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The geolocation accuracy of spaceborne LiDAR (Light Detection And Ranging) data is important for quantitative forest inventory. Geolocation errors in Global Ecosystem Dynamics Investigation (GEDI) footprints are almost unavoidable because of the instability of orbital parameter estimation and GNSS (Global Navigation Satellite Systems) positioning accuracy. This study calculates the horizontal geolocation error of multiple temporal GEDI footprints using a waveform matching method, which compares original GEDI waveforms with the corresponding simulated waveforms from airborne LiDAR point clouds. The results show that the GEDI footprint geolocation error varies from 3.04 m to 65.03 m. In particular, the footprints from good orbit data perform better than those from weak orbit data, while the nighttime and daytime footprints perform similarly. After removing the system error, the average waveform similarity coefficient of multi-temporal footprints increases obviously in low-waveform-similarity footprints, especially in weak orbit footprints. When the waveform matching effect is measured using the threshold of the waveform similarity coefficient, the waveform matching method can significantly improve up to 32% of the temporal GEDI footprint datasets from a poor matching effect to a good matching effect. In the improvement of the ratio of individual footprint waveform similarity, the mean value of the training set and test set is about two thirds, but the variance in the test set is large. Our study first quantifies the geolocation error of the newest version of GEDI footprints (Version 2). Future research should focus on the improvement of the detail of the waveform matching method and the combination of the terrain matching method with GEDI waveform LiDAR.

In recent decades, the melting of the Greenland Ice Sheet (GrIS) has become one of the major causes of global sea-level rise. Supraglacial lakes (SGLs) are typical hydrological features produced on the surface of the GrIS during the melt seasons. The existence and evolution of SGLs play an important role in the melting process of the ice sheet surface. To understand the distribution and recent changes of SGLs in Greenland, this study developed a random forest (RF) algorithm incorporating the texture and morphological features to automatically identify SGLs based on the Google Earth Engine (GEE) platform. Sentinel-2 imagery was used to map the SGLs inventory in Greenland during the 2016–2018 melt seasons and to explore the spatial and temporal variability characteristics of SGLs. Our results show changes in SGLs from 2016 to 2018, with the total area decreasing by ~1152.22 km2 and the number increasing by 1134; SGLs are mainly distributed in western Greenland (SW, CW, NW) and northeastern Greenland (NE), where the NE region has the largest number of observed SGLs and the largest SGL was with the surface area of 16.60 km2 (2016). SGLs were found to be most active in the area with the elevation of 800–1600 m and the slope of 0–5°, and showed a phenomenon of retreating to lower elevation areas and developing to steeper slope areas. Our work provided a method for rapid inventory of SGLs. This study will help monitor the mass balance of the GrIS and predict future rapid ice loss from Greenland.

This paper investigates a numerical method for solving fractional partial integro-differential equations (FPIDEs) arising in American Contingent Claims, which follow finite moment log-stable process (FMLS) with jump diffusion and regime switching. Mathematically, the prices of American Contingent Claims satisfy a system of d problems with free-boundary values, where d is the number of regimes of the market. In addition, an optimal exercise boundary is needed to setup with each regime. Therefore, a fully implicit scheme based on the penalty term is arranged. In the end, numerical examples are carried out to verify the obtained theoretical results, and the impacts of state variables in our model on the optimal exercise boundary of American Contingent Claims are analyzed.

Accurate building extraction is crucial for urban understanding, but it often requires a substantial number of building samples. While some building datasets are available for model training, there remains a lack of high-quality building datasets covering urban and rural areas in China. To fill this gap, this study creates a high-resolution GaoFen-7 (GF-7) Building dataset utilizing the Chinese GF-7 imagery from six Chinese cities. The dataset comprises 5,175 pairs of 512 × 512 image tiles, covering 573.17 km 2 . It contains 170,015 buildings, with 84.8% of the buildings in urban areas and 15.2% in rural areas. The usability of the GF-7 Building dataset has been proved with seven convolutional neural networks, all achieving an overall accuracy (OA) exceeding 93%. Experiments have shown that the GF-7 building dataset can be used for building extraction in urban and rural scenarios. The proposed dataset boasts high quality and high diversity. It supplements existing building datasets and will contribute to promoting new algorithms for building extraction, as well as facilitating intelligent building interpretation in China.

Lateritic soils in tropical regions feature cohesive textures and high specific resistance, driving up energy demands for tillage and harvesting machinery. However, current equipment designs lack discrete element models that account for soil moisture variations, and the microscopic effects of water content on lateritic soil deformation remain poorly understood. This study aims to calibrate and validate discrete element method (DEM) models of lateritic soil at varying moisture contents of 20.51%, 22.39%, 24.53%, 26.28%, and 28.04% by integrating the Hertz–Mindlin contact mechanics with bonding and JKR cohesion models. Key parameters in the simulations were calibrated through systematic experimentation. Using Plackett–Burman design, critical factors significantly affecting axial compressive force—including surface energy, normal bond stiffness, and tangential bond stiffness—were identified. Subsequently, Box–Behnken response surface methodology was employed to optimize these parameters by minimizing deviations between simulated and experimental maximum axial compressive forces under each moisture condition. The calibrated models demonstrated high fidelity, with average relative errors of 4.53%, 3.36%, 3.05%, 3.32%, and 7.60% for uniaxial compression simulations across the five moisture levels. Stress–strain analysis under axial loading revealed that at a given surface displacement, both fracture dimensions and stress transfer rates decreased progressively with increasing moisture content. These findings elucidate the moisture-dependent micromechanical behavior of lateritic soil and provide critical data support for DEM-based design optimization of soil-engaging agricultural implements in tropical environments.

In this paper, we consider the optimization problem of dividends for the terminal bankruptcy model, in which some money would be returned to shareholders at the state of terminal bankruptcy, while accounting for the tax rate and transaction cost for dividend payout. Maximization of both expected total discounted dividends before bankruptcy and expected discounted returned money at the state of terminal bankruptcy becomes a mixed classical-impulse stochastic control problem. In order to solve this problem, we reduce it to quasi-variational inequalities with a nonzero boundary condition. We explicitly construct and verify solutions of these inequalities and present the value function together with the optimal policy.

There are large fertilization/more times/environment complex during natural rubber tree in the process of the growth cycle and difficult to monitor, aim to the special rubber particles fertilizer easy to deliquescence and cause pipe blockage and then lead to fertilization breaks leakage problem, this paper designed a kind of convenient for visual surveillance of rubber particles ditching fertilizer structure. Optimal designed and analyzed reliability its structure characteristics/working principle/core components and monitoring method. The self-running power suspension test shows that when the moisture content of the fertilizer is ⩽0.16, the scraping structure has no obvious stick with fertilizer go down smoothly, and the strip breaking rate index is ⩽ 0.06, the structure basic function indexes meet the design requirements

Market crashes often appear in daily trading activities and such instantaneous occurring events would affect the stock prices greatly. In an unstable market, the volatility of financial assets changes sharply, which leads to the fact that classical option pricing models with constant volatility coefficient, even stochastic volatility term, are not accurate. To overcome this problem, in this paper we put forward a dynamic elasticity of variance (DEV) model by extending the classical constant elasticity of variance (CEV) model. Further, the partial differential equation (PDE) for the prices of European call option is derived by using risk neutral pricing principle and the numerical solution of the PDE is calculated by the Crank-Nicolson scheme. In addition, Kalman filtering method is employed to estimate the volatility term of our model. Our main finding is that the prices of European call option under our model are more accurate than those calculated by Black-Scholes model and CEV model in financial crashes.

Interest rate is an important macrofactor that affects asset prices in the financial market. As the interest rate in the real market has the property of fluctuation, it might lead to a great bias in asset allocation if we only view the interest rate as a constant in portfolio management. In this paper, we mainly study an optimal investment strategy problem by employing a constant elasticity of variance (CEV) process and stochastic interest rate. The assets of investment for individuals are supposed to be composed of one risk-free asset and one risky asset. The interest rate for risk-free asset is assumed to follow the Cox–Ingersoll–Ross (CIR) process, and the price of risky asset follows the CEV process. The objective is to maximize the expected utility of terminal wealth. By applying the dual method, Legendre transformation, and asymptotic expansion approach, we successfully obtain an asymptotic solution for the optimal investment strategy under constant absolute risk aversion (CARA) utility function. In the end, some numerical examples are provided to support our theoretical results and to illustrate the effect of stochastic interest rates and some other model parameters on the optimal investment strategy.

In the existing agricultural machinery field, the agricultural machinery is generally cumbersome, the transportation, disassembly and assembly, maintenance procedures are more complicated, and the functions are single, and the versatility is not good. This article combines the industrial design standardization and modularization ideas for the trenching fertilizer application machine. Model construction and assembly practice were carried out. The whole machine was built according to independent functions, and divided into racks, fertilisers, trenching assemblies, suspension systems, transmission systems, etc. Modeling of the rack, modeling of the fertilizer turntable, and plowing the modeling process is elaborated. By assembling the functional components and standard parts, the modular design idea can be reused and exchanged in the design of the trenching fertilizer machine. The relevant modules can form the final product through the arrangement and combination. The final assembly entity can be a variety of products, which can better meet the customer's customization needs; the repetitive use of similarity can make the whole product more convenient in manufacturing and maintenance, and the overall machine coordination and appearance are more beautiful.