Search Results (237)

The application of low-field time-domain nuclear magnetic resonance (TD-NMR) to measure water content and assess moisture-related relaxation behavior in sludge samples has been investigated. The results of TD-NMR measurements on 26 dewatered sludge samples revealed a strong correlation between sludge water content and key features of the T₂ distribution curves, including the maximum relaxation time and peak area, demonstrating the potential of the TD-NMR method for estimating sludge moisture content. No consistent relationship was observed between the peaks in T₂ relaxation distribution curves obtained by Inverse Laplace Transform (ILT) and the expected water fraction ratios, apparently because the sludge structure is highly variable from sample to sample. Despite the complex and heterogeneous nature of sludge samples, the direct correspondence between key features of the T₂ relaxation curves and moisture content demonstrates the high potential of TD-NMR as a tool for rapid and reliable moisture monitoring, even in an online device configuration. Full article

Accurate discretization of the orientation distribution function (ODF) is essential for reliable microstructural modeling of polycrystalline aggregates. This work proposes a novel texture discretization method that achieves high-fidelity ODF approximation even with a small number of orientations using only grain volume information. The core idea is to extend conventional inverse transform sampling by reconstructing the source samples before inversion. This reconstruction suppresses discretization errors induced by random sampling fluctuations and improves adaptability to non-uniform grain size distributions (GSDs). To preserve texture diversity under the same ODF, spatial shuffling and subsequent unscrambling of grain positions are introduced. The total variation distance (TVD) is adopted as a global metric to quantify discretization errors, and key influential factors are systematically analyzed, particularly the binning strategies. Error comparisons demonstrate that, within the typical range of grain numbers (10²–10³), the TVD of the proposed method is one order of magnitude lower than that of the conventional method, with its standard deviation two orders of magnitude smaller. The randomness and periodicity of discretized textures are further investigated, thereby elucidating the underlying mechanisms for the newly introduced advantages. This method provides a robust and efficient framework for texture modeling with consideration of GSDs. Full article

Deep learning approaches for egocentric video understanding often lack a principled theoretical treatment of stability, particularly when dealing with the sparse, noisy, and temporally ambiguous observations characteristic of first-person imaging. In this work, we frame egocentric video question answering not merely as a classification task, but as an ill-posed inverse problem aimed at reconstructing latent semantic intent from stochastically perturbed visual signals. To address the instability inherent in standard dual-encoder architectures, we present a framework with a mathematical interpretation that incorporates gated cross-modal interaction within the transformer backbone. Formally, the video-side update analyzed in this work is defined as a learnable convex combination of unimodal feature representations and cross-modal attention residuals; the full implementation applies analogous gated cross-modal updates bidirectionally. From a regularization perspective, the gating mechanism can be interpreted as an adaptive parameter that balances data fidelity against language-conditioned structural constraints during feature reconstruction. We provide the Bounded Update Property (Lemma 1) and an analytical layer-wise sensitivity bound and empirically demonstrate that the proposed framework achieves measurable improvements in both accuracy and stability on the EgoTaskQA and MSR-VTT benchmarks. On EgoTaskQA, our model improves accuracy from 27.0% to 31.7% (+4.7 pp) and reduces the accuracy drop under 50% frame drop from 3.93 pp to 0.94 pp. On MSR-VTT, our model improves accuracy by 13.0 pp over the dual-encoder baseline. Under severe perturbation (50% frame drop) on MSR-VTT, our model retains 97.7% of its clean performance, whereas the baseline exhibits near-zero drop accompanied by majority-class behavior. These results provide empirical evidence that the proposed interaction induces stable behavior under perturbations in an ill-posed multimodal inference setting, mitigating sensitivity to sampling variability while preserving query-relevant temporal structure. Furthermore, an entropy-based analysis indicates that the gating mechanism prevents excessive diffusion of attention, promoting coherent temporal reasoning. Overall, this work offers a mathematically informed perspective on designing interaction mechanisms for stable multimodal systems, with a focus on robust reasoning under temporal ambiguity. Full article

Soil heavy metal pollution caused by mineral resource extraction activities poses a serious threat to the ecological environment within and surrounding mining areas. As a highly concealed toxic heavy metal, arsenic (As) urgently requires the establishment of efficient pollution monitoring methods to achieve pollution prevention and control, as well as environmental remediation in mining areas. This study investigated the feasibility of hyperspectral remote sensing inversion for soil heavy metal arsenic based on ZY-1 02E hyperspectral satellite imagery, focusing on a mining area and its surrounding soils in Sanlisha’an, Wuxuan County, Guangxi. Full Constrained Least Squares (FCLS) was employed to separate mixed pixels and enhance soil spectral contributions in ZY-1 02E imagery, thereby mitigating vegetation interference. Six mathematical transformations, including RT, AT, FD, RTFD, ATFD, and SD, were applied to both the original and enhanced spectra to enhance spectral features. The correlations between the transformed spectra, as well as the original image spectra (S), and soil As concentration were analyzed; then the spectra strongly correlated with soil As concentration were selected to construct Ratio Spectral Index (RSI) and Normalized Difference Spectral Index (NDSI). Correlation matrices were calculated between RSI/NDSI indices and As concentration. Sensitive features were screened using an improved Successive Projection Algorithm (SPA). As concentration inversion was also performed with four models: traditional regression models, PLSR and MLR, and ensemble learning models (RF and XGBoost). In the soil contribution-enhanced spectral modeling results, the optimal transformation–index combination is ATFD-NDSI. The performance indicators of each model are as follows: MLR test set R² = 0.65, PLSR test set R² = 0.62, RF test set R² = 0.7, and XGBoost test set R² = 0.64. The results indicate that the ATFD-NDSI-RF ensemble model provides the best performance. By integrating multiple decision trees, RF effectively handles complex nonlinear relationships, thus enhancing the accuracy and generalization ability of predication. The analysis of NDSI–ATFD–RF inversion results based on sampling points indicates that model error correlates with the pollution intensity gradient, showing greater errors, especially in high-concentration areas, but still maintaining strong correlations (tailings reservoir: r = 0.92, forested areas: r = 0.96, and cropland: r = 0.83). The spatial distribution reveals that the inversion results are closely similar to the spatial distribution of IDW interpolation. Areas with high As concentrations are concentrated in the tailings reservoir and in the southeastern part of the study area. The correlation coefficient between the inversion results and IDW interpolation is 0.6, which further verifies that the inversion results effectively reproduce the spatial distribution trend of highly polluted areas. Full article

High-resolution marine gravity fields are critical for interpreting seafloor structure, investigating marine geodynamics, and enabling gravity-aided navigation. However, sparse shipborne observations, heterogeneous multi-source geodetic datasets, and the inability of conventional methods to handle nonlinear inversion limit accurate gravity recovery. To overcome these limitations, we propose a spectral physics-informed constraint deep-learning framework based on a multi-channel Swin Transformer to reconstruct high-resolution marine gravity anomaly fields. The model ingests multi-source geodetic inputs organized as 64 × 64 grid patches centered near each computation point and fuses them to predict the target gravity anomaly. We adopt a remove–compute–restore (RCR) strategy that isolates residual gravity signals, which improves numerical stability and accelerates training. Inputs include satellite-altimetry-derived vertical gravity gradients, vertical deflections, mean sea surface height, and topography; the model is trained on over 430,000 shipborne gravity samples from the South China Sea (0–30° N, 105–125° E). To enforce physical consistency, we embed a spectral-domain physics constraint derived from potential-field theory into the loss function; this constraint helps recover short-wavelength gravity signals. We also introduce an adaptive multi-domain multi-scale feature fusion module (AMAMFF) to improve the integration of heterogeneous inputs, and we demonstrate its benefits in experiments across complex terrain. Validation against independent shipborne gravity checkpoints yields an RMS error of 3.09 mGal, indicating a substantial performance advantage over existing deep-learning approaches and conventional gravity-field models. Full article

Sulphatic evaporites represent a critical challenge for underground engineering due to their high solubility, swelling potential, and sensitivity to changing hydraulic and thermal conditions. In this study, we investigate the temperature-dependent dissolution behavior and microstructural evolution of Triassic sulphate rocks consisting of anhydrite and minor portions of gypsum from the Western Alps. Twelve cylindrical samples were immersed in CaSO₄-saturated water solutions at 15 °C, 40 °C, and 60 °C for six months. Periodic mass and volume measurements were combined with Scanner Electron Microscope (SEM) imaging to quantify dissolution and document mineralogical transformations. All samples experienced progressive mass loss, whereas volumetric changes remained below measurement resolution. Dissolution pathways varied strongly with temperature. At 15 °C, dissolution occurred mainly along anhydrite grain boundaries, producing rounded crystal edges, while less effect was observed in the gypsum veins, leaving the intergranular layers preserved. In contrast, at 40–60 °C, gypsum was preferentially dissolved, generating porosity around comparatively unaltered anhydrite grains. These results qualitatively reproduce the temperature-controlled solubility inversion between gypsum and anhydrite predicted by thermodynamic models. No secondary gypsum precipitation or swelling features were observed. The experimental evidence highlights the role of temperature and hydraulic regime in controlling the stability of sulphate rocks and provides insights relevant to tunnel excavation, underground storage facilities, and geomechanical modeling in evaporitic settings. Full article

Paleogeomorphology exerts first-order control on the distribution of structural hydrocarbon reservoirs across regional unconformities, whereas variations in pore-throat architecture and flow capacity among different geomorphic units further govern hydrocarbon migration pathways and accumulation sites. Therefore, high-resolution reconstruction of regional paleogeomorphology is essential for effective exploration. This study investigates the Yanwu area of the Ordos Basin, where pre-Jurassic paleogeomorphology was reconstructed based on detailed stratigraphic analyses of the Yan’an Formation and the Yan-10 oil-bearing interval, and its influence on reservoir formation was systematically evaluated. Paleogeomorphology was delineated using well-log-based compensated impression methods integrated with localized 3D seismic inversion. Reservoir samples from distinct geomorphic units were analyzed through thin-section petrography, FESEM imaging, high-pressure mercury intrusion, and visualized micro-scale hydrocarbon charging experiments to characterize pore-throat systems and flow behavior. Four geomorphic units—paleohighs, slope zones, terraces, and valleys—were identified. Seismic inversion across the Yanwu tributary valley and the Honghe paleovalley confirms the reliability of the reconstructed geomorphology. Reservoirs within slope zones and terraces exhibit superior pore-throat structures, dominated by intergranular and dissolution pores, and display grid-like displacement patterns with higher ultimate recovery in micro-charging tests. Portions of the paleohighs show comparable reservoir quality and flow capacity. Results indicate that slope zones and terraces represent the most favorable hydrocarbon accumulation domains. Where overlying strata provide effective sealing, hydrocarbons preferentially accumulate on structural highs within these geomorphic units; in contrast, insufficient sealing transforms them into efficient migration conduits. Certain paleohighs may also host structural-high accumulations when capped by effective traps. The clarified accumulation patterns across geomorphic units offer a robust framework for guiding hydrocarbon exploration and reserve growth in regions with similar tectono-sedimentary settings. Full article

In order to solve the problems of insufficient medical image feature extraction, high classification accuracy, and computational complexity in automatic diagnosis of skin lesions in the edge computing environment, this paper proposes a real-time pseudo-multimodal low-delay diagnosis framework, SCGViT, based on a vision transformer. The framework is constructed around three functional objectives: mitigating data imbalance through generative modeling, capturing diverse representations via multi-dimensional perception, and optimizing feature fusion through adaptive refinement. Firstly, using Class-Conditioned Generative Adversarial Networks (CGANs) simulates manifolds of minority class samples in latent space, achieving preliminary balance of data distribution. Secondly, a branch feature-extraction path is constructed to simulate inversion (INV) and infrared (IR) modes in the original visual primary color mode (RGB), in order to achieve multi-dimensional perception. Finally, a cross-attention mechanism is combined for cross-branch feature aggregation, and a channel-attention mechanism (squeeze and excitation) is embedded for secondary refinement of the mixed global local features to enhance the representation ability of key pathological regions by integrating complementary structural and contrast information. The experimental results on the HAM10000 dataset showed that the F1 score reached 0.973, the inference speed reached 304.439 FPS, the parameter count was only 0.524 M, and the computational complexity was only 0.866 G FLOPs, achieving a balance between high accuracy and light weight. Full article

Stochastic Process Mining, in particular, Markov processes, is used to represent uncertainty and variability in Activities of Daily Living (ADLs). However, the Markov models inherently assume that the time spent in each state must follow an exponential distribution. This presents a significant challenge to model real-life complexities in ADLs. Therefore, this paper employs semi-Markov models on publicly available ADL event logs to model state durations, where results are validated via goodness-of-fit tests (Kullback–Leibler, Kolmogorov–Smirnov, Cramér–von Mises). Synthetic durations are generated using the inverse transform sampling technique. To simulate dementia-based behaviours, the weights of the mixture model are altered to reflect prolonged duration in napping, toileting, meal, and drink preparation. These anomalies are then detected through the employment of log-likelihood ratio and chi-square tests. Experimental results demonstrate that the proposed approach can be used to reliably identify abnormal ADL durations, offering a proven framework to track early detection of behavioural shifts, and showcasing the effectiveness of detecting duration-based anomalies in ADL. By identifying such anomalies, our work aims to detect deterioration in the smart home resident’s condition, focusing in particular on their ability to execute different ADLs. Full article

Accurate evaluation of the standard normal cumulative distribution function is fundamental in many areas of mathematics, statistics, and applied computation, yet no closed-form expression in elementary functions exists. We present a simple analytic approximation based on a logistic function with a cubic argument, designed to preserve symmetry, monotonicity, and analytic invertibility. The parameters of the approximation are obtained through numerical optimization over a wide domain, targeting both maximum absolute error and root-mean-square error. The resulting function achieves uniformly low approximation error and significantly reduces error relative to the classical logistic approximation, while remaining competitive with commonly used high-accuracy numerical methods. Unlike rational or high-degree polynomial approximations, the proposed form admits an explicit inverse, making it convenient for applications requiring analytic quantile evaluation or inverse transform sampling. Numerical error analysis and illustrative examples demonstrate that the approximation provides a practical balance between accuracy, simplicity, and analytic tractability. Full article

Accurate determination of Soil Organic Carbon (SOC), which is the foundation of soil health and safeguards ecological and food security, is crucial in local agricultural production. We aimed to investigate the influence of soil texture on hyperspectral models for predicting SOC content and to evaluate the role of different preprocessing methods and feature band selection algorithms in improving modeling efficiency. Laboratory-determined SOC content and hyperspectral reflectance data were obtained using soil samples from daylily cultivation areas in Yunzhou District, Datong City. Mathematical transformations, including Savitzky–Golay smoothing (SG), First Derivative (FD), Second Derivative (SD), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV), were applied to the spectral reflectance data. Feature bands extracted based on the successive projection algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to establish SOC content inversion models employing four algorithms: partial least-squares regression (PLSR), Random Forest (RF), Backpropagation Neural Network (BP), and Convolutional Neural Network (CNN). The results indicate the following: (1) Preprocessing can effectively increase the correlation between the soil spectral reflectance process and SOC content. (2) SPA and CARS effectively screened the characteristic bands of SOC in daylily cultivated soil from the spectral curves. The SPA algorithm and CARS selected 4–11 and 9–122 bands, respectively, and both algorithms facilitated model construction. (3) Among all the constructed models, the FD-CARS-PLSR performed most prominently, with coefficients of determination (R²) for the training and validation sets reaching 0.93 and 0.83, respectively, demonstrating high model stability and reliability. (4) Incorporating soil texture as an auxiliary variable into the PLSR inversion model improved the inversion accuracy, with accuracy gains ranging between 0.01 and 0.05. Full article

For microradian-level phasemeters aimed at space-based gravitational wave detection, the analog front-end circuitry plays a critical role in determining the system’s phase noise. This paper focuses on the symmetric differential structure-based operational amplifier analog front-end between the Quadrant Photodiode output and the high-resolution ADC input. An equivalent additive noise model is established, and the mechanism of noise conversion into phase noise is derived. The noise performance within the target 5–25 MHz band is evaluated through LTspice simulations and experimental verification. Experimental results show that, after suppressing sampling timing jitter with a 37.5 MHz pilot tone, the noise contribution of the front-end analog circuit to the phasemeter system is significantly better than the phase measurement noise requirement of 2π $\mathsf{\mu }\mathrm{r}\mathrm{a}\mathrm{d}/{\mathrm{H}\mathrm{z}}^{1/2}$ in the 0.1 mHz–1 Hz band for space-based gravitational wave detection. Compared with a transformer-based front-end, the differential amplifier solution exhibits significant advantages in low-frequency noise suppression and signal stability. Further analysis using the digital phase-locked loop closed-loop transfer function confirms that the noise amplitude is proportional to phase noise and inversely proportional to signal amplitude, providing a theoretical basis for analog front-end circuit optimization and system-level noise budgeting. The results offer a reliable reference for the design of high-precision phasemeters and the engineering implementation of space-based gravitational wave detection missions. Full article

The catalytic oxidation of CO is of great technological importance for the treatment of vehicle and industrial exhaust gases. PtCe-catalysts of low-temperature CO oxidation were prepared by the impregnation of ZSM-5 zeolite (Z) with aqueous solutions of H₂PtCl₆ and Ce(NO₃)₃, varying the order of metal deposition and thermal treatment conditions. The relationships between structure transformations and catalyst performance were established based on the SEM, TEM, EDX, DRIFT, and X-ray photoelectron spectroscopies data. For the Ce/Pt/Z sample, in which cerium was deposited after platinum, the 100% CO conversion temperature was only 120 °C. The inverse deposition sequence of metals (Pt/Ce/Z catalyst) resulted in CO oxidation at a higher temperature that can be decreased to 110 °C by redox treatment. The prepared catalysts were also active in the CO oxidation in excess hydrogen (PROX) but were not selective enough. However, the activity of PtCe-modified ZSM-5 enhanced greatly in the repeated cycles of CO oxidation (TOX) after testing in PROX. It is suggested that enhancing the interaction between Pt and Ce is a key factor in tuning the catalyst performance. The 0.2 wt.% Pt catalysts showed the best performance and provided complete CO conversion at 95 °C, which is a pronounced result for low-loaded Pt catalysts. Full article

Staggered synthetic aperture radar (SAR) is an innovative concept capable of achieving an ultrawide continuous swath with fine azimuth resolution by variable pulse repetition interval. However, the inherent data gaps and nonuniform sampling introduce severe azimuth artifacts, degrading image quality. Existing methods can mitigate these artifacts but struggle to effectively balance imaging quality and computational cost, especially under low oversampling conditions. To address this challenge, this paper proposes a low-artifact preview image generation method for staggered SAR. First, the artifact characteristics are analyzed through the derivation of a staggered SAR signal model. Then, a three-stage processing framework is introduced, consisting of constant-gradient phase extrapolation, artifact-based inverse filtering, and result fusion. Additionally, data nonuniformity is addressed using a weighted nonuniform fast Fourier transform. Simulation results demonstrate that the proposed method significantly improves processing speed compared to existing techniques while maintaining good imaging quality, making it suitable for rapid scene screening in wide-area SAR applications. Full article

MnCo₂O₄ and CoMn₂O₄ were successfully synthesized on a stainless-steel substrate using the hydrothermal method. The structural and morphological characteristics of the spinel samples were investigated using X-ray diffraction (XRD) and scanning electron microscopy (SEM). The electronic and vibrational properties were studied through X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR). Electrochemical properties were also evaluated using a three-electrode system associated with an electrochemical workstation. The studies revealed that the inversion of Mn and Co cation distribution between the spinel structure sites not only modifies the crystal structure and morphology but also alters specific functional properties. MnCo₂O₄ crystallized in a cubic spinel phase, exhibiting spherical particles, pronounced microstrain, and stronger metal–oxygen bonding. In contrast, CoMn₂O₄ adopted a tetragonal spinel structure with rod-like crystallites, lower microstrain, and more flexible bonding environments. Electrochemical impedance spectroscopy further revealed distinct charge-transfer dynamics, indicating differences in surface redox activity. This comparative analysis elucidates how cation site occupancy governs the performance of the synthesized spinel oxides and underscores their potential as efficient catalysts or catalyst supports for redox and energy-related applications. Full article