Search Results (2,844)

Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH₄) in the atmosphere is important for greenhouse gas emission management. Traditional XCH₄ retrieval methods are complex, while machine learning can be used to model nonlinear relationships by analyzing large datasets, providing an efficient alternative. This study proposes an XGBoost algorithm-based retrieval method to improve the efficiency of atmospheric XCH₄ retrieval. First, the key wavelengths affecting XCH₄ retrieval were determined using a radiative transfer model. The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH₄ products, and auxiliary data were matched to construct the dataset. The dataset constructed was used to train the XGBoost model and obtain the TRO_XGB_XCH₄ model. Finally, the accuracy of the proposed model was evaluated using various parameter values and validated against XCH₄ products and Total Carbon Column Observing Network (TCCON) ground-based observations. The results showed that the proposed TRO_XGB_XCH₄ model had a tenfold cross-validation accuracy R of 0.978, a ground-based validation R of 0.749, and a temporal extension accuracy R of 0.863. Therefore, the accuracy of the TRO_XGB_XCH₄ retrieval model is comparable to that of the official TROPOMI L2 product. Full article

Limb-sparing techniques for appendicular primary bone tumors are still associated with a high rate of complications. Three-dimensional (3D)-printed patient-specific instruments could reduce these complications. The aim of this study is to describe a limb-sparing surgery using 3D-printed patient-specific guides (PSGs) and an endoprosthesis (PSE) to treat femoral chondrosarcoma in a dog. An eight-year-old female Golden Retriever presented with persistent lameness of the right hind limb, reluctance to move and difficulty in maintaining a standing position. Palpation of the right femur revealed an approximately 4 cm painful lesion. Cytological analysis of the needle aspiration supported the clinical and radiological suggestion of a cartilaginous bone neoplasm. Computed tomography (CT) scans suggested the presence of an aggressive lesion on the right distal femur. CT scans of the femur and tibia were then reconstructed using a bone tissue algorithm and processed with computer-aided design (CAD) software, which allowed for performing virtual surgical planning (VSP) and the fabrication of both the PSG and the PSE. Anti-inflammatory drugs and monoclonal antibodies were used for pain management while waiting for surgery. Adjuvant chemotherapy was also administered. An ostectomy of the distal third of the femur to completely remove the tumor was performed with the designed PSG, while the bone defect was filled with the designed PSE. Histopathological examination of the osteotomized bone segment confirmed a grade 2 central chondrosarcoma. There was no excessive tumor growth during the 28 days between the CT scans and surgery. Both PSG and PSE fitted perfectly to the bone surfaces. PSG eliminated the need for intraoperative imaging and ensured a faster and more accurate osteotomy. PSE optimized load sharing and eliminated the complications of the commercial endoprosthesis, such as incongruity and the need for manual intraoperative adjustment. Overall, the use of VSP, 3D-printed PSG and PSE significantly reduced surgical time, risk of infection and intra- and postoperative complications. Full article

The current research presents a practical approach to assess energy performance and thermal comfort in households through monitoring campaigns. The campaigns are conducted in a Greek city, involving the installation of low-intrusive recording devices for hourly electricity consumption, indoor temperature, and relative humidity in different residences in winter and summer periods. The recorded indoor environmental conditions are initially compiled to the Predicted Mean Vote (PMV) index, followed by the formulation of databases of hourly electricity consumption, PMV and local outdoor climate conditions retrieved by an official source of meteorological conditions. A special algorithm for database processing was developed which takes into account the eligibility of data series, i.e., only the ones corresponding to non-zero electricity consumption are treated as eligible. First, the sequential temporal progress of energy consumption and thermal comfort is produced towards the assessment of energy-use intensity and thermal comfort patterns. Secondly, through summing of the electricity consumption within 0.5-step PMV intervals, under three outdoor temperature intervals with approximately the same number of eligible measurements, reliable interrelations of energy consumption and PMV are obtained even for residences with limited amount of measured data. It is revealed that the weekly electricity consumption ranged within 0.15–3.59 kWh/m² for the winter cases and within 0.29–1.72 kWh/m² for the summer cases. The acceptable range of −1 ≤ PMV ≤ 1 interval holds an occurrence frequency from 69.46% to 93.39% and from 37.94% to 70.31% for the winter and summer examined cases, respectively. Less resistance to discomfort conditions is observed at most of the summer examined households exhibiting the electricity peak within the 1 ≤ PMV ≤ 1.5 interval, contrary to the winter cases for which the electricity peak occurred within the −1 ≤ PMV ≤ −0.5 interval. The study provides graphical relationships of PMV and electricity consumption under various outdoor temperatures paving the way for correlating thermal comfort and energy consumption. Full article

As a comprehensive reflection of the thermal characteristics of the urban environment, the urban heat island (UHI) effect has triggered a series of ecological and environmental issues. Existing studies on the UHI effect in Shijiazhuang, the capital of Hebei Province, China, have primarily focused on spatial–temporal distribution characteristics and migration trends, with less focus on the influences of other contributing factors. This study focuses on Shijiazhuang city, using Landsat ETM+/OLI data from 2000 to 2020 to analyze the spatiotemporal traits of the UHI effect. The mono-window algorithm (MW) was used to retrieve land surface temperatures (LSTs), and the seasonal autoregressive integrated moving average (SARIMA) model was used to predict LST trends. Key factors such as the normalized difference vegetation index (NDVI), digital elevation model (DEM), population (POP), precipitation (PPT), impervious surface (IPS), potential evapotranspiration (PET), particulate matter 2.5 (PM2.5), and night light (NL) were analyzed using spatial autocorrelation to explore their dynamic relationship with the UHI. Specifically, a multi-scale analysis model was developed to search for the optimum urban spatial scale, enabling a comprehensive assessment of the spatiotemporal evolution and drivers of the UHI in Shijiazhuang. The UHI showed pronounced spatial clustering, expanding annually by 44.288 km², with a southeastward shift. Autumn exhibited the greatest reduction in UHI, while predictions suggested peak temperatures in summer 2027. According to the bivariate clustering analysis, the NDVI was the most influential factor in mitigating the UHI, while the IPS spatially showed the most significant enhancement in the UHI in the central urban areas. Other factors generally promoted the UHI after 2005. The multi-scale geographically weighted regression (MGWR) model was best fitted at a 3 km × 3 km scale. Considering the joint effects of multiple factors, the ranking of contributing factors to the model prediction is as follows: PET > DEM > NDVI > IPS > PPT > PM2.5 > NL > POP. The interactive effects, especially between the PET and DEM, reach a significant value of 0.72. These findings may address concerns regarding both future trends and mitigation indications for UHI variations in Shijiazhuang. Full article

We investigate the retrieval of ozone (O₃) profiles, with a particular focus on tropospheric O₃, from backscattered ultraviolet radiances measured by the TROPOspheric Monitoring Instrument (TROPOMI), using the UV2 (300–332 nm) and UV3 (305–400 nm) channels independently. An optimal estimation retrieval algorithm, originally developed for the Ozone Monitoring Instrument (OMI), was extended as a preliminary step toward integrating multiple satellite ozone profile datasets. The UV2 and UV3 channels exhibit distinct radiometric and wavelength calibration uncertainties, leading to inconsistencies in retrieval accuracy and convergence stability. A yearly “soft” calibration mitigates overestimation and cross-track-dependent biases (“stripes”) in tropospheric ozone retrievals, enhancing retrieval consistency between UV2 and UV3. Convergence stability is ensured by optimizing the measurement error constraints for each channel. It is shown that our research product outperforms the standard product (UV1 and UV2 combined) in capturing the seasonal and long-term variabilities of tropospheric ozone. An agreement between the retrieved tropospheric ozone and ozonesonde measurements is observed within 0–3 DU ± 5.5 DU (R = 0.75), which is better than that of the standard product by a factor of two. Despite lacking Hartley ozone information in UV2 and UV3, the retrieved stratospheric ozone columns have good agreement with ozonesondes (R = 0.96). Full article

In this paper, we introduce atmospheric adaptive optics (AO) system architectures that utilize scintillation-resistant wavefront sensors based on iterative phase retrieval (IPR) techniques (described in detail in Part I) for closed-loop mitigation of atmospheric turbulence-induced wavefront aberrations in strong intensity scintillation conditions. The objective is to provide a framework (mathematical and numerical models, performance metrics, control algorithms, and wave-optics modeling and simulation results) for the potential integration of IPR-based wavefront sensing techniques into the following major atmospheric optics system types: directed energy laser beam projection, remote laser power delivery (remote power beaming), and free-space optical communications. Theoretical analysis and numerical simulation results demonstrate that the proposed closed-loop AO system architectures and control algorithms can be uniquely applicable for addressing one of the most challenging AO problems of turbulence effects mitigation in the presence of strong-intensity scintillations. Full article

Rapid and accurate dynamic monitoring and quantitative analysis of red tide disasters are of significant practical importance to national economic development. Remote sensing technology is an effective means for monitoring red tides. This paper utilizes GOCI satellite data and employs a quasi-analytical algorithm (QAA) to retrieve the spectral curves of phytoplankton absorption coefficients. On the basis of a detailed analysis of the differences in the spectral curves of the phytoplankton absorption coefficients between red tide and non-red tide waters, we establish a red tide identification algorithm for the East China Sea on the basis of phytoplankton absorption coefficients. The algorithm is applied to multiple red tide events in the East China Sea. The results indicate that this algorithm can effectively determine the occurrence locations of red tides and extract relevant information about them. Full article

Private information retrieval (PIR) enables a client to retrieve a specific element from a server’s database without disclosing the index that was queried. This work introduces three improvements to the efficient single-server PIR protocol Spiral. We found that performing a modulus switching towards expanded ciphertexts can improve the server throughput. Secondly, we apply two techniques called the composite $\mathsf{NTT}$ algorithm and approximate decomposition to Spiral to further improve it. We conduct comprehensive experiments to evaluate the concrete performance of our protocol, and the results confirm an approximately 1.7 times faster overall throughput than Spiral. Full article

As carbon dioxide (CO₂) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO₂ retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes a method to predict the concentration of column-averaged CO₂ (XCO₂) from shortwave infrared hyperspectral satellite data, using machine learning to avoid the iterative computations of the physical method. The training dataset is constructed using the Orbiting Carbon Observatory-2 (OCO-2) spectral data, XCO₂ retrievals from OCO-2, surface albedo data, and aerosol optical depth (AOD) measurements for 2019. This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. This model was then applied to retrieve CO₂ column concentrations for 2020. The results showed a correlation of 0.760 with Total Carbon Column Observing Network (TCCON) measurements, which is higher than the correlation of 0.739 with satellite product data, verifying the effectiveness of the retrieval method. Full article

Cloud liquid water (CLW) and total precipitable water (TPW) are two important parameters for weather and climate applications. These parameters are typically retrieved at 23.8 GHz and 31.4 GHz. Historically, the CLW and TPW physical retrievals always required the sea surface temperature (SST) and sea surface wind speed (SSW), which are difficult to obtain from conventional measurements. This study employs the multilayer perceptron (MLP) model to retrieve SST and SSW from FY-3F Microwave Radiometer Imager (MWRI) observations. Collocated with ERA5 reanalysis data, the MLP model predicts SST well, with a correlation coefficient of 0.98, the root mean squared error (RMSE) of 1.10, and mean absolute error (MAE) of 0.70 K. For SSW, the correlation coefficient is 0.82, RMSE is 1.80, and MAE is 1.30 m/s, respectively. The SST and SSW parameters derived from MWRI are then used to retrieve CLW and TPW based on the observations from the Microwave Temperature Sounder (MWTS) onboard the FY-3F satellite. The spatial distributions of CLW and TPW derived from this new algorithm agree well with those from ERA5 data. Cloud liquid water (CLW) and total precipitable water (TPW) are crucial parameters for weather and climate applications. The integration of physical and AI-based algorithms enables the retrieval of CLW and TPW directly from FY-3F satellite observations. This approach overcomes the limitations imposed by the need for other data sources, such as ERA5 reanalysis data, and offers distinct advantages in terms of data processing timeliness. Full article

Soil Moisture (SM) plays a crucial role in agricultural production, ecology, and sustainable development. The prevailing resolution of microwave-based SM products is notably coarse, typically spanning from 10 to 50 km, which might prove inadequate for specific applications. In this research, various single-model machine learning algorithms have been employed to study SM downscaling, each with its own limitations. In contrast to existing methodologies, our research introduces a pioneering algorithm that amalgamates diverse individual models into an integrated Stacking framework for the purpose of downscaling SM data within the Shandian River Basin. This basin spans the southern region of Inner Mongolia and the northern area of Hebei province. In this paper, factors exerting a profound influence on SM were comprehensively integrated. Ultimately, the surface variables involved in the downscaling process were determined to be Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Surface Reflectance (SR), Evapotranspiration (ET), Digital Elevation Model (DEM), slope, aspect, and European Space Agency-Climate Change Initiative (ESA-CCI) product. The goal is to generate a 1 km SM downscaling dataset for a 16-day period. Two distinct models are constructed for the SM downscaling process. In one case, the downscaling is followed by the inversion of SM, while in the other case, the inversion is performed after the downscaling analysis. We also employ the Categorical Features Gradient Boosting (CatBoost) algorithm, a single model, for analytical evaluation in identical circumstances. According to the results, the accuracy of the 1 km SM obtained using the inversion-followed-by-downscaling model is higher. Furthermore, it is observed that the stacking algorithm, which integrates multiple models, outperforms the single-model CatBoost algorithm in terms of accuracy. This suggests that the stacking algorithm can overcome the limitations of a single model and improve prediction accuracy. We compared the predicted SM and ESA-CCI SM; it is evident that the predicted results exhibit a strong correlation with ESA-CCI SM, with a maximum Pearson correlation coefficient (PCC) value of 0.979 and a minimum value of 0.629. The Mean Absolute Error (MAE) values range from 0.002 to 0.005 m³/m³, and the Root Mean Square Error (RMSE) ranges from 0.003 to 0.006 m³/m³. Overall, the results demonstrate that the stacking algorithm based on multi-model integration provides more accurate and consistent retrieval and downscaling of SM. Full article

This research introduces novel analytical methods for evaluating multi-UAV persistent search and retrieval with stochastic target appearance (PSR-STA) scenarios. Traditional approaches that rely on single aggregate effectiveness measures for a scenario fail to capture the complex spatiotemporal dynamics of multi-UAV operations and provide limited insights into improving search performance. To address these limitations, we present a comprehensive analysis framework combining temporal and spatial analysis techniques. For temporal analysis, we employ a graphical comparison of line charts and discrete Fourier transform analysis to identify shared temporal patterns across scenarios. Spatial patterns are analyzed through principal components analysis and random forest surrogate modeling with profiling to understand non-linear parameter influences. Additionally, we introduce trellis charts for integrated visualization and analysis of combined spatiotemporal patterns. This research builds on a case study developed in a previous case study of multi-UAV PSR-STA. While the previous work established foundational algorithms and metrics for multi-UAV PSR-STA, this study introduces sophisticated spatiotemporal analysis techniques that reveal deep insights into system behavior and enable a nuanced understanding of UAV search performance across varied scenarios. Full article

Facing the severe global wildfire challenge and the need for advanced prediction, this study analysed the evolving research in forest fire prediction using an LDA-BERT similarity model. Due to climate change, human activities, and natural factors, forest fires threaten ecosystems, society, and the climate system. The vast existing literature on forest fire prediction makes it challenging to identify research themes manually. The proposed LDA-BERT model combines LDA and BERT. LDA was used for topic mining, determining the optimal number of topics by calculating the semantic consistency. BERT was employed in word vector training, using topic word probabilities as weights. The cosine similarity algorithm and normalisation were used to measure the topic similarity. Through empirical research on 13,552 publications from 1980–2023 retrieved from the Web of Science database, several key themes were identified, such as “wildfire risk management”, “vegetation and habitat changes”, and “climate change and forests”. Research trends show a shift from macro-level to micro-level studies, with modern technologies becoming a focus. Multidimensional scaling revealed a hierarchical theme distribution, with themes closely related to forest fires being dominant. This research offers valuable insights for the scientific community and policymakers, facilitating understanding these changes and contributing to wildfire mitigation. However, it has limitations like subjectivity in theme-representative word selection and needs further improvement in threshold setting and model performance evaluation. Future research can optimise these aspects and integrate emerging technologies to enhance forest fire prediction research. Full article

Introduction: Recent advancements in diagnostic imaging technologies have significantly improved the field of dental medicine. This review examines these new imaging techniques and their impact on enhancing accuracy, enabling early detection, and facilitating effective treatment planning in dentistry. Methods: A bibliometric and content analysis was conducted on 61 peer-reviewed articles retrieved from the Scopus database, published between 2019 and 2024. The selection criteria focused on studies exploring advances in dental diagnosis through innovative imaging methods and personalized techniques for identifying oral pathologies. The bibliometric approach analyzed publication trends, while content analysis categorized emerging technologies and their clinical applications. Results: Our findings indicate a notable shift towards integrating cutting-edge technologies, including Cone Beam Computed Tomography (CBCT), artificial intelligence (AI), and biosensors. These advancements have significantly improved diagnostic accuracy, particularly in complex cases such as periodontal diseases, dental fractures, and oral infections. Studies demonstrate that molecular diagnostics and AI-driven algorithms enhance the personalization of treatment plans, optimizing patient outcomes. Conclusions: Emerging diagnostic technologies have the potential to enhance both the quality and efficiency of dental care. However, their implementation is challenged by high costs, the need for specialized training, and disparities in access. Future research should focus on refining AI-driven diagnostic models, addressing regulatory considerations, and expanding the clinical validation of novel imaging tools. As these technologies evolve, they are expected to increase diagnostic specificity, leading to more precise, patient-centered treatment approaches. Ultimately, these advancements offer substantial opportunities to transform dental practice by providing faster, less invasive, and more reliable diagnoses. Full article

Evapotranspiration (ET) plays a crucial role in the surface water cycle and energy balance, and accurate ET estimation is essential for study in various domains, including agricultural irrigation, drought monitoring, and water resource management. Remote sensing (RS) technology presents an efficient approach for estimating ET at regional scales; however, existing RS retrieval algorithms for ET are intricate and necessitate a multitude of parameters. The land surface temperature–vegetation index (LST-VI) space method and statistical regression by machine learning (ML) offer the benefits of simplicity and straightforward implementation. This study endeavors to identify the optimal long-term sequence LST-VI space method and ML for ET estimation under conditions of limited observed variables, (LST, VI, and near-surface air temperature). A comparative analysis of their performance is undertaken using ground-based flux observations and MOD16 ET data. The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = −0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = −0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. Nevertheless, when compared to MOD16 data, both approaches exhibit varying degrees of underestimation. The results of this study can contribute to water resource management and offer a fresh perspective on remote sensing estimation methods for ET. Full article