pulse durations. Then, the uncertainty evaluation is validated through 45 actual measurements, using multiple SEs, and the same water positions and pulse durations. The work proves the viability and the performance of the presented TDR inversion method for both localization measurements and for their uncertainty evaluation under different experimental conditions. More generally, it establishes a general framework for TDR measurements and uncertainty evaluation combining physical modeling, simulation-based uncertainty evaluation, and experimental verification. Full article

Creases in origami reflectarray antennas (ORAs) impose layout exclusion zones that invalidate conventional shaped beam synthesis, assuming continuous periodic apertures. A crease-compatible shaped beam synthesis approach is presented, in which crease-intersecting elements are treated as constrained reflectors by removing only their patches while retaining a continuous ground plane, thereby translating geometric restrictions into explicit amplitude/phase constraints. These constraints are incorporated into a modified alternating projection method (MAPM) via an iteration-updated ternary state matrix and a revised inverse projector, where the amplitudes of internal elements are kept prescribed, and only their phases are iteratively optimized. A 15 GHz hexagonal twist ORA using triangular-ring unit cells is designed to generate a sector beam in the xoz plane and a pencil beam in the yoz plane. Full-wave simulations demonstrate a peak gain of 26.4 dBi with sidelobe levels below −16.1 dB, validating the proposed beam shaping synthesis with crease constraints for ORAs. Full article

To address the existing challenges in mold slag thickness measurement—such as the susceptibility of contact sensors to high-temperature degradation and the limitation of non-contact methods to detecting only the upper slag surface—this study proposes an integrated approach that fuses millimeter-wave radar and eddy current sensors for measuring mold slag thickness in a continuous casting mold. The method innovatively combines two sensing principles: the millimeter-wave radar employs an improved FFT-CZT² high-precision ranging algorithm to perform high-resolution scanning of the solid slag upper surface, reconstructing its topography (error: $\pm$1 mm), while Mel-frequency cepstral coefficients (MFCC) are applied to extract features from the radar intermediate-frequency signals, combined with an enhanced PSO-BP neural network algorithm to predict the thickness of the solid slag layer (error: $\pm$5 mm). Concurrently, an eddy current sensor monitors the liquid slag–molten steel interface position (error: $\pm$1 mm). Through dual-sensor data fusion, the upper surface topography data and solid slag thickness obtained from the radar are spatially registered in three dimensions with the molten steel level information derived from the eddy current sensor. This integration ultimately enables the non-contact synchronous measurement of three key parameters within the mold: solid slag layer thickness, liquid slag layer thickness inversion, and molten steel level. Furthermore, by reconstructing the upper slag surface morphology, the method successfully resolves practical issues such as uneven material distribution, local material deficiency, or excessive feeding. Preliminary experimental verification confirms that the proposed method maintains stable performance even under high-temperature and complex environmental conditions. It thus provides a real-time, accurate, and full-cross-section monitoring solution for mold slag in continuous casting, offering significant practical value for the development of smart steel plants. Full article

We propose a coupling-aware equivalent source reconstruction framework for reproducing complex three-dimensional electromagnetic (EM) environments inside an anechoic chamber. A measured or simulated target field is represented by a finite set of physically realizable equivalent source antennas whose positions and complex excitations are identified by solving a nonlinear high-dimensional inverse problem. To ensure physical fidelity, the forward model explicitly accounts for mutual coupling through a full-wave Method-of-Moments (MoM) formulation, avoiding the inaccuracies of idealized uncoupled superposition. The inverse problem is efficiently solved using a hybrid evolutionary optimization scheme that combines an adaptive differential evolution strategy with stagnation-triggered CMA-ES refinement, augmented by a lightweight surrogate-based pre-screening to reduce expensive full-wave evaluations. The optimized source configuration is directly deployed in a microwave anechoic chamber, where the reconstructed field is measured on an observation plane and compared against the target field. The experimental results demonstrate close agreement in both amplitude and spatial distribution, while the proposed optimization pipeline substantially reduces the number of full-wave evaluations required for convergence. This work enables accurate repeatable chamber emulation of outdoor or in situ EM scenarios for robust system-level testing and evaluation. Full article