Principal component analysis on meteorological data in UTM KL

The high usage of fossil fuel to produce energy for the increasing demand of energy has been the primary culprit behind global warming. Renewable energies such as solar energy can be a solution in preventing the situation from worsening. Solar energy can be harnessed using available system such as solar thermal cogeneration systems. However, for the system to function smoothly and continuously, knowledge on solar radiation’s intensity several minutes in advance are required. Though there exist various solar radiation forecast models, most of the existing models requires high computational time. In this research, principal component analysis were applied on the meteorological data collected in Universiti Teknologi Malaysia Kuala Lumpur to reduce the dimension of the data. Dominant factors obtained from the analysis is expected to be useful for the development of solar radiation forecast model

Principal Component Analysis on Meteorological Data in UTM KL

The high usage of fossil fuel to produce energy for the increasing demand of energy has been the primary culprit behind global warming. Renewable energies such as solar energy can be a solution in preventing the situation from worsening. Solar energy can be harnessed using available system such as solar thermal cogeneration systems. However, for the system to function smoothly and continuously, knowledge on solar radiation’s intensity several minutes in advance are required. Though there exist various solar radiation forecast models, most of the existing models requires high computational time. In this research, principal component analysis were applied on the meteorological data collected in Universiti Teknologi Malaysia Kuala Lumpur to reduce the dimension of the data. Dominant factors obtained from the analysis is expected to be useful for the development of solar radiation forecast model

Expression of FGFR1 is an independent prognostic factor in triple-negative breast cancer

10.1007/s10549-015-3371-xBreast Cancer Research and Treatment151199-11

Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems

The high usage of fossil fuel to produce energy for the increasing demand of energy has been the primary culprit behind global warming. Alternative energy supply is thus necessary in order to prevent the situation from worsening. Recently, renewable energies such as solar energy has emerged as potential alternative energy resources due to its abundance all over the globe Solar energy can be harnessed using available system such as solar thermal cogeneration systems. However, fluctuations of solar radiation is one of the main challenge faced by the implementation of solar thermal cogeneration system due to its high variability. In order to have solar thermal cogeneration systems function smoothly and continuously, knowledge on solar radiation’s intensity several minutes in advance are required. While there exist various solar radiation forecast models, most of the proposed model are time consuming. In this research, a new methodology to forecast solar radiation via several meteorological data that incorporates dimension reduction technique is proposed. Based on the proposed methodology, two prediction models, Artificial Neural Network and statistical are established

Cancer stem cell and epithelial�mesenchymal transition markers predict worse outcome in metaplastic carcinoma of the breast

10.1007/s10549-015-3299-1Breast Cancer Research and Treatment150131-4