Effects of Insecticides on Pest Populations and Their Natural Enemies in Soybean Field

In the 5-time field experiments and broad demonstrations, effects of volume and application formulae of several pesticides on the population densities of major inset pests and natural enemies in the soybean field were determined. Results from the Ducan’s multiple range test indicated that 300 g/ha of Omethoate (fine granule) and 45 g/ha of Fenvalerate in the seedling stage and 300 g/ha of Chloromethiuron suspensoid in the flowering stage may control pests and protect major natural enemies. Application of these pesticides in the corresponding soybean stages is an effective way that mediates the conflict between chemical and biological controls in the field. There are over 170 species of natural enemies whose hosts are known in the soybean field in China. These natural enemies play important roles in controlling the soybean pests. However, farmers still strongly rely on chemicals to control these pests because natural enemies are not able to timely curb the pest infestations when there is a pest outbreak. Frequent chemical application in a higher volume will ruin the ecological balance. The conflict between biological and chemical controls has become a hot issue in the soybean production worldwide. Our objectives in this study are to decide the effective low concentrations of commonly used pesticides, determine the effect of the third and fourth generations of pesticides on the major insect pests and natural enemies in the soybean field and propose feasible control methods by coordinating control and chemical controls.Originating text in Chinese.Citation: Qi, Yaoxun, Ma, Zhengquan, Shan, De'An, Gao, Xiaohua, Wang, Qisheng. (1987). Effects of Insecticides on Pest Populations and Their Natural Enemies in Soybean Field. Plant Protection (Institute of Plant Protection, CAAS, China), 13, 4-6

Asynchronous Federated Learning for Edge-assisted Vehicular Networks

Vehicular networks enable vehicles support real-time vehicular applications through training data. Due to the limited computing capability, vehicles usually transmit data to a road side unit (RSU) at the network edge to process data. However, vehicles are usually reluctant to share data with each other due to the privacy issue. For the traditional federated learning (FL), vehicles train the data locally to obtain a local model and then upload the local model to the RSU to update the global model, thus the data privacy can be protected through sharing model parameters instead of data. The traditional FL updates the global model synchronously, i.e., the RSU needs to wait for all vehicles to upload their models for the global model updating. However, vehicles may usually drive out of the coverage of the RSU before they obtain their local models through training, which reduces the accuracy of the global model. It is necessary to propose an asynchronous federated learning (AFL) to solve this problem, where the RSU updates the global model once it receives a local model from a vehicle. However, the amount of data, computing capability and vehicle mobility may affect the accuracy of the global model. In this paper, we jointly consider the amount of data, computing capability and vehicle mobility to design an AFL scheme to improve the accuracy of the global model. Extensive simulation experiments have demonstrated that our scheme outperforms the FL schemeComment: This paper has been submitted to WCS

Asynchronous Federated Learning Based Mobility-aware Caching in Vehicular Edge Computing

Vehicular edge computing (VEC) is a promising technology to support real-time applications through caching the contents in the roadside units (RSUs), thus vehicles can fetch the contents requested by vehicular users (VUs) from the RSU within short time. The capacity of the RSU is limited and the contents requested by VUs change frequently due to the high-mobility characteristics of vehicles, thus it is essential to predict the most popular contents and cache them in the RSU in advance. The RSU can train model based on the VUs' data to effectively predict the popular contents. However, VUs are often reluctant to share their data with others due to the personal privacy. Federated learning (FL) allows each vehicle to train the local model based on VUs' data, and upload the local model to the RSU instead of data to update the global model, and thus VUs' privacy information can be protected. The traditional synchronous FL must wait all vehicles to complete training and upload their local models for global model updating, which would cause a long time to train global model. The asynchronous FL updates the global model in time once a vehicle's local model is received. However, the vehicles with different staying time have different impacts to achieve the accurate global model. In this paper, we consider the vehicle mobility and propose an Asynchronous FL based Mobility-aware Edge Caching (AFMC) scheme to obtain an accurate global model, and then propose an algorithm to predict the popular contents based on the global model. Experimental results show that AFMC outperforms other baseline caching schemes.Comment: This paper has been submitted to The 14th International Conference on Wireless Communications and Signal Processing (WCSP 2022

An optimized encoding algorithm for systematic polar codes

Many different encoding algorithms for systematic polar codes (SPC) have been introduced since SPC was proposed in 2011. However, the number of the computing units of exclusive OR (XOR) has not been optimized yet. According to an iterative property of the generator matrix and particular lower triangular structure of the matrix, we propose an optimized encoding algorithm (OEA) of SPC that can reduce the number of XOR computing units compared with existing non-recursive algorithms. We also prove that this property of the generator matrix could extend to different code lengths and rates of the polar codes. Through the matrix segmentation and transformation, we obtain a submatrix with all zero elements to save computation resources. The proportion of zero elements in the matrix can reach up to 58.5{\%} from the OEA for SPC when the code length and code rate are 2048 and 0.5, respectively. Furthermore, the proposed OEA is beneficial to hardware implementation compared with the existing recursive algorithms in which signals are transmitted bidirectionally

High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks

Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keep the privacy of users. In the FEEL system, vehicles upload data to the edge servers, which train the vehicles' data to update local models and then return the result to vehicles to avoid sharing the original data. However, the cache queue in the edge is limited and the channel between edge server and each vehicle is a time varying wireless channel, which makes a challenge to select a suitable number of vehicles to upload data to keep a stable cache queue in edge server and maximize the learning accuracy. Moreover, selecting vehicles with different resource statuses to update data will affect the total amount of data involved in training, which further affects the model accuracy. In this paper, we propose a vehicle selection scheme, which maximizes the learning accuracy while ensuring the stability of the cache queue, where the statuses of all the vehicles in the coverage of edge server are taken into account. The performance of this scheme is evaluated through simulation experiments, which indicates that our proposed scheme can perform better than the known benchmark scheme.Comment: This paper has been submitted to China Communication