Use of natural clinoptilolite for the removal of lead, copper and zinc in fixed bed column

This work deals with the removal of lead, copper and zinc from aqueous solutions by using natural zeolite (clinoptilolite). Fixed bed experiments were performed, using three different volumetric flow rates of 5, 7 and 10bed volume/h, under a total normality of 0.01N, at initial pH of 4 and ambient temperature (25 degrees C). The removal efficiency increased when decreasing the flow rate and the following selectivity series was found: Pb(2+)>Zn(2+)> or =Cu(2+). Conductivity measurements showed that lead removal follows mainly ion exchange mechanism, while copper and zinc removal follows ion exchange and sorption mechanism as well.     

Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases

Spatio-temporal databases deal with geometries changing over time. In general, geometries cannot only change in discrete steps, but continuously, and we are talking about moving objects. If only the position in space of an object is relevant, then moving point is a basic abstraction; if also the extent is of interest, then the moving region abstraction captures moving as well as growing or shrinking regions. We propose a new line of research where moving points and moving regions are viewed as 3-D (2-D space+time) or higher-dimensional entities whose structure and behavior is captured by modeling them as abstract data types. Such types can be integrated as base (attribute) data types into relational, object-oriented, or other DBMS data models; they can be implemented as data blades, cartridges, etc. for extensible DBMSs. We expect these spatio-temporal data types to play a similarly fundamental role for spatio-temporal databases as spatial data types have played for spatial databases. The paper explains the approach and discusses several fundamental issues and questions related to it that need to be clarified before delving into specific designs of spatio- temporal algebras.     

Greedy learning of multiple objects in images using robust statistics and factorial learning

We consider data that are images containing views of multiple objects. Our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image must be explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a method to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion, and present results showing successful extraction of objects from real images.     

Lactose uptake rate measurements by C-labelled lactose reveals promotional activity of porous cellulose in whey fermentation by kefir yeast

Lactose uptake rate by kefir yeast, immobilized on tubular cellulose and gluten pellets during fermentation of lactose and whey, was monitored using ¹⁴C-labelled lactose. Results illustrated that, in all cases, lactose uptake rate was strongly correlated with fermentation rate and the fermentation’s kinetic parameters were improved by kefir yeast entrapped in tubular cellulose. As a result, twofold faster fermentations were achieved in comparison with kefir yeast immobilized on gluten. This is probably due to cluster and hydrogen bonds formation between cellulose and inhibitors, such as Ca⁺⁺ and generated lactic acid, by which they leave the liquid medium. The findings, regarding the promotional effect of cellulose, seem promising for application in industrial whey fermentations.     

Low-temperature thermal drying of Saccharomyces cerevisiae starter culture for food production

The production of low-temperature thermally-dried cells of Saccharomyces cerevisiae at 32 °C is examined in the present investigation. Cells are obtained in a thin layer after heating for 4 h in an incubator. This thermally-dried starter culture showed acceptable fermentation efficiency, proved by repeated batch fermentations of glucose. The starter culture, when compared with freeze-dried cells, showed better fermentation ability. Storage of thermally-dried S. cerevisiae for one month showed resistance to loss of fermentation efficiency. The chemical composition of volatiles produced during fermentation were similar to those obtained with freeze-dried and wet starter culture fermentations. The thermally-dried cells effectively ferment at low temperatures.     

α‐Alumina nanospheres from nano‐dispersed boehmite synthesized by a wet chemical route

In this work, stable aqueous suspensions of nano‐boehmite were developed through a hybrid wet‐chemical route that uses hyperbranched dendritic poly(ethylene)imine (PEI) as template material for boehmite formation aiming at the development of a deagglomerated α‐alumina nanopowder after calcination. The method involves firstly the interaction between the Al precursor and PEI followed by the hydrolysis and polycondensation reactions. The study was aiming to investigate the effect of solids content and pH during reactions on both the stability of the final suspension and the morphology of the resulting nanocrystals. For this purpose, the suspensions were evaluated through viscosity measurements, zeta potential analyses, FT‐IR, DLS and sedimentation studies, whereas after the proper centrifugation, drying, and calcination steps, the as‐received nanocrystals were evaluated through SEM, TEM and XRD studies. In addition, the boehmite nanopowder was studied using Thermogravimetric and Differential Thermal Analysis, whereas its sinterability was evaluated by dilatometric measurements. The investigation showed that the conditions employed affect greatly both the morphology of nanocrystals as well as the dispersion and the stability of the suspensions. The boehmite suspension with the optimum dispersion and stability can lead, after calcination at 1050°C, to a fine deagglomerated α‐alumina nanopowder with a mean size at about 10 nm.