Search Results (634)

Spectral computed tomography (SCT) enables material decomposition, artifact reduction, and contrast enhancement, leveraging symmetry principles across its technical framework to enhance material differentiation and image quality. However, its nonlinear data acquisition process involving noise and scatter leads to a highly ill-posed inverse problem. To address this, we propose a dual-domain iterative reconstruction network that combines joint learning reconstruction with physical process modeling, which also uses the symmetric complementary properties of the two domains for optimization. A dedicated physical module models the SCT forward process to ensure stability and accuracy, while a residual-to-residual strategy reduces the computational burden of model-based iterative reconstruction (MBIR). Our method, which won the AAPM DL-Spectral CT Challenge, achieves high-accuracy material decomposition. Extensive evaluations also demonstrate its robustness under varying noise levels, confirming the method’s generalizability. This integrated approach effectively combines the strengths of physical modeling, MBIR, and deep learning. Full article

The design of emulsions at the nanoscale is a significant application of nanotechnology. For spherical droplets and a given volume of dispersed phase, the nanometre size of droplets inversely increases the total area, $A={\displaystyle \frac{3V}{r}}$, allowing greater contact with organic and inorganic materials during application. In topical applications, not only is cell contact increased, but also permeability in the cell membrane. Nanoemulsions typically achieve kinetic stability rather than thermodynamic stability, so their commercial application requires reasonable resistance to flocculation and coalescence, which can be affected by temperature changes. Therefore, their thermoresponsive characterisation becomes relevant. In this work, we analyse this response in an O/W nanoemulsion of Palmarosa for antibacterial purposes that has already shown stability for one year at controlled room temperature. We now study hysteresis processes and the behaviour of the statistical distribution in droplet size by Dynamic Light Scattering, obtaining remarkable stability under temperature changes up to 50 °C. This includes a maintained chemical composition observed using Fourier Transform Infrared Spectroscopy and the preservation of antibacterial properties analysed through optical density tests on cultures and the Spread-Plate technique for bacteria colony counting. We obtain practically closed hysteresis curves for some tracers of droplet size distributions through controlled thermal cycles between 10 °C and 50 °C, exhibiting a non-linear behaviour in their distribution. In general, the results show notable physical, chemical, and antibacterial stability, suitable for commercial applications. Full article

This study investigates the influence of chromatic dispersion on the performance of Brillouin optical time-domain analysis (BOTDA) sensors, particularly under high-pump-power conditions, where nonlinear effects become significant. By incorporating dispersion terms into the coupled amplitude equations of stimulated Brillouin scattering (SBS), we theoretically analyzed the dispersion-induced pulse broadening effect and its impact on the Brillouin gain spectrum (BGS). Numerical simulations revealed that dispersion leads to a moderate broadening of pump pulses, resulting in slight changes to BGS characteristics, including increased peak power and reduced linewidth. To explore the interplay between dispersion and nonlinearity, we built a gain-based BOTDA experimental system and tested two types of fibers, namely standard single-mode fiber (SMF) with anomalous dispersion and dispersion-compensating fiber (DCF) with normal dispersion. Experimental results show that SMF is more prone to modulation instability (MI), which significantly degrades the signal-to-noise ratio (SNR) of the BGS. In contrast, DCF effectively suppresses MI and provides a more stable Brillouin signal. Despite SMF exhibiting narrower BGS linewidths, DCF achieves a higher SNR, aligning with theoretical predictions. These findings highlight the importance of fiber dispersion properties in BOTDA design and suggest that using normally dispersive fibers like DCF can improve sensing performance in long-range, high-power applications. Full article

LiTaO₃ crystals doped with Cr³⁺ and Nd³⁺ ions are promising for developing active nonlinear laser media. In this work, the defect structure of LiTaO₃ crystals, including those doped with Cr³⁺ and Nd³⁺, is examined. X-ray patterns of all six investigated LiTaO₃:Cr:Nd crystals are identical and correspond to a highly perfect structure. Using optical microscopy, the presence of defects of various shapes, microinhomogeneities, and lacunae was revealed. The optical absorption and Raman scattering spectra of a series of nonlinear, optical, double-doped LiTaO₃:Cr³⁺:Nd³⁺ (0.06 ≤ [Cr³⁺] ≤ 0.2; 0.2 ≤ [Nd³⁺] ≤ 0.45 wt%) crystals showed that at concentrations of doping Cr³⁺ ions less than 0.09 wt% and Nd³⁺ ions less than 0.25 wt%, the crystal structure is characterized by a low level of defects, and the optical transmission spectra characterized by narrow lines corresponding to electron transitions in Nd³⁺ ions. In this case, for the radiative transition in the cation sublattice, the existence of three nonequivalent neodymium centers is observed, and for the radiative transition, two nonequivalent centers are observed. IR absorption spectroscopy in the OH⁻-stretching vibration range revealed two main spectral regions: 3463–3465 cm⁻¹, associated with stoichiometry changes, and 3486–3490 cm⁻¹, linked to complex defects such as (V^-_Li)-OH and (Ta⁴⁺_Li)-OH. A distinct low-intensity line at ~3504 cm⁻¹ was observed only in doped crystals, attributed to (Nd²⁺_Li)-OH defects that significantly distort the oxygen-octahedral clusters due to the larger ionic radius of Nd³⁺ compared to Ta⁵⁺. In contrast, Cr-related defects cause only minor distortions. The Klauer method indicated that the highest concentration of OH⁻-groups occurs in the LiTaO₃:Cr³⁺ (0.09 wt%):Nd³⁺ (0.25 wt%) crystal, where multiple complex defects are present. Full article

Extensive numerical simulations were performed in MATLAB R2020b based on the classical nonlinear Thomson scattering theory and single-electron model, to systematically examine the influence of initial phase in tightly focused linearly polarized laser pulses on the radiation characteristics of multi-energy-level electrons. Through our research, we have found that phase variation from 0 to 2π induces an angular bifurcation of peak radiation intensity, generating polarization-aligned symmetric lobes with azimuthal invariance. Furthermore, the bimodal polar angle decreases with the increase of the initial energy. This phase-controllable bimodal distribution provides a new solution for far-field beam shaping. Significantly, high-harmonic intensity demonstrates π-periodic phase-dependent modulation. Meanwhile, the time-domain pulse width also exhibits 2π-cycle modulation, which is synchronized with the laser electric field period. Notably, electron energy increase enhances laser pulse peak intensity while compressing its duration. The above findings demonstrate that the precise control of the driving laser’s initial phase enables effective manipulation of the radiation’s spatial characteristics. Full article

Main-chain conjugated and non-conjugated polyelectrolytes are an important class of materials that have many technological applications ranging from fire-retardant materials to carbon-nanotube composites, nonlinear optical materials, electrochromic materials for smart windows, and optical sensors for biomolecules. Here, we describe a series of poly(pyridinium salt)s-fluorene containing 9,9-bis(4-aminophenyl)fluorene moieties with various organic counterions that were synthesized using ring-transmutation polymerization and metathesis reactions, which are non-conjugated polyelectrolytes. Their chemical structures were characterized by Fourier transform infrared (FTIR), proton (¹H) and fluorine 19 (¹⁹F) nuclear magnetic resonance (NMR) spectrometers, and elemental analysis. They exhibited polyelectrolytic behavior in dimethyl sulfoxide. Their lyotropic liquid-crystalline phases were examined by polarizing optical microscopy (POM) and small angle X-ray scattering (SAXS) studies. Their emission spectra exhibited a positive solvatochromism on changing the polarity of solvents. They emitted greenish-yellow lights in polar organic solvents. They formed aggregates in polar aprotic and protic solvents with the addition of water (v/v, 0–90%), whose λ_em peaks were blue shifted. Full article

Arginine plays a critical role in biomolecular interactions due to its guanidinium side chain, which enables multivalent electrostatic and hydrogen bonding contacts. In this study, atomistic molecular dynamics simulations were conducted across a broad concentration range (26–605 mM) to investigate the thermodynamic and structural features of arginine self-assembly in aqueous solution. Key observables—including hydrogen bond count, radius of gyration, contact number, and isobaric heat capacity—were analyzed to characterize emergent behavior. A three-regime aggregation pattern (dilute, cooperative, and saturated) was identified and quantitatively modeled using the Hill equation, revealing a non-linear transition in clustering behavior. Spatial analyses were supplemented with trajectory-based clustering and radial distribution functions. The heat capacity peak observed near 360 mM was interpreted as a thermodynamic signature of hydration rearrangement. Trajectory analyses utilized both GROMACS tools and the MDAnalysis library. While force field limitations and single-replica sampling are acknowledged, the results offer mechanistic insight into how arginine concentration modulates molecular organization—informing the understanding of biomolecular condensates, protein–nucleic acid complexes, and the design of functional supramolecular systems. The findings are in strong agreement with experimental observations from small-angle X-ray scattering and differential scanning calorimetry. Overall, this work establishes a cohesive framework for understanding amino acid condensation and reveals arginine’s concentration-dependent behavior as a model for weak, reversible molecular association. Full article

This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and for the development of portable near-infrared (NIR) instruments. Thirty soybean samples from diverse sources were collected, and 360 spectral measurements were acquired using a 900–1700 nm NIR spectrometer after grinding and standardized sampling. To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. Specifically, the SPASNV + D₁–RF combination achieved an RPD of 14.7 for moisture, CARS–SNV + D₁–RF reached 5.9 for protein, and CARS–SG + D₂–RF attained 12.0 for fat, all significantly surpassing alternative methods and demonstrating a strong nonlinear learning capacity and predictive precision. These findings show that integrating optimal preprocessing and feature selection strategies can markedly enhance the predictive accuracy in NIR-based soybean analyses. The RF model offers exceptional stability and performance, providing both technical reference and theoretical support for the development of portable NIR devices and practical rapid-quality assessment systems for soybeans in industrial applications. Full article

Sensors are devices that can detect changes in the external environment and convert them into signals. They are widely used in fields like industrial automation, smart homes, medical devices, automotive electronics, and the Internet of Things (IoT), enabling real-time data collection to enhance system intelligence and efficiency. With advancements in technology, sensors are evolving toward miniaturization, high sensitivity, and multifunctional integration. This paper employs the Direct Sampling Method (DSM) and neural networks to reconstruct the shape of perfect electric conductors from the sensed electromagnetic field. Transverse electric (TE) electromagnetic waves are transmitted to illuminate the conductor. The scattered fields in the x- and y-directions are measured by sensors and used in the method of moments for forward scattering calculations, followed by the DSM for initial shape reconstruction. The preliminary shape data obtained from the DSM are then fed into a U-net for further training. Since the training parameters of deep learning significantly affect the reconstruction results, extensive tests are conducted to determine optimal parameters. Finally, the trained neural network model is used to reconstruct TE images based on the scattered fields in the x- and y-directions. Owing to the intrinsic strong nonlinearity in TE waves, different regularization factors are applied to improve imaging quality and reduce reconstruction errors after integrating the neural network. Numerical results show that compared to using the DSM alone, combining the DSM with a neural network enables the generation of high-resolution images with enhanced efficiency and superior generalization capability. In addition, the error rate has decreased to below 15%. Full article

There is growing interest in measuring the temperature-dependent dielectric properties of bio-tissues using dual-mode techniques (scattering measurements and thermal treatment). Uncooled coaxial antennas are preferred for their direct contact with the measured medium and reduced complexity; however, they exhibit structural changes during ablation due to the thermal expansion of polytetrafluoroethylene (PTFE). This paper presents an experimental study on PTFE expansion in an uncooled coaxial insulated monopole antenna in response to changes in the tissue’s thermal environment. Furthermore, it presents a methodology to mitigate these effects through coaxial annealing. The investigation consists of two distinct experiments: characterising PTFE expansion and assessing the effects of annealing through microwave ablation. This was achieved by simulating the thermal effects experienced during ablation by immersing the test antenna in heated peanut oil. PTFE expansion was measured through camera monitoring and using a toolmaker’s microscope, revealing two expansion modalities: linear PTFE expansion and non-linear plastic deformation from manufacturing processes. The return loss during ablation and consequential changes in the ablated lesion were also assessed. Antenna pre-annealing increased resilience against structural changes in the antenna, improving lesion ellipticity. Therefore, this study establishes a fabrication method for achieving an uncooled thermally stable antenna, leading to an optimised dual-mode ablation procedure, enabling quasi-real-time permittivity measurement of the surrounding tissue. Full article

We will show, in any space dimension $d\ge 3$, the decay and scattering in the energy space for the solution to the damped nonlinear Schrödinger equation posed on ${\mathbb{R}}^d\times \mathbb{T}$ and initial data in $H^1({\mathbb{R}}^d\times \mathbb{T})$. We will also derive new bilinear Morawetz identities and corresponding localized Morawetz estimates. Full article

Wave energy converters (WECs) have gained significant attention as a promising renewable energy source. Optimal control strategies, crucial for maximizing energy extraction, have traditionally relied on linear models based on small motion assumptions. However, recent studies indicate that these models do not adequately capture the complex dynamics of WECs, especially when large motions are introduced to enhance power absorption. The nonlinear Froude–Krylov (FK) forces, particularly in heaving-point-absorbers with varying cross-sectional areas, are acknowledged as key contributors to this discrepancy. While high-fidelity computational models are accurate, they are impractical for real-time control applications due to their complexity. This paper presents a parameterized approach for expressing nonlinear FK forces across a wide range of point-absorber buoy shapes inspired by implementing real-time, model-based control laws. The model was validated using measured force data for a stationary spherical buoy subjected to regular waves. The FK model was also compared to a closed-form buoyancy model, demonstrating a significant improvement, particularly with high-frequency waves. Incorporating a scattering model further enhanced force prediction, reducing error across the tested conditions. The outcomes of this work contribute to a more comprehensive understanding of FK forces across a broader range of buoy configurations, simplifying the calculation of the excitation force by adopting a parameterized algebraic model and extending this model to accommodate irregular wave conditions. Full article

This review summarizes the application of physics-informed neural networks (PINNs) for solving higher-order nonlinear partial differential equations belonging to the nonlinear Schrödinger equation (NLSE) hierarchy, including models with external potentials. We analyze recent studies in which PINNs have been employed to solve NLSE-type evolution equations up to the fifth order, demonstrating their ability to obtain one- and two-soliton solutions, as well as other solitary waves with high accuracy. To provide benchmark solutions for training PINNs, we employ analytical methods such as the nonisospectral generalization of the AKNS scheme of the inverse scattering transform and the auto-Bäcklund transformation. Finally, we discuss recent advancements in PINN methodology, including improvements in network architecture and optimization techniques. Full article

This paper studies the coupling effect of laser intensity and waist radius on a series of electron radiation characteristics, such as radiation power, electron trajectory, a time spectrum, and a frequency spectrum. Through a large number of numerical simulations, based on the study of all radiation patterns, the influence laws and mechanisms of these two parameters are revealed. The larger the light intensity ${\mathit{a}}_{\mathbf{0}}$, the greater the influence of the waist radius ${\mathit{\omega }}_{\mathbf{0}}$ on the symmetry of the radiation power. When the light intensity is the same, the larger the waist radius, the greater the radiation power, the better the collimation, and the radiation power approximately satisfies a quadratic function. These results indicate that the radiation power can be rapidly increased by seven orders of magnitude through adjustment. Through a numerical simulation, we have obtained high-energy, high-frequency, high-collimation, good directivity, and supercontinuum X-rays. The above research has a pioneering guiding significance for the modulation of X-rays in optical laboratories. Full article

In realistic hyperspectral image (HSI) cross-scene classification tasks, it is ideal to obtain target domain samples during the training phase. Therefore, a model needs to be trained on one or more source domains (SD) and achieve robust domain generalization (DG) performance on an unknown target domain (TD). Popular DG strategies constrain the model’s predictive behavior in synthetic space through deep, nonlinear source expansion, and an HSI generation model is usually adopted to enrich the diversity of training samples. However, recent studies have shown that the activation functions of neurons in a network exhibit asymmetry for different categories, which results in the learning of task-irrelevant features while attempting to learn task-related features (called “feature contamination”). For example, even if some intrinsic features of HSIs (lighting conditions, atmospheric environment, etc.) are irrelevant to the label, the neural network still tends to learn them, resulting in features that make the classification related to these spurious components. To alleviate this problem, this study replaces the common nonlinear generative network with a specific linear projection transformation, to reduce the number of neurons activated nonlinearly during training and alleviate the learning of contaminated features. Specifically, this study proposes a dimensionally decoupled spatial spectral linear extrapolation (SSLE) strategy to achieve sample augmentation. Inspired by the weakening effect of water vapor absorption and Rayleigh scattering on band reflectivity, we simulate a common spectral drift based on Markov random fields to achieve linear spectral augmentation. Further considering the common co-occurrence phenomenon of patch images in space, we design spatial weights combined with label determinism of the center pixel to construct linear spatial enhancement. Finally, to ensure the cognitive unity of the high-level features of the discriminator in the sample space, we use inter-class contrastive learning to align the back-end feature representation. Extensive experiments were conducted on four datasets, an ablation study showed the effectiveness of the proposed modules, and a comparative analysis with advanced DG algorithms showed the superiority of our model in the face of various spectral and category shifts. In particular, on the Houston18/Shanghai datasets, its overall accuracy was 0.51%/0.83% higher than the best results of the other methods, and its Kappa coefficient was 0.78%/2.07% higher, respectively. Full article