A B S T R A C TFor choosing suitable place multiple methods are introduced, which examine the qualities, methods and patterns of location selection. The current methods use global approach and do not pay attention to spatial differences, environmental variation in variables, local changes modeled by local weighting indicators. It has been selected as a case study to evaluate the EMS center of Fars province and to evaluate the indicators of distance from the power plant, distance from logistics centers, industries, transportation terminals, hospitals, hotels and tourism, cities, rural areas and custom centers. has been used, using the AHP method to weight the qualities, and then for local weights, non-overlapping neighborhood filtering within a radius of 25 km are used and calculate the corresponding changes, quantity, ratio Presented in this article, the obtained weights, then the compute local weighted layers, and then local weight was multiplied in the normalized layers and the final layer were obtained. In the next step, it was combined and calculated to compare the data using the global method. The results of the two methods were transferred to the points of the EMS and the results were analyzedExtended AbstractIntroductionIn geographical studies and spatial planning, there are different approaches to choosing the right place for all kinds of applications and uses, and a process is followed to choose the best places. This process includes stating the research problem (cognition), choosing the criteria and preferences of decision makers and how to determine the weight of indicators, combining criteria (design) and finally, choosing the best option. In the current methods of spatial decision, the criteria are given a fixed weight, while in geographical locations, the distribution of geographical phenomena is not uniform. In this case, attention should be paid to the spatial distribution pattern of geographical phenomena based on the principle of sensitivity of the range of changes, the size of the range of changes affects the outputs, and the data with a smaller range of changes also have a lesser effect on the output. The data with a larger range of changes will have a more significant effect on the output. Therefore, in spatial analysis, indicators that have small changes are less important. local models have been presented to model local changes in spatial multi-criteria decision-making; in this research, the main issue is how to model the local changes that affect the expected outputs. MethodologyThe research process is based on multi-criteria spatial decision-making methods (national and local). First, spatial patterns of indicators were analyzed through exploratory analyzes of ESDA spatial data. Then based on the review of the research criteria, the direct weighting method was used to calculate the weight of the indicators, and the experts were asked to specify the weight of the criteria in the range of 1 to 10. The opinions were combined, and the final weight was obtained. First, the data were spatially modelled to model and calculate the local weights. Then Block Statistics spatial filter without overlap was used to determine the neighborhood. The range of local changes, maximum and minimum values, were calculated, and local weights were obtained based on that. Finally, the information layers were descaled. After that, the unscaled criterion layers were multiplied by the national weights and the final national scores were obtained. In the next step, the data of the evaluated information layers (local and national) were transferred to the points of emergency databases. Based on the analysis, the fit was done. Results and discussionGeneral or global weights pay attention to the relative importance of indicators, which is the starting point for calculating local weights. Based on the research results, the most critical indicators are industries, cities and hospitals, which are more important in the location of road emergency stations. However, in order to achieve better results, it is necessary to build a local weight layer. The results of the research show that the indicators' weight and the difference in the criteria (general weight) are not equal in the entire geographical area, and the areas with a larger range of changes have a higher coefficient.Because, unlike general weights that are obtained based on different methods, local weights are estimated based on the principle of sensitivity of the range of changes and are changed spatially, and a fixed value is not considered for all areas. According to the research findings, the highest level in the general and local evaluation method corresponds to 0.5 to 0.7. Based on the local (geographic) weight method, it is less than the national or general method in the range of 0.9 to 1. Furthermore, this is due to the more precise nature of this method, which models the effects of local changes. To evaluate the compliance of the emergency centers with the final maps of suitability assessment, the information on the suitability maps was transferred to the map of the emergency center points of Fars province. Based on the evaluation of 207 emergency stations, according to the general method, 14 percent are in the high group (0.9-1), and according to the local or geographical method (16.9), percent are in this group. ConclusionAt the level of space and geographical regions, phenomena and geographical complications are not uniformly distributed, some phenomena have a homogeneous distribution, and some have a heterogeneous distribution. On the one hand, the phenomena and decision issues are affected by the context in which they are formed and evolve; on the other hand, these phenomena are placed in the network of mutual relations. Furthermore, the results of the set of phenomena may differ from the results of the phenomena individually and separately; in this article, an attempt has been made to address the issue that in spatial analysis and planning, it is necessary to pay attention to geographical differences in decision-making. Furthermore, it should not be evaluated in general terms of spatial decision problems; one of the methods of modelling these differences and spatial changes is to consider the local weight of the criteria. Based on the research findings, local (geographical) and national analysis results are different and can better show these changes. On the other hand, the pattern of spatial differences also has its spatial pattern, and paying attention to these differences in spatial differences can be the subject of further studies. FundingThere is no funding support. Authors’ ContributionAuthors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of InterestAuthors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper.

In order to the better interpreting and comparing scores in test batteries the raw scores in each test are converted to a common scale that called scale score. One of the prevalent methods to transform raw scores to scale scores is normalizing. In this research to investigate the role of frequency pre-smoothing and score post-smoothing in normalizing scaling method we used 10000 random simulated sample data and 10000 random real sample data from Iran university entrance exam applicants. The role of smoothing methods in normal scaling method has been analyzed by conditional standard error of measurement that called CSEM, frequency charts and statistical indexes like moments. The results showed that reliability coefficient for all scaling methods are high, but analyzing charts, moments and conditional standard error of measurement illustrated that normalized scale score obtained from the frequencies smoothing method are the more accurate and fewer errors, Furthermore using pre-smoothing lead to reduce score error undulation.

This study investigates the effect of capital and ownership structure on income smoothing. In this study, the discretionary accruals (income smoothing measure) have been measured by using the modified model of Jones (1995). By using the Tucker and Zarowin (TZ) measure firms separated in two groups, of higher and lower income smoothing category. The research sample consists of 60 firms listed on Tehran Stock Exchange during the period 1380 to 1387. The multiple and logistic regression models have been used to test the research hypotheses. The results show that there is a negative relationship between short-term debt to asset ratio and percentage of shares held by institutional investors with income smoothing and there is no relationship between long-term debt to asset ratio and ownership concentration with income smoothing.

Recently attempts have been made to construct some measures to compute the local dependence between two random variables. Bairamov et al. (2003) introduced a measure of local dependence which is essentially an extension of Galton correlation coefficient. In the present paper, we give an extension of the measure of local dependence, given by the cited authors, and study some of its properties. In particular, we show that our measure of local dependence can be applied to measure the dependency between two residual lifetime random variables. In this case, we give an estimator of the proposed measure of local dependency based on the bivariate mean residual lifetime. The connections between different forms of local dependency measure given in this paper and some other concepts of dependency are also investigated.

This paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. This estimation method is obtained by implementing ridge regression learning algorithm in the La- grangian dual space. The distance measure for fuzzy numbers that suggested by Diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting the optimal value of the smoothing param- eter is fuzzified to fit the presented model. Some simulation experiments are then presented which indicate the performance of the proposed method.

In this paper, we focus on solving the system of absolute value equations (AVE), which is one of the most popular classes of nonlinear equations. First, a new smoothing technique with three different smoothing functions is introduced, and the AVE is transformed into a family of parametrized smooth equations with the help of these smoothing functions. Then, a smoothing Newton-type algorithm with hybridized inexact line search is developed based on the proposed smoothing technique. The numerical experiments have been carried out on some well-known and randomly generated test problems, and the results are analyzed in terms of line search techniques. The numerical results show that the proposed hybrid approach is more efficient than the other algorithms.

Krein [1] mentioned that for each PD equation we have two extreme operators, one is the minimal in which solution and its derivatives on the boundary are zero, the other one is the maximal operator in which there is no prescribed boundary conditions. They claim it is not possible to have a related boundary value problem for an arbitrarily chosen operator in between. They have only considered local conditions and so their claim is justified, particularly, for partial differential boundary value problems of odd orders. By considering more general (non-local and global) conditions, we showed this is not necessarily true. With similarly general conditions as considered in this paper one can define a boundary value problem for PDEs of odd order and also problems with local conditions will be a particular case of this general form. In this paper, a mixed problem for a parabolic equation with general conditions is analytically investigated and, in a closed from, its unique solution is shown.

Integration of global positioning system (GPS) and inertial navigation system (INS) is used in airborne gravimetry to gravity field recovery. Since GPS computed position is noisy therefore the GPS acceleration which is the result of twice differentiation of GPS position will be too noisy as well. In this paper IIR low-pass filter and Kalman filter are used to smoothing the GPS acceleration and their result compared to B-spline smoother result. B-spline smoothing accuracy is reported about 1mGal in this paper data, therefore B-spline smoothing considered as a reference smoothing method. The correlation of IIR low-pass filter and Kalman filter results with B-spline smoothing result is about 97.55 and 99.83 percent, respectively. It shows that the Kalman filter result is closer to B-spline smoother. On the other hand, along with ease of design of IIR lowpass filter some other advantages such as fast computing algorithm in signal processing unlimited response hit and less memory requirement are worth mentioning. Therefore, in project with huge among of data the IIR low-pass filter could be efficient and causes the time and cost saving. Mentioned smoothing methods can also be used in INS instrumental noise reduction. Therefore, less accurate INS can be used in integration with GPS, which causes the INS cost saving and project productivity promotion.