Software-defined networking: guidelines for experimentation and validation in large-scale real world scenarios

Part 1: IIVC WorkshopInternational audienceThis article thoroughly details large-scale real world experiments using Software-Defined Networking in the testbed setup. More precisely, it provides a description of the foundation technology behind these experiments, which in turn is focused around OpenFlow and on the OFELIA testbed. In this testbed preliminary experiments were performed in order to tune up settings and procedures, analysing the encountered problems and their respective solutions. A methodology consisting of five large-scale experiments is proposed in order to properly validate and improve the evaluation techniques used in OpenFlow scenarios

Small-sample corrections for score tests in Birnbaum-Saunders regressions

In this paper we deal with the issue of performing accurate small-sample inference in the Birnbaum-Saunders regression model, which can be useful for modeling lifetime or reliability data. We derive a Bartlett-type correction for the score test and numerically compare the corrected test with the usual score test, the likelihood ratio test and its Bartlett-corrected version. Our simulation results suggest that the corrected test we propose is more reliable than the other tests.Comment: To appear in the Communications in Statistics - Theory and Methods, http://www.informaworld.com/smpp/title~content=t71359723

Tailored virtual reality for smart physiotherapy

Remote sensing and Virtual Reality (VR) are technologies that create new development opportunities in the field of serious games with application in physiotherapy. Thus, during a physiotherapy training session expressed by a game around the remote sensing of user body motion provides measurements that can be used for objective evaluation of physical therapy outcomes. In this work is presented a serious game for physiotherapy characterized by Kinect natural user interface and a set of VR games developed in the Unity3D. To provide patient electronic health record, game remote configuration as well as for data presentation for physiotherapist a mobile application was developed. Additionally, several training results expressed by upper limb, neck and spine angles are included in the paper.info:eu-repo/semantics/acceptedVersio

Braquiária na agropecuária brasileira: uma história de sucesso.

bitstream/item/99413/1/cap1.pd

BioCode gold-nanobeacon for the detection of fusion transcripts causing chronic myeloid leukemia

This work was supported by Fundacao para a Ciencia e Tecnologia, Ministry of Science and Education (FCT/MEC) [PTDC/QUI-QUI/112597/2009; PTDC/BBB-NAN/1812/2012; CIGMH (PEST-OE/SAU/UI0009/2011); SFRH/BD/87836/2012 to M.C. and SFRH/BPD/88322/2012 to L.G.]; and by Unidade de Ciencias Biomoleculares Aplicadas-UCIBIO [(UID/Multi/04378/2013) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007728)].BACKGROUND: Gold-nanobeacons (Au-nanobeacons) have proven to be versatile systems for molecular diagnostics and therapeutic actuators. Here, we present the development and characterization of two gold nanobeacons combined with Förster resonance energy transfer (FRET) based spectral codification for dual mode sequence discrimination. This is the combination of two powerful technologies onto a single nanosystem. RESULTS: We proved this concept to detect the most common fusion sequences associated with the development of chronic myeloid leukemia, e13a2 and e14a2. The detection is based on spectral shift of the donor signal to the acceptor, which allows for corroboration of the hybridization event. The Au-nanobeacon acts as scaffold for detection of the target in a homogenous format whose output capability (i.e. additional layer of information) is potentiated via the spectral codification strategy. CONCLUSIONS: The spectral coded Au-nanobeacons permit the detection of each of the pathogenic fusion sequences, with high specificity towards partial complementary sequences. The proposed BioCode Au-nanobeacon concept provides for a nanoplatform for molecular recognition suitable for cancer diagnostics.publishersversionpublishe

Integração lavoura-pecuária-floresta (iLPF): região sul.

bitstream/item/79516/1/0000005512-ILPF-REGIAO-SUL.pdfInformação de capa: [Apostila do] Curso de Capacitação do Programa ABC (Agricultura de Baixa Emissão de Carbono)

Integração lavoura-pecuária-floresta (iLPF): Região Sul.

A integração Lavoura-Pecuária-Floresta (iLPF) tem como grande objetivo a mudança do sistema de uso da terra, fundamentando-se na integração dos componentes do sistema produtivo, visando atingir patamares cada vez mais elevados de qualidade do produto, qualidade ambiental e competitividade. Portanto, apresenta-se como uma estratégia para maximizar efeitos desejáveis no ambiente, aliando o aumento da produtividade com a conservação de recursos naturais no processo de intensificação de uso das áreas já desmatadas no Brasil.bitstream/item/82226/1/0000005512-ILPF-REGIAO-SUL.pdfInformação de capa: [Apostila do] Curso de Capacitação do Programa ABC (Agricultura de Baixa Emissão de Carbono)

Efeito de indutores de resistência na incidência do mal do Panamá em banana maçã.

O mal do Panamá ou murcha de Fusarium, causado por Fusarium oxysporum Schlechtend.: Fr. f.sp. cubense (E.F. Smith) W.C. Snyder & Hansen, é uma das doenças mais importantes da bananeira (Musa spp.). Primeiramente relatado na Austrália, esta doença está presente em todas as regiões produtoras do mundo. Nas décadas de 1950 e 1960, o mal do Panamá dizimou a bananicultura da América Central e Caribe, então constituída unicamente da variedade Gros Michel (Daly & Walduck, 2006). No Brasil, onde foi detectado em 1930 sobre banana Maçã (Cordeiro et al., 2003), essa doença causa perdas acentuadas na produção de frutos em todo o País (Cordeiro et al., 1993), notadamente na variedade Maçã e naquelas do subgrupo Prata.pdf 202

Predicting wet age-related macular degeneration (AMD) using DARC (detecting apoptosing retinal cells) AI (artificial intelligence) technology

Objectives: To assess a recently described CNN (convolutional neural network) DARC (Detection of Apoptosing Retinal Cells) algorithm in predicting new Subretinal Fluid (SRF) formation in Age-related-Macular-Degeneration (AMD). Methods: Anonymized DARC, baseline and serial OCT images (n = 427) from 29 AMD eyes of Phase 2 clinical trial (ISRCTN10751859) were assessed with CNN algorithms, enabling the location of each DARC spot on corresponding OCT slices (n = 20,629). Assessment of DARC in a rabbit model of angiogenesis was performed in parallel. Results: A CNN DARC count >5 at baseline was significantly (p = 0.0156) related to development of new SRF throughout 36 months. Prediction rate of eyes using unique DARC spots overlying new SRF had positive predictive values, sensitivities and specificities >70%, with DARC count significantly (p < 0.005) related to the magnitude of SRF accumulation at all time points. DARC identified earliest stages of angiogenesis in-vivo. Conclusions: DARC was able to predict new wet-AMD activity. Using only an OCT-CNN definition of new SRF, we demonstrate that DARC can identify early endothelial neovascular activity, as confirmed by rabbit studies. Although larger validation studies are required, this shows the potential of DARC as a biomarker of wet AMD, and potentially saving vision-loss