Design and optimization of haze prediction model based on particle swarm optimization algorithm and graphics processor.

With the rapid expansion of industrialization and urbanization, fine Particulate Matter (PM2.5) pollution has escalated into a major global environmental crisis. This pollution severely affects human health and ecosystem stability. Accurately predicting PM2.5 levels is essential. However, air quality forecasting currently faces challenges in processing vast data and enhancing model accuracy. Deep learning models are widely applied for their superior learning and fitting abilities in haze prediction. Yet, they are limited by optimization challenges, long training periods, high data quality needs, and a tendency towards overfitting. Furthermore, the complex internal structures and mechanisms of these models complicate the understanding of haze formation. In contrast, traditional Support Vector Regression (SVR) methods perform well with complex non-linear data but struggle with increased data volumes. To address this, we developed CUDA-based code to optimize SVR algorithm efficiency. We also combined SVR with Genetic Algorithms (GA), Sparrow Search Algorithm (SSA), and Particle Swarm Optimization (PSO) to identify the optimal haze prediction model. Our results demonstrate that the model combining intelligent algorithms with Central Processing Unit-raphics Processing Unit (CPU-GPU) heterogeneous parallel computing significantly outpaces the PSO-SVR model in training speed. It achieves a computation time that is 6.21-35.34 times faster. Compared to other models, the Particle Swarm Optimization-Central Processing Unit-Graphics Processing Unit-Support Vector Regression (PSO-CPU-GPU-SVR) model stands out in haze prediction, offering substantial speed improvements and enhanced stability and reliability while maintaining high accuracy. This breakthrough not only advances the efficiency and accuracy of haze prediction but also provides valuable insights for real-time air quality monitoring and decision-making.

Deeply Coupled Convolution-Transformer With Spatial-Temporal Complementary Learning for Video-Based Person Re-Identification.

Advanced deep convolutional neural networks (CNNs) have shown great success in video-based person re-identification (Re-ID). However, they usually focus on the most obvious regions of persons with a limited global representation ability. Recently, it witnesses that Transformers explore the interpatch relationships with global observations for performance improvements. In this work, we take both the sides and propose a novel spatial-temporal complementary learning framework named deeply coupled convolution-transformer (DCCT) for high-performance video-based person Re-ID. First, we couple CNNs and Transformers to extract two kinds of visual features and experimentally verify their complementarity. Furthermore, in spatial, we propose a complementary content attention (CCA) to take advantages of the coupled structure and guide independent features for spatial complementary learning. In temporal, a hierarchical temporal aggregation (HTA) is proposed to progressively capture the interframe dependencies and encode temporal information. Besides, a gated attention (GA) is used to deliver aggregated temporal information into the CNN and Transformer branches for temporal complementary learning. Finally, we introduce a self-distillation training strategy to transfer the superior spatial-temporal knowledge to backbone networks for higher accuracy and more efficiency. In this way, two kinds of typical features from same videos are integrated mechanically for more informative representations. Extensive experiments on four public Re-ID benchmarks demonstrate that our framework could attain better performances than most state-of-the-art methods.

Isolation and identification of acetaminophen degrading strain Shinella sp. HZA2.

Acetaminophen (APP), frequently used as analgesic and antipyretic drug in our life, is potentially toxic to both animals and humans. A novel acetaminophen degrading strain HZA2, was isolated from the activated sludge, and identified as Shinella sp. based on its 16S rRNA gene sequence analysis, morphological, physiological, and biochemical characterizations. This strain could degrade 100 mg L-1 acetaminophen completely within 12 h, and it was also a very effective strain for the degradation of high concentration of acetaminophen below 3000 mg L-1 under the optimal condition. The optimal degrading conditions of acetaminophen by HZA2 were pH 7.5 and 32.7 °C by the analysis of response surface methodology. Exogenous carbon source could enhance the biodegradation of acetaminophen. During the process, the intermediate metabolites were identified as 4-aminophenol and hydroquinone via gas chromatography-mass spectrometry analysis. The results indicated that strain HZA2 may be a promising bacterium for the bioremediation of acetaminophen pollutions.