Comparison between Ozonation and Photo-Fenton Processes for Pesticide Methomyl Removal in Advanced Greenhouses

So-called “Advanced Greenhouses” are a new approach to the concept of protected agriculture. Among other technological and structural improvements, these facilities give the possibility of recycling the irrigation surplus water, rich in lixiviates, salts, pesticides and its metabolites. After many cycles, the current is so concentrated on those substances that it becomes necessary for the presence of a membrane separation stage which brine, highly concentrated on those named pollutants, has to be treated before being sent to the public sewage system. Advanced Oxidation Processes, among other chemical treatments, can be considered an alternative to process this current effluent. In this work, concentrated aqueous solutions of methomyl as model pesticide (200 mg·L^?1) have been subjected to two of those processes: ozonation and photo-Fenton reaction. Analysis of the elimination of the pesticide itself and the grade of mineralization achieved have shown how, while the ozonation is the most effective process decomposing the pesticide (eliminating the total concentration in 60 minutes), the photo-Fenton reaction mineralizes successfully the 40% of the total organic load (the ozonation only can cope with 20%) but only decompose a 40% of the pesticide. Evolution of biodegradability and toxicity of the effluent along both processes was also analyzed. Intermediates generated both by ozonation and photo-Fenton did not increase the biodegradability of the treated effluents. Nevertheless, while acute toxicity just after 15 minutes of treatment with ozone is notably higher than for raw solution, and it is maintained till the end of the experiment (120 min), though, toxicity along photo-Fenton reaction has two growing and decreasing regions, always shows lower values than the provoked during ozonation. None of the two assayed processes has been proved to increase biocompatibility of highly concentrated methomyl solutions.     

GAssist vs. BioHEL: critical assessment of two paradigms of genetics-based machine learning

This paper reports an exhaustive analysis performed over two specific Genetics-based Machine Learning systems: BioHEL and GAssist. These two systems share many mechanisms and operators, but at the same time, they apply two different learning paradigms (the Iterative Rule Learning approach and the Pittsburgh approach, respectively). The aim of this paper is to: (a) propose standard configurations for handling small and large datasets, (b) compare the two systems in terms of learning capabilities, complexity of the obtained solutions and learning time, (c) determine the areas of the problem space where each one of these two systems performs better, and (d) compare them with other well-known machine learning algorithms. The results show that it is possible to find standard configurations for both systems. With these configurations the systems perform up to the standards of other state-of-the-art machine learning algorithms such as Support Vector Machines. Moreover, we identify the problem domains where each one of these systems have advantages and disadvantages and propose ways to improve the systems based on this analysis.     

Insight from data analytics with an automotive aftermarket SME

There is enormous general interest in the automotive sector, and there are many sets of informative data openly available to the public. Companies involved in the automotive aftermarket sector have access to further masses of data, and this can be analysed to provide valuable insights to complement those obtainable from the internet and popular press, discovering hidden knowledge and financial benefit within the data by using statistical analysis, big data analytics, and data science. This article gives examples of insight derived from data giving great value to stakeholders in the sector. The examples are followed by a discussion of the issues involved in applying data science and ensuring that solutions are implemented and are sustainable within the business partner companies.     

Prediction of topological contacts in proteins using learning classifier systems

Evolutionary based data mining techniques are increasingly applied to problems in the bioinformatics domain. We investigate an important aspect of predicting the folded 3D structure of proteins from their unfolded residue sequence using evolutionary based machine learning techniques. Our approach is to predict specific features of residues in folded protein chains, in particular features derived from the Delaunay tessellations, Gabriel graphs and relative neighborhood graphs as well as minimum spanning trees. Several standard machine learning algorithms were compared to a state-of-the-art learning method, a learning classifier system (LCS), that is capable of generating compact and interpretable rule sets. Predictions were performed for various degrees of precision using a range of experimental parameters. Examples of the rules obtained are presented. The LCS produces results with good predictive performance and generates competent yet simple and interpretable classification rules.     

KEEL: a software tool to assess evolutionary algorithms for data mining problems

This paper introduces a software tool named KEEL which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational. Supported by the Spanish Ministry of Science and Technology under Projects TIN-2005-08386-C05-(01, 02, 03, 04 and 05). The work of Dr. Bacardit is also supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant GR/T07534/01.     

Coupled photochemical-biological system to treat biorecalcitrant wastewater.

The aim of the present work is to study a coupled system to treat biorecalcitrant wastewaters. The combination consists of an advanced oxidation process (AOP) named photo-Fenton (Ph-F), which is a photochemical treatment and a sequencing batch biofilter reactor (SBBR). The synthetic wastewater used to optimise this process is a solution of 200 ppm of 4-chlorophenol (4-CP). The first part of the work is the study of the biodegradability enhancement achieved by the photochemical process, measured as the ratio between the biochemical oxygen demand (BOD5) and the chemical oxygen demand (COD). The second step is the start-up and optimisation of the biological process. The results showed that it is necessary to severely treat the toxic solution (with 500 ppm of [H2O2]0) in order to achieve more than 90% of TOC removal in the whole process. The photochemical and biological treatments lasted 50 minutes and 24 hours, respectively.     

Synthesis of δ-lactonic pheromones ofXylocopa hirsutissima andVespa orientalis and an allomone of some ants of genusCamponotus

Simple preparations ofcis-3,6-dimethyltetrahydro-2-pyrone, VII,6-n-undecyltetrahydro-2-pyrone, XVIIb, and 6-n-pentyl-5,6-dihydro-2-pyrone, XVIa, have been achieved. Products VII and XVIIb, respectively, are the major constituent of the pheromonal blend of a carpenter bee (Xylocopa hirsutissima) and the pheromone of the queens of the Oriental hornet (Vespa orientalis). The lactone XVIa is a suspected defensive allomone in two species of formicine ants of the genusCamponotus. All three compounds have been prepared from dehydroacetic acid, I, a cheap and industrially available starting material.