Abstract High-temperature radiative hydrodynamics serves as the core theoretical foundation for numerical simulation of inertial confinement fusion (ICF). Its characteristics, such as strong nonlinear coupling, dynamic interface discontinuity, and instability evolution, pose severe challenges to accurate modeling. As a core mathematical tool for describing complex geometric and physical field evolution, differential manifold provides an innovative solution to break through the bottlenecks of multi-field coupling and interface modeling in high-temperature radiative hydrodynamics. Taking the critical Rayleigh-Taylor (R-T) instability during ICF target implosion as the research object, this paper systematically constructs and verifies a modeling system for high-temperature radiative plasma based on a differential manifold. The core innovation lies in abstracting the dynamically evolving plasma flow field as a four-dimensional pseudo-Riemannian manifold, defining key physical quantities such as density, temperature, and radiation intensity as tensor fields on the manifold, accurately revealing the evolution law of plasma interface perturbations through geometric quantities including manifold curvature and connection, and innovatively deriving the covariant form of radiative hydrodynamics governing equations, which fundamentally avoids the dependence on traditional coordinate systems. Simulation results confirm that the differential manifold method can effectively solve the distortion problem of traditional Eulerian grids. The prediction error of the initiation position of R-T instability is less than 7%, and the calculation accuracy of interface radiative energy transport is improved by 8%. This study not only realizes the systematic application of differential manifold in the field of ICF high-temperature radiative hydrodynamics, providing mathematical support for the accurate prediction and control of instability, but also expands the application boundary of differential manifold in the modeling of complex fields in fusion physics.

Production of legible typography on the television screen is affected by technological va ri ables unknown to the printed media. Specifi c problems of type distortion and decay in television transmission are described. To counteract these problems the Graphi c Arts Department of CBS News experimented with various typefaces and developed CBS News 36; research results ar(' illustrated and discussed.

Abstract In membrane structure damage detection, surface textures of membrane materials frequently interfere with damage identification and geometric feature extraction. To address the issues of global detail blurring caused by conventional spatial-domain filtering and the problems of insufficient texture suppression or distortion in non-textured regions induced by frequency-domain filtering due to its poor adaptability to fixed parameters and spectral leakage, this paper proposes a texture suppression method that integrates spatial and frequency domains. First, spatial mathematical models are established for three typical types of membrane textures (quasi-sinusoidal texture, orthogonal grid texture, and diagonal grid texture) to quantitatively analyze their frequency-domain energy distribution characteristics. Based on this analysis, the orientation, scale, and bandwidth parameters of multi-scale Gabor filters are derived, eliminating dependence on manual parameter tuning. Second, a spatial-domain fusion of multi-scale Gabor responses generates a texture energy (TE) map, which integrates with Otsu’s adaptive thresholding and morphological opening–closing operations to precisely segment textured regions and generate optimized masks. Finally, median filtering for suppression is applied only within the textured regions identified by the masks, thereby maximizing the preservation of structural integrity in non-textured regions. Experimental results demonstrate that the proposed texture suppression based on Texture Modeling and multi-scale Gabor spatial-frequency fusion method effectively combines the precision of the frequency domain with the localization advantages of the spatial domain. It achieves significant texture suppression while yielding an average peak signal-to-noise ratio improvement exceeding 10 dB compared to the gradient operator method. When compared to Fourier band-stop filtering, wavelet threshold denoising, and Gaussian filtering methods, it achieves optimal values for both mean gradient magnitude (4.67) and TE (0.03), indicating significantly superior suppression performance over the comparative methods.

When amplifying digital pulse signals in an all-digital transmitter (ADTx), the switched-mode power amplifier (SMPA) introduces pulse distortion, which in turn leads to nonlinear distortion. This paper focuses on the nonlinear distortion problem caused by the delay effect in ADTx, analyzing the generation mechanism of pulse distortion during the switching amplification process and establishing a mathematical model based on a five-level RF-PWM outphasing ADTx. Numerical simulations are then conducted using the pulse distortion error model to examine the impact of nonlinear distortion caused by delay errors on the output pulse waveform of the ADTx, as well as on performance metrics such as the error vector magnitude (EVM) and adjacent channel power ratio (ACPR). Pulse distortions are compensated according to the distortion characteristics. After applying the proposed compensation method, the ACPR of the signal improves by 5 dB, and the EVM decreases from 3.4% to approximately 0.4%, resulting in a significant improvement in signal quality.

Rethinking the value of dynamic and static feature planes in 4D reconstruction of deformable tissues.

Unmanned aerial vehicles (UAVs) provide rich thermal imagery data crucial for vision-related tasks such as feature extraction, image enhancement, and data synthesis. Acquiring high-resolution (HR) thermal images for UAVs remains challenging due to sensor hardware constraints. Guided super-resolution (SR) methods leverage HR visible images to enhance thermal images reconstruction from low-resolution (LR) inputs. However, these methods demonstrate limitations in restoring fine grained textural details. To solve the problem, we propose a hierarchical cross-modal mamba with multi-head reconstruction network (HCM-Mamba) for UAV thermal images SR. The framework introduces a residual state-space block (RSSB) that directly models long-range spatial dependencies, enabling robust contextual correlation learning for sparse, small targets in complex aerial scenes. To alleviate the problem of modal differences between thermal and visible images, we propose a cross-modal integration framework, which includes a hierarchical cross-modal bridging encoder (HCB-Encoder) (interaction, refinement, and enhancement). The complementary features are hierarchically aligned and integrated through a dual attention mechanism and multi-scale aggregation. In order to address the problems of structure distortion and detail loss in traditional end-to-end architectures, we propose a multi head collaborative reconstruction mechanism that ensures coherent optimization of global structure and local texture while decoupling the feature learning process. Extensive experiments have shown that HCM-Mamba has achieved significant improvements in multiple evaluation metrics, maintaining structure integrity, and restoring perceived real details under real-world UAV imaging conditions.

The main topic of the article is the influence of color reproduction during the digitization of works by marine artists on artistic and emotional expressiveness.The problem of distortion of the original color scale during digitization is considered, which can lead to a loss of depth of figurative understanding, a change in the semantic load and mood of the picture and, as a result, to a weakening of the artistic effect and emotional impact on the viewer.The study used a visual-analytical approach combined with a method of comparative analysis of original works and their digital reproductions played on the screens of various devices.(tablet and laptop).The comparison was carried out taking into account changes in the color scheme and the nature of the audience's emotional reaction.As material, works with different color solutions were selected from the collections of the Mykolaiv Regional Art Museum named after V. V. Vereshchagin and the Ochakov Museum of Marine Painting named after Rufin Sudkovsky, which allowed to cover a wide range of visual images.The article focuses on the role of color in the formation of an artistic image, which determines the emotional understanding of the holistic content of the work.The aspects of color action are considered, which allow artists to convey the author's intention and form a corresponding emotional response in the viewer.In our case, the causes of color deformations that occur during digital display of images are determined.The importance of accurate color reproduction in the digitization of works of art to preserve their aesthetic and emotional value is determined.The importance of digital technologies as a tool for preserving cultural heritage is emphasized, which expands access to works of art and performs not only an informational, but also a cultural and educational function.It is noted that involvement in fine arts contributes to the development of artistic perception, the formation of stable aesthetic preferences in a wide audience, as well as the strengthening of visual culture in society.The research conducted may be useful to specialists in the field of museum studies, art history, digital humanities, which provides art history with new tools for the study and analysis of art, as well as in the training of specialists in artistic and creative specialties, which is relevant in the conditions of active use of digital devices in museum practice and education.

Abstract To address the response distortion issues of intensified complementary metal oxide semiconductor (ICMOS) camera, a novel pixel-level correction method is proposed, termed the three-stage deviation compensation method. The method specifically targets three types of distortion, namely column stripe noise, nonlinear intensity response of a single pixel, and inconsistent intensity response of the whole image field. In the first stage, column stripe noise is estimated using the statistical analysis of multiple images captured under dark field conditions. In the second stage, a pixel response–integrated correction method is designed to solve two other distortion problems mentioned above. K-means outlier detection is applied to process the original image data, thereby constructing ideal linear response curves for each pixel following outlier removal. Third-order polynomial fitting is subsequently applied to correct response deviations. In the third stage, to improve the accuracy of response calibration, we design a feature vector based on multiple critical optical parameters of ICMOS camera, which is used to train the support vector regression improved by genetic algorithm for secondary deviation compensation. The corresponding experiment findings demonstrate that the uniformity and linearity of the pixel response have been improved by approximately 30.0% and 1.5%, respectively, compared with our previously proposed two-step calibration method.

Image generation technology using generative adversarial networks has been widely used in front-end design, but existing models have problems such as fuzzy generation and limited style expression. This study proposes an improved StyleGAN (Style Generative Adversarial Network) model to achieve style transfer and high-quality generation of front-end interface elements. An additional module is added after the generator to calculate the mutual information between the latent variables and the output of the network layer, and integrate it into the discriminator loss function for joint optimization to enhance the ability to control details. The overall FID (Frechet Inception Distance) value of the images generated by the improved model on the Rico dataset reaches 12.5, and the MS-SSIM (Multi-Scale Structural Similarity) reaches 0.92. Among them, the IS (Inception Score) value of the image generated for the Navigation Menu category reaches 7.41, which is about 14.2% higher than the baseline StyleGAN. The method used effectively solves the problem of detail distortion in front-end page generation, and realizes the precise mapping of style features and design elements through the mutual information constraint mechanism, providing a highly customizable technical framework for front-end intelligent design.

User ratings and reviews on marketplaces are subject to systematic distortions, creating serious risks for e-commerce participants and reducing the efficiency of market mechanisms. This study presents a comprehensive analysis of information distortion problems, covering the process from rating formation to its systematic accounting. The aim of the work is to systematize factors of information distortion on marketplaces and develop metrics for quantitative assessment of reliability. An interdisciplinary approach is applied, integrating economic theories, psychological concepts, as well as knowledge from behavioral economics and computer science. The research expands our understanding of information economics in the context of digital platforms, revealing relationships between information quality and market participant behavior. The results have practical significance for marketplace developers, regulators, and users, providing a foundation for creating effective information quality control mechanisms.

The purpose of this article is to identify the specific features of the “East-West” narrative as a marker of the Russian cultural code in media discourse. The research material consists of media texts from the “Culture” section of the “Izvestia” newspaper information portal, published on the website of the federal mass media between January 1 and October 28, 2025. The relevance of studying the specifics of the Russian cultural code implementation in media discourse is determined by the growing significance of axiosphere issues in the life of modern Russian society, various types of individual activity from the position of the axiological approach, and the creation of a regulatory framework defining the value and substantive aspects of culture. In the course of the research, the descriptive method, methods of partial sampling and contextual analysis, and linguistic and cultural analysis were applied. The content of contemporary media texts is characterized by ideological modality, which is not a modality of utterance, but a modality of discourse realized in language. The Russian cultural code is considered, a number of components of which are civilizational signs of Russia. Since the language of mass media has a cognitive-ideological level, in this work the components of the Russian cultural code were considered as linguacultural concepts objectified in media texts. It is established that in the media texts studied, the “East-West” narrative actualizes significant cultural and social issues, including problems of distortion of collective historical memory, morality, and behavior in the family. Despite the changing geopolitical agenda, the “East-West” narrative includes mostly positive and neutral connotations regarding both the East and the West. This circumstance testifies to the special unique status of Russia.

To address the issues of imprecise user segmentation, inadequate handling of fuzzy evaluation information, and low recommendation accuracy in current electricity retail package recommendations, a novel recommendation method based on K-prototypes clustering and interval-valued intuitionistic fuzzy theory is proposed. First, a multi-dimensional user profile is constructed, incorporating five numerical tags—such as monthly average electricity consumption and monthly load factor—and two categorical tags: industry characteristics and value-added service demand. The K-prototypes algorithm is employed to cluster users, effectively resolving the profile distortion problem caused by the neglect of categorical features in traditional K-means clustering. Second, interval-valued intuitionistic fuzzy numbers are introduced to transform user linguistic evaluations into quantitative indicators. A projection measure-based model is established to objectively determine attribute weights, thereby eliminating subjective weighting bias. Finally, a comprehensive ranking of electricity retail packages is generated by integrating satisfaction levels of similar users and similar measures of new users. The recommendation performance is validated using Root Mean Square Error (RMSE), Kendall’s τ, Normalized Discounted Cumulative Gain (NDCG@5), and Discrimination Index (S). A case study involving users from a region in China demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) to 0.32, which is 31.25% lower than the next best traditional method (K-prototypes + equal weight clustering with RMSE = 0.48), accurately addresses the core demands of diverse user groups, significantly improves recommendation precision and user satisfaction, and exhibits substantial practical application value.

Abstract Accurate spectral analysis of high-energy astrophysical sources often relies on comparing observed data to incident spectral models convolved with the instrument response. However, for gamma-ray bursts and other high-energy transient events observed at high count rates, significant distortions (e.g., pile-up, dead time, and large signal trailing) are introduced, complicating this analysis. We present a method framework to address the model dependence problem, especially to solve the problem of energy spectrum distortion caused by instrument signal pile-up due to high counting rate and high-rate effects, applicable to X-ray, gamma-ray, and particle detectors. Our approach combines physics-based Monte Carlo (MC) simulations with a model-independent spectral inversion technique. The MC simulations quantify instrumental effects and enable correction of the distorted spectrum. Subsequently, the inversion step reconstructs the incident spectrum using an inverse response matrix approach, conceptually equivalent to deconvolving the detector response. The inversion employs a convolutional neural network, selected for its numerical stability and effective handling of complex detector responses. Validation using simulations across diverse input spectra demonstrates high fidelity. Specifically, for 27 different parameter sets of the brightest gamma-ray bursts, goodness-of-fit tests confirm the reconstructed spectra are in excellent statistical agreement with the input spectra and residuals are typically within ±2 σ . This method enables precise analysis of intense transients and other high-flux events, overcoming limitations imposed by instrumental effects in traditional analyses.

Aiming at the problems of voltage distortion caused by voltage drop nonlinearity of voltage source inverter (VSI) tubes, poor recognition accuracy of traditional grey wolf optimization (GWO) in identifying parameters of permanent magnet synchronous motor (PMSM), and slow convergence speed at the later stage, a memory self-learning grey wolf optimization (MSLGWO) algorithm with voltage error compensation is proposed. First, the output voltage error caused by switching tube voltage drop in different conduction states of the inverter is compensated to mitigate its impact on parameter identification. Then, cat mapping is employed to generate the initial position of the grey wolves, combined with an inverse learning strategy to find and select the superior solution among them to secure the variety of the initial population. In addition, the rate of convergence is accelerated by using a cosine-varying convergence factor to maintain a balance between global and local search capabilities. Lastly, inspired by the particle swarm optimization algorithm, a memory-based self-learning mechanism is incorporated to leverage the past experiences of individual wolves. Compared with traditional GWO, the proposed MSLGWO with voltage compensation reduces the identification error by at least 50.0% and completes the process within 0.11 s.

Abstract To achieve accurate rate of rock hardness recognition in coal mine roadway excavation, support, and anchor operations, vibration and acoustic signals during the cutting process of rocks with different hardness is collected using a self-developed experimental platform for rock hardness recognition in Excavation-Support-Anchor equipment. A cross-modal rock hardness recognition method based on Hyperbolic Tangent Adaptive Space Learning (HTASL) is proposed. The method first segments the collected vibration and acoustic signals into fixed-length sample segments to construct an initial rock hardness sample set. Time-frequency domain features of each sample are extracted to form a 27-dimensional feature vector that characterizes rock hardness properties. Aiming at the problems of sample structural scatter deviation and feature space distortion caused by noise and redundant information interference during cross-modal feature fusion, the sample structural scatter of rock hardness is obtained through modal expansion. A label-aware adaptive module is designed to constrain the geometric relationship of samples, constructing a feature space that integrates latent structures and label information, and establishing a local adaptive structural scatter to solve the problem of local distortion in the feature space. Meanwhile, we design a hyperbolic tangent structural scatter correction mechanism. The sample structural scatter is decomposed into singular vectors and singular values, and the hyperbolic tangent structural scatter is reconstructed after correcting the singular values through hyperbolic tangent constraints. This scatter is integrated into the modal expansion framework to build the HTASL model. Through theoretical derivation, the analytical solution of the projection direction is obtained, realizing cross-modal rock hardness feature extraction with strong class separability. The effectiveness of the HTASL method is verified by comparative experiments and ablation experiments on the dataset from the self-developed Excavation-Support-Anchor experimental platform.

To solve the problem of feature distortion induced by excessive smoothing or sharpening during the reconstruction of 3D point cloud, a feature-preserving probabilistic reconstruction method is proposed. First, low-rank estimation of local curvature of point cloud is realized by space-based principal component analysis in order to better locate sharp features, and the normal field of point cloud is adaptively optimized based on curvature which guarantees accurate normal constraints during sharp feature recovery. Then, the probability reconstruction model of point cloud is established according to Bayesian statistic inference. And the posterior probability distribution of each point position is simulated by sequential Monte Carlo method within particle filtering framework. In order to improve the convergence, the resampling range is adjusted according to the local noise. Finally, the Bayesian reconstruction of point cloud is achieved using resampling with normal and spherical constraints for different regions which takes the advantage of multiple hypothesis testing, and the problems of feature degradation and irregular edges are solved. Compared with several state-of-the-art methods, the experimental results show that the proposed algorithm has improved subjective visual effects, reconstruction accuracy, and efficiency, and the reconstruction error is reduced by 46.84% while the running time is decreased by 33.34% on average. The proposed method can effectively restore original features while suppressing noise, which improve the quality of point cloud.

Moisture content is one of the key indicators for evaluating the quality of apricots. When moisture levels fluctuate over an excessively wide range, scattering effects and absorption characteristics interfere with each other, making it difficult for a single model to achieve accurate predictions across the entire range. This study investigates precision modeling methods applicable to different moisture intervals based on spectral morphological features. By extracting the spectral morphological features of the water-sensitive regions (peak and valley) and conducting Pearson correlation analysis, the spectral morphological feature parameters with relatively strong correlations were selected, and they were combined with the characteristic bands to construct a segmented model for water content intervals. The results indicate that spectral morphological features of apricots within the 25–40% and 40–55% moisture range exhibit a certain correlation with moisture content. A weak correlation is observed in the 55–70% moisture range. After preliminary fusion modeling of spectral morphological features and characteristic bands for apricots across different moisture ranges, further analysis revealed that moisture content models based on valley morphology features and characteristic bands outperformed those based on peak morphology features and characteristic bands, demonstrating superior representational capability. By establishing a fusion model based on the spectral morphological parameters selected through Pearson’s method and the characteristic bands, the detection accuracy and model stability in the 25–70% moisture content range have been effectively improved. Among all the models covering different moisture content ranges, the model for the 55–70% moisture content range has the best prediction effect. The correlation coefficient of its prediction set reaches 0.8723, and the Ratio of Performance to Interquartile Range (RPIQ) is 2.5220, indicating that this range is the most suitable for establishing a high-precision quantitative moisture content detection model. This research effectively solved the problem of spectral response distortion caused by wide variations in moisture content and improved the prediction accuracy of the moisture content detection model for apricots.

With the development of deep learning technology, virtual try-on technology has developed important application value in the fields of e-commerce, fashion, and entertainment. The recently proposed Leffa technology has addressed the texture distortion problem of diffusion-based models, but there are limitations in that the bottom detection inaccuracy and the existing clothing silhouette persist in the synthesis results. To solve this problem, this study proposes CaP-VTON (Clothing-Agnostic Pre-Inpainting Virtual Try-On). CaP-VTON integrates DressCode-based multi-category masking and Stable Diffusion-based skin inflation preprocessing; in particular, a generated skin module was introduced to solve skin restoration problems that occur when long-sleeved images are converted to short-sleeved or sleeveless ones, introducing a preprocessing structure that improves the naturalness and consistency of full-body clothing synthesis and allowing the implementation of high-quality restoration considering human posture and color. As a result, CaP-VTON achieved 92.5%, which is 15.4% better than Leffa, in short-sleeved synthesis accuracy and consistently reproduced the style and shape of the reference clothing in visual evaluation. These structures maintain model-agnostic properties and are applicable to various diffusion-based virtual inspection systems; they can also contribute to applications that require high-precision virtual wearing, such as e-commerce, custom styling, and avatar creation.

This study examines the performance of hybrid steel-GFRP pipes compared to steel pipes, with a focus on bonding properties and the occurrence of internal corrosion. Some pipes were worn screw-shaped to mimic the effects of corrosion. The hybrid material was manufactured from two steel pipes reinforced with GFRP, bonded with polyester resin and 10% styrene to reduce viscosity and prevent bubble formation. Distortion problems during the manufacture of the specimens are addressed. Results indicate greater deformation in the worn pipes than in the steel-only specimens, whereas the hybrid material showed no significant difference between the two types. The hybrid material supported higher loads in some probes, but only two hybrid probes failed. Strain gauges measured the deformations, and the composite material's behavior was examined under a microscope. The hybrid material presented a lower flexural modulus and greater compliance to cracking. Despite the performance of the proposed hybrid material not being able to stand up to steel’s superior mechanical properties, the study offers useful insights and recommendations for future research, backed by stress-strain graphs.

Introduction: The sensorless control technology for permanent magnet synchronous motors typically employs a sliding mode observer to obtain rotor position and speed information based on the back electromotive force. This study aims to improve the inherent chattering and poor observation performance of the traditional sliding mode observer (SMO) in the rotor position estimation of the surface-- mounted permanent magnet synchronous motor. Methods: The super twisting algorithm (STA) is introduced to improve the traditional SMO, and the super twisting sliding mode observer (STA-SMO) is constructed to solve the chattering problem of the traditional SMO. According to different speeds, the sliding mode variable gain coefficient is designed, and a continuous function L(x) is introduced as a switching function to make the switching of the sliding mode surface smoother. Considering the problem of stator current distortion caused by dead zone, the harmonic suppression strategy of adaptive notch filter (ANF) based on the least mean square (LMS) algorithm is studied and combined with the STA-SMO method to construct a position sensorless control system considering current harmonic compensation. Comparative verification under different speed conditions is carried out to verify the control performance of the method studied in this study under a wide speed range. Results: Firstly, the speed information is introduced as a variable into the gain coefficient of the traditional STA-SMO, and the parameters are adjusted with speed, which solves the parameter matching problem in different speed domains of STA-SMO and effectively improves the stability of the observer. On this basis, the current harmonic compensation strategy based on LMS-ANF is introduced. According to the characteristics of the adaptive filter, the harmonic current of a specific wave can be extracted, and the acquisition current is compensated to suppress the influence of current harmonics on the estimation results of the observer, which further improves the accuracy of the observer. Discussion: The proposed STA-SMO with LMS-ANF harmonic compensation demonstrates superior performance over traditional SMO, effectively reducing chattering and improving stability across wide speed ranges. Experimental results confirm its robustness under dynamic loads and adaptability to speed transitions, with chattering reduced by 1.1%. The LMS-ANF strategy mitigates current harmonics, enhancing low-speed accuracy. While the method balances simplicity and reliability, future work could address near-zero-speed performance and computational efficiency for broader industrial applications. Conclusion: The STA-SMO + LMS-ANF proposed in this study can effectively improve the anti-interference ability of the observer, adapt to the application of a wide speed range, and have strong robustness and higher observer accuracy.