Abstract. Malaysia is planning to build a nuclear power plant (NPP) by 2030 to diversify the national electricity supply and resources. Selection of an NPP site must consider various factors, especially nuclear safety consideration to fulfil the nuclear safety objectives. Environmental Risk Assessment Analysis is a part of safety requirements by the International Atomic Energy Agency (IAEA) prior to the NPP commissioning process. Risk Assessments Analysis (RIA) is compulsory for the NPP site evaluation. One of RIA methods are Radioactive Dispersion Analysis using probabilistic risk analysis software. It is also important to perform studies to estimate the impact to the neighbouring population in the case of a nuclear accident at the power plant. In the present work, aimed to study the impact of a hypothetical nuclear accident by simulating the dispersion pattern of radionuclides originated from a candidate site at Manjung, Perak. The work has been performed using the HotSpot Health Physics codes. Two types of radionuclides have been considered namely I at major towns in Perak such as Lumut and Sitiawan are 1.2 mSv and 9.9 mSv. As for Taiping, Ipoh, Kampar, and Teluk Intan the estimated TEDE is around 0.2 mSv and 1.6 mSv respectively. In conclusion, the dispersion can reach as far as 80 km from the site. However, estimated annual effective dose is not more than 1 mSv limit, which is considered acceptable in the point of view of radiological health risk for human and the environment.
We propose an improved solution to the three-stage DNA motif prediction approach. The threestage approach uses only a subset of input sequences for initial motif prediction, and the initial motifs obtained are employed for site detection in the remaining input subset of non-overlaps. The currently available solution is not robust because motifs obtained from the initial subset are represented as a position weight matrices, which results in high false positives. Our approach, called DeepFinder, employs deep learning neural networks with features associated with binding sites to construct a motif model. Furthermore, multiple prediction tools are used in the initial motif prediction process to obtain a higher number of positive hits. Our features are engineered from the context of binding sites, which are assumed to be enriched with specificity information of sites recognized by transcription factor proteins. DeepFinder is evaluated using several performance metrics on ten chromatin immunoprecipitation (ChIP) datasets. The results show marked improvement of our solution in comparison with the existing solution. This indicates the effectiveness and potential of our proposed DeepFinder for large-scale motif analysis.
It has come to attention that Malaysia have been aiming to build its own nuclear power plant (NPP) for electricity generation in 2030 to diversify the national energy supply and resources. As part of the regulation to build a NPP, environmental risk assessment analysis which includes the atmospheric dispersion assessment has to be performed as required by the Malaysian Atomic Energy Licensing Board (AELB) prior to the commissioning process. The assessment is to investigate the dispersion of radioactive effluent from the NPP in the event of nuclear accident. This article will focus on current development of locally developed atmospheric dispersion modeling code based on Gaussian Plume model. The code is written in Fortran computer language and has been benchmarked to a readily available HotSpot software. The radionuclide release rate entering the Gaussian equation is approximated to the value found in the Fukushima NPP accident in 2011. Meteorological data of Mersing District, Johor of year 2013 is utilized for the calculations. The results show that the dispersion of radionuclide effluent can potentially affect areas around Johor Bahru district, Singapore and some parts of Riau when the wind direction blows from the North-northeast direction. The results from our code was found to be in good agreement with the one obtained from HotSpot, with less than 1% discrepancy between the two.
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