Evaluating machine learning performance in predicting sodium adsorption ratio for sustainable soil-water management in the eastern Mediterranean.

Soil salinization is a critical global issue for sustainable agriculture, impacting crop yields and posing a threat to achieving the Sustainable Development Goal (SDG) of ensuring food security. It is necessary to monitor it in detail and uncover its underlying factors at a regional scale. In this context, the present study aimed to evaluate soil health in the eastern Mediterranean region by using the Sodium Adsorption Ratio (SAR) as an indicator of soil salinity in three distinct soil horizons. The main objective of the research was to evaluate the performance of four machine learning (ML) models, including Random Forest (RF), Nu Support Vector Regression (NuSVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Gradient Boosting Regression (GBR), for accurate prediction of SAR following the Recursive Feature Elimination (RFE) as a feature selection method. Moreover, SHapely Additive exPlanations (SHAP) was applied as sensitivity analysis to identify the most influential covariates. Main findings of the research revealed that the average clay content in the surface horizon (H10-25cm) was 50.5% ± 10.4, which significantly increased to 57.5% ± 8.7 (p < 0.05). No significant mean differences were detected between the studied horizons for SAR and Na+. ML output revealed that NuSVR outperformed other algorithms in accurately predicting outcomes during both the training and testing stages. Moreover, Scenario 2 (SC2) with seven selected features from the RFE method facilitated highly accurate SAR predictions. Overall, the performance of ML models is ranked as NuSVR > GBR > ANN-MLP > RF. Lastly, SHAP sensitivity analysis identified CEC, Ca+2, Mg+2, and Na+ as the most influential variables for SAR prediction in both the training and testing stages. Hence, the research yielded valuable insights for efficient agricultural soil management at a regional level using state-of-the-art technology.

Study of the Synergistic Influence of Plasma Electrolytic Oxidation and Wet Post-Treatment on the Surface of AA7075.

In this study, we enhanced the corrosion and microbial resistance of aluminum 7075 alloys by applying a thin layer of alumina through plasma electrolytic oxidation (PEO) in an alkali-silicate electrolyte. In addition, the influence of film sealing on coated aluminum alloy 7075 was studied in detail, specifically in oil and water at 100 °C after treatment. The surface and cross-sectional morphology, element composition, and phase composition of the PEO coatings were characterized by using scanning electron microscopy (SEM) assisted with energy-dispersive X-ray spectrometry (EDS) and X-ray diffraction (XRD), respectively. The corrosion resistance of the coating on AA7075 PEO was evaluated before and after post-treatment using hot water and hump oil at 100 °C. This assessment was conducted by using various electrochemical techniques, including open-circuit potential (OCP), linear polarization resistance (LPR), potentiodynamic polarization scan (PD), electrochemical impedance spectroscopy (EIS), and cyclic potentiodynamic scan (CPS). The results showed that the corrosion resistance of the AA7075 alloy was significantly improved after the PEO coating. The AA7075 + SF, among all of the examined alloys, exhibited superior corrosion properties, due to its fat sealing. This is probably due to the formation of a mixed fatty acid layer from oil on the surface of the AA7075 PEO, which synthesizes a hydrophobic layer. Interestingly, the samples treated with PEO showed a great resistance to microbial growth.

Comparison Between Patient and Plastic Surgeon Aesthetic Analysis in Rhinoplasty Consultation.

The surgeon's aesthetic analysis of the nose is based on scientific measures of its proportions and dimensions. Because the primary aim of rhinoplasty is targeted at the patient's satisfaction with self-image, patients' perception and satisfaction are of paramount importance. The aim of this study was to evaluate surgeon versus patient nasal aesthetic analysis. Method: A cross-sectional study was conducted on 57 primary rhinoplasty consultations during the period June and September 2017 at the Plastic Surgery Clinic in King Fahad Hospital-Hofuf. The surgeon and the patients were handed identical questionnaires before the consultations. The questionnaire has 27 components regarding the nasal appearance. Results: The surgeon's and the patients' perceptions regarding reliability was assessed by Cohen's Kappa and Pearson's correlation coefficient. There was moderate agreement with the overall appearance of the nose (κ = 0.2-0.39). The most agreed-upon components were "dorsal hump" (κ = 0.6, P = 0.001) and "tip drops down" (κ = 0.41, P = 0.002). The columella and the suitability of the front part of the nose had the largest disagreement (κ = -0.06 and κ = -0.09, respectively). The level of agreement among most of the questionnaires' components was slight or nonexistent (κ = 0.004-0.39). Conclusions: The surgeon and patients have a minimum agreement regarding the view of nasal appearance, mostly with the suitability of the front part and the columella. The parts of the nose agreed upon the most were "dorsal hump" and "tip drops down". Exploring the differences between patient and surgeon aesthetic analysis of the nose will aid in addressing the discrepancies and improving surgical outcome and satisfaction.

Assessing the impacts of agricultural drought (SPI/SPEI) on maize and wheat yields across Hungary.

This study examined the physical properties of agricultural drought (i.e., intensity, duration, and severity) in Hungary from 1961 to 2010 based on the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The study analyzed the interaction between drought and crop yield for maize and wheat using standardized yield residual series (SYRS), and the crop-drought resilient factor (CDRF). The results of both SPI and SPEI (-3, -6) showed that the western part of Hungary has significantly more prone to agricultural drought than the eastern part of the country. Drought frequency analysis reveals that the eastern, northern, and central parts of Hungary were the most affected regions. Drought analysis also showed that drought was particularly severe in Hungary during 1970-1973, 1990-1995, 2000-2003, and 2007. The yield of maize was more adversely affected than wheat especially in the western and southern regions of Hungary (1961-2010). In general, maize and wheat yields were severely non-resilient (CDRF < 0.8) in the central and western part of the country. The results suggest that drought events are a threat to the attainment of the second Sustainable Development Goals (SDG-2). Therefore, to ensure food security in Hungary and in other parts of the world, drought resistant crop varieties need to be developed to mitigate the adverse effects of climate change on agricultural production.

Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe.

The Modified Fournier Index (MFI) is one of the indices that can assess the erosivity of rainfall. However, the implementation of the artificial neural network (ANN) for the prediction of the MFI is still rare. In this research, climate data (monthly and yearly precipitation (pi, Ptotal) (mm), daily maximum precipitation (Pd-max) (mm), monthly mean temperature (Tavg) (°C), daily maximum mean temperature (Td-max) (°C), and daily minimum mean temperature (Td-min) (°C)) were collected from three stations in Hungary (Budapest, Debrecen, and Pécs) between 1901 and 2020. The MFI was calculated, and then, the performance of two ANNs (multilayer perceptron (MLP) and radial basis function (RBF)) in predicting the MFI was evaluated under four scenarios. The average MFI values were between 66.30 ± 15.40 (low erosivity) in Debrecen and 75.39 ± 15.39 (low erosivity) in Pecs. The prediction of the MFI by using MLP was good (NSEBudapest(SC3) = 0.71, NSEPécs(SC2) = 0.69). Additionally, the performance of RBF was accurate (NSEDebrecen(SC4) = 0.68, NSEPécs(SC3) = 0.73). However, the correlation coefficient between the observed MFI and the predicted one ranged between 0.83 (Budapest (SC2-MLP)) and 0.86 (Pécs (SC3-RBF)). Interestingly, the statistical analyses promoted SC2 (Pd-max + pi + Ptotal) and SC4 (Ptotal + Tavg + Td-max + Td-min) as the best scenarios for predicting MFI by using the ANN-MLP and ANN-RBF, respectively. However, the sensitivity analysis highlighted that Ptotal, pi, and Td-min had the highest relative importance in the prediction process. The output of this research promoted the ANN (MLP and RBF) as an effective tool for predicting rainfall erosivity in Central Europe.