This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was assessed by the determination (...) coefficient (R²), the sum squared error (SSE) and a review of fit graphs. The results demonstrate the value of ANNs for prediction modeling. Drawing on supervised learning and back propagation, the ANN-based prediction models adopt an architecture of [18-15-1] for zinc, [18-11-1] for manganese, and [18-8-1] for boron, and perform effectively with a single cached layer. It was found that the MLR-based prediction models are substantially less accurate than those based on the ANNs. In addition, the physical-chemical parameters being investigated are nonlinearly correlated with the levels of heavy metals in the surface waters of the Oued Inaouen watershed flowing towards Inaouen. (shrink)

The educational process has been dramatically transformed by technology; this transformation has caused the emergence of central concepts, among which is distance learning. The latter has brought new opportunities to learn from; meanwhile, it has presented challenges that have impacted learning in many ways. Thus, the present study investigated the implementation of E-learning among EFL students at Moulay Ismail University to identify students' perceptions about this type of learning and consider the pros and cons to ensure best practice solutions (...) that endorse online learning quality. The study included 315 students at Moulay Ismail University in Morocco who completed an online questionnaire and used descriptive analysis because the data was presented in simple summaries that aimed to describe the basic features of the data and measures. The findings, both quantitative and qualitative, indicated that e-learning is still in its early stages. At the same time, students are not entirely prepared to supersede traditional learning with distance education due to some reasons, in particular, such as lack of university support in offering resources to learn online, lack of internet connection, malfunction of technological tools, etc. All in all, EFL students prefer blended learning because it allows them to combine a new technological experience with a traditional one. Meaning that e-learning should not be excluded entirely from the educational system, but it can be used only as a support to foster EFL students' academic achievements. (shrink)