Depressive anxiety is one of the most emotional disorders in our industrial societies. Many treatments of phobias exist and are based on plant extracts therapies, which play an important role in the amelioration of the behavior. Our study aimed to evaluate the adaptogenic activity of different essential oils provided from local plants: Cinnamomum camphora (Camphora), Eucalyptus globulus (Blue gum), Lavandula stœchas (Topped lavender) and Rosmarinus officinalis (Rosemary) on Wistar rats. The adaptogenic activity was evaluated on the elevated plus-maze. The efficacy of the extract (200 mL/kg) was compared with the standard anxiolytic drug Diazepam® 1 mg. Animals administered by the essential oil of Lavandula stœchas, Cinnamomum camphora, Rosmarinus officinalis and Eucalyptus globulus showed a behavior similar to those treated with Diazepam®. For groups treated with the following essential oils: Rosmarinus officinalis, Lavandula stoechas and Cinnamomum camphora at a dose of 200 mL/kg, we notice an increase in the time spent on the open arms of the elevated plus-maze and a decrease in time spent on the closed arms of the elevated plus-maze, especially for Rosmarinus officinalis, which explains the anxiolytic effect of these plants. We also notice a decrease in the number of entries in closed arms, open arms and the number of passing to the central square. The increase in the number of entries to open arms with Eucalyptus globulus essential oil shows a reduction in anxiety behavior in rodents and this shows that these plants have an inhibitory effect.

Purpose. The selection of an appropriate variographic model is crucial in geostatistics to obtain accurate estimates of mineral reserves. The aim of this work is to develop a reserve estimation tool using a geostatistical approach. Methodology. The geostatistical approach is based on selecting the most representative variographic models for the studied variables. The model selection is done by applying a cross-validation procedure leave-one-out (LOOCV). LOOCV is a resampling technique used in statistical analysis and machine learning to estimate the generalization error of a model and compare the performance of different models. The studied variables are then estimated using ordinary kriging. Findings. The application of the proposed approach has resulted in satisfactory results in terms of dispersion of grades and thicknesses of mineralized layers in a phosphate deposit. To evaluate the quality of the adjustment models obtained, efficiency factors such as Nash-Sutcliffe, and RMSE (Root Mean Square Error), were employed. These factors provide quantitative measures of the agreement between the observed and predicted values. The NSE (Nash-Sutcliffe efficiency) and RMSE (root mean square error) values of 0.572 and 6.599, respectively, indicate a better fit and greater accuracy of the adjustment models. The accuracy and efficiency criteria of the studied variables have acceptable values, with a mean square error (MSE) of 1.54 · 10-7. Originality. The combination of the least squares and LOOCV methods in the geostatistical analysis leads to improved estimation precision, greater reliability in representing the spatial variability of the parameters, and enhanced confidence in the validity of the adjustment models. Practical value. The development of a computer code for this geostatistical approach provides a practical tool for decision-makers to use in the management and exploitation of mining sites. Overall, this study has contributed to the advancement of geostatistical techniques and their application in the mining industry.

The object of the research consists of testing the suitability of the vector normalization procedure (NP) in the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method. One of the most problematic steps of the Multi-Criteria Decision Making (MCDM) process is related to the application of NPs by default to transform different measurement units of criteria into a comparable unit. This is because of the absence of a universal agreement that defines which NP is the most suitable for a given MCDM method. In the literature, there are thirty-one available NPs, each one of them has its strengths and weaknesses and, accordingly, can efficiently be applied to an MCDM method and even worst to another. Let’s note that many NPs (e. g., NPs of sum, max-min, vector, and max) have been used by default (i. e., without suitability study) in the TOPSIS method. Consequently, outcomes of multi-criteria evaluation and rankings of alternatives considered in the decision problems could have led to inconsistent solutions, and, therefore, decision-makers could have made irrational or inappropriate decisions. That’s why suitability studies of NPs become indispensable. Moreover, a description of the methodology, proposed in this research, is outlined as follows: 1) method of weighting based on an ordinal ranking of criteria and Lagrange multiplier (for determining criteria weights); 2) TOPSIS method (for ranking considered alternatives); 3) a statistical approach with 3-estimate (for comparing effects generated by the used NPs). In the research, twelve different NPs are compared to each other in the TOPSIS method via a numerical example, which deals with the wheel loader selection problem. The results of the comparison indicate that, amongst the twelve different NPs analyzed in this suitability study, vector NP has the lesser effect on the considered alternatives’ evaluation outcomes, when used with the TOPSIS method. The vector NP-TOPSIS approach can therefore be applied to solve multi-criteria decision problems. Its application further allows the decision-makers and users to better select efficient solutions and, consequently, to make conclusive decisions.