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This study aimed to investigate how enteric pathogens and their biofilm populations on fresh produce survive according to time that contamination has occurred on leaves and contamination route: seed irrigation water.

Cress was contaminated in two different ways: contamination of seeds and irrigation water with 8-log MPN/mL bacterial load, Salmonella Newport, Escherichia coli O157:H7, O104:H4 or O78:H2. While contaminated seeds were cultivated for seed contamination, contaminated irrigation was applied at the end of each week to separate groups of samples obtained from cultivated surface-sterile seeds to understand how long these pathogens could survive until harvest.

The results indicated these pathogens survived until harvest, and formed biofilms on cress leaves grown using both contaminated seeds and irrigation water. No significant difference was observed among populations of Salmonella and E. coli groups in terms of survival (∼4.5–6.0 log MPN/g) and biofilm formation (∼4.4–5.7 log MPN/g) for contamination by seed. Also, SEM images revealed biofilm-like structures, the proofs of the attachment of these pathogens on leaf surfaces.

From our knowledge this is the first study focusing on the survival and biofilm formation of one Salmonella serotype (Newport) and three E. coli serotypes (O157:H7, O104:H4, and O78:H2), representing enterohemorrhagic and enteroaggregative E. coli pathogenic subgroups, under the same irrigation and growth schemes. Furthermore, this study mimics the contamination of seeds and irrigation water with sewage or wastewater and may shed light on contamination of fresh produce grown using poor wastewater treatment.

Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.

This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.

The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.