In this paper, we investigate a UAV-assisted Mobile Edge Computing (MEC) system that is deployed with multiple UAVs to provide timely data processing services to disaster-stricken areas. In our model, since base stations are unavailable in disaster-affected areas, we solely employ UAVs as MEC servers, as well as enable real-time data transmission during UAV flights by estimating and compensating for the Doppler Frequency Shift (DFS). Subsequently, an optimization problem is formulated to jointly optimize the trajectories and offloading strategies of multiple UAVs to minimize the task completion time. We enhance the performance of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm by using a weighted strategy algorithm, and we thus propose the Weighted-Strategy-Based Multi-Agent Deep Deterministic Policy Gradient (WMDDPG) algorithm for optimizing UAV trajectories. We employ the Greedy-Based Simulated Annealing (GSA) algorithm to overcome the limitations of the greedy algorithm and to obtain the best offloading strategy. The results demonstrate the effectiveness of the proposed WMDDPG-GSA algorithm, as it outperforms benchmark algorithms.

Forest biodiversity is an important component of biological diversity that should not be disregarded. The question of how to evaluate it has sparked scholarly inquiry and discussion. The purpose of this paper is to describe the principles of general linear regression, the selection of model variables in OLS autoregressive modelling, model coefficient testing, analysis of variance of autoregressive models, and model evaluation indicators in order to clarify the suitability of GWR models for solving biomass-related data problems. The GWR 4.0 program was used to create a spatially weighted autoregressive model. Model testing and an accuracy analysis were performed on the model. Following a comparison and study with the general linear regression model, it was discovered that the geographically weighted autoregressive model is better suited to defining spatially correlated data than the general linear regression model.