In this paper, a quantitative structure?retention relationship (QSRR) model was developed for predicting the retention indices (log RI) of 36 constituents of essential oils. First, the chemical structure of each compound was sketched using HyperChem software. Then, molecular descriptors covering different information of molecular structures were calculated by Dragon software. The results illustrated that linear techniques, such as multiple linear regression (MLR), combined with a successful variable selection procedure are capable of generating an efficient QSRR model for predicting the retention indices of different compounds. This model, with high statistical significance (R2 = 0.9781, Q2 LOO = 0.9691, Q2 ext = 0.9546, Q2 L(5)O = 0.9667, F = 245.27), could be used adequately for the prediction and description of the retention indices of other essential oil compounds. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-5-out cross-validation, bootstrap, randomization test and validation through the test set.

The partitioning tendency of pesticides, in this study herbicides in particular, into different environmental compartments depends mainly of the physicochemical properties of the pesticides itself. Aqueous solubility (S) indicates the tendency of a pesticide to be removed from soil by runoff or irrigation and to reach surface water. The experimental procedure for determining the aqueous solubility of pesticides is very expensive and difficult. QSPR methods are often used to estimate the aqueous solubility of herbicides. The artificial neural network (ANN) and support vector machine (SVM) methods, always associated with selection of a genetic algorithm (GA) of the most important variable, were used to develop QSPR models to predict the aqueous solubility of a series of 80 herbicides. The values of log S of the studied compounds were well correlated with the descriptors. Considering the pertinent descriptors, a Pearson correlation squared coefficient (R2) of 0.8 was obtained for the ANN model with a structure of 5-3-1 and 0.8 was obtained for the SVM model using the RBF function for the optimal parameters values: C = 11.12; ? = 0.1111 and ? = 0.222.

This study investigated the microbiological and parasitic quality of sewage sludge treated in drying beds in Algeria, aiming to contribute to a better understanding of the factors influencing sludge safety for potential agricultural applications in the Algerian context. The research focused on various sludge types (liquid, semi-solid, and solid) and their behavior across different seasons. Standard microbiological methods were employed to quantify total coliforms, fecal streptococci, E. coli, and Clostridium. Controls were implemented to ensure accuracy, with positive controls validating detection methods using known quantities of microorganisms and parasites, while negative controls confirmed the absence of contamination in the testing environment. Parasitic contamination was assessed through microscopic examination for protozoa and helminths. Results revealed substantial variation in microbial concentrations across sludge types and seasons. Liquid sludge, particularly during summer, exhibited the highest levels of total coliforms (up to 7.021 log10) and E. coli (up to 6.049 log10), while solid sludge showed lower counts. Seasonal trends indicated increased microbial levels during warmer months. Parasitic contamination was prevalent in 81% of samples, with protozoan cysts (e.g., Giardia intestinalis and Endolimax nanus) and helminth eggs detected. Despite reducing microbial loads, drying processes alone were insufficient, leaving significant contamination. Enhanced protocols are needed, such as longer drying periods, chemical disinfectants, or advanced technologies like anaerobic digestion or composting. This highlights the need for locally adapted treatment strategies. Furthermore, this research provides specific recommendations for improving sewage sludge management practices in Algeria, taking into account the unique environmental and agricultural context of the country.

The search for an eco-freindly larvicide suitable for vector control requires a budget and considerable time to carry out experiments. Fortunately, the use of QSAR modeling allows the prediction of larvicidal activity of structurally diverse chemicals against mosquitoes in a way quick and costless. This approach can be helpful to study for making biolarvicide with highest ability to destroy mosquito larvae. We propose a QSAR model using two different statistical methods, multiple linear regression (MLR) and Support vector machine (SVM) for predicting the larvicidal activity of 30 compounds of essential oil (EOs) isolated from the root of Asarum heterotropoides against Culex pipiens pallens. A model with four theoretical descriptors derived from Dragon software was developed applying the genetic algorithm (GA)-variable subset selection (VSS) procedure. The statistical parameters, R2 = 0.9716, Q2LOO = 0.9595, s = 0.1690 of the model developed by MLR showed a good predictive capability for log LC50 values. The comparison between the results of SVM and MLR models showed that the SVM model present a good alternative to construct a QSAR model for the prediction of the larvicidal activity.

A simple linear model was used to investigate the correlation between the n-octanol/water partition coefficient (kow) of non-substituted fused polycyclic aromatic hydrocarbons (PAHs). Among (74) 3D-geometrically tested descriptors calculated using the Dragon software, volume V turned out to be the best descriptor to model the considered endpoint (with a squared correlation coefficient (R2) of 0.9844 and a standard error of estimation (s) of 0.132 log units). The correlation coefficient cross-validation (Q2) between experimental and predicted log kow for training and test sets was 0.9811 (for training set) and 0.9828 (for test set), respectively. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-5-out cross-validation, bootstrap, randomization tests, and validation through the test set.