Interaction of triton X-100 on silica: A relationship between surface characteristics and adsorption isotherms

Adsorption of Triton X-100 on various silica substrates has been investigated. A number of solids, including a natural quartz, this quartz washed with HCl acid and subsequently heated at 1273 K; two aerosils and one Kieselgel silicas were studied. These solids exhibit surface areas in the range of 5 to 430 m² g^?1. All the Triton adsorption isotherms display an S-shape at the adsorption temperatures studied (298 and 308 K). It has been found that the pretreatments of natural quartz (by water washing, impurities removed by acid and/or high temperature calcination) affect considerably the amounts of TX-100 adsorbed. Measurements of surface composition have been made by X-ray photoelectron spectroscopy (XPS) with particular emphasis on the presence of impurities and on the number of OH groups at the surface of the samples. The nature of the surface hydroxyl has also been studied by infrared spectroscopy. Furthermore, the specific number of hydroxyl groups on the surface of the silica samples has been determined by thermogravimetric analysis. Finally an attempt to correlate solid surface characteristics with adsorption isotherms has been developed.     

Coupling GIS with Hydrologic and Hydraulic Flood Modelling

Geographic Information Systems (GIS) have been recognised as a powerful means to integrate and analyse data from various sources in the context of comprehensive floodplain management. As part of this comprehensive approach to floodplain management, it is very important to be able to predict the consequences of different scenarios in terms of flooded areas and associated risk. Hydrologic and hydraulic modelling plays a crucial role and there is much to gain in incorporating these modelling capabilities in GIS. This is still a rather complex task and research is being done on the full integration of these models. Interfacing between these models and GIS may be a very efficient way of overcoming the difficulties and getting very good results in terms of engineering practice. This paper presents results based on the use of Intergraph GIS coupled with Idrisi GIS. Using these two systems substantially increased the flexibility of using GIS as a tool for flood studies. A lumped (XSRAIN) and a distributed (OMEGA) hydrologic models were used to simulate flood hydrographs. The well known HEC-2 Hydraulic model was used to compute flooded areas. These models were applied in the Livramento catchment with very good results. The computation of flooded areas for different flood scenarios, and its representation in GIS, can be used in the assessment of affected property and associated damages. This is a very useful GIS-based approach to floodplain management.     

Telephone booster sessions for optimizing smoking cessation for patients in rehabilitation centers.

Smokers with smoking-related diseases who are hospitalized in rehabilitation centers should be offered smoking cessation. This is the first study evaluating whether telephone booster sessions after intensive inpatient treatment are an effective strategy. The present study was conducted in 13 rehabilitation centers for somatic disorders as a prospective multicenter study with a randomized treatment-control group design. We compared abstinence rates after hospital discharge from treatment that included a group smoking cessation program with (treatment group) and without telephone booster sessions (control group). Data from 290 smokers were analyzed. After 6 and 12 months the treatment group achieved abstinence rates twice as high as those of the control group. Men profited more from telephone booster sessions than did women. Results indicated that telephone booster sessions were highly effective (even) after an inherently intensive group program during a hospital stay. Further research should focus on the special needs of women receiving telephone counseling.     

Effects of temperature, ammonium and glucose concentrations on yeast growth in a model wine system

In enology, alcoholic fermentation is a complex process involving several mechanisms. Slow and incomplete alcoholic fermentation is a chronic problem for the wine industry and factors leading to sluggish and stuck fermentations have been extensively studied and reviewed. The most studied cause of sluggish and stuck fermentation is the nitrogen content limitation. Nevertheless, other factors, such as temperature of fermentation and sugar concentration can affect the growth of yeasts. In this study we modelled the yeast growth‐cycle in wine model system as a function of temperature, sugar and ammonium concentrations; the individual effects and the interaction of these factors were analysed by means of a quadratic response surface methodology. Cell concentrations and weight loss were monitored in the whole wine fermentation process. The results of central composite design show that lower is the availability of nitrogen, higher is the cell growth rate; moreover, initial nitrogen concentration also influences survival time of Saccharomyces cerevisiae.     

Effects of Particle Size of Bulk Drug and food on the Bioavailability of U-78875 in Dogs

The effects of particle size and food on the absolute bioavailability of U-78875 in dogs after oral administration of either a suspension or tablet dosage form were investigated. A reduction of particle size caused a significant increase in bioavailability along with an increase in dissolution rate. Additionally, both suspension and tablet dosage forms administered after food caused an increase in bioavailability. Thus, to accelerate drug dissolution, a reduction of U-78875 particle size from the unmilled state is important for the optimization of formulation compositions. To increase the bioavailability of U-78875, postprandial dosing should be considered.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Hybrid photocatalytic membranes embedding decatungstate for heterogeneous photooxygenation

The incorporation of decatungstate in polymeric membranes provides new heterogeneous photocatalysts for the oxidation of organic substrates under oxygen atmosphere at 25 °C. Photocatalytic membranes have been prepared yielding polymeric films with a high thermal, chemical and mechanical stability (PVDF, PDMS, Hyflon). Surface spectroscopy techniques including transmittance and reflectance UV-Vis and FT-IR have been used to assess the photocatalyst integrity within the polymeric support. Catalyst screening has been performed under both homogeneous and heterogeneous photooxygenation conditions. The photocatalyst activity has been evaluated in terms of the substrate conversion, turnover numbers, and recycling experiments. A membrane induced selectivity behavior has been evidenced by comparison with homogeneous oxidations.     

Network regression with predictive clustering trees

Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, called NCLUS, that explicitly considers autocorrelation when building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multi-target prediction, including multi-target regression and multi-target classification. We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results show that our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approaches.