Search Results (45,054)

Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP data source, were no earlier than 1980. We identified a close relationship between the tree-ring index (TRI) and vegetation carbon dioxide uptake (as measured by GPP) and then developed a nested TRI-GPP model to reconstruct spatially explicit GPP values since 1895 from seven tree-ring chronologies. The model performance in both phases was acceptable: We chose general regression neural network regression and random forest regression in Phase 1 (1895–1937) and Phase 2 (1938–1985). With the simulated and real GPP maps, we observed that the GPP for grassland and overall GPP were increasing. The GPP landscape patterns were stable, but in recent years, the GPP’s increasing rate surpassed any other period in the past 130 years. The main local climate driver was the Palmer Drought Severity Index (PDSI), and GPP had a significant positive correlation with PDSI in the growing season (June, July, and August). With the GPP maps derived from the nested TRI-GPP model, we can create fine-scale GPP maps to understand vegetation change and carbon uptake over the past century. Full article

Research related to Cerradão vegetation focuses more on the floristic-structural aspect, with rare studies on the quantification of volume and biomass stocks, and even fewer investigating the increments of these attributes. Using a systematic sampling method with subdivided strips and 400 m² plots, the density found was 1135, 1165, and 1229 trees/ha in 2012, 2020, and 2023, respectively, in Lajeado State Park, Tocantins State, Brazil. Volume was estimated using the equation v=0.000085D2.122270H0.666217, and biomass was estimated using the equation AGB=0.0673ρD2H0.976. Vegetation dynamics were assessed using growth increment, recruitment, mortality, turnover rate, and time. The results indicated that dynamics have increased since the start of monitoring. Typical Cerrado species, in the strict sense, were replaced by those from forest environments. The total production in volume and biomass was 160.91 m³/ha and 118.10 Mg/ha, respectively, in 2023. The species of Emmotum nitens, Mezilaurus itauba, Ocotea canaliculata, and Sacoglottis guianensis showed the highest increment values in volume and biomass. For the community, the average values were 4.04 m³/ha/year and 3.54 Mg/ha/year. The community has not yet reached its carrying capacity and stores a significant amount of biomass. This is influenced by the transition of the study area from an exploited environment to a conservation unit (park) and by its location in a transitional area with the Amazon biome. Full article

Ecosystem services (ESs) serve as the foundation for sustaining human life and development, with vegetation status playing a crucial role in influencing the supply of these services. This study focuses on the Yellow River Basin (YRB), where we quantitatively examined the main ESs indicators from 2010 to 2020. We explored the trends in fractional vegetation cover (FVC) and ESs, as well as the constraint relationship between FVC and total ecosystem services (TES). The findings are as follows. (1) From 2010 to 2020, FVC, landscape aesthetics (LA), soil conservation (SC), food production (FP), and TES in the YRB demonstrated an upward trend, whereas water yield (WY) exhibited a downward trend. (2) A constraint relationship exists between FVC and LA, SC, WY, and TES, with the constraint line taking on a hump-like shape. (3) The threshold value of the constraint line between FVC and LA, SC, WY, and TES are approximately 80%. Below this value, FVC does not impose a constraint effect on LA, SC, WY, and TES, but above 80%, a strong constraint effect emerges, leading to a reduction in LA, SC, WY, and TES. These results offer a valuable data reference for guiding future vegetation restoration and ecological engineering efforts in the region. Full article

Selecting seedlings of varying sizes and effectively managing root pruning are key challenges in transplantation. However, the effects of seedling size and root pruning on transplantation outcomes are not fully understood. This study classified one-year-old Populus ‘Beilinxiongzhu-01’ seedlings into three size categories based on height: large (308.75 ± 9.66 cm), medium (238.00 ± 7.71 cm), and small (138.92 ± 7.18 cm). In early March of the subsequent year, root pruning was applied with varying intensities based on root collar diameter: low (15 times), medium (7.5 times), and high (3.75 times). A control group without pruning was also included. Over the year, key phenological and morphological traits were monitored. The results showed that (1) root pruning significantly impacted the phenology of seedlings, accelerating root emergence, delaying early leaf phenology, increasing the dieback rate, and postponing end-of-season defoliation. Mortality and the rapid growth phase were not significantly affected. Larger seedlings exhibited earlier end-of-season defoliation and higher dieback rates early in the growing season, while smaller seedlings advanced in early leaf development. (2) Except under low or no pruning, root pruning reduced seedling height (H), diameter at breast height (DBH), and root collar diameter (RCD). However, across all treatments, these indicators remained higher in larger seedlings compared to smaller ones. Under medium- and high-intensity pruning, smaller seedlings exhibited higher relative growth rates and larger leaf areas than larger seedlings, with the reduction in these variables becoming more pronounced as seedlings increased in size. Notably, only larger seedlings demonstrated a reduction in maximum growth rate, suggesting greater vulnerability to root pruning. In summary, root pruning induced significant phenological and morphological differences across seedling sizes. While smaller seedlings showed some response to pruning, larger seedlings experienced more pronounced phenological disruptions and growth inhibition. Full article

Jointly building the Economic Corridor of the Eurasia Continent (ECEC), which is one of the most important parts of the Silk Road Economic Belt, is a pivotal initiative for fostering regional development. Forests, which serve as a green foundation of economic resilience, underpin this effort. However, there is an imbalance in ecological status due to differences in natural resources and the social economy along the economic corridor. This imbalance has led to alterations in landscapes, yet the specific changes and their underlying relationships are still much less understood. Here, we quantitatively detected changes in the forest landscape and its ecological efforts over the ECEC via widespread, satellite-based and long-term land cover maps released by the European Space Agency (ESA) Climate Change Initiative (CCI). Specifically, the coupling between changes in forest coverage and landscape patterns, e.g., diversity, was further examined. The results revealed that forest coverage fluctuated and declined over the ECEC from 1992 to 2018, with an overall reduction of approximately 9784.8 km² (i.e., 0.25%). Conversions between forests and other land cover types were widely observed. The main displacements occurred between forests and grasslands/croplands (approximately 48%/21%). Moreover, the landscape diversity in the study area increased, as measured by the effective diversity index (EDI), during the study period, despite obvious spatial heterogeneity. Notably, this pattern of landscape diversity was strongly associated with forest displacement and local urban development through coupling analysis, consequently indicating increasing fragmentation rather than biological diversity. This study highlights the coupled relationship between quantitative and qualitative changes in landscapes, facilitating our understanding of environmental protection and policy management. Full article

This article explores the important, and yet often overlooked, solid-state structures of selected bioaromatic compounds commonly found in lignin hydrogenolysis oil, a renewable bio-oil that holds great promise to substitute fossil-based aromatic molecules in a wide range of chemical and material industrial applications. At first, single-crystal X-ray diffraction (SCXRD) was applied to the lignin model compounds, dihydroconiferyl alcohol, propyl guaiacol, and eugenol dimers, in order to elucidate the fundamental molecular interactions present in such small lignin-derived polyols. Then, considering the potential use of these lignin-derived molecules as building blocks for polymer applications, structural analysis was also performed for two chemically modified model compounds, i.e., the methylene-bridging propyl-guaiacol dimer and propyl guaiacol and eugenol glycidyl ethers, which can be used as precursors in phenolic and epoxy resins, respectively, thus providing additional information on how the molecular packing is altered following chemical modifications. In addition to the expected H-bonding interactions, other interactions such as π–π stacking and C–H∙∙∙π were observed. This resulted in unexpected trends in the tendencies towards the crystallization of lignin compounds. This was further explored with the aid of DSC analysis and CLP intermolecular energy calculations, where the relationship between the major interactions observed in all the SCXRD solid-state structures and their physico-chemical properties were evaluated alongside other non-crystallizable lignin model compounds. Beyond lignin model compounds, our findings could also provide important insights into the solid-state structure and the molecular organization of more complex lignin fragments, paving the way to the more efficient design of lignin-based materials with improved properties for industrial applications or improving downstream processing of lignin oils in biorefining processes, such as in enhancing the separation and isolation of specific bioaromatic compounds). Full article

Preharvest crop yield estimation is crucial for achieving food security and managing crop growth. Unmanned aerial vehicles (UAVs) can quickly and accurately acquire field crop growth data and are important mediums for collecting agricultural remote sensing data. With the rapid development of machine learning, especially deep learning, research on yield estimation based on UAV remote sensing data and machine learning has achieved excellent results. This paper systematically reviews the current research of yield estimation research based on UAV remote sensing and machine learning through a search of 76 articles, covering aspects such as the grain crops studied, research questions, data collection, feature selection, optimal yield estimation models, and optimal growth periods for yield estimation. Through visual and narrative analysis, the conclusion covers all the proposed research questions. Wheat, corn, rice, and soybeans are the main research objects, and the mechanisms of nitrogen fertilizer application, irrigation, crop variety diversity, and gene diversity have received widespread attention. In the modeling process, feature selection is the key to improving the robustness and accuracy of the model. Whether based on single modal features or multimodal features for yield estimation research, multispectral images are the main source of feature information. The optimal yield estimation model may vary depending on the selected features and the period of data collection, but random forest and convolutional neural networks still perform the best in most cases. Finally, this study delves into the challenges currently faced in terms of data volume, feature selection and optimization, determining the optimal growth period, algorithm selection and application, and the limitations of UAVs. Further research is needed in areas such as data augmentation, feature engineering, algorithm improvement, and real-time yield estimation in the future. Full article

Surface quality represents a critical challenge in additive manufacturing (AM), with surface roughness serving as a key parameter that influences this aspect. In the aerospace industry, the surface roughness of the aviation components is a very important parameter. In this study, a typical Al alloy, AlSi10Mg, was selected to study its surface roughness when using Laser Powder Bed Fusion (LPBF). Two Random Forest (RF) models were established to predict the upper surface roughness of printed samples based on laser power, laser scanning speed, and hatch distance. Through the study, it is found that a two-dimensional (2D) RF model is successful in predicting surface roughness values based on experimental data. The best and minimum surface roughness is 2.98 μm, which is the minimum known without remelting. More than two-thirds of the samples had a surface roughness of less than 7.7 μm. The maximum surface roughness is 11.28 μm. And the coefficient of determination (R²) of the model was 0.9, also suggesting that the surface roughness of 3D-printed Al alloys can be predicted using ML approaches such as the RF model. This study helps to understand the relationship between printing parameters and surface roughness and helps print components with better surface quality. Full article

The apple jewel beetle (AJB), Agrilus mali Matsumura (Coleoptera: Buprestidae), is a dangerous pest of commercial apple orchards across China, the largest apple production country in the world, and has recently become invasive in the Xinjiang Uygur Autonomous Region (XUAR) of northwestern China, where wild apple forests also occur. This pest poses a serious threat to apple production and wild apple forests throughout the world. Global warming is expected to change the geographical distribution of A. mali in China, but the extent of this is unknown. Based on empirical data from 1951 to 2000, a MaxEnt model was used to forecast the ecological distribution of A. mali under three different climate scenarios projected in the fifth report of the Intergovernmental Panel on Climate Change. The results showed that the most important variables were the maximum temperature of November, precipitation in January, and minimum temperatures in April. Under all climate scenarios, the forecasted suitable regions for A. mali in China will expand northward in the 2050s and 2070s. The forecasted highly suitable regions will be 1.11–1.34 times larger than they are currently, and their central distributions will be 61.57–167.59 km further north. These findings suggest that the range and damage caused by A. mali in China will increase dramatically in the future. Monitoring and management measures should be implemented urgently to protect both the commercial apple industry and wild apple resources. Full article

In the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extracting key values from electricity invoices, including customer data, bill breakdown, electricity consumption, or marketer data. We evaluate several machine learning techniques, such as Naive Bayes, Logistic Regression, Random Forests, or Support Vector Machines. Our approach relies on a bag-of-words strategy and custom-designed features tailored for electricity data. We validate our method on the IDSEM dataset, which includes 75,000 electricity invoices with eighty-six fields. The model converts PDF invoices into text and processes each word separately using a context of eleven words. The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. The study also explores the advantages of our custom features and evaluates the performance of unseen documents. The precision obtained with Support Vector Machines is 91.86% on average, peaking at 98.47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices. Full article

Selenium is an indispensable trace element in the human body that plays an important role in maintaining life activities. The consumption of Se-rich crops provides a practical and effective way for the body to supplement Se. However, the Se content in crops is affected by the soil Se content and the interactions between other elements in the soil. In this study, the Tibetan Plateau of China was chosen as the study area. The random forest algorithm was applied to select four key indicators—selenium (Se), bioavailable phosphorus (P), cadmium (Cd), and bioavailable copper (Cu)—from 29 soil variables to predict the Se content in rapeseed, wheat, potato, pasture, and chrysanthemum crops. The results showed that, despite the rich soil Se resources in the Tibetan Plateau, only 20% of the crop samples met the national Se enrichment standard (>0.07 mg kg⁻¹). Compared with the traditional multiple linear regression method, the random forest model is more accurate, efficient, and reliable in predicting the Se content of crops. In cross-species crop prediction, which refers to the simultaneous cultivation and analysis of multiple distinct crop species within the same agricultural setting, the random forest model demonstrated superior performance, marking a significant breakthrough in cross-species crop research. This approach effectively eliminates the tedious process of conducting repetitive individual evaluations for different crop types in the same region, highlighting its innovative significance. Meanwhile, the Tibetan Plateau, known as the “Roof of the World”, is also of great research value. These results provide valuable references for the planning and management of Se-enriched farmlands, which will help improve the yield and quality of Se-enriched crops and promote the growth of farmers’ interests. Full article

Forests are both an irreplaceable natural resource and a vital economic asset for all humankind. Based on the data of counties in mainland China from 2007 to 2020, the article explores the direct impact and spatial spillover effects of the policy implementation on the economic growth of counties with the help of the forest city pilot policy and the policy evaluation model. The results reveal that policy implementation can have a positive economic growth effect on the pilot counties, which, in turn, can significantly increase the size of the county’s GDP, the level of GDP per capita, and the total amount of nighttime lighting brightness. The implementation of forest city construction can bring about 2.74% of total GDP size, about 2.63% of per capita GDP development level, and about 7.25% of nighttime light brightness to the county on average. Cost–benefit analysis also indicates that forest city construction can bring about a comprehensive economic benefit of approximately CNY 686.453 million (approximately USD 96.82 million) to the counties. The rapid improvement in labor productivity, significant influx of high-end factors, and continuous expansion of market potential are important mechanisms through which policy implementation promotes economic growth in pilot counties. While promoting economic growth in the pilot counties, forest city construction can also have positive spatial spillover effects on neighboring areas in the pilot counties. Furthermore, when the deficits in atmospheric vapor pressure and annual evapotranspiration are used as instrumental variables for forest city construction, the empirical estimates are not significantly altered. In the process of building forest cities, county governments should be wary of issues such as the high cost of forest maintenance. This study provides a Chinese model and policy reference for other countries and regions in the world to deal with the relationship between forest city construction and county economic growth. Full article

Background/Objectives: This study aims to evaluate the performance of various classification algorithms and resampling methods across multiple diagnostic and prognostic cancer datasets, addressing the challenges of class imbalance. Methods: A total of five datasets were analyzed, including three diagnostic datasets (Wisconsin Breast Cancer Database, Cancer Prediction Dataset, Lung Cancer Detection Dataset) and two prognostic datasets (Seer Breast Cancer Dataset, Differentiated Thyroid Cancer Recurrence Dataset). Nineteen resampling methods from three categories were employed, and ten classifiers from four distinct categories were utilized for comparison. Results: The results demonstrated that hybrid sampling methods, particularly SMOTEENN, achieved the highest mean performance at 98.19%, followed by IHT (97.20%) and RENN (96.48%). In terms of classifiers, Random Forest showed the best performance with a mean value of 94.69%, with Balanced Random Forest and XGBoost following closely. The baseline method (no resampling) yielded a significantly lower performance of 91.33%, highlighting the effectiveness of resampling techniques in improving model outcomes. Conclusions: This research underscores the importance of resampling methods in enhancing classification performance on imbalanced datasets, providing valuable insights for researchers and healthcare professionals. The findings serve as a foundation for future studies aimed at integrating machine learning techniques in cancer diagnosis and prognosis, with recommendations for further research on hybrid models and clinical applications. Full article

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management. Full article