The current study was conducted in 2013 to identify the seeds of three species of Amaranthus, Amaranthus viridis L., Amaranthus retroflexus L. and Amaranthus albus L., by using the artificial neural network (ANN) and canonical discriminant analysis (CDA) methods. To begin with, photographs were taken of the seeds and 13 morphological characteristics of each seed extracted as predictor variables. Backward regression was used to find the most influential variables and seven variables were derived. Thus, predictor variables were divided into two sets of 13 and seven morphological characteristics. The results showed that the recognition accuracy of the ANN made using 13 and seven predictor variables was 81.1 and 80.3%, respectively. Meanwhile, recognition accuracy of the CDA using the seven and 13 predictor variables was 74.0 and 75.7%, respectively. Therefore, in comparison to CDA, ANN showed higher identification accuracy; however, the difference was not statistically significant. Identification accuracy for A. retroflexus was higher using the CDA method than ANN, while the ANN method had higher recognition accuracy for A. viridis than CDA. In addition, use of 13 predictor variables yielded a greater identification accuracy than seven. The results of the current study showed that using seed morphological characteristics extracted by computer vision could be effective for reliable identification of the similar seeds of Amaranthus species.

There are many parameters in agriculture that change over time in a sigmoid pattern. In the current study, the double logistic function was used to describe and simulate dry matter (DM) variation of winter rapeseed plant and to explain the growth rate under water stress. Irrigation treatments were full irrigation at all growth stages, water stress during the vegetative stage in early spring, water stress at flowering and podding stages, water stress at grain filling stage and rain-fed treatment with supplemental irrigation at time of planting. A high value for the goodness of fit (0.996) and low value for normalized root mean square error (0.085) showed that the double logistic function can describe and simulate DM variation of rapeseed accurately. DM predicted by the double logistic equation based on growing degree day was slightly closer to the measured DM compared with the DM predicted by the double logistic equation based on days after planting. Results showed that growth rate before the winter cold period was lower than that after this period. There were two maximum growth rates for winter rapeseed: the first occurred before cold period and another after.

Balamuthia mandrillaris is an opportunistic free-living amoeba that has been reported to cause cutaneous lesions and Balamuthia amoebic encephalitis. The biology and environmental distribution of B. mandrillaris is still poorly understood and isolation of this pathogen from the environment is a rare event. Previous studies have reported that the presence of B. mandrillaris in the environment in Iran may be common. However, no clinical cases have been reported so far in this country. In the present study, a survey was conducted in order to evaluate the presence of B. mandrillaris in hot-spring samples of northern Iran. A total of 66 water samples were analysed using morphological and molecular tools. Positive samples by microscopy were confirmed by performing PCR amplification of the 16S rRNA gene of B. mandrillaris. Sequencing of the positive amplicons was also performed to confirm morphological data. Two of the 66 collected water samples were positive for B. mandrillaris after morphological and molecular identification. Interestingly, both positive hot springs had low pH values and temperatures ranging from 32 °C to 42 °C. Many locals and tourists use both hot springs due to their medicinal properties and thus contact with water bodies containing the organism increases the likelihood of infection. To the best of our knowledge, this is the first report on the isolation of B. mandrillaris from hot-spring sources related to human activity. Therefore, B. mandrillaris should be considered as a possible causative agent if cases of encephalitis are suspected following immersion in hot springs in addition to Acanthamoeba and Naegleria.

In this paper, the combination of neural network (NN), proportional derivative (PD), and robust controller are used for determining the maximum load-carrying capacity (MLCC) of articulated robots, subject to both actuator and end-effector deflection constraints. The proposed technique is then applied to articulated robots, and MLCC is obtained for a given trajectory. In the practical simulations, it's impossible to determine the parameters of robot model exactly, so the trajectory tracking performance of the proportional integral derivative (PID) and computed torque methods significantly decrease. The PD control of robot has major problem, it cannot guarantee zero steady state error. For this reason, the NN controller with PD and robust controller are used. The multilayer neural network is also used to compensate gravity and friction effects. By using Lyapunov Direct Method it is shown that the stability of closed loop system would be guaranteed, if the weights of multilayer had certain learning rules. Standard back propagation algorithm is used as a learning algorithm to update the connection weights of the NN controller. The simulation results of the proposed adaptive robust neural network (ARNN) controller are compred with sliding mode and feedback linearization methods for flexible joint robot, and compared with open loop controller for 3D industrial robot. The obtained results assured the robustness and improvement in MLCC in the presence of uncertainties in dynamic model of the robot arm and external disturbances. In fact, adaptive robust NN controller suppresses disturbances accurately and achieves very small errors between commanded and actual trajectories.

Maximum load carrying capacity (MLCC) of flexible robot manipulators is computed based on closed-loop approach. In open-loop approach, controller is not considered, so the end effector deviation from the predefined path is significant and the accuracy constraint restrains the maximum payload before actuators go into saturation mode. In order to improve the MLCC, a method based on closed-loop strategy is presented. Since in this case the accuracy is improved the actuators constraint is not a major concern and full power of actuators can be used. Since controller can play an important role in improving the maximum payload, a sliding mode based partial feedback linearization controller is designed. Furthermore, a fuzzy variable layer is used in sliding mode design to boost the performance of the controller. However, the control strategy required measurements of elastic variables velocity that are not conveniently measurable. So a nonlinear observer is designed to estimate these variables. Stability analysis of the proposed controller and state observer are performed on the basis of Lyapunov's direct method. In order to verify the effectiveness of the presented method, simulation is done for a two-link flexible manipulator. The obtained maximum payload in open-loop and closed-loop cases is compared and the superiority of the method is illustrated and the results are discussed.