Search Results (918)

To address challenges in agent path planning within complex environments—particularly slow convergence speed, high path redundancy, and insufficient smoothness—this paper proposes KDB-RRT*, a novel algorithm built upon RRT.* This method integrates a bidirectional search strategy with a three-layer optimization framework: ① accelerated node retrieval via KD-tree indexing to reduce computational complexity; ② enhanced exploration efficiency through goal-biased dynamic circle sampling and a bidirectional gravitational field guidance model, coupled with adaptive step size adjustment using a Sigmoid function for directional expansion and obstacle avoidance; and ③ trajectory optimization employing DP algorithm pruning and cubic B-spline smoothing to generate curvature-continuous paths. Additionally, a multi-level collision detection framework integrating Separating Axis Theorem (SAT) pre-judgment, R-tree spatial indexing, and active obstacle avoidance strategies is incorporated, ensuring robust collision resistance. Extensive experiments in complex environments (Z-shaped map, loop-shaped map, and multi-obstacle settings) demonstrate KDB-RRT’s superiority over state-of-the-art methods (Optimized RRT*, RRT*-Connect, and Informed-RRT*), reducing average planning time by up to 97.9%, shortening path length by 5.5–21.4%, and decreasing inflection points by 40–90.5%. Finally, the feasibility of the algorithm’s practical application was further verified based on the ROS platform. The research results provide a new method for efficient path planning of intelligent agents in unstructured environments, and its three-layer optimization framework has important reference value for mobile robot navigation systems. Full article

Anthocyanins are important secondary metabolites that impart color to fruits, and their biosynthesis is regulated by light. AP2/ERF transcription factors represent one of the largest TF families in plants and play pivotal roles in regulating plant growth and development, secondary metabolism, and stress responses. However, their comprehensive profile in mango (Mangifera indica L.) and their role in mango anthocyanin biosynthesis remain largely unclear. In this study, genome-wide identification and analysis of the AP2/ERF gene family in mango were conducted. A total of 240 family members were identified and classified into five subfamilies. Phylogenetic tree, conserved motif, and gene structure analyses revealed high conservation within the same subfamily and significant divergence among different subfamilies. Synteny analysis indicated that segmental and tandem duplication events played a major role in the expansion of the MiAP2/ERF family. Organ-specific expression profiles based on RNA-seq data uncovered the expression patterns of MiAP2/ERF genes in different plant organs. Furthermore, RNA-seq analyses related to light-induced anthocyanin accumulation, including preharvest “bagging–debagging” treatment and postharvest UV-B/white light and blue light treatments, identified a subset of MiAP2/ERF genes with significant light-responsive trends. The expression patterns of six blue-light-induced MiAP2/ERF genes were validated by means of qPCR. In summary, this study provides a comprehensive theoretical characterization of the AP2/ERF family in mango and reveals its potential role in light-induced anthocyanin accumulation, thereby establishing a solid theoretical foundation for subsequent investigations into gene functions and molecular mechanisms. Full article

Rapidly exploring Random Trees (RRT) provide probabilistic completeness but often explore inefficiently in high-DOF manipulation tasks. We address this by proposing a contact-aware, two-level planner that couples a learned toggle–subgoal predictor with a conditional diffusion sampler in joint space under a completeness-preserving mixture with uniform sampling. An upper ResNet-based network predicts task-relevant milestones from RGB images: grasp/release “toggle” configurations and intermediate joint-space subgoals that serve as phase-wise, receding-horizon targets between consecutive contact events. Conditioned on these predictions and the current state, a lower-level diffusion model samples tree-extension segments—joint-space directions and step lengths—instead of absolute configurations. These proposals act as a drop-in replacement for uniform sampling in standard RRT/RRT-Connect, while a nonzero fraction of uniform samples preserves probabilistic completeness. By biasing growth toward contact-relevant regions, the planner concentrates the search near feasible approach manifolds without altering nearest-neighbor, steering, or collision-checking primitives. In mug pick-and-place simulations, the proposed method achieves higher success rates than diffusion and other sequence-based policies trained by imitation learning, and requires fewer RRT expansions than uniform and goal-biased RRT as well as prior learning-guided samplers based on CVAE and conditional GAN, under identical collision checking and iteration limits. Full article

Pruning is a regular and essential urban tree maintenance practice aimed at sustaining overall health, ecosystem services, and public safety. However, knowledge of post-pruning recovery dynamics remains limited, which in turn hinders accurate assessments of growth and ecological functions. To address this, we examined recovery dynamics of Acacia auriculiformis, a common urban species. Tree height and crown radius were recorded monthly for 12 months after pruning. Trees were classified into two size groups based on diameter at breast height (DBH, trunk diameter measured at 1.3 m above ground): medium (DBH < 45 cm) and large (DBH ≥ 45 cm). A generalized linear mixed model (GLMM), appropriate for repeated measures and non-normal data, was fitted using a Tweedie distribution and a log-link function to model the recovery pattern. Results showed continuous growth over time, with medium-sized trees presenting significantly higher crown radius growth than large trees (p = 0.006), while height growth did not differ (p = 0.788). The best model for height included time (AIC = −846.4), whereas crown recovery was best modelled by time and size class (AIC = −1586.6). These findings demonstrate that, in this study, medium-sized A. auriculiformis generally recover faster, especially in crown expansion. This exploratory study suggests that tree size may influence post-pruning recovery and can provide a reference for subsequent differentiated management studies. The morphological modeling further provides preliminary quantitative evidence for annual recovery dynamics in urban A. auriculiformis. Full article

Floating offshore wind is now receiving much attention as an expansion to bottom-fixed, especially in deep waters with large wind resources. In this regard, improving the performance and efficiency of floating offshore wind farms (FOWFs) is currently a highly addressed topic. The inter-array (IA) cable connection is a key aspect to be optimised. Due to floating offshore wind (FOW) particularities such as dynamic cable designs, higher power capacities, and challenging installation, IA cabling is expected to be a primary cost driver for commercial-scale FOWFs. Therefore, IA cabling optimisation can lead to large cost reductions. In this work, an optimisation with an adaptive particle swarm optimisation (PSO) algorithm for such wind farms is proposed, considering the floating substructures’ horizontal translations and its impact on the dynamic cable length. The method provides an optimised IA connection, reducing acquisition costs and power losses by using a clustered minimum spanning tree (MST) as an initial solution and improving it with the PSO algorithm. The PSO achieves a reduction in the levelised cost of energy (LCOE) between 0.018% (0.022 EUR/MWh) and 0.10% (0.12 EUR/MWh) and a reduction in cable acquisition costs between 0.18% (0.3 M EUR) and 1.34% (3.8 M EUR) compared to the initial solution, showing great potential for future commercial-sized FOWFs. Full article

Understanding how long-term local climate zone (LCZ) dynamics interact with rapid urbanization and land surface temperature (LST) changes is essential for sustainable planning in megaregion-scale urban clusters. In this paper, we propose a multi-feature local sample transfer method to obtain LCZ maps from 2000 to 2020 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) and then analyze spatiotemporal changes in LCZs and their impacts on surface thermal environments. Results show the following: (1) The proposed multi-feature local sample transfer approach significantly improves the efficiency of long-term LCZ mapping by greatly reducing the effort required for sample acquisition. (2) The built types (LCZ1–10) increased by 1.34% overall, with large low-rise (LCZ8) showing the greatest expansion (4.72%). The compact low-rise (LCZ3) was the only built type to decline, decreasing by 2.02%. (3) Urbanization has produced a contiguous warming core that expands outward from the central metropolitan zones, thereby promoting the UHI coalescence. (4) Dense trees (LCZA) and large low-rise (LCZ8) exerted the strongest influence on LST. Large low-rise (LCZ8) consistently exhibited the highest warming contribution in Foshan, Zhongshan, and Dongguan. In coastal cities including Shenzhen, Hong Kong, and Macao, the largest LST increases occurred when water (LCZG) areas were converted to bare rock or paved (LCZE) or cs (LCZ1–10). Overall, the results highlight the strong coupling between urbanization and surface heating, providing critical insights for urban climate adaptation and integrated land-use planning in rapidly urbanizing megaregions. Full article

This paper investigates the motion planning problem for symmetric UAV cooperative lifting in confined spaces. A dynamic model of the symmetric UAV cooperative lifting system is established, and differential flatness analysis is employed to transform nonlinear dynamics into constraints on flat outputs, thereby simplifying the motion planning process. The planning framework consists of two levels: path planning and trajectory planning. For path planning, a reinforcement learning-based bidirectional RRT (RLDB-BiRRT) method is proposed, which integrates the random tree expansion mechanism with the DDPG algorithm to achieve adaptive directional bias. This approach effectively mitigates the issues of low search efficiency and excessive redundant nodes inherent in traditional RRT algorithms. For trajectory planning, an adaptive safe flight corridor (SFC) construction method is introduced, combining symmetric ellipsoids and convex polyhedra to generate high-quality linear constraints. Building upon the proposed motion planning method and leveraging differential flatness analysis, a unified planning framework is developed that seamlessly integrates the reinforcement learning-enhanced path planning with adaptive safe corridor construction and differential-flatness-based trajectory optimization, specifically designed for symmetric UAV cooperative lifting tasks in confined spaces. This integrated approach enhances corridor space utilization and ensures trajectory continuity. Simulation experiments validate the effectiveness of the proposed methods, demonstrating their capability to generate dynamically feasible, smooth, and safe transportation trajectories in confined environments, while effectively constraining load swing and UAV attitude angles. This study provides theoretical foundations and practical references for the application of symmetric UAV cooperative lifting in low-altitude logistics and emergency transportation scenarios. Full article

Path planning is a critical capability for unmanned aerial vehicles (UAVs) operating in complex 2D environments such as agricultural fields or indoor facilities—scenarios where flight altitude is often constrained and safe, smooth trajectories are essential. While the sampling-based Bidirectional RRT* (BI-RRT*) algorithm offers asymptotic optimality and improved computational efficiency, it frequently generates paths that lack the curvature continuity, obstacle clearance, and low turning angles required for stable drone flight. To address these limitations, this paper proposes a bi-directional rapid exploration random tree algorithm based on cooperative expansion strategy (CE-BI-RRT*) specifically designed for UAVs path planning in cluttered 2D settings. In terms of expansion, for different environments, the algorithm successively tests the direct expansion strategy, the intelligent deflection strategy and the improved artificial potential field method, as these strategies can quickly guide the two trees to the target while avoiding obstacles. In terms of ChooseParent and Rewire, the path length, path smoothness and safety distance are comprehensively considered in the path cost function, and a rotation strategy is applied to make the path away from obstacles after rewiring, so as to realize the gradual optimization of the path. The final path is further refined using a cubic Bezier curve optimization technique to ensure smooth transitions and continuous curvature. Evaluation results confirm its search performance when benchmarked against mainstream randomized motion planning algorithms. Full article

Invasive seed predators can severely affect the reproduction of long-lived trees, especially when host range expansion occurs. The beetle Specularius impressithorax (Chrysomelidae: Bruchinae), native to Africa, has become established in Hawaiʻi where it attacks the endemic coral tree (Erythrina sandwicensis; Wiliwili). Here, we report the infestation of an African coral tree (E. livingstoniana) by this beetle and assess its performance and oviposition patterns on native and non-native hosts. Field surveys showed that eggs were aggregated on both hosts but more abundant on E. sandwicensis than on E. livingstoniana. Laboratory assays revealed no difference in larva-to-adult survival between the two hosts, although adults emerging from E. sandwicensis were larger. Choice tests indicated no oviposition preference between the two Erythrina species, despite the larger seed size of E. sandwicensis. To explore potential host range expansion, trials were run on economic legumes with varying phylogenetic distance from Erythrina, which showed oviposition on peanut (Arachis hypogaea) with low but successful survival (10.3%), while no development occurred on broad bean or pigeon pea. More E. sandwicensis seeds germinated when infested by a single early-stage larva (70% germination) than when uninfested (20%), suggesting that minimal seed predation may facilitate germination because previously reported greater damage induced by infestation through adulthood reduces germination. Our findings highlight the ecological flexibility of an invasive bruchine, its potential to exploit other Faboideae plants, and the dual role of seed predators as both threats and facilitators of seed germination. These results have implications for conservation of endemic coral trees and for understanding invasion dynamics of shared seed predators. Additionally, we examined non-botanical substrate filled with seed powder for oviposition and compiled global host records of S. impressithorax to contextualize its host range expansion. Full article

Large-scale tree planting programs that store carbon provided by wood and non-wood products are being promoted to mitigate climate change. Assessing the biomass pool of plantations is thus an essential task in forest ecology. This study investigated biomass allocation and allometric equations for above- and belowground components along an age-sequence of Pinus tabuliformis plantations (8, 18, 32, and 46 years old) in northern Hebei Province, China. The biomass of each tree component (root, stem, branch, foliage) was quantified by destructive harvesting. Allometric equations and biomass conversion and expansion factors (BCEFs) were subsequently developed for each tree component. The mean above- and belowground biomass was 5.86, 20.05, 41.26, and 135.28 kg tree⁻¹ and 1.73, 3.42, 11.39, and 27.30 kg tree⁻¹ in the 8-, 18-, 32-, and 46-year-old stands, respectively. The proportion of stem biomass to total tree biomass increased from 28.7% for the 8-year-old stand to 55.8% for 46-year-old stand. In contrast, the contributions of foliage and branch decreased along the chronosequence. The root contribution to total tree biomass also showed a declining trend with stand age. Allometric models based on diameter at breast height showed a good fit (p < 0.001) and incorporating stand age as an additional variable improved the fit of allometric equations (higher R² and lower ACI) for branch, aboveground, root, and total tree biomass. BCEFs decreased for all tree components as stand age increased. These findings indicate that changes in tree biomass allocation and allometry across stand development must be considered to improve estimates of plantation biomass and carbon stocks at regional and national scales. Full article

Planning algorithms are essential for reducing computational complexity in robotics and autonomous driving. While the Rapidly exploring Random Tree Star (RRT*) algorithm offers probabilistic completeness and asymptotic optimality, its practical efficiency is hampered by slow convergence, high initial path cost, and excessive invalid sampling due to uninformed tree expansion. To address these limitations, this study introduces the A-RRT* algorithm. The key improvements include: utilizing an A* path to define an adaptive sampling region for faster initial solution quality and convergence; incorporating a goal bias strategy to guide random node generation; and implementing a steering angle criterion during parent node reselection within a multi-iteration replanning framework to refine the path to global optimality. Simulation confirm that the proposed A-RRT* algorithm effectively enhances planning efficiency and path quality compared to the comparison algorithms. Specifically, it reduces the initial solution time by up to 55%, lowers the initial path cost by 4.5–12.5%, and achieves final path cost that is 1.6–9.8% shorter. Full article

Over the last fifteen years, the Google Earth Engine (GEE) has become a pivotal tool for large-scale geospatial analysis, with growing applications in urban environmental monitoring. This review examines the peer-reviewed literature, published between 2015 and 2024, that utilizes GEE to evaluate urban environments through remote sensing-derived indices. The literature search strategy was guided by predefined search terms, which were applied to online databases including Scopus and Google Scholar. The inclusion criteria for this review comprised English-language publications, limited to articles only from journals, while book series, books, and conference articles were excluded. The eligibility criteria applied aimed to identify peer-reviewed studies that applied GEE to urban contexts using vegetation, thermal, greenness, or density indices. Studies without a clear urban focus or not employing GEE as a primary tool were excluded. The selection process followed a structured methodological flow, where a total of 291 studies were identified that fulfilled the applied criteria. This review indicates that key methodological trends encompass both conventional techniques, such as Random Forests (RFs), Support Vector Machines (SVMs), and classification/regression trees, as well as emerging machine learning algorithms, with Landsat, Sentinel, and MODIS as the most commonly used satellite datasets. The articles included in this review show a geographic focus, with over 44% of publications from China, 11% from the United States, and 9% from India, while the rest of the countries identified in this review contribute fewer than 5% each, suggesting that there is a significant opportunity for research in underrepresented regions. The main result of this review is that GEE proves to be an effective, scalable, and reproducible platform for urban environmental analysis, with most studies focusing on vegetation and thermal indices using Landsat, Sentinel, and MODIS data. As GEE has become one of the most widely used platforms for urban environmental monitoring, future research should focus on addressing challenges such as the standardization of indices, the consistency of methodological approaches, and the expansion of global coverage through advanced cloud-based geospatial frameworks. Full article

Cultivation practices such as fruit thinning and soil management with ground covers are commonly applied in Citrus orchards, yet their physiological impact on young trees remains poorly documented. This study evaluated the effects of manual fruit thinning and weed-control mesh on vegetative growth, fruit development, and leaf mineral composition of Citrus sinensis L. Osbeck cv. ‘Navelina’ grafted on Citrus macrophylla. A six-month field experiment was conducted in southeastern Spain under semi-arid Mediterranean conditions using six treatments that combined different soil coverage and subsurface drainage systems. After physiological fruit drop, trees were standardized to ten fruits per plant. Vegetative parameters (canopy and trunk dimensions), fruit growth (size, juice content), and foliar nutrient concentrations were monitored. Trees with ground cover showed significantly greater canopy expansion and juice yield compared to uncovered controls. A negative correlation between fruit number and canopy-to-fruit volume ratio highlighted the trade-off between vegetative vigor and fruit load. Foliar analysis revealed lower micronutrient concentrations (Fe, Mn, B, Zn) in uncovered trees, suggesting reduced nutritional status. These findings demonstrate that combining early thinning with weed-control mesh promotes vegetative vigor, improves juice yield, and enhances nutrient uptake, providing practical insights for optimizing orchard establishment and early Citrus productivity in water-limited environments. Full article

Phoracantha semipunctata is a global quarantine pest, which is fatal to various tree species of the Eucalyptus. Currently, this pest has landed and colonized Guangdong province, China. Previously, there was very limited research information on P. semipunctata in China, which basically describes the taxonomic status. Field investigations found that the climatic ecological niche of the pest is continuing to expand. With global warming and the globalization of trade, signs of expansion may intensify the spread. In order to prevent any further spread of P. semipunctata, it is important to clarify its geographic distribution in China. In this study, the algorithm Random Forests was used to project the potential geographic distribution of P. semipunctata in China currently and in the future. Our results showed that temperature seasonality (Bio4) and the precipitation of the coldest quarter (Bio19) are key environmental factors limiting the current distribution of P. semipunctata in China. Currently, P. semipunctata has been found in Guangdong province, colonizing in the triangle zone composed of Zhanjiang City, Qingyuan City, and Jieyang City, with the projected potential suitable distribution area of 50.88 × 10⁴ km². Under future climate scenarios, the total suitable distribution area is projected to increase, from Fujian province toward the north to Guangxi province toward the north. Building on these results, we predicted the potential future spread pattern of P. semipunctata and developed priority measures for its management. These findings provide a theoretical basis for designing effective quarantine and control strategies against P. semipunctata. Full article

The study examines the use of environmentally friendly materials, such as metakaolin and cement, in various proportions to stabilize expansive plastic soils and assess their effects on the soil–water characteristic curve (SWCC). Metakaolin, a supplementary cementitious material with a lower carbon footprint than ordinary cement, enhances soil behavior through pozzolanic reactions. The incorporation of metakaolin and cement reduced the fitting parameter “a,” linked to the air-entry value (AEV), indicating that treated soils desaturate at lower suction values due to improved aggregate formation and pore structure. With increasing stabilizer content, the SWCC shifted toward lower suction values, reflecting improved hydraulic performance and reduced moisture sensitivity. The fitting parameter “n,” representing desaturation capacity and pore size distribution, increased with stabilizer content, suggesting a more uniform and durable soil structure. Overall, using metakaolin and cement enhances expansive soils’ structural and hydraulic behavior while conserving cement and reducing CO₂ emissions. Machine learning models, Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN), were developed to predict SWCC. The RF model achieved the best accuracy (R² = 0.9063, adjusted R² = 0.8631), demonstrating the reliability of ML in evaluating green soil stabilization methods. Full article