Search Results (26,408)

Characterizing the spatial distribution of particles smaller than 10 μm (PM₁₀) is of great importance for air quality management yet is very challenging because of the sparseness of air quality monitoring stations. In this study, we use a model-agnostic meta-learning-trained artificial neural network (MAML-ANN) to estimate the concentrations of PM₁₀ at 60 m × 60 m spatial resolution by combining satellite-derived aerosol optical depth (AOD) with meteorological data. The network is designed to regress from the predictors at a specific time to the ground-level PM₁₀ concentration. We utilize the ANN model to capture the time-specific nonlinearity among aerosols, meteorological conditions, and PM₁₀, and apply MAML to enable the model to learn the nonlinearity across time from only a small number of data samples. MAML is also employed to transfer the knowledge learned from coarse spatial resolution to high spatial resolution. The MAML-ANN model is shown to accurately estimate high-resolution PM₁₀ in Beijing, with coefficient of determination of 0.75. MAML improves the PM₁₀ estimation performance of the ANN model compared with the baseline using pre-trained initial weights. Thus, MAML-ANN has the potential to estimate particulate matter estimation at high spatial resolution over other data-sparse, heavily polluted, and small regions. Full article

Surfactants play a crucial role in tertiary oil recovery by reducing the interfacial tension between immiscible phases, altering surface wettability, and improving foam film stability. Oil reservoirs have high temperatures and high pressures, making it difficult and hazardous to conduct lab experiments. In this context, molecular dynamics (MD) simulation is a valuable tool for complementing experiments. It can effectively study the microscopic behaviors (such as diffusion, adsorption, and aggregation) of the surfactant molecules in the pore fluids and predict the thermodynamics and kinetics of these systems with a high degree of accuracy. MD simulation also overcomes the limitations of traditional experiments, which often lack the necessary temporal–spatial resolution. Comparing simulated results with experimental data can provide a comprehensive explanation from a microscopic standpoint. This article reviews the state-of-the-art MD simulations of surfactant adsorption and resulting interfacial properties at gas/oil–water interfaces. Initially, the article discusses interfacial properties and methods for evaluating surfactant-formed monolayers, considering variations in interfacial concentration, molecular structure of the surfactants, and synergistic effect of surfactant mixtures. Then, it covers methods for characterizing microstructure at various interfaces and the evolution process of the monolayers’ packing state as a function of interfacial concentration and the surfactants’ molecular structure. Next, it examines the interactions between surfactants and the aqueous phase, focusing on headgroup solvation and counterion condensation. Finally, it analyzes the influence of hydrophobic phase molecular composition on interactions between surfactants and the hydrophobic phase. This review deepened our understanding of the micro-level mechanisms of oil displacement by surfactants and is beneficial for screening and designing surfactants for oil field applications. Full article

Over the past two decades, urbanization in China has been advancing rapidly. The intricate effects of urbanization on vegetation growth in the urban core have been studied and reported. However, the percentage of impervious surfaces in the urban core, as defined in previous studies, was relatively low, and included some pixels containing farmland and water bodies. Consequently, their results may be affected by urbanization processes, such as the transformation of land types. Hence, this paper extracted 100% impervious surfaces from 2000 to 2022 as urban core areas in China using a 30 m resolution China land cover dataset (CLCD), which completely excluded the effect of urbanization itself on the experimental results, obtaining the trend of vegetation change in the real urban core area. Employing the remote sensing imagery of the Enhanced Vegetation Index (EVI) from 2000 to 2022, we analyzed the growth of vegetation in 1559 urban cores and the surrounding rural areas in China. The study’s findings revealed that the majority of the core areas (85.3%) studied in this paper exhibited a significant (p < 0.05) increase in vegetation, indicating that the various urban greening policies in China have been effective. However, only about 23.7% (369) of the urban core areas showed a faster increase in vegetation than the rural areas. This suggests that for most urban cores (1190), vegetation increase is not as pronounced as it is in surrounding rural areas. Additionally, the EVI rate of change in the urban cores obtained using CLCD versus MODIS land cover data significantly differed. The latter obtained a less pronounced trend of vegetation growth compared to the former, attributable to the disparity in their spatial resolution and the methodology used to define urban areas. The study underscores the importance of vegetation growth and its distribution in various urban core areas to comprehend the dynamics of urban cores’ vegetation growth and to offer insights for the subsequent formulation of greening policies. Moreover, data with different resolutions will significantly impact the results, thus highlighting the necessity of employing high spatial resolution data for more comprehensive research. Full article

Urban wetlands represent a significant ecosystem type within urban landscapes. The quantitative assessment of their ecological service value holds great significance in guiding and improving the urban habitat. However, due to the insufficient spatial resolution of traditional low-to-medium resolution remote sensing imagery for surface monitoring, previous studies have conducted relatively limited research on the ecosystem services of urban wetlands. In this paper, based on multi-source data including multi-scale remote sensing data, a spatial-temporal fusion model and multiple ecological parameter inversion models were employed to invert three key ecological parameters at high spatial resolution, thereby assessing the ecosystem service values (ESVs) of urban wetlands. Taking the East Lake Scenic Area (ELSA) in Wuhan as an example, the dynamics of its ecosystem services’ value components were comparatively analyzed. The results indicate that, while the total value of ecosystem services declined slightly in 2015 compared to 2011, there was a notable increase in their value to CNY 3.219 billion by 2019, which represents a doubling of the total value relative to 2011. This trend could be primarily attributed to a significant rise in cultural services within the region. Specifically, the value of tourism services reached CNY 2.090 billion in 2019, representing a threefold increase compared to 2011. This demonstrates that ecosystem services in the ELSA have been significantly optimized and enhanced through associated ecological projects. Further research should investigate the mechanisms by which urbanization affects these crucial ecosystem services, particularly the characterization of cultural services in urban wetlands, and develop more effective strategies to enhance urban resilience and sustainable development. Full article

Purpose: This article introduces the Pentacam^® Cornea OCT (optical coherence tomography). This advanced corneal imaging system combines rotating ultra-high-resolution spectral domain OCT with sub- 2-micron axial resolution and Scheimpflug photography. The purpose of this study is to present the first experience with the instrument and its potential for corneal diagnostics, including optical biopsy. Methods: In this prospective study, the Pentacam^® Cornea OCT was used to image the corneas of seven patients. The novel wide-angle pericentric scan system enables optimal OCT imaging performance for the corneal layer structure over the entire width of the cornea, including the limbal regions. A detailed analysis of the resulting images assessed the synergism between the OCT and Scheimpflug photography. Results: The Pentacam^® Cornea OCT demonstrated significantly improved image resolution and ability to individualize corneal layers with high quality. There is a synergism between the OCT high-definition signal to individualize details on the cornea and Scheimpflug photography to detect and quantify corneal scattering. The noncontact exam was proven safe, user-friendly, and effective for enabling optical biopsy. Conclusions: Pentacam^® Cornea OCT is an advancement in corneal imaging technology. The ultra-high-resolution spectral domain OCT and Scheimpflug photography provide unprecedented detail and resolution, enabling optical biopsy and improving the understanding of corneal pathology. Further studies are necessary to compare and analyze the tomographic reconstructions of the cornea with the different wavelengths, which may provide helpful information for diagnosing and managing corneal diseases. Full article

Soil serves as a repository of human history, preserving artifacts within its horizons. However, the presence of chemically reactive remnants, such as ancient slags, can significantly impact the surrounding soil environment. This paper addresses this scarcely explored issue by focusing on soil contamination arising from pre-Roman slag deposits at the Monte Romero archaeological site in southwest Spain, dating back to the Tartessian period (c. 7th century BC). Through the high-resolution microscopy examination of slag wastes and the trace element analysis of soil samples by ICP-OES, this study evaluated current contamination status using a multi-index approach. The results revealed markedly high levels of Pb (>5000 mg kg⁻¹), Cu (up to 2730 mg kg⁻¹), and As (up to 445 mg kg⁻¹) in the soil compared to a control sample. The identification of secondary complex compounds like Cu arsenates and Pb arsenates/antimonates within slag cavities suggests post-depositional weathering processes, leading to the dispersion of potentially toxic elements into the surrounding soil. Assessments through indices of contamination and potential ecological risk highlighted severe contamination, particularly concerning Ag, Pb, Sb, Cu, and As. This study underscores the importance of addressing potential environmental hazards associated with archaeological sites hosting remnants of metal production. Full article

License plate recognition is a pivotal challenge in surveillance applications, predominantly due to the low resolution and diminutive size of license plates, which impairs recognition accuracy. The advent of AI-based super-resolution techniques offers a promising avenue to ameliorate the resolution of such images. Despite the deployment of various super-resolution methodologies, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), the quest for satisfactory outcomes in license plate image enhancement persists. This paper introduces “DiffPlate”, a novel Diffusion Model specifically tailored for license plate super-resolution. Leveraging the unprecedented capabilities of Diffusion Models in image generation, DiffPlate is meticulously trained on a dataset comprising low-resolution and high-resolution pairs of Saudi license plates, curated for our surveillance application. Our empirical analysis substantiates that DiffPlate markedly eclipses state-of-the-art alternatives such as SwinIR and ESRGAN, evidencing a 26.47% and 37.32% enhancement in Peak Signal-to-Noise Ratio (PSNR) against these benchmarks, respectively. Furthermore, DiffPlate achieves superior performance in terms of Structural Similarity Index (SSIM), with a 4.88% and 16.21% improvement over SwinIR and ESRGAN, respectively. Human evaluative studies further corroborate that images refined by DiffPlate were preferred 92% more frequently compared to those processed by other algorithms. Through DiffPlate, we present a new solution to the license plate super-resolution challenge, demonstrating significant potential for adoption in real-world surveillance systems. Full article

The development of green spaces in urban parks can significantly enhance the quality of the urban and ecological environment. This paper utilizes 2021 Gaofen-7 (GF-7) satellite remote sensing images as its primary data source and uses deep learning algorithms for the precise extraction of the green space coverage within Beijing’s fifth ring road. It also incorporates the park points of interest (POI) information, road data, and other auxiliary data to extract green park space details. The analysis focuses on examining the relationship between supply and demand in the spatial allocation of green park spaces from an accessibility perspective. The main findings are as follows: (1) The application of deep learning algorithms improves the accuracy of green space extraction by 10.68% compared to conventional machine methods. (2) The distribution of parks and green spaces within the fifth ring road of Beijing is uneven, showing a clear pattern of “more in the north and less in the south”. The accessibility within a five-minute service radius achieves a coverage rate of 46.65%, with a discernible blind zone in the southeast. (3) There is an imbalance in the per capita green space location entropy within the fifth ring road of Beijing, there is a big difference in per capita green space location entropy (44.19), and social fairness needs to be improved. The study’s outcomes unveil the intricate relationship between service capacity and spatial allocation, shedding light on the supply and demand dynamics of parks and green spaces within Beijing’s fifth ring road. This insight will contribute to the construction of ecologically sustainable and aesthetically pleasing living spaces in modern megacities. Full article

The Amazon region is home to many plant species, many of which have not been studied. The objective was to evaluate the physicochemical properties, bioactive compounds, and antioxidant activity of Phytelephas tenuicalis (tintiuk), Grias neuberthii (apai), Euterpe oleracea (acai), and Mauritia flexuosa (brown moriche). Physicochemical analyses were carried out on fresh fruit from local markets. Bioactive compounds (carotenoids, phenolics, vitamin C, and organic acids) were quantified in the freeze-dried pulp by rapid-resolution liquid chromatography (RRLC), and antioxidant activity was determined by ABTS and DPPH assays. The results showed high soluble solids (10.7 °Brix) and ascorbic acid (67.3 mg/100 g DW) in tintiuk; β-carotene (63.4 mg/100 g DW) and malic acid (19.6 g/100 g DW) in brown moriche; quercetin (944.2 mg/100 g DW) and antioxidant activity by ABTS (6.7 mmol ET/100 g DW) in apai; and citric acid (2.1 g/100 g DW) in acai. These results indicate interesting bioactive properties that could increase the consumption of these fruits nationally and internationally, benefiting local farmers and stimulating the development of new products in functional food, medicine, and cosmetics. Full article

Background/Objectives: To compare the epithelial thickness changes and the changes in epithelial wavefront aberrometry following spherical versus astigmatic myopic small incision lenticule extraction (SMILE). Methods: Eighty-six eyes of 86 patients who underwent SMILE were included in this retrospective study. A total of 43 eyes underwent myopic spherical correction (spherical group) and 43 eyes underwent myopic cylindrical correction (cylindrical group). The groups were matched according to the spherical equivalent of surgically corrected refraction. Subjective manifest refraction as well as high-resolution anterior segment optical coherence tomography (MS-39; CSO; Florence, Italy) were obtained preoperatively as well as 3 months postoperatively. The latter was utilized for computing epithelial wavefront aberrometry in addition to epithelial thickness mapping. Results: Epithelial thickness increased significantly in both groups after SMILE (p < 0.01). In the cylindrical group, epithelial thickening was more pronounced on the flat meridian compared to the steep meridian (p = 0.04). In both groups, epithelial wavefront aberrometry showed a significant postoperative increase in the epithelium’s spherical refractive power, causing a myopization of −0.24 ± 0.42 diopters (D) in the spherical group (p < 0.01) and −0.41 ± 0.52 D in the cylindrical group (p < 0.0001). While no significant changes in epithelial cylindrical refractive power were observed in the spherical group, a significant increase was noted in the cylindrical group from −0.21 ± 0.24 D to −0.37 ± 0.31 D (p = 0.01). In both groups, epithelial higher-order aberrations increased significantly (p < 0.001). Conclusions: Postoperative epithelial remodeling after SMILE alters lower-order (sphere and cylinder) and higher-order aberrations of the corneal epithelial wavefront and might contribute to refractive undercorrection, especially in astigmatic corrections. Epithelial wavefront aberrometry can be used to quantify the refractive effect of epithelial remodeling processes after keratorefractive surgery. Full article

The high sensitivity and picosecond time resolution of single-photon avalanche diodes (SPADs) can improve the operational range and imaging accuracy of underwater detection systems. When an underwater SPAD imaging system is used to detect targets, backward-scattering caused by particles in water often results in the poor quality of the reconstructed underwater image. Although methods such as simple pixel accumulation have been proven to be effective for time–photon histogram reconstruction, they perform unsatisfactorily in a highly scattering environment. Therefore, new reconstruction methods are necessary for underwater SPAD detection to obtain high-resolution images. In this paper, we propose an algorithm that reconstructs high-resolution depth profiles of underwater targets from a time–photon histogram by employing the K-nearest neighbor (KNN) to classify multiple targets and the background. The results contribute to the performance of pixel accumulation and depth estimation algorithms such as pixel cross-correlation and ManiPoP. We use public experimental data sets and underwater simulation data to verify the effectiveness of the proposed algorithm. The results of our algorithm show that the root mean square errors (RMSEs) of land targets and simulated underwater targets are reduced by 57.12% and 23.45%, respectively, achieving high-resolution single-photon depth profile reconstruction. Full article

The molecular machines of life, proteins, are made up of twenty kinds of amino acids, each with distinctive side chains. We present a geometrical analysis of the protrusion statistics of side chains in more than 4000 high-resolution protein structures. We employ a coarse-grained representation of the protein backbone viewed as a linear chain of C_α atoms and consider just the heavy atoms of the side chains. We study the large variety of behaviors of the amino acids based on both rudimentary structural chemistry as well as geometry. Our geometrical analysis uses a backbone Frenet coordinate system for the common study of all amino acids. Our analysis underscores the richness of the repertoire of amino acids that is available to nature to design protein sequences that fit within the putative native state folds. Full article

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records. Full article

The utilization of metabolomics and lipidomics analytical platforms in the study of pathogenic microbes is slowly expanding. These research approaches will significantly contribute to the establishment of microbial metabolite and lipid databases of significant value to all researchers in microbiology. In this review, we present a high-level overview of some examples of biomarkers that can be used to detect the presence of microbes, monitor the expansion/decline of a microbe population, and add to our understanding of microbe biofilms and pathogenicity. In addition, increased knowledge of the metabolic functions of pathogenic microbes can contribute to our understanding of microbes that are utilized in diverse industrial applications. Our review focuses on lipids, secondary metabolites, and non-ribosomal peptides that can be monitored using electrospray ionization high-resolution mass spectrometry (ESI-HRMS). Full article

This study investigates the predictability of downslope windstorms located in Santa Barbara County, California, locally referred to as Sundowner winds, from both observed relationships and a high-resolution, operational numerical weather prediction model. We focus on April 2022, during which the Sundowner Winds Experiment (SWEX) was conducted. We further refine our study area to the Montecito region owing to some of the highest wind measurements occurring at or near surface station MTIC1, situated on the coast-facing slope overlooking the area. Fires are not uncommon in this area, and the difficulty of egress makes the population particularly vulnerable. Area forecasters often use the sea-level pressure difference ($\Delta$SLP) between Santa Barbara Airport (KSBA) and locations to the north such as Bakersfield (KBFL) to predict Sundowner windstorm occurrence. Our analysis indicates that $\Delta$SLP by itself is prone to high false alarm rates and offers little information regarding downslope wind onset, duration, or magnitude. Additionally, our analysis shows that the high-resolution rapid refresh (HRRR) model has limited predictive skill overall for forecasting winds in the Montecito area. The HRRR, however, skillfully predicts KSBA-KBFL $\Delta$SLP, as does GraphCast, a machine learning weather prediction model. Using a logistic regression model we were able to predict the occurrence of winds exceeding 9 m s${}^{-1}$ with a high probability of detection while minimizing false alarm rates compared to other methods analyzed. This provides a refined and easily computed algorithm for operational applications. Full article