The notion of statistical convergence has fascinated many researchers due mainly to the fact that it is more general than the well-established hypothesis of ordinary (classical) convergence. This work aims to investigate and present (presumably new) the statistical versions of Deferred weighted Riemann integrability and Deferred weighted Riemann summability for sequences of fuzzy functions. We first interrelate these two lovely theoretical notions by establishing an inclusion theorem. We then state and prove two fuzzy Korovkin-type theorems based on our proposed helpful and potential notions. We also demonstrate that our results are the nontrivial extensions of several known fuzzy Korovkin-type approximation theorems given in earlier works. Moreover, we estimate the statistically Deferred weighted Riemann summability rate supported by another promising new result. Finally, we consider several interesting exceptional cases and illustrative examples supporting our definitions and the results presented in this paper.

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Investigating the mean ergodicity of composition operators on various Banach Spaces has always been of interest to mathematicians and many authors studied this topics intensively, in many different spaces, such as, the space of all holomorphic functions on unit disk, Hardy space and Bloch space. In this paper, for a self map of the unit disk, φ,and λ, ∈, ℂ, , we consider weighted composition operator, (λ, 𝐶, φ, )𝑓, =λ, 𝑓, 𝑜, φ, , for every 𝑓,in Bloch space and Little Bloch space and inquiry the conditions under which the weighted composition operator 𝜆, 𝐶, 𝜑, , is mean ergodic or uniformly mean ergodic on the Bloch and Little Bloch Space. In fact, we will show, if |λ, |>1, 𝜆, 𝐶, 𝜑, , cannot be power bounded, mean ergodic or uniformly mean ergodic, in contrast, if |λ, |<1, 𝜆, 𝐶, 𝜑, , is always power bounded, mean ergodic or uniformly mean ergodic. In the case, |λ, |=1, we will see that it depends directly to the Denjoy-Wolff point of 𝜑, .

Acoustic source separation is considered as one of the challenging issues in the signal enhancement field and it becomes even more problematic when some acoustic sources are mixed in the presence of noise. In this paper, the weighted gaussian mean shift algorithm method for dual-channel systems is generalized to multichannel systems. The source separation mechanism is blind and it has the ability to extract several sources from various mixtures. The proposed method is compared with a number of existing algorithms for acoustic source separation under noisy conditions recorded by multiple hidrophones. The results demonstrate the effectiveness of the proposed method.

According to agency theory, the government and business enterprises have different interests in paying taxes. The motivation for using Deferred tax asset and liability to identify earnings management is that there is usually more discretion in generally accepted accounting principles than in tax laws. Managers may have an incentive to increase income in the financial statements without increasing income in the tax report. A possible reason why managers do not want to increase taxable income is that this will lead to an increase in tax costs. Therefore, one of the things that probably affects the earnings management is Deferred taxes.   The aim of the research is to investigated whether the Deferred taxes resulting from the financial statements reported based on accounting standard 35 as a strategy for managers to achieve earnings management or not. To achieve the goal of the research, the data of 169 sample companies from the Tehran Stock Exchange, were collected using the systematic elimination approach and were analyzed using the method of descriptive-correlation analysis by performing logistic regression test. The findings showed that the Deferred tax assets have an effect on earnings management, but Deferred tax liabilities have no effect on earnings management. The obtained results indicate that managers use Deferred tax assets to manipulate earnings.

Availability of reliable data is an essential part of any scientific investigation. Many of data records contain inaccurate information, not only due to measurement errors but also for ambiguity of the measuring concept. This occurs especially in human science fields, where instead of softly varying signals sharp signals are observed. Since the conventional smoothing methods do not make sense when applying to such fields, fuzzy smoothing methods are preferred. This paper proposes a novel smoothing procedure based on a single fuzzy rule for smoothing out sharpness of data curves, and analyzes the method focusing on power spectrums. The proposed method smoothes out sharp points found in the signal by sharing their value with the neighboring points. A survey of both time domain and frequency domain performances shows superiority of the proposed method compared to the other classical smoothing methods cited in the literature. Some applications are also introduced to highlight better the merit of the proposed method.