Photovoltaic application of O-doped Wittichenite-Cu 3 BiS 3: from microscopic properties to maximum efficiencies

The electronic properties and the low environmental impact of Cu 3 BiS 3 make this compound a promising material for low-cost thin film solar cell technology. From the first principles, the electronic properties of the isoelectronic substitution of S by O in Cu 3 BiS 3 have been obtained using two different exchange-correlation potentials. This compound has an acceptor level below the conduction band, which modifies the opto-electronic properties with respect to the host semiconductor. In order to analyze a possible efficiency increment with respect to the host semiconductor, we have calculated the maximum efficiency of this photovoltaic absorber material.

Maximum Likelihood Estimation of Modal Parameters in Structures Using the Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on maximum likelihood estimation (MLE) with the expectation maximization (EM) algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. The benchmark structure is a four-story, two-bay by two-bay steel-frame scale model structure built in the Earthquake Engineering Research Laboratory at the University of British Columbia, Canada. This paper focuses on Phase I of the analytical benchmark studies. A MATLAB-based finite element analysis code obtained from the IASC-ASCE SHM Task Group web site is used to calculate the dynamic response of the prototype structure. A number of 100 simulations have been made using this MATLAB-based finite element analysis code in order to evaluate the proposed identification method. There are several techniques to realize system identification. In this work, stochastic subspace identification (SSI)method has been used for comparison. SSI identification method is a well known method and computes accurate estimates of the modal parameters. The principles of the SSI identification method has been introduced in the paper and next the proposed MLE with EM algorithm has been explained in detail. The advantages of the proposed structural identification method can be summarized as follows: (i) the method is based on maximum likelihood, that implies minimum variance estimates; (ii) EM is a computational simpler estimation procedure than other optimization algorithms; (iii) estimate more parameters than SSI, and these estimates are accurate. On the contrary, the main disadvantages of the method are: (i) EM algorithm is an iterative procedure and it consumes time until convergence is reached; and (ii) this method needs starting values for the parameters. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using both the SSI method and the proposed MLE + EM method. The numerical results show that the proposed method identifies eigenfrequencies, damping ratios and mode shapes reasonably well even in the presence of 10% measurement noises. These modal parameters are more accurate than the SSI estimated modal parameters.

Cell cycle-dependent colocalization of BARD1 and BRCA1 proteins in discrete nuclear domains

Germ-line mutations of the BRCA1 gene predispose women to early-onset breast and ovarian cancer by compromising the gene’s presumptive function as a tumor suppressor. Although the biochemical properties of BRCA1 polypeptides are not understood, their expression pattern and subcellular localization suggest a role in cell-cycle regulation. When resting cells are induced to proliferate, the steady-state levels of BRCA1 increase in late G1 and reach a maximum during S phase. Moreover, in S phase cells, BRCA1 polypeptides are hyperphosphorylated and accumulate into discrete subnuclear foci termed “BRCA1 nuclear dots.” BRCA1 associates in vivo with a structurally related protein termed BARD1. Here we show that the steady-state levels of BARD1, unlike those of BRCA1, remain relatively constant during cell cycle progression. However, immunostaining revealed that BARD1 resides within BRCA1 nuclear dots during S phase of the cell cycle, but not during the G1 phase. Nevertheless, BARD1 polypeptides are found exclusively in the nuclear fractions of both G1- and S-phase cells. Therefore, progression to S phase is accompanied by the aggregation of nuclear BARD1 polypeptides into BRCA1 nuclear dots. This cell cycle-dependent colocalization of BARD1 and BRCA1 indicates a role for BARD1 in BRCA1-mediated tumor suppression.