Impact of oxygen concentration during the deposition of window layers on lowering the metastability effects in Cu(In,Ga)Se2/CBD Zn(S,O) based solar cell

The purpose of the present paper is to focus on the impact of oxygen gas partial pressure during the sputtering of i‐ZnO and ZnMgO on the transient behavior of Cu(In,Ga)Se₂ (CIGSe) based solar cells parameters when a CBD‐Zn(S,O) buffer layer is used. Based on electrical characterization of cells, it is observed that the effect of light soaking is different on J–V characteristics depending on whether oxygen is or is not present during the first deposition time of the i‐ZnO or ZnMgO layers. In fact, when cells are prepared with standard i‐ZnO, the efficiencies are very low and a pronounced transient behavior is observed. However, when the first 10 nm of i‐ZnO or ZnMgO is formed by sputtered layer without adding oxygen during the process, depending on the thickness of the buffer layer, the transient effects strongly decreases. It is then possible to get stable cells reaching efficiencies quite similar to the CdS reference cells, especially with ZnMgO, without any post‐treatments. Copyright © 2015 John Wiley & Sons, Ltd.     

Effect of dehydroxylation on the specific heat of simple clay mixtures

Specific heats of four clays (standard reference kaolins, commercial kaolin and montmorillonite) before and after dehydroxylation have been measured. The results were compared with handbook data for the thermal chemical properties of solids. Good agreement has been obtained for the reference kaolin before any thermal treatment. Then, following thermal treatments at 500 °C, 600 °C and 700 °C, dehydroxylation leads to a progressive decrease of heat capacity per unit mass. After dehydroxylation, heat capacity values for all the studied materials are rather similar and agree closely with those estimated by the rule of mixtures. Finally, an empirical relation describing the specific heat capacity (C) in J kg⁻¹ K⁻¹ of dehydroxylated kaolin from 40 °C to 1100 °C is proposed: C = 1128 + 0.102T − 36 × 10⁶T⁻² where T is in K.     

Fungal fucoidanase production by solid-state fermentation in a rotating drum bioreactor using algal biomass as substrate

Fucoidanase enzymes able to degrade fucoidan were produced by solid-state fermentation (SSF). The fermentation assays were initially carried out in a laboratory-scale rotating drum bioreactor, and two fungal strains (Aspergillus niger PSH and Mucor sp. 3P) and three algal substrates (untreated, autohydrolyzed, and microwave processed seaweed Fucus vesiculosus) were evaluated. Additionally, fermentations were carried out under rotational (10 rpm) and static conditions in order to determine the effect of the agitation on the enzyme production. Agitated experiments showed advantages in the induction of the enzyme when compared to the static ones. The conditions that promoted the maximum fucoidanase activity (3.82 U L^?1) consisted in using Mucor sp. 3P as fungal strain, autohydrolyzed alga as substrate, and the rotational system. Such conditions were subsequently used in a 10 times larger scale rotating drum bioreactor. In this step, the effect of controlling the substrate moisture during the enzyme production by SSF was investigated. Moreover, assays combining the algal substrate with an inert support (synthetic fiber) were also carried out. Fermentation of the autohydrolyzed alga with the moisture content maintained at 80% during the fermentation with Mucor sp. 3P gave the highest enzyme activity (9.62 U L^?1).     

Spatial Quality Evaluation of Pedestrian Streets

This paper presents an assessment of the spatial quality of a pedestrian street located in downtown Bauru, S~o Paulo State, Brazil, from the application of some urban sustainability indicators. Multi-method was used to evaluate the thermal comfort quality of users, urban accessibility and preservation of architectural heritage. The results show that the sustainability indicators, apart from being effective tools in identifying problems, can assist local managers in decision making, planning and monitoring of pedestrian public space. These indicators contribute to a broad diagnosis of the pedestrian street and also the formulation of programs, projects and municipal policies, aimed at providing a better life quality for pedestrians.     

Experimental determination of the absorbed dose to water in a scanned proton beam using a water calorimeter and an ionization chamber

The absorbed dose to water is the reference physical quantity for the energy absorbed in tissue when exposed to beams of ionizing radiation in radiotherapy. The SI unit of absorbed dose to water is the gray (Gy = 1 J/kg). Ionization chambers are used as the dosimeters of choice in the clinical environment because they show a high reproducibility and are easy to use. However, ionization chambers have to be calibrated in order to convert the measured electrical charge into absorbed dose to water. In addition, protocols require these conversion factors to be SI traceable to a primary standard of absorbed dose to water. We present experimental results where the ionization chamber used for the dosimetry for the scanned proton beam facility at PSI is compared with the direct determination of absorbed dose to water from the METAS primary standard water calorimeter. The agreement of 3.2% of the dose values measured by the two techniques are within their respective statistical uncertainties.     

An Efficient Algorithm for Spectral Analysis of Heart Rate Variability

We present a simple efficient algorithm for the derivation of a heart rate signal from the electrocardiogram. We demonstrate that the amplitude spectrum of this heart rate signal more closely matches that of the input signal to an integral pulse frequency modulation (IPFM) model of the heart's pacemaker than do the spectra of other ECG-derived heart rate signals. The applicability of this algorithm in cross-spectral analysis between heart rate and other physiologic signals is also discussed.     

New statistical software for the proportional hazards model with current status data

Currently, there are some statistical software packages such as intcox, survBayes, and BITE that are designed to analyze current status data (in which failure times are known to be either left- or right-censored) based on the proportional hazards model. They, however, either fail to directly provide standard errors for the estimated regression parameters or require frailty terms. As a result, practitioners often analyze their data using packages for right-censored data. By so doing, they mistreat left-censored observations as exact. This paper describes our newly developed statistical software for the proportional hazards model with current status data. The software is implemented in the R and C languages and consists of the following two simple steps: (a) find MLEs of the regression parameter and the cumulative hazard function; (b) compute the variance-covariance matrix of the regression parameter estimator by using the generalized missing information principle (GMIP) developed by Kim [Kim, J.S., 2003b. Efficient estimation for the proportional hazards model with left-truncated and Case 1 interval-censored data. Statista Sinica 13 (2), 519-537]. Our simulation study results show that our method works well in terms of bias, standard error, and power. By treating current status data as right-censored data, we also show the discrepancy in terms of bias, standard error, and power. Real examples are provided to illustrate the use of the software. This method can be extended to both general interval-censored data and truncated and interval-censored data.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.