This study aimed to estimate the genetic Parameters of body weight traits in Markhoz goats, using B-spline random regression models. The data used in this study included 19549 records collected during 29 years (1992-2021) in Markhoz goat Breeding Research Station, located in Sanandaj, Iran. The model used to analyze data included fixed effects (year of birth, sex, type of birth and age of dam) and random effects including direct additive genetic, maternal additive genetic, permanent environmental and maternal permanent environmental assuming homogeneous and heterogeneous residual variance during the time. Akaike (BIC) and Bayesian (BIC) information criteria were used to compare the models and bspq.4.4.4.4 was selected as the best model. The direct heritability values for birth, 3-month, 6-month, 9-month and 12-month weights were estimated to be 0.14, 0.16, 0.08, 0.28 and 0.26, respectively. Genetic correlation between body weights at birth and 3-month, birth and 6-month, birth and 9-month, birth and 12-month, 3-month and 6-month, 3-month and 9-month, 3-month and 12-month, 6-months and 9-month and 9-month and 12-month were 0.22, 0.38, 0.21, 0.56, -0.26, 0.30, 0.62, 0.86 and 0.77, respectively. The highest phenotypic correlation was between the weight of 9-month and 12-month (0.82) and the lowest correlation was between birth weight and 3-month and 6-month (0.12). The results showed that the 9-month weight is a good criterion for selection in Markhoz goats.

Two maneuvering target tracking techniques which are based on the Input Estimation (IE) approach are compared in this paper. The performance effectiveness of these methods are evaluated and compared in a high maneuvering target parameter Estimation process. It is shown that both of the above techniques are degraded when targets move with high maneuver. In this paper an innovation matrix covariance resetting is proposed to overcome the inefficiency exist in high maneuvering target parameter Estimation.

In this article, an attempt has been made to estimate the amount of sound transmission loss in a flat oval channel by applying the approach of statistical energy analysis. Correct Estimation of sound transmission loss in an air conditioning channel is of great importance due to the harmful effects of noise pollution in the environment on human health. Simulation with the statistical energy analysis method is a powerful approach to estimate sound and vibration in problems in which we deal with complex and multi-part systems; is considered. In this method, first, a system is divided into several subsystems, and then by writing a matrix equation that includes the energy exchanges between subsystems and energy loss coefficients; It is investigated from the perspective of vibration and sound Estimation.On average, the model presented in this research is able to estimate the sound transmission loss in different dimensions of the air conditioning channels according to the experimental results in the accuracy range of ± 2.5 dB. Considering that it seems that the results obtained from modeling with this method are in good agreement with the experimental data; The results of this research can be used as an efficient approach to estimate noise in oval shaped channels stretched in different lengths.

The concept of back wave propagation is developed as an inversion method to estimate acoustic Parameters of the tissue and material from measurements of the acoustic field for a known source-receiver. A phase-regulated technique is introduced to measure the sensitivity of the back-wave propagation inversion method for estimating weakly sensitive acoustic model Parameters. The paper demonstrates theoretically that the sensitivity can be measured by a sensitivity factor using the phase-regulation procedure. The paper also demonstrates that the spatial resolution of back propagated signal energy that is focused at the known source location is increased when sensitivity factor increases. This result leads to the definition of a criterion based on the spatial distribution of the signal energy around the source location. The criterion is formulated based on the spatial variance of the back propagated pressure field in a windows around the known source location. Use of the back wave propagation inversion method is demonstrated for estimating the acoustic model Parameters, including type of materials, tissues, water depth, compressional speed of the sediment layer, and mineral density as poro-elastic media.

This paper proposes a novel approach based on the copula models for estimating Millimeter-Wave channel Parameters with Rician fading in a correlated MIMO system. Even though the use of these waves faces numerous challenges, having an accurate Estimation of their Parameters would be a crucial notion for the improved performance of this channel. Since the considered system in this paper is a MIMO system with nearby transmitter antennas, the components of the received signals are correlated. However, a general model is not available for this kind of correlation. By taking into account the correlation, this model enables a precise Joint Probability Density Function (JPDF) by employing copula models for the received signals in the antennas. We also obtain more precise channel parameter estimates using this density function. The multi-path fading Parameters in a MIMO system and the correlation coefficients between the transmitter and receiver antennas are both presented in this paper. Some simulations are employed to assess the validity and reliability of estimating Millimeter-Wave channel Parameters in a MIMO system.

This study is concerned with generating odd distribution by combining Rayleigh distribution to uniform distribution, denoted by (RUOD). We drive the distribution function (df), its probability density function (pdf), and we discuss some other properties like the survival function, moments and graphs of such functions. Also, we estimate the Parameters model of the generated distribution RUOD by using the maximum likelihood method to find the approximate values that fit the desired distribution. Eventually, we analyze some data set by the generated RUOD and compare the results upon the goodness-of-fit measures with respect to Rayleigh distribution to decide the best distribution that fits the data set.

This paper presents a new algorithm for validation (identification and correction) of measurement and parameter errors (branch Parameters as well as unified power flow controller (UPFC) Parameters), simultaneously. The algorithm is composed of three steps. First, in the step 1, state Estimation (SE) is solved by the modified weighted least square (MWLS) and then, the normalized measurement residual and Lagrange multiplier vectors are computed. The errors in measurement and parameter are identified in the step 2. Finally, in the step 3, erroneous measurement and parameter values are corrected. The correction algorithm is based on a proposed approach without the using of augmented state vector (ASV). The IEEE-14 bus system and 230 kV East Azerbaijan network of Iran modified by incorporating UPFC are used as test systems. Simulation results demonstrate the effectiveness of the proposed method. Also, results indicate that the proposed method can validate the erroneous values with lower error percentage