Alleviating sample imbalance in water quality assessment using the VAE-WGAN-GP model.

Water resources are essential for sustaining human life and promoting sustainable development. However, rapid urbanization and industrialization have resulted in a decline in freshwater availability. Effective prevention and control of water pollution are essential for ecological balance and human well-being. Water quality assessment is crucial for monitoring and managing water resources. Existing machine learning-based assessment methods tend to classify the results into the majority class, leading to inaccuracies in the outcomes due to the prevalent issue of imbalanced class sample distribution in practical scenarios. To tackle the issue, we propose a novel approach that utilizes the VAE-WGAN-GP model. The VAE-WGAN-GP model combines the encoding and decoding mechanisms of VAE with the adversarial learning of GAN. It generates synthetic samples that closely resemble real samples, effectively compensating data of the scarcity category in water quality evaluation. Our contributions include (1) introducing a deep generative model to alleviate the issue of imbalanced category samples in water quality assessment, (2) demonstrating the faster convergence speed and improved potential distribution learning ability of the proposed VAE-WGAN-GP model, (3) introducing the compensation degree concept and conducting comprehensive compensation experiments, resulting in a 9.7% increase in the accuracy of water quality assessment for multi-classification imbalance samples.

Label-free bacterial colony detection and viability assessment by continuous-wave terahertz transmission imaging.

Timely and accurate bacterial detection is critical for various health and safety applications, which promotes the continuous development of versatile optical sensors for bacterial investigations. Here, we report a new strategy for bacterial colony sensing using terahertz (THz) imaging with minimal assay procedures. The proposed method utilizes the acute sensitivity of THz wave to the changes in the water content and cellular structures. Single bacterial colonies of 4 bacterial species were directly distinguished using THz imaging by utilizing their differences in THz absorption. In addition, the distribution of mixed bacterial samples has been demonstrated by THz imaging, which demonstrated that the target bacterium could be easily recognized. Furthermore, we investigated the differentiation of bacterial viability, which indicated that bacteria under different living states could be distinguished by THz imaging because of their different hydration levels and cellular structures. Our results suggest that THz imaging has the potential to be used for mixed bacterial sample detection and bacterial viability assessment in a label-free and nondestructive manner.