Temporal Profiles and Spectral Lags of XRF 060218

The spectral and temporal properties of the non-thermal emission ofthe nearby XRF 060218 in 0.3-150 keV band are studied. We show that both the spectral energy distribution and the light curve properties suggest the same origin of the non-thermal emission detected by {\em Swift} BAT and XRT. This event has the longest pulse duration and spectral lag observed to date among the known GRBs. The pulse structure and its energy dependence are analogous to typical GRBs. By extrapolating the observed spectral lag to the {\em CGRO/BATSE} bands we find that the hypothesis that this event complies with the same luminosity-lag relation with bright GRBs cannot be ruled out at $2\sigma$ significance level. These intriguing facts, along with its compliance with the Amati-relation, indicate that XRF 060218 shares the similar radiation physics as typical GRBs.Comment: 9 pages in emulateapj format, including 4 figures and 1 table, accepted for publication in ApJ Letter

Reversibility and Improved Hydrogen Release of Magnesium Borohydride

Desorption and subsequent rehydrogenation of Mg(BH_4)_2 with and without 5 mol % TiF_3 and ScCl_3 have been investigated. Temperature programmed desorption (TPD) experiments revealed a significant increase in the rate of desorption as well as the weight percentage of hydrogen released with additives upon heating to 300 °C. Stable Mg(B_xH_y)_n intermediates were formed at 300 °C, whereas MgB_2 was the major product when heated to 600 °C. These samples were then rehydrogenated and subsequently characterized with powder X-ray diffraction (pXRD), Raman, and NMR spectroscopy. We confirmed significant conversion of MgB_2 to fully hydrogenated Mg(BH_4)_2 for the sample with and without additives. TPD and NMR studies revealed that the additives have a significant effect on the reaction pathway during both dehydrogenation and rehydrogenation reactions. This work suggests that the use of additives may provide a valid pathway for improving intrinsic hydrogen storage properties of magnesium borohydride

A Patient-specific Wear Prediction Framework for an Artificial Knee Joint with Coupled Musculoskeletal Multibody-dynamics and Finite Element Analysis

A novel wear prediction framework was developed by coupling a patient-specific lower extremity musculoskeletal multibody dynamics model with the finite element contact mechanics and wear model of total knee replacement. The tibiofemoral contact forces and kinematics were influenced by articular surface wear, and in turn, the variations from the knee dynamics resulted in increases in the volumetric wear of 404.41 mm3 after 30 million cycle simulation from 380.86 mm3 from the traditional wear prediction using fixed load/motions. The developed patient-specific wear prediction framework provided a reliable virtual platform for investigating articular surface wear of total knee replacements

Comparison of ν-support vector regression and logistic equation for descriptive modeling of Lactobacillus plantarum growth

Due to the complexity and high non-linearity of bioprocess, most simple mathematical models fail to describe the exact behavior of biochemistry systems. As a novel type of learning method, support vector regression (SVR) owns the powerful capability to characterize problems via small sample, nonlinearity, high dimension and local minima. In this paper, we developed a ν-SVR model with genetic algorithms (GA) in the pre-estimate in Lactobacillus plantarum fermentation by comparing the predicting capability of logistic model and SVR model. 5-fold cross validation technique was applied in the SVR train to avoid over-fitting. The information of SVR parameters were obtained in the generation of 150 and the optimal parameters were C= 235.8935, σ= 8.3608, ν=0.7587. Correspondingly, the logistic model parameters μmax and xmax were estimated as 0.4791 and 0.3498, respectively. The experimental results demonstrated that, SVR model excelled the logistic model based on the normalized mean square error (NMSE), mean absolute percentage error (MAPE) and the Pearson correlation coefficient R. We found that the ν-SVR model optimized by genetic algorithms could be a potential monitoring method for prediction of biomass.Key words: Support vector regression, genetic algorithm, logistic model, prediction of biomass