Metal speciation and pH effect on Pb,Cu, Zn and Cd biosorption onto Sphaerotilus natans: Langmuir-type empirical model

Biosorption data of lead, copper, zinc and cadmium onto Sphaerotilus natans at different equilibrium pH (3-5 units) were here reported and analysed. Experimental results outlined the positive effect of pH increase on pollutant uptake and also the biomass affinity series (Pb > Cu > Zn > Cd) reflecting the hydrolytic properties of metals. An original empirical model was proposed to represent the effect of pH on heavy metal biosorption inserting q(max) vs. pH empirical functions into the classical Langmuir isotherm.     

A review of energy efficiency label of street lighting systems

There are very few countries that have provisions addressing the energy efficiency of the whole street lighting system, such as Spain or the Netherlands. Nevertheless, there is not an agreement about how energy efficiency must be assessed. The Spanish Government contemplates it in the Royal Decree 1890/2008 with the goal of improving energy savings and efficiency. However, this has not obtained the expected results. Nowadays, energy efficiency of this kind of systems is assessed using a label. In the case of Spain, this label only assesses one magnitude. The contributions of this paper are two evaluation systems (kiviat diagram and pie chart) which assess five magnitudes: lamps, energy efficiency index, light pollution, renewable energy contribution, and harness of the luminous flux using dimming. After that, a survey was done to study several subjects: (1) if citizens are aware about the efficiency of street lighting systems, (2) whether the sample of colors used in the label is adequate, and (3) if our proposed systems could replace the current evaluation system. Finally, the paper finishes with the conclusions of the survey.     

Computational evolutionary inverse lagrangian puff model

This article presents a model based on the principles of evolutionary computation and on the principles of dispersion of an Inverse lagrangian puff model of the type Backward gaussian puff tracking in order to determine the spatial behavior over time of the concentration for PM_2.5 and PM₁₀ in a study area.     

Planned missing data designs in psychological research.

The authors describe 2 efficiency (planned missing data) designs for measurement: the 3-form design and the 2-method measurement design. The 3-form design, a kind of matrix sampling, allows researchers to leverage limited resources to collect data for 33% more survey questions than can be answered by any 1 respondent. Power tables for estimating correlation effects illustrate the benefit of this design. The 2-method measurement design involves a relatively cheap, less valid measure of a construct and an expensive, more valid measure of the same construct. The cost effectiveness of this design stems from the fact that few cases have both measures, and many cases have just the cheap measure. With 3 brief simulations involving structural equation models, the authors show that compared with the same-cost complete cases design, a 2-method measurement design yields lower standard errors and a higher effective sample size for testing important study parameters. With a large cost differential between cheap and expensive measures and small effect sizes, the benefits of the design can be enormous. Strategies for using these 2 designs are suggested. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

WENO schemes based on upwind and centred TVD fluxes

In this paper we propose to use second-order TVD fluxes, instead of first-order monotone fluxes, in the framework of finite-volume weighted essentially non-oscillatory (WENO) schemes. We call the new improved schemes the WENO-TVD schemes. They include both upwind and centred schemes on non-staggered meshes. Numerical results suggest that our schemes are superior to the original schemes used with first-order monotone fluxes. This is especially so for long time evolution problems containing both smooth and non-smooth features.     

Methylcyclohexane dehydrogenation on Rh/γ-Al2O3 catalysts

The influence of the Rh loading on the surface properties and catalytic behaviour of Rh/γ-Al₂O₃ catalysts has been studied. The series of catalysts presents differences in metal dispersion, reducibility, surface composition and catalytic activity. All the data reported suggest that the differences in catalytic behaviour in the methylcyclohexane dehydrogenation reaction can be explained in terms of electron-deficient rhodium clusters, essentially when the metal particle size becomes smaller than 15 Å.     

Professionals in mutual help groups: Impact on social climate and members' behavior.

This study assessed the impact of professional involvement in mutual help groups for the mentally ill. Social climate data and behavioral data on members in groups led either by a mental health professional (n?=?36) or an indigenous group member (n?=?70) were compared. The results of the study indicated a more formal, psychologically directed approach in the professionally led groups. Although professional involvement in mutual help groups does not necessarily produce different member outcomes, it does seem to affect both the perceptions and the behavior of members. Professionals should be cautious when they get involved in mutual help groups to avoid professionalizing them. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Secretions of Stingless Bees: The Dufour Gland of Nannotrigona testaceicornis

The Dufour gland of Nannotrigona testaceicornis is a large, wide, pear-shaped sac. The gland secretion consists chiefly of the diterpene ester all-trans-geranylgeranyl acetate (64% of the total), together with a complex mixture of small amounts of cyclic ketals; mono-, sesqui-, and diterpene compounds; acetates; and other oxygenated compounds. Samples of N. testaceicornis collected at two sites in Brazil and one in México shared the same composition of their glands, suggesting that the species is uniform over this wide geographical area.     

Self-adaptive systems: A survey of current approaches,research challenges and applications

Self-adaptive software is capable of evaluating and changing its own behavior, whenever the evaluation shows that the software is not accomplishing what it was intended to do, or when better functionality or performance may be possible. The topic of system adaptivity has been widely studied since the mid-60s and, over the past decade, several application areas and technologies relating to self-adaptivity have assumed greater importance. In all these initiatives, software has become the common element that introduces self-adaptability. Thus, the investigation of systematic software engineering approaches is necessary, in order to develop self-adaptive systems that may ideally be applied across multiple domains. The main goal of this study is to review recent progress on self-adaptivity from the standpoint of computer sciences and cybernetics, based on the analysis of state-of-the-art approaches reported in the literature. This review provides an over-arching, integrated view of computer science and software engineering foundations. Moreover, various methods and techniques currently applied in the design of self-adaptive systems are analyzed, as well as some European research initiatives and projects. Finally, the main bottlenecks for the effective application of self-adaptive technology, as well as a set of key research issues on this topic, are precisely identified, in order to overcome current constraints on the effective application of self-adaptivity in its emerging areas of application.     

Prediction of Particlulate Air Pollution using Neural Techniques

We have analysed the possibility of predicting hourly average concentrations of suspended atmospheric particulate matter with aerodynamic diameter less than 2.5 microns (PM2.5) several hours in advance using data obtained in downtown Santiago, Chile. By performing some standard tests used in the study of dynamical systems, we are able to extract some features of the time series of data. We use this information to estimate the amount of data on the past to be used as input to a neural network in order to predict future values of PM2.5 concentrations. We show that improvement of predictions is possible by using another neural network for noise reduction on the original series. The best results are obtained with a type of neural network which is equivalent to a linear regression. Up to six hours in advance, predictions generated in this way have significantly smaller errors than predictions based on the persistence of the long term average of the data.