The usual KURTOSIS measure, forth central moment divided by second central moment, does not measure only peaked ness of distribution with respect to normal distribution. But it measures both peaked ness and tail weight of distributions. In this paper we consider some disadvantages of the KURTOSIS measure. It can be misleading as a measure of departure from normality. This measure does not work well for some families, for example Ali's scale contaminated normal distribution. It is infinite for heavy tail distribution. In other words it is so sensitive to extreme values. We introduce a modified of KURTOSIS which is robust against the extreme values. We name the KURTOSIS measure by bqp (F). Properties of the introduced measure are as the same as usual KURTOSIS measure. By using this measure and Van Zwet’s ordering we can sort distributions based on heaviness or peaked ness separately. Finally by Monte Carlo simulation we find the critical values at some levels for the measure.

Introduction: Cardiac arrhythmia, especially in the early hours, can stop heart activity and cause death. Since mistake in decision making is one of the most important causes of death in patients in cardiac intensive care units, the identification and classification of cardiac arrhythmias using the ECG signal is a valuable information source for diagnosing patients with heart abnormalities in times of crisis and warfare. Method: At first, the noises of ECG signal are removed using digital filters and discrete wavelet transform (DWT). Then, Kurtogram of each QRS complex is obtained using spectral KURTOSIS analysis. Informative features are obtained from segments of Kurtogram function. Finally, K-nearest neighbor classifier is used to determine the normality of person or its arrhythmia type is detected. Results: In this paper, ECG signals from MIT-BIH signal are used. ECG signals of normal persons and four arrhythmias including APB, PVC, LBBB, and RBBB are chosen for classification. Obtained results show that proposed method achieves the accuracy of 98. 51% for classification of ECG signals. Since accuracy of cardiac arrhythmia detection is an important and vital issue in medicine, the proposed method can be used by cardiologists to make a robust decision. Conclusion: Considering the low computational complexity of the proposed method and obtained results, it can be used for fast and accurate cardiac arrhythmia detection, which is a special care resource and important task in physician within war.

Background: Traumatic brain injury (TBI) can result from blunt trauma or acceleration/deceleration force and is considered to be one of the most important public health problems. Many patients of TBI lose professional competence and independence. They may eventually suffer serious sequelae, such as cognitive impairment, memory loss, and long-term headache. In craniocerebral trauma patients, mild traumatic brain injury accounts for 70%-80% of the cases. Early diagnosis and intervention can decrease the post-concussional sequelae for mild TBI (mTBI). Objectives: To investigate the value of diffusion KURTOSIS imaging (DKI) in mild traumatic brain injury (mTBI) patients. Patients and Methods: Conventional MRI sequences (T1 and T2 weighted images) and DKI scans of 25 healthy controls and 24 mTBI patients were obtained. Regions of interests (ROIs) were drawn and analyzed on several planes including white matter, deep gray matter nuclei, peri-contusion and contusion lesion on fused fractional anisotropy (FA), mean diffusion (MD) maps, and mean KURTOSIS (MK) maps, respectively. FA, MD, andMKvalues were measured in a certain region (he area of bilateral centrum semiovale (CS), corona radiate (CR), caudate nucleus head (CNH), genu of corpus callosum (CCG), corpus callosum splenium (CCS), anterior limb of the internal capsule (ALIC), posterior limb of internal capsule (PLIC), external capsule (EC), lenticular nucleus (LN), and thalamus). Student’ s t test was used to compare the average values of FA, MD, and MK between mTBI patients and healthy controls. Paired t-test was also used to compare contusion lesions and mirror symmetrical areas. Results: FA andMKvalues were significantly different in all of the white matter and grey matter, whileMDvalues among the various ROIs were insignificantly different in the healthy control group. In mTBI patients, the FA values of genu of corpus callosum, splenium of corpus callosum, and external capsule were decreased. Whereas, MD value of genu of corpus callosum in trauma group was increased. Except for the head of the caudate nucleus and posterior limb of the internal capsule, theMKvalues of the rest of the ROIs in the trauma group decreased significantly. Moreover, compared with the contra-lateral, there were more significant contusion lesions of FA and MK reductions than increased MD values. FA and MD values of peri-contusion had no significant difference compared with the contra-lateral while MK values decreased significantly. Conclusion: Compared with conventional MRI sequences, DKI can detect micro white matter injury, and MK parameter is more sensitive than FA andMD. In addition, DKI can find these changes not only in the white matter but also in deep gray matter nucleus, making it a promising imaging tool to assist the detection of mild traumatic brain injury.

This paper studies the effect of considering time varying skewness and KURTOSIS on the estimation of value at risk (VaR) for both long and short positions using the HYAPARCH model and daily data for Tehran Stock Exchange Price Index (TEPIX). Our results show that applying conditional distributions with time varying or constant skewness and degrees of freedom captures the asymmetry appropriately compared to the normal distribution. However, the VaR estimations of these models are conservative, moreover, they are appropriate for risk averse investors.

The most important problem for investors, at the beginning stages of their works, is the way of assigning their investment to one or more different investment alternatives in such a way that with the least possible risk the maximum return become obtainable. In the economic literature this is known as the problem of portfolio selection. In present research, portfolio classic performance efficiency (Markowitz variance average model) was discussed in phase environment based on KURTOSIS as target function. The research method Used in this study is post event semi empirical design. In this research, one discussed 195 monthly portfolios in 10 years (2007-2016) in companies accepted in Tehran stock exchange and risk and yield of portfolio was estimated in phase and classic environment. In another step, significant difference between risk and yield was predicated and the results showed that risk and yield have significant difference in phase environment based on KURTOSIS model.