Minimal Decision Rules Based on the A Priori Algorithm

Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensi- ble (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our ap- proach, in a post-processing stage, we apply the Apriori algorithm to reduce the decision rules obtained through rough sets. The set of dependencies thus obtained will help us discover irrelevant attribute values

Effect of replacing conventional Italian ryegrass by organic nitrogen source systems on chemical soil properties

Aim of study: To evaluate agronomic performance and changes on soil chemical properties in two types of managements: conventional or sustainable.Area of study: Principality of Asturias, Spain.Material and methods: On a sandy-clay-loam texture soil, three winter forage legumes (faba bean, red clover and white lupin), in monoculture or mixed with Italian ryegrass and with organic fertilization (sustainable management) versus Italian ryegrass in monoculture and inorganic fertilization (conventional management) were evaluated during three consecutive years. After the harvest in spring, the rotations were completed with maize crop with the purpose to evaluate the effect of the sustainable management on forage yield and soil chemical parameters.Main results: The results showed that faba bean and red clover in monoculture and mixed with Italian ryegrass had better edaphic quality than Italian ryegrass in monoculture, and white lupin in monoculture or mixed with Italian ryegrass. Faba bean in monoculture and mixed with Italian ryegrass, both with organic fertilization, could be competitive crops since both had yields comparable to Italian ryegrass in monoculture with inorganic fertilization.Research highlights: Current agricultural practice could be changed for a more sustainable management system, including organic fertilization and legume crops

Two Interventions to Improve Knowledge of Scientific and Dissemination Articles in First-Year University Students

The representations of science in mass media have shown a significant increase in the last years. However, mass media dissemination activities can extend to pseudoscience due to the fact that not all scientific news are published with the same rigour. Thus, we aimed to develop two theoretical-practical interventions among first-year university students with the purpose of improving their knowledge about scientific studies and original scientific sources, as well as to critically analyze dissemination of scientific research in media. The interventions had a positive impact on knowledge about scientific information sources, particularly Pubmed, in addition to reducing the number of incorrect features linked to both scientific and dissemination articles, suggesting the importance of interventions focused on misconceptions. However, students showed knowledge of correct features of scientific articles, independently of our intervention, and they made more mistakes when attributing incorrect features to scientific articles when compared to dissemination ones

Undergraduate research in the Faculty of Informatics of Madrid

To receive the degree in Computer Engineering, students of the Faculty of Informatics of the Polytechnical University of Madrid, must carry out a Project under the supervision of a professor. Such a Project, may consist of the resolution of a problem coming from the "real world", by using the adequate technics learned by the student, or, on the contrary, may be in fact the first research work carried out by the student, as a training for the further realization of a Ph. D. Thesis. In this paper, we describe several works of this kind, carried out under our supervision, pertaining to the field of Data and Knowledge Engineering

Functional aspects of databases: A research project within the framework of the European program HCM

The authors describe a program intended to help increase the human resources available for research and technological development which will be needed by the Member States of the European Community in the coming years. The main aim is the training of research scientists by mobility and the formation of networks. The program will cover all scientific and technological sectors, and areas of the social and human sciences that are able to improve European competitiveness, such as economic and management science, environmental economics, and the interconnections between science, technology, and society. The activities carried out under the program are: the development of a system of research fellowships, the creation and development of scientific and technical cooperation networks across all the regions of the Community, and the organization of a series of high level meetings at the cutting edge of scientific and technical knowledge

Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimers disease

Linear mixed effects (LME) modelling under both frequentist and Bayesian frameworks can be used to study longitudinal trajectories. We studied the performance of both frameworks on different dataset configurations using hippocampal volumes from longitudinal MRI data across groups-healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients, including subjects that converted from MCI to AD. We started from a big database of 1250 subjects from the Alzheimer's disease neuroimaging initiative (ADNI), and we created different reduced datasets simulating real-life situations using a random-removal permutation-based approach. The number of subjects needed to differentiate groups and to detect conversion to AD was 147 and 115 respectively. The Bayesian approach allowed estimating the LME model even with very sparse databases, with high number of missing points, which was not possible with the frequentist approach. Our results indicate that the frequentist approach is computationally simpler, but it fails in modelling data with high number of missing values

INTERGROWTH-21st versus a customized method for the prediction of neonatal nutritional status in hypertensive disorders of pregnancy

Background Hypertensive disorders of pregnancy (HDP) generate complications and are one of the principal causes of maternal, foetal, and neonatal mortality worldwide. It has been observed that in pregnancies with HDP, the incidence of foetuses small for their gestational age (SGA) is twice as high as that in noncomplicated pregnancies. In women with HDP, the identification of foetuses (SGA) is substantially important, as management and follow-up are determined by this information. Objective The objective of this study was to evaluate whether the INTERGROWTH-21st method or customized birthweight references better identify newborns with an abnormal nutritional status resulting from HDP. Method A comparative analysis study was designed with two diagnostic methods for the prediction of neonatal nutritional status in pregnancies with HDP. The performance of both methods in identifying neonatal malnutrition (defined by a neonatal body mass index < 10(th) centile or a ponderal index < 10(th) centile) was assessed by calculating sensitivity, specificity, positive predictive value, negative predictive value, diagnostic odds ratio, Youden's index and probability ratios. Results The study included 226 pregnant women diagnosed with HDP. The customized method identified 45 foetuses as small for gestational age (19.9%), while the INTERGROWTH-21st method identified 27 newborns with SGA (11.9%). The difference between proportions was statistically significant (p < 0.01). Using body mass index (< 10(th) centile) as a measure of nutritional status, newborns identified as SGA by the customized method showed a higher risk of malnutrition than those identified as SGA by INTERGROWTH-21st (RR: 4.87 (95% CI: 1.86-12.77) vs. 3.75 (95% CI: 1.49-9.43)) (DOR: 5.56 (95% CI: 1.82-16.98) vs. 4.84 (95% CI: 1.51-15.54)) Even when using Ponderal index (< 10(th) centile), newborns identified as SGA by the customized method showed a higher risk of malnutrition than those identified as SGA by INTERGROWTH-21st (RR 2.37 (95% CI: 1.11-5.05) vs. 1.68 (95% CI: 0.70-4.03))(DOR 2.62 (95% CI: 1.00-6.87) vs. 1.90 (95% CI: 0.61-5.92)). Conclusion In pregnant women with HDP, the predictive ability of the customized foetal growth curves to identify neonatal malnutrition appears to surpass that of INTERGROWTH-21st