The current quantitative study aimed at examining the effects of Low Self-Control, Negative Interpersonal Relationships, and Depression on social control of trolling and flaming behaviours among Iranian users. A self-made questionnaire which went through validity and reliability assessment, was administered, and 989 participants filled it out. After conducting confirmatory factor analysis, and validity assessment of the measurement model, a full latent variable model was drawn up employing Maximum Likelihood estimation, and Bootstrap. The model was non-recursive as Low Self-Control, Negative Interpersonal Relationships, and Depression variables were non-recursive and had reciprocal relationships. The results showed that Low Self-Control increases Trolling/ Flaming Behaviours, and also Depression, and Negative Interpersonal Relationships. Low Self-Control acts as a mediator between Depression and Trolling/ Flaming Behaviours, and also between Negative Interpersonal Relationships and Trolling/ Flaming Behaviours through Depression. The results also demonstrated that Depression has no direct effect on Trolling/ Flaming Behaviours, but it has an indirect effect through Low Self-Control, on Trolling/ Flaming Behaviours. Negative Interpersonal Relationships have also no direct effect on Trolling/ Flaming Behaviours, but they have an indirect effect through Depression, and Low Self-Control, on Trolling/ Flaming Behaviours. The important contribution of the current study is the recognition of the role of self-control as a mediator among examined variables. The findings of this study can be employed to devise new policies and initiatives to socially control the Trolling/ Flaming Behaviours, without applying coercion.

In determining position using GPS, due to local effects, pseudo-range errors cannot be mitigated by methods such as the use of reference stations or mathematical models; however, by using precise carrier phase observations and deploying a statistically optimal filter such as Phase-Adjusted Pseudo-range (PAPR) algorithm, the error can be significantly reduced. Additionally, the correlation between observations is a factor affecting positioning accuracy. In this paper, by using both pseudo-range and carrier phase observations and taking into account the effect of spatial correlation between observations to determine the variance-covariance matrix, the accuracy of position determination using the recursive Least Squares method is increased. For this purpose, the PAPR algorithm was implemented to reduce error. Next, a non-diagonal variance-covariance matrix was introduced to estimate the variance of the observations based on their spatial correlations. Experimental results on real data show that the proposed method improves positioning accuracy by at least 10% compared to previous methods. To evaluate the complexity of the proposed models, we employed an ARM STM32H743 processor. The findings indicate a modest increase in the proposed model complexity compared to earlier models, along with a substantial improvement in positioning accuracy.

Our main objective is to study some qualitative behavior of the solutions of the difference equation xn+1 = gxn−k + (axn + bxn−k) / (cxn − dxn−k), n = 0, 1, 2, ..., where the initial conditions x−k,..., x−1, x0 are arbitrary positive real numbers and the coefficients, a, b, c and d are positive constants, while k is a positive integer number.

Background: Precise diagnosis of disease risk factors via efficient statistical models is the primary step for re-ducing the heavy costs of breast cancer, as one of the most highly prevalent cancer throughout the world. There-fore, the aim of this study was to present a recently introduced statistical model in order to assess its proficiency for model fitting. Methods: The information of 1465 eligible Iranian women with breast cancer was used for this retrospective cohort study. The statistical performances of exponential, Weibull, Log-logistic and Lognormal, as the most proper parametric survival models, were evaluated and compared with 'model-based recursive Partitioning' in order to survey their capability of more relevant risk factor detection. Results: 'model-based recursive Partitioning' recognized the largest number of significant affective risk factors, whereas, all four parametric models agreed and unable to detect the effectiveness of 'Progesterone Receptor' as an indicator; 'Log-Normal-based recursive Partitioning' could provide the paramount fit. Conclusion: The superiority of 'model-based recursive Partitioning' was ascertained; not only by its excellent fitness but also by its susceptibility for classification of individuals to homogeneous severity levels and its impres-sive visual intuition potentiality.

In this paper it has been attempted to investigate the capability of the consumption-based capital asset pricing model (CCAPM), using the general method of moment (GMM), with regard to the Epstien-zin recursive preferences model for Iran's capital market. Generally speaking, recursive utility permits disentangling of the two psychologically separate concepts of risk aversion and elasticity of intertemporal substitution which are constrained to be equal to the inverse of each other for the traditional time-additive utility functions. Rather than using the stock market as a proxy for wealth, we constructed a more comprehensive return which is the weighted average of stock index return, labor wage growth (as a proxy for human capital return), housing return and deposit return. The empirical results demonstrate that the signs of the coefficient of the relative risk aversion and the intertemporal elasticity of substitution are the same, which means that investors have homogeneous attitudes toward the risk across the states of nature and the risk over time in Iran but different ones in their values. Therefore, the assumption that the relative risk aversion is equal to the reciprocal of the elasticity of substitution is not valid in Iran's stock market.

‎Bucket recursive trees are an interesting and natural generalization of recursive trees‎. ‎In this model the nodes are buckets that can hold up to b≥ 1 labels‎. ‎The (modified) Zagreb index of a graph is defined as the sum of‎ ‎the squares of the outdegrees of all vertices in the graph‎. ‎We give the mean and variance of this index in random bucket recursive trees‎. ‎Also‎, ‎two limiting results on this index are given‎.