Wheat ground beetle Zabrus tenebrioides (Goeze) (Coleoptera: Carabidae) is an important wheat pest in Iran and some regions of the world. This pest causes damage to wheat by feeding on the root, stalk, and leaves. Farmers try to control this pest by the foliar application of different insecticides, but this method causes damage to the environment. To find an effective method to control this pest, the thiamethoxam insecticide (Cruiser®) efficiency was evaluated using seed treatment in this study. An experiment based on a randomized complete block design with four treatments and four replications was conducted in a field in Ramhormoz city, Khuzestan province (Iran). The treatments were 150 and 200 ml of thiamethoxam plus 2350 ml of water for seed treatment, 2,000 ppm of diazinon by field spraying at the wheat tillering stage, and a control. The results indicated that the average plant density in 150 and 200 ml of thiamethoxam (333.58 and 333.28 plant/m2, respectively) was more than those of diazinon (258.28 plant/m2) and the control (182.12 plant/m2). The average ear density in 150 and 200 ml of thiamethoxam (513.78 and 506.12 plant/m2, respectively) was more than those of diazinon (321.22 plant/m2) and the control (260.86 plant/m2). According to the present results, farmers can use 150 ml of thiamethoxam plus 2350 ml of water for 100 kg of seeds to control this pest by seed treatment.

Damage control surgery (DCS) represents a staged surgical approach to the treatment of critically surgical approach to the treatment of critically injured trauma patients. Originally described in the context of hepatic trauma and post injury induced coagulopathy, the indications for DCS have extended to the management of extra abdominal trauma and non traumatic acute abdominal emergencies. Significant progress in trauma based resuscitation techniques had le to improved outcomes and a reduction in the DCS requirement.

In this paper a semi-active structural damage control strategy using MR dampers and Neural Networks is presented. A multilayer feed-forward neural network has been designed. The input layer is relative displacement of stories and the output layer is the voltage needed for MR damper. The neural network is learned to predict the voltage needed for MR damper that can minimize the Park & Ang damage index of structure. Genetic algorithm has been used to learn the neural network. The Park & Ang damage index of the structure has been used as the fittnees function of the genetic algorithm. To evaluate the structural control system a nonlinear 3 story benchmark building has been selected. The results show the the proposed structural control system can effectively reduce the Park & Ang damage index of the structure.

A common method for controlling a group of parallel converters in decentralized control strategy structure in an island microgrid, the use is the droop-down characteristics of frequency ω-P and voltage E-Q. However, the problem with using this method is that the reactive power is not properly distributed (in proportion to the capacity of the micronutrients) between the micronutrients, which may lead to overload in the converters. Microgrids may also suffer from dynamic stability problems such as power fluctuations, which can be increased by switching between active and reactive power control. To avoid this problem, the X / R ratio of transmission lines is an important parameter that should be carefully considered in the design of micronutrient controllers. By linearizing and simplifying conditions, the control system conversion function model becomes a single input-single output system, which is efficient enough to show the relationship between control parameters such as slope of droop characteristics and derivative sentences, virtual impedance, and voltage controllers. Using this model, stability conditions for different parameters are analyzed. Also, to improve power distribution stability, common droop strategies are modified by adjusting the slope as well as adding nonlinear sentence sections. This approach reduces the coupling between active and reactive power control and reduces the dependence of power distribution on grid parameters such as the X / R ratio. To evaluate the reliability of the proposed model, the simulation results in a sample island microgrid in MATLAB software are presented.

Civil infrastructures, nowadays, are an indispensable part of society, and any unpredicted damage can cause severe economic and life loss. Hence, developing smart structures has been the topic of many studies during the past decades. In this article, developing a smart structure by synthesizing structural control and health monitoring is suggested by extracting damage-sensitive features from the active control force. The autoregressive models have been deployed to extract damage-sensitive features in the time domain. Then, quadratic discriminant analysis is utilized to discriminant between different damage states of the structure. The active control force is obtained by two model reference adaptive controllers, namely the MIT rule and Lyapunov stability theorem, to attenuate the structural vibration caused by Gaussian white noise excitations. The proposed approach has been numerically studied on a three-story shear building with active ideal controllers in all floors. Results indicate that the proposed approach can detect the potential damage, as well as its severity and location, precisely.

In this paper, the artificial intelligence is employed to design a Fault-Tolerant Controller (FTC) for structural vibrations. The FTC is designed to reduce the probability of damage considering sensor fault. For this purpose, Neural Networks (NNs) are used as fault detection and accommodation and fuzzy logic is used as a controller. This control strategy requires two groups of neural networks. The first group of neural networks finds the faulty sensor by estimating the structural responses and comparing them with the responses obtained from the sensors. The second group has the task of estimating the response of the faulty sensor using data obtained from healthy sensors. To evaluate this method, the time history analysis of a 3-story benchmark building equipped with accelerometers and active actuators has been used. This evaluation is based on determining the probability of structural damage and the generation of fragility curves under forty ground motions. To develop fragility curves, the criteria specified in the FIMA 356 (IO, LS and CP) for the moment frame based on the inter-story drift are used. This study show that in the absence of the neural networks, sensor fault reduces the performance of the fuzzy controller and it is even possible to increase the structural responses compared to the structure without the controller. In addition, results demonstrate that the proposed control strategy can rectify the deterioration of sensor faults and decrease the probability of failure.