Search Results (1,651)

Background: Diabetes-related foot diseases represent a global health problem because of the associated complications, the risk of amputation, and the economic burden on health systems. Negative pressure wound therapy (NPWT) is a technique that uses sub-atmospheric pressure to help promote wound healing by reducing the inflammatory exudate while keeping the wound moist, inhibiting bacterial growth, and promoting the formation of granulation tissue. Objective: This study aimed to assess the effectiveness of NPWT in preventing major amputation in diabetic patients with complicated foot or lower limb infections and to contextualize the results through a review of the existing literature. Materials and methods: We conducted a retrospective study at the First Surgical Department of “Dr. I. Cantacuzino” Clinical Hospital in Bucharest, Romania, over a 15-year period, including 30 consecutive adult patients with diabetes and soft tissue foot or lower limb infections treated with NPWT. Patients with non-diabetic ulcers, incomplete medical data, or aged under 18 were excluded. All patients underwent initial surgical debridement, minor amputation, or drainage procedures, followed by the application of NPWT using a standard protocol. Dressings were changed every 2–4 days for a total of 7–10 days. Antibiotic therapy was adapted according to the culture results. The primary outcome was limb preservation, defined as avoidance of major amputation. Secondary outcomes included in-hospital mortality and wound status at discharge. Results: NPWT was associated with a favorable outcome in 24 patients (80%), defined by wound granulation or healing without the need for major amputation. Five patients (16.6%) underwent major amputation because of failure of the primary lesion treatment, and one patient died. No statistically significant association was observed between the outcomes and standard classification scores (WIFI, IWGDF, and TPI). A comprehensive literature review helped to integrate these findings into the existing pool of knowledge. Conclusions: NPWT may support limb preservation in selected diabetic foot cases. While the retrospective design and the small sample size of the study limit generalizability, these results reinforce the need for further controlled studies to evaluate NPWT in real-life clinical settings. The correct use of NPWT combined with etiological treatment may offer a maximum chance to avoid major amputation in patients with diabetes-related foot diseases. Full article

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring. Full article

The concentration characteristics of SO₂, NO₂, O₃, and CO in the marine atmosphere are of great significance for understanding air–sea interactions and regional atmospheric chemical processes. However, due to the challenging conditions of marine monitoring, long-term continuous observational data remain scarce. To address this gap, this study proposes a Transfer Learning–Convolutional Neural Network (TL-CNN) model that integrates ERA5 meteorological data, EAC4 atmospheric composition reanalysis data, and ground-based observations through multi-source data fusion. During data preprocessing, the Data Interpolating Empirical Orthogonal Function (DINEOF), inverse distance weighting (IDW) spatial interpolation, and Gaussian filtering methods were employed to improve data continuity and consistency. Using ERA5 meteorological variables as inputs and EAC4 pollutant concentrations as training targets, a CNN-based inversion framework was constructed. Results show that the CNN model achieved an average coefficient of determination (R²) exceeding 0.80 on the pretraining test set, significantly outperforming random forest and deep neural networks, particularly in reproducing nearshore gradients and regional spatial distributions. After incorporating transfer learning and fine-tuning with station observations, the model inversion results reached an average R² of 0.72 against site measurements, effectively correcting systematic biases in the reanalysis data. Among the pollutants, the inversion of SO₂ performed relatively poorly, mainly because emission reduction trends from anthropogenic sources were not sufficiently represented in the reanalysis dataset. Overall, the TL-CNN model provides more accurate pollutant concentration fields for offshore regions with limited observations, offering strong support for marine atmospheric environment studies and assessments of marine ecological effects. It also demonstrates the potential of combining deep learning and transfer learning in atmospheric chemistry research. Full article

Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article

Based on GPM satellite observations during June to September from 2014 to 2023, deep convective systems (DCSs) over the Tibetan Plateau and Sichuan Basin exhibited distinct spatiotemporal and structural characteristics. Over the Plateau, DCSs were primarily concentrated in the central and eastern regions, with echo-top heights typically ranging from 15 to 17 km and 40 dBZ echo tops mostly found between 6 and 8 km. In contrast, the Basin displayed a more spatially uniform distribution of convection, characterized by lower echo-top heights (12–14 km) and higher 40 dBZ echo tops. Although both regions experienced a seasonal peak in DCS frequency in July, their diurnal variations differed significantly. The Plateau exhibited a pronounced unimodal peak between 13:00 and 16:00, which was driven by strong surface heating. In the Basin, a bimodal pattern was observed, with elevated frequencies during 23:00–02:00 and 08:00–11:00. This pattern was likely influenced by local thermodynamic and topographic conditions. The altitude of maximum corrected radar reflectivity (MaxCRF) was predominantly between 4 and 7 km over the Plateau and confined to 2–4 km over the Basin. Over the Plateau, DCS frequency increased significantly with elevation, consistent with the enhancing role of high terrain, whereas no comparable relationship was found in the Basin. Instead, convective activity in the Basin appeared to be modulated primarily by atmospheric instability and moisture availability, highlighting the contrasting environmental controls between the two regions. Full article

Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article

This study aimed to determine the optimal application sequence of cold atmospheric helium plasma (CAP) with fluoride varnish to enhance enamel protection and fluoride uptake. A total of 91 bovine incisor teeth were randomly assigned into seven groups (n = 13 each): negative control (C, no treatment), comparative controls [helium gas (He, gas only)], helium plasma (P, plasma only)], positive control [fluoride varnish (V)], and three experimental groups: plasma followed by varnish (PV), varnish followed by plasma (VP), and plasma before and after varnish (PVP). Specimens were analyzed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX), and microhardness testing at 0, 24, and 48 h post-treatment. SEM revealed that helium plasma treatment enhanced the even dispersion of fluoride and reduced imperfections on the enamel surface. EDX analysis indicated significant alterations in the elemental composition, particularly with respect to the amount of fluoride (F) and the calcium-to-phosphorus (Ca/P) ratios. In the PVP group (CAP before and after varnish), the fluoride atomic percentage increased notably from 1.21% (varnish group) to 7.31% at 48h. Concurrently, the Ca/P ratio increased from 1.95 to 2.39 corresponding with a statistically significant 24% improvement in enamel hardness (repeated-measures ANOVA with Bonferroni correction, p < 0.05). The timing of CAP application critically affects fluoride absorption and enamel hardening. This study clearly demonstrates how sequential CAP treatment maximizes fluoride effectiveness, offering a promising route for non-invasive caries prevention. Full article

The successful launch of Korea Multipurpose Satellite-3/Advanced Earth Imaging Sensor System (KOMPSAT-3/AEISS) on 18 May 2012 allowed the Republic of Korea to meet the growing demand for high-resolution satellite imagery. However, like all satellite sensors, KOMPSAT-3/AEISS experienced temporal changes post-launch and thus requires ongoing evaluation and calibration. Although more than a decade has passed since launch, the KOMPSAT-3/AEISS mission and its multi-year data archive remain widely used. This study focused on the cross-calibration of KOMPSAT-3/AEISS with Sentinel-2A/Multispectral Instrument (MSI) by comparing the radiometric responses of the two satellite sensors under similar observation conditions, leveraging the linear relationship between Digital Numbers (DN) and top-of-atmosphere (TOA) radiance. Cross-calibration was performed using near-simultaneous satellite images of the same region, and the Spectral Band Adjustment Factor (SBAF) was calculated and applied to account for differences in spectral response functions (SRF). Additionally, Bidirectional Reflectance Distribution Function (BRDF) correction was applied using MODIS-based kernel models to minimize angular reflectance effects caused by differences in viewing and illumination geometry. This study aims to evaluate the radiometric consistency of KOMPSAT-3/AEISS relative to Sentinel-2A/MSI over Baotou scenes acquired in 2022–2023, derive band-specific calibration coefficients and compare them with prior results, and conduct a side-by-side comparison of cross-calibration and vicarious calibration. Furthermore, the cross-calibration yielded band-specific gains of 0.0196 (Blue), 0.0237 (Green), 0.0214 (Red), and 0.0136 (NIR). These findings offer valuable implications for Earth observation, environmental monitoring, and the planning and execution of future satellite missions. Full article

In this work, we tested the performance of automated atmospheric PM_2.5 monitoring instruments and contrasted the data from automated measurements with those from filter-based reference measurements. The tested instruments include four brands of beta attenuation instruments (two were made in China, D1 and D2; the other two were imported from other countries, I1 and I2) and one brand of a light scattering instrument (also imported from another country, I3). The automated monitoring data were corrected based on the reference tests. The total testing period lasted 18 months. The objective of this work is to evaluate the influences of environmental factors on the performance of different automated instruments, and to improve the accuracy of the automated instruments by using a correction method. The results showed that contrasted with the reference tests, the absolute errors (MAE, mean absolute error; SD, standard deviation; and RMSE, root mean square error) of the automated monitoring instruments werehigher for temperature (T ≤ 10 °C), humidity (60% ≤ RH < 80%), and PM_2.5 concentrations (PM_2.5 ≥ 75 μg/m³). Meanwhile, the relative errors (CV, coefficient of variation; and NRMSE, normalized root mean square error) of the automated monitoring instruments were higher for humidity (RH > 80%) and PM_2.5 concentrations (PM_2.5 < 15 μg/m³). For winter data, it proved challenging to pass the reference test, which was based on a linear regression between 24-h average automated monitoring data and the integrated filter-based PM_2.5 data (aka the KBR test). Before corrections, the pass rates of D1, D2, I1, I2, and I3 in the rolling KBR tests are 57.7%, 51.3%, 41.1%, 21%, and 90.2%, respectively. After corrections, the rates increase to 79.6%, 86.6%, 81.8%, 58.9%, and 91.8%, respectively. The coefficient corrections (corrections of system errors) have made the most prominent contribution to improving the pass rates of the winter samples. The quarterly correction method can significantly improve the data accuracy of automated monitoring instruments. Full article

The health impact of air pollutants has generated a trend in the design and manufacture of portable, personal and fixed PM monitoring systems to help reduce exposure to air pollutants. However, these devices still need to be improved and properly evaluated to compete with environmental monitors in the market. In this work, a test chamber with controlled environmental conditions and wireless connectivity is developed for the evaluation of low-cost portable and personal PM sensors. The developed system ensures rapid evaluation tests ranging from seconds to hours to corroborate prolonged operation and correct calibration. The system is controlled by a Python-based graphical user interface (GUI) and monitors PM concentration, altitude, relative humidity, atmospheric pressure, illuminance, and temperature measurements. Fifty measurement tests with a duration of 10 min each were conducted to ensure robust performance and data transfer. Subsequently, four calibration tests were conducted using two SENSIRION SPS30 (SPS A and SPS B) personal PM sensors and two PMS5003 (PMS A and PMS B) personal PM sensors. The Prana Air PAS-OUT-01 sensor served as the reference to calculate the correlations and the descriptive statistics between each sensor to be calibrated. A contamination source was employed utilizing a monodispersed aerosol generator for 0.46 µm latex polystyrene particle atomization. Linear regression was applied during the calibration to determine the calibration coefficients, which were then used to adjust the sensor readings in the respective code and descriptive statistics of the sensor calibration tests were calculated. For the PMS5003 sensors, the Pearson correlation coefficients (r) after calibration were PMS A: 0.9870 and PMS B: 0.9898 compared to their uncalibrated values of PMS A: 0.9828 and PMS B: 0.9863. In contrast, the uncalibrated SPS A sensor initially had a correlation of 0.9939, which slightly decreased to 0.9917 after calibration. Meanwhile, the uncalibrated SPS B sensor showed a correlation of 0.9422, which improved to 0.9715 after calibration. Full article

Accurate ground deformation monitoring with interferometric synthetic aperture radar (InSAR) is often hindered by tropospheric delays caused by atmospheric pressure, temperature, and water vapor variations. While models such as ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) provide first-order corrections, they often leave residual errors dominated by small-scale turbulent effects. To address this, we present a novel variational autoencoder with clustering (VAE-clustering) approach that performs unsupervised separation of atmospheric and deformation signals, followed by noise component removal via density-based clustering. The method is integrated into the MintPy pipeline for automated velocity and displacement time-series retrieval. We evaluate our approach on Sentinel-1 interferograms from three case studies: (1) land subsidence in Mashhad, Iran (2015–2022), (2) land subsidence in Tehran, Iran (2018–2021), and (3) postseismic deformation after the 2021 Acapulco earthquake. Across all cases, the method reduced the velocity standard deviation by approximately 70% compared to the ERA5 corrections, leading to more reliable displacement estimates. These results demonstrate that VAE-clustering can effectively mitigate residual tropospheric noise, improving the accuracy of large-scale InSAR time-series analyses for geohazard monitoring and related applications. Full article

Many research fields, such as urban planning, urban climate and environmental assessment, require information on the distribution of buildings. In this study, we used U-Net to segment buildings from WorldView-3 imagery. To improve the accuracy of building segmentation, we undertook two endeavors. First, we investigated the optimal order of atmospheric correction (AC) and panchromatic sharpening (pan-sharpening) and found that performing AC before pan-sharpening results in higher building segmentation accuracy than after pan-sharpening, increasing the average IoU by 9.4%. Second, we developed a new multi-task self-supervised learning (SSL) network to pre-train VGG19 backbone using 21 unlabeled WorldView images. The new multi-task SSL network includes two pretext tasks specifically designed to take into account the characteristics of buildings in satellite imagery (size, distribution pattern, multispectral, etc.). Performance evaluation shows that U-Net combined with an SSL pre-trained VGG19 backbone improves building segmentation accuracy by 15.3% compared to U-Net combined with a VGG19 backbone trained from scratch. Comparative analysis also shows that the new multi-task SSL network outperforms other existing SSL methods, improving building segmentation accuracy by 3.5–13.7%. Moreover, the proposed method significantly saves computational costs and can effectively work on a personal computer. Full article

Loading of the Earth’s crust due to variations in global atmospheric pressure can displace the position of geodetic stations. However, the station displacements induced by the diurnal and semidiurnal atmospheric tides (S₁-S₂) are often neglected during Global Positioning System (GPS) processing. We first studied the magnitudes of S₁-S₂ deformation in the Earth’s center of mass (CM) frame and compared the global S₁-S₂ grid models provided by the Global Geophysical Fluid Center (GGFC) and the Vienna Mapping Function (VMF) data server. The magnitude of S₁-S₂ tidal displacement can reach 1.5 mm in the Up component at low latitudes, approximately three times that of the horizontal components. The most significant difference between the GGFC and VMF grid models lies in the phase of S₂ in the horizontal components, with phase discrepancies of up to 180° observed at some stations. To investigate the effects of S₁-S₂ corrections on GPS coordinates, we then processed GPS data from 108 International GNSS Service (IGS) stations using the precise point positioning (PPP) method in two processing strategies, with and without the S₁-S₂ correction. We observed that the effects of S₁-S₂ on daily GPS coordinates are generally at the sub-millimeter level, with maximum root mean square (RMS) coordinate differences of 0.18, 0.08, and 0.51 mm in the East, North, and Up components, respectively. We confirmed that part of the GPS draconitic periodic signals was induced by unmodeled S₁-S₂ loading deformation, with the amplitudes of the first two draconitic harmonics induced by atmospheric tide loading reaching 0.2 mm in the Up component. Moreover, we recommend using the GGFC grid model for S₁-S₂ corrections in GPS data processing, as it reduced the weighted RMS of coordinate residuals for 45.37%, 46.30%, and 53.70% of stations in the East, North, and Up components, respectively, compared with 39.81%, 44.44%, and 50.00% for the VMF grid model. The effects of S₁-S₂ on linear velocities are very limited and remain within the Global Geodetic Observing System (GGOS) requirements for the future terrestrial reference frame at millimeter level. Full article

With the aim of addressing the problem of spot drift caused by laser transmission in atmospheric turbulence, the effects of different weather conditions such as sunny, cloudy, rainy and sandstorm conditions on spot drift were measured at 0.42 km, 2 km and 10 km transmission distances, and the correction performance of a fast-steering mirror (FSM) was evaluated. The results show that under weak-turbulence conditions such as sunny, cloudy and short-distance conditions, the mean and variance of spot drift are relatively small, and the disturbance is dominated by low frequency. The FSM achieves more effective correction, significantly reduces the drift amplitude and improves the system stability. Under strong-turbulence conditions such as rainy days, dust storms and long distances, the mean and variance of drift increase significantly, and the spot disturbance frequency is higher. The response ability of the FSM to high-frequency disturbance is limited, and the correction effect decreases. In general, the FSM is more suitable for low-intensity disturbance scenarios, and its correction performance has certain limitations under strong-disturbance and long-distance conditions. Full article

In this study, we present a novel data assimilation framework, the Ensemble Kalman Filter One-Dimensional Variational (EnKF1D-Var) framework, which assimilates observations from a Ground-based Microwave Radiometer (GMWR), a Mie–Raman Aerosol Lidar (MRL), and a Global Navigation Satellite System Meteorology sensor (GNSS/MET). The framework integrates multi-source vertical observations of water vapor and temperature with hourly temporal and 15 m vertical resolutions, driven by GFS forecasts. Three-month-long studies from May to July 2024 at Anqing Station in subtropical China demonstrate that the EnKF1D-Var retrievals reduce biases in temperature and humidity within the low troposphere, especially for daytime retrievals, by dynamically updating the observational error covariance matrices. Maximum humidity corrections reach up to 0.075 g/kg (120 PPMV), and temperature bias reductions exceed 3%. Incremental analysis reveals that the contribution to bias correction differs across instruments. GNSS/MET plays a dominant role in temperature adjustment, while GMWR provides supplementary support. In contrast, the majority of the improvements in water vapor retrieval can be attributed to MRL observations. This study achieved a reasonable application of multiple ground-based remote sensing observations, providing a new approach for the inversion of temperature and humidity profiles in the atmospheric boundary layer. Full article