Search Results (35)

Syrphid flies (Syrphidae) are among the most significant groups of insect pollinators with approximately 6300 described species worldwide. Within China, more than 15% species have been reported but their diversity and distribution pattern are not well understood. Based on recent collections and published literature records, this study aimed to assess the species diversity, richness, evenness, and distribution pattern of Syrphidae in China. Biodiversity was measured using various indices such as Simpson’s diversity index, the Shannon–Wiener diversity index, Simpson’s reciprocal index, the Shannon equitability index, and the Margalef index. The results indicated that most of the indices showed highest values within Sichuan, Shaanxi, Yunnan, Taiwan, Tibet, and Gansu provinces. However, the lowest values of most of these indices were seen in Tianjin, Chongqing, and Hongkong. The ranges of these values were 0.69–5.55, 0.67–1.00, and 1.44–46.26 for the Shannon–Wiener index, Simpson’s index, and the Margalef index, respectively. Based on UMAP (Uniform Manifold Approximation and Projection) clustering approaches, all provinces of China were divided into two groups where group 1 showed 16 provinces having similar values to each other in a UMAP1 and UMAP2 plot, whereas 17 provinces were categorized into group 2. This clustering was further refined by a hierarchical clustering dendrogram where group 2 was further refined into two subgroups, where three provinces were separated into one small group including Hongkong, Chongqing, and Tianjin because of the lowest values of most of the indices. These results provide significant insights into the species richness and distribution of syrphid flies and inform strategies to help maintain these pollinators to support sustainable agriculture. Full article

Accurate crop yield prediction is essential for sugarcane growers, as it enables them to predict harvested biomass, guiding critical decisions regarding acquiring agricultural inputs such as fertilizers and pesticides, the timing and execution of harvest operations, and cane field renewal strategies. This study is based on an experiment conducted by researchers from the Commonwealth Scientific and Industrial Research Organisation (CSIRO), who employed a UAV-mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials subjected to varying nitrogen (N) fertilization regimes in the Wet Tropics region of Australia. The predictive performance of models utilizing multispectral features, LiDAR-derived features, and a fusion of both modalities was evaluated against a benchmark model based on the Normalized Difference Vegetation Index (NDVI). This work utilizes the dataset produced by this experiment, incorporating other regressors and features derived from those collected in the field. Typically, crop yield prediction relies on features derived from direct field observations, either gathered through sensor measurements or manual data collection. However, enhancing prediction models by incorporating new features extracted through regressions executed on the original dataset features can potentially improve predictive outcomes. These extracted features, nominated in this work as meta-features (MFs), extracted through regressions with different regressors on original features, and incorporated into the dataset as new feature predictors, can be utilized in further regression analyses to optimize crop yield prediction. This study investigates the potential of generating MFs as an innovation to enhance sugarcane crop yield predictions. MFs were generated based on the values obtained by different regressors applied to the features collected in the field, allowing for evaluating which approaches offered superior predictive performance within the dataset. The kNN meta-regressor outperforms other regressors because it takes advantage of the proximity of MFs, which was checked through a projection where the dispersion of points can be measured. A comparative analysis is presented with a projection based on the Uniform Manifold Approximation and Projection (UMAP) algorithm, showing that MFs had more proximity than the original features when projected, which demonstrates that MFs revealed a clear formation of well-defined clusters, with most points within each group sharing the same color, suggesting greater uniformity in the predicted values. Incorporating these MFs into subsequent regression models demonstrated improved performance, with ${\overline{R}}^2$ values higher than 0.9 for MF Grad Boost M3, MF GradientBoost M5, and all kNN MFs and reduced error margins compared to field-measured yield values. The ${\overline{R}}^2$ values obtained in this work ranged above 0.98 for the AdaBoost meta-regressor applied to MFs, which were obtained from kNN regression on five models created by the researchers of CSIRO, and around 0.99 for the kNN meta-regressor applied to MFs obtained from kNN regression on these five models. Full article

With the rapid development of China’s economic construction and the increasing scale of the project, more and more complex engineering geological problems have put forward higher requirements for engineering geological researchers. As the core link of engineering geological research, engineering geological evaluation provides a key scientific basis for solving engineering geological problems. The engineering geological evaluation model is a good tool and means to support the realization of the evaluation method. Therefore, it is urgent to study three-dimensional engineering geological evaluation modeling systematically. In view of the current situation that the construction methods of the three-dimensional engineering geological evaluation model in the field of infrastructure construction at home and abroad are not uniform, this paper briefly summarizes the research progress of the three-dimensional engineering geological evaluation model. It focuses on three-dimensional geological modeling and the three-dimensional engineering geological space evaluation index system. This study discusses the main methods for comprehensive engineering geological evaluation and the construction of a three-dimensional geological model. At the same time, in response to the low accuracy of current three-dimensional engineering geological evaluation models and their insufficient integration with numerical simulations, this paper proposes improvement suggestions and outlines the development trends of such models. The purpose of this paper is to deepen the engineering geological evaluation work, promote its sustainable development, and lay the foundation for the study of a three-dimensional engineering geological evaluation model so as to cope with more complex engineering geological challenges in the future. Full article

Aphids are significant pests of winter wheat, causing damage by feeding on plant sap and reducing crop yield and quality. This study evaluates the potential of hyperspectral remote sensing (350–2500 nm) and machine learning (ML) models for classifying healthy and aphid-infested wheat canopies. Field-based hyperspectral measurements were conducted at three growth stages—T1 (stem elongation–heading), T2 (flowering), and T3 (milky grain development)—with infestation levels categorized according to established economic thresholds (ET) for each growth stage. Spectral data were analyzed using Uniform Manifold Approximation and Projection (UMAP); vegetation indices; and ML classification models, including Logistic Regression (LR), k-Nearest Neighbors (KNNs), Support vector machines (SVMs), Random Forest (RF), and Light Gradient Boosting Machine (LGBM). The classification models achieved high performance, with F1-scores ranging from 0.88 to 0.99, and SVM and RF consistently outperforming other models across all input datasets. The best classification results were obtained at T2 with an F1-score of 0.98, while models trained on the full spectrum dataset showed the highest overall accuracy. Among vegetation indices, the Modified Triangular Vegetation Index, MTVI (r_pb = −0.77 to −0.82), and Triangular Vegetation Index, TVI (r_pb = −0.66 to −0.75), demonstrated the strongest correlations with canopy condition. These findings underscore the utility of canopy spectra and vegetation indices for detecting aphid infestations above ET levels, allowing for a clear classification of wheat fields into “treatment required” and “no treatment required” categories. This approach provides a precise and timely decision making tool for insecticide application, contributing to sustainable pest management by enabling targeted interventions, reducing unnecessary pesticide use, and supporting effective crop protection practices. Full article

The term “engineering cancer” refers to expansive soil, whose properties threaten the stability and safety of structures. As a result, appropriate steps must be taken to guarantee the sustainable development of buildings. To explore the impact of freeze–thaw cycles (FTCs) on the microscopic characteristics of unsaturated expansive soil in the cold region, the mineralogical composition and microstructure were analyzed using X-ray diffraction (XRD), thermogravimetric analysis, and scanning electron microscopy (SEM). The influence of repeated FTCs on the characteristics of particle morphology and pore structure in expansive soil was quantitatively examined. The findings indicate that, in comparison to other expansive soil samples, the Yanji expansive soil is particularly susceptible to failures due to its high sand content and low liquid limit. The FTCs significantly alter the microstructure, leading to increased complexity in the particle edge shapes, a transition in particle distribution from dispersed to more concentrated, a reduction in larger particles, and a more intricate spatial arrangement of particles. As moisture content rises, the impact of FTCs becomes increasingly pronounced. The particle distribution’s area probability index and fractal dimension are identified as medium-variability parameters, with a high-variation coefficient before the 3rd FTC, which then gradually decreases. The repeated FTCs result in particle breakage and agglomeration, causing the particle size to become more uniform and the soil’s microstructure to stabilize after 3–5 FTCs. These findings contribute to understanding the FTC behavior of expansive soils, provide theoretical support and scientific guidance for disaster prevention and control measures, as well as for the sustainable development of engineering projects involving expansive soil sites. Full article

Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing classification accuracy. While principal component analysis (PCA) is commonly used to address this issue, its effectiveness relative to other feature extraction (FE) methods in BSM remains underexplored. This study aims to enhance ML classifier accuracy in BSM by evaluating various FE techniques that mitigate multicollinearity among vegetation indices. Using composite burn index (CBI) data from the 2014 Carlton Complex fire in the United States as a case study, we extracted 118 vegetation indices from seven Landsat-8 spectral bands. We applied and compared 13 different FE techniques—including linear and nonlinear methods such as PCA, t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), Isomap, uniform manifold approximation and projection (UMAP), factor analysis (FA), independent component analysis (ICA), multidimensional scaling (MDS), truncated singular value decomposition (TSVD), non-negative matrix factorization (NMF), locally linear embedding (LLE), spectral embedding (SE), and neighborhood components analysis (NCA). The performance of these techniques was benchmarked against six ML classifiers to determine their effectiveness in improving BSM accuracy. Our results show that alternative FE techniques can outperform PCA, improving classification accuracy and computational efficiency. Techniques like LDA and NCA effectively capture nonlinear relationships critical for accurate BSM. The study contributes to the existing literature by providing a comprehensive comparison of FE methods, highlighting the potential benefits of underutilized techniques in BSM. Full article

Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA’s EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban. Full article

Sequence alignment is a critical factor in the variant analysis of genomic research. Since the FM (Ferrainas–Manzini) index was developed, it has proven to be a model in a compact format with efficient pattern matching and high-speed query searching, which has attracted much research interest in the field of sequence alignment. Such characteristics make it a convenient tool for handling large-scale sequence alignment projects executed with a small memory. In bioinformatics, the massive success of next-generation sequencing technology has led to an exponential growth in genomic data, presenting a computational challenge for sequence alignment. In addition, the use of a heterogeneous computing system, composed of various types of nodes, is prevalent in the field of HPC (high-performance computing), which presents a promising solution for sequence alignment. However, conventional methodologies in short-read alignment are limited in performance on current heterogeneous computing infrastructures. Therefore, we developed a parallel sequence alignment to investigate the applicability of this approach in NUMA-based (Non-Uniform Memory Access) heterogeneous architectures against traditional alignment algorithms. This proposed work combines the LF (longest-first) distribution policy with the EP (enhanced partitioning) strategy for effective load balancing and efficient parallelization among heterogeneous architectures. The newly proposed LF-EP-based FM aligner shows excellent efficiency and a significant improvement over NUMA-based heterogeneous computing platforms. We provide significantly improved performance over several popular FM aligners in many dimensions such as read length, sequence number, sequence distance, alignment speedup, and result quality. These resultant evaluation metrics cover the quality assessment, complexity analysis, and speedup evaluation of our approach. Utilizing the capabilities of NUMA-based heterogeneous computing architectures, our approach effectively provides a convenient solution for large-scale short-read alignment in the heterogeneous system. Full article

High-performance coding solutions are urgently needed for the storage and transmission of 3D point clouds due to the development of 3D data acquisition facilities and the increasing scale of acquired point clouds. Video-based point cloud compression (V-PCC) is the most advanced international standard for compressing dynamic point clouds. However, it still has serious issues of time consumption and the large size of the occupancy map. Considering the aforementioned issues, based on V-PCC, we propose the Voxel Selection-based Refining Segmentation (VS-RS), which is used to accelerate the refining segmentation process of the point cloud. Furthermore, the data-adaptive patch packing (DAPP) is proposed to reduce the size of the occupancy map. In order to specify the effect of the improvement, we also designed novel evaluation indicators. Experimental results show that the proposed method achieves a Bjøntegaard Delta rate (BD-rate) gain of −1.58% in the V-PCC benchmark. Additionally, it reduces encoding time by up to 31.86% and reduces the size of the occupancy map by up to 20.14%. Full article

Cold exposure remains a significant public health concern, particularly in the Arctic regions prone to extremely cold weather. While the physical health impacts of cold exposure are well documented, understanding the social vulnerability aspects is crucial for effective mitigation and policy development. This study investigates the multifaceted dimensions of social vulnerability in the face of cold temperatures across various communities in Alaska. Alaska, renowned for its extreme cold temperatures and harsh environmental conditions, poses unique challenges to its residents, particularly in the context of social vulnerability. Drawing on a combination of quantitative data analysis and qualitative insights, we examine the factors contributing to social vulnerability, including demographic, economic, geographic, and infrastructural elements, in terms of the Extremely Cold Social Vulnerability Index, for seven Public Health Regions in Alaska. The Universal Thermal Climate Index in two very cold categories (<−27 °C) was used to identify cold exposure. Factors such as income, housing quality, health status, and resilience of the population play crucial roles in determining an individual or community’s sensitivity to, and ability to cope with, cold temperatures. Our analysis reveals that social vulnerability in Alaska is not uniform but varies significantly among regions. The research findings highlight the importance of considering factors of both sensitivity and adaptivity in understanding and addressing social vulnerability, thereby informing the development of targeted strategies and policies to enhance the resilience of Alaskan communities. As cold temperatures are projected to continue to challenge the region, addressing social vulnerability is essential for ensuring the well-being and safety of Alaska’s diverse populations. Full article

The southern portion of the Qinghai–Tibet Plateau (QTP) and the central Himalayan region are home to the Mt. Qomolangma (Everest) National Nature Reserve (QNNR), which is the world’s highest nature reserve and is distinguished by delicate natural ecosystems and unique geographic features. Analyzing regional vegetation trends, as well as the impacts of natural and anthropogenic variables on vegetation coverage, is crucial for local environmental protection and sustainable development. In this study, the variation patterns of the MOD13Q1 Normalized Difference Vegetation Index (NDVI) data were explored, and the responses of vegetation development to both natural and anthropogenic parameters were investigated by applying trend analysis and partial correlation analysis, as well as the partial least squares-structural equation model (PLS-SEM). To better comprehend the spatial characteristics and interrelationships between NDVI and various parameters under different vegetation types, the Uniform Manifold Approximation and Projection (UMAP) was employed for dimensionality reduction and visualization. The results illustrated that between 2000 and 2018, the reserve greened up at a rate of 0.00073/a (p < 0.05), with vegetation improvement areas accounting for 49.46%. The major climatic driver for the greening trend of vegetation was temperature. Topography (especially elevation) remains dominant in regulating vegetation development in the QNNR, despite a progressively growing impact of hydrothermal conditions on vegetation development. Additionally, the implementation of environmental initiatives has stifled the adverse impacts of human activity. Full article

Due to the influence of the high proportion of renewable energy penetration, the time-varying and complex operation mode of the new power system is gradually increasing, leading to a lack of fineness and practicality of traditional operation modes. To this end, a new visual extraction method for fine operation mode of power system is proposed. Specifically, aiming at the dimensional problem between high-dimensional electrical characteristic variables, a power grid operation data preprocessing method based on maximum absolute standardization (MaxAbs) is designed. Then, in order to reduce the impact of redundant features on the accuracy of the operation mode extraction results, the Pearson correlation coefficient is introduced to optimize the feature space relationship matrix, constructing a screening model of operating mode characteristic variables based on pearson kernel principal component analysis (P_KPCA). Then, with the clustering elbow index as the constraint condition, a K-means algorithm based on improved particle swarm optimization (IPSO-Kmeans) was proposed to realize fine operation mode extraction. Finally, the experimental analysis is carried out with the actual operation data of the power grid for one year and based on uniform manifold approximation and projection (UMAP) to visualize the extraction results of the operation mode. The validity and accuracy of the proposed method are verified. Full article

The transition zone between the Mu Us Sandy Land and the Loess Plateau is considered an ecologically fragile area. However, significant changes in land use have occurred in the past few decades due to changes in land policies and the implementation of major national ecological projects. Despite this, there is still a lack of clear investigation into the impact of these changes on the landscape structure and ecological health of the area. This study utilizes high-resolution annual land use data from China, along with multi-index models and algorithms, to comprehensively analyze regional land use changes, landscape patterns, and the ecological environment’s quality. Through a comprehensive analysis of various factors, including changes in quantity, transformation in land types, spatial dynamics, landscape structure, and ecological quality, we aim to provide a better understanding of the complex interactions between land use and ecological systems in this area. The research results indicate that: (1) Since 2000, 9057.4 km² of land in the study area has undergone changes. The grassland area has the largest increase, the forest area has the fastest growth rate, while cropland and barren land have decreased to varying degrees, and impervious surface has slightly expanded. (2) The movement trajectory of the center of gravity for different land types is closely related to human activities such as land development and utilization, as well as ecological restoration. Land changes have resulted in an escalation of landscape fragmentation, a reduction in landscape diversity, and a decline in the uniform distribution of different types. (3) Ecological land is the key to improving the ecological environment. The increase in ecological land area in the study area has led to an improvement in the quality of the ecological environment. The net contribution rate of land change to ecological improvement reaches 1.99%. The analysis methods and perspectives used in this study can be applied to other similar studies. The study’s findings enhance the understanding of how land and vegetation changes affect the ecological environment in this crucial area. They are of great significance in guiding the development and utilization of land resources and the implementation of ecological environment projects. Full article

Coastal wetlands are natural complexes situated between terrestrial and marine ecosystems and are one of the most productive ecosystems in terms of global biomass production. However, under the influence of intensive human activity, global coastal wetlands have undergone rapid degradation. In this study, RS technology, landscape ecology, and object-oriented methods were used to interpret remote sensing images from different periods and analyze the dynamic changes in landscape patterns and their driving mechanisms in coastal wetlands in the Luoyangjiang River estuary from 1983 to 2021 by considering changes in the landscape pattern index. The results show that the patch areas of all the types of wetland landscapes in the Luoyangjiang River estuary changed, and the patch areas of mangroves and Spartina alterniflora increased. The patch density of the coastal wetlands increased significantly, the index of mangrove aggregation increased, and the index of separation decreased. From the perspective of the overall characteristic value of the landscape pattern, the landscape diversity index and the evenness index of the study area gradually increased, and the difference in the proportion of different types of landscape was reduced. Additionally, the patch number and patch diversity significantly increased, the maximum patch index and the spread index decreased, and the landscape separation index significantly increased. Rapid urbanization and the implementation of many ecological restoration projects were shown to be the main factors driving changes in the landscape indices of coastal wetlands in the Luoyangjiang River estuary. In the study period, rapid urbanization significantly reduced the area of coastal wetlands, and the implementation of ecological restoration projects increased the fragmentation, heterogeneity, and dispersion of wetland landscapes in the study area and decreased the aggregation of wetland landscapes. Moreover, the distribution of all the types of landscapes gradually became more uniform. Full article

Mineral resource-based cities have formed a large number of goafs due to the long-term mining of coal. It is of great significance to make full use of the abandoned land resources above the goaf to promote the transformation and development of resource-based cities. In order to avoid the threat of surface residual deformation to the proposed construction project, it is an urgent problem to obtain the stability results of the construction site accurately. First of all, based on the principles of relevance, hierarchy, representativeness and feasibility of index selection, 10 indexes are selected to construct the stability evaluation index system. Then the subjective weight and objective weight of evaluation indexes are determined based on improved AHP, rough set and CRITIC methods, which improves the accuracy of the determination of the index weights. In addition, the membership degree of each index is determined using the cloud model. Finally, the stability grade can be obtained according to the maximum membership degree theory. The above researches are applied to evaluate the stability of the Mianluan expressway construction site, and the results show that the stability level of the study area is not uniform and that there are two states: stable and basically stable. Finally, a sensitivity analysis of the subjective weight of each index is carried out, the index stopping time has the highest sensitivity to weight (12.44%), which is far lower than the corresponding weight change rate of 100%, indicating that the determination of weight is scientific and reasonable. These things considered, the reliability of the evaluation result is indirectly verified according to the field leveling. This research can provide a reference for the effective utilization of land resources above an old goaf. Full article