Poder calorífico da madeira e do carvão vegetal: lignina e carbono fixo como determinantes.

Resumo

Balanço energético da produção de Eucalyptus benthamii para uso em programas de bioenergia.

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POLYMER DEGRADATION IN TURBULENT DRAG REDUCING FLOWS IN PIPES

The drag reduction by addition of high molecular additives has been investigated by a number of researchers since it was reported by Toms more than 60 years ago. One of the most significant limitations in drag reduction is the polymer degradation, which is caused by the turbulent structures. Researches have demonstrated that many parameters affect the polymer efficient, as: molecular weight, Reynolds number, concentration and temperature. In the present work we investigate this degradation phenomenon in a pipe flow apparatus device, for aqueous solutions of three different polymers: Polyethylene Oxide (PEO), Polyacrylamide (PAM) and Xanthan Gum (XG).The first two are known as flexible molecules while the last one is considered rigid. The dependence of polymer scission on molecular weight, concentration and Reynolds number is analyzed. We report how the drag reduction decreases when the flow pass repeatedly through the pipe and how the pressure loss measured in the apparatus increases, despite to the fact that the experiment was conducted at a fixed inlet pressure. It is worth noting that the mechanism of loss of efficiency for the XG solutions seems to be completely different from that observed for PEO and PAM, the flexible materials

Imaginary Phases in Two-Level Model with Spontaneous Decay

We study a two-level model coupled to the electromagnetic vacuum and to an external classic electric field with fixed frequency. The amplitude of the external electric field is supposed to vary very slow in time. Garrison and Wright [{\it Phys. Lett.} {\bf A128} (1988) 177] used the non-hermitian Hamiltonian approach to study the adiabatic limit of this model and obtained that the probability of this two-level system to be in its upper level has an imaginary geometric phase. Using the master equation for describing the time evolution of the two-level system we obtain that the imaginary phase due to dissipative effects is time dependent, in opposition to Garrison and Wright result. The present results show that the non-hermitian hamiltonian method should not be used to discuss the nature of the imaginary phases in open systems.Comment: 11 pages, new version, to appear in J. Phys.

Blood Group Antigen Studies Using Cdte Quantum Dots And Flow Cytometry.

New methods of analysis involving semiconductor nanocrystals (quantum dots [QDs]) as fluorescent probes have been highlighted in life science. QDs present some advantages when compared to organic dyes, such as size-tunable emission spectra, broad absorption bands, and principally exceptional resistance to photobleaching. Methods applying QDs can be simple, not laborious, and can present high sensibility, allowing biomolecule identification and quantification with high specificity. In this context, the aim of this work was to apply dual-color CdTe QDs to quantify red blood cell (RBC) antigen expression on cell surface by flow cytometric analysis. QDs were conjugated to anti-A or anti-B monoclonal antibodies, as well as to the anti-H (Ulex europaeus I) lectin, to investigate RBCs of A1, B, A1B, O, A2, and Aweak donors. Bioconjugates were capable of distinguishing the different expressions of RBC antigens, both by labeling efficiency and by flow cytometry histogram profile. Furthermore, results showed that RBCs from Aweak donors present fewer amounts of A antigens and higher amounts of H, when compared to A1 RBCs. In the A group, the amount of A antigens decreased as A1 > A3 > AX = Ael, while H antigens were AX = Ael > A1. Bioconjugates presented stability and remained active for at least 6 months. In conclusion, this methodology with high sensibility and specificity can be applied to study a variety of RBC antigens, and, as a quantitative tool, can help in achieving a better comprehension of the antigen expression patterns on RBC membranes.104393-440

Regional Genetic Structure in the Aquatic Macrophyte Ruppia cirrhosa Suggests Dispersal by Waterbirds

The evolutionary history of the genus Ruppia has been shaped by hybridization, polyploidisation and vicariance that have resulted in a problematic taxonomy. Recent studies provided insight into species circumscription, organelle takeover by hybridization, and revealed the importance of verifying species identification to avoid distorting effects of mixing different species, when estimating population connectivity. In the present study, we use microsatellite markers to determine population diversity and connectivity patterns in Ruppia cirrhosa including two spatial scales: (1) from the Atlantic Iberian coastline in Portugal to the Siculo-Tunisian Strait in Sicily and (2) within the Iberian Peninsula comprising the Atlantic-Mediterranean transition. The higher diversity in the Mediterranean Sea suggests that populations have had longer persistence there, suggesting a possible origin and/or refugial area for the species. The high genotypic diversities highlight the importance of sexual reproduction for survival and maintenance of populations. Results revealed a regional population structure matching a continent-island model, with strong genetic isolation and low gene flow between populations. This population structure could be maintained by waterbirds, acting as occasional dispersal vectors. This information elucidates ecological strategies of brackish plant species in coastal lagoons, suggesting mechanisms used by this species to colonize new isolated habitats and dominate brackish aquatic macrophyte systems, yet maintaining strong genetic structure suggestive of very low dispersal.Fundacao para a Cincia e Tecnologia (FCT, Portugal) [PTDC/MAR/119363/2010, BIODIVERSA/0004/2015, UID/Multi/04326/2013]Pew FoundationSENECA FoundationMurcia Government, Spain [11881/PI/09]FCT Investigator Programme-Career Development [IF/00998/2014]Spanish Ministry of Education [AP2008-01209]European Community [00399/2012]info:eu-repo/semantics/publishedVersio

Visual analytics for collaborative human-machine confidence in human-centric active learning tasks

Active machine learning is a human-centric paradigm that leverages a small labelled dataset to build an initial weak classifier, that can then be improved over time through human-machine collaboration. As new unlabelled samples are observed, the machine can either provide a prediction, or query a human ‘oracle’ when the machine is not confident in its prediction. Of course, just as the machine may lack confidence, the same can also be true of a human ‘oracle’: humans are not all-knowing, untiring oracles. A human’s ability to provide an accurate and confident response will often vary between queries, according to the duration of the current interaction, their level of engagement with the system, and the difficulty of the labelling task. This poses an important question of how uncertainty can be expressed and accounted for in a human-machine collaboration. In short, how can we facilitate a mutually-transparent collaboration between two uncertain actors - a person and a machine - that leads to an improved outcome?In this work, we demonstrate the benefit of human-machine collaboration within the process of active learning, where limited data samples are available or where labelling costs are high. To achieve this, we developed a visual analytics tool for active learning that promotes transparency, inspection, understanding and trust, of the learning process through human-machine collaboration. Fundamental to the notion of confidence, both parties can report their level of confidence during active learning tasks using the tool, such that this can be used to inform learning. Human confidence of labels can be accounted for by the machine, the machine can query for samples based on confidence measures, and the machine can report confidence of current predictions to the human, to further the trust and transparency between the collaborative parties. In particular, we find that this can improve the robustness of the classifier when incorrect sample labels are provided, due to unconfidence or fatigue. Reported confidences can also better inform human-machine sample selection in collaborative sampling. Our experimentation compares the impact of different selection strategies for acquiring samples: machine-driven, human-driven, and collaborative selection. We demonstrate how a collaborative approach can improve trust in the model robustness, achieving high accuracy and low user correction, with only limited data sample selections