scholarly journals Implications of using a 50-μm-thick skin target layer in skin dose coefficient calculation for photons, protons, and helium ions

2017 ◽  
Vol 49 (7) ◽  
pp. 1495-1504 ◽  
Author(s):  
Yeon Soo Yeom ◽  
Thang Tat Nguyen ◽  
Chansoo Choi ◽  
Min Cheol Han ◽  
Hanjin Lee ◽  
...  
2020 ◽  
Vol 4 (2) ◽  
pp. 722-729
Author(s):  
Usman Sani ◽  
Bashir Gide Muhammad ◽  
Dimas Skam Joseph ◽  
D. Z. Joseph

Poor implementation of quality assurance programs in the radiation industry has been a major setback in our locality. Several studies revealed that occupational workers are exposed to many potential hazards of ionizing radiation during radio-diagnostic procedures, yet radiation workers are often not monitored. This study aims to evaluate the occupational exposure of the radiation workers in Federal Medical Centre Katsina, and to compare the exposure with recommended occupational radiation dose limits. The quarterly readings of 20 thermo-luminescent dosimeters (TLDs') used by the radiation workers from January to December, 2019 were collected from the facility's radiation monitoring archive, and subsequently assessed and analyzed. The results indicate that the average annual equivalent dose per occupational worker range from 0.74 to 1.20 mSv and 1.28 to 2.21 mSv for skin surface and deep skin dose, measured at 10 mm and 0.07 mm tissue depth respectively. The occupational dose was within the recommended national and international limits of 5 mSv per annum or an average of 20 mSv in 5 years. Therefore, there was no significant radiation exposure to all the occupational workers in the study area. Though, the occupational radiation dose is within recommended limit, this does not eliminate stochastic effect of radiation. The study recommended that the occupational workers should adhere and strictly comply with the principles of radiation protection which includes distance, short exposure time, shielding and proper monitoring of dose limits. Furthermore, continuous training of the radiation workers is advised.


2021 ◽  
Author(s):  
Jonas Andersson ◽  
Daniel R. Bednarek ◽  
Wesley Bolch ◽  
Thomas Boltz ◽  
Hilde Bosmans ◽  
...  
Keyword(s):  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mikolaj Grabowski ◽  
Ewa Grzanka ◽  
Szymon Grzanka ◽  
Artur Lachowski ◽  
Julita Smalc-Koziorowska ◽  
...  

AbstractThe aim of this paper is to give an experimental evidence that point defects (most probably gallium vacancies) induce decomposition of InGaN quantum wells (QWs) at high temperatures. In the experiment performed, we implanted GaN:Si/sapphire substrates with helium ions in order to introduce a high density of point defects. Then, we grew InGaN QWs on such substrates at temperature of 730 °C, what caused elimination of most (but not all) of the implantation-induced point defects expanding the crystal lattice. The InGaN QWs were almost identical to those grown on unimplanted GaN substrates. In the next step of the experiment, we annealed samples grown on unimplanted and implanted GaN at temperatures of 900 °C, 920 °C and 940 °C for half an hour. The samples were examined using Photoluminescence, X-ray Diffraction and Transmission Electron Microscopy. We found out that the decomposition of InGaN QWs started at lower temperatures for the samples grown on the implanted GaN substrates what provides a strong experimental support that point defects play important role in InGaN decomposition at high temperatures.


Nano Letters ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2989-2996
Author(s):  
Roméo Juge ◽  
Kaushik Bairagi ◽  
Kumari Gaurav Rana ◽  
Jan Vogel ◽  
Mamour Sall ◽  
...  

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 230
Author(s):  
Jaechan Cho ◽  
Yongchul Jung ◽  
Seongjoo Lee ◽  
Yunho Jung

Binary neural networks (BNNs) have attracted significant interest for the implementation of deep neural networks (DNNs) on resource-constrained edge devices, and various BNN accelerator architectures have been proposed to achieve higher efficiency. BNN accelerators can be divided into two categories: streaming and layer accelerators. Although streaming accelerators designed for a specific BNN network topology provide high throughput, they are infeasible for various sensor applications in edge AI because of their complexity and inflexibility. In contrast, layer accelerators with reasonable resources can support various network topologies, but they operate with the same parallelism for all the layers of the BNN, which degrades throughput performance at certain layers. To overcome this problem, we propose a BNN accelerator with adaptive parallelism that offers high throughput performance in all layers. The proposed accelerator analyzes target layer parameters and operates with optimal parallelism using reasonable resources. In addition, this architecture is able to fully compute all types of BNN layers thanks to its reconfigurability, and it can achieve a higher area–speed efficiency than existing accelerators. In performance evaluation using state-of-the-art BNN topologies, the designed BNN accelerator achieved an area–speed efficiency 9.69 times higher than previous FPGA implementations and 24% higher than existing VLSI implementations for BNNs.


2015 ◽  
Vol 31 ◽  
pp. e47
Author(s):  
J. Greffier ◽  
C. Van Ngog Ty ◽  
G. Bonniaud ◽  
B. Ledermann ◽  
S. Ovtchinnikoff ◽  
...  

1997 ◽  
Vol 72 (6) ◽  
pp. 835-841 ◽  
Author(s):  
D. C. Taylor ◽  
E. M. A. Hussein ◽  
P. S. Yuen

1984 ◽  
Vol 136 (4) ◽  
pp. 232-236 ◽  
Author(s):  
H.P. von Arb ◽  
F. Dittus ◽  
H. Heeb ◽  
H. Hofer ◽  
F. Kottmann ◽  
...  
Keyword(s):  

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