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Water pollution is an increasing global issue that societies are facing and is threating human health, ecosystem functions and agriculture production. The distinguished features of artificial intelligence (AI) based modeling can deliver a deep insight pertaining to rising water quality concerns. The current study investigates the predictive performance of gene expression programming (GEP), artificial neural network (ANN) and linear regression model (LRM) for modeling monthly total dissolved solids (TDS) and specific conductivity (EC) in the upper Indus River at two outlet stations. In total, 30 years of historical water quality data, comprising 360 TDS and EC monthly records, were used for models training and testing. Based on a significant correlation, the TDS and EC modeling were correlated with seven input parameters. Results were evaluated using various performance measure indicators, error assessment and external criteria. The simulated outcome of the models indicated a strong association with actual data where the correlation coefficient above 0.9 was observed for both TDS and EC. Both the GEP and ANN models remained the reliable techniques in predicting TDS and EC. The formulated GEP mathematical equations depict its novelty as compared to ANN and LRM. The results of sensitivity analysis indicated the increasing trend of input variables affecting TDS as HCO3− (22.33%) > Cl− (21.66%) > Mg2+ (16.98%) > Na+ (14.55%) > Ca2+ (12.92%) > SO42− (11.55%) > pH (0%), while, in the case of EC, it followed the trend as HCO3− (42.36%) > SO42−(25.63%) > Ca2+ (13.59%) > Cl− (12.8%) > Na+ (5.01%) > pH (0.61%) > Mg2+ (0%). The parametric analysis revealed that models have incorporated the effect of all the input parameters in the modeling process. The external assessment criteria confirmed the generalized outcome and robustness of the proposed approaches. Conclusively, the outcomes of this study demonstrated that the formulation of AI based models are cost effective and helpful for river water quality assessment, management and policy making.

The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the comparison of individual supervised ML models, such as gene expression programming (GEP) and artificial neural network (ANN), with that of an ensemble learning model, i.e., random forest (RF), for predicting river water salinity in terms of electrical conductivity (EC) and dissolved solids (TDS) in the Upper Indus River basin, Pakistan. The projected models were trained and tested by using a dataset of seven input parameters chosen on the basis of significant correlation. Optimization of the ensemble RF model was achieved by producing 20 sub-models in order to choose the accurate one. The goodness-of-fit of the models was assessed through well-known statistical indicators, such as the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and Nash–Sutcliffe efficiency (NSE). The results demonstrated a strong association between inputs and modeling outputs, where R2 value was found to be 0.96, 0.98, and 0.92 for the GEP, RF, and ANN models, respectively. The comparative performance of the proposed methods showed the relative superiority of the RF compared to GEP and ANN. Among the 20 RF sub-models, the most accurate model yielded the R2 equal to 0.941 and 0.938, with 70 and 160 numbers of corresponding estimators. The lowest RMSE values of 1.37 and 3.1 were yielded by the ensemble RF model on training and testing data, respectively. The results of the sensitivity analysis demonstrated that HCO3− is the most effective variable followed by Cl− and SO42− for both the EC and TDS. The assessment of the models on external criteria ensured the generalized results of all the aforementioned techniques. Conclusively, the outcome of the present research indicated that the RF model with selected key parameters could be prioritized for water quality assessment and management.

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The purpose of the research increases the awareness of dentist about common diseases and their tests and to know the normal value for each test. Prospective study Survey containing multiple choice questions and checklist to know the level of knowledge of dentist and dental student it was digital survey estimation of sample size 120 participants and collected and analyzed statistically. Five questions above 80% and average of 71% for all questions. The results showed least awareness in the answers was the answers of questions 6 and 14. (Q6. what is/are the tests used to measure blood pressure?) And correct answer was sphygmomanometer and Ambulatory blood pressure monitoring and 47.1% of participants answered it correctly. (Q14. what is the normal Thrombin Time?) The correct answer was between 9 to 13 seconds and 28.9% of participants answered it correctly. In the other hands the highest awareness in the answers was the answers of questions 7,8 and 12. (Q7. what is the normal Platelet Count?) The correct answer was 150,000 to 450,000/mm 3 and 86.6% of participants answered it correctly. (Q8. what is the normal Bleeding Time?) The correct answer was Between 1 to 6 minutes and 90.1% of participants answered it correctly. The education level factor in the group statistic showed the highest mean were general practitioner and consultants due to the knowledge and the experience while dental interns and dental students showed the lowest mean because the dental student lack the knowledge. There is awareness in the dentists and dental students because the average of the correct answers 71% but needs further more courses and lectures in this topic need to be implemented lab investigations importance, needs, interpretation and procedures. Which of the lab investigation is mandatory for the dentist? Availability of lab investigation in the new building of the dental hospital in king Saud University.

Osseointegration, which refers to the direct attachment of dental implants (DIs) to the neighboring host bone, is a great predictor of the therapeutic efficacy of DIs. The factors that determine whether DIs are successful have been evolving over time. Presently, these factors comprise the lack of mobility at the beginning of the prosthetic stage. DIs have a high chance of success; however, they occasionally fail. The failure of properly implanted DIs has been attributed to inappropriate patient identification; inadequate dental hygiene associated with microbial plaque buildup. The three most significant factors that lead to early implant failure appear to be a paucity of basic stability, surgical injury, and infection. Due to disruption of the basic bone recovery process, initial symptoms of infection may portend a far more serious outcome than if similar issues arise later. It appears that peri-implantitis and occlusal overloading are the main causes of late failure. Among the predisposing variables for implant problems and failure include poor prosthesis fabrication and subpar implant design.