GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models

Expression and trans-specific polymorphism of self-incompatibility RNases in Coffea (Rubiaceae)

Self-incompatibility (SI) is widespread in the angiosperms, but identifying the biochemical components of SI mechanisms has proven to be difficult in most lineages. Coffea (coffee; Rubiaceae) is a genus of old-world tropical understory trees in which the vast majority of diploid species utilize a mechanism of gametophytic self-incompatibility (GSI). The S-RNase GSI system was one of the first SI mechanisms to be biochemically characterized, and likely represents the ancestral Eudicot condition as evidenced by its functional characterization in both asterid (Solanaceae, Plantaginaceae) and rosid (Rosaceae) lineages. The S-RNase GSI mechanism employs the activity of class III RNase T2 proteins to terminate the growth of "self" pollen tubes. Here, we investigate the mechanism of Coffea GSI and specifically examine the potential for homology to S-RNase GSI by sequencing class III RNase T2 genes in populations of 14 African and Madagascan Coffea species and the closely related self-compatible species Psilanthus ebracteolatus. Phylogenetic analyses of these sequences aligned to a diverse sample of plant RNase T2 genes show that the Coffea genome contains at least three class III RNase T2 genes. Patterns of tissue-specific gene expression identify one of these RNase T2 genes as the putative Coffea S-RNase gene. We show that populations of SI Coffea are remarkably polymorphic for putative S-RNase alleles, and exhibit a persistent pattern of trans-specific polymorphism characteristic of all S-RNase genes previously isolated from GSI Eudicot lineages. We thus conclude that Coffea GSI is most likely homologous to the classic Eudicot S-RNase system, which was retained since the divergence of the Rubiaceae lineage from an ancient SI Eudicot ancestor, nearly 90 million years ago.United States National Science Foundation [0849186]; Society of Systematic Biologists; American Society of Plant Taxonomists; Duke University Graduate Schoolinfo:eu-repo/semantics/publishedVersio

Self- and peer assessment may not be an accurate measure of PBL tutorial process

<p>Abstract</p> <p>Background</p> <p>Universidade Cidade de São Paulo adopted a problem-based learning (PBL) strategy as the predominant method for teaching and learning medicine. Self-, peer- and tutor marks of the educational process are taken into account as part of the final grade, which also includes assessment of content. This study compared the different perspectives (and grades) of evaluators during tutorials with first year medical students, from 2004 to 2007 (n = 349), from seven semesters.</p> <p>Methods</p> <p>The tutorial evaluation method was comprised of the students' self assessment (SA) (10%), tutor assessment (TA) (80%) and peer assessment (PA) (10%) to calculate a final educational process grade for each tutorial. We compared these three grades from each tutorial for seven semesters using ANOVA and a post hoc test.</p> <p>Results</p> <p>A total of 349 students participated with 199 (57%) women and 150 (42%) men. The SA and PA scores were consistently greater than the TA scores. Moreover, the SA and PA groups did not show statistical difference in any semester evaluated, while both differed from tutor assessment in all semesters (Kruskal-Wallis, Dunn's test). The Spearman rank order showed significant (p < 0.0001) and positive correlation for the SA and PA groups (r = 0.806); this was not observed when we compared TA with PA (r = 0.456) or TA with SA (r = 0.376).</p> <p>Conclusion</p> <p>Peer- and self-assessment marks might be reliable but not valid for PBL tutorial process, especially if these assessments are used for summative assessment, composing the final grade. This article suggests reconsideration of the use of summative assessment for self-evaluation in PBL tutorials.</p

A 3D Human Posture Approach for Activity Recognition Based on Depth Camera

Human activity recognition plays an important role in the context of Ambient Assisted Living (AAL), providing useful tools to improve people quality of life. This work presents an activity recognition algorithm based on the extraction of skeleton joints from a depth camera. The system describes an activity using a set of few and basic postures extracted by means of the X-means clustering algorithm. A multi-class Support Vector Machine, trained with the Sequential Minimal Optimization is employed to perform the classification. The system is evaluated on two public datasets for activity recognition which have different skeleton models, the CAD-60 with 15 joints and the TST with 25 joints. The proposed approach achieves precision/recall performances of 99.8 % on CAD-60 and 97.2 %/91.7 % on TST. The results are promising for an applied use in the context of AAL