Thermodynamics of Horndeski Black Holes with Generalized Uncertainty Principle

Horndeski theory is the most general scalar-tensor extension of General Relativity with second order field equations. It may be interesting to study the effects of the Generalized Uncertainty Principle on a static and asymptotically flat shift symmetric solutions of the Horndeski black holes. With this motivation, here we obtain the modified black hole temperatures in shift symmetric Horndeski gravity by employing the Generalized Uncertainty Principle. Using the corrected temperature, the entropy and heat capacity are calculated with details. We also investigate the tunneling probability of particles from Horndeski black holes horizon and possible correlations between the emitted modes (particles).Comment: 17page

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels

Influenza vaccination coverage and obstacles in healthcare workers (HCWs) and the follow up of side effects: A multicenter investigation in Iran

Introduction. Influenza is a highly contagious acute respiratory illness. Due to the high exposure of hospital personnel, widespread vaccination of these high-risk groups seems to be a necessity in healthcare centers. To determine vaccination coverage in the personnel of four tertiary referral collegiate hospitals in 2019, and to further investigate individual obstacles for Influenza vaccination. Methods. In this cross-sectional descriptive study, 637 personnel were selected randomly from distinctive hospitals in a list-wised. Ones vaccinated filled the side effects questionnaire and who not vaccinated filled the vaccination obstacles questionnaire. Results. The mean vaccination coverage was 29.4% and the coverage difference among centers was not statistically significant (p=0.192). The following items had the most impact on personnel decision: confidence about one’s immune system (p<0.05), the experience of side effects from previous vaccinations (p=0.011), attitude about vaccination in colleagues (p=0.021) and work experience (p<0.05). About 23% of vaccinated individuals reported side effects following vaccination and the most common side effect was mild cold symptoms with 12.3% prevalence. Conclusion. The results of the current study revealed that influenza vaccination coverage among HCWs is not satisfactory in Iran. Hospital authorities and infection control units should plan to remove the obstacles of influenza vaccination

Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN

In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer (RMSE:1.21, MAE:0.878, NSE:0.93, PBIAS:0.15, R2:0.93), second piezometer (RMSE:1.22, MAE:0.881, NSE:0.92, PBIAS:0.17, R2:0.94), and third piezometer (RMSE:1.23, MAE:0.911, NSE:0.91, PBIAS:0.19, R2:0.94) in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modelling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models

Potential assessment of water harvesting from local wastewater treatment plants (Case study: Rotating Biological Contactor, RBC)

In dry regions the reuse of treated wastewater plays a significant role in management, operation, scheduling and utilization of water resources. In design and operate of the sewage treatment plants , it is essential to measure and forecast the harvested water from wastewater plants and balance the groundwater depletion with these new resources. In this study, potential water harvested by local wastewater treatment plants, Rotating Biological Contactor (RBC) is determined and balanced with water requirement of plants. Based on the design basis of RBC, the production ratio of 80 % is used and the produced discharge ranges from 4 up to 8 liter per seconds, with 140000 cubic meters per year. To quantify the balancing between RBC produced water and irrigation water requirements, a plant-by-plant water requirement is calculated and the operation rule of groundwater wells in the case study are determined and proposed as an action plan to the operator of wells.  Based on the results it was observed that the RBC can supply two times of pistachio orchard (23 hectares) irrigation requirements or 70 percent of the landscape and green space water needs in the case study

Qualitative Zoning of Shahr-e-Babak Aquifer Based on its Corrosiveness, Sedimentation, and Applicability for Agricultural, Drinking, and Pressure Irrigation Uses

Water quality management in groundwater aquifers requires accurate water quality monitoring to ensure they meet a variety of relevant standards. Given the rather few reported studies in the field, the present study was designed and implemented to investigate the groundwater quality of Shahr-e-Babak aquifer that is exploited for agricultural, mining, drinking, and industrial consumptions. The kriging and IDW (power:1-3) techniques with spherical, exponential, and Gaussian variogramsare were compared using R2, RMSE, MAE, and RSS indices to find the optimum model for determining the spatial variability of sixteen groundwater quality parameters. Multi-purpose zoning of groundwater quality was accomplished in the ArcGIS environment in terms of the Wilcox, Schuler, drip and sprinkle irrigation as delineated by Iran Power Ministry, corrosiveness, and sedimentation standards as well as the WHO and IRISI indices before spatial correlations were determined accordingly. Based on the water quality zoning maps thus derived, Langelier, Corrosiveness, and Ryznar indices were several times larger than the threshold levels across the whole aquifer plain, indicating the wide corrosiveness of the water in industrial applications. The results also revelaed that 93% of the groundwater in the plain area was classified as C4-S1 and C4-S2, which are unsuitable for irrigation, while only 1.3% of the groundwater was acceptable for drinking uses. Drip irrigation zoning revealed that 64% of the plain area had the lowest water quality.The undesirable quality zones in a vast area of the aquifer investigated calls for accurate quality monitoring and management to meet the development objectives in the region

Accuracy and uncertainty analysis of artificial neural network in predicting saffron yield in the south Khorasan province based on meteorological data

Because of saffron yield sensitivity and the effects of climate on its performance, and also due to the nonlinear nature of crop yield functions, the Artificial Neural Network (ANN) model is employed in this study for prediction and uncertainty analysis of saffron yield in the South Khorasan province based on 20 years of data. The input vector of the ANN model was optimized from 37 parameters through correlation and variance inflation. The optimum architecture of the model was derived as 1-2-4-11 with a sigmoidal activation function based on the results at three stages of training, testing and verification. The root mean square error (RMSE) and mean absolute error (MAE) were equal to 0.3 and 0.5 in the training step and 0.7 and 1 in the test step, respectively. These results indicate that the ANN is a suitable model for predicting saffron yield. Uncertainty analysis based on R2, d-factor and 95%PPU showed that despite use of inadequate data, model prediction showed acceptable prediction bounds and predicted a satisfactorily saffron yield trend. The R2 values were equal to 0.92 and 0.58 in the training and test steps, respectively, which are statistically significant at the

GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables

Abstract Accurate prediction of soil temperature (Ts) is critical for efficient soil, water and field crop management. In this study, hourly Ts variations at 5, 10, and 30 cm soil depth were predicted for an arid site (Sirjan) and a semi-humid site (Sanandaj) in Iran. Existing machine learning models have high performance, but suffer from uncertainty and instability in prediction. Therefore, GLUE approach was implemented to quantify model uncertainty, while wavelet coherence was used to assess interactions between Ts and meteorological parameters. Standalone machine learning models (adaptive neuron fuzzy interface system (ANFIS), support vector machine model (SVM), radial basis function neural network (RBFNN), and multilayer perceptron (MLP)) were hybridized with four optimization algorithms (sunflower optimization (SFO), firefly algorithm (FFA), salp swarm algorithm (SSA), particle swarm optimization (PSO)) to improve Ts prediction accuracy and reduce model uncertainty. For both arid and semi-humid sites, ANFIS-SFO produced the most accurate performance at studied soil depths. At best, hybridization with SFO (ANFIS-SFO, MLP-SFO, RBFNN-SFO, SVM-SFO) decreased RMSE by 5.6%, 18%, 18.3%, and 18.2% at 5 cm, 11.8%, 10.4%, 10.6%, and 12.5% at 10 cm, and 9.1%, 12.1%, 13.9%, and 14.2% at 30 cm soil depth compared with the respective standalone models. GLUE analysis confirmed the superiority of hybrid models over the standalone models, while the hybrid models decreased the uncertainty in Ts predictions. ANFIS-SFO covered 95%, 94%, and 96% observation data at 5, 10, and 30 cm soli depths, respectively. Wavelet coherence analysis demonstrated that air temperature, relative humidity, and solar radiation, but not wind speed, had high coherence with Ts at different soil depths at both sites, and meteorological parameters mostly influenced Ts in upper soil layers. In conclusion, uncertainty analysis is a necessary and powerful technique to obtain an accurate and realistic prediction of Ts. In contrast, wavelet coherence analysis is a useful tool to investigate the most effective variables that strongly affect predictions