Constantinos V. Chrysikopoulos

BACKGROUND The potential of copper nanoparticles (Cu‐NPs) to be used as an alternative control strategy against olive fruit flies ( Bactrocera oleae ) with reduced sensitivity to the pyrethroid deltamethrin and the impact of both nanosized and bulk copper hydroxide (Cu(OH) 2 ) on the insect's reproductive and endosymbiotic parameters were investigated. RESULTS The application of nanosized and bulk copper applied by feeding resulted in significant levels of adult mortality, comparable to or surpassing those achieved with deltamethrin at recommended doses. Combinations of Cu‐NPs or copper oxide nanoparticles (CuO‐NPs) with deltamethrin significantly enhanced the insecticide's efficacy against B. oleae adults. When combined with deltamethrin, Cu‐NPs significantly reduced the mean total number of offspring compared with the control, and the number of stings, pupae, female and total number of offspring compared with the insecticide alone. Both bulk and nanosized copper negatively affected the abundance of the endosymbiotic bacterium Candidatus Erwinia dacicola which is crucial for the survival of B. oleae larvae. CONCLUSION The Cu‐NPs can aid the control of B. oleae both by reducing larval survival and by enhancing deltamethrin performance in terms of toxicity and reduced fecundity, providing an effective anti‐resistance tool and minimizing the environmental footprint of synthetic pesticides by reducing the required doses for the control of the pest. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A substantial portion of the water supply and sanitation (WatSan) infrastructure in the rural areas of developing countries is currently not operating. This failure is due to the inappropriate implementation of WatSan technologies and the lack of decision-making resources. This study explores the application of several machine learning classification algorithms to predict the optimal WatSan system effectively. The proposed classification methods are Logistic Regression, Random Forest, Support Vector Machine, CatBoost, and Neural Network. The practicality of these classification methods was tested using a dataset comprising 774 water technology options. Several experiments were conducted to obtain the highest possible classification accuracy of the capacity requirement level (CRL) in terms of accuracy and F1 score classification metrics. Our findings suggest that CatBoost, with the addition of the synthetic minority oversampling technique (SMOTE), outperforms the other algorithms in classifying WatSan technology options.