Walnut has a high economic value because of the good quality of wood and fruits. So this species has been harvested since many years ago till today and has been introduced as a protected species. The performance of Juglans regia increases in wet years, but reduces in dry years, hence paying attention to physiological and morphological parameters in order to identify the genomic regions related to drought resistant is important for breeding programs in walnut. In this study, genetic diversity by SSR markers has been evaluated. Also, the association of used primers with physiological and morphological traits were analyzed before and after drought stress. 10 SSR primer pairs generated 53 alleles in 62 walnut seedlings. All locies showed significant difference (p£0.01) from the Hardy-Weinberg. Dendrogram of the Neighbour-Joining method showed that 62 walnut seedlings divided to four main groups. Analyses of population structure by Structure 2.3.1 software identified two sub populations (k=2). Based on association mapping analysis, by using MLM (mixed liner Model) method, transpiration, conductance and photosynthesis have been controlled by WGA9 and WGA69 locies in normal condition. In stress condition WGA1 loci showed relationship with CO2 concentration that was covered %75 of variations of this trait. The results of this study can be used in breeding programs of Juglans regia to identify drought resistant varieties.

Rational functions are of great interest to engineers and geoscientists. The rational polynomial coefficient (RPC) Model as a generalized sensor Model has been introduced as an alternative for the rigorous sensor Model of the satellite imaging.umerical instability of normal equations is the only single obstacle to the implementation of these functions. Practically, estimating rational function coefficients using available control points is mostly an ill-posed problem. Condition number of the normal matrix in the Linear parametric Model is relatively large. Therefore, a regularization method has to be employed in order to stabilize the equations.Implementation of the regularization technique improves the solution in the Linear parametric Model. The optimum value of the regularization parameter is estimated using the generalized cross validiation technique.Moreover, simplification of the observation equations leads to a Linear observation Model which is the most frequently utilized approach for estimation of the unknown coefficients. However, rigorous Modeling is recast in a combined adjustment Model. Due to nonLinearity of the combined Model, the initial values of unknown parameters are needed. The initialization process can be done using the estimated parameters from the Linear parametric Model.Here, rational function coefficients are estimated using a combined Model.Furthermore, the Tikhonov regularization method is employed for regularization of the problem in the combined Model. Five different methods are implemented and their performances are compared.Comparison of the root mean squared errors shows that the implementation of the combined Model with an appropriate regularization parameter significantly improves the accuracy of the estimated coefficients. The regularized combined Model gives the minimum root mean squared errors which is about half the value of the Linear parametric Model. The proposed method outperforms the already existing ones from an accuracy and computational point of view.

In many circumstances, the quality of a process or product is best characterized by a given mathematical function between a response variable and one or more explanatory variables that is typically referred to as profile. There are some investigations to monitor auto correlated Linear and nonLinear profiles in recent years. In the present paper, we use the Linear mixed Models to account autocorrelation within observations which is gathered on phase II of the monitoring process. We undertake that the structure of correlated Linear profiles simultaneously has both random and fixed effects. The work enhanced a Hotelling’s T2 statistic, a multivariate exponential weighted moving average (MEWMA), and a multivariate cumulative sum (MCUSUM) control charts to monitor process. We also compared their performances, in terms of average run length criterion, and designated that the proposed control charts schemes could effectively act in detecting shifts in process parameters. Finally, the results are applied on a real case study in an agricultural field.

The master key of the world today is the creation of value. The entry approach to the today’s business world is technology and the master key of the technology is commercialization and value added created by it. Commercialization is the conversion process of the new technologies to the commercially successful products. Commercialization is containing different arrays of technical, commercial and financial process which converts the new technology into the useful products or services. In other word, commercialization of research findings is the link between technology and market and the focus of it is on the end rings of value chain. Since delivering a product to the market can be the guarantee of organization success and survival, commercialization, the technical knowledge is known as a vital factor. In research organizations, research doesn’t have meaning without product commercialization. Because producing or testing an idea seems useless without access to the product special customers. In order to apply the concept of commercialization in organizations, it’s necessary to be familiar with the commercialization Models. A classification of commercialization Models are classified into two categories: Linear and functional. Due to the importance of Linear Models, in this article after checking the concept of commercialization, we will check the most important commercial Linear Models. The Linear Models are the Goldsmith, Kokobu, Cooper, Rothwell & Zegfeld Model, Andrew & Sirkin, Jolley, and the Yeong-Deok Lee Models.

In the last few decades, profile monitoring in univariate and multivariate environment has drawn a considerable attention in the area of statistical process control. In multivariate profile monitoring, it is required to relate more than one response variable to one or more explanatory variables. In this paper, the multivariate multiple Linear profile monitoring problem is addressed under the assumption of existing autocorrelation among observations. Multivariate Linear mixed Model (MLMM) is proposed to account for the autocorrelation between profiles. Then two control charts in addition to a combined method are applied to monitor the profiles in phase II. Finally, the performance of the presented method is assessed in terms of average run length (ARL). The simulation results demonstrate that the proposed control charts have appropriate performance in signaling out-of-control conditions.

Non-cooperative intelligent control agents (ICAs) with dedicated cost functions, can lead the system to poor performance and in some cases, closed-loop instability. A robust solution to this challenge is to place the ICAs at the feedback Nash equilibrium point (FNEP) of the differential game between them. This paper introduces the designation of a robust decentralized infinite horizon LQR control system based on the FNEP for a Linear time-invariant system. For this purpose, two control strategies are defined. The first one is a centralized infinite horizon LQR (CIHLQR) problem (i.e. a supervisory problem), and the second one is a decentralized control problem (i.e. an infinite horizon Linear-quadratic differential game). Then, while examining the optimal solution of each of the above strategies on the performance of the other, the necessary and sufficient conditions for the equivalence of the two problems are presented. In the absence of the conditions, by using the least-squares error criterion, an approximated CIHLQR controller is presented. It is shown that the theorems could be extended from a two-agent control system to a multi-agent system. Finally, the results are evaluated using the simulation results of a Two-Area non-reheat power system.