Introduction: Growth, yield and quality of okra (Abelmoschus esculentus (L.) Moench) are related to fertilizer application, being nitrogen (N) the most outstanding, due to its direct relationship with photosynthesis and vegetative growth of the plant. Objective: The objective was to evaluate the agronomic and productivity characteristics of okra as a function of N dose. Materials and methods: The study was conducted at the experimental area of Campus Gurupi, the Universidad Federal de Tocantins (UFT), Brazil, in two planting periods (autumn/winter and spring/summer). The experimental design used was randomized block design (RBD) with six treatments (50, 100, 150, 150, 200 and 250 kg N ha^-1) and four replications. Urea was used as a source of N. The characteristics evaluated were: productivity, average fruit mass, height and plant chlorophyll index. Results: Productivity and plant height were superior in the fall/winter crop. Mean fruit mass and chlorophyll index were not influenced by planting time. For productivity, a linear response was obtained with increasing dose up to the limit of the N dose used (250 kg ha^-1), with a mean value higher than 14 t of fruit. Mean mass and plant height responded linearly to increasing N dose. Nitrogen affected the chlorophyll index, with maximum values of 45.96 and 47.19, observed in the two evaluation periods. Conclusion: Planting time and N content in the soil interacted with plant height, being favorable in the period without precipitation. N influenced all the characteristics, demonstrating the importance of nitrogen fertilization in the development of okra plants.

In this study, we define the unrestricted Pell and Pell-Lucas quaternions. We give generating functions, Binet formulas and some generalizations of well-known identities such as Vajda’s, Catalan’s, Cassini’s d’Ocagne’s identities.

In this paper, all the forests, woodlands and trees in the administrative area of Zhaoling Township in Chuzhou City of Huai'an City were collected and analyzed. The total area of the administrative area is 4852 hectares, the forest coverage rate is 22.07%, and the forest greening rate is 26.13%. This index has exceeded 20% of the forest coverage rate of the well - off society. Tree species is particularly serious. In the forest system (pure forest), the area of pure forest of poplar is accounted for 99.9% of the whole forest area. In the four tree systems, the number of poplar trees accounted for 80% of the total number of trees in the whole tree, and the total amount of poplar trees accounted for 98%. The poplar pure forest age group structure disorders, the unit area is low. The ratio of total area of poplar pure forest in Zhongling and young forests was 92.9%, and the ratio of total area of poplar pure forest and mature forest was 7.1%. The ratio of mature forest and the ratio of mature forest was 0.7%, and the proportion of each group was obviously abnormal.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.