Search Results (4,058)

This paper studied a multi-channel self-mixing interferometric vibration measurement system based on a vertical-cavity surface-emitting laser array. A 1 × 8 laser array was utilized to irradiate multiple positions of a vibrating target to establish independent measurement channels. The reflected light beams, carrying the vibration information of each position, were redirected back into the laser and coherently mixed with the original optical field, generating the self-mixing interference. The interferometric signals were measured by monitoring the junction voltage variations across the terminals of the VCSEL array. A denoising filtering method based on the variational mode decomposition with Hausdorff distance was proposed to improve the signal-to-noise ratio. Furthermore, the vibration waveforms of different positions were reconstructed using the Hilbert transform-based orthogonal phase demodulation technology. Both simulations on synthetic signals and experiments with real datasets were conducted to validate the feasibility and stability of the proposed method. Due to the array detection configuration, the system boasted a simple and compact structure, low power consumption, and easy extensibility, laying the groundwork for high accuracy and multi-dimensional vibration detection in industrial applications. Full article

Inverse Synthetic Aperture Radar (ISAR) images are extensively used in Radar Automatic Target Recognition (RATR) for non-cooperative targets. However, acquiring training samples for all target categories is challenging. Recognizing target classes without training samples is called Zero-Shot Learning (ZSL). When ZSL involves multiple modalities, it becomes Multi-modal Zero-Shot Learning (MZSL). To achieve MZSL, a framework is proposed for generating ISAR images with optical image aiding. The process begins by extracting edges from optical images to capture the structure of ship targets. These extracted edges are used to estimate the potential locations of the target’s scattering centers. Using the Geometric Theory of Diffraction (GTD)-based scattering center model, the edges’ ISAR images are generated from the scattering centers. Next, a mapping is established between the edges’ ISAR images and the actual ISAR images. Neighbor-Adapted Local Linear Embedding (NALLE) generates pseudo-ISAR images for the unseen classes by combining the edges’ ISAR images with the actual ISAR images from the seen classes. Finally, these pseudo-ISAR images serve as training samples, enabling the recognition of test samples. In contrast to the network-based approaches, this method requires only a limited number of training samples. Experiments based on simulated and measured data validate the effectiveness. Full article

Previously, it was suggested that the “persona-driven” approach can contribute to producing sufficiently diverse synthetic training data for Large Language Models (LLMs) that are currently about to run out of real natural language texts. In our paper, we explore whether personas evoked from LLMs through HCI-style descriptions could indeed imitate human-like differences in authorship. For this end, we ran an associative experiment with 50 human participants and four artificial personas evoked from the popular LLM-based services: GPT-4(o) and YandexGPT Pro. For each of the five stimuli words selected from university websites’ homepages, we asked both groups of subjects to come up with 10 short associations (in Russian). We then used cosine similarity and Mahalanobis distance to measure the distance between the association lists produced by different humans and personas. While the difference in the similarity was significant for different human associators and different gender and age groups, neither was the case for the different personas evoked from ChatGPT or YandexGPT. Our findings suggest that the LLM-based services so far fall short at imitating the associative thesauri of different authors. The outcome of our study might be of interest to computer linguists, as well as AI/ML scientists and prompt engineers. Full article

The ZnO/hydroxyapatite nanocomposite was prepared by attrition in a planetary mill from hydroxyapatite (HA) and ZnO nanopowders. The photocatalytic degradation of synthetic dye, methyl orange (MO), was evaluated under stirring and UV irradiations by measuring the spectroscopically UV-VIS absorbance of the solution in order to determine the remanent dye concentration. The samples CZH3 (75% ZnO) and CZH4 (25% ZnO) highlighted the best MO retention from aqueous solution by adsorption and photodegradation effects. The high absorbance of the proposed nanocomposites showed their potential to be used as photocatalysts for wastewater treatment to enable the retention of organic pollutants. Full article

The purpose of this paper is to evaluate and compare the degree of servitization of the economies of European Union countries and to assess the importance of servitization for sustainable development. This study used Eurostat data from the years 2000 and 2023. Using Hellwig’s synthetic measure of development, four groups of countries differing in the degree of servitization in each of the years studied were created and then compared in the context of the differences between them, including population density, the share of the service sector in employment and gross value-added creation, and the level of gross value added created by the service sector per capita. The results showed that a high degree of servitization characterizes mainly the rich countries of the EU-15, while a lower one applies mainly to the countries of Central and Eastern Europe. The service sector increased its share in employment, structure, and gross value-added creation during the period under review. High population density was also a factor conducive to the development of services, but its increase did not always coexist with an increase in the degree of servitization of the economy. Servitization drives development and facilitates optimal use of resources. However, high levels of servitization are not always reflected in good values of sustainable development. Servitization processes can be stimulated by adequate economic development policies, but the methods and actions taken in this regard should be adapted to the level of economic development and the specifics of each country. Full article

Background: Corneal allogenic intrastromal ring segments (CAIRSs) offer a novel, biocompatible alternative to synthetic intracorneal ring segments (ICRSs). This review aims to evaluate the clinical outcomes of CAIRS. Methods: Inclusion criteria were studies with a minimum of 20 eyes and six months of follow up. The primary outcome measure was uncorrected distance visual acuity (UDVA). The secondary outcomes were a change in corrected distance visual acuity (CDVA), spherical equivalent (SE), mean keratometry (K-mean), maximum keratometry (K-max), K1, K2, and pachymetry. Results: The primary outcome UDVA improved from 0.83 ± 0.15 to 0.40 ± 0.08 logMAR (p = 0.01), while CDVA improved from 0.52 ± 0.22 to 0.19 ± 0.09 logMAR (p = 0.01). K-max decreased from 57.8 ± 1.09 D to 53.57 ± 2.66 D (p < 0.01), and K-mean reduced from 49.27 ± 0.28 D to 45.30 ± 1.46 D (p < 0.01). An average of 84.92% ± 11.4% of eyes had an improvement in UDVA. No major complications or significant visual acuity deterioration were reported. Conclusions: CAIRSs serve as an alternative to synthetic ICRSs and even corneal transplantation in some cases. They represent a safe, effective, and biocompatible promising advancement in corneal ectasia management to improve visual acuity and corneal topography with minimal complications. Full article

Corn (Zea mays L.) productivity is often compromised by phytosanitary challenges, with fungal disease like Curvularia leaf spot being particularly significant. While synthetic fungicides are commonly used, there is growing interest in exploring alternative compounds that are effective against pathogens, ensure food safety, and have low toxicity to non-target organisms. In this study, we examined the biochemical changes in corn plants treated with Lippia sidoides essential oil and its major compound, thymol. Both treatments serve as preventive measures for inoculated plants and induced resistance. We tested five concentrations of each product in in vivo experiments. After evaluating the area under the disease progress curve, we analyzed leaf samples for enzymatic activities, including superoxide dismutase, catalase, ascorbate peroxidase, and chitinase. Phytoalexin induction was assessed using soybean cotyledons and sorghum mesocotyls. Cytotoxicity tests revealed lower toxicity at concentrations below 50 µL/mL. Both essential oil and thymol stimulated the production of reactive oxygen species, with thymol primarily activating catalase and L. sidoides oil increasing ascorbate peroxidase levels. Both thymol and L. sidoides were also key activators of chitinase. These findings suggest that L. sidoides essential oil and thymol are promising candidates for developing biological control products to enhance plant defense against pathogens. Full article

Electricity consumption data form the foundation for the efficient and reliable operation of smart grids and are a critical component for ensuring effective data mining. However, due to factors such as meter failures and extreme weather conditions, anomalies frequently occur in the data, which adversely impact the performance of data-driven applications. Given the near full-rank nature of low-voltage distribution area electricity consumption data, this paper employs clustering to enhance the low-rank property of the data. Addressing common issues such as missing data, sparse noise, and Gaussian noise in electricity consumption data, this paper proposes a multi-norm optimization model based on low-rank matrix theory. Specifically, the truncated nuclear norm is used as an approximation of matrix rank, while the $L_1$-norm and F-norm are employed to constrain sparse noise and Gaussian noise, respectively. The model is solved using the Alternating Direction Method of Multipliers (ADMM), achieving a unified framework for handling missing data and noise processing within the model construction. Comparative experiments on both synthetic and real-world datasets demonstrate that the proposed method can accurately recover measurement data under various noise contamination scenarios and different distributions of missing data. Moreover, it effectively separates principal components of the data from noise contamination. Full article

Complex pore structures with multiple inclusions challenge the predictive accuracy of rock physics models. This study introduces a novel method for estimating a single equivalent pore aspect ratio that optimizes rock physics model predictions by minimizing discrepancies with experimental measurements in porous rocks with multiple inclusions with variable aspect ratios and proportions. The proposed methodology uses digital rock physics numerical simulations for validation. A comparative analysis is conducted between the equivalent aspect ratio derived from optimized rock physics models, numerical simulations, and the aspect ratio distribution estimated from digital rock samples. The approach is tested on both synthetic and real core samples, demonstrating its robustness and applicability to field data, including core samples and well log data. The validation results confirm that the method enhances predictive accuracy and offers a versatile framework for addressing pore complexity in subsurface rock formations. Full article

Long synthetic aperture radar (SAR) offers the advantage of achieving higher resolution by utilizing longer synthetic aperture times, which makes it a promising technology for ocean observation in the future. However, compared to SAR systems with shorter synthetic aperture times, it suffers more severely from issues such as image defocusing, blurring and artifacts during the observation of maritime targets, due to motion errors. To improve the quality of SAR imaging against motion errors in long synthetic aperture time scenarios, this paper proposes a combined motion compensation (MOCO) method based on subaperture processing. The method first divides the full aperture data into several subapertures. Within each subaperture, the platform is assumed to move at approximately constant velocity. The major imaging step is then combined with two motion compensation operations, which are performed individually within each subaperture. Then, the processed subaperture data are stitched together, and finally, the residual errors are compensated by the third MOCO, resulting in the final image. By simulating maritime observation targets with point targets, simulation results demonstrate that the proposed MOCO algorithm effectively reduce the influence of motion errors, suppress the sidelobe interference to the imaging, and improve the focusing accuracy. Compared with other classical MOCO algorithms, the ISLR_r and ISLR_a metrics show improvements of 0.2662 and 0.8170 dB, respectively. Further verification of the proposed method is conducted by processing the imaging results of measured sea surface data. The proposed algorithm produces clearer wave textures and achieves better imaging performance on targets such as ships in the sea. This result validates the effectiveness and superiority of the proposed method. The proposed method effectively addresses the need for high-precision motion error compensation in high-resolution imaging within long synthetic aperture time system. Full article

Leaky waveguides (LWs) are low-refractive-index films deposited on glass substrates. In these, light can travel in the film while leaking out at the film–substrate interface. The angle at which light can travel in the film is dependent on its refractive index and thickness, which can change with pH when the film is made of pH-responsive materials. Herein, we report an LW comprising a waveguide film made of a synthetic hydrogel containing the monomers acrylamide and N-[3-(dimethylamino)propyl]methacrylamide (DMA) and a bisacrylamide crosslinker for pH measurements between 4 and 8. The response of the LW pH sensor was reversible and the response times were 0.90 ± 0.14 and 2.38 ± 0.22 min when pH was changed from low to high and high to low, respectively. The reported LW pH sensor was largely insensitive to typical concentrations of common interferents, including sodium chloride, urea, aluminum sulfate, calcium chloride, and humic acid. Compared to a glass pH electrode, the measurement range is smaller but is close to the range required for monitoring the pH of drinking water. The pH resolution of the hydrogel sensor was ~0.004, compared to ~0.01 for the glass electrode. Full article

Lakes are a crucial component of inland water bodies, and changes in their water levels serve as key indicators of global climate change. Traditional methods of lake water level monitoring rely heavily on hydrological stations, but there are problems such as regional representativeness, data stability, and high maintenance costs. The satellite altimeter is an essential tool in lake research, with the Synthetic Aperture Radar (SAR) altimeter offering a high spatial resolution. This enables precise and quantitative observations of lake water levels on a large scale. In this study, we used Sentinel-3A SAR Radar Altimeter (SRAL) data to establish a more reasonable lake height inversion algorithm for satellite-derived lake heights. Subsequently, using this technology, a systematic analysis study was conducted with Qinghai Lake as the case study area. By employing regional filtering, threshold filtering, and altimeter range filtering techniques, we obtained effective satellite altimeter height measurements of the lake surface height. To enhance the accuracy of the data, we combined these measurements with GPS buoy-based geoid data from Qinghai Lake, normalizing lake surface height data from different periods and locations to a fixed reference point. A dataset based on SAR altimeter data was then constructed to track lake surface height changes in Qinghai Lake. Using data from the Sentinel-3A altimeter’s 067 pass over Qinghai Lake, which has spanned 96 cycles since its launch in 2016, we analyzed over seven years of lake surface height variations. The results show that the lake surface height exhibits distinct seasonal patterns, peaking in September and October and reaching its lowest levels in April and May. From 2016 to 2023, Qinghai Lake showed a general upward trend, with an increase of 2.41 m in lake surface height, corresponding to a rate of 30.0 cm per year. Specifically, from 2016 to 2020, the lake surface height rose at a rate of 47.2 cm per year, while from 2020 to 2022, the height remained relatively stable. Full article

Cassava peels are rich in polysaccharides but highly unexplored and underutilized, as they could be used to meet the increasing demand for clean-label foods. This study investigated the effect of temperature on the solubilization of cassava peel during hydrothermal treatment to determine the emulsifying ability of solubilized cassava peel (SCP). Subcritical water conditions were employed via hydrothermal (120–200 °C; 2 MPa) or autoclave (127 °C; 0.2 MPa) treatments to solubilize cassava peels. The composition of the SCPs was determined, and their emulsifying ability was assessed using interfacial tension and zeta potential measurements. Under the best treatment conditions (140 °C at 2 MPa [hydrothermal]; 127 °C at 0.2 MPa [autoclave]), SCPs reduced interfacial tension against soybean oil to 12.9 mN/m and 13.4 mN/m, respectively. A strengthened co-emulsifier system was developed by incorporating SCPs with Quillaja saponins (QS) or Tween 20 to enhance the performance. Dynamic interfacial tension and zeta potential measurements revealed synergistic interactions, showing a remarkable reduction in interfacial tension from 12.94 to 5.33 mN/m. This suggests that the SCP has a surfactant-like structure owing to its amphiphilic structure and hydrophobic chains (nonpolar region) attached to the -OH functional group (polar region). Combining a second surface-active compound or co-emulsifier results in an additive effect, reducing the interfacial tension. These findings provide novel insights into carbohydrate-saponin binding and elucidate the impact of peel composition, concentration, and hydrothermal treatment conditions on co-emulsifier system performance, which will assist in the development of emulsifiers, contributing to the advancement of clean-label food technologies, effectively replacing synthetic emulsifiers in food formulations, and offering both sustainability and functionality. A systematic investigation of processing conditions and co-emulsifier interactions provides a practical framework for developing high-performance natural emulsifiers from agricultural waste. Full article

The deployment density of surface sensors can significantly impact the accuracy of subsurface shallow seismic field energy inversion. With finite budget constraints, it is often not feasible to deploy a large number of sensors, resulting in limited seismic signal acquisition that hinders accurate inversion of the shallow subsurface explosions. To address the challenge of insufficient sensor signals needed for inversion, we conducted a study on a subsurface shallow wavefield data interpolation method based on transfer learning. This method is designed to increase the overall signal acquisition by interpolating signals at target locations from limited measurement points. Our research employs neural networks to interpolate real seismic data, supplementing the sampled signals. Given the lack of extensive samples from actual data collection, we devised a training approach that combines numerically simulated signals with real collected signals. Initially, we performed conventional interpolation training using a deep interpolation network with complete synthetic gather images obtained from numerical simulations. Subsequently, the feature extraction part was frozen, and the interpolation network was transferred to real datasets, where it was trained using incomplete gather images. Finally, these incomplete gather images were re-input into the trained network to obtain interpolated results at the target locations. Our study demonstrates the superiority of our method by comparing it with two other interpolation networks and validating the effectiveness of transfer learning through four sets of ablation experiments in the actual test. This method can also be applied to other shallow geological structures to generate a large number of seismic signals for energy inversion. Full article

The issue of gas permeability of porous beds is important for the development of a new generation of clean energy sources, especially in the context of unconventional energy storage. Detailed experimental studies were carried out to demonstrate the gas permeability of porous materials: in situ karbonizat and natural and synthetic pumice. The measure of gas permeability was the volumetric gas flow velocity resulting from the permissible pressure difference forcing the gas flow in a given axis (X, Y, Z) on a sample of a cube-shaped porous material. A novelty is the indication of correlation with selected materials exhibiting features of unconventional energy storage. Assessment of the gas permeability coefficient for selected material features shows an increasing trend for epoxy resin, dacite, in situ carbonizate and pumice. On the other hand, for carbonate rocks, mudstones and shales, there is a decrease in gas permeability. The indicated porous materials can be storage tanks of unconventional energy carriers. In an innovative way, a material (halloysite) was indicated that has the ability to store and be a source of transport in the form of a cylindrical model (nanotube) for future implementation of isotropic features of porous materials. Full article