Effect of minerals on casein micelle stability of cows' milk

The effects of minerals on casein micelle stability of individual cows' milk, throughout a complete lactation, were investigated. Calcium and calcium ions, magnesium, phosphorus, sodium, potassium and citrate contents were analysed, together with the following physical properties of milk; pH, ethanol stability, rennet clotting time and coagulum firmness. There was an inverse non-linear relationship between free calcium ion concentration and ethanol stability (ES; r=0.84). Rennet coagulation time showed a weaker relationship with free calcium ion concentration (r=0.44) but a stronger relationship with pH (r=0.66). In addition, samples containing higher amounts of free calcium ions produced a firmer gel. Citrate in natural samples acts as a stabilizing factor, as it slightly improves milk stability. Potassium, on the other hand, exhibited a negative correlation, but only with rennet clotting time (r=-0.52). Throughout lactation the average values were; free Ca2+ concentration 1.88 mM, pH 6.63, ES 83.2% and clotting time 13.6 min. The equilibrium relationship between pH and free Ca2+ concentration was investigated by adjusting milk pH from 5.9 to 7.1, using acid and alkali. There was a good inverse linear relationship between pH and log (free Ca2+) for individual milk samples, with a gradient of -0.62 and a standard deviation of 0.042.     

BDD anodic oxidation as tertiary wastewater treatment for the removal of emerging micro‐pollutants,pathogens and organic matter

This work reports for the first time the removal of 17α‐ethynylestradiol (EE2), a synthetic estrogen hormone, from secondary treated effluents by electrochemical oxidation. Experiments were conducted in a single compartment reactor comprising a boron‐doped diamond (BDD) anode and a zirconium cathode. EE2, in the range 100–800 µg L^?1, was spiked in the post‐chlorination effluent of a municipal treatment plant and oxidized at 0.9–2.6 mA cm^?2 current density. Complete degradation of 100 µg L^?1 EE2 was achieved in 7 min at 2.1 mA cm^?2 and inherent conditions, while the addition of 0.1 mol L^?1 NaCl achieved removal in just a few seconds. The process was then tested in the pre‐chlorination effluent at 2.1 mA cm^?2 and inherent conditions; complete E. coli killing and EE2 removal occurred in just 1.5 and 3.5 min, respectively, while overall estrogenicity (assessed by the YES assay) and residual organic matter (in terms of chemical oxygen demand (COD)) decreased by 50% and 85% after 30 min, respectively. These results clearly show the potential of BBD electrochemical oxidation to serve as an efficient tertiary wastewater treatment. Copyright © 2011 Society of Chemical Industry     

eInvoke: Secure e-Invoicing based on web services

Electronic Invoicing services (e-Invoicing) will have a pivotal role in all the stages of handling Value Added Tax (VAT) for European Member States. Through a systematic introduction of e-invoicing, tax administrators will be able to implement new tools and procedures to carry out alternative controls that are less intrusive on the trading partners. Nevertheless, successful European e-invoicing implementations need to be in compliance with the corresponding European Directive 2001/115/EC. Most contemporary e-Invoicing implementations are proprietary and based on EDI, thus demonstrating great deficiencies. This paper presents an open electronic invoicing system named eInvoke, based on XML, XML cryptography and Web Services, that addresses all security requirements imposed by the Directive. Our e-Invoicing system has been accepted by “CEN/ISSS e-Invoicing Focus Group on Standards and Developments on electronic invoicing relating to VAT Directive 2001/115/EC” and its summary appears in CEN 2003 report [13] (pg 79–85), forwarded to EC as a recommendation. Alexandros Kaliontzoglou holds a Degree in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece. Since 2001 he is a PhD candidate in the area of Network and Information Systems Security at the Telecommunications laboratory in the School of Electrical and Computer Engineering of NTUA. Since April 2000 he has been working for Expertnet S.A. as a security engineer specializing at Web Technologies and network applications, and he has been active both in European research projects in the 5th and 6th Framework Programme (eMayor, Intelcities, SELIS, Reshen, La Mer, TSEC, WebSig) and projects of the Greek private sector. His research interests focus in the area of IT Security, Service Oriented Architectures, Web Services, Software Engineering, e-Government, e-Commerce and Public Key Infrastructures. Pelagia Boutsi has obtained the Degree in Informatics from the University of Piraeus, Greece, in November 2001. Since April 2002, she is a PhD candidate in the area of Security Information at the Computer Science Department of University of Piraeus. Since September 2001 she is employed at Expertnet S.A. as member of the Technical Department. Her current research interests are in the fields of PKIs, XML and XML Security. She has participated in European research projects and projects of the private sector. Despina Polemi has obtained the Degree in Applied Mathematics from Portland State University(USA) in 1984, Ph.D. in Applied Mathematics (Coding Theory) from City University of New York (Graduate Center) in 1991. She held teaching positions (1984– 1995) in Queens College and Baruch College of City University of NewYork. From 1991 to 1996 was assistant professor (tenure track) in State University of New Yorkat Farmingdale in the department of Mathematics. During 1996–2002 she was an associate researcher in ICCS. From 2000 to 2003 she acted as President of the BoD in a security consulting company Expertnet (www.expertnet.net.gr) and technical manager of the company from 2000–2004. She nowa Professor in the University of Piraeus R&D department. Her current research interests are in the fields of cryptology, security and e-business. She has over ninety publications in the above areas. She has received many research grants from various organizations such as the Danish Research Foundation, MSI Army Research Office/Cornell University, IEEE, State University of New York (SUNY), and The Graduate School of City University of New York (CUNY). She has been project manager (PM)/technical manger (TM) in security projects of various programmes such as National Security Agency (NSA), Dr. Nuala McGann Drescher Foundation, Greek Ministry of Defense, INFOSEC TELEMATICS for Administrations (COSACC), the Fifth Framework IST Programme (HARP, BEE, SEED, WebSig, TSEC, CORAS, RESHEN, SEED, La Mer, SECRETS) and the 6FP (e-Mayor, Intelcities, BIOSEC, SELIS). She participated in the EC security projects of the programs COST, ACTS, and NATOs’ security projects. She is a member of IEEE. She serves as an evaluator, reviewer and expert in the European Commission and consultant for the FP6.     

Aerosol lidar intercomparison in the framework of the EARLINET project. 3. Raman lidar algorithm for aerosol extinction, backscatter, and lidar ratio

An intercomparison of the algorithms used to retrieve aerosol extinction and backscatter starting from Raman lidar signals has been performed by 11 groups of lidar scientists involved in the European Aerosol Research Lidar Network (EARLINET). This intercomparison is part of an extended quality assurance program performed on aerosol lidars in the EARLINET. Lidar instruments and aerosol backscatter algorithms were tested separately. The Raman lidar algorithms were tested by use of synthetic lidar data, simulated at 355, 532, 386, and 607 nm, with realistic experimental and atmospheric conditions taken into account. The intercomparison demonstrates that the data-handling procedures used by all the lidar groups provide satisfactory results. Extinction profiles show mean deviations from the correct solution within 10% in the planetary boundary layer (PBL), and backscatter profiles, retrieved by use of algorithms based on the combined Raman elastic-backscatter lidar technique, show mean deviations from solutions within 20% up to 2 km. The intercomparison was also carried out for the lidar ratio and produced profiles that show a mean deviation from the solution within 20% in the PBL. The mean value of this parameter was also calculated within a lofted aerosol layer at higher altitudes that is representative of typical layers related to special events such as Saharan dust outbreaks, forest fires, and volcanic eruptions. Here deviations were within 15%.     

Stability of feature selection algorithms: a study on high-dimensional spaces

With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally, we show how stability profiles can support the choice of a feature selection algorithm. Alexandros Kalousis received the B.Sc. degree in computer science, in 1994, and the M.Sc. degree in advanced information systems, in 1997, both from the University of Athens, Greece. He received the Ph.D. degree in meta-learning for classification algorithm selection from the University of Geneva, Department of Computer Science, Geneva, in 2002. Since then he is a Senior Researcher in the same university. His research interests include relational learning with kernels and distances, stability of feature selection algorithms, and feature extraction from spectral data. Julien Prados is a Ph.D. student at the University of Geneva, Switzerland. In 1999 and 2001, he received the B.Sc. and M.Sc. degrees in computer science from the University Joseph Fourier (Grenoble, France). After a year of work in industry, he joined the Geneva Artificial Intelligence Laboratory, where he is working on bioinformatics and datamining tools for mass spectrometry data analysis. Melanie Hilario has a Ph.D. in computer science from the University of Paris VI and currently works at the University of Geneva’s Artificial Intelligence Laboratory. She has initiated and participated in several European research projects on neuro-symbolic integration, meta-learning, and biological text mining. She has served on the program committees of many conferences and workshops in machine learning, data mining, and artificial intelligence. She is currently an Associate Editor of theInternational Journal on Artificial Intelligence Toolsand a member of the Editorial Board of theIntelligent Data Analysis journal.     

Approximate analysis of serial flow lines with multiple parallel-machine stations

This paper studies serial flow lines, in which each station consists of multiple identical reliable parallel machines. The parallel machines of different work stations are not necessarily identical, viz., station processing times are assumed to be exponentially distributed with non-identical mean service rates. Initially, a model consisting of two stations with multiple parallel machines and an intermediate buffer is solved analytically, by developing a recursive algorithm that generates the transition matrix for any value of the intermediate buffer capacity. This model is used as a decomposition block for solving larger lines. More specifically, the decomposition block is solved via exact Markovian analysis and then the decomposition equations and an algorithm that simultaneously solves them are derived in order to evaluate the performance measures of large production systems with multiple parallel-machine stations. Numerical results are provided for large production lines with up to 1000 workstations. These results are compared against simulation and the average percentage error is found to be very small.     

Dynamic action recognition based on dynemes and Extreme Learning Machine

In this paper, we propose a novel method that performs dynamic action classification by exploiting the effectiveness of the Extreme Learning Machine (ELM) algorithm for single hidden layer feedforward neural networks training. It involves data grouping and ELM based data projection in multiple levels. Given a test action instance, a neural network is trained by using labeled action instances forming the groups that reside to the test sample’s neighborhood. The action instances involved in this procedure are, subsequently, mapped to a new feature space, determined by the trained network outputs. This procedure is performed multiple times, which are determined by the test action instance at hand, until only a single class is retained. Experimental results denote the effectiveness of the dynamic classification approach, compared to the static one, as well as the effectiveness of the ELM in the proposed dynamic classification setting.     

Silhouette-based human action recognition using sequences of key poses

In this paper, a human action recognition method is presented in which pose representation is based on the contour points of the human silhouette and actions are learned by making use of sequences of multi-view key poses. Our contribution is twofold. Firstly, our approach achieves state-of-the-art success rates without compromising the speed of the recognition process and therefore showing suitability for online recognition and real-time scenarios. Secondly, dissimilarities among different actors performing the same action are handled by taking into account variations in shape (shifting the test data to the known domain of key poses) and speed (considering inconsistent time scales in the classification). Experimental results on the publicly available Weizmann, MuHAVi and IXMAS datasets return high and stable success rates, achieving, to the best of our knowledge, the best rate so far on the MuHAVi Novel Actor test.