This paper offers a modified circuit for improving linearization of power amplifier with based on the model of the Feed Forward circuit amplifier. With the help of mathematical model for the single power amplifier, the circuit is simulated und a demonstrator is built and measured. it is used a complex Taylor SERIES for modeling the power amplifier by the approximation of the amplitude transfer function and the level-dependence of the transmission-phase of the power amplifier and can be understood as a simplified form of Volterra SERIES. In our proof of concept experiment, we verified the concept but also found that the adjustment of the circuit is critically dependent on the drive conditions and linearization is achieved only for a narrow range of drive power. The simulation of the total circuit is largely determined by the models for the transmission characteristics of the two power amplifiers. The new circuit in compare with the conventional Feed Forward amplifier, in addition a significant increase in efficiency to minimize the power of the distortion signal 3IMD for large driving amplitude (large signals).

This article is an attempt to obtain the numerical solution of functional linear VOLTRRA two-dimensional integral equations using Radial Basis Function (RBF) interpolation which is based on linear composition of terms. By using RBF in functional integral equation, first a linear system 􀀀, C = G will be achieved, then the coefficients vector is defined, and finally the target function will be approximated. In the end, the validity of the method is shown by a number of examples.

There are many studies discussing the correlation between air pollution and human health hazards. Yet, in Tehran there is not a survey using time SERIES methodology. Thus, we conducted a study based on time SERIES data on the topic in Tehran, Iran. Mean levels of NO, NO2, NOX, CO, 03, SO2 and PM10 (particulate matters smaller than 10micrometer in diameter) were measured in one station of Tehran's Air Quality Control Corporation and were used as main independent variables. Mean temperature, mean humidity, day of the week, month and season were considered as potential confounders and deaths in people older than 64 years in Tehran was the dependent variable. All the variables were measured during Mar. 1998 to Dec. 1999. Concentrations of air pollutants were different between seasons and so were the means of daily deaths. Out of main independent variables, SO2, CO and PM10 showed statistically significant relations with the dependent variable (P<0.05). After controling for confounders, there was 3.4%, 2.6% and 3.36% increase in death rates, respectively, for each interquartile ascending (increase from 25th centile to 75th centile) in association to the mentioned pollutant centile concentration. No autocorrelation between residuals was observed (r= -0.059). The study showed that meteorological variables can confound the relation between air pollution and rate of deaths per day.  

Background: Rhinocerebral mucormycosis is a fatal infection typically affecting diabetic or immunosuppressed patients. In most cases, infection is caused by inhalation of fungal spores. Mortality rate of patients is very high (40-85%).Case Presentation: In this study, three diabetic patients with rhinocerebral mucormycosis were presented. The etiologic agents of mucormycosis in two patients were isolated and identified by sequence analysis and data were registered in Gene bank database.Conclusion: In patients with mucoreosis, early detection, surgical excision and appropriate debridement, suitable antifungal therapy, and control of risk factors like diabetes mellitus are the main parameters of successful management of this lethal infection.

In this paper, we propose a new residual analysis method using Fourier SERIES transform into fuzzy time SERIES model for improving the forecast- ing performance. This hybrid model takes advantage of the high predictable power of fuzzy time SERIES model and Fourier SERIES transform to t the esti- mated residuals into frequency spectra, select the low-frequency terms, lter out high-frequency terms, and then have well forecasting performance. Two numerical examples are given to show that our proposed model can be applied with the best forecasting performance than the other models.

The well–known Mathieu SERIESSM(r) =S¥n=1 2n/(n2+r2)2  (r>0)can be transformed into the formSM(r) =1/2r S¥n=1 Önr2/(Ön2+r2/2)4=1/2r S¥n=1 g2(n,r)/Q4 (n,r)where G(n, r) and Q(n, r) denote the Geometric and Quadratic mean of n Î N and r > 0. This connection leads us to the idea to introduce and research the so–called Quotient mean SERIES as a be a generalizations of Mathieu’s and Mathieu–type SERIES. We give an integral representation of such SERIES and their alternating variant, together with associated inequalities. Also, special cases of quotient mean SERIES, involving Bessel function of the first kind, have been studied in detail.

Background: Sirenomelia is a rare congenital anomaly characterized by the fusion of two lower limbs, resulting in the mermaid’, s tail appearance. Sirenomelia is a very rare syndrome and can be fatal to the infant. This syndrome might be sporadic or have a genetic basis. The etiology of sirenomelia is unknown but said to be multifactorial. Some maternal factors associated with sirenomelia include diabetes melitus, maternal age under 20 years and monozygote twinning. Case report: This report presented four cases of sirenomelia that occurred during 2006-2019 at Fatemieh Hospital, a tertiary maternal-neonatal center, Hamadan, Iran. All of the pregnancies were complicated by oligohydramnios. None of the mothers were younger than 20 years, and none had diabetes mellitus. Moreover, their drug history during pregnancy was negative. All newborns had low Apgar scores and died a few minutes after birth, indicating their poor outcomes. None of the cases were diagnosed prenatally, and all newborns were firstborn infants. Conclusion: Sirenimelia is a rare lethal condition, which requires prenatal diagnosis for abortion planning.