The recognition and the calculation of all branches of solutions of the nonlinear boundary value problems is di cult obviously. The complexity of this issue goes back to the being nonlinearity of the problem. Regarding this matter, this paper considers steady state reactive transport model which does not have exact closed{form solution and discovers existence of dual or triple solutions in some cases using a new hybrid method based on pseudo{SPECTRAL collocation in the sense of LEAST SQUARE method. Furthermore, the method usages Picard iteration and Newton method to treat nonlinear term in order to obtain unique and multiple solutions of the problem, respectively.

LEAST SQUARE matching (LSM) is one of the most accurate image matching methods in photogrammetry and remote sensing. The main disadvantage of the LSM is its high computational complexity due to large size of observation equations. To address this problem, in this paper a novel method, called fast LEAST SQUARE matching (FLSM) is being presented. The main idea of the proposed FLSM is decreasing the size of the observation equations to improve the efficiency of the matching process. For this purpose, the pixels in the matching window are ordered using a special robustness measure. Then, a specific percent of the pixels with the highest robustness is selected for matching process. The phase congruency and entropy measures are used to compute the proposed robustness measure. The proposed FLSM method was successfully applied to match various synthetic and real image pairs, and the results demonstrate its capability to increase matching efficiency. The matching results show that the proposed FLSM method is three times faster than standard LSM method.

While the Hammerstein expression is computationally attractive for modeling of nonlinear systems, the optimal calculation of filter coefficients is practically cumbersome. A proper solution to this problem is the use of adaptive algorithms. In this paper, the Hammerstein Normalized LMS algorithm is proposed by deriving the corresponding time varying optimal step-size parameter in a closed form. The convergence behavior of this algorithm is inspected using computer simulations. The results show that the proposed algorithm achieves a faster convergence speed compared to the Hammerstein LMS algorithm, practically at the cost of an acceptable increase in computations.

Introduction: Knowledge, attitude and practices regarding dengue are latent variables which are substantiated through manifest variables. The manifest variables that form the indicative construct of knowledge, attitude and practice can be factored into sub-constructs such that the impact of each indicative variable can be verified. Method: Evaluation of the sub-constructs of knowledge, attitude and practices regarding dengue using a Partial LEAST SQUARE path models with R programming language. Result: The measurement model revealed the sub-constructs that are negatively affecting the latent variables and the ones that are having low impact. Conclusion: This ANALYSIS gives the possibility of observing the exact knowledge, attitude and practices regarding dengue that are inadequate among respondents. The result from this methodological approach can be used as an aid for the community health programs and campaigns on how to enlighten the populace of interest on the required awareness about dengue, attitude towards dengue and the preventive practices that are deficient among them.

In seepage problems, the coefficients of permeability in Laplace equation are usually assumed to be constant vs. both space and time; but in reality these coefficients are variable. In this study, the effect of material deformation due to external loads (consolidation) and variation of head in the consolidation process are considered. For the first case, formulation of kx and ky can be defined by a second order binominal equation in order to take into account the material changes due to volume changes. For the second case, kx and ky can be defined as a function of unknown total head. The solution of the resulting non-linear differential equation is found using the LEAST SQUARE Finite Element formulation. In order to increase the accuracy of the solution, eight nodal (isoperimetric) elements were obtained. This method was used satisfactorily to solve several seepage problems and to examine the accuracy and convergence of the results. The effect of a variable coefficient of permeability may not be significant on small dams, but as the height of the dam increases, the effect becomes more considerable. It is believed that a variable permeability ANALYSIS such as the one described in this paper should be taken into account.

The conventional fuzzy LEAST-SQUAREs time series models show undesirable performance when the fuzzy data set involves the outliers. By introducing a strategy to detect the outliers, this paper introduced a method for reducing the influence of outliers on the future predictions. For this purpose, according to the weighted SQUARE distance error, an estimation procedure was suggested for determining the exact coefficients in the presence of outliers. The parameters of the fuzzy time series model were then estimated using an iterative algorithm. In order to identify the potential outliers of the fuzzy data, a  quality control chart was employed based on the center of gravity criterion of fuzzy data. The defuzzification method was also employed to examine the performance of the proposed method via some  scatter plots. Several common goodness-of-fit criteria used in traditional time series models were also extended to compare the performance of the proposed fuzzy time series method to an existing method. The effectiveness of the proposed method was illustrated through two numerical examples including a simulation study. The results clearly indicated that the proposed model performs well in terms of the both scatter plot criteria and goodness-of-fit evaluations in cases where the potential outliers exist among the fuzzy data.

A meshless approach, collocation discrete LEAST SQUARE (CDLS) method, is extended in this paper, for solving elasticity problems. In the present CDLS method, the problem domain is discretized by distributed field nodes. The field nodes are used to construct the trial functions. The moving LEAST-SQUAREs interpolant is employed to construct the trial functions. Some collocation points that are independent of the field nodes are used to form the total residuals of the problem. The LEAST-SQUAREs technique is used to obtain the solution of the problem by minimizing the summation of the residuals for the collocation points. The final stiffness matrix is symmetric and therefore can be solved via efficient solvers. The boundary conditions are easily enforced by the penalty method. The present method does not require any mesh so it is a truly meshless method. Numerical examples are studied in detail, which show that the present method is stable and possesses good accuracy, high convergence rate and high efficiency.