Staphylococcal growth and enterotoxin production in the presence of meat cultures (non LAB)

The effect of meat cultures (non lactic acid bacteria) on the growth and production of enterotoxins and thermonuclease by Staphylococcus aureus was studied. Micrococcus varians did not affect growth nor the synthesis of metabolites. Levels of enterotoxins A, B and D produced by the respective S. aureus strains were reduced by S. xylosus, S. saprophyticus and S. carnosus. The two latter species prevented production of enterotoxin C₁ and S. xylosus markedly reduced the amount produced. The three coagulase-negative staphylococci showed little inhibitory effect on the growth of S. aureus. Penicillium nalgiovense did not show inhibitory activity against the four S. aureus strains. Debaryomyces hansenii slightly inhibited growth of the enterotoxin A-producing strain, but reduced enterotoxin synthesis at 30 °C. Thermonuclease was detected whenever enterotoxins were detected though the influence of the effector organism was dependent on the test strain.     

Fractional-Fourier-transform calculation through the fast-Fourier-transform algorithm

A method for the calculation of the fractional Fourier transform (FRT) by means of the fast Fourier transform (FFT) algorithm is presented. The process involves mainly two FFT's in cascade; thus the process has the same complexity as this algorithm. The method is valid for fractional orders varying from -1 to 1. Scaling factors for the FRT and Fresnel diffraction when calculated through the FFT are discussed.     

Endoscopic sclerotherapy is useful in Dieulafoy''s disease

1.5% Capsaicin (Cap) or Vehicle was respectively used to treat the right or left sciatic nerve in 20 Sprague-Dawley rats. On the seventh day, the 20 rats were at random divided into electroacupuncture (EA) group and non-EA group, the spinal cord corresponding to the afferent segments of sciatic nerve was taken out for observing the changes of acetylcholinesterase (AChE) activity and [3H]-quinuelidinylbenzylate (QNB) binding sites in the spinal dorsal horn (SDH). The results were as follows: (1) EA "Huantiao" could enhance AChE activity in the SDH and decrease [3H]-QNB binding sites; (2) Cap treating sciatice nerve could weaken AChE activity in the SDH and merease [3H]-QNB binding sites; (3) Cap treatment could inhibit or partially inhibit the actions of EA as above. The results indicated that ACh participated in the primary afferent of acupuncture information and might exist in Cap-sensitive neurons.     

A Non-Uniform Predictor-Observer for a Networked Control System

International Journal of Control, Automation and Systems - This paper presents a Non-Uniform Predictor-Observer (NUPO) based control approach in order to deal with two of the main problems related...     

Solving identity delegation problem in the e-government environment

At present, many countries allow citizens or entities to interact with the government outside the telematic environment through a legal representative who is granted powers of representation. However, if the interaction takes place through the Internet, only primitive mechanisms of representation are available, and these are mainly based on non-dynamic offline processes that do not enable quick and easy identity delegation. This paper proposes a system of dynamic delegation of identity between two generic entities that can solve the problem of delegated access to the telematic services provided by public authorities. The solution herein is based on the generation of a delegation token created from a proxy certificate that allows the delegating entity to delegate identity to another on the basis of a subset of its attributes as delegator, while also establishing in the delegation token itself restrictions on the services accessible to the delegated entity and the validity period of delegation. Further, the paper presents the mechanisms needed to either revoke a delegation token or to check whether a delegation token has been revoked. Implications for theory and practice and suggestions for future research are discussed.     

Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly being produced in many fields of research. Although current algorithms are useful for fairly large datasets, scaling problems are found when the number of instances is in the hundreds of thousands or millions. When we face huge problems, scalability becomes an issue, and most algorithms are not applicable.Thus, paradoxically, instance selection algorithms are for the most part impracticable for the same problems that would benefit most from their use. This paper presents a way of avoiding this difficulty using several rounds of instance selection on subsets of the original dataset. These rounds are combined using a voting scheme to allow good performance in terms of testing error and storage reduction, while the execution time of the process is significantly reduced. The method is particularly efficient when we use instance selection algorithms that are high in computational cost. The proposed approach shares the philosophy underlying the construction of ensembles of classifiers. In an ensemble, several weak learners are combined to form a strong classifier; in our method several weak (in the sense that they are applied to subsets of the data) instance selection algorithms are combined to produce a strong and fast instance selection method.An extensive comparison of 30 medium and large datasets from the UCI Machine Learning Repository using 3 different classifiers shows the usefulness of our method. Additionally, the method is applied to 5 huge datasets (from three hundred thousand to more than a million instances) with good results and fast execution time.     

Students’ expectations of,and experiences in e-learning: Their relation to learning achievements and course satisfaction

Within only a few years, the use of e-learning has increased rapidly in Austria. In certain subjects, up to 60% of university students report using e-learning platforms at least ‘sometimes’ or ‘frequently’ (Unger & Wroblewski, 2006). Yet, which aspects of e-learning do students consider important for their learning achievements and course satisfaction? This question was addressed by surveying 2196 students from 29 universities in Austria about their expectations of, and experiences in e-learning. Multiple regression analyses using Mplus 4.21 were carried out to investigate how different facets of students’ expectations and experiences are related to perceived learning achievements and course satisfaction.     

SENFIS: a Sensor Node File System for increasing the scalability and reliability of Wireless Sensor Networks applications

In the last years the Wireless Sensor Networks’ (WSN) technology has been increasingly employed in various application domains. The extensive use of WSN posed new challenges in terms of both scalability and reliability. This paper proposes Sensor Node File System (SENFIS), a novel file system for sensor nodes, which addresses both scalability and reliability concerns. SENFIS can be mainly used in two broad scenarios. First, it can transparently be employed as a permanent storage for distributed TinyDB queries, in order to increase the reliability and scalability. Second, it can be directly used by a WSN application for permanent storage of data on the WSN nodes. The experimental section shows that SENFIS implementation makes an efficient use of resources in terms of energy consumption, memory footprint, flash wear levelling, while achieving execution times similarly with existing WSN file systems.     

An adaptive, real-time, traffic monitoring system

In this paper we describe a computer vision-based traffic monitoring system able to detect individual vehicles in real-time. Our fully integrated system first obtains the main traffic variables: counting, speed and category; and then computes a complete set of statistical variables. The objective is to investigate some of the difficulties impeding existing traffic systems to achieve balanced accuracy in every condition; i.e. day and night transitions, shadows, heavy vehicles, occlusions, slow traffic and congestions. The system we present is autonomous, works for long periods of time without human intervention and adapts automatically to the changing environmental conditions. Several innovations, designed to deal with the above circumstances, are proposed in the paper: an integrated calibration and image rectification step, differentiated methods for day and night, an adaptive segmentation algorithm, a multistage shadow detection method and special considerations for heavy vehicle identification and treatment of slow traffic. A specific methodology has been developed to benchmark the accuracy of the different methods proposed.     

Two-stage cascaded classification approach based on genetic fuzzy learning for speech/music discrimination

Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents a robust and effective approach for speech/music discrimination, which relies on a two-stage cascaded classification scheme. The cascaded classification scheme is composed of a statistical pattern recognition classifier followed by a genetic fuzzy system. For the first stage of the classification scheme, other widely used classifiers, such as neural networks and support vector machines, have also been considered in order to assess the robustness of the proposed classification scheme. Comparison with well-proven signal features is also performed. In this work, the most commonly used genetic learning algorithms (Michigan and Pittsburgh) have been evaluated in the proposed two-stage classification scheme. The genetic fuzzy system gives rise to an improvement of about 4% in the classification accuracy rate. Experimental results show the good performance of the proposed approach with a classification accuracy rate of about 97% for the best trial.