Influence of the light spectrum on growth, development, and nutrients contents of okra was studied by growing okra (Abelmoschus esculentus L.) under three different LED-based irradiations defined by their peak wavelength at 455.45 ± 1.80 nm (B455), 522.27 ± 1.46 nm (G522), and 635.03 ± 1.33 nm (R635), respectively in the blue, green, and red regions of the visible spectrum. The photosynthetic photon flux density (PPFD) of 200 μmol m−2 s−1 was provided by the LEDs for 18 h daily. Leaves macronutrients and micronutrients concentration and plant biometric parameters were measured 60 days after sowing; the evolution of biometric parameters was also monitored during the growing period. Results related to biometric parameters have shown that highest leaf area, plant height, and fresh and dry weight were achieved under B455 light; both R635 and G522 lights produce the highest quantity of leaves; and largest stem diameters were observed under B455 and G522 lights. Regarding mineral contents, highest calcium, phosphorus, and manganese concentrations were obtained under R635 light; highest sodium content was observed under G522 light; and the highest nitrogen content was obtained under both B455 and G522 lights. However, there were no significant differences observed for potassium, magnesium, and zinc concentrations among the three light treatments. These results revealed that selective spectrum in artificial lighting design can be strategically used to optimize the plant growth, development, and mineral contents uptake under controlled environments.

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Intelligent Tutoring Systems (ITS) are computer-based learning environments that aim to imitate to the greatest possible extent the behavior of a human tutor in their capacity as a pedagogical and subject expert. One of the major challenges of these systems is to know how to adapt the training both to changing requirements of all kinds and to student knowledge and reactions. The activities recommended by these systems mainly involve active student performance prediction that, nowadays, becomes problematic in the face of the expectations of the present world. In the associated literature, several approaches, using various attributes, have been proposed to solve the problem of performance prediction. However, these approaches have failed to take advantage of the synergistic effect of students' social and emotional factors as better prediction attributes. This paper proposes an approach to predict student performance called SoEmo-WMRMF that exploits not only cognitive abilities, but also group work relationships between students and the impact of their emotions. More precisely, this approach models five types of domain relations through a Weighted Multi-Relational Matrix Factorization (WMRMF) model. An evaluation carried out on a data sample extracted from a survey carried out in a general secondary school showed that the proposed approach gives better performance in terms of reduction of the Root Mean Squared Error (RMSE) compared to other models simulated in this paper.

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