Abstract
Nowadays, and considering flexibility, industrial robots still present some drawback that prevent them to be used in vast fields of the industry. One of their major limitations is related with their perception skills. In this area, and although the many developments verified on 3D object recognition systems in the research sphere, the number of solutions appearing in the industry level has been slow. Hence, this article tries to clarify some of the motives that difficult the technology transference (in what concerns object recognition) between both worlds. At the same time, it will be presented an industrial case scenario (inserted in an European Project) where some of the problems enumerated during the article are present.

Abstract
Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes - which is most clearly seen in blood - though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.

Abstract

Abstract
The idea behind the present work is the application of a Mixed Integer Linear Programming (MILP) model for the reconfiguration and performance analysis of weakly-meshed distribution networks with emphasis on the variability of demand and the integration of wind power generation at specific buses. The ultimate goal is the minimization of active power losses for the corresponding optimal topology in the short-term operation considering key distribution parameters and power factor constraints. A distribution system case study with twenty wind scenarios is considered to evaluate the impact of wind integration in distribution networks. The effectiveness of a simplified linearized model for the study of active power losses and the impacts of wind generation and periodic demand variation on node voltages, branch currents and grid reconfigurations under normal operation conditions is illustrated by the case study. Issues resulting from solving a 25-bus case study are presented and discussed.

Abstract
This letter presents experimental results of a refractive index sensor using a bent optical fiber taper. The approach of this sensor is based on an in-line Michelson interferometer implemented with a single mode tapered fiber with a cleaved tip end and changing tilt angle, enabling to tune its refractive index sensitivity. Several radii of curvature are tested and their refractive index sensitivities are analyzed for a refractive index range between 1.333 and 1.405. A clear enhancement of the sensor response is achieved at specific taper bending radii. A substantial improvement in the refractive index sensitivity, at values very close to distilled water, is obtained with a radius of curvature of 11 mm. A significant enhancement of the sensor response is also achieved for a refractive index close to 1.40 with a radius of curvature of 16.5 mm. (c) 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:921-924, 2015

Abstract
The ultimate goal of feature selection is to select the smallest subset of features that yields minimum generalization error from an original set of features. This effectively reduces the feature space, and thus the complexity of classifiers. Though several algorithms have been proposed, no single one outperforms all the other in all scenarios, and the problem is still an actively researched field. This paper proposes a new hybrid parallel approach to perform feature selection. The idea is to use a filter metric to reduce feature space, and then use an innovative wrapper method to search extensively for the best solution. The proposed strategy is implemented on a shared memory parallel environment to speedup the process. We evaluated its parallel performance using up to 32 cores and our results show 30 times gain in speed. To test the performance of feature selection we used five datasets from the well known NIPS challenge and were able to obtain an average score of 95.90% for all solutions.