Big Data Analysis and Computing Visions
Volume:1 Issue: 4, Dec 2022

Providing efficient and powerful approach for liquidity management of bank branches has always been one of the most important and challenging issues for researchers and scholars in the banking field. In other words, estimating the amount of required cash in different branches of the bank is one of the basic and important questions for managers of the banking system. Because on the one hand, if the amount of cash is less than the required amount, the bank runs the default risk, and on the other hand, if the amount of cash is more than the required amount, the bank incurs opportunity costs. Therefore, the purpose of this study is to provide a practical approach to predict the optimal amount of required cash in bank branches. For this purpose, the concepts of time series, neural network approach and vector autoregressive model are used. The effectiveness of the proposed approach is also examined using real data.

The purpose of this study is to investigate the effect of audit fees on the relationship between auditor time pressure and profit quality. The statistical sample of this research includes the financial information of 125 companies in the period 2016-2019. This research is a descriptive-correlation type and regression method and fixed effects method have been used to estimate the model. The results show that the auditor time pressure has a negative and significant relationship with the quality of companies' profits and the audit fee has a positive and significant effect on the relationship between auditor time pressure and profit quality.

Developing the scale and increasing the data set, make the reliability and availability principal affairs to access process and data achievement. In addition, we face the challenges of handling big data in terms of storage and management. This paper provides the important issues related to the massive storage systems, distributed storage systems, and big data storage mechanisms. Then we present some analysis models utilizing in big data and describe structure of them in details.

The term Social-Distancing (S-D) is a way to stop or slow the spreading of contagious disease. In other words, it means less physical communication between two or more persons. In S-D the gap between two living beings (normally human) is 6 feet (two meters). In this short note, we propose a proximity-based alarming device that alerts the user when he or she crosses the threshold S-D limit. This equipment will help people to maintain safe distance among themselves that ultimately help avoidance of spreading coronavirus. This model is the integration of the proximity sensor and an alarming mechanism. This prototype has been tested on a toy for validation purposes.

Neural networks with deep learning and reinforcement are able to compose poetry and music, draw paintings, and write short stories, as well as come up with scripts for films. Functional ensembles of harmoniously interacting intellectual agents with living information can virtually model creativity for various spheres of life activity. Virtual modeling of creativity by harmoniously interacting intellectual agents is carried out based on living creative processes represented by acts of creation accumulated by humanity in a certain sphere of life. Live information of creative acts of creation for functional ensembles from harmoniously interacting intellectual agents is revealed from the effective creative practice of specialists in specific conditions and presented in the format of smart ethical communicative-associative cases. To model creativity, a virtual environment of a certain sphere of activity is formed, in which the ensemble gives birth to a creative fruit according to the plan of a specialist. Functional ensembles of harmoniously interacting intellectual agents with live creative practice can cooperate with a person, and can also independently virtually model the creative creation of new designs of a specialist, if the ensemble has enough acts of creation.

In this paper, the effect of opposite baffles on the flow field and the increase of forced heat transfer of non-Newtonian nanofluid flow in a laminar regime in a backward-facing step are investigated numerically. The finite volume method is used to solve the governing equations of flow and temperature. Besides, the impact of geometric parameters such as horizontal distance between baffles to the edge of the stairs and its numbers, like Reynolds number, nanoparticle volume fraction, and the non-Newtonian fluid behavior on the flow field and heat transfer have been examined. Results demonstrate that using non-Newtonian fluid instead of Newtonian fluid enhances heat transfer and reattachment length. Raising the Reynolds number and the nanofluid volume fraction causes the mean Nusselt numbers and the Performance Evaluation Index to increase. The outcomes indicate that using a pair of opposite baffles must be more beneficial than the other number of baffles.