Taheri-Saramad x-ray detector (TSXD): a novel high spatial resolution x-ray imager based on ZnO nano scintillator wires in polycarbonate membrane.

A novel x-ray imager based on ZnO nanowires is designed and fabricated. The proposed architecture is based on scintillation properties of ZnO nanostructures in a polycarbonate track-etched membrane. Because of higher refractive index of ZnO nanowire compared to the membrane, the nanowire acts as an optical fiber that prevents the generated optical photons to spread inside the detector. This effect improves the spatial resolution of the imager. The detection quantum efficiency and spatial resolution of the fabricated imager are 11% and <6.8 µm, respectively.

A Novel Scheme for Optimal Control of a Nonlinear Delay Differential Equations Model to Determine Effective and Optimal Administrating Chemotherapy Agents in Breast Cancer.

BACKGROUND: Determining the optimal and effective scheme for administrating the chemotherapy agents in breast cancer is the main goal of this scientific research. The most important issue here is the amount of drug or radiation administrated in chemotherapy and radiotherapy for increasing patient's survival. This is because in these cases, the therapy not only kills the tumor cells, but also kills some of the healthy tissues and causes serious damages. In this paper we investigate optimal drug scheduling effect for breast cancer model which consist of nonlinear ordinary differential time-delay equations. METHODS: In this paper, a mathematical model of breast cancer tumors is discussed and then optimal control theory is applied to find out the optimal drug adjustment as an input control of system. Finally we use Sensitivity Approach (SA) to solve the optimal control problem. RESULTS: The goal of this paper is to determine optimal and effective scheme for administering the chemotherapy agent, so that the tumor is eradicated, while the immune systems remains above a suitable level. Simulation results confirm the effectiveness of our proposed procedure. CONCLUSION: In this paper a new scheme is proposed to design a therapy protocol for chemotherapy in Breast Cancer. In contrast to traditional pulse drug delivery, a continuous process is offered and optimized, according to the optimal control theory for time-delay systems.

Classification of restrictive and obstructive pulmonary diseases using spirometry data.

Spirometry provides useful information for diagnosis purposes in the pulmonary system. A decision support system could help in using this useful noninvasive test more reliably. Bayesian reasoning could aggregate the results of a group of neural network classifiers to improve the diagnosis of pulmonary diseases and discriminating between its restrictive and obstructive patterns.

Neutron spectra unfolding in Bonner spheres spectrometry using neural networks.

The neural network method has been used for the unfolding of neutron spectra in neutron spectrometry by Bonner spheres. A back propagation algorithm was used for training of neural networks. 4 mm x 4 mm bare LiI (Eu) and in a polyethylene sphere set: 2, 3, 4, 5, 6, 7, 8, 10, 12, 18 inch diameter have been used for unfolding of neutron spectra. Neural networks were trained by 199 sets of neutron spectra, which were subdivided into 6, 8, 10, 12, 15 and 20 energy bins and for each of them an appropriate neural network was designed and trained. The validation was performed by the 21 sets of neutron spectra. A neural network with 10 energy bins which had a mean value of error of 6% for dose equivalent estimation of spectra in the validation set showed the best results. The obtained results show that neural networks can be applied as an effective method for unfolding neutron spectra especially when the main target is neutron dosimetry.

Estimation of the radon concentration in soil related to the environmental parameters by a modified Adaline neural network.

A new method based on adaptive linear neuron (Adaline) is used to estimate the radon concentration in soil associated with the environmental parameters. Analysis of the data obtained from a site in Thailand indicates that our proposed method is able to differentiate temporal variation of radon concentration related to the environmental parameters from those caused by phenomena in the earth (e.g. earthquake). The result also shows agreement between our method and another method based on impulse responses from multivariable time series (complex mathematical equations).

Layered neural networks based analysis of radon concentration and environmental parameters in earthquake prediction.

A layered neural network (LNN) has been employed to estimate the radon concentration in soil related to the environmental parameters. This technique can find any functional relationship between the radon concentration and the environmental parameters. Analysis of the data obtained from a site in Thailand indicates that this approach is able to differentiate time variation of radon concentration caused by environmental parameters from those arising by anomaly phenomena in the earth (e.g. earthquake). This method is compared with a linear computational technique based on impulse responses from multivariable time series. It is indicated that the proposed method can give a better estimation of radon variations related to environmental parameters that may have a non-linear effect on the radon concentration in soil, such as rainfall.