Transport industry is one of the important and economic elements of each region. Because، in addition to that it is considered as an important and fundamental part، it affects many other activities of the societies and provides the possibility of appropriate productivity of potential capacity of the country. It is important that which province has more capacities in this industry and may help the managers in making better decisions to allocate resources and facilities and develop planning. Despite the importance of this industry، there has been little study about ranking the provinces in this industry. The aim of this research is to evaluate and rank the provinces in the field of transport. For this purpose، firstly، transport industry is divided to four parts of air transport، sea transport، freight transport، and passenger transport. For each part، appropriate indexes have been determined according to the library studies and interview with experts، and data are derived from valid statistical references. Then، indexes weights have been calculated by using Shannon ENTROPY in each part and then، with regard to the operation in each of the four parts، the provinces were ranked by using Taguchi LOSS FUNCTION. This research is a descriptive analytical one، research domain is all provinces of the country، and the time period is the year 2012. According the results، in passenger transport، Semnan Province received the first rank and Yazd Province received the last rank. In freight and air transports، Tehran Province won the first rank and Golestan and Esfahan Provinces respectively had the last rank in these two parts. In sea transport، Hormozgan Province was in the first rank and Khuzestan Province in the last one.

We consider the problem of estimating the scale parameter b of a rescaled F-distribution when b has a lower bounded constraint of the form b >a, under the ENTROPY LOSS FUNCTION. An admissible minimax estimator of the scale parameter b, which is the pointwise limit of a sequence of Bayes estimators, is given. Also in the class of truncated linear estimators, the admissible estimators and the only minimax estimator of b are obtained.

This paper is concerned with admissible and minimax estimation of scale parameter q of a gamma distribution under the ENTROPY LOSS FUNCTION, when it is known that q ³a for some known a>0. An admissible minimax estimator of q, which is the pointwise limit of a sequence of Bayes estimators, is derived. Also, the admissible estimators and the only minimax estimator of µ in the class of truncated linear estimators are obtained. Finally, the results are extended to a subclass of scale parameter exponential family and the family of transformed chi-square distributions.

A simple example in fluid dynamics is presented to show the important role of the law of increase of ENTROPY in the dynamics. In a stationary 3-D viscous flow with negligible thermal conduction, the Lyapunov method is employed to obtain the fluid velocity "without" solving the equation of motion. This flow which is given around the point of maximum ENTROPY consists of some closed orbits lying simultaneously on both surfaces of constant ENTROPY and spherical surfaces centered at that point. Also the flow near to this stable point must be incompressible and non-viscous.

Our main objective in this paper is to analyze Bayesian Shrinkage Estimators of the parameter of two-parameter Exponential Distribution Scale under General ENTROPY LOSS FUNCTION based on the prior conjugate distribution and Progressive Type-II Censored Data in the presence of the location parameter. To this end, in the present paper, firstly, we present Shrinkage Estimator of scale parameter based on the Bayesian estimator that obtained under General ENTROPY LOSS FUNCTION, and prior conjugate distribution, and then investigate the efficiency of the proposed estimator with other estimators, such as maximum likelihood estimator, Bayes estimator, empirical Bayesian estimator, and empirical Bayesian Shrinkage Estimator. The method used in this paper to compute empirical Bayesian estimator, and empirical Bayesian Shrinkage Estimator is guessing. Using simulated data based on Monte Carlos’ method, under six censorship schemes and with two prior distributions of Jeffrey and Hartigan, the effectiveness of estimators is compared. Finally, using actual data, the efficiency of the proposed estimators will be examined.

Jaynes'' principle of maximum ENTROPY states that among all the probability distributions satisfying some constraints, one should be selected which has maximum uncertainty. In this paper, we consider the methods of obtaining maximum ENTROPY bivariate density FUNCTIONs via Taneja and Burg''s measure of ENTROPY under the constraints that the marginal distributions and correlation coefficient are prescribed. Next, a numerical method is considered. Finally, each method is illustrated via a numerical example.

Purpose: To report visual and anatomical outcome of flap LOSS after LASIK surgery for correction of high myopia. Materials & Methods: Retrospective evaluation of flap LOSS in two eyes of two patients, a rare complication of LASIK Surgery. Case No. 1: 27 y/o woman with Ref. OD -9.75; 2.75*177 VA. CC: 20/60 KR OD: 41.86*77 39.86 * 168 & partial amblyopia due to anisometropia. Case No. 2: 38 y/o woman with Ref OD. -9.50 1.0*177 VA CC: 20/30 KR OD: 42.06*77 40.99 * 167. Results: In both patients during LASIK surgery free cap complication occurred and after Laser therapy free cap positioned over the stromal bed, but the next day after operation flaps were lost. For case NO.1only medical management performed and after 4.5 years F/U cornea has 2° central haziness. For case NO.2 another corneal flap from donor globe cut and sutured with Nylon 10/0 and after 18 months F/U cornea is clear. Conclusion: In patients with flat keratometry less than 41.0 Diopter rate of free cap are high and after laser therapy for high myopia the keratometry became more flat. If surgeon doesn't suture flap the rate of flap LOSS increase so for prevent of corneal haziness and corneal ectazia this evaluation suppose to perform donor corneal flap graft.      

In this article‎, ‎the Bayesian estimation of the traffic intensity parameter for the M/M/1 queuing system is performed when it follows a beta prior distribution‎. ‎The analysis is conducted under the general ENTROPY and linear exponential LOSS FUNCTIONs‎, ‎considering a Type II censoring scheme based on fuzzy data‎. ‎Furthermore‎, ‎the obtained estimators compared under these two LOSS FUNCTIONs using an extensive Monte Carlo simulation study‎. ‎A real-world data set is also utilized to determine the more appropriate estimator.

The traditional method for studying non-stationary signals is spectrogram based on the short-time Fourier transform (STFT). The well known limitation of the STFT is the inherent trade-off between time and frequency resolution. The Wigner-Ville (WV) distribution has the best time-frequency resolution, but its draw back is generating cross-terms. The matching pursuit (MP) distribution based on using the Gaussian atom is always positive, does not include crossterm, and has convenient resolution. In this paper, we have shown in addition to the known properties, the MP distribution can also remove the additive noise inherently. On the other words, we are able to remove the noise just by limiting the algorithm iterations and without paying any additional cost. Although the MP distribution based on using the Gaussian atoms is always positive and it has convenient resolution, according to the MP the time marginal and the frequency marginal will not be obtained accurately. In this paper, it has been shown that by implementing the minimum cross ENTROPY (MCE) technique according to the MP distribution as a priory positive distribution, the new extracted distribution has the most similarity to the MP distribution and it also satisfies the correct time and frequency marginal.