Search Results (44,188)

In recent years, accurate field monitoring has been a research hotspot in the domains of aerial remote sensing and satellite remote sensing. In view of this, this study proposes an innovative cross-platform super-resolution reconstruction method for remote sensing images for the first time, aiming to make medium-resolution satellites capable of field-level detection through a super-resolution reconstruction technique. The progressive growing generative adversarial network (PGGAN) model, which has excellent high-resolution generation and style transfer capabilities, is combined with a deep residual network, forming the Res-PGGAN model for cross-platform super-resolution reconstruction. The Res-PGGAN architecture is similar to that of the PGGAN, but includes a deep residual module. The proposed Res-PGGAN model has two main benefits. First, the residual module facilitates the training of deep networks, as well as the extraction of deep features. Second, the PGGAN structure performs well in cross-platform sensor style transfer, allowing for cross-platform high-magnification super-resolution tasks to be performed well. A large pre-training dataset and real data are used to train the Res-PGGAN to improve the resolution of Sentinel-2’s 10 m resolution satellite images to 0.625 m. Three evaluation metrics, including the structural similarity index metric (SSIM), the peak signal-to-noise ratio (PSNR), and the universal quality index (UQI), are used to evaluate the high-magnification images obtained by the proposed method. The images generated by the proposed method are also compared with those obtained by the traditional bicubic method and two deep learning super-resolution reconstruction methods: the enhanced super-resolution generative adversarial network (ESRGAN) and the PGGAN. The results indicate that the proposed method outperforms all the comparison methods and demonstrates an acceptable performance regarding all three metrics (SSIM/PSNR/UQI: 0.9726/44.7971/0.0417), proving the feasibility of cross-platform super-resolution image recovery. Full article

This study aimed to delve deeper into the alterations in the microbial residual carbon (MRC) accumulation in the Yangtze River’s wetland ecosystems as a consequence of afforestation and to evaluate their impact on soil organic carbon (SOC). The hypothesis posited that afforestation could foster soil aggregation by augmenting arbuscular mycorrhizal fungi (AMF) hyphae and glomalin-related soil protein (GRSP) in deep soil, thereby suppressing the proliferation of genes pivotal to microbial residue decomposition and enhancing MRC accumulation. We collected soil samples at 0–20, 20–40, 40–60, 60–80 and 80–100 cm respectively. Metagenomic sequencing, the quantification of soil amino sugars and MRC, soil aggregate distribution profiling and the measurement of AMF mycelium length density alongside GRSP levels were analyzed. Our findings showed that afforestation notably elevated the concentration of soil amino sugars and the levels of total and fungal MRC, with increases ranging from 53%–80% and 82%–135%, respectively, across the five soil depths examined, in stark contrast to the eroded, non-afforested control. The role of MRC in the SOC was observed to escalate with increasing soil depth, with afforestation markedly amplifying this contribution within the 40–60 cm, 60–80 cm and 80–100 cm soil layers. The study concludes that the SOC content in the deeper soil horizons post-afforestation witnessed a significant rise, paralleled by a substantial increase in both total and fungal MRC, which exhibited a robust positive correlation with the SOC levels. This underscores the pivotal role that amino sugar accumulation from microbial residues plays in the retention of SOC in the deeper soil layers of afforested regions, challenging the conventional wisdom that plant residues are recalcitrant to decomposition within forested SOC matrices. Full article

The integration of advanced control systems in prostheses necessitates the accurate identification of human locomotion activities, a task that can significantly benefit from EEG-based measurements combined with machine learning techniques. The main contribution of this study is the development of a novel framework for the recognition and classification of locomotion activities using electroencephalography (EEG) data by comparing the performance of different machine learning algorithms. Data of the lower limb movements during level ground walking as well as going up stairs, down stairs, up ramps, and down ramps were collected from 10 healthy volunteers. Time- and frequency-domain features were extracted by applying independent component analysis (ICA). Successively, they were used to train and test random forest and k-nearest neighbors (kNN) algorithms. For the classification, random forest revealed itself as the best-performing one, achieving an overall accuracy up to 92%. The findings of this study contribute to the field of assistive robotics by confirming that EEG-based measurements, when combined with appropriate machine learning models, can serve as robust inputs for prosthesis control systems. Full article

The dry leaf of Turpinia arguta (Lindl.) Seem. is used in traditional Chinese medicine as a quick-acting product for sore throat and pharyngitis relief. Samples of T. arguta were collected from 40 different habitats mostly located in Jiangxi Province, and leaf appearance traits and dry matter yield were analyzed. HPLC was used to quantify ligustroflavone and rhoifolin, the pharmacological-quality markers for this species, according to the 2020 Edition of Chinese Pharmacopoeia. The correlations between leaf-measurable traits, ligustroflavone and rhoifolin contents, and climate factors were subsequently assessed using Pearson’s two-tailed correlation test and redundancy analysis. The highest dry matter yields were found in locations S(G-mt), Q(J-t), and A(X-t). Ligustroflavone and rhoifolin contents ranged from 0.023% to 1.336% and 0.008% to 0.962%, respectively; the highest levels of ligustroflavone and rhoifolin compounds were found in locations A(X-t) and Y(B-mt). Leaf morphology was influenced by the mean temperature of the warmest quarter, while leaf thickness was affected by the minimum temperature of the coldest month, precipitation seasonality, and solar radiation. Larger and thicker leaves predicted higher yields of ligustroflavone and rhoifolin compounds in T. arguta under Chinese southern conditions, such as those in Anyuan and Quannan counties. Our findings suggest that enhancing the mean diurnal temperature range and implementing appropriate shading during cultivation can enhance the ligustroflavone and rhoifolin compounds of T. arguta. Full article

Accurately assessing tree mortality probability in the context of global climate changes is important for formulating scientific and reasonable forest management scenarios. In this study, we developed a climate-sensitive individual tree mortality model for Masson pine using data from the seventh (2004), eighth (2009), and ninth (2014) Chinese National Forest Inventory (CNFI) in Hunan Province, South–Central China. A generalized linear mixed-effects model with plots as random effects based on logistic regression was applied. Additionally, a hierarchical partitioning analysis was used to disentangle the relative contributions of the variables. Among the various candidate predictors, the diameter (DBH), Gini coefficient (GC), sum of basal area for all trees larger than the subject tree (BAL), mean coldest monthly temperature (MCMT), and mean summer (May–September) precipitation (MSP) contributed significantly to changes in Masson pine mortality. The relative contribution of climate variables (MCMT and MSP) was 44.78%, larger than tree size (DBH, 32.74%), competition (BAL, 16.09%), and structure variables (GC, 6.39%). The model validation results based on independent data showed that the model performed well and suggested an influencing mechanism of tree mortality, which could improve the accuracy of forest management decisions under a changing climate. Full article

Modern web services widely provide RESTful APIs for clients to access their functionality programmatically. Fuzzing is an emerging technique for ensuring the reliability of RESTful APIs. However, the existing RESTful API fuzzers repeatedly generate invalid requests due to unawareness of errors in the invalid tested requests and lack of effective strategy to generate legal value for the incorrect parameters. Such limitations severely hinder the fuzzing performance. In this paper, we propose DynER, a new test case generation method guided by dynamic error responses during fuzzing. DynER designs two strategies of parameter value generation for purposefully revising the incorrect parameters of invalid tested requests to generate new test requests. The strategies are, respectively, based on prompting Large Language Model (LLM) to understand the semantics information in error responses and actively accessing API-related resources. We apply DynER to the state-of-the-art fuzzer RESTler and implement DynER-RESTler. DynER-RESTler outperforms foREST on two real-world RESTful services, WordPress and GitLab with a 41.21% and 26.33% higher average pass rate for test requests and a 12.50% and 22.80% higher average number of unique request types successfully tested, respectively. The experimental results demonstrate that DynER significantly improves the effectiveness of test cases and fuzzing performance. Additionally, DynER-RESTler finds three new bugs. Full article

Measuring soil quality and the use of indicators for its evaluation is a worldwide challenge. In Garrotxa Volcanic Zone Natural Park (northeastern Spain), different parameters related to oribatid mites as indicators of soil quality were evaluated under different land uses: forest, pasture, and a biennial double-crop rotation of forage crops. In forage crops, previous fertilization management (one based on mineral fertilizers, three on cattle manure, and one using both types) was also evaluated. Three samplings (April, June, and September) were performed over one season. Fifty-four oribatid species belonging to 28 families were identified. Abundance was the lowest in June for all land uses (average of 1184 individuals m⁻²). In the study period, abundance, diversity (Shannon index, H’), and dominance (Berger–Parker index, d) varied with different land uses, with the highest values of abundance and H’ in forests (9287 individuals m⁻² and 2.19, respectively) and the lowest dominance in forests (d = 0.29) without differences between the other uses. Additionally, in the studied parameters, no differences were associated with previous fertilization management in forage crops. Hypochthoniella minutissima, Xenillus (X.) tegeocranus characterized the forest system, Epilohmannia cylindrica minima the forage crops, and Tectocepheus sarekensis the pasture. In pasture, the dominance of the parthenogenetic species Tectocepheus sarekensis raises concerns about potential management constraints. Full article

Corn silage is the main feed in the diet of dairy cows and other ruminant livestock. Silage corn feed is very susceptible to spoilage and corruption due to the influence of aerobic secondary fermentation during the silage process. At present, silage quality testing of corn feed mainly relies on the combination of sensory evaluation and laboratory measurement. The sensory review method is difficult to achieve precision and objectivity, while the laboratory determination method has problems such as cumbersome testing procedures, time-consuming, high cost, and damage to samples. In this study, the external sensory quality grading model for different qualities of silage corn feed was established using hyperspectral data. To explore the feasibility of using hyperspectral data for external sensory quality grading of corn silage, a hyperspectral system was used to collect spectral data of 200 corn silage samples in the 380–1004 nm band, and the samples were classified into four grades: excellent, fair, medium, and spoiled according to the German Agricultural Association (DLG) standard for sensory evaluation of silage samples. Three algorithms were used to preprocess the fodder hyperspectral data，including multiplicative scatter correction (MSC), standard normal variate (SNV), and S–G convolutional smoothing. To reduce the redundancy of the spectral data, variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS) were used for feature wavelength selection, and linear discriminant analysis (LDA) algorithm was used for data dimensionality reduction, constructing random forest classification (RFC), convolutional neural networks (CNN) and support vector machines (SVM) models. The best classification model was derived based on the comparison of the model results. The results show that SNV-LDA-SVM is the optimal algorithm combination, where the accuracy of the calibration set is 99.375% and the accuracy of the prediction set is 100%. In summary, combined with hyperspectral technology, the constructed model can realize the accurate discrimination of the external sensory quality of silage corn feed, which provides a reliable and effective new non-destructive testing method for silage corn feed quality detection. Full article

Ecological environment quality reflects the overall condition and health of the environment. Analyzing the spatiotemporal dynamics and driving factors of ecological environment quality across large regions is crucial for environmental protection and policy-making. This study utilized the Google Earth Engine (GEE) platform to efficiently process large-scale remote sensing data and construct a multi-scale Remote Sensing Ecological Index (RSEI) based on Landsat and Sentinel data. This approach overcomes the limitations of traditional single-scale analyses, enabling a comprehensive assessment of ecological environment quality changes across provincial, municipal, and county levels in Fujian Province. Through the Mann–Kendall mutation test and Sen + Mann–Kendall trend analysis, the study identified significant change points in the RSEI for Fujian Province and revealed the temporal dynamics of ecological quality from 1987 to 2023. Additionally, Moran’s I statistic and Geodetector were employed to explore the spatial correlation and driving factors of ecological quality, with a particular focus on the complex interactions between natural factors. The results indicated that: (1) the integration of Landsat and Sentinel data significantly improved the accuracy of RSEI construction; (2) the RSEI showed a consistent upward trend across different scales, validating the effectiveness of the multi-scale analysis approach; (3) the ecological environment quality in Fujian Province experienced significant changes over the past 37 years, showing a trend of initial decline followed by recovery; (4) Moran’s I analysis demonstrated strong spatial clustering of ecological environment quality in Fujian Province, closely linked to human activities; and (5) the interaction between topography and natural factors had a significant impact on the spatial patterns of RSEI, especially in areas with complex terrain. This study not only provides new insights into the dynamic changes in ecological environment quality in Fujian Province over the past 37 years, but also offers a scientific basis for future environmental restoration and management strategies in coastal areas. By leveraging the efficient data processing capabilities of the GEE platform and constructing multi-scale RSEIs, this study significantly enhances the precision and depth of ecological quality assessment, providing robust technical support for long-term monitoring and policy-making in complex ecosystems. Full article

Water is a fundamental resource for Chile’s productive structure, which is more important in arid areas, and especially with agricultural uses. This study was based on two basins (Cogotí and Illapel) located in the Coquimbo Region of north-central Chile. In this region, surface water rights were closed in 2002 and the only current option is the use of groundwater. These basins have high water demands due to the use of surface and groundwater for agricultural purposes, a fact that should influence the sustainability of groundwater reserves over time. The objective of this study was to determine how much agricultural use has affected the availability of groundwater in two basins. Under the previous context, the evolution of agricultural irrigation surfaces was evaluated using Landsat images and forest classifications. Similarly, groundwater reserves were evaluated using the recessive curves of hydrographs associated with the beginning of each hydrological year. The results show an increase in the agricultural area between 1996 and 2016, with a subsequent decrease, while groundwater reserves denoted significant decreases over time. In conclusion, a significant decrease in the volumes of groundwater reserves in both basins was observed, a decrease that is consistent with the increase in irrigated areas. Full article

Aboveground biomass (AGB) is a vital indicator for studying carbon sinks in forest ecosystems. Semiarid forests harbor substantial carbon storage but received little attention due to the high spatial–temporal heterogeneity that complicates the modeling of AGB in this environment. This study assessed the performance of different data sources (annual monthly time-series radar was Sentinel-1 [S1]; annual monthly time series optical was Sentinel-2 [S2]; and single-temporal airborne light detection and ranging [LiDAR]) and seven prediction approaches to map AGB in the semiarid forests on the border between Gansu and Qinghai Provinces in China. Five experiments were conducted using different data configurations from synthetic aperture radar backscatter, multispectral reflectance, LiDAR point cloud, and their derivatives (polarimetric combination indices, texture information, vegetation indices, biophysical features, and tree height- and canopy-related indices). The results showed that S2 acquired better prediction (coefficient of determination [R²]: 0.62–0.75; root mean square error [RMSE]: 30.08–38.83 Mg/ha) than S1 (R²: 0.24–0.45; RMSE: 47.36–56.51 Mg/ha). However, their integration further improved the results (R²: 0.65–0.78; RMSE: 28.68–35.92 Mg/ha). The addition of single-temporal LiDAR highlighted its structural importance in semiarid forests. The best mapping accuracy was achieved by XGBoost, with the metrics from the S2 and S1 time series and the LiDAR-based canopy height information being combined (R²: 0.87; RMSE: 21.63 Mg/ha; relative RMSE: 14.45%). Images obtained during the dry season were effective for AGB prediction. Tree-based models generally outperformed other models in semiarid forests. Sequential variable importance analysis indicated that the most important S1 metric to estimate AGB was the polarimetric combination indices sum, and the S2 metrics were associated with red-edge spectral regions. Meanwhile, the most important LiDAR metrics were related to height percentiles. Our methodology advocates for an economical, extensive, and precise AGB retrieval tailored for semiarid forests. Full article

The family Bondarzewiaceae is an important and diverse group of macrofungi associated with wood as white rotting fungi, and some species are forest tree pathogens. Currently, there are nine genera and approximately 89 species in the family, distributed in tropical, subtropical, and temperate climates. To address the phylogenetic relationships among the genera, a combined ITS-28S dataset was subjected to maximum likelihood (ML), Bayesian inference (BI), and time divergence analyses using the BEAST package. Both ML and BI analyses revealed two major clades, where one major clade consisted of Amylosporus, Stecchericium, and Wrightoporia austrosinensisa. The second major clade is composed of Bondarzewia, Heterobasidion, Gloiodon, Laurilia, Lauriliella, and Wrightoporia, indicating that these genera are phylogenetically similar. Wrightoporia austrosinensisa recovered outside of Wrightoporia, indicating that this species is phylogenetically different from the rest of the species of the genus. Similarly, time divergence analyses suggest that Bondarzewiaceae diversified around 114 million years ago (mya), possibly during the Early Cretaceous Epoch. The genus Amylosporus is well resolved within the family, with an estimated stem age of divergent around 62 mya, possibly during the Eocene Epoch. Further, the species of the genus are recovered in two sister clades. One sister clade consists of species with pileate basidiomata and generative hyphae with clamp connections, corresponding to the proposed section Amylosporus sect. Amylosporus. The other consists of species having resupinate basidiomata and generative hyphae without clamps, which is treated here as Amylosporus sect. Resupinati. We provided the key taxonomic characters, known distribution, number of species, and stem age of diversification of each section. Furthermore, we also described a new species, Amylosporus wadinaheezicus, from Oman, based on morphological characters of basidiomata and multigene sequence data of ITS, 28S, and Tef1-α. With pileate basidiomata and phylogenetic placement, the new species is classified under the proposed A. sect. Amylosporus. An identification key to the known species of Amylosporus is presented. Ecology and distribution of species of the genera in the family are discussed. Full article

Vertical water jets present significant challenges for hydraulic structures due to their potential to cause erosion and structural damage. This study aimed to predict the dimensionless pressure coefficient (C_p) of vertical water jets by examining the relationships between experimental parameters, such as Froude number, slope, and the ratio of waterfall height over the product of the Froude number and diameter, referred to as α, using machine learning models. Two hundred forty controlled experiments were conducted, with pressure data collected. To address the problem’s non-linearity, six machine learning models were tested: linear regression, K-nearest neighbors, decision tree, support vector regression, random forest, and XGBoost. The XGBoost model outperformed others, achieving an R-squared of 0.953 and a Root Mean Squared Error (RMSE) of 0.191. Residual analysis validated its better performance, demonstrating that it delivered the most accurate predictions with minimal bias. Feature importance analysis revealed the Froude number was the most significant predictor, followed by slope and diameter. This study emphasizes the importance of the Froude number in predicting jet behavior and shows the efficacy of advanced machine learning models in capturing complex fluid dynamics, providing valuable insights for optimizing engineering applications such as water jet cutting and cooling systems. Full article

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 10⁴ km²), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km²) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes. Full article

The northern bark beetle, Ips duplicatus, is an emerging economic pest, reportedly infesting various species of spruce (Picea spp.), pine (Pinus spp.), and larch (Larix spp.) in Central Europe. Recent climate changes and inconsistent forest management practices have led to the rapid spread of this species, leaving the current monitoring strategies inefficient. As understanding the molecular components of pheromone detection is key to developing novel control strategies, we generated antennal transcriptomes from males and females of this species and annotated the chemosensory proteins. We identified putative candidates for 69 odorant receptors (ORs), 50 ionotropic receptors (IRs), 25 gustatory receptors (GRs), 27 odorant-binding proteins (OBPs), including a tetramer-OBP, 9 chemosensory proteins (CSPs), and 6 sensory neuron membrane proteins (SNMPs). However, no sex-specific chemosensory genes were detected. The phylogenetic analysis revealed conserved orthology in bark beetle chemosensory proteins, especially with a major forest pest and co-habitant, Ips typographus. Recent large-scale functional studies in I. typographus chemoreceptors add greater significance to the orthologous sequences reported here. Nevertheless, identifying chemosensory genes in I. duplicatus is valuable to understanding the chemosensory system and its evolution in bark beetles (Coleoptera) and, generally, insects. Full article