In irradiation process, instead of traverse on the targeted cells, there is side
effect happens to non-targeted cells. The targeted cells that had been irradiated with
ionizing radiation emits damaging signal molecules to the surrounding and then, dam-
age the bystander cells. The type of damage considered in this work is the number of
double-strand breaks (DSBs) of deoxyribonucleic acid (DNA) in cell’s nucleus. By us-
ing mathematical approach, a mechanistic model that can describe this phenomenon is
developed based on a structured population approach. Then, the accuracy of the model
is validated by its ability to match the experimental data. The Particle Swarm (PS)
optimization is employed for the data fitting procedure. PS optimization searches the
parameter value that minimize the errors between the model simulation data and exper-
imental data. It is obtained that the mathematical modelling proposed in this paper is
strongly in line with the experimental data.
A new topic of Zero Energy Building (ZEB) is getting famous in research area
because of its goal of reaching zero carbon emission and low building cost. Renewable
energy system is one of the ideas to achieve the objective of ZEB. Genetic Algorithm (GA)
is widely used in many research areas due to its capability to escape from a local minimal
to obtain a better solution. In our study, GA is chosen in sizing optimization of the
number of photovoltaic, wind turbine and battery of a hybrid photovoltaic-wind-battery
system. The aim is to minimize the total annual cost (TAC) of the hybrid energy system
towards the low cost concept of ZEB. Two GA parameters, which are generation number
and population size, have been analysed and optimized in order to meet the minimum
TAC. The results show that the GA is efficient in minimizing cost function of a hybrid
photovoltaic-wind-battery system with its robustness property.
A mechanistic model has been used to explain the effect of radiation. The
model consists of parameters which represent the biological process following ionizing
radiation. The parameters in the model are estimated using local and global optimiza-
tion algorithms. The aim of this study is to compare the efficiency between local and
global optimization method, which is Pattern Search and Genetic Algorithm respectively.
Experimental data from the cell survival of irradiated HeLa cell line is used to find the
minimum value of the sum of squared error (SSE) between experimental data and sim-
ulation data from the model. The performance of both methods are compared based on
the computational time and the value of the objective function, SSE. The optimization
process is carried out by using the built-in function in MATLAB software. The parameter
estimation results show that genetic algorithm is more superior than pattern search for
this problem.