Search Results (1,051)

Geochemical maps are essential visualization tools for studying the distribution patterns of elements on the Earth’s surface. They provide critical insights into geological structure, mineralization processes, and environmental evolution. Traditional interpolation methods often fail to adequately reconstruct high-frequency details in geochemical maps with low sampling density. This study proposes a super-resolution (SR) reconstruction method for geochemical maps based on an enhanced U-Net architecture, validated in the Gouli area of Qinghai Province. By integrating residual blocks, multi-scale neural networks, and constraints from topographic features (elevation, slope, aspect) and geological map embeddings, our method enhances the resolution of stream sediment geochemical maps from 1:50,000 to 1:25,000 scale. Experimental results demonstrate that the proposed method outperforms SRCNN, VDSR, and standard U-Net models in both peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Specifically, with all constraints incorporated, the method achieves maximum and mean PSNR values of 38.486 and 25.334, respectively, and maximum and mean SSIM values of 0.968 and 0.817. The reconstructed high-resolution (HR) geochemical maps exhibit superior detail clarity and maintain strong spatial correlation with the original HR data. Studies have shown that this method can effectively learn multi-scale geochemical patterns and detect subtle anomalies missed in low-resolution (LR) maps. Moreover, the reconstructed HR geochemical maps exhibit better alignment with the Ag, Cu, and Pb anomalies in known mineralization zones (Maixiulongwa and Sanchakou areas), thereby providing strong support for precise mineral exploration. Full article

Background/Objectives: We provide a framework for determining how far into the future the spatiotemporal dynamics of tumor growth can be accurately predicted using routinely available magnetic resonance imaging (MRI) data. Our analysis is applied to a coupled set of reaction-diffusion equations describing the spatiotemporal development of tumor cellularity and vascularity, initialized and constrained with diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data, respectively. Methods: Motivated by experimentally acquired murine glioma data, the rat brain serves as the computational domain within which we seed an in silico tumor. We generate a set of 13 virtual tumors defined by different combinations of model parameters. The first parameter combination was selected as it generated a tumor with a necrotic core during our simulated ten-day experiment. We then tested 12 additional parameter combinations to study a range of high and low tumor cell proliferation and diffusion values. Each tumor is grown for ten days via our model system to establish “ground truth” spatiotemporal tumor dynamics with an infinite signal-to-noise ratio (SNR). We then systematically reduce the quality of the imaging data by decreasing the SNR, downsampling the spatial resolution (SR), and decreasing the sampling frequency, our proxy for reduced temporal resolution (TR). With each decrement in image quality, we assess the accuracy of the calibration and subsequent prediction by comparing it to the corresponding ground truth data using the concordance correlation coefficient (CCC) for both tumor and vasculature volume fractions, as well as the Dice similarity coefficient for tumor volume fraction. Results: All tumor CCC and Dice scores for each of the 13 virtual tumors are >0.9 regardless of the SNR/SR/TR combination. Vasculature CCC scores with any SR/TR combination are >0.9 provided the SNR ≥ 80 for all virtual tumors; for the special case of high-proliferating tumors (i.e., proliferation > 0.0263 day⁻¹), any SR/TR combination yields CCC and Dice scores > 0.9 provided the SNR ≥ 40. Conclusions: Our systematic evaluation demonstrates that reaction-diffusion models can maintain acceptable longitudinal prediction accuracy—especially for tumor predictions—despite limitations in the quality and quantity of experimental data. Full article

Background: A cataract is a clouding of the normally clear lens that obscures the passage of light, effectively reducing clarity and sharpness of vision. Although this disease can affect both children and adults, the most common type is the age-related cataract (ARC). The literature describes many potential agents associated with cataract development. However, this study focuses on modifiable factors, especially nutritional ones and those that may induce oxidative stress. The objective of the present study was to assess serum selenium (Se), copper (Cu), and zinc (Zn) concentrations, as well as the copper/zinc molar ratio (Cu/Zn molar ratio), total antioxidant status (TAS), total oxidant status (TOS), and oxidative stress index (OSI), of patients with ARC in relation to their dietary habits. Methods: A total of 68 patients with ARC and 64 healthy volunteers, with ages ranging from 48 to 92 years, were included in this study. The experimental material collected from the participants consisted of blood samples, which were tested for Se, Cu, and Zn concentrations using atomic absorption spectrometry (AAS). Oxidative stress (OS) parameters, such as TAS and TOS, were estimated spectrophotometrically. In addition, a food frequency questionnaire (FFQ) was used to collect information on the dietary habits of ARC patients. Results: Statistical analysis of the data revealed that the concentrations of Se, Cu, and Zn in serum were significantly lower in ARC patients compared to the controls. In the ARC group, some elements of dietary behavior had a significant effect on the levels of the examined elements and OS parameters. Conclusions: Thus, eventual alterations to one’s diet appear to be worth considering in the context of maintaining homeostasis and adequate mineral levels in ARC patients. Full article

The synergistic preparation of geopolymer from lithium slag, fly ash, and slag for underground construction can facilitate the extensive recycling of lithium slag. The effects of different activator moduli and water–binder ratios on the mechanical properties and damage mechanisms of the lithium-slag-based geopolymer were investigated by uniaxial compression tests and acoustic emission (AE) monitoring. The results show that, based on a comprehensive evaluation of peak stress, crack closure stress, plastic deformation stress, and elastic modulus, the optimal activator modulus is determined to be 1.0, and the optimal water-to-binder ratio is 0.42. At low modulus values (0.8 and 1.0) and low water–binder ratio (0.42), the AE events exhibit a steady pattern, indicating slow crack initiation and propagation within the geopolymer; with the increasing activator modulus and water-to-binder ratios, the frequency of AE events increases significantly, indicating more-frequent crack propagation and stress mutation within the geopolymer. Similarly, when the modulus is 0.8 or 1.0 and the water–binder ratio is 0.42, the sample presents a macroscopic tensile failure mode; as the modulus and water–binder ratio increase, the sample presents a tensile–shear composite failure mode. The energy evolution laws of geopolymer specimens with different activator moduli and water-to-binder ratios were analyzed, and a damage constitutive model was established. The results indicate that, with optimized mix proportions, the material can be used as a supporting material for underground spaces. Full article

Occluded person re-identification (Re-ID) faces significant challenges, mainly due to the interference of occlusion noise and the scarcity of realistic occluded training data. Although data augmentation is a commonly used solution, the current occlusion augmentation methods suffer from the problem of dual inconsistencies: intra-sample inconsistency is caused by misaligned synthetic occluders (an augmentation operation for simulating real occlusion situations); i.e., randomly pasted occluders ignore spatial prior information and style differences, resulting in unrealistic artifacts that mislead feature learning; inter-sample inconsistency stems from information loss during random cropping (an augmentation operation for simulating occlusion-induced information loss); i.e., single-scale cropping strategies discard discriminative regions, weakening the robustness of the model. To address the aforementioned dual inconsistencies, this study proposes the unified Multi-Aligned and Multi-Scale Augmentation (MA–MSA) framework based on the core principle of ”synthetic data should resemble real-world data”. First, the Frequency–Style–Position Data Augmentation (FSPDA) module is designed: it ensures consistency in three aspects (frequency, style, and position) by constructing an occluder library that conforms to real-world distribution, achieving style alignment via adaptive instance normalization and optimizing the placement of occluders using hierarchical position rules. Second, the Multi-Scale Crop Data Augmentation (MSCDA) strategy is proposed. It eliminates the problem of information loss through multi-scale cropping with non-overlapping ratios and dynamic view fusion. In addition, different from the traditional serial augmentation method, MA–MSA integrates FSPDA and MSCDA in a parallel manner to achieve the collaborative resolution of dual inconsistencies. Extensive experiments on Occluded-Duke and Occluded-REID show that MA–MSA achieves state-leading performance of 73.3% Rank-1 (+1.5%) and 62.9% mAP on Occluded-Duke, and 87.3% Rank-1 (+2.0%) and 82.1% mAP on Occluded-REID, demonstrating superior robustness without auxiliary models. Full article

Proton spin–lattice and spin–spin NMR relaxation studies were conducted on lung tissue samples from 10 patients. For each case, relaxation properties of tumor tissue were compared with those of the corresponding reference tissue. The spin–lattice relaxation measurements were performed over a wide frequency range, from 10 kHz to 10 MHz, spanning three orders of magnitude. These were complemented by both spin–lattice and spin–spin relaxation data acquired at 18.7 MHz. Notably, the spin–spin relaxation process exhibited a bi-exponential character. This relaxation behavior was quantitatively analyzed using dedicated models to achieve two main goals: to evaluate the diagnostic potential of low-field NMR relaxometry, and to gain insights into the dynamics of water and macromolecules in tissue, in comparison with aqueous solutions of proteins and polymers. The frequency dependence of the spin–lattice relaxation rates was well described by a power-law function, with an exponent of approximately 0.3 closely matching the theoretical prediction for reptation dynamics in polymer systems, associated with the intermolecular relaxation contribution. The combined analysis of spin–lattice and spin–spin relaxation data revealed specific parameters (such as ratios between the relaxation rates or between the amplitudes of individual relaxation components) that can be considered as potential markers of pathological changes affecting molecular dynamics in tissues. Full article

Background: Communication difficulty restricts education, healthcare, and social participation, yet population-level data for Saudi Arabia have been scarce. This study analysed the 2017 Saudi National Disability Survey to estimate prevalence, describe severity and demographic patterns, and identify factors linked to these difficulties. Objectives: We aimed to estimate national and regional prevalence, assess severity, and gender differences, and identify socio-demographic and disability-related correlates. Methods: A cross-sectional, two-stage stratified cluster sample of 33,575 households (weighted N = 20,408,362 citizens) provided self-reported data on communication difficulty and socio-demographics. Weighted frequencies described prevalence and multivariable logistic regression identified independent correlates. Results: Among all Saudi citizens, 7.1% reported at least one functional difficulty, and of this group 15.7%—equivalent to 1.1% of the total population (n = 226,510)—had a communication difficulty; within that communication difficulty stratum, (n = 185,508) (0.9% of all citizens) experienced it alongside additional impairments, whereas (n = 41,002) (0.2% of all citizens) reported communication difficulty in isolation. The communication difficulties exhibit significant regional variation, ranging from 0.45% in Najran to 1.55% in Aseer. Most cases were classified as being associated with some difficulty (72%); females were over-represented in the extreme category despite a modest male excess overall (adjusted odds ratio [AOR] = 1.09). Higher education, married status, and bilateral first-cousin marriage (AOR = 1.22) were associated with greater risk. Chronic disease (44%) and perinatal causes (13%) predominated, and 84% of cases co-occurred with at least one other disability. Independent predictors included a long duration (AOR = 4.18), disease or delivery-related cause, and consanguinity. Conclusions: Findings highlight geographically clustered need, genetic risk factors, and substantial multimorbidity, indicating the importance of region-specific screening, premarital counselling, and integrated rehabilitation within chronic disease services. Full article

The harsh conditions of the space environment necessitate advanced materials capable of withstanding extreme temperature fluctuations and ultraviolet (UV) radiation. While epoxy-based composites are widely utilized in aerospace due to their favorable strength-to-weight ratio, they are prone to degradation, especially under prolonged high-energy UV-C exposure. This study investigated the mechanical and chemical stability of epoxy composites reinforced with nanosilica at 0, 2, 5, and 10 wt% before and after UV-C irradiation. Dynamic mechanical analysis (DMA) revealed that increased nanosilica content enhanced the storage modulus below the glass transition temperature (Tg) but reduced both Tg and the damping factor. Following UV-C exposure, all samples showed a decrease in storage modulus and Tg; however, composites with higher nanosilica content maintained better property retention. Frequency sweeps corroborated these findings, indicating improved instantaneous modulus but accelerated relaxation with increased nanosilica. Fourier-transform infrared (FTIR) spectroscopy of UV-C-exposed samples demonstrated significant oxidation and carboxylic group formation in neat epoxy, contrasting with minimal spectral changes in nanosilica-modified composites, signifying improved chemical resistance. Overall, nanosilica incorporation substantially enhances the thermomechanical and oxidative stability of epoxy composites under simulated space conditions, highlighting their potential for more durable performance in low Earth orbit applications. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

This study investigates the acoustic performance of Ecoflex™ 00-35, a highly flexible silicone rubber, for use in soft and adaptable vibration and noise control systems. Under normal conditions, Ecoflex™ 00-35 consists of two components—Part A and Part B—which are mixed and cured at room temperature to form an elastomer. In this study, curing parameters such as the A/B mixing ratio, thinning agent addition, and curing pressure were varied to examine their effects on acoustic behavior. The microstructure of the prepared samples was analyzed using scanning electron microscopy (SEM), while sound absorption properties were measured using impedance tubes. Test results demonstrated that modifying curing parameters, applying vacuum, and incorporating a thinning agent increased the average cell diameter, leading to the fabrication of a moderate sound absorber with a sound absorption coefficient ranging from 0.35 to 0.60 in the low- to mid-frequency ranges. Further enhancement in low-frequency absorption was achieved by applying low pressure for a short duration, allowing cell expansion. In contrast, the addition of a thinning agent significantly improved absorption at higher frequencies. These findings highlight the influence of processing conditions on the acoustic behavior of soft silicone elastomers and provide valuable insights into their structure–property relationships. Ultimately, this study contributes to the development of advanced materials for acoustic damping and noise control applications. Full article

Colorectal cancer (CRC) is driven by a complex spectrum of somatic mutations and structural variants that contribute to tumor heterogeneity and therapy resistance. In this study, we performed a comparative analysis of short-read Illumina and long-read Nanopore sequencing technologies across multiple CRC sample groups, encompassing diverse tissue morphologies. Our evaluation included general base-level metrics—such as nucleotide ratios, sequence match rates, and coverage—as well as variant calling performance, including variant allele frequency (VAF) distributions and pathogenic mutation detection rates. Focusing on clinically relevant genes (KRAS, BRAF, TP53, APC, PIK3CA, and others), we characterized platform-specific detection profiles and completed the ground truth validation of somatic KRAS and BRAF mutations. Structural variant (SV) analysis revealed Nanopore’s enhanced ability to resolve large and complex rearrangements, with consistently high precision across SV types, though recall varied by variant class and size. To enable direct comparison with the Illumina exome panel, we applied an exonic position reference file. To assess the impact of depth and PCR amplification, we completed an additional high-coverage Nanopore sequencing run. This analysis confirmed that PCR-free protocols preserve methylation signals more accurately, reinforcing Nanopore’s utility for integrated genomic and epigenomic profiling. Together, these findings underscore the complementary strengths of short- and long-read sequencing platforms in high-resolution cancer genomics, and we highlight the importance of coverage normalization, epigenetic fidelity, and rigorous benchmarking in variant discovery. Full article

This paper presents a low-power continuous-time delta-sigma (CT-DS) analog-to-digital converter (ADC) for use in high-sensitivity automotive-grade sub-1 GHz receivers in emerging wireless sensors network applications. The proposed ADC employs a third-order Cascade of Integrators FeedForward and Feedback (CIFF-B) loop filter operating at a sampling frequency of 150 MHz to achieve high energy efficiency and robust noise shaping. A low-noise phase-locked loop (PLL) is integrated to provide high-precision clock signals. The loop filter combines active-RC and GmC integrators with the source degeneration technique to optimize power consumption and linearity. To minimize complexity and enhance stability, a 1-bit quantizer with isolation switches and return-to-zero (RZ) digital-to-analog converters (DACs) are used in the modulator. With a 500 kHz bandwidth, the sensitivity of the receiver is −105.5 dBm. Fabricated in a 180 nm standard CMOS process, the prototype achieves a peak signal-to-noise ratio (SNR) of 76.1 dB and a signal-to-noise and distortion ratio (SNDR) of 75.3 dB, resulting in a Schreier figure of merit (FoM) of 160.7 dB based on SNDR, while consuming only 0.8 mA from a 1.8 V supply. Full article

Electroencephalography (EEG) is a non-invasive biosensing platform with a spatial-frequency content that is of significant relevance for a multitude of aspects in the neurosciences, ranging from optimal spatial sampling of the EEG to the design of spatial filters and source reconstruction. In the past, simplified spherical head models had to be used for this analysis. We propose a method for spatial frequency analysis in EEG for realistically shaped volume conductors, and we exemplify our method with a five-compartment Boundary Element Method (BEM) model of the head. We employ the recently developed technique for spatial harmonic analysis (Sphara), which allows for spatial Fourier analysis on arbitrarily shaped surfaces in space. We first validate and compare Sphara with the established method for spatial Fourier analysis on spherical surfaces, discrete spherical harmonics, using a spherical volume conductor. We provide uncertainty limits for Sphara. We derive relationships between the signal-to-noise ratio (SNR) and the required spatial sampling of the EEG. Our results demonstrate that conventional 10–20 sampling might misestimate EEG power by up to 50%, and even 64 electrodes might misestimate EEG power by up to 15%. Our results also provide insights into the targeting problem of transcranial electric stimulation. Full article

Accurate and non-invasive assessment of fat and muscle composition is crucial for biomedical monitoring to track health conditions in humans and pets, as well as for classifying meats in the meat industry. This study introduces a cost-effective, multifunctional ultra-wideband microwave system operating from 2.4 to 4.4 GHz, designed for rapid and non-destructive quantification of fat thickness, muscle thickness, and fat-to-muscle ratio in diverse ex vivo samples, including pork, beef, and oil–water mixtures. The compact handheld device integrates essential RF components such as a frequency synthesizer, directional coupler, logarithmic power detector, and a dual-polarized Vivaldi antenna. Bluetooth telemetry enables seamless real-time data transmission to mobile- or PC-based platforms, with each measurement completed in a few seconds. To enhance signal quality, a two-stage denoising pipeline combining low-pass filtering and Savitzky–Golay smoothing was applied, effectively suppressing noise while preserving key spectral features. Using a random forest regression model trained on resonance frequency and signal-loss features, the system demonstrates high predictive performance even under limited sample conditions. Correlation coefficients for fat thickness, muscle thickness, and fat-to-muscle ratio consistently exceeded 0.90 across all sample types, while mean absolute errors remained below 3.5 mm. The highest prediction accuracy was achieved in homogeneous oil–water samples, whereas biologically complex tissues like pork and beef introduced greater variability, particularly in muscle-related measurements. The proposed microwave system is highlighted as a highly portable and time-efficient solution, with measurements completed within seconds. Its low cost, ability to analyze multiple tissue types using a single device, and non-invasive nature without the need for sample pre-treatment or anesthesia make it well suited for applications in agri-food quality control, point-of-care diagnostics, and broader biomedical fields. Full article

Introduction: Asthma is a chronic airway inflammatory disease characterized by variable airflow limitation and intermittent symptoms. In well-controlled asthma, auscultation and spirometry often appear normal, making diagnosis challenging. Moreover, bronchial provocation tests carry a risk of inducing acute bronchoconstriction. This study aimed to develop a non-invasive, objective, and reproducible diagnostic method using machine learning-based lung sound analysis for the early detection of asthma, even during stable periods. Methods: We designed a machine learning algorithm to classify controlled asthma patients and healthy individuals using respiratory sounds recorded with a digital stethoscope. We enrolled 120 participants (60 asthmatic, 60 healthy). Controlled asthma was defined according to Global Initiative for Asthma (GINA) criteria and was supported by normal spirometry, no pathological auscultation findings, and no exacerbations in the past three months. A total of 3600 respiratory sound segments (each 3 s long) were obtained by dividing 90 s recordings from 120 participants (60 asthmatic, 60 healthy) into non-overlapping clips. The samples were analyzed using Mel-Frequency Cepstral Coefficients (MFCCs) and Tunable Q-Factor Wavelet Transform (TQWT). Significant features selected with ReliefF were used to train Quadratic Support Vector Machine (SVM) and Narrow Neural Network (NNN) models. Results: In 120 participants, pulmonary function test (PFT) results in the asthma group showed lower FEV1 (86.9 ± 5.7%) and FEV1/FVC ratios (86.1 ± 8.8%) compared to controls, but remained within normal ranges. Quadratic SVM achieved 99.86% accuracy, correctly classifying 99.44% of controls and 99.89% of asthma cases. Narrow Neural Network achieved 99.63% accuracy. Sensitivity, specificity, and F1-scores exceeded 99%. Conclusion: This machine learning-based algorithm provides accurate asthma diagnosis, even in patients with normal spirometry and clinical findings, offering a non-invasive and efficient diagnostic tool. Full article