Random Sample Points Research Articles

Multi-attribute diagnosis of urban flood-bearing bodies based on integrated learning with Stacking–GPR–QPSO coupling

Flood-bearing bodies are urban components directly impacted and damaged by disasters. Current methods for attribute identification and diagnosis of flood-bearing bodies, relying on real-time monitoring, are inadequate for pre-disaster forecasting and lack comprehensiveness. To reduce the uncertainty associated with single data sources, a Dual Path Network (DPN) method was employed to extract feature vectors based on multi-source datasets. A meta-classifier was constructed by integrating five base learners using Stacking, optimized by Quantum Particle Swarm Optimization (QPSO)-enhanced Gaussian Process Regression, forming an ensemble learner for predicting urban spatial classification. Utilizing GIS proximity analysis functions, attributes of functional zones, spatial attributes of points of interest (POI), and flood loss were assigned to each flood-bearing body grid. By overlaying urban flood inundation maps, multi-attribute diagnosis of flood-bearing bodies was achieved. The Jinshui District of Zhengzhou, China, is selected as the study area. The results show: (1) Predictions of urban functional zone categories in four other districts of Zhengzhou showed an average accuracy rate of 78.5 % through random sampling point validation. The threshold effect of prediction accuracy at different scales was significant. (2) Simulated flood economic losses for recurrence intervals of 1 year, 5 years, 10 years, 20 years, 50 years, and 100 years exhibited an exponential growth trend. (3) The multiple flood-bearing attributes of each flooded grid can be diagnosed. Finally, the model was effectively verified by simulating and comparing historical data from the “7·20” flood event in Zhengzhou.

Characteristics of Vegetation and Soil of Degraded Grasslands and Their Relationships in Xizang

To investigate the characteristics of grassland degradation on a regional scale in Xizang, data on grassland degradation from the second grassland survey of Xizang and 12 vegetation and soil indicators from the National Tibetan Plateau Data Center were collected. Using ArcMap, 10 000 random sample points were selected on raster data （excluding non-grassland, desertification, and salinization data, leaving 7 949 valid sample points）. The multi-value extraction to-point method was applied to extract degradation and indicator data for each sample point. The characteristics of degraded grassland vegetation and soil and their relationships were analyzed in Xizang. Moreover, random forest modeling was conducted to predict the trend of grassland ecosystem changes. The results indicated that： ① The grasslands in Xizang were primarily composed of alpine steppe and alpine meadow types, accounting for 45.83% and 41.15% of the valid sample points, respectively. ② With the intensification of grassland degradation, the number of steppe-type species among the 17 grassland types gradually decreased, and the proportion of steppe dominated by species such as Stipa purpurea and Carex moorcroftii decreased, whereas the proportion of miscellaneous grasses and Dasiphora fruticosa increased. ③ As the degree of degradation increased, vegetation indicators generally showed a declining trend, with soil total nitrogen, total phosphorus, total potassium, and organic carbon decreasing, whereas soil pH and bulk density increased, and soil moisture content was not significant. ④ A positive correlation exists between soil moisture content, total nitrogen, total phosphorus, total potassium, organic carbon, vegetation cover, net primary productivity of vegetation, normalized difference vegetation index, aboveground biomass, and habitat quality. However, there was a negative correlation between pH and soil bulk density, and the correlation coefficients among various indicators decreased with the intensification of degradation. ⑤ The random forest simulation results showed that during the degradation process, the contribution rates of soil bulk density and habitat quality both exceeded 12%, with the model prediction accuracy reaching 78%. The study revealed that grassland degradation in Xizang was closely related to soil bulk density and habitat quality, indicating that higher soil bulk density or lower habitat quality may correspond to more severe grassland degradation. This provides a scientific basis for future grassland conservation and management.

Method for Brillouin gain spectrum recovery based on compressed sensing with convex optimization

The traditional Brillouin optical fiber distributed sensors obtain the Brillouin gain spectrum (BGS) through frequency-by-frequency sweeping acquisition, which can be time-consuming and data intensive. These characteristics put a lot of pressure on data storage, especially on signal processing. Compressed sensing is a method represented by random sampling to reduce the number of acquisition frequencies, but the results obtained may be unstable. In this paper, we have proposed a reconstruction algorithm based on compressed sensing with convex optimization (COP), which can recover the whole BGS by collecting only 10% of the acquisition frequencies. The recovered BGS can attain a RMSE similar to the fully collected BGS. The proposed algorithm also provides more accurate and stable performances for different random sampling points compared to existing reconstruction methods. For example, for a 10% sampling percentage, with a reduction in error of 2.24 and 0.40 MHz, values are lower than those employing the orthogonal matching pursuit (OMP) and the regularized orthogonal matching pursuit (ROMP), respectively. Moreover, the reconstruction results of the proposed method are more stable for different random sampling points, with a reduction in standard deviation of 2.58 and 0.07 MHz.

Winter Wheat Mapping Method Based on Pseudo-Labels and U-Net Model for Training Sample Shortage

In recent years, the semantic segmentation model has been widely applied in fields such as the extraction of crops due to its advantages such as strong discrimination ability, high accuracy, etc. Currently, there is no standard set of ground true label data for major crops in China, and the visual interpretation process is usually time-consuming and laborious. The sample size also makes it difficult to support the model to learn enough ground features, resulting in poor generalisation ability of the model, which in turn makes the model difficult to apply in fine extraction tasks of large-area crops. In this study, a method to establish a pseudo-label sample set based on the random forest algorithm to train a semantic segmentation model (U-Net) was proposed to perform winter wheat extraction. With the help of the GEE platform, Winter Wheat Canopy Index (WCI) indicators were employed in this method to initially extract winter wheat, and training samples (i.e., pseudo labels) were built for the semantic segmentation model through the iterative process of “generating random sample points—random forest model training—winter wheat extraction”; on this basis, the U-net model was trained with multi-time series remote sensing images; finally, the U-Net model was employed to obtain the spatial distribution map of winter wheat in Henan Province in 2022. The results illustrated that: (1) Pseudo-label data were constructed using the random forest model in typical regions, achieving an overall accuracy of 97.53% under validation with manual samples, proving that its accuracy meets the requirements for U-Net model training. (2) Utilizing the U-Net model, U-Net++ model, and random forest model constructed based on pseudo-label data for 2022, winter wheat mapping was conducted in Henan Province. The extraction accuracy of the three models is in the order of U-Net model > U-Net++ model > random forest model. (3) Using the U-Net model to predict the winter wheat planting areas in Henan Province in 2019, although the extraction accuracy decreased compared to 2022, it still exceeded that of the random forest model. Additionally, the U-Net++ model did not achieve higher classification accuracy. (4) Experimental results demonstrate that deep learning models constructed based on pseudo-labels exhibit higher classification accuracy. Compared to traditional machine learning models like random forest, they have higher spatiotemporal adaptability and robustness, further validating the scientific and practical feasibility of pseudo-labels and their generation strategies, which are expected to provide a feasible technical pathway for intelligent extraction of winter wheat spatial distribution information in the future.

Orthogonal systems based stable estimator for non parametric regression problems

. In this work, we study a univariate as well as a multivariate Jacobi polynomial-based estimator for solving the fundamental problem of non parametric (NP) regression. The proposed NP regression estimator aims to approximate a possibly very low regular regression function from the knowledge of its noised values given at some n random sampling points. We first study the univariate version of the proposed estimator, then extend this study to the general multivariate case with random sampling vectors drawn according to an unknown distribution. Our proposed estimator has the advantage of being stable with reasonable time complexity. Moreover, it has an optimal convergence rate under the weak condition that the regression function has a certain low Sobolev smoothness property. Although the proposed estimators are studied under the hypothesis that the random sampling vectors are supported on the d dimensional unit hypercube, by applying trivial transformations, the results of this work are still valid in the more general setting of any d dimensional hyperrectangle. Finally, the theoretical results of this work are illustrated by some numerical simulations on synthetic as well as real datasets.

Simple Conditional Spatial Query Mask Deformable Detection Transformer: A Detection Approach for Multi-Style Strokes of Chinese Characters

In the Chinese character writing task performed by robotic arms, the stroke category and position information should be extracted through object detection. Detection algorithms based on predefined anchor frames have difficulty resolving the differences among the many different styles of Chinese character strokes. Deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points with no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause a reduction in the speed of the vector learning stroke features in the detection head. In view of this problem, a new detection method for multi-style strokes of Chinese characters, called the simple conditional spatial query mask deformable DETR (SCSQ-MDD), is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contributions and resample the points without contributions to address the randomness of the correlation calculation among the reference points. Secondly, by separating the content query and spatial query of the transformer decoder, the dependence of the prediction task on the content embedding is relaxed. Finally, the detection model without predefined anchor frames based on the SCSQ-MDD is constructed. Experiments are conducted using a multi-style Chinese character stroke dataset to evaluate the performance of the SCSQ-MDD. The mean average precision (mAP) value is improved by 3.8% and the mean average recall (mAR) value is improved by 1.1% compared with the deformable DETR in the testing stage, illustrating the effectiveness of the proposed method.

Design and research of residual film pollution monitoring system based on UAV

To evaluate residual film recovery quality and rapidly monitor residual film pollution in agricultural fields, a residual film pollution monitoring system based on UAV, ground station, and cloud server was proposed in this research. It integrated a random sampling point generation (RSPG) algorithm and a key-lock pairing (KLP) algorithm for fast planning of flight paths, and used the U-Net model to rapidly segment residual films from images and calculate residual film coverage. The results showed that the average deviation between the actual distance and the ideal distance of the randomly generated sampling points using the RSPG algorithm was 0.0733 m, and the average deviation of the azimuth angles was 0.2°. The KLP algorithm significantly reduced path planning time, while maintaining a high shortest path rate and reducing memory consumption. The U-Net model achieved an optimal mean intersection over union (MIOU) of 85.63 % on the test set. The simulation and field verification of the system found that automatic UAV path planning and rapid residual film image segmentation were achieved. The coefficient of determination (R2) between the predicted values and the manually labelled values was 0.966, and the root-mean-square error (RMSE) was 0.1173 %. The average relative error between the predicted residual film coverage and the manually labelled coverage was 11.55 %, and the average detection time of the monitoring system was 422.6 s. Therefore, the UAV-based residual film pollution monitoring system can realize integrated automatic sampling and rapid residual film pollution assessment. This study provides technical support for accurate assessment and control of residual film pollution.

Research on a random sampling method for bulk grain based on the M-Unet and SGBM algorithms

In response to the challenge of limited real-time capability in identifying and positioning sampling points during random sampling of bulk grains in industrial settings, this paper introduces a novel approach. It combines the utilization of a lightweight neural network model, known as M-Unet, with the Semi-Global Block Matching (SGBM) algorithm to achieve the random sampling of bulk grains. The process commences with the deployment of a binocular camera to capture image data from the grain loading area, followed by binocular correction. Recognizing the constraints associated with the traditional U-Net network model, characterized by large parameters and suboptimal real-time performance, we introduce the M-Unet network, which is a more efficient alternative. M-Unet incorporates MobileNet as a substitute for the U-Net network encoder, resulting in a significant improvement in real-time performance and segmentation quality. Subsequently, the M-Unet network is employed to partition the corrected left image into multiple regions based on predefined sampling principles, and random sampling points are generated within each region. To determine the three-dimensional spatial positioning of these sampling points, we employ the SGBM algorithm, a semi-global stereo matching technique, on the left and right images of the corrected grain loading area. Experimental results demonstrate that the proposed method exhibits robust real-time performance and effectively recognizes grain loading areas. Furthermore, this method is suitable for deployment on portable devices, offering novel insights and methodologies for automating and advancing the intelligence of grain sampling processes. This innovation effectively addresses the challenges associated with low efficiency and human-related factors in traditional grain sampling. The findings presented in this paper carry substantial significance in safeguarding the interests of grain farmers, reducing food losses, minimizing waste, and enhancing the intelligence of sampling processes.

Determining Resampling Ratios Using BSMOTE and SVM-SMOTE for Identifying Rare Attacks in Imbalanced Cybersecurity Data

Machine Learning is widely used in cybersecurity for detecting network intrusions. Though network attacks are increasing steadily, the percentage of such attacks to actual network traffic is significantly less. And here lies the problem in training Machine Learning models to enable them to detect and classify malicious attacks from routine traffic. The ratio of actual attacks to benign data is significantly high and as such forms highly imbalanced datasets. In this work, we address this issue using data resampling techniques. Though there are several oversampling and undersampling techniques available, how these oversampling and undersampling techniques are most effectively used is addressed in this paper. Two oversampling techniques, Borderline SMOTE and SVM-SMOTE, are used for oversampling minority data and random undersampling is used for undersampling majority data. Both the oversampling techniques use KNN after selecting a random minority sample point, hence the impact of varying KNN values on the performance of the oversampling technique is also analyzed. Random Forest is used for classification of the rare attacks. This work is done on a widely used cybersecurity dataset, UNSW-NB15, and the results show that 10% oversampling gives better results for both BMSOTE and SVM-SMOTE.

Google Earth Engine Based Spatio-Temporal Changes of Bafa Lake from 1984 to 2022

The water resource management is crucial to protect environment and ecological cycle. The detection of temporal and spatial changes in the lake's water extent is important for sustainable land planning. Therefore, the areal changes over the wetlands must be well monitored. Bafa Lake is an essential downstream water in the Büyük Menderes Basin which is the largest river basin of the Aegean Region. Google Earth Engine (GEE) is an easy-to-use online remote sensing data processing platform based on cloud computing. In this study, the long-term spatio-temporal changes of Bafa Lake between 1984-2022 have been analyzed using Landsat-5/8 satellite images on the GEE platform. A total of 1093 Landsat images were processed. The annual water areas were computed through composite images per year. The water area extraction was done using the normalized water difference index (NDWI). The minimum and maximum lake water areas in 38 years were detected as 5474 ha and 6789 ha in 1990 and 2006, respectively. In the accuracy assessment according to random sampling points, the Overall Accuracy (OA) was calculated as 98% and the kappa coefficient as 0.96. The water surface area was increased by 3.9% from 1984 to 2022. Between 2015-2022, the maximum increase or decrease in the lake area compared to the previous year observed as less than 1%. Therefore, there has not been a notable variation in the water area of Bafa Lake in the past few years.

Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR

Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding path tracking, the Linear Quadratic Regulator (LQR) algorithm is widely used in various control applications due to its efficient stability and ease of implementation. However, the relatively empirical selection of its weight matrix can affect the control effect. This study suggests a path planning and tracking control framework for autonomous vehicles based on an upgraded RRT* and Particle Swarm Optimization Linear Quadratic Regulator (PSO-LQR) to address the abovementioned issues. Firstly, according to the driving characteristics of autonomous vehicles, a variable sampling area is used to limit the generation of random sampling points, significantly reducing the number of iterations. At the same time, an improved Artificial Potential Field (APF) method was introduced into the RRT* algorithm, which improved the convergence speed of the algorithm. Utilizing path pruning based on the maximum steering angle constraint of the vehicle and the cubic B-spline algorithm to achieve path optimization, a continuous curvature path that conforms to the precise tracking of the vehicle was obtained. In addition, optimizing the weight matrix of LQR using POS improved path-tracking accuracy. Finally, this article’s improved RRT* algorithm was simulated and compared with the RRT*, target bias RRT*, and P-RRT*. At the same time, on the Simulink–Carsim joint simulation platform, the PSO-LQR is used to track the planned path at different vehicle speeds. The results show that the improved RRT* algorithm optimizes the path search speed by 34.40% and the iteration number by 33.97%, respectively, and the generated paths are curvature continuous. The tracking accuracy of the PSO-LQR was improved by about 59% compared to LQR, and its stability was higher. The position error and heading error were controlled within 0.06 m and 0.05 rad, respectively, verifying the effectiveness and feasibility of the proposed path planning and tracking control framework.

Research on obstacle avoidance motion planning method of manipulator in complex multi scene

In order to improve the efficiency and success rate of obstacle avoidance motion planning of industrial manipulator in complex multi scenes, a collision detection model between manipulator and obstacles based on cylinder and sphere bounding box is established, and an improved RRT*algorithm based on heuristic probability fusion artificial potential field method(P-artificial potential field RRT*, PAPF-RRT*) is proposed. The probability target bias and random sampling point optimization strategy are introduced into the sampling, and the location optimization constraints are applied to the sampling points to enhance the sampling guidance and quality. In order to change the expansion direction of the traditional new node and the local optimization problem in special environment, the target gravity, obstacle repulsion and adaptive step size of the artificial potential field method are combined, so that the algorithm can guide the expansion direction and step size of the new node in real time within the resultant force range generated by APF, reducing excessive exploration and the expansion of the collision region. The Cubic B-spline is used to interpolate and optimize the planned path to reduce the complexity of the path and improve the flexibility of the path. The simulation results in two-dimensional and three-dimensional multi scenes show that the present algorithm reduces the average path search time by 31.22% and shortens the path length by 17.32% comparing with the traditional RRT*algorithm. The visual simulation results show that the present algorithm can make the manipulator successfully avoid obstacles and run to the target point quickly and smoothly.

Field trial: disinfection of contaminated anesthetic masks for piglets

This paper aimed to assess the success of cleaning and disinfection on microbiological contamination of anesthetic masks, which were used for automated isoflurane anesthesia for surgical castration of male piglets. Data collection took place on 11 farms in Southern Germany between September 2020 and June 2022. Each farm was visited three times (one farm having two different anesthesia devices was visited six times), and microbiological assessments took place at four sample points (SP): after unpacking the masks (SP0), after disinfection before anesthesia (SP1), after anesthesia of all piglets to be castrated in this run (SP2), and after disinfection after anesthesia (SP3). The microbiological assessment included the determination of total bacteria count, total count of hemolytic and non-hemolytic mesophilic aerotolerant bacteria and a qualitative detection of indicator bacteria Escherichia (E.) coli, extended-spectrum beta-lactamase-producing E. coli (ESBL) and methicillin-resistant Staphylococcus aureus (MRSA). For analysis, a generalized linear mixed model was applied using farms and farm visits as random effects and sampling points nested in farm visits as fixed effect. The fixed effect was highly significant for all three variables (total bacteria count, total count of hemolytic and non-hemolytic mesophilic aerotolerant bacteria) (p < 0.001). The bacterial counts at SP0 were about the same as at SP3. Concerning indicator bacteria, their presence was highest at SP2 and lowest at SP3. No indicator bacteria were present at SP1. It can be concluded that disinfection of anesthetic masks, especially before performing anesthesia, may effectively protect piglets of the following batch against unwanted transmission of pathogens. These findings will help farmers plan cleaning and disinfection activities.

Fine‐scale accuracy assessment of the 2016 National Land Cover Dataset for stream‐based wildlife habitat

AbstractEfficiently and effectively identifying and assessing potential wildlife habitat and important ecological resources is essential as rapid anthropogenic land use change alters and detrimentally affects terrestrial and aquatic habitats. Accuracy assessment of remotely sensed data supports ecological planning and management decisions, and is especially important when using freely available, coarse‐resolution spatial datasets, such as the National Land Cover Dataset (NLCD). A popular dataset designed for application at larger regional to national spatial scales, the NLCD has been used in finer scale studies outside of its intended use, often without the imperative support of field‐based accuracy assessment. We ground‐truthed stratified random sampling points to assess the accuracy of the 2016 NLCD at a fine spatial scale relevant to stream‐based ecological research. Our results demonstrated an overall accuracy of &lt;65%, less than the United States Geological Survey recommended accuracy of ≥85%. Results indicated that the NLCD may not be effective as a tool for stream‐level studies and could provide erroneous results for fine‐scale habitat assessment and planning when used as the only reference dataset. When conducting ecological research, it is important to consider the appropriate scale, resolution, and limitations of available datasets to achieve the most accurate results.

Path Planning of Indoor Air Sampling Pipe in Substations Based on Fast Extended Random Number Algorithm

The substation is a fire accident-prone place, the substation fire automatic alarm system optimization design is an organic part of the fire safety work, and the substation air sampling pipe laying structure design method is an important link. In this paper, the substation model and sampling space are established, and the root node of the tree is searched by fast random in the RRT random algorithm to generate random sampling points and find out the path node nearest to the random sampling points. If so, the distance between the two points is less than the length, and the target point is considered to have been reached. This method can search the three-dimensional space quickly and solve the problem of the path planning of the air sampling pipe in the complex environment of the substation.

White-tailed deer (Odocoileus virginianus) nutritional carrying capacity over two decades after exclusion

Studies on the impacts of white-tailed deer (Odocoileus virginianus; hereafter deer) herbivory on vegetation biomass and nutritional availability are limited. Considering deer can shift plant species composition and abundance through selective browsing, we sought to investigate whether deer could influence their own nutritional carrying capacity. Using paired control and exclosure experimental units constructed in 2000 on three Wildlife Management Areas in the Upper Coastal Plain, Lower Coastal Plain, and Mississippi Alluvial Valley (hereafter Delta) ecoregions of Mississippi, we collected biomass samples following methods initially used on the sites in 2005. Biomass data were collected during July – August of 2021 at 15 random sampling points per experimental unit. Additionally, representative samples of each plant species were collected and analyzed for crude protein content to determine nutritional carrying capacity. By analyzing biomass data from 2005 and 2021, we found between-year differences, but no differences by treatment (exclosure vs. control). Notably, there was a simultaneous increase in shade tolerant species and decrease in shade intolerant species from 2005 to 2021, and a decrease in preferred forage species. Nutritional carrying capacity was only determined using 2021 data and revealed a lack of treatment effects. The differences between ecoregions demonstrated abundant low quality forage on both Coastal Plain sites (Upper and Lower) and a comparatively low amount of forage that was higher in quality in the Delta. Results suggest changing understory light conditions and a need for additional management if increasing available forage for deer is a desired management outcome. Specifically, regional differences in nutritional availability between the Coastal Plain sites and the Delta site illustrate the potential for active management to aid in the promotion of herbaceous understory forage species.

Continuous Monitoring of the Mining Activities, Restoration Vegetation Status and Solar Farm Growth in Coal Mine Region Using Remote Sensing Data

Abstract Land reclamation of previously mined regions has been incorporated in the mining process as awareness of environmental protection has grown. In this study, we used the open-pit coal mine Oslomej in R. N. Macedonia to demonstrate the activities related to the monitoring process of the study area. We combined the Google Earth Engine (GEE) computing platform with the Landsat time-series data, Normalized Difference Vegetation Index (NDVI), Random Forest (RF) algorithm, and the LandTrendr algorithm to monitor the mining impacts, land reclamation, and the solar farm growth of the coalfield region between 1984 and 2021. The data from the sequential Landsat archive that was used to construct the spatiotemporal variability of the NDVI over the Oslomej mine site (1984-2021) and the pixel-based trajectories from the LandTrendr algorithm were used to achieve accurate measurements and analysis of vegetation disturbances. The different land use/land cover (LULC) classes herbaceous, water, mine, bare land, and solar farm in the Oslomej coalfield area were identified, and the effects of LULC changes on the mining environment were discussed. The RF classification algorithm was capable of separating these LULC classes with accuracies exceeding 90 %. We also validated our results using random sample points, field knowledge, imagery, and Google Earth. Our methodology, which is based on GEE, effectively captured information on mining, reclamation, and solar farm change, providing annual data (maps and change attributes) that can help local planners, policymakers, and environmentalists to better understand environmental influences connected to the ongoing conversion of the mining areas.

Expected invariants of simplicial complexes obtained from random point samples

Expected invariants of simplicial complexes obtained from random point samples

Edaphic diversity of cunaxid mites (Acari: Prostigmata) in different tree formations from northern region of Rio Grande Do Sul State, Brazil

Areas of natural vegetation can favor biodiversity since they have a higher number of native plants and less impact of human activities compared to areas with agricultural crops. Orange and pine forestry stand out among the important monoculture crops for Brazilian agribusiness. In the orange crop, agrochemicals are used to control pests; in pine, there is no need to apply agrochemicals as often, however, biodiversity can be impacted by allelopathic effects of the plant. There are some mite families associated with these environments; among them the cunaxid mites present are associated with both the soil and the litter, feeding on springtails, nematodes and other small organisms. The objective of this study was to evaluate the cunaxid diversity associated with Natural Vegetation (NV), Pine Forestry (PF) and Orange Cultivation (OC) in the municipalities of Mormaço and Soledade, state of Rio Grande do Sul, Brazil. Three litter and soil collections were carried out at 12 random sampling points, in previously demarcated areas within each environment. In total, 330 cunaxids were collected, 57.6% in NV, 32.7% in OC and 9.7% in PF. Among the species found, Coleoscirus tuberculatus Den Heyer, Cunaxa n. sp., Neobonzia n. sp. and Pulaeus quadrisolenidius Castro &amp; Den Heyer were exclusively associated with NV, while Rubroscirus sp. only with OF and Dactiloscirus sp. with OC. The most abundant species was Neocunaxoides ovatus Lin, Zhang &amp; Ji, with 130 specimens and the rarest was Neobonzia n. sp., with only one specimen.

Improved Bidirectional RRT* Algorithm for Robot Path Planning

In order to address the shortcomings of the traditional bidirectional RRT* algorithm, such as its high degree of randomness, low search efficiency, and the many inflection points in the planned path, we institute improvements in the following directions. Firstly, to address the problem of the high degree of randomness in the process of random tree expansion, the expansion direction of the random tree growing at the starting point is constrained by the improved artificial potential field method; thus, the random tree grows towards the target point. Secondly, the random tree sampling point grown at the target point is biased to the random number sampling point grown at the starting point. Finally, the path planned by the improved bidirectional RRT* algorithm is optimized by extracting key points. Simulation experiments show that compared with the traditional A*, the traditional RRT, and the traditional bidirectional RRT*, the improved bidirectional RRT* algorithm has a shorter path length, higher path-planning efficiency, and fewer inflection points. The optimized path is segmented using the dynamic window method according to the key points. The path planned by the fusion algorithm in a complex environment is smoother and allows for excellent avoidance of temporary obstacles.