Aiming to solve the problems with easy false detection of small targets in river floating object detection and deploying an overly large model, a new method is proposed based on improved YOLOv5s. A new data augmentation method for small objects is designed to enrich the dataset and improve the model’s robustness. Distinct feature extraction network levels incorporate different coordinate attention mechanism pooling methods to enhance the effective feature information extraction of small targets and improve small target detection accuracy. Then, a shallow feature map with 4-fold down-sampling is added, and feature fusion is performed using the Feature Pyramid Network. At the same time, bilinear interpolation replaces the up-sampling method to retain feature information and enhance the network’s ability to sense small targets. Network complex algorithms are optimized to better adapt to embedded platforms. Finally, the model is channel pruned to solve the problem of difficult deployment. The experimental results show that this method has a better feature extraction capability as well as a higher detection accuracy. Compared with the original YOLOv5 algorithm, the accuracy is improved by 15.7%, the error detection rate is reduced by 83% in small target task detection, the detection accuracy can reach 92.01% in edge testing, and the inference speed can reach 33 frames per second, which can meet the real-time requirements.

Icarapin is a bee venom protein found to induce IgE-mediated allergic reaction. In this study, icarapin of Asian honey bee was cloned and sequenced. By in silico screening, S198 was found to be the potential antigenic site. This site was changed to cysteine and coupled with PEG5K. Compared to the wild type icarapin and the S198C variant, PEGylated S198C variant induced lower level of IgG and IgE antibodies in mice, showing that it is indeed located in an antigenic site. Our work may be generalized to other proteins for the discovery of antigenic sites and the reduction of antigenicity

In mobile wireless multimedia sensor networks' image compression task collaborations, the existing methods do not consider the dynamic changes in the processing ability and the locations of the cooperative nodes. when processing the image compression tasks, these methods will cause frequent interruptions and result in data re-transmission for those tasks. in this paper, an image compression task collaboration algorithm based on dynamic alliance is proposed to solve this problem in mobile wireless multimedia sensor networks. first, a dynamic task alliance is established by the camera node based on the location, computing capability and resource usage of ordinary nodes. then, the location and average moving velocity of the camera nodes and ordinary nodes are considered to calculate the task stable execution time. the image compression task is divided into an image transfer sub task and an image compression sub task based on the task stable execution time. finally, an image compression task collaboration allocation optimization model is established according to the transmission time, the execution time, the execution cost, and the network energy consumption. the gradient method is used to realize the cooperative allocation of image compression tasks. simulation results show that the proposed algorithm can realize task load balancing, reduce the execution time, and network energy consumption.

Water environment monitoring has always been an important method of water resource environmental protection. In practical applications, there are problems such as large water bodies, long monitoring periods, and large transmission and processing delays. Aiming at these problems, this paper proposes a framework and method for detecting floating objects on water based on the sixth-generation mobile network (6G). Using satellite remote sensing monitoring combined with ground-truth data, a regression model is established to invert various water parameters. Then, using chlorophyll as the main reference indicator, anomalies are detected, early warnings are given in a timely manner, and unmanned aerial vehicles (UAVs) are notified through 6G to detect targets in abnormal waters. The target detection method in this paper uses MobileNetV3 to replace the VGG16 network in the single-shot multi-box detector (SSD) to reduce the computational cost of the model and adapt to the computing resources of the UAV. The convolutional block attention module (CBAM) is adopted to enhance feature fusion. A small target data enhancement module is used to enhance the network identification capability in the training process, and the key-frame extraction module is applied to simplify the detection process. The network model is deployed in system-on-a-chip (SOC) using edge computing, the processing flow is optimized, and the image preprocessing module is added. Tested in an edge environment, the improved model has a 2.9% increase in detection accuracy and is 55% higher in detection speed compared with SSD. The experimental results show that this method can meet the real-time requirements of video surveillance target detection.

Gas leakage is one of the most frequent types of accidents in the petrochemical industry. It is imperative to use mobile sensors to monitor such an accident area. The new and effective way is to use drones and helicopters that spray wireless sensors from the air to monitor harmful gases and to locate the gas leaking source. However, the sprayed wireless sensors will be distributed randomly around the accident area, and it is a challenge to obtain effective coverage. This paper proposes a gravitation-based redeployment algorithm for sensors (GRSS) that considers the virtual boundary forces and the gas concentration in a 3-D accident monitoring area. A priority-based redeployment algorithm for sensors (PRSS) is proposed to further improve the coverage and simplify the 3-D redeployment problem. PRSS considers the layer priorities of the monitoring area to control the movements of the mobile sensors. The simulation results show that the GRSS and PRSS methods can achieve better coverage and utilize less distance compared with the random algorithm and 3-D self-deployment.

The prediction of drug properties plays a vital role in drug research. However, the drug property prediction accuracy of traditional methods is limited due to their inability to fully capture molecular structure and function information. As a result, the use of graph neural networks has attracted significant attention as an effective drug property prediction approach. Nevertheless, traditional graph neural networks still exhibit certain drawbacks in this regard, including their disregard of the interaction information between nodes and edges, the loss of local information during global pooling operations, and the absence of feature fusion mechanisms. This study proposes an enhanced graph neural network (GNN) model that incorporates an attention mechanism, multiscale pooling, an adaptive weight generator, and an activation function to predict drug properties. A comparative analysis with the conventional graph neural network model reveals significant improvements in terms of predicting the side effects of drugs on the heart and liver, with increases of 1%, 7%, and 13%. Furthermore, the enhanced graph neural network model exhibits good performance across the remaining two datasets. Empirical findings underscore the efficacy of the model in drug property prediction tasks, and it is characterized by enhanced predictive precision and robust performance outcomes.