Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system

The Effects of Metformin on Stereological and Ultrastructural Features of the Ovary in Streptozotocin-induced Diabetes Adult Rats: an Experimental Study

Background: Diabetes is a chronic disease that can affect almost all of the body organs, including male and female reproductive systems. Objective: This study was designed to investigate the preventive effects of metformin on stereological and ultrastructure characteristics of the ovary in the streptozotocin-induced diabetes adult female rats. Materials and Methods: Seventy adult (8-10 wk) female Sprague-Dawley rats (180-200 gr) were equally divided, as follows: (n = 10/each) control; STZ-induced diabetes (single dose of 65 mg/kg STZ, IP); metformin-treated (50 mg/100 gr of body weight, orally); diabetic-metformin-treated; sham 1, (single dose of sodium citrate); sham 2, (0.5 ml of daily oral distilled water); and sham 3, (sodium citrate + distilled water treated). The body mass index, ovarian weight, blood sugar level, cholesterol, and triglyceride were measured. The stereological and ultrastructural features of ovary were assessed. Results: The blood sugar of induced-diabetic rats was increased (p < 0.01). The BMI (p < 0.01), number of granulosa cells (p = 0.04), primordial, primary and secondary follicles (p = 0.03), total volume of ovary (p < 0.01) and cortex, nucleus diameter ratio to the ooplasm were decreased. The number of atretic follicles in the diabetic and diabetic + metformin-treated rats were increased (p < 0.01). The ultrastructural characteristics of ovary were more damaged in diabetic rats. Conclusion: Diabetes has destructive effects on ovarian follicles and causes follicular atresia. Also, the size of oocytes, numbers of granulosa cells and ooplasmic organelles, which are involved in the folliculogenesis are affected by diabetes and metformin has no preventive effects. Key words: Diabetes, Metformin, Ovary, Tissue

State of the art survey of deep learning and machine learning models for smart cities and urban sustainability

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems