Progressive collapse studies generally assess the performance of the structure under gravity and blast loads, while earthquakes may also lead to the progressive collapse of a damaged structure. In this study, the progressive collapse response of concentrically braced dual SYSTEMS WITH steel moment-resisting frames was assessed under seismic loads through pushover analysis using triangular and uniform lateral load patterns. Two different bracing types (X and inverted V braces) were considered, and their performances were compared under different lateral load patterns using the nonlinear static alternate path method recommended in the Unified Facilities Criteria (UFC) guideline. Eventually, the seismic progressive collapse resistance of models was compared to their progressive collapse response under gravity loads. These studies showed that models under the seismic progressive collapse loads satisfied UFC acceptance criteria and limited rehabilitation objective. The structures had better performance under seismic progressive collapse than models under gravity loads because of more resistance, ductility, suitable load redistribution, and more structural elements that participated in load redistribution. Furthermore, despite studies on progressive collapse under gravity loads, the dual system WITH X braces showed better progressive collapse performance (more resistance, residual reserve strength ratio and ductility) under seismic loads than the model WITH inverted V braces.

In this study, a robust controller is designed for fuzzy network control SYSTEMS (NCSs) using the static output feedback. Delay and data packet dropout affect on the stability of network control SYSTEMS, and therefore, the asymptotic stability condition is established considering delay and data packet dropout. Delay is time-varying while the lower and upper bounds for delay is defined, and the number of data packet dropout is unknown. Data DRIFT is also an important phenomena that may occur when data is transmitted from sensors to the controller and from the controller to actuators. This phenomenon is modeled as a stochastic variable WITH a probabilistic distribution. For stability analyses, Lyapunov–Krasovskii functions, which depend on the limits of delay and data packet dropout, are used. Results of controller design are derived as Linear Matrix Inequalities (LMIs). A numerical example is adopted to show the effectiveness of the proposed approach.

In statistics it is often assumed that sample observations are independent. But sometimes in practice, observations are somehow dependent on each other. Spatiotemporal data are dependent data which their correlation is due to their spatiotemporal locations. Spatiotemporal models arise whenever data are collected across both time and space. Therefore such models have to be analyzed in terms of their spatial and temporal structure. Usually a spatiotemporal random field {Z(s, t) : (s, t) Î D x T} is used for modeling the spatiotemporal data, where D Ì Rd, d ³ 1 is a space region and T Í R is a time region. One of the fundamental subjects in analyzing such data is prediction. In spatial statistics, assuming that the spatiotemporal random field Z(s,.t) is stationary WITH finite variance at all coordinates (s, t) Î D x T, and spatiotemporal covariance function C(h, u) = cov (Z(s, t)j Z(s + h, t + u)) exists, the unknown value of the random field at a given location (s0, t0) is usually predicted WITH kriging as the best linear unbiased predictor. In practice, the spatiotemporal covariance function is unknown and a positive definite function should be fitted to the estimates of the covariance function. To ensure that a valid spatiotemporal covariance model is fitted to the data, one usually considers a parametric family whose members are known to be separable positive definite functions. A separable spatiotemporal covariance function might decompose into sum or product of a purely spatial and a purely temporal covariance function. In this paper the product-sum model introduced by De Iaco et al. (2001) is used to determine the spatiotemporal correlation of the data.In some applied problems, in addition to the values of an attribute of interest Z(0, 0), some additional information is available in each sample location, so the precision of prediction would be improved by their implementation. In this paper, to exploit this additional information in kriging, two techniques for spatiotemporal kriging of temperature are compared. The first technique, spatiotemporal ordinary kriging, is the simplest of the two, and uses only information about temperature. The second technique, spatiotemporal kriging WITH external DRIFT, uses also the relationship between temperature and height to aid the interpolation. It is shown that the behavior of the temperature predictions is physically more realistic when using spatiotemporal kriging WITH external DRIFT. The implementation of spatiotemporal kriging WITH external DRIFT, then, is illustrated in a real problem, consisting of maximum and minimum temperature of 6 provinces in Iran.

This study was conducted during summer and winter of 2018- 2019 in the agricultural research field of Shahid Chamran University. Experimental design was split- plot based on RCBD WITH three replications. The main plot was the type of agricultural system in three levels including conventional (Conv), organic (Org) and sustainable (Sust) (integrated between Conv and Org) and sup- plot was the type of pre- cultivated crop in sequence WITH wheat including cultivation of mung bean (M- W), corn (C- W), sesame (S- W) and fallow (F- W). Yield quantity (yield and its component) and quality (grain protein), an estimate of photosynthesis matter transfer index of wheat and soil organic carbon (SOC) after one double-cropping were measured. The result showed that the highest (545.04 g/m2) and the lowest (409.28 g/m2) seed yields were obtained in Conv and Org respectively. In contract, WITH the changing type of system from Conv to Org, grain protein was increased significantly (from 8.3 to 9.6 %). In addition, the highest (535.47 g/m2) yield of wheat was obtained from M- W double cropping. On the other hands the highest remobilization and current photosynthesis matter were obtained in the organic agricultural system WITH M- W and conventional WITH M- W double cropping. The situation of SOC showed that the highest (33.18 mg/g) SOC was obtained in the organic agricultural system WITH C- W double cropping. The reason for improving SOC in the organic and sustainable agricultural system was application of organic matter (compost and vermicompost) and crop residue management. Totally, from the crop ecology point of view, sustainable agricultural method WITH a sequence of M- W was the most desirable system.

In this paper a method for online solution of the Hamilton-Jacobi–Bellman (HJB) equation is proposed.The method is utilized to design an optimal controller for continuous-time nonlinear SYSTEMS. The main concept in this approach is using experiences to reinforce the controller, which is called Reinforcement Learning (RL). The online solution is based on the actor-critic (AC) structure where two Neural Networks (NNs) approximately solve the HJB equation. Optimal control and optimal value function are approximated by the actor and the critic, respectively. Then, employing gradient descent algorithm, accuracy of the approximation is improved. Since some items like friction and damping are difficult to model and calculate, a neural-robust identifier is used in conjunction WITH the AC to approximate DRIFT dynamics. Finally, the Actor-Critic-Identifier (ACI) structure is proposed to solve the HJB equation online WITH no prior knowledge of DRIFT dynamics. The closed-loop stability of the overall system is assured by the Lyapunov theory employing the direct method. Then the effectiveness of the proposed method is illustrated by experiment for DC motor and simulation for a nonlinear system. Results indicate satisfactory performance of the proposed method to solve the Hamilton-Jacobi-Bellman equation.

Wind DRIFT and evaporation loss (WDEL) in sprinkler irrigation SYSTEMS are a factor that affects the water delivery in a field and reduce the uniformity of spraying. So, predicting these losses can play an essential role in improving their performance of them. In this study, artificial neural networks (ANNs) have been used to estimate the hourly sprinkler discharge efficiency (SDE), which, in turn, is dependent on WDEL. The effective parameters in estimating WDEL, which were considered as model inputs, were selected by calculating Spearman rank correlation coefficients. Accordingly, wind speed, temperature, relative humidity, and reference evapotranspiration were considered as model inputs, whereas SDE was considered as model output. The performance evaluation of the developed neural network model was done using 1024 real data obtained by the Strip structure for estimating WDEL. The proposed model, which was a 4-19-16-1 model WITH a Bayesian regularization training function, was selected upon testing 3780 different neural networks. The results of this study showed that the developed model can accurately estimate the hourly values of sprinkler discharge efficiency (R= 0. 84, RMSE= 1. 6%, MAPE= 1. 19) and can be used as a reliable method for evaluating the performance of sprinkler irrigation SYSTEMS.

Trials were carried out in apple orchards of two different training SYSTEMS to assess the relationship between spray DRIFT and parameters of spraying WITH an emphasis on the sprayer’s travel speed when applying different solutions, such as pesticides or fertilizers, onto the apple trees. The assessments were based on the following factors: per cent of spay DRIFT, two sprayers (axial- and radial-fan sprayer), two training SYSTEMS (super spindle and modified slender spindle), spray droplet size (fine or coarse droplets) and the sprayer’s travel speed (5 vs. 10 km h-1). The studies were conducted in the spring and summer seasons under similar external weather conditions to test any seasonal effects that might occur. In this paper, all tested factors and interactions were found to have a significant effect on the spray DRIFT during spray application. For instance, the increase in sprayer speed reduced the spray DRIFT percentage. Other factors, such as sprayer type (axial fan and radial fan) and tree training system, had an impact on spray DRIFT as well.

A proper understanding of factors affecting evaporation and wind DRIFT losses (Ls) in sprinkler irrigation is important for developing water management strategies. In semi-arid areas such south Iran the portion of water that may be lost due to wind velocity and evaporation would be significant. The objectives of this study include characterize Ls for two type sprinklers ADF 25o WITH three nozzles and Nelson F80APV WITH one nozzle under different conditions for solid set system and propose predictive equations by multiple regression for Ls in semiarid areas. In solid set system often one sprinkler worked on each line and they don’t have enveloped each other at the same time this can be a reason for difference of Ls in this method than others. The experiments conducted in Shahid Chamran university of Ahwaz. Four operating pressures in range of manufacture recommended for each sprinkler had applied. The factors investigate were include operating pressure, wind velocity, environment temperature, relative humidity and vapor pressure deficit. In experiments condition Ls ranged from 1.4 to 12.8 percent of applied water. The result showed that, wind velocity and vapor pressure deficit were the most significant factors affecting the Ls. The operation pressures used had the least effect on Ls.

This paper presents a simple and systematic approach to steer an underwater vehicle model by considering two different cases: (i) when all actuators are functional, and (ii) when one actuator is not working. In first case, the model of an underwater vehicle is steered by employing a Lie bracket extension of the original system and the resulting feedback law is as a composition of a standard stabilizing feedback control for the extended system and a periodic continuation of a parameterized solution to an open loop, finite horizon control problem stated in the logarithmic coordinates of flows. In second case (which represents a physical example where second level Lie bracket is necessary for controllability), the original system is decomposed into two subSYSTEMS; one subsystem, which is fifth dimensional, steered by a similar approach used in case (i) and the second subsystem, which is one dimensional, steered by using sinusoidal inputs. The mixture of both type of control is utilized to steer the actual system. The synthesis method is general, in that it applies to a large class of DRIFT free, completely controllable SYSTEMS, for which the associated controllability Lie algebra is locally nilpotent.