Outbreak of serogroup C meningococcal disease associated with campus bar patronage

Between February 1991 and April 1992, eight undergraduates at a US residential university and one at a nearby 2-year college contracted serogroup C meningococcal disease. A case-control investigation with 20 controls per case, oropharyngeal carriage surveys, and multilocus enzyme electrophoresis (MEE) of serogroup C isolates were used to identify factors contributing to the outbreak. All eight sterile-site isolates from cases were closely related by MEE and were similar (though not identical) to the strain associated with the 1991-1992 epidemic of meningococcal disease in eastern Canada. Disease was associated with cigarette smoking (p = 0.012), recent patronage of campus-area bars (p = 0.034), estimated amount of time spent in campus-area bars (p = 0.0003), and, especially, recent patronage of one specific bar, bar A (p = 0.0006; odds ratio = 23.1, 95% confidence interval 3.0-571.5). In carriage surveys, 1,528 throat cultures taken from (primarily student) noncases yielded only five (0.3%) strains that were identical by MEE to those from cases. Two of these were found among 22 cultures obtained from bar A employees in spring 1992. Some cases in this outbreak may have followed transmission of the epidemic strain in bar A. Campus bar environments may facilitate the spread of meningococcal disease among teenagers and young adults.     

Improving the Performance of Feedforward Neural Networks by Noise Injection into Hidden Neurons

The generalization ability of feedforward neural networks (NNs) depends on the size of training set and the feature of the training patterns. Theoretically the best classification property is obtained if all possible patterns are used to train the network, which is practically impossible. In this paper a new noise injection technique is proposed, that is noise injection into the hidden neurons at the summation level. Assuming that the test patterns are drawn from the same population used to generate the training set, we show that noise injection into hidden neurons is equivalent to training with noisy input patterns (i.e., larger training set). The simulation results indicate that the networks trained with the proposed technique and the networks trained with noisy input patterns have almost the same generalization and fault tolerance abilities. The learning time required by the proposed method is considerably less than that required by the training with noisy input patterns, and it is almost the same as that required by the standard backpropagation using normal input patterns.     

A Variable Current-Limit Control Scheme for a Bi-directional Converter used in Ultracapacitor Applications

In ultracapacitor applications, generally, a bi-directional converter is connected to a DC bus and is designed to compensate rapid load variations on the bus. During transient phases, overloaded DC bus can push the converter out of its operating limits. For providing the necessary power, converters should be put in parallel, while each converter is limited into its optimal operating range. In a boost converter, this operating limit can be related to the inductor current and UC voltage. In this study, a variable current-limit is proposed for inductor current which then determines the operating range of the boost converter. This method will provide stability of the converter during overload transients. An experimental setup consisting of a bi-directional converter, a controllable load/source, and an ultracapacitor is presented, to validate the proposed method. Several scenarii are applied to analyze the performance of the system in overloaded phases and theoretical and experimental results are presented.     

Electronic properties of Cu(In,Ga)Se2 solar cells on stainless steel foils without diffusion barrier

Compared with rigid glass, manufacturing of Cu(In,Ga)Se₂ (CIGS) solar cells on flexible stainless steel (SS) substrates has potential to reduce production cost because of the application of roll‐to‐roll processing. Up to now, high‐efficiency cells on SS could only be achieved when the substrate is coated with a barrier layer (e.g. SiO_x or Si₃N₄) for hindering the diffusion of impurities, especially Fe, into the CIGS layer. In this paper, the effect of these impurities on the electronic transport properties of the device is investigated. Using admittance spectroscopy, the presence of a deep defect level at around 320 meV is observed, which deteriorates the efficiency of the solar cells. Furthermore, it is shown that reducing substrate temperature during CIGS deposition is an effective alternative to a barrier layer for reducing diffusion of detrimental Fe impurities into the absorber layer. By applying a CIGS growth process for deposition at low substrate temperatures, an efficiency of 17.7%, certified by Fraunhofer Institute ISE, Freiburg, was achieved on Mo/Ti‐coated SS substrate without an additional metal‐oxide or metal‐nitride impurity diffusion barrier layer. Copyright © 2012 John Wiley & Sons, Ltd.     

Multi-dimensional Capon spectral estimation using discrete Zhang neural networks

The minimum variance spectral estimator, also known as the Capon spectral estimator, is a high resolution spectral estimator used extensively in practice. In this paper, we derive a novel implementation of a very computationally demanding matched filter-bank based a spectral estimator, namely the multi-dimensional Capon spectral estimator. To avoid the direct computation of the inverse covariance matrix used to estimate the Capon spectrum which can be computationally very expensive, particularly when the dimension of the matrix is large, we propose to use the discrete Zhang neural network for the online covariance matrix inversion. The computational complexity of the proposed algorithm for one-dimensional (1-D), as well as for two-dimensional (2-D) and three-dimensional (3-D) data sequences is lower when a parallel implementation is used.     

Revealing the Dynamics of Hybrid Metal Halide Perovskite Formation via Multimodal In Situ Probes

The exploration of the synthetic space of halide perovskites hinges on an enormous number of parameters requiring time‐consuming experimentation to decouple and optimize. Here, the formation of the prototype material CH₃NH₃PbI₃ (MAPbI₃) is investigated at different time and length scales using multimodal in situ measurements to monitor the evolution of crystalline phases, morphology, and photoluminescence as a function of the lead precursors. Kinetically fast formation of crystalline precursor phases already during the spin‐coat deposition is observed using lead iodide (PbI₂) or lead chloride (PbCl₂) routes. These precursor phases most likely template final MAPbI₃ film morphology. In particular, the emergence of the “needle‐like” structure is shown to appear before film annealing. In situ photoluminescence measurements suggest nanoscale nucleation followed by rapid nuclei densification and growth. Using this multimodal in situ approach, different formation pathways can be identified either via precursor phases in the PbI₂ and PbCl₂ routes or direct perovskite formation from molecular building blocks as observed in the lead acetate (PbAc₂) route. Correlation of in situ results with photovoltaic device performance demonstrates the power of in situ multimodal techniques, paves the way to a fast screening of synthetic parameters, and ultimately leads to controlled synthetic procedures that yield high‐efficiency devices.     

Development of an emitter for industrial silicon solar cells using the doped oxide solid source diffusion technique

The aim of this paper is to demonstrate for the first time the feasibility of fabricating large-area screen-printed monocrystalline silicon solar cells using the Doped Oxide Solid Source (DOSS) diffusion technique. This process was applied to form the n⁺p emitter junction from highly doped sources prepared in a POCl₃ ambient. The diffusions were performed under a pure nitrogen flow in the temperature range 900–1050°C. In this investigation attention was devoted to the influence of the source doping level on the fill factor. The solar cells were fabricated on industrial quality 4-inch Cz wafers using a simple processing sequence incorporating screen-printed contacts and a TiO₂ antireflection coating deposited by spin-on. Fill factors as high as 79% were obtained. The potential benefit of retaining for passivation purposes the thin residual oxide grown during phosphorus diffusion was evaluated. These first experiments led to a cell efficiency close to 10%.     

Influence of Ni and Cr impurities on the electronic properties of Cu(In,Ga)Se2 thin film solar cells

Deposition of Cu(In,Ga)Se₂ (CIGS) thin film solar cells on metallic substrate is an attractive approach for development of low cost solar modules. However, in such devices, special care has to be taken to avoid diffusion of impurities, such as Fe, Ni, and Cr, from the substrate into the active layers. In this work, the influence of Ni and Cr impurities on the electronic properties of CIGS thin film solar cells is investigated in detail. Impurities were introduced into the CIGS layer by diffusion during the CIGS deposition process from a Ni or Cr precursor layer below the Mo electrical back contact. A high temperature and a low temperature CIGS deposition process were applied in order to correlate the changes in the photovoltaic parameters with the amount of impurities diffused into the absorber layer. Solar cells with Ni and Cr impurities show a reduction in the device performance, whereas the effect was most pronounced in Ni containing devices. The presence of deep defect levels in the absorber layer was identified with admittance spectroscopy and can be related to Ni and Cr impurities, which diffused into the CIGS layer according to secondary ion mass spectroscopy depth profiles and inductively coupled plasma mass spectrometry. Copyright © 2014 John Wiley & Sons, Ltd.