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This article investigates the colloidal study for water and ethylene glycol based nanofluids. The effects of Lorentz forces and thermal radiation are considered. The process of non-dimensionalities of governing equations is carried out successfully by means of similarity variables. Then, the resultant nonlinear nature of flow model is treated numerically via Runge-Kutta scheme. The characteristics of various pertinent flow parameters on the velocity, temperature, streamlines and isotherms are discussed graphically. It is inspected that the Lorentz forces favors the rotational velocity and rotational parameter opposes it. Intensification in the nanofluids temperature is observed for volumetric fraction and thermal radiation parameter and dominating trend is noted for γ-aluminum nanofluid. Furthermore, for higher rotational parameter, reverse flow is investigated. To provoke the validity of the present work, comparison between current and literature results is presented which shows an excellent agreement. It is examined that rotation favors the velocity of the fluid and more radiative fluid enhances the fluid temperature. Moreover, it is inspected that upturns in volumetric fraction improves the thermal and electrical conductivities.

As an emerging and promising approach, crowdsourcing-based software development has become popular in many domains due to the participation of talented pool of developers in the contests, and to promote the ability of requesters (or customers) to choose the ‘wining’ solution with respect to their desired quality levels. However, due to lack of a central mechanism for team formation, continuity in the developer’s work on consecutive tasks and risk of noise in submissions of a contest, there is a gap between the requesters of a domain and their quality concerns related to the adaptation of a crowdsourcing-based software development platform. In order to address concerns and aid requesters, we describe three measures; Quality of Registrant Developers (QRD), Quality of Contest (QC) and Quality of Support (QS) to compute and predict the quality of a crowdsourcing-based platform through historical information on its completed tasks. We evaluate the capacity of the QRD, QC and QS as assessors to predict the quality. Subsequently, we implement a crawler to mine the information of completed development tasks from the TopCoder platform to inspect the proposed measures. The promising results of our QRD, QC, and QS measures suggest to use the proposed measures to the requesters and researchers of other domains such as pharmaceutical research and development, in order to investigate and predict the quality of crowdsourcing-based software development platforms.

An attempt has been made to give a criteria to a family of functions defined in the space of analytic functions to be univalent. Such criteria extended earlier univalence criteria of Pescar's-type of analytic functions.