Search Results (1,951)

The paper uses the model of the coupled Boltzmann transport equations, numerically modeled using the lattice Boltzmann method, which describes the impact of an ultra-short laser on thin metal surfaces. The main reason for using this method is the need to create an appropriate model on a very small scale, both in terms of space and time. In the present study, a three-dimensional model is used, for which, in the case of optimization or identification tasks, the characteristics of the obtained numerical solution are quite different from those of other numerical methods and models. The proposed and numerically implemented task of identifying parameters characterizing the laser pulse, such as the power of the stationary laser source, the laser-beam absorptivity coefficient, and the radius of the laser beam, appropriately illustrates the problem. The problem has also been solved for ideal deterministic (no noise) and randomly disturbed (with noise) values of measured temperatures. Three different optimization algorithms are used to solve the inverse task. Several variants of the identification tasks, differing in terms of the number of temperature measurement points, are solved. The results and effectiveness of the identification tasks are compared for the best solution, as well as the statistical measures. Full article

Fractal dimension (Frac) and lacunarity (Lac) are frequently proposed as biomarkers of multiscale image complexity, but their incremental value over standardized radiomics remains uncertain. We position both measures within the Image Biomarker Standardisation Initiative (IBSI) feature space by running a fully reproducible comparison in two settings. In a baseline experiment, we analyze $N=1000$ simulated $64\times 64$ textured ROIs discretized to $N_g=64$, computing 92 IBSI descriptors together with Frac (box counting) and Lac (gliding box), for 94 features per ROI. In a wavelet-augmented experiment, we analyze $N=1000$ ROIs and add level-1 wavelet descriptors by recomputing first-order and GLCM features in each sub-band (LL, LH, HL, and HH), contributing $4\times (19+19)=152$ additional features and yielding 246 features per ROI. Feature similarity is summarized by a consensus score that averages z-scored absolute Pearson and Spearman correlations, distance correlation, maximal information coefficient, and cosine similarity, and is visualized with clustered heatmaps, dendrograms, sparse networks, PCA loadings, and UMAP and t-SNE embeddings. Across both settings a stable two-block organization emerges. Frac co-locates with contrast, difference, and short-run statistics that capture high-frequency variation; when wavelets are included, detail-band terms from LH, HL, and HH join this group. Lac co-locates with measures of large, coherent structure—GLSZM zone size, GLRLM long-run, and high-gray-level emphases—and with GLCM homogeneity and correlation; LL (approximation) wavelet features align with this block. Pairwise associations are modest in the baseline but become very strong with wavelets (for example, Frac versus GLCM difference entropy, which summarizes the randomness of gray-level differences, with $\left|r\right|\approx 0.98$; and Lac versus GLCM inverse difference normalized (IDN), a homogeneity measure that weights small intensity differences more heavily, with $\left|r\right|\approx 0.96$). The multimetric consensus and geometric embeddings consistently place Frac and Lac in overlapping yet separable neighborhoods, indicating related but non-duplicative information. Practically, Frac and Lac are most useful when multiscale heterogeneity is central and they add a measurable signal beyond strong IBSI baselines (with or without wavelets); otherwise, closely related variance can be absorbed by standard texture families. Full article

The article presents a study of the computational complexity and efficiency of various parallel algorithms that implement the numerical solution of the equation in the hereditary $\alpha \left(t\right)$-model of radon volumetric activity (RVA) in a storage chamber. As a test example, a problem based on such a model is solved, which is a Cauchy problem for a nonlinear fractional differential equation with a Gerasimov–Caputo derivative of a variable order and variable coefficients. Such equations arise in problems of modeling anomalous RVA variations. Anomalous RVA can be considered one of the short-term precursors to earthquakes as an indicator of geological processes. However, the mechanisms of such anomalies are still poorly understood, and direct observations are impossible. This determines the importance of such mathematical modeling tasks and, therefore, of effective algorithms for their solution. This subsequently allows us to move on to inverse problems based on RVA data, where it is important to choose the most suitable algorithm for solving the direct problem in terms of computational resource costs. An analysis and an evaluation of various algorithms are based on data on the average time taken to solve a test problem in a series of computational experiments. To analyze effectiveness, the acceleration, efficiency, and cost of algorithms are determined, and the efficiency of CPU thread loading is evaluated. The results show that parallel algorithms demonstrate a significant increase in calculation speed compared to sequential analogs; hybrid parallel CPU–GPU algorithms provide a significant performance advantage when solving computationally complex problems, and it is possible to determine the optimal number of CPU threads for calculations. For sequential and parallel algorithms implementing numerical solutions, asymptotic complexity estimates are given, showing that, for most of the proposed algorithm implementations, the complexity tends to be $n^2$ in terms of both computation time and memory consumption. Full article

Spaceborne Synthetic Aperture Radar (SAR) can be applied for monitoring tropical cyclones (TCs), but co-polarized C-band SAR suffers from signal saturation such that it is improper for high wind-speed conditions. In contrast, cross-polarized SAR data does not suffer from this issue, but the retrieval algorithm needs more deliberation. Previously, many geophysical model functions (GMFs) have been developed using cross-polarized data, which obtain wind speeds using the complex relationships described by radar backscatter, incidence angle, wind direction, and radar look direction. In this regard, the rapid development of artificial intelligence technology has provided versatile machine learning methods for such a nonlinear inversion problem. In this study, we comprehensively compare the wind-speed retrieval performance of several models including Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN), which were developed based on spatio-temporal matching and correlation analysis of stepped frequency microwave radiometer (SFMR) and dual-polarized Sentinel-1 SAR data after noise removal. A data set with ~2800 samples is generated during TCs for training and validating the inversion model. The generalization ability of different models is tested by the reserved independent data. When using similar parameters with GMFs, RF inversion has the highest accuracy with a Root Mean Square Error (RMSE) of 3.40 m/s and correlation coefficient of 0.94. Furthermore, considering that the sea surface temperature is a crucial factor for generating TCs and influencing ocean backscattering, its effects on the proposed RF model are also explored, the results of which show improved wind-speed retrieval performances. Full article

Satellite-derived bathymetry (SDB) using Ice, Cloud, and Land Elevation satellite-2 (ICESat-2) LiDAR data and remote sensing images faces challenges in the difficulty of uniform coverage of the inversion area by the bathymetric control points due to the linear sampling pattern of ICESat-2. This study proposes a novel virtual control point optimization framework integrating inverse distance weighting (IDW) and spectral confidence analysis (SCA). The methodology first generates baseline bathymetry through semi-empirical band ratio modeling (control group), then extracts virtual control points via SCA. An optimization scheme based on spectral confidence levels is applied to the control group, where high-confidence pixels utilized a residual correction-based strategy, while low-confidence pixels employed IDW interpolation based on a virtual control point. Finally, the preceding optimization scheme uses weighting-based fusion with the control group to generate the final bathymetry map, which is also called the optimized group. Accuracy assessments over the three research areas revealed a significant increase in accuracy from the control group to the optimized group. When compared with in situ data, the determination coefficient (R²), RMSE, MRE, and MAE in the optimized group are better than 0.83, 1.48 m, 12.36%, and 1.22 m, respectively, and all these indicators are better than those in the control group. The key innovation lies in overcoming ICESat-2’s spatial sampling limitation through spectral confidence stratification, which uses SCA to generate virtual control points and IDW to adjust low-confidence pixel values. It is also suggested that when applying ICESat-2 satellite data in active–passive-fused SDB, the distribution of training data in the research zone should be adequately considered. Full article

This study proposes a fast material parameter evaluation method for multilayer thin-film structures based on machine learning technology to solve the problems of long time and low efficiency in the traditional material parameter inversion process. Nanoindentation experiments are first conducted to establish an experimental basis across film stacks. A two-dimensional elasto-plastic model of the indentation process is then built to generate a large set of load–depth curves, which serve as training data for a machine learning model. Trained on simulated curves and validated against measurements, the model enables fast inverse identification of layer-wise plastic parameters and interfacial cohesive properties. The experimental results show that the method has high accuracy and efficiency in the inversion of interlayer cohesion parameters, and the correlation coefficient R² is 0.99 or more. Compared with traditional methods, the pipeline supports batch analysis of multiple datasets and delivers parameter estimates within 1 h, substantially shortening turnaround time while improving result reliability. This method can not only effectively solve the challenges faced by traditional material evaluation, but also provide a new and effective tool for the performance evaluation and optimization design of multilayer thin-film materials. It has broad application prospects and potential value. Full article

This paper describes a laboratory set-up designed to support hands-on learning of heat transfer principles in aerospace engineering education. Developed within the framework of experiential and project-based learning, the set-up enables students to experimentally characterize the convective coefficient of a cooling fan and the thermo-optical properties of aluminum plates with different surface coatings, specifically their absorptivity and emissivity. A custom-built, LED-based radiation source (the ESAT Sun simulator) and a calibrated temperature acquisition system are used to emulate and monitor radiative heating under controlled conditions. Simplified physical models are developed for both the ESAT Sun simulator and the plates that capture the dominant thermal dynamics via first-order energy balances. The laboratory workflow includes real-time data acquisition, curve fitting, and thermal model inversion to estimate the convective and thermo-optical coefficients. The results demonstrate good agreement between the model predictions and observed temperatures, which supports the suitability of the set-up for education. The proposed activities can strengthen the student’s understanding of convective and radiative heat transport in aerospace applications while also fostering skills in data analysis, physical and numerical reasoning, and system-level thinking. Opportunities exist to expand the material library, refine the physical modeling, and evaluate the long-term pedagogical impact of the educational set-up described here. Full article

Background: Sodium/iodide symporter (NIS) is a membrane protein involved in iodide transport into cells, making it a key component of thyroid physiology and radioiodine therapy for thyroid cancer. Although NIS is expressed in many extrathyroidal tissues, including breast tumors, its functional role and prognostic significance in these contexts remain a subject of active investigation. Understanding the mechanisms regulating NIS, its influence on cellular processes such as migration and metastasis, and its connection with transcription factors like FOXA1 could contribute to the development of new therapeutic strategies for breast cancer treatment. This study aims to investigate the correlation between sodium/iodide symporter (NIS) expression and response to neoadjuvant chemotherapy in patients with triple-negative breast cancer (TNBC). Methods: The current retrospective study included 161 TNBC patients who received neoadjuvant chemotherapy followed by mastectomy. NIS expression was assessed via immunohistochemistry, graded semi-quantitatively from 0 to 3+. The Residual Cancer Burden (RCB) scale was used to evaluate the response to chemotherapy. Statistical analysis included Lilliefors tests and Kendall’s tau correlation coefficient. Publicly available Cancer Genome Atlas datasets were analyzed to assess the relationship between NIS and FOXA1 expression. Results: NIS immunopositivity was observed in 69.5% of TNBC samples compared to 63.3% GATA-3-positive and 31.0% of Mammaglobin-positive samples. While no significant correlation was found between NIS expression and age, TNM stage, or Ki-67, a statistically significant moderate positive correlation (τ = 0.481, p < 0.01) was identified between NIS expression and RCB index, indicating that higher NIS expression was associated with a poorer response to neoadjuvant chemotherapy. TCGA data analysis revealed a statistically significant increase in NIS mRNA expression in FOXA1-mutated TNBC samples compared to FOXA1-wild-type samples (p < 0.05). Younger patients exhibited higher Ki-67 levels (τ = −0.416, p < 0.05). Conclusions: Higher NIS expression correlates with chemoresistance to neoadjuvant chemotherapy in TNBC patients. This phenomenon may be linked to FOXA1 activity, suggesting that NIS may represent a potential biomarker for chemoresistance in TNBC. The inverse correlation between patient age and Ki-67 levels may be associated with a different mutational landscape in younger patients. Full article

This paper addresses the joint design of transmit waveforms and receive filters for multiple-input multiple-output (MIMO) radar systems in the presence of signal-dependent clutter and steering vector mismatch. A low-complexity peak searching algorithm is developed to maximize the output signal-to-clutter-plus-noise ratio (SCNR) under a constant-modulus constraint. Different from existing approaches, this paper decomposes the receive filter into a spatial beamformer and a temporal filter to reduce the dimensionality of matrix inversion. The angular uncertainty of the target direction is discretized, and a peak searching strategy identifies the optimal error angle, which is then used to optimize the initial phases of the transmit waveform subcarriers. Based on the optimized initial phases, the estimates of the target angle and steering vector are updated, and the receive filter coefficients are further modified, thereby improving the output SCNR. Numerical simulations are provided to evaluate the performance of the proposed approach compared with existing mismatch-robust methods. The results show that the proposed method preserves inter-subcarrier orthogonality, achieves near-ideal output SCNR with reduced computational complexity, and enables real-time acquisition of more accurate target angles. Full article

The transient electromagnetic method (TEM) is one of the most common and effective techniques for detecting anomalies in tunnel engineering. Traditional TEM interpretations often assumed a half-space model, which can be problematic in enclosed environments such as tunnels. This limitation often results in poor inversion accuracy, especially when full-space effects are pronounced. Utilizing thin-layer theory, this study derived analytical expressions for TEM responses under both full-space and half-space conditions and defined a ratio coefficient relating full-space and half-space responses in the presence of anomalous bodies. Furthermore, a sensitivity analysis of this ratio coefficient was conducted under varying conditions of surrounding rock and anomalous bodies, exploring the laws governing the full-space effect for anomalous bodies in tunnels. The research results indicate that when changes in surrounding rock or anomalous body conditions enhance the TEM response—such as when the anomalous body is closer to the receiver coil, thicker, or exhibits a larger conductivity contrast with the surrounding rock—the ratio coefficient between full-space and half-space responses decreases, potentially approaching 1.0. Conversely, when these changes weaken the anomalous body response, the ratio coefficient increases, approaching approximately 2.5, as observed under homogeneous medium conditions. Moreover, the off-time exerts a significant influence on the ratio coefficient. The earlier off-times yield larger coefficients, whereas the coefficient gradually approaches 1.0 with increasing time. Finally, this study elucidates the dynamic characteristics of the full-space effect and establishes a functional relationship describing the primary factors influencing the ratio coefficient. The findings provide a theoretical foundation for improving TEM detection of anomalous bodies in tunnels. Full article

We study an integral transform—here called the Master Integral Transform—in which the kernel is an arbitrary entire function of finite order. When the nonzero Taylor coefficients of the kernel have positive Beurling–Malliavin density, we prove completeness and global injectivity in a Cauchy-weighted Hilbert space, and we furnish explicit Mellin–Fourier inversion formulae with exponentially decaying integrands. Classical Fourier, Laplace, and Mellin transforms appear only as strict special cases. Beyond these, we establish structural properties (multiplier/composition law, dilation covariance, parameter regularity) and present applications not captured by fixed-kernel frameworks, including inverse-kernel identification and hybrid boundary value models, e.g., the Poisson–Airy pair produces a closed-form transformed Green’s function and a solvable variable-coefficient PDE, illustrating capabilities unavailable to fixed-kernel frameworks. Full article

Analysis of tree vegetation recovery on abandoned agricultural lands is one of the key tasks in landscape research. This study considered the factors of forest cover of postagrogenic lands typical of the Central Russian forest-steppe. We applied a combination of geoinformation and statistical methods to analyze the relationship between climatic, geomorphological, and soil factors and the forest cover of abandoned agricultural lands. The results of this study showed varying strengths of the relationship between the climatic factors of the warm and cold seasons and the afforestation rate of postagrogenic lands. In the flat terrain region, warm-season climatic variables have a major effect on forest cover. Among the climatic factors, the precipitation of the warmest quarter and the hydrothermal coefficient show the strongest direct correlation with the forest cover of the abandoned agricultural lands. The accumulated temperature over the period with values above 10 °C and the average temperature of the warmest quarter show the strongest inverse correlation with forest cover. It has been established that soil type has a significant impact on the rate of abandoned lands afforestation. Forest cover on even-aged abandoned agricultural lands on gray forest soils (Haplic Phaeozems) is, on average, twice that of chernozem soils. The variation in forest cover is higher on abandoned croplands located on Chernozem. We analyzed forest cover as a variable dependent on various environmental conditions and proposed a number of multivariate regression models that estimate forest cover as a response to a combination of climatic, geomorphological, and soil conditions. As a result, the influence of various factors on the afforestation rate of postagrogenic lands was quantitatively shown. Full article

Background: Adolescents suffering from obesity are at higher risk of bone fragility due to hepatic steatosis, which may lead to an inflammatory microenvironment in the bone marrow. We therefore aimed to assess the reliability of measuring the bone marrow fat fraction (BMFF) and T2* of the lumbar vertebral marrow using the proton density fat fraction (PDFF) sequence for adolescents with obesity and liver steatosis. Method: This was an observational feasibility pilot study on adolescents living with obesity and liver steatosis. Anthropometric measurements were obtained. Participants underwent abdominal MRI, MR elastography and dual-energy X-ray absorptiometry (DXA). Regions of interest were drawn using the radiology interface from the central L1 to L4 vertebrae on fat and T2* maps from the PDFF sequence. ImageJ was used to measure abdominal compartment fat areas. Descriptive analyses, the intraclass correlation coefficient, and correlation results were obtained from anthropometric, adiposity, BMFF, and T2* measurements. Results: We recruited 23 adolescents with a body mass index > 85th percentile and mean age = 14.7 years (interval 12–17 years), and n = 18 (78%) were boys. BMFF and T2* measurements were successful in 100% of cases. The intra-operator reproducibility of the BMFF and T2* measurements was excellent: ICC = 0.99 (95% confidence interval (CI) [0.986; 0.999]) and ICC = 0.99 (95% CI [0.992; 0.999]), respectively. The inter-operator ICC was good for BMFF (ICC = 0.89; 95% CI [0.705; 0.963]) and moderate for T2* (ICC = 0.66; 95% CI [0.239; 0.873]). Only BMFF was inversely correlated with vertebral-bone mineral density (r = −0.67; p = 0.0009). However, T2* measurements showed a positive linear relationship with the total body fat tissue percentage measured by DXA (r = 0.48; p = 0.03) and the total abdominal fat area (r = 0.45; p = 0.04). Conclusions: PDFF could be a reliable imaging biomarker for bone health assessment in adolescents living with obesity. Full article

Hyperspectral remote sensing provides a powerful tool for crop nutrient monitoring and precision fertilization, yet its application is hindered by high-dimensional redundancy and inter-band collinearity. This study aimed to improve maize nitrogen estimation by constructing three types of two-dimensional full-band spectral indices—Difference Index (DI), Simple Ratio Index (SRI), and Normalized Difference Index (NDI)—combined with spectral preprocessing methods (raw spectra (RAW), first-order derivative (FD), and second-order derivative (SD)). To optimize feature selection, three strategies were evaluated: Grey Relational Analysis (GRA), Pearson Correlation Coefficient (PCC), and Variable Importance in Projection (VIP). These indices were then integrated into machine learning models, including Backpropagation Neural Network (BP), Random Forest (RF), and Support Vector Regression (SVR). Results revealed that spectral index optimization substantially enhanced model performance. NDI consistently demonstrated robustness, achieving the highest grey relational degree (0.9077) under second-derivative preprocessing and improving BP model predictions. PCC-selected features showed superior adaptability in the RF model, yielding the highest test accuracy under raw spectral input (R² = 0.769, RMSE = 0.0018). VIP proved most effective for SVR, with the optimal SD–VIP–SVR combination attaining the best predictive performance (test R² = 0.7593, RMSE = 0.0024). Compared with full-spectrum input, spectral index optimization effectively reduced collinearity and overfitting, improving both reliability and generalization. Spectral index optimization significantly improved inversion accuracy. Among the tested pipelines, RAW-PCC-RF demonstrated robust stability across datasets, while SD-VIP-SVR achieved the highest overall validation accuracy (R² = 0.7593, RMSE = 0.0024). These results highlight the complementary roles of stability and accuracy in defining the optimal pipeline for maize nitrogen inversion. This study highlights the pivotal role of spectral index optimization in hyperspectral inversion of maize nitrogen content. The proposed framework provides a reliable methodological basis for non-destructive nitrogen monitoring, with broad implications for precision agriculture and sustainable nutrient management. Full article

Wearable, rectifiable aerosol photometers (WRAPs), instruments with combined nephelometer and on-board filter-based sampling capabilities, generally show strong correlations with reference instruments across a range of ambient and household PM_2.5 concentrations. However, limited data exist on their performance when challenged by mixed aerosol exposures, such as those found in dusty occupational environments. Understanding how these instruments perform across a spectrum of environments is critical, as they are increasingly used in human health studies, including those in which concurrent PM_2.5 and coarse dust exposures occur simultaneously. The authors collected co-located, ~24 h. breathing zone gravimetric and nephelometer PM_2.5 measures using the MicroPEM v3.2A (RTI International) and the UPAS v2.1 PLUS (Access Sensor Technologies). Samples were collected from adult brick workers (n = 93) in Nepal during work and non-work activities. Median gravimetric/arithmetic mean (AM) PM_2.5 concentrations for the MicroPEM and UPAS were 207.06 (interquartile range [IQR]: 216.24) and 737.74 (IQR: 1399.98) µg/m³, respectively (p < 0.0001), with a concordance correlation coefficient (CCC) of 0.26. The median stabilized inverse probability-weighted nephelometer PM_2.5 concentrations, after gravimetric correction, for the MicroPEM and UPAS were 169.16 (IQR: 204.98) and 594.08 (IQR: 1001.00) µg/m³, respectively (p-value < 0.0001), with a CCC of 0.31. Digital microscope photos and electron micrographs of filters confirmed large particle breakthrough for both instruments. A possible explanation is that the miniaturized pre-separators were overwhelmed by high dust exposures. This study was unique in that it evaluated personal PM_2.5 monitors in a high dust occupational environment using both gravimetric and nephelometer-based measures. Our findings suggest that WRAPs may substantially overestimate personal PM_2.5 exposures in environments with concurrently high PM_2.5 and coarse dust levels, likely due to large particle breakthrough. This overestimation may obscure associations between exposures and health outcomes. For personal PM_2.5 monitoring in dusty environments, the authors recommend traditional pump and cyclone or impaction-based sampling methods in the interim while miniaturized pre-separators for WRAPs are designed and validated for use in high dust environments. Full article