MONTE CARLO SIMULATION OF RADIATION FIELD OPTIMIZATION FOR MEDICAL LINAC

A method for designing an X-ray flatness filter for medical electron linac is developed. It is used in the optimization process in the electron beam radiation system. Monte Carlo simulation method is used and two examples of real radiation system optimization processes for China-made medical electron linac are provided： 15 MV X- ray system of BJ-20 linac, and 12 MeV electron system of BJ-14. Results are verified by using the traditional method.

Robust flat filtering DSP based control of the boost converter

The article deals with the design and implementation of a flat filter tracking digital controller for a boost DC-DC power converter. A highly perturbed switched boost converter circuit is shown to be efficiently controlled, in a trajectory tracking task for its non-minimum phase output, by means of a suitable linear filter, here addressed as a flat filter. Flat filtering is a natural robust version of generalized proportional integral control （GPIC） by which the effects of arbitrary time varying exogenous disturbances, unknown endogenous nonlinearities and un-modeled dynamics can be jointly attenuated in a conceptually similar fashion to observer-based active disturbance rejection control （ADRC） and algebraic identification based model free control （MFC） but： a） without using extended state observers and b） respecting the original system order in a time-varying simplified model while avoiding algebraic estimation techniques. The proposed control technique based on the TMS320F28335 digital signal processor chip is tested by means of realistic simulations and experimental setup.

Identification and prioritization of differentially expressed genes for time-series gene expression data

Identification of differentially expressed genes （DEGs） in time course studies is very useful for understanding gene function, and can help determine key genes during specific stages of plant development. A few existing methods focus on the detection of DEGs within a single biological group, enabling to study temporal changes in gene expression. To utilize a rapidly increasing amount of single-group time-series expression data, we propose a two-step method that integrates the temporal characteristics of time-series data to obtain a B-spline curve fit. Firstly, a fiat gene filter based on the Ljung-Box test is used to filter out flat genes. Then, a B-spline model is used to identify DEGs. For use in biological experiments, these DEGs should be screened, to determine their biological importance. To identify high-confidence promising DEGs for specific biological processes, we propose a novel gene prioritization approach based on the partner evaluation principle. This novel gene prioritization ap- proach utilizes existing co-expression information to rank DEGs that are likely to be involved in a specific biological process/condition. The proposed method is validated on the Arabidopsis thaliana seed germination dataset and on the rice anther development expression dataset.