Zero-shot generalization for predicting viral concentrations and evaluating removal efficiencies across wastewater matrices.

ABSTRACT: Predicting viral particles on new unseen data across wastewater matrices (WMs) in aerobic membrane bioreactor (AeMBR)-based wastewater treatment plants (WWTPs) remains an open challenge due to the process drifts involved in the treatment stages. Efficient data augmentation approaches based on Markov chain (MCM), Markov chain and multivariate Gaussian (MMCM), Gaussian mixture (GMM) and Copula (CM) were proposed to generate synthetic data from physicochemical parameters, virometry, and PCR-based method. Dual-attention long short-term memory network (DA-LSTM) with new generative models was proposed to predict viral particles and evaluate the removal efficiencies across AeMBRs, thereby handling effluent processing drifts. The DA-LSTM combines attention mechanisms to adaptively adjust the weigh

SUBMITTER: Chen J 

PROVIDER: S-EPMC12645020 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature