Evaporation is a key element for water resource management, hydrological modelling, and irrigation system designing. Monthly evaporation (Ep) was projected by deploying three machine learning (ML) models included Extreme Gradient Boosting, ElasticNet Linear Regression, and Long Short-Term Memory; and two empirical techniques namely Stephens-Stewart and Thornthwaite. The aim of this study is to develop a reliable generalised model to predict evaporation throughout Malaysia. In this context, monthly meteorological statistics from two weather stations in Malaysia were utilised for training and testing the models on the basis of climatic aspects such as maximum temperature, mean temperature, minimum temperature, wind speed, relative humidity, and solar radiation for the period of 2000-2019. For every approach, multiple models were formulated by utilising various combinations of input parameters and other model factors. The performance of models was assessed by utilising standard statistical measures. The outcomes indicated that the three machine learning models formulated outclassed empirical models and could considerably enhance the precision of monthly Ep estimate even with the same combinations of inputs. In addition, the performance assessment showed that Long Short-Term Memory Neural Network (LSTM) offered the most precise monthly Ep estimations from all the studied models for both stations. The LSTM-10 model performance measures were (R2 = 0.970, MAE = 0.135, MSE = 0.027, RMSE = 0.166, RAE = 0.173, RSE = 0.029) for Alor Setar and (R2 = 0.986, MAE = 0.058, MSE = 0.005, RMSE = 0.074, RAE = 0.120, RSE = 0.013) for Kota Bharu.
We aimed to systematically review evidence pertaining to the safety and efficacy of nitazoxanide in treating infectious diarrhea. On September 21, 2017, we identified relevant studies using 12 databases. The estimates of the included studies were pooled as a risk ratio (RR). We conducted a network and pairwise random-effects meta-analysis for both direct and indirect comparisons of different organisms that are known to cause diarrhea. The primary and secondary analysis outcomes were clinical response until cessation of illness, parasitological response and adverse events. We included 18 studies in our analysis. In cryptosporidiosis, the overall estimate favored nitazoxanide in its clinical response in comparison with placebo RR 1.46 [95% CI 1.22-1.74; P-value <0.0001]. Network meta-analysis among patients with Giardia intestinalis showed an increase in the probability of diarrheal cessation and parasitological responses in comparison with placebo, RR 1.69 [95% CI 1.08-2.64, P-score 0.27] and RR 2.91 [95% CI 1.72-4.91, P-score 0.55] respectively. In Clostridium difficile infection, the network meta-analysis revealed a non-significantly superior clinical response effect of nitazoxanide to metronidazole 31 days after treatment RR 1.21 [95% CI 0.87-1.69, P-score 0.26]. In Entamoeba histolytica, the overall estimate significantly favored nitazoxanide in parasitological response with placebo RR 1.80 [95% CI 1.35-2.40, P-value < 0.001]. We highlighted the effectiveness of nitazoxanide in the cessation of diarrhea caused by Cryptosporidium, Giardia intestinalis and Entamoeba histolytica infection. We also found significant superiority of NTZ to metronidazole in improving the clinical response to G. intestinalis, thus it may be a suitable candidate for treating infection-induced diarrhea. To prove the superiority of NTZ during a C. difficile infection may warrant a larger-scale clinical trial since its superiority was deemed insignificant. We recommend nitazoxanide as an appropriate option for treating infectious diarrhea.