Search Results (45,974)

Understanding how pollen assemblages represent the local and regional vegetation composition is crucial for palaeoecological research. Here, we analyze 102 surface moss/soil pollen samples collected from four study regions located in various boreal forest vegetation types in Central Siberia. Despite Larix being the most prevalent tree generus in the study area, the proportion of Larix pollen can be as low as 0.6–1.5% (0.4–4.7% on average) even in localities with a high canopy density of the species. No relationship between the quantity of Larix pollen in the spectra and the abundance of Larix in the local vegetation was found. The dominant components of the pollen assemblages are Betula and Alnus fruticosa. The pollen value of Picea is low (2.6–8.2% on average), with higher proportions at sample plots where spruce is abundant in forests. Pinus is a highly prevalent pollen species within its geographical range, comprising up to 40% of pollen assemblages. Outside of the range, the ratio of Pinus pollen was higher in habitats with low canopy density and in treeless ecosystems. The composition of herbaceous pollen and spores is significantly affected by the local plant community, offering more comprehensive insights into past vegetation patterns. Full article

Soil fauna play an important role in litter decomposition and affect the “home-field advantage” (HFA) of litter decomposition. However, how this effect is modulated by the microenvironment needs further investigation. We conducted a reciprocal transplant experiment of litter decomposition using different mesh-size litterbags across litter and soil layers in subtropical coniferous (Pinus massoniana) and broad-leaved (Quercus variabilis) forests. Our results revealed a pronounced HFA in P. massoniana. P. massoniana litter decomposed faster in its home habitat by 40.6% in the litter layer and 10.2% in the soil layer in coarse mesh bags and by 21.8% in the litter layer and 21.4% in the soil layer in fine mesh bags. However, Q. variabilis litter showed faster decomposition in its home soil layer by 10.8% and 4.3% for coarse and fine mesh bags, whereas in the litter layer it decomposed faster in the away habitat by 16.7% and 20.6% for coarse and fine mesh bags, respectively. Higher soil mesofauna abundance and microbial activities in the coniferous forest compared to the broad-leaved forest drive the observed HFA of litter decomposition. Especially in the litter layer, the abundance of mesofauna was 89.8% higher in the coniferous forest. Coarse mesh bags generally facilitated a higher decomposition rate across litter and soil layers, likely due to a better interaction between soil mesofauna and extracellular enzyme activity. The HFA index exhibited distinct seasonal fluctuations, peaking in October for coarse mesh bags and in April for fine mesh bags within the litter layer, while soil layer peaks occurred in August and April. Notably, an increase in Acarina abundance strongly correlated with enhanced decomposition and HFA effects in the litter layer during October. This study revealed the sensitivity of HFA to the soil layer and soil fauna and underscores the complex role of the microclimate in shaping interactions among soil microorganisms, litter quality, and mesofauna, thereby enriching our understanding of litter decomposition dynamics in forest ecosystems. Full article

Forests play a crucial role in climate change mitigation by acting as a carbon sink. Understanding the influence of soil properties on carbon stocks in forests is essential for developing effective forest management strategies. The aim of the study was to assess the impact of soil texture on carbon stocks in the biomass of deciduous and coniferous tree stands of a forest-steppe ecotone. Soil samples were collected from 55 soil pits, and forest inventory data were obtained from eight permanent sample plots. The results showed that the distribution of mechanical particles in soils, particularly the stocks of silt and clay, significantly influenced the accumulation of carbon in tree stands. The stock of silt and clay was shown to increase with an increase in the diversity of tree species in forests and carbon stocks in forest stands. While soil organic carbon stocks did not exhibit a clear relationship with tree stand carbon stocks, a strong positive correlation (r = 0.802, p < 0.05) was found between the stocks of fine particles in the 2 m root-inhabited soil layer and the carbon stocks in tree biomass. The study provides a classification of forest types based on soil texture, which can facilitate differentiated forest management strategies for enhancing the carbon sequestration potential of forest ecosystems in the forest-steppe zone. Full article

The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang region of Henan Province, China, as our study area and proposed an optimization framework to improve GEDI canopy height estimation accuracy. This framework includes correcting geolocation errors in GEDI footprints, screening and analyzing features that affect estimation errors, and combining two regression models with feature selection methods. Our findings reveal a geolocation error of 4 to 6 m in GEDI footprints at the orbital scale, along with an overestimation of GEDI canopy height in the South Taihang region. Relative height (RH), waveform characteristics, topographic features, and canopy cover significantly influenced the estimation error. Some studies have suggested that GEDI canopy height estimates for areas with high canopy cover lead to underestimation, However, our study found that accuracy increased with higher canopy cover in complex terrain and dense vegetation. The model’s performance improved significantly after incorporating the canopy cover parameter into the optimization model. Overall, the R² of the best-optimized model was improved from 0.06 to 0.61, the RMSE was decreased from 8.73 m to 2.23 m, and the rRMSE decreased from 65% to 17%, resulting in an accuracy improvement of 74.45%. In general, this study reveals the factors affecting the accuracy of GEDI canopy height estimation in areas with complex terrain and dense vegetation cover, on the premise of minimizing GEDI geolocation errors. Employing the proposed optimization framework significantly enhanced the accuracy of GEDI canopy height estimates. This study also highlighted the crucial role of canopy cover in improving the precision of GEDI canopy height estimation, providing an effective approach for forest monitoring in such regions and vegetation conditions. Future studies should further improve the classification of tree species and expand the diversity of sample tree species to test the accuracy of canopy height estimated by GEDI in different forest structures, consider the distortion of optical remote sensing images caused by rugged terrain, and further mine the information in GEDI waveforms so as to enhance the applicability of the optimization framework in more diverse forest environments. Full article

Heterosis, or hybrid vigor, refers to the phenomenon where hybrid progenies outperform their parents in traits such as yield and resistance. This phenomenon has been widely applied in plant breeding. Recent advances in high-throughput genomics have significantly advanced our understanding of heterosis. This review systematically summarizes the genetic, molecular, and epigenetic mechanisms underlying heterosis. Furthermore, we discuss recent advances in predictive methods for heterosis and their applications in improving growth rate, resistance to abiotic stresses, and wood yield in tree species. We also explore the role of tree genomics in unraveling the mechanisms underlying heterosis, emphasizing the potential of integrating high-resolution genomics, single-cell sequencing, and spatial transcriptomics to achieve a comprehensive understanding of heterosis from the molecular to spatial levels. Building on this, CRISPR-based gene-editing technologies can be employed to precisely edit heterotic loci, enabling the study of allele function. Additionally, molecular marker-assisted selection (MAS) can be utilized to identify heterotic loci in parental lines, facilitating the selection of optimal hybrid combinations and significantly reducing the labor and time costs of hybrid breeding. Finally, we review the utilization of heterosis in tree breeding and provide a forward-looking perspective on future research directions, highlighting the potential of integrating multi-omics approaches and emerging gene-editing tools to revolutionize tree hybrid breeding. Full article

The loss of native forests in Argentina has been a concern, driven by factors such as agriculture expansion and urbanization. Therefore, understanding the conservation status of sampled populations and their adaptation to different plant communities is essential. This research focused on the heterogeneity analysis of the associated woody and semi-woody vegetation to Hexachlamys edulis (O. Berg) Kausel and D. Legrand, a species commonly known as “ubajay” in Entre Ríos, Argentina. The study aimed to record the species present in the populations, explore plant communities associated with H. edulis, identify other potentially useful agroforestry species, compare locations based on the similarity of accompanying species, and explain the conservation status of each population. Results revealed a total of 71 species belonging to 39 families. The Myrtaceae family was the most relevant, particularly in terms of native species representation. The analysis of biodiversity indicators, including richness, the Shannon index, and dominance revealed variations among the studied sites. The anthropic indicator highlighted the impact of human activity, with Concordia showing a higher ratio of native-to-exotic species. Cluster analysis and ordination techniques revealed groupings of censuses from the same localities, indicating differences in vegetation composition between sites. Significant differences in species composition were found among the sampled populations. Overall, the study can serve as baseline information for future research on the dynamics of vegetation in these areas and on the studied H. edulis species. Finally, these findings contribute to understanding how wild species like H. edulis adapt to different plant communities, which might be valuable for developing new agroecological approaches or identifying potential companion planting species in future agricultural systems. Full article

The application of slow-release nitrogen fertilizer not only economizes labor input, but also decreases the frequency of use of mechanical intakes, with significant implications in advancing modern intensive agricultural production. Whether slow-release nitrogen fertilizer application can influence the association between microbial diversity and soil multifunctionality remains controversial. This study analyzed the spatial variances of soil environmental factors, soil multifunctionality, and their correlations with bacterial and fungal communities under five nitrogen application rates. The key factors influencing the dominant microbial species and community structures at different spatial locations were determined by the slow-release nitrogen fertilizer application rate, and the driving factors and dominant species of soil multifunctionality were identified. In contrast to the control group, moderate slow-release nitrogen fertilizer application enhanced soil multifunctionality and ameliorated the resilience of microbial diversity loss at diverse spatial locations resulting from irrational nitrogen fertilizer application. The resilience of the fungal community to disturbances caused by fertilization was lower than that of the bacterial community. Bacterial diversity exhibited a significant correlation with soil multifunctionality, and the soil multifunctionality intensity under 240 kg ha⁻¹ treatment increased by 159.01% compared to the CK. The main dominant bacterial communities and the dominant fungal community Ascomycota affected soil multifunctionality through slow-release nitrogen fertilizer application. Structural equation modeling and random forest analysis demonstrated that bacterial community diversity, particularly in bulk soil and the rhizosphere, community composition, and soil nitrogen form are the primary driving factors of soil multifunctionality. Results indicated that the microbial niche alterations induced by slow-release nitrogen fertilizer application positively affect soil multifunctionality. Full article

At present, achieving efficient, sustainable, and safe transportation has led to increasing attention on driving behavior recognition and advancements in autonomous driving. Identifying diverse driving styles and corresponding types is crucial for providing targeted training and assistance to drivers, enhancing safety awareness, optimizing driving costs, and improving autonomous driving systems responses. However, current studies mainly focus on specific driving scenarios, such as free driving, car-following, and lane-changing, lacking a comprehensive and systematic framework to identify the diverse driving styles. This study proposes a novel, data-driven approach to driving-style recognition utilizing naturalistic driving data NGSIM. Specifically, the NGSIM dataset is employed to categorize car-following and lane-changing groups according to driving-state extraction conditions. Then, characteristic parameters that fully represent driving styles are optimized through correlation analysis and principal component analysis for dimensionality reduction. The K-means clustering algorithm is applied to categorize the car-following and lane-changing groups into three driving styles: conservative, moderate, and radical. Based on the clustering results, a comprehensive evaluation of the driving styles is conducted. Finally, a comparative evaluation of SVM, Random Forest, and KNN recognition indicates the superiority of the SVM algorithm and highlights the effectiveness of dimensionality reduction in optimizing characteristic parameters. The proposed method achieves over 97% accuracy in identifying car-following and lane-changing behaviors, confirming that the approach based on naturalistic driving data can effectively and intelligently recognize driving styles. Full article

Drought is a complex environmental hazard to ecosystems and society. Decision-making on drought management options requires evaluating and predicting the extremity of future drought events. In this regard, quantifiable indices such as the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the standardized streamflow index (SSI) have been commonly used to characterize meteorological and hydrological drought. In general, the estimation and prediction of the indices require an extensive range of precipitation (SPI and SPEI) and discharge (SSI) datasets in space and time domains. However, there is a challenge for long-term and spatially extensive data availability, leading to the insufficiency of data in estimating drought indices. In this regard, this study uses satellite precipitation data to estimate and predict the drought indices. SPI values were calculated from the precipitation data obtained from the Centre for Hydrometeorology and Remote Sensing (CHRS) data portal for a study water basin. This study employs a hydrological model for calculating discharge and drought in the overall basin. It uses random forest (RF) and support vector regression (SVR) as machine learning models for SSI prediction for time scales of 1- and 3-month periods, which are widely used for establishing interactions between predictors and predictands that are both linear and non-linear. This study aims to evaluate drought severity variation in the overall basin using the hydrological model and compare this result with the machine learning model’s results. The results from the prediction model, hydrological model, and the station data show better correlation. The coefficients of determination obtained for 1-month SSI are 0.842 and 0.696, and those for the 3-month SSI are 0.919 and 0.862 in the RF and SVR models, respectively. These results also revealed more precise predictions of machine learning models in the longer duration as compared to the shorter one, with the better prediction result being from the SVR model. The hydrological model-evaluated SSI has 0.885 and 0.826 coefficients of determination for the 1- and 3-month time durations, respectively. The results and discussion in this research will aid planners and decision-makers in managing hydrological droughts in basins. Full article

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption. Full article

Background/Objectives: The recurrence of glioblastoma is an inevitable event in this disease’s course. In this study, we sought to identify the metabolomic signature in patients with recurrent glioblastomas undergoing surgery and radiation therapy. Methods: Blood samples collected prospectively from six patients with recurrent IDH-wildtype glioblastoma who underwent one surgery at diagnosis and a second surgery at relapse were analyzed using untargeted gas chromatography–time-of-flight mass spectrometry to measure metabolite abundance. The data analysis techniques included univariate analysis, correlation analysis, and a sample t-test. For predictive modeling, machine learning (ML) algorithms such as multinomial logistic regression, gradient boosting, and random forest were applied to predict the classification of samples in the correct treatment phase. Results: Comparing samples after the first surgery and after the relapse surgeries to the pre-operative samples showed a significant decrease in sorbitol and mannitol; there was a significant increase in urea, oxoproline, glucose, and alanine. After chemoradiation, two metabolites, erythritol and 6-deoxyglucitol, showed a decrease, with a cut-off of three and a significant reduction for 6-deoxyglucitol, while 2,4-difluorotoluene and 9-myristoleate showed an increase post radiation, with a fold-change cut-off of three. The gradient-boosting ML model achieved a high performance for the prediction of tumor conditions in patients with glioblastoma who had undergone relapse surgery. Conclusions: We developed an ML predictor for tumor phase based on the plasma metabolomic profile. Our study suggests the potential of combining metabolomics with ML as a new tool to stratify the risk of tumor progression in patients with glioblastoma. Full article

LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based IoT networks. Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data. We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). Using metrics such as accuracy, precision, recall, and F1-score, CAE-FL emerged as the top performer, achieving 97.27% accuracy and a balanced precision, recall, and F1-score of 0.97, with IF-FL close behind at 96.84% accuracy. Competitive performance from OCSVM-FL and LOF-FL, along with the comparable results of K-Means-FL, highlighted the robustness of clustering-based detection methods in this context. Visual analyses using confusion matrices and ROC curves provided further insights into each model’s effectiveness in detecting tampered signals. This research underscores the capability of federated learning to enhance privacy and security in anomaly detection for LoRa networks, even against unknown attacks, marking a significant advancement in securing IoT communications in sensitive applications. Full article

Biobased foams have the potential to serve as eco-friendly alternatives to petroleum-based foams, provided they achieve comparable thermomechanical and physical properties. We propose a facile approach to fabricate eco-friendly cellulose nanofibril (CNF)-reinforced thermomechanical pulp (TMP) fiber-based foams via an oven-drying process with thermal conductivity as low as 0.036 W/(m·K) at a 34.4 kg/m³ density. Acrodur^®, iron chloride (FeCl₃), and cationic polyacrylamide (CPAM) were used to improve the foam properties. Acrodur^® did not have any significant effect on the foamability and density of the foams. Mechanical, thermal, cushioning, and water absorption properties of the foams were dependent on the density and interactions of the additives with the fibers. Due to their high density, foams with CPAM and FeCl₃ at a 1% additive dosage had significantly higher compressive properties at the expense of slightly higher thermal conductivity. There was slight increase in compressive properties with the addition of Acrodur^®. All additives improved the water stability of the foams, rendering them stable even after 24 h of water absorption. Full article

Nitrate is among the most widely occurring contaminants in groundwater on a global scale, posing a serious threat to drinking water supplies. With the advancement of urbanization and mountainous agriculture, the nitrate in the groundwater of Wanzhou District in the Three Gorges Reservoir Area has formed a complex combination of pollution sources. To more accurately identify the sources of nitrate in groundwater, this study integrates hydrochemical methods and environmental isotope techniques to analyze the sources and transformation processes in shallow groundwater nitrate under different land-use types. Furthermore, the Bayesian isotope mixing model (MixSAIR) is employed to calculate the contribution rates in various nitrate sources. The results indicate that nitrate is the primary form of inorganic nitrogen in shallow groundwater within the study area, with nitrate concentrations in cultivated groundwater generally higher than those in construction land and forest land. The transformation process of nitrate is predominantly nitrification, with little to no denitrification observed. In cultivated shallow groundwater, nitrate mainly originates from chemical fertilizers (36.3%), sewage and manure (35.4%), and soil organic nitrogen (24.7%); in forested areas, nitrate primarily comes from atmospheric precipitation (35.3%), chemical fertilizers (31.3%), and soil organic nitrogen (22.1%); while in constructed areas, nitrate mainly derives from chemical fertilizers (46.0%) and sewage and manure (32.2%). These results establish a scientific foundation for formulating groundwater pollution control and management strategies in the region and serve as a reference for identifying nitrate sources in groundwater in regions with comparable hydrogeological features and pollution profiles. Full article

Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R² and RMSE of the model based on source III were better than the R² and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R² by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R² of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R² of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R² of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work. Full article