As the global population grows and urbanization accelerates, the garbage that is generated continues to increase. This waste causes serious pollution to the ecological environment, affecting the stability of the global environmental balance. Garbage detection technology can quickly and accurately identify, classify, and locate many kinds of garbage to realize the automatic disposal and efficient recycling of waste, and it can also promote the development of a circular economy. However, the existing garbage detection technology has some problems, such as low precision and a poor detection effect in complex environments. Although YOLOv5 has achieved good results in garbage detection, the detection results cannot meet the requirements in complex scenarios, so this paper proposes a garbage detection model, YOLOv5-OCDS, based on an improved YOLOv5. Replacing the partial convolution in the neck with Omni-Dimensional Dynamic Convolution (ODConv) improves the expressiveness of the model. The C3DCN structure is constructed, and parts of the C3 structures in the neck are replaced by C3DCN structures, allowing the model to better adapt to object deformation and target scale change. The decoupled head is used for classification and regression tasks so that the model can learn each class’s characteristics and positioning information more intently, and flexibility and extensibility can be improved. The Soft Non-Maximum Suppression (Soft NMS) algorithm can better retain the target’s information and effectively avoid the problem of repeated detection. The self-built garbage classification dataset is used for related experiments, and the mAP@50 of the YOLOv5-OCDS model is 5.3% higher than that of the YOLOv5s; the value of mAP@50:95 increases by 12.3%. In the experimental environment of this study, the model’s Frames Per Second (FPS) was 61.7 f/s. In practical applications, when we use some old GPU, such as the GTX1060, it can still reach 50.3 f/s, so that real-time detection can be achieved. Thus, the improved model suits garbage detection tasks in complex environments.

In the multi-class recognition task for unmanned aerial vehicle (UAV) radio frequency (RF) signals, noise interference and complex backgrounds can significantly degrade recognition performance. The stability and accuracy of existing recognition methods often fail to meet the requirements of practical applications. To enhance the stability and accuracy of UAV RF signal recognition, especially to mitigate performance degradation in complex backgrounds, a UAV RF signal classification method, MD-Net, is proposed that integrates Adaptive Time-Frequency Masking and a dual-network architecture. First, an Adaptive Time-Frequency Masking mechanism is constructed. By analyzing the energy distribution of RF signals in the time-frequency domain, the masking region is automatically determined, ensuring that the training data maintains a diverse distribution across different interference scenarios. This significantly improves the model’s anti-interference performance and discriminative stability in complex environments. Subsequently, a dual-branch recognition network architecture is designed, integrating a multi-layer perceptron (MLP) and a long short-term memory (LSTM) network. The MLP extracts static amplitude features from the signals, while the LSTM learns time-series features. These two feature types are then fused to achieve complementary characteristics, ultimately enabling accurate classification of UAV RF signals. Extensive comparative experiments conducted on the DroneRF dataset demonstrate that the MD-Net model achieves an average recognition accuracy of 85.58%, an improvement of 5.27 percentage points over the baseline model. The experimental results show that Adaptive Time-Frequency Masking can effectively enhance the model’s adaptability to real-world interference environments, while the dual-network fusion mechanism fully integrates static amplitude and time-series features, providing a feasible and highly reliable technical approach for UAV RF signal recognition.

With social development and scientific and technological progress, China is committed to building a service-oriented government, which will put forward higher requirements for the government administration efficiency and levels, and at the same time incorporates civil servants' perceptions of government management into the thinking of transforming administrative methods. Based on computational neuroscience, this paper studies civil servants' cognitive evaluation of performance appraisal, and more realistically simulates the entire evaluation process. The research results show that there are differences in the cognitive evaluation of civil servants at different stages of performance appraisal. At the same time, under the influence of information interference, cognitive evaluation will also show various changes. Based on computational neuroscience, this paper studies the civil servants' cognitive evaluation of performance appraisal, which is beneficial to the orderly development of performance appraisal, and can also promote the transformation of government management concepts and functions.

This paper aims to set up new rules for processing seawater quality monitoring data collected by photoelectric sensor network, and mine out the useful information contained in the data. For this purpose, the immune algorithm was introduced to the classical genetic algorithm, the fitness function was designed, and the crossover and mutation probabilities were adjusted, thus creating the adaptive immune genetic algorithm (IIGA). The new algorithm was described in details and applied in an actual case. Through the comparison between the IIGA, IGA and apriori algorithms, the author concluded that the IIGA not only shortened the mining time, but also ensured the operation accuracy. The research findings are of great importance to the association rules mining in various fields.