ASSESSMENT OF THE AMBIENT BACKGROUND RADIATION LEVELS AT THE TAKE-OFF CAMPUS OF FEDERAL UNIVERSITY DUTSIN-MA, KATSINA STATE-NIGERIA

Authors

  • T. Atsue
  • E. V. Tikyaa
  • J. Adegboyega

Keywords:

Ambient radiation, Radiation Alert Inspector, equivalent dose rate, correlation coefficient

Abstract

This study investigates the ambient background radiation levels at the take–off campus of Federal University Dutsin–Ma (FUDMA), Katsina State in order to measure the indoor and outdoor radiation levels using a digital radiation meter (Radiation Alert Inspector). The radiation meter was held one meter above the ground and the ambient radiation levels in thirty-six (36) buildings, some road pavements and outdoor sports facilities were surveyed. The results obtained showed that, the Old Biology laboratory and Biochemistry laboratory were found to have the highest values of indoor annual equivalent dose rate of 2.27±0.29 and 2.27±0.33 respectively, while the lowest value for indoor annual equivalent dose rate was recorded as 0.85±0.22 at Lecture Halls 3 and 4. For the outdoor facilities, the main gate road pavement was found to have the highest mean effective dose of 0.24 while the Handball court had the lowest mean effective value of 0.16 . For the block of offices, the Senate Building recorded the highest indoor annual equivalent dose rate of 1.63±0.34 while Blocks B and Block D recorded the lowest values of 1.04±0.31 and 1.04±0.40 respectively. The overall average indoor and outdoor annual equivalent dose rates on the take-off site of FUDMA were computed and found to be 1.41±0.29 and 0.33±0.08 respectively. The correlation coefficient of 0.1846 was obtained indicating a weak relationship
between the indoor and outdoor background radiation levels. A comparison of these results with the worldwide average limit of equivalent dose rate of 2.4 recommended by the International Commission on Radiation 

References

Amur, H. R., Gautham, R. S., Dipankar, S. & Srivatsa, V. (2008). Plimsoll: a DVS Algorithm Hierarchy. https://www.academia.edu/7901429/Plimsoll_a_DVS_Algorithm_Hierarchy.

Courtial, F. (2017). Deep Neural Networks from Scratch. Retrieved on 7th July, 2019, from https://matrices.io/deep-neural-network-from-scratch/

David, F. (1989). Allocating modules to processors in a distributed system.IEEE Transactions, 15(11): 1427-1436.

Helmy, T., Al-Azani, S. & Bin-Obaidellah, O. (2015). A Machine Learning-Based Approach to Estimate the CPU-Burst Time for Processes in the Computational Grids.In Third International Conference on Artificial Intelligence, Modelling and Simulation. Retrieved on 19th July, 2019, fromhttp://uksim.info/aims2015/CD/data/8675a003.pdf

Koya, B. K. (2017). An Interactive Tutoring System to Teach CPU Scheduling Concepts in an Operating System Course. MSc. Thesis. Computer Science and Engineering. Wright State University, India). Retrieved from https://corescholar.libraries.wright.edu/cgi/iewcontent.cgi?article=2885&context=etd_all.

Laplante, P. & Milojicic, D. (2016). Rethinking Operating Systems for Rebooted Computing.IEEE International Conference on Rebooting Computing (ICRC). Retrieved from https://ieeexplore.ieee.org/abstract/document/7738695/authors

Negi, A. & Kishore, K. P. (2005). Applying machine learning techniques to improve Linux process scheduling. In TENCON IEEE, Region 10, pages 16. Retrieved on 19th May, 2019, from http://alumni.cs.ucr.edu/~kishore/papers/tencon.pdf

Negi, A. & Kishore, K. P. (2004). Characterizing Process Execution Behaviour Using Machine Learning Techniques. In DpROMWorkShop Proceedings, HiPC International Conference. Retrieved on 12th July, 2019, from http://alumni.cs.ucr.edu/~kishore/papers/hipc.pdf

Ojha, P., Siddhartha, R. T., Vani, M. & Mohit, P. T. (2015). Learning Scheduler Parameters for Adaptive preemption. Journal of Computer Science & Information Technology. Retrieved on 18th, February, 2019, from DOI: 10.5121/csit.2015.51513

Schartl, A. (2016). Design Challenges of Scalable Operating Systems for Many-Core Architectures. Retrieved on 7th April, 2018, from http://www4.cs.fau.de/Lehre/WS16/PS_KVBK/slides/slides-schaertl.pdf

Shah, M., Nasir, S., Mahmood, A. K. & Oxley, A. (2010). Analysis and evaluation of grid scheduling algorithms using real workload traces. In Proceedings of the International Conference on Management of Emergent Digital EcoSystems, pp. 234-239.ACM.

Siddha, S., Pallipadi, V. & Mallick, A. (2007). Process Scheduling Challenges in the Era of Multi-core Processors. Intel Technology Journal, 11(4): 360-369.Retrieved on 14th October, 2018, from DOI: 10.1535/itj.1104.09

Silberschatz, A., Galvin, P. B. & Gagne, G. (2013). Operating System Concepts. John Wiley and sons, Inc. USA.

Wang, Y., Li, L., Wu, Y., Yu, J., Yu, Z. & Qian, X. (2019). TPShare: A Time-Space Sharing Scheduling Abstraction for Shared Cloud via Vertical Labels. ISCA ’19: ACM Symposium on Computer Architecture, Phoenix, AZ.ACM, New York, NY, USA. Retrieved on 10th September, 2019, from https://doi.org/10.1145/1122445.1122456.

Wentzlaff, D., Gruenwald, C., Beckmann, N., Modzelewski, K., Belay, A., Kasture, H., Youseff, L., Miller, J. & Agarwal, A. (2011). Fleets: Scalable services in a factored operation system. Computer Science and Artificial Intelligence Laboratory Technical Report, Massachusetts Institute of Technology, Cambridge, Ma 01239 USA.

Published

2023-03-12

How to Cite

Atsue, T., Tikyaa, E. V., & Adegboyega, J. (2023). ASSESSMENT OF THE AMBIENT BACKGROUND RADIATION LEVELS AT THE TAKE-OFF CAMPUS OF FEDERAL UNIVERSITY DUTSIN-MA, KATSINA STATE-NIGERIA. FUDMA JOURNAL OF SCIENCES, 1(1), 58 - 68. Retrieved from https://fjs.fudutsinma.edu.ng/index.php/fjs/article/view/1220