Extreme-ultraviolet interferometry at 15.5 nm using multilayer optics

The development of multilayer mirror technology capable of operating in the range of 3-30 nm and the construction of thin membranes with excellent uniformity and strength have made it possible to design and implement a Mach-Zehnder interferometer operating at 15.5 nm. We have tested this interferometer by using a soft x-ray laser as a source, and we show its use in probing high-density plasmas.     

Relating instance hardness to classification performance in a dataset: a visual approach

Machine Learning - Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The...     

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.     

Influence of deposition and annealing parameters on some properties of YBCO films prepared by spray pyrolysis

Superconducting YBCO films were prepared by chemical deposition of aerosol generated from nitrate solutions by pneumatic and ultrasonic atomizers. Single crystalline YSZ and MgO substrates were used. Two different substrate temperatures were applied, with the chemical composition of starting solutions being adjusted accordingly. Deposited precursor films were thermally processed under two basically different conditions, i.e., either in atmospheric O₂ atT900°C, or in atT600°C. The influence of these different deposition and annealing parameters onT _c andJ _c values, microstructure, and surface morphology is reported. By optimizing our technological procedure, a real possibility exists to prepare (by means of aerosol deposition) YBCO films withJ _c values of at least 10⁴–10⁵ A/cm², at processing temperatures (both deposition and annealing as well) not exceeding 600–700°C.     

Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling

In the classification framework there are problems in which the number of examples per class is not equitably distributed, formerly known as imbalanced data sets. This situation is a handicap when trying to identify the minority classes, as the learning algorithms are not usually adapted to such characteristics. An usual approach to deal with the problem of imbalanced data sets is the use of a preprocessing step. In this paper we analyze the usefulness of the data complexity measures in order to evaluate the behavior of undersampling and oversampling methods. Two classical learning methods, C4.5 and PART, are considered over a wide range of imbalanced data sets built from real data. Specifically, oversampling techniques and an evolutionary undersampling one have been selected for the study. We extract behavior patterns from the results in the data complexity space defined by the measures, coding them as intervals. Then, we derive rules from the intervals that describe both good or bad behaviors of C4.5 and PART for the different preprocessing approaches, thus obtaining a complete characterization of the data sets and the differences between the oversampling and undersampling results.     

An experiment for improving students performance in secondary and tertiary education by means of m-learning auto-assessment

Mobile learning is considered an evolution of e-learning that embraces the ubiquitous nature of current computational systems in order to improve teaching and learning. Within this context it is possible to develop mobile applications oriented to learning, but it is also important to assess to what extent such applications actually work. In this paper we present a new tool designed to reinforce students’ knowledge by means of self-assessment. Improvement in student achievement was evaluated and an attitudinal survey was also carried out to measure student attitudes towards this new tool. Three different experimental groups were selected for this research, with students aged from 14 to 21 years old, including high-school and university students. Results show that this kind of tool improves student achievement, especially amongst younger learners, with a relatively low impact on current teaching activities and methodology.     

Context-sensitive dependency pairs

Termination is one of the most interesting problems when dealing with context-sensitive rewrite systems. Although a good number of techniques for proving termination of context-sensitive rewriting (CSR) have been proposed so far, the adaptation to CSR of the dependency pair approach, one of the most powerful techniques for proving termination of rewriting, took some time and was possible only after introducing some new notions like collapsing dependency pairs, which are specific for CSR. In this paper, we develop the notion of context-sensitive dependency pair (CSDP) and show how to use CSDPs in proofs of termination of CSR. The implementation and practical use of the developed techniques yield a novel and powerful framework which improves the current state-of-the-art of methods for automatically proving termination of CSR.     

A quality evaluation methodology for health-related websites based on a 2-tuple fuzzy linguistic approach

Nowadays, the patients and physicians use the health-related websites as an important information source and, therefore, it is critical the quality evaluation of health- related websites. The quality assessment of health-related websites becomes especially relevant because their use imply the existence of a wide range of threats which can affect people’s health. Additionally, website quality evaluation can also contribute to maximize the exploitation of invested resources by organizations in the development of user-perceived quality websites. But there is not yet a clear and unambiguous definition of the concept of website quality and the debate about quality evaluation on the Web remains open. In this paper, we present a qualitative and user-oriented methodology for assessing quality of health-related websites based on a 2-tuple fuzzy linguistic approach. To identify the quality criteria set, a qualitative research has been carried out using the focus groups technique. The measurement method generates linguistic quality assessments considering the visitors’ judgements with respect to those quality criteria. The combination of the linguistic judgements is implemented without a loss of information by applying a 2-tuple linguistic weighted average operator. This methodology means an improvement on quality evaluation of health websites through the commitment to put users first.     

Differential evolution solution to the SLAM problem

A new solution to the Simultaneous Localization and Modelling problem is presented in this paper. The algorithm is based on the stochastic search for solutions in the state space to the global localization problem by means of a differential evolution algorithm. This non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) focuses on the most likely areas according to the perception and up to date motion information. The population evolves using the log-likelihood of each candidate pose according to the observation and the motion errors derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model.The proposed SLAM algorithm operates in two steps: in the first step the ELF filter is used at local level to re-localize the robot based on the robot odometry, the laser scan at a given position and a local map where only a low number of the last scans have been integrated. In the second step, the aligned laser measures and the corrected robot poses are used to detect whether the robot is revisiting a previously crossed area (i.e., a cycle in the robot trajectory exists). Once a cycle is detected, the Evolutive Localization Filter is used again to estimate the accumulated residual drift in the detected loop and then to re-estimate the robot poses in order to integrate the sensor measures in the global map of the environment.The algorithm has been tested in different environments to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.