Concentrator multijunction solar cell characteristics under variable intensity and temperature

The performance of multijunction solar cells has been measured over a range of temperatures and illumination intensities. Temperature coefficients have been extracted for three‐junction cell designs that are in production and under development. A simple diode model is applied to the three‐junction performance as a means to predict performance under operating conditions outside the test range. These data may be useful in guiding the future optimization of concentrator solar cells and systems. Copyright © 2008 John Wiley & Sons, Ltd.     

Adsorption performance of mesoporous silicas towards a cationic dye. Influence of mesostructure on adsorption capacity

The dye adsorption performance of four mesoporous silicas with different structure and textural properties, MCM-41, MCM-48, SBA-15 and mesocellular silica foam (MCF), was studied and compared by using toluidine blue O (TBO) as dye model in aqueous solution. These materials were characterized by X-ray diffraction (XRD), small-angle X-ray scattering, nitrogen adsorption-desorption analyses, and transmission electron microscopy (TEM). The effect of some parameters such as adsorbent dosage, contact time, temperature, and pH on the TBO removal in aqueous solution was studied. Results showed that adsorption capacity raised when adsorbent dosage, contact time and pH solution were increased while an increase in temperature decreased the adsorption of TBO. Langmuir, Freundlich and Temkin isotherm models were employed to elucidate the adsorption mechanism while the adsorption rate data were analyzed according to the pseudo-first and second-order kinetic models. Results showed that adsorption of TBO onto MCM-48, SBA-15, and MCF fitted well the Freundlich isotherm model while the kinetic studies showed that adsorption process could be better described by the pseudo-second-order model for all mesoporous silicas. Finally, some solvents were evaluated to carried out dye desorption from the TBO-loaded mesoporous silicas founding that acetic acid was the most efficient.     

Environmental-barrier coating ceramics for resistance against attack by molten calcia-magnesia-aluminosilicate (CMAS) glass: Part I,YAlO3 and γ-Y2Si2O7

The high-temperature (1500?°C) interactions of two promising dense, polycrystalline EBC ceramics, YAlO₃ (YAP) and γ-Y₂Si₂O₇, with a calcia-magnesia-aluminosilicate (CMAS) glass have been explored as part of a model study. Despite the fact that the optical basicities of both the EBC ceramics and the CMAS are similar, they both react with the CMAS. In the case of the Si-free YAlO₃, the reaction zone is small and it comprises three regions of reaction-crystallization products, including Y-Ca-Si apatite solid-solution (ss) and Y₃Al₅O₁₂ (YAG)(ss). In contrast, only Y-Ca-Si apatite(ss) forms in the case of Si-containing γ-Y₂Si₂O₇, and the reaction zone is an order-of-magnitude thicker. These CMAS interactions are analyzed in detail, and are found to be strikingly different than those observed in Y-free EBC ceramics (β-Yb₂Si₂O₇ and β-Sc₂Si₂O₇) in the accompanying Part II paper. This is attributed to the presence of the Y in the YAlO₃ and γ-Y₂Si₂O₇ EBC ceramics.     

Neural networks as a prognostic tool for patients with non-small cell carcinoma of the lung

Patients with non-small cell carcinoma of the lung (NSCLC) have a poor prognosis (64 and 41% survival rates in Stages I and II). It is currently not possible to predict which patients with Stage I or II NSCLC will survive the disease. Sixty-seven patients with NSCLC, including 49 patients with Stage I NSCLC and 18 with Stage II disease (11 with squamous cell carcinomas, 35 with adenocarcinomas, and 21 with large cell carcinomas) were treated with lobectomy and followed for a minimum of 5 years. The tumors were studied with DNA flow cytometry and quantitative immunocytochemical studies for proliferation cell nuclear antigen, p53 protein, and MIB-1. The data were analyzed with backpropagation neural networks, univariate analysis of variance, the Kaplan-Meier survival method, and Cox proportional hazards model. The dependent variables were "free of disease" and "recurrence or dead from disease." Twenty neural network models were trained, using all cases but one, after 1883 to 2000 training cycles. At 5 years, 30 patients were free of disease and 37 were dead or had recurrence. Proliferating cell nuclear antigen was the only statistically significant prognostic factor by univariate analysis of variance and Cox proportional hazards analysis. The S phase was statistically significant by univariate analysis of variance (P <.05). All of the 20 models classified the test cases correctly. Study with backpropagation neural networks using multiple prognostic features from patients with NSCLC suggests that this technology might be useful for prediction of survival. This preliminary study must be validated with data from a larger group of patients with NSCLC before its clinical adequacy is established.     

Quantifying the influence of cold water intrusions in a shallow,coastal system across contrasting years: Green Bay,Lake Michigan

We present water column thermal structure for two climatically different years: 2012, which experienced abnormally warm spring and summer air temperatures preceded by a relatively low ice winter and 2013, which experienced cooler than average spring and average summer air temperatures and preceded by average ice conditions. Mean bottom water temperatures for the season and during cold water intrusions were significantly warmer in 2012 than 2013 leading to a significantly reduced stratified season in 2012. Cold water intrusions were driven into southern Green Bay by southerly winds while intrusions were terminated when winds switched to persistent northerly winds. 2012 observed a significant increase in northerly winds relative to 2013, decreasing cold water intrusion presence and duration but winds did not fully explain the difference in thermal conditions for southern Green Bay. These cold bottom waters drive stratification in polymictic southern Green Bay while dimictic waters were found to have significantly warmer bottom temperatures during 2012 and a deeper mixed layer. Our observations suggest that relatively shallow (<20?m), seasonally stratified systems may not increase in stratification strength and duration under a warming climate; rather, changing wind climatology and surface heat flux can inform the degree to which the mixing regime can be expected to change and impact stratification and thermal structure of coastal systems. We discuss the biogeochemical implications of different thermal regimes, particularly within the context of multiple drivers of physical water column structure in eutrophic, stratified coastal systems.     

Competence-based recommender systems: a systematic literature review

ABSTRACT

Competence-based learning is increasingly widespread in many institutions since it provides flexibility, facilitates the self-learning and brings the academic and professional worlds closer together. Thus, the competence-based recommender systems emerged taking the advantages of competences to offer suggestions (performance of a learning experience, assistance of an expert or recommendation of a learning resource) to the user (learner or instructor). The objective of this work is to conduct a new Systematic Literature Review (SLR) concerning competence-based recommender systems to analyse in relation to their nature and assessment of competences an others key factors that provide more flexible and exhaustive recommendations. To do so, a SLR research methodology was followed in which 25 competence-based recommender systems related to learning or instruction environments were classified according to multiple criteria. We evaluate the role of competences in these proposals and enumerate the emerging challenges. Also a critical analysis of current proposals is carried out to determine their strengths and weakness. Finally, future research paths to be explored are grouped around two main axes closely interlinked; first about the typical challenges related to recommender systems and second, concerning ambitious emerging challenges.     

Endosomal escape of protein nanoparticles engineered through humanized histidine-rich peptides

Poly-histidine peptides such as H6(HHHHHH)are used in protein biotechnologies as purification tags,protein-assembling agents and endosomal-escape entities.The pleiotropic properties of such peptides make them appealing to design protein-based smart materials or nanoparticles for imaging or drug delivery to be produced in form of recombinant proteins.However,the clinical applicability of H6-tagged proteins is restricted by the potential immunogenicity of these segments.In this study,we have explored several humanized histidine-rich peptides in tumor-targeted modular proteins,which can specifically bind and be internalized by the target cells through the tumoral marker CXCR4.We were particularly interested in exploring how protein purification,self-assembling and endosomal escape perform in proteins containing the variant histidine-rich tags.Among the tested candidates,the peptide H5 E(HEHEHEHEH)is promising as a good promoter of endosomal escape of the associated fulllength protein upon endosomal internalization.The numerical modelling of cell penetration and endosomal escape of the tested proteins has revealed a negative relationship between the amount of protein internalized into target cells and the efficiency of cytoplasmic release.This fact demonstrates that the His-mediated,proton sponge-based endosomal escape saturates at moderate amounts of internalized protein,a fact that might be critical for the design of protein materials for cytosolic molecular delivery.