Search Results (607)

Two-dimensional indium selenide (InSe) is a promising material for next-generation electronics, characterized by a small electron effective mass and ultrahigh room-temperature mobility. This review systematically examines the fundamental physics and emerging quantum phenomena intrinsic to InSe. It also addresses the recent discovery of sliding ferroelectricity, which breaks macroscopic spatial inversion symmetry and yields robust polarization states without conventional displacive ionic dynamics. Technological progress from mechanical exfoliation to scalable bottom-up metal–organic chemical vapor deposition is evaluated. The review also covers advanced architecture enabled by InSe, including sub-3 nm-node logic transistors and nonvolatile ferroelectric synaptic devices. Finally, key challenges involving stoichiometric control, back-end-of-line-compatible integration, and environmental instability are discussed in the context of future low-power and neuromorphic computing. Full article

Linear dichroism (LD) optical absorption spectroscopy historically has found substantial yet still limited application in broad areas of science. In particular, full-dipole-tensor reconstruction has been onerous, usually requiring tedious and difficult measurements on single crystals at many orientations using a four-circle goniometer. As a consequence, it is very seldom done. Here, we propose, and test by numerical simulation, a simpler, faster, novel method of determining the full dipole optical absorption tensor of homogeneous planar films in real time as a function of energy (or wavelength), while requiring only minimal additional time and instrumentation. The goal of this paper is to explain the theory and to demonstrate the effectiveness and stability of the procedure using synthetic data sets. Experimental implementation and testing is deferred to future work and publications. The full-tensor spectrum, after construction from the experimental data, allows one to instantly calculate the absorption for any selected polarization direction, even those that are physically inaccessible to experimental measurement. Although our specific application in this paper is X-ray Absorption Fine Structure (XAFS) Spectroscopy, the method should be applicable to UV–Vis, IR, THz, microwave, and other wavelengths. A strength of this measurement modality is that full-tensor data can be acquired using essentially the same sort of scanning geometry that is normally used for XAFS, with only a discrete shift in the spin axis orientation between groups of scans. The additional instrumentation needed to determine the five Fourier components of the signal at each energy is minimal; two angles gives ten parameters, while six are strictly needed. Robust inversion from data to tensor elements is demonstrated, implemented via simple matrix multiplication. Outside of XAFS, FTMAPS is also expected to be applicable to diverse scientific and technological areas such as oriented bio-molecular films, semiconductor and materials physics, and process control of thin-film photovoltaics and semiconductors. Full article

Electrocardiogram (ECG) signals are essential for arrhythmia detection; however, they are frequently degraded by noise during acquisition, and their evaluation is vulnerable to data-leakage and patient-overlap issues that can compromise model assessment. Therefore, in this study, we propose an image-encoding-based arrhythmia classifier combined with a morphology-aware denoising autoencoder. We evaluate signals under a corrected, patient-independent protocol in which every model-selection decision was made on a separate validation partition. On a leakage-free test partition, the autoencoder achieved an SNR improvement of 5.63 dB and a correlation coefficient of 0.801, improving R-peak amplitude preservation and redetection accuracy under moderate-to-severe noise while introducing measurable morphology degradation when the input was already lightly contaminated. The proposed model encodes the denoised, beat-centered signal into images through an interleaved-grouping outer product with sorting and flipping, and classifies them with a multi-scale three-dimensional convolutional network. Under this protocol, the proposed model obtained the highest macro-F1 among five image-encoding architectures, but did not outperform four models operating directly on the denoised signal (macro-F1 32.5% versus 37.3–40.0%), indicating that the proposed encoding does not improve overall five-class classification under rigorous inter-patient evaluation. A controlled test showed that the encoding is exactly invariant to global signal-polarity inversion, unlike the sequential models. This targeted invariance, rather than a general accuracy advantage, is the contribution reported here. Full article

High-precision urban vegetation mapping is essential for assessing carbon sink capacities, mitigating the urban heat island effect, and supporting sustainable development. Although deep learning and high-resolution remote sensing have advanced automated vegetation monitoring, existing models still face challenges when a common segmentation architecture is evaluated under different imaging geometries. In this study, Cityscapes and ISPRS Vaihingen are treated as two independent benchmarks representing perspective street-level imagery and orthographic aerial imagery, rather than as simultaneous cross-view inputs. “Background dominance” caused by perspective distortion and the “gridding artifacts” inherent in orthographic textures severely constrain segmentation accuracy across varying vegetation scales, particularly for small targets. To address these limitations, we propose a Scale-Aware Mixture of Experts (SA-MoE) architecture for fine-grained vegetation segmentation under two distinct imaging views, together with a scene-specific optimization strategy. The core SA-MoE framework consists of two main components. First, the spatial gating network uses a temperature polarization mechanism with τ = 0.5 to adjust the initial logit maps, sharpening expert-weight differences while preserving stable gradient propagation. Second, we use a heterogeneous expert group with five parallel branches: a pixel-level expert, three spatial experts with different dilation rates, and a global average-pooling expert. A dynamic pixel-level weighted fusion mechanism is then applied, decoupling feature extraction from receptive-field allocation. Furthermore, to address the heterogeneity of “hard samples” and “label noise” across the two benchmark settings, we introduce a scene-specific optimization strategy. Our findings show that the Focal-Dice (FD) loss is more suitable for perspective scenes with severe target imbalance and hard-to-classify vegetation targets, whereas the Cross-Entropy (CE) loss is more robust to boundary jitter in orthographic imagery. Comparative experiments on the Cityscapes (perspective view) and ISPRS Vaihingen (orthographic view) datasets reveal that SA-MoE achieves a highly competitive balance between computational efficiency and fine-grained segmentation, particularly in micro-target recall. Notably, the recall for extra-small (XS) scale targets in the aerial dataset improved by 3.21 percentage points compared to the second-best model. For the street-level dataset, our model achieved competitive global performance in terms of Overall Accuracy (OA), Precision, and F1-Score. However, we also observed a performance trade-off, where Transformer-based models maintained an advantage in preserving fine boundary details for these extra-small targets. In the routing analysis, we observed a pattern that we refer to as “receptive field inversion”, in which the model assigns lower weights to large-dilation experts for large canopy regions in orthophotos. We interpret this pattern as a plausible routing hypothesis. Overall, SA-MoE offers an efficient and adaptive solution for urban vegetation mapping under two imaging views. Full article

Background/Objectives: Microplastics (MPs) are emerging environmental contaminants with potential implications for human health. Experimental studies suggest that MPs accumulate in the liver and promote inflammatory and fibrotic changes, but evidence from human tissues remains limited. This study investigated the presence of suspected MP-like particles in post-mortem human liver tissue and their associations with clinical, biochemical, hematological and histopathological parameters. Methods: Post-mortem liver tissue samples were collected from 55 adults. Suspected MP-like particles were extracted using hydrogen peroxide digestion, filtration, Nile Red staining and image-based quantification. Histopathological evaluation assessed inflammation, fibrosis, necrosis and fatty liver degeneration. Polarized light microscopy was used as a supportive morphological assessment method. Statistical analyses included Mann–Whitney U tests, Fisher’s exact tests and Spearman correlation analysis. Results: Detectable hepatic suspected MP-like particles were identified in 9 of 55 individuals (16.4%). Individuals with detectable suspected MP-like particles had significantly lower alanine aminotransferase (ALT) levels and leukocyte counts. The hepatic suspected MP-like particle burden showed weak positive correlation with liver fibrosis and inflammation and weak inverse correlation with ALT levels and leukocyte count. Fisher’s exact test showed that liver fibrosis and liver inflammation were significantly associated with detectable hepatic suspected MP-like particles, with higher unadjusted odds observed in the corresponding 2 × 2 contingency tables. No statistically significant associations were found for liver necrosis or fatty liver degeneration. Conclusions: Detectable suspected MP-like particles were identified in post-mortem human liver tissues and were more closely associated with fibrotic and inflammatory histopathological changes than with routine biochemical abnormalities. Larger studies using standardized detection methods are needed to confirm these results. Full article

Landau–Zener–Stückelberg–Majorana (LZSM) interference describes the coherent superposition of nonadiabatic transitions when a quantum system is driven through an avoided crossing, but its classical optical analog remains largely unexplored. This work establishes an optical-resonator-based platform for exploring LZSM interference in atomic polarization dynamics using temporal coupled-mode theory. By exploiting the formal correspondence between the cavity mode and atomic polarization in a weakly driven two-level system (TLS), exact analytical solutions are derived for linear frequency sweeps and sinusoidal modulation. The results reveal that the Landau–Zener transition of atomic polarization occurs even when the input frequency transiently sweeps across resonance. The finite temporal memory inherent in the cavity response gives rise to Stückelberg interference, manifesting as oscillations in transmission and cavity energy. The dependence of the interference pattern on sweep rate, linewidth, and modulation is systematically analyzed, and a non-monotonic behavior of oscillation amplitude versus the cavity linewidth is identified. Furthermore, the conventional critical coupling concept is reexamined in the nonadiabatic regime, where counterintuitively the deepest transmission dip occurs under over-coupling rather than critical coupling. Finally, an inverse-design approach engineering both the amplitude and frequency of the input frequency is introduced to tailor the LZSM interference pattern. Our results provide a new platform to all-optical simulation of complex quantum interference. Full article

SAR and InSAR are fundamental sensing modalities for all-weather, day-and-night Earth observation because they operate independently of solar illumination and retain sensitivity to scene structure under conditions that often limit optical imaging. Recent Deep Learning (DL) methods have improved SAR image interpretation, inverse imaging, target recognition, and InSAR-based deformation analysis, but many purely data-driven pipelines still neglect the forward sensing model, coherent scattering physics, speckle statistics, phase structure, and acquisition geometry that shape radar observations. This paper develops a physics-aware perspective on learning for SAR and InSAR. The literature is organized along two complementary axes: the physical constraint dimensions that govern radar measurements, namely polarization, scattering, signal-domain structure, resolution, and interferometric phase/coherence, and the integration modes through which such structure enters modern learning systems, including representation design, model-guided architectures, and learning-assisted inverse problems. We further discuss representative applications, dataset and evaluation issues, domain-shift challenges, and trustworthy deployment. Finally, two constructive illustrative case studies are included: one on sparse SAR imaging with an explicit SAR sensing matrix, and one on InSAR phase-domain estimation under wrapped phase, coherence variation, and physics-aware regularization. Together, they illustrate how physics-aware learning compares with classical structure-preserving baselines and unconstrained black-box learning in representative amplitude-driven and phase-driven inverse settings. Full article

Two-dimensional (2D) antiferromagnets are highly promising for next-generation spintronics due to their ultrafast dynamics and robustness against stray fields; however, their practical application is severely hindered by the vanishing magneto-optic effects restricted by strict crystal symmetries. In this work, we propose a universal physical mechanism to activate and manipulate the magneto-optic Faraday effect in 2D fully compensated bilayer antiferromagnets using an external vertical electric field. By constructing a comprehensive tight-binding model and performing first-principles calculations on bilayer ${\mathrm{VSe}}_2$, we demonstrate that the applied electric field explicitly breaks the spatial inversion and combined $\mathcal{PT}$ symmetries. This symmetry breaking lifts the Kramers degeneracy, inducing a pronounced spin splitting that, in conjunction with intrinsic spin–orbit coupling, generates non-vanishing Berry curvature. Consequently, the previously forbidden Faraday rotation angle is activated from zero to a significant non-zero value, and its rotation direction can be deterministically reversed by switching the electric field polarity. Our findings provide profound physical insights into the symmetry-modulated light–matter interactions and pave the way for designing fully electrically controllable, energy-efficient antiferromagnetic opto-spintronic devices. Full article

Based on the polarity balance reconstruction, chaotic system with conditional symmetry is coined by introducing absolute value function and trigonometric functions with property of slope polarity inversion. Suitable functions returning the polarity balance based on offset boosting, conditionally symmetric chaotic attractors in the system, can be effectively positioned under different regimes of conditional symmetry. As a result, different amounts of coexisting conditionally symmetric attractors are reproduced even tending to infinity. For comparison, other external functions such as the hyperbolic tangent function can be employed to construct mandatory polarity balance, and thus coexisting attractors of artificial symmetry can be obtained. The evolution of the basin of attraction is analyzed for the observation of offset parameter-dominated multistability. A simplified circuit is designed for verifying the coexisting conditionally symmetric attractors. Full article

Accurate estimation of soil surface moisture (SSM) is essential for various applications, including hydrological modeling, precision agriculture, and drought monitoring. With advancements in SAR data acquisition and modeling techniques, accurate retrieval of SSM has become increasingly feasible. The sensitivity of C-band (5.36 GHz) SAR data to the orientation of surface roughness with respect to the sensor look angle significantly influences surface soil moisture estimation results. Hence, to address this research gap, a modified eigen-space transformation was employed to obtain optimized surface roughness estimates, known as synthetic surface roughness (SSR). The results of the proposed SSR showed adequate statistical significance with the field-scale surface roughness values (r = 0.77, RMSE = 0.08) for all orientation angles, compared with the cross-polarization ratio (CPR) (r = 0.70, RMSE = 0.35) and Inverse of Anisotropy (A_I) (r = 0.59, RMSE = 0.49) measures. The modified Dubois model was inverted to retrieve the dielectric constant (ε_SSR) using SSR as a roughness proxy, demonstrating reliable performance for surface roughness conditions up to 4 cm and in situ dielectric constant values up to 8.5 (~29% volumetric soil moisture). However, exceeding this threshold leads to an underestimation of ε_SSR (Bias= −0.43), and hence a new framework, the optimized dielectric model (ε_ODM), was introduced using a piecewise-constrained angular projection correction. The volumetric moisture content (mv_ODM) retrieved from ε_ODM was promising (r = 0.86, RMSE = 0.04, Bias = 0.02) across a wide range of soil moisture conditions when compared with widely adopted backscattering models, namely Mod. Dubois, Calibrated IEM, Oh, and Mod. Oh. Full article

Tight reservoirs exhibit complex pore structures and strong heterogeneity, which complicate reservoir characterization. Although spectral induced polarization (SIP) is sensitive to pore structure, existing polarization models are formulated at the pore scale and are difficult to connect with measurements across different core volumes. We adopted an improved membrane polarization model and coupled it with a three-dimensional impedance network to investigate two-stage SIP upscaling: from pore systems to core-scale networks, and from local core volumes represented by subnetworks to larger parent cores. The framework was used to determine the representative elementary volume (REV), evaluate subnetwork representativeness, examine fracture-controlled heterogeneity, and compare simulations with laboratory measurements. The results show that REV size is controlled by the distribution ranges of pore radius and pore length. Homogeneous networks converge with sufficient sampling, whereas subnetworks from strongly heterogeneous fractured cores may exhibit relative deviations exceeding tenfold when fracture structures are not adequately captured. Impedance network inversion can reproduce measured spectra, but the recovered pore distributions are non-unique and represent electrically equivalent structures. Laboratory-scale SIP data may not fully capture large-scale heterogeneity; integrating logging or imaging information may, therefore, help constrain cross-scale non-local effects and improve the reliability of comprehensive reservoir evaluation. 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

We present newly derived approximate analytical solutions (AAS) of the motion, free and forced modes of dynamics of two viscoelastic rheological Maxwell–Faraday discrete dynamic systems (RMFDDSFTPEP), creeper type, and fractional type, with piezoelectric polarization property of the Faraday piezoelectric element. They always occur in paired rheological discrete dynamic systems depending on the order of sparse coupling of rheological basic light elements in standard light-binding structures and their connections with the rigid body and the fixed point. The research results presented in the paper AAS for the creep-flow EFM of creep-flow dynamics of a RMFDDSFTPEP, which characterizes the SLFTMF coupling set of models, which includes piezoelectric effects. To avoid the inversion of complex, fractional-order expressions of the Laplace transforms (LT), the terms of the complex expressions are expanded into power orders, based on the assumption that certain ratios are small relative to unity. The inverse LT is performed term by term to obtain AAS in the time domain. Full article

In this study, the effect of Ti content on the electrochemical behavior and passive film growth mechanism of Ni₅₀Nb_50−xTi_x (x = 10, 15, and 20 at.%) metallic glasses produced via melt spinning was investigated. Structural characterization via X-ray diffraction (XRD) and transmission electron microscopy (TEM) confirmed the fully glassy nature and chemical homogeneity of all alloys. Electrochemical performance was evaluated in a 3.5 wt.% NaCl solution using potentiodynamic and potentiostatic polarization, as well as electrochemical impedance spectroscopy (EIS). The results showed that increasing Ti content improves corrosion resistance by reducing corrosion and passive current densities and increasing charge-transfer resistance. The Ni₅₀Nb₃₀Ti₂₀ alloy exhibited the best electrochemical performance, associated with the formation of a more stable and protective passive film. The passive film growth mechanism was analyzed using the High-Field Model (HFM). A linear relationship between inverse capacitance and anodic potential confirmed that ionic transport through the oxide layer governs passive film growth. The calculated electric field strength decreased systematically with increasing Ti content, suggesting the formation of passive films with lower defect density and enhanced barrier properties. These results demonstrate that adding Ti significantly enhances the passivation behavior of Ni-Nb metallic glasses and promotes the formation of stable oxide films with improved corrosion resistance in chloride-containing environments. Full article

Social media has emerged as a crucial environment for examining public sentiment during disasters, providing immediate insights into collective emotions and urgent expectations. This research examines the emotional reactions expressed on Turkish posts shared on the X platform (formerly Twitter) following the 6 February 2023 earthquake by employing an integrated method that combines topic modeling and topic-based sentiment analysis. Data were collected between 10 February 2023 and 28 February 2023. A large dataset consisting of 305,000 tweets was compiled, and 296,836 tweets remained for analysis after preprocessing and filtering procedures. Latent Dirichlet Allocation (LDA), enhanced with term frequency-inverse document frequency weighting and bigram extraction techniques, was applied to identify prominent themes, including rescue operations, appeals for assistance, communication about missing persons, and disaster management. The sentiment polarity within each topic was determined using a hybrid deep learning model incorporating Bidirectional Encoder Representations from Transformers (BERT) embeddings Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM) layers, and FastText representations. This model reached a classification accuracy of 94%, with F1-scores of 0.91 and 0.95, recall values of 0.90 and 0.96, and precision values of 0.92 and 0.95, achieving higher performance than the evaluated baseline models. The findings indicate that supportive, solidarity-oriented, and resilience-related communication patterns were among the most frequently observed positive sentiment expressions, whereas negative sentiments appeared more frequently in discussions regarding delays in aid delivery and perceived shortcomings in institutional response. This study presents a scalable and flexible framework for analyzing sentiment in Turkish-language crisis communication, providing insights that may support disaster response monitoring and decision-making processes as well as the development of systems for tracking public reactions in real time. Full article