Search Results (98)

Accurate monitoring of soil moisture in arid agricultural regions is essential for improving crop production and the efficient management of water resources. This study focuses on Shihezi City in Xinjiang, China. We propose a novel method for soil moisture retrieval by integrating Sentinel-1 and Sentinel-2 remote sensing data. Dual-polarization parameters (VV + VH and VV × VH) were constructed and tested. Pearson correlation analysis showed that these polarization combinations carried the most useful information for soil moisture estimation. We then applied Shapley Additive exPlanations (SHAP) for feature selection, and a Stacking model was used to perform soil moisture inversion based on the selected features. SHAP values derived from the coupled support vector regression (SVR) and random forest regression (RFR) models were used to select an additional six key features for model construction. Building on this framework, a comparative analysis was conducted to evaluate the predictive performance of multivariate linear regression (MLR), RFR, SVR, and a Stacking model that integrates these three models. The results demonstrate that the Stacking model outperformed other approaches in soil moisture retrieval, achieving a higher R² of 0.70 compared to 0.52, 0.61, and 0.62 for MLR, RFR, and SVR, respectively. This process concluded with the use of the Stacking model to generate a county-level farmland soil moisture distribution map, which provides an objective and practical approach to guide agricultural management and the optimized allocation of water resources in arid regions. Full article

In HCV-coinfected people with HIV (PWH), there are still conflicting data regarding the long-term metabolic impact of HCV eradication. The aim of the study is to investigate long-term changes in controlled attenuation parameter (CAP) and metabolic profile after sustained virological response (SVR) post-direct acting antivirals (DAAs) in PWH. This is a retrospective observational study including individuals with HIV/HCV coinfection, followed as outpatients at San Raffaele Hospital, who achieved SVR post-DAAs. Individuals were assessed for metabolic parameters before and after the start of DAAs. Univariate and multivariate mixed linear models were calculated to estimate crude mean changes in CAP, metabolic parameters, and weight; slopes were reported with the corresponding 95% confidence intervals (95% CI). Overall, during a median follow-up of 4.02 years (interquartile range, IQR 3.04–4.80), the mean percent increase in CAP was 2.86/year (p < 0. 0001), and the mean decrease in stiffness was –4.28 (p = 0.003). Additionally, total cholesterol (p < 0.0001), high-density lipoprotein (HDL) cholesterol (p = 0.001), triglycerides (p < 0.0001), glucose (p < 0.0001), and Body Mass Index (BMI) (p < 0.0001) increased over time. A long-term follow-up in PWH with SVR post-DAAs showed an overall significant increase in CAP and worsening of the metabolic profile, suggesting a higher risk of developing liver steatosis and metabolic alterations over time. Full article

There is a lack of a systematic comparison framework that can assess models in both single-step and multi-step forecasting situations while balancing accuracy, training efficiency, and prediction horizon. This study aims to evaluate the predictive capabilities of machine learning and deep learning models in water quality time series forecasting. It made use of 22-month data with a 4 h interval from two monitoring stations located in a tributary of the Pearl River. Six models, specifically Support Vector Regression (SVR), XGBoost, K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) Network, Gated Recurrent Unit (GRU), and PatchTST, were employed in this study. In single-step forecasting, LSTM Network achieved superior accuracy for a univariate feature set and attained an overall 22.0% (Welch’s t-test, p = 3.03 × 10⁻⁷) reduction in Mean Squared Error (MSE) compared with the machine learning models (SVR, XGBoost, KNN), while RNN demonstrated significantly reduced training time. For a multivariate feature set, the deep learning models exhibited comparable accuracy but with no model achieving a significant increase in accuracy compared to the univariate scenario. The KNN model underperformed across error evaluation metrics, with the lowest accuracy, and the XGBoost model exhibited the highest computational complexity. In multi-step forecasting, the direct multi-step PatchTST model outperformed the iterated multi-step models (RNN, LSTM, GRU), with a reduced time-delay effect and a slower decrease in accuracy with increasing prediction length, but it still required specific adjustments to be better suited for the task of river water quality time series forecasting. The findings provide actionable guidelines for model selection, balancing predictive accuracy, training efficiency, and forecasting horizon requirements in environmental time series analysis. Full article

Backgrounds: Direct-acting antiviral (DAA) therapy, which achieves a high sustained virological response (SVR) rate, has been established as a standard treatment for patients with hepatitis C virus (HCV) infection. However, the risk factors for postoperative recurrence in patients with HCV-related hepatocellular carcinoma (HCC) detected after the achievement of an SVR by DAAs are unknown. Methods: The clinical records of 95 patients with initial HCV-related HCC detected after DAA-SVR achievement, who underwent liver resection between September 2014 and December 2020, were retrospectively reviewed. Patients with major vascular invasion and/or SVR achievement induced by interferon-based therapy were excluded. In this study, the patients were divided into two groups according to their alcohol intake status: without alcohol abuse (<80 g of ethanol each day for at least 5 years, n = 85) and with (continuous) alcohol abuse (n = 10). The risk factors for recurrence after liver resection were investigated, with special reference to the alcohol intake status. Results: The 3- and 5-year disease-free survival (DFS) rates after liver resection were 68.7% and 55.3%, respectively. Univariate and multivariate analyses identified alcohol abuse [hazard ratio (HR) 3.36, p = 0.004] and tumor size (HR 2.53, p = 0.010) as independent risk factors for postoperative recurrence. The 3- and 5-year postoperative DFS rates were 72.2% and 61.5% for patients without alcohol abuse and 40.0% and 13.3% for those with alcohol abuse (p = 0.001). Conclusions: Continuous alcohol abuse is a risk factor for recurrence after surgery of HCC detected after the achievement of DAA-SVR. Full article

Accurate estimation of rapeseed yield is crucial for harvesting decisions and improving efficiency and output. Machine learning (ML) models driven by remote sensing data are widely used for yield prediction. This study explores the generality of feature-based rapeseed yield prediction models across different varieties and years. Seven vegetation indices (VIs) and twenty-four texture features (TFs) were calculated from UAV-based imagery. Pearson’s correlation coefficient was used to assess variable sensitivity at different growth stages, and the variable importance score (VIP) from the random forest (RF) model was used for feature selection. Three ML regression methods—RF, support vector regression (SVR), and partial least squares regression (PLSR)—were applied using the single-stage VI, selected multi-stage VI, and multivariate VI-TFs for yield prediction. The best yield model was selected through cross-validation and tested for temporal fit using cross-year data. Results showed that the multi-stage VI and RF model achieved the highest accuracy in the training dataset (R² = 0.93, rRMSE = 7.36%), while the multi-stage VI and PLSR performed best in the test dataset (R² = 0.62, rRMSE = 15.20%). However, this study demonstrated that the addition of TFs could not enhance the robustness of rapeseed yield estimation. Additionally, the model updating strategy improved the RF model’s temporal fit, increasing R² by 25% and reducing the rRMSE to below 10%. This study highlights the potential of the multi-stage VI for rapeseed yield prediction and offers a method to improve the generality of yield prediction models over multiple years, providing a practical approach for meter-scale yield mapping and multi-year prediction. Full article

Background: To date, little is known about correlations between liver dysfunction and circulatory and cardiac abnormalities (e.g.,: mitral valve, MV) in patients with chronic liver disease (CLD). This study aimed to assess a potential parallelism between liver dysfunction and cardiovascular involvement and identify the factors associated with structural and functional MV disorders. Methods. Among 995 patients with CLD, 346 were enrolled and compared with 168 controls without liver disease. According to the degree of liver disease, patients were classified as patients with chronic hepatitis (142) or with liver cirrhosis (Child-A: 70; Child-B: 65; Child-C: 69). Results: Among the chronic hepatitis group, resting heart rate (HR) and left ventricular (LV) mass were higher than in the control group (p = 0.0008), whereas systemic vascular resistance (SVR) was lower (p = 0.01). Among cirrhotic patients, resting HR, left atrium dimensions/volumes, LV walls thickness, LV mass, cardiac output (CO), isovolumetric relaxation time (IVRT), deceleration time (DT) and prevalence of aortic stenosis were higher than in non-cirrhotic patients (p = 0.02), whereas the e/a ratio and SVR were lower (p = 0.0001). Among Child-B/C, CO, IVRT, DT, prevalence of MV regurgitation and MV calcification score were higher than in the remaining patients (p = 0.02), whereas SVR was lower (p < 0.0001). Among cirrhotic patients with MV regurgitation, Child–Pugh score, liver disease duration, resting HR, left chambers dimensions/mass, CO, IVRT, DT and MV calcification score were higher compared to patients without regurgitation (p < 0.000), whereas mean blood pressure, e/a ratio and SVR were lower (p = 0.008). At multivariate analysis, Child–Pugh score, liver disease duration, left chambers volume/mass and MV calcification score were independently associated with MV regurgitation in cirrhotic patients. Child–Pugh score and MV calcification score strongly correlated in cirrhotic patients (r = 0.68, 95% CI 0.60–0.75, p < 0.0001). Conclusions: The magnitude of cardiac morpho/functional abnormalities is associated with the severity of liver dysfunction. Structural and functional MV abnormalities could represent a novel sign of cardiac involvement in liver cirrhosis. The severity and duration of liver disease, the enlargement of cardiac chambers and leaflet calcium accumulation could play a key role. Full article

Accurate prediction of the height of water-conducting fissure zone (HWCFZ) is an important issue in coal water control and a prerequisite for ensuring the safe production of coal mines. At present, the prediction model of HWCFZ has some issues such as poor prediction accuracy. Based on the widely collected measured data of the HWCFZ in different coal mines in northern Shaanxi Province, China, the HWCFZ in shallow-buried coal seams is categorized into two types, i.e., typical shallow-buried coal seams and near-shallow-buried seams, according to the different depths of burial and base-loading ratios. On the basis of summarizing the research results of the previous researchers, three factors, namely, mining thickness, coal seam depth, and working length, were selected, and the data of the height of the water-conducting fissure zone in the study area were analyzed by using a multivariate nonlinear regression method. Subsequently, each group of the data was randomly divided into training data and validation data with a ratio of 70:30. Then, the training data were used to build a neural network model (BP), random forest model (RF), a hybrid integration of particle swarm optimization and the support vector machine model (PSO-SVR), and a hybrid integration of genetic algorithm optimization and the support vector machine model (GA-SVR). Finally, the test samples were used to test the model accuracy and evaluate the generalization ability. Accordingly, the optimal prediction model for the typical shallow-buried area and near-shallow-buried area of Jurassic coal seams in northern Shaanxi was established. The results show that the HWCFZ for the typical shallow-buried coal seam is suitable to be determined by the multivariate nonlinear regression method, with an accuracy of 0.64; the HWCFZ for near-shallow-buried coal seams is suitable to be predicted by the two-factor PSO-SVR computational model of mining thickness and the burial depth, with a prediction accuracy of 0.84; and machine learning methods are more suitable for near-shallow-buried areas, dealing with small-scale data and discrete data. Full article

Aerosols and their chemical composition exert an influence on the atmospheric environment, global climate, and human health. However, obtaining the chemical composition of aerosols with high spatial and temporal resolution remains a challenging issue. In this study, using the NR-PM1 collected in the Beijing area from 2012 to 2013, we found that the annual average concentration was 41.32 μg·m⁻³, with the largest percentage of organics accounting for 49.3% of NR-PM1, followed by nitrates, sulfates, and ammonium. We then established models of aerosol chemical composition based on a machine learning algorithm. By comparing the inversion accuracies of single models—namely MLR (Multivariable Linear Regression) model, SVR (Support Vector Regression) model, RF (Random Forest) model, KNN (K-Nearest Neighbor) model, and LightGBM (Light Gradient Boosting Machine)—with that of the combined model (CM) after selecting the optimal model, we found that although the accuracy of the KNN model was the highest among the other single models, the accuracy of the CM model was higher. By employing the CM model to the spatially and temporally matched AOD (aerosol optical depth) data and meteorological data of the Beijing–Tianjin–Hebei region, the spatial distribution of the annual average concentrations of the four components was obtained. The areas with higher concentrations are mainly situated in the southwest of Beijing, and the annual average concentrations of the four components in Beijing’s southwest are 28 μg·m⁻³, 7 μg·m⁻³, 8 μg·m⁻³, and 15 μg·m⁻³ for organics, sulfates, ammonium, and nitrates, respectively. This study not only provides new methodological ideas for obtaining aerosol chemical composition concentrations based on satellite remote sensing data but also provides a data foundation and theoretical support for the formulation of atmospheric pollution prevention and control policies. Full article

Accurately forecasting carbon prices plays a vital role in shaping environmental policies, guiding investment strategies, and accelerating the development of low-carbon technologies. However, traditional forecasting models often face challenges related to information leakage and boundary effects. This study proposes a novel extended sliding window decomposition (ESWD) mechanism to prevent information leakage and mitigate boundary effects, thereby enhancing decomposition quality. Additionally, a fully data-driven multivariate empirical mode decomposition (MEMD) technique is incorporated to further improve the model’s capabilities. Partial decomposition operations, combined with high-resolution and full-utilization strategies, ensure mode consistency. An empirical analysis of China’s largest carbon market, using eight key indicators from energy, macroeconomics, international markets, and climate fields, validates the proposed model’s effectiveness. Compared to traditional LSTM and SVR models, the hybrid model achieves performance improvements of 66.6% and 23.5% in RMSE for closing price prediction, and 73.8% and 10.8% for opening price prediction, respectively. Further integration of LSTM and SVR strategies enhances RMSE performance by an additional 82.7% and 8.3% for closing prices, and 30.4% and 4.5% for opening prices. The extended window setup (EW10) yields further gains, improving RMSE, MSE, and MAE by 11.5%, 35.4%, and 23.7% for closing prices, and 4.5%, 8.4%, and 4.2% for opening prices. These results underscore the significant advantages of the proposed model in enhancing carbon price prediction accuracy and trend prediction capabilities. Full article

Eucalyptus is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of Eucalyptus biomass is important for plantation forest management and the prediction of carbon stock in terrestrial ecosystems. In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. The results of the study concluded that the prediction model accuracy of the natural transformed regression equations (R² = 0.873, RMSE = 0.312 t/ha, RRMSE = 0.0091) outperformed that of the machine learning algorithms at the individual tree scale. Among the machine learning models, the SVR prediction model accuracy was the best (R² = 0.868, RMSE = 7.932 t/ha, RRMSE = 0.231). In this study, UAV-LiDAR-based data had great potential in predicting the AGB of Eucalyptus trees, and the tree height parameter had the strongest correlation with AGB. In summary, the combination of UAV LiDAR data and machine learning algorithms to construct a predictive forest AGB model has high accuracy and provides a solution for carbon stock assessment and forest ecosystem assessment. Full article

The study of chlorophyll fluorescence parameters is very important for understanding plant photosynthesis. Monitoring cotton chlorophyll fluorescence parameters via spectral technology can aid in understanding the photosynthesis, growth, and stress of cotton fields in real time and provide support for cotton growth regulation and planting management. In this study, cotton plot experiments with different water treatments were set up to obtain the spectral reflectance of the cotton canopy, the maximum photochemical quantum yield (Fv/Fm), and the photochemical quenching coefficient (qP) of leaves at different growth stages. Support vector machine regression (SVR), random forest regression (RFR), and artificial neural network regression (ANNR) were used to establish a fluorescence parameter inversion model of the cotton canopy leaves. The results show that the original spectrum was transformed by multivariate scattering correction (MSC), the standard normal variable (SNV), and continuous wavelet transform (CWT), and the model constructed with Fv/Fm passed accuracy verification. The SNV-SVR model at the budding stage, the MSC-SVR model at the early flowering stage, the SNV-SVR model at the full flowering stage, the MSC-SVR model at the flowering stage, and the CWT-SVR model at the full boll stage had the highest estimation accuracy. The accuracies of the three spectral preprocessing and qP models were verified, and the MSC-SVR model at the budding stage, SNV-SVR model at the early flowering stage, MSC-SVR model at the full flowering stage, SNV-SVR model at the flowering stage, and CWT-SVR model at the full boll stage presented the highest estimation accuracies. Full article

Background: Hepatocellular carcinoma (HCC) remains a significant concern for patients with chronic hepatitis C (HCV), even after achieving a sustained virological response (SVR) with direct-acting antivirals (DAAs) or interferon (IFN)-based therapies. This study compared the risk of HCC in patients with HCV who achieved SVR through the DAA versus IFN regimens. Methods: A retrospective analysis was conducted on 4806 HCV patients, without coinfection nor prior HCC history, treated at the Chang Gung Memorial Hospital, Taiwan (DAA: 2825, IFN: 1981). Kaplan–Meier and Cox regression analyses with propensity score matching (PSM) were used to adjust for baseline differences. Results: DAA-treated patients exhibited a higher incidence of HCC than IFN-treated patients before and after PSM (after PSM: annual: 1% vs. 0.5%; 6-year: 6% vs. 3%, p = 0.01). Both DAA and IFN patients had a decreased HCC incidence during follow-up (>3 vs. <3 years from the end of treatment: DAA: 1.43% vs. 1.00% per year; IFN: 0.47% vs. 0.36% per year, both p < 0.05). HCC incidence was higher in the first three years post-SVR in DAA-treated ACLD patients and then decreased (3.26% vs. 1.39% per year, p < 0.01). In contrast, HCC incidence remained constant in the non-ACLD and IFN-treated groups. Multivariate Cox regression identified age ≥ 60, male sex, BMI, AFP ≥ 6 ng/mL, FIB-4, and ACLD status as independent risk factors for HCC, but antiviral regimens were not an independent factor for HCC. Conclusion: DAA treatment significantly affects HCC risk primarily within three years post-treatment, especially in younger HCV patients with ACLD. HCC incidence was reduced after three years in ACLD patients treated by DAA, but continued surveillance was still necessary. However, patients under 60 without advanced liver disease may require less intensive follow-up. Full article

Background: Despite achieving a sustained virological response (SVR) with direct-acting antivirals (DAAs), an unexpected increase in the occurrence rate of hepatocellular carcinoma (HCC) has been observed among HCV-treated patients. This study aims to assess the long-term follow-up of HCV patients treated with DAAs who achieved an SVR to investigate the potential for late-onset HCC. Methods: In this prospective multicenter study, we enrolled consecutive HCV patients treated with DAAs following Italian ministerial guidelines between 2015 and 2018. Exclusion criteria included active HCC on imaging, prior HCC treatment, HBV or HIV co-infection, or liver transplant recipients. Monthly follow-ups occurred during treatment, with subsequent assessments every 3 months for at least 48 months. Abdominal ultrasound (US) was performed within two weeks before starting antiviral therapy, supplemented by contrast-enhanced ultrasonography (CEUS), dynamic computed tomography (CT), or magnetic resonance imaging (MRI) to evaluate incidental liver lesions. Results: Of the 306 patients completing the 48-months follow-up post-treatment (median age 67 years, 55% male), all achieved an SVR. A sofosbuvir-based regimen was administered to 72.5% of patients, while 20% received ribavirin. During follow-up, late-onset HCC developed in 20 patients (cumulative incidence rate of 6.55%). The pattern of HCC occurrence varied (median diameter 24 mm). Multivariate and univariate analyses identified liver stiffness, diabetes, body mass index, and platelet levels before antiviral therapy as associated factors for late HCC occurrence. Conclusions: Our findings suggest that late HCC occurrence may persist despite achieving SVR. Therefore, comprehensive long-term follow-up, including clinical, laboratory, and expert ultrasonography evaluations, is crucial for all HCV patients treated with DAAs. Full article

Sea level rise (SLR) attributed to the melting of ice caps and thermal expansion of seawater is of great global significance to vast populations of people residing along the world’s coastlines. The extent of SLR’s impact on physical coastal areas is determined by multiple factors such as geographical location, coastal structure, wetland vegetation and related oceanic changes. For coastal communities at risk of inundation and coastal erosion due to SLR, the modelling and projection of future sea levels can provide the information necessary to prepare and adapt to gradual sea level rise over several years. In the following study, a new model for predicting future sea levels is presented, which focusses on two tide gauge locations (Darwin and Milner Bay) in the Northern Territory (NT), Australia. Historical data from the Australian Bureau of Meteorology (BOM) from 1990 to 2022 are used for data training and prediction using artificial intelligence models and computation of mean sea level (MSL) linear projection. The study employs a new double data decomposition approach using Multivariate Variational Mode Decomposition (MVMD) and Successive Variational Mode Decomposition (SVMD) with dimensionality reduction techniques of Principal Component Analysis (PCA) for data modelling using four artificial intelligence models (Support Vector Regression (SVR), Adaptive Boosting Regressor (AdaBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN-BiGRU). It proposes a deep learning hybrid CNN-BiGRU model for sea level prediction, which is benchmarked by SVR, AdaBoost, and MLP. MVMD-SVMD-CNN-BiGRU hybrid models achieved the highest performance values of 0.9979 (d), 0.996 (NS), 0.9409 (L); and 0.998 (d), 0.9959 (NS), 0.9413 (L) for Milner Bay and Darwin, respectively. It also attained the lowest error values of 0.1016 (RMSE), 0.0782 (MABE), 2.3699 (RRMSE), and 2.4123 (MAPE) for Darwin and 0.0248 (RMSE), 0.0189 (MABE), 1.9901 (RRMSE), and 1.7486 (MAPE) for Milner Bay. The mean sea level (MSL) trend analysis showed a rise of 6.1 ± 1.1 mm and 5.6 ± 1.5 mm for Darwin and Milner Bay, respectively, from 1990 to 2022. Full article

To clarify the risk factors for the aggravation of esophagogastric varices (EGVs) after hepatitis C virus (HCV) eradication with direct-acting antiviral (DAA) therapy, we enrolled 167 consecutive patients with HCV-related compensated cirrhosis who achieved a sustained virological response (SVR) after DAA therapy. During a median of 69 months, EGVs were aggravated in 42 (25%) patients despite SVR. The cumulative 1-, 3-, 5-, and 10-year aggravated EGV rates were 7%, 23%, 25%, and 27%, respectively. Multivariate analysis identified a platelet count < 11.0 × 10⁴/μL, LSM ≥ 18.0 kPa, total bile acid ≥ 33.0 μmol/L, and a diameter of left gastric vein (LGV) ≥ 5.0 mm at HCV eradication as independent risk factors for EGV aggravation post-SVR. In groups that met all of these risks, the cumulative EGV aggravation rates at 1, 3, and 5 years were 27%, 87%, and 91%, respectively. However, none of the patients who had only one or none of the risk factors experienced EGV aggravation. Platelet count, LSM, total bile acid, and diameter of LGV at HCV eradication were associated with aggravated EGV post-SVR. EGVs tend to worsen as two or more of these risk factors increase. Full article