Estimating Response Ratios from Continuous Outcome Data

Several methods for imputing the number of responders from summary continuous outcome data in randomized controlled trials exist. A method by Furukawa and others was used in the quite common case that only such summary continuous outcome measures, but not the actual numbers of responders, are reported in order to estimate response rates (probabilities) for different treatments and response ratios between treatments in such trials. The authors give some empirical justification, but encourage search for theoretical support and further empirical exploration. In particular, a problem that needs to be addressed is that randomness in baseline score is not taken into consideration. This will be done in the present paper. Assuming a binormal model for the data, we compare theoretically the true response rate for a single treatment arm to the theoretical response rate underlying two versions of the suggested imputation method. We also assess the performance of the method numerically for some choices of model parameters. We show that the method works satisfactorily in some cases, but can be seriously biased in others. Moreover, assessing the uncertainty of the estimates is problematic. We suggest an alternative Bayesian estimation procedure, based directly on the normal model, which avoids these problems and provides more precise estimates when applied to simulated data sets.     

Synthesis of carbon nanofibers@MnO2 3D structures over copper foil as binder free anodes for lithiumion batteries

A 3D structured composite of carbon nanofibers@MnO2 on copper foil is reported here as a binder free anode of lithium ion batteries, with high capacity, fast charge/discharge rate and good stability. Carbon nanofiber yarns were synthesized directly over copper foil through a floating catalyst method. The growth of carbon nanofiber yarns was significantly enhanced by mechanical polishing of the copper foils, which can be attributed to the increased surface roughness and surface area of the copper foils. MnO2 was then grown over carbon nanofibers through spontaneous reduction of potassium permanganate by the carbon nanofibers. The obtained composites of carbon nanofibers@MnO2 over copper foil were tested as an anode in lithium ion batteries and they show superior electrochemical performance. The initial reversible capacity of carbon nanofibers@MnO2 reaches up to around 998 mAh g-1 at a rate of 60 mmA g-1 based on the mass of carbon nanofibers and MnO2 . The carbon nanofibers@MnO2 electrodes could deliver a capacity of 630 mAh g-1 at the beginning and maintain a capacity of 440 mmAh g-1 after 105 cycles at a rate of 600 mA g-1 . The high initial capacity can be attributed to the presence of porous carbon nanofiber yarns which have good electrical conductivity and the MnO2 thin film which makes the entire materials electrochemically active. The high cyclic stability of carbon nanofibers@MnO2 can be ascribed to the MnO2 thin film which can accommodate the volume expansion and shrinking during charge and discharge and the good contact of carbon nanofibers with MnO2 and copper foil.     

Synthesis of carbon nanofibers@MnO2 3D structures over copper foil as binder free anodes for lithium ion batteries

A 3D structured composite of carbon nanofibers@MnO₂ on copper foil is reported here as a binder free anode of lithium ion batteries, with high capacity, fast charge/discharge rate and good stability. Carbon nanofiber yarns were synthesized directly over copper foil through a floating catalyst method. The growth of carbon nanofiber yarns was significantly enhanced by mechanical polishing of the copper foils, which can be attributed to the increased surface roughness and surface area of the copper foils. MnO₂ was then grown over carbon nanofibers through spontaneous reduction of potassium permanganate by the carbon nanofibers. The obtained composites of carbon nanofibers@MnO₂ over copper foil were tested as an anode in lithium ion batteries and they show superior electrochemical performance. The initial reversible capacity of carbon nanofibers@MnO₂ reaches up to around 998 mAh·g^?1 at a rate of 60 mmA·g^?1 based on the mass of carbon nanofibers and MnO₂. The carbon nanofibers@MnO₂ electrodes could deliver a capacity of 630 mAh·g^?1 at the beginning and maintain a capacity of 440 mmAh·g^?1 after 105 cycles at a rate of 600 mA·g^?1. The high initial capacity can be attributed to the presence of porous carbon nanofiber yarns which have good electrical conductivity and the MnO₂ thin film which makes the entire materials electrochemically active. The high cyclic stability of carbon nanofibers@MnO₂ can be ascribed to the MnO₂ thin film which can accommodate the volume expansion and shrinking during charge and discharge and the good contact of carbon nanofibers with MnO₂ and copper foil.     

A Comparison of an Analytical Approach and a Standard Simulation Approach in Bayesian Forecasting Applied to Monthly Data from Insurance of Companies

In this paper, we apply the theory of Bayesian forecasting and dynamic linear models, as presented in West and Harrison (1997), to monthly data from insurance of companies. The total number reported claims of compensation is chosen to be the primary time series of interest. The model is decomposed into a trend block, a seasonal effects block and a regression block with a transformed number of policies as regressor. An essential part of the West and Harrison (1997) approach is to find optimal discount factors for each block and hence avoid the specification of the variance matrices of the error terms in the system equations. The BATS package of Pole et al. (1994) is applied in the analysis. We compare predictions based on this analytical approach with predictions based on a standard simulation approach applying the BUGS package of Spiegelhalter et al. (1995). The motivation for this comparison is to gain knowledge on the quality of predictions based on more or less standard simulation techniques in other applications where an analytical approach is impossible. The predicted values of the two approaches are very similar. The uncertainties, however, in the predictions based on the simulation approach are far larger especially two months or more ahead. This partly indicates the advantages of applying optimal discount factors and partly the disadvantages of at least a standard simulation approach for long term predictions.     

Optimizing carbon coating parameters for obtaining SiO2/C anodes with improved electrochemical performance

In this work, we present a comprehensive and systematic study on the use of low-cost and highly abundant carbon precursors to obtain SiO₂/C anodes with superior electrochemical performance towards Li-ions. Different SiO₂/C composites are prepared by soaking silica nanoparticles in solutions containing 20 wt%, 40 wt%, or 60 wt% of glucose, sucrose, or cornstarch, followed by thermal decomposition of the carbohydrates at 850 °C or 1200 °C. Structural, microstructural, and textural differences on the composites derived from the different carbon coating treatments are related to the electrochemical performance of the anodes. Composites containing final carbon contents close to 15 wt% show a complete coverage of the SiO₂ particles with a nanometric carbon layer and exhibit the best electrochemical results. The increase in the annealing temperature from 850 to 1200 °C reduces the porosity of the carbon layer and increases its level of ordering, both having positive effects on the overall electrochemical performance of the electrodes. SiO₂/C composites coated with 40 wt% sucrose and heat treated at 1200 °C display the best electrochemical performance, delivering a reversible specific capacity of 723 mAhg⁻¹ at 50 mAg⁻¹ after 100 cycles, which is considerably higher than the reversible capacity of 233 mAhg⁻¹ obtained with the uncoated material cycled under the same conditions.

     

Solid solubility and phase transitions in the system LaNb1−xTaxo4

The solid solubility between LaNbO₄ and LaTaO₄ was investigated by X-ray diffraction, and a two-phase region was observed in the composition region LaNb_1−xTa_xO₄ where 0.4?x?0.8. Single-phase LaNb_1−xTa_xO₄ (0?x?0.4) with the monoclinic Fergusonite structure at ambient temperature, was observed to transform to a tetragonal Scheelite structure by in-situ high-temperature X-ray diffraction, and the phase transition temperature was shown to increase with increasing Ta-content. This ferroelastic to paraelastic second-order phase transition was described by Landau theory using spontaneous strain as an order parameter. The thermal expansion of LaNb_1−xTa_xO₄ (0?x0.4) was shown to be significantly higher below the phase transition than above. Single-phase LaNb_1−xTa_xO₄ (0.8?x?1) with another monoclinic crystal structure at ambient temperature was shown to transform to an orthorhombic crystal structure by X-ray diffraction and differential scanning calorimetry. The phase transition temperature was observed to decrease with decreasing Ta-content. Finally, orthorhombic LaTaO₄ could also be transformed to monoclinic LaTaO₄ at ambient temperature by applying a uniaxial pressure of 150-170 MPa, reflecting the lower molar volume of monoclinic LaTaO₄.     

Enantiomeric cannabidiol derivatives: synthesis and binding to cannabinoid receptors

(-)-Cannabidiol (CBD) is a major, non psychotropic constituent of cannabis. It has been shown to cause numerous physiological effects of therapeutic importance. We have reported that CBD derivatives in both enantiomeric series are of pharmaceutical interest. Here we describe the syntheses of the major CBD metabolites, (-)-7-hydroxy-CBD and (-)-CBD-7-oic acid and their dimethylheptyl (DMH) homologs, as well as of the corresponding compounds in the enantiomeric (+)-CBD series. The starting materials were the respective CBD enantiomers and their DMH homologs. The binding of these compounds to the CB(1) and CB(2) cannabinoid receptors are compared. Surprisingly, contrary to the compounds in the (-) series, which do not bind to the receptors, most of the derivatives in the (+) series bind to the CB(1) receptor in the low nanomole range. Some of these compounds also bind weakly to the CB(2) receptor.     

Bayesian Hierarchical Space–time Modeling of Earthquake Data

Stochastic earthquake models are often based on a marked point process approach as for instance presented in Vere-Jones (Int. J. Forecast., 11:503–538, 1995). This gives a fine resolution both in space and time making it possible to represent each earthquake. However, it is not obvious that this approach is advantageous when aiming at earthquake predictions. In the present paper we take a coarse point of view considering grid cells of 0.5 × 0.5°, or about 50 × 50 km, and time periods of 4 months, which seems suitable for predictions. More specifically, we will discuss different alternatives of a Bayesian hierarchical space–time model in the spirit of Wikle et al. (Environ. Ecol. Stat., 5:117–154, 1998). For each time period the observations are the magnitudes of the largest observed earthquake within each grid cell. As data we apply parts of an earthquake catalogue provided by The Northern California Earthquake Data Center where we limit ourselves to the area 32–37° N, 115–120° W for the time period January 1981 through December 1999 containing the Landers and Hector Mine earthquakes of magnitudes, respectively, 7.3 and 7.1 on the Richter scale. Based on space-time model alternatives one step earthquake predictions for the time periods containing these two events for all grid cells are arrived at. The model alternatives are implemented within an MCMC framework in Matlab. The model alternative that gives the overall best predictions based on a standard loss is claimed to give new knowledge on the spatial and time related dependencies between earthquakes. Also considering a specially designed loss using spatially averages of the 90th percentiles of the predicted values distribution of each cell it is clear that the best model predicts the high risk areas rather well. By using these percentiles we believe that one has a valuable tool for defining high and low risk areas in a region in short term predictions.      

Synthesis of Monodisperse Silicon Quantum Dots Through a K-Naphthalide Reduction Route

Si quantum dots (Si q-dots) with a size below ~5?nm have great potential in electronics and photovoltaics and are candidate materials for down conversion of light due to their strong photoluminescence (PL) properties. Proper control of size and size distribution as well as the surface characteristics of the Si q-dots are critical for applications in order to control the PL response. Here we report on the synthesis of Si q-dots by a chemical route using potassium-naphthalide as a reducing agent. A narrow size distribution of the Si q-dots, with size in the range from 3 to 30?nm, was achieved by controlling the concentration of the reduction agent, the concentration of silicon tetrachloride (SiCl₄) precursor, temperature and the reaction time. The crystallinity and the narrow size distribution of Si q-dots were demonstrated by electron microscopy and electron diffraction. The optical absorption and PL response in the blue region of the visible spectrum is reported for 3.1?±?0.6?nm octanoxy capped Si q-dots and 4.2?±?1.4?nm methoxy capped Si q-dots in 1,2-dimethoxyethane solution. A quantum efficiency of (1.63?±?0.16)?×?10^?3% was detected for the octanoxy terminated Si q-dots.