Contribución experimental a la calorimetría de los cementos hidráulicos

Se describe un procedimiento práctico para calibrar los calorímetros utilizados en la determinación del calor de hidratación de cementos hidráulicos por el denominado método del frasco aislante. A partir de la exactitud con la que pueden determinarse los calores de hidratación, se calculan los valores teóricos de la precisión con los cuales deben conocerse el coeficiente de pérdida y la masa térmica de los frascos calorimétricos. Se comparan los mismos con los determinados en este trabajo. Se describe con amplitud el equipo utilizado en las mediciones .In this work it is described a practical procedure to measure the calorimeters used to determine the hydration heat of hydraulic concretes by the method of isolating flasks• Taking into account the accuracy in determining the hydration heats, it is possible to calculate theoretical values of precision by means of which the heat loss coefficient and the thermic mass of the calorimetric flasks must be known. These results are compared to those established in this work. The equipment used in measuring is widely described

Detection of bacterial contaminants by the larval development test.

The resistance diagnosis of gastrointestinal nematodes of sheep and goats to commercial anthelmintics is an important tool to guide farmers about the use of the most effective dewormer in their flocks, promoting sustainable production.Boletim de Indústria Animal, v. 74

Determination of parasite resistance status to anthelmintics through the larval development test.

The control of gastrointestinal nematodes of small ruminants has been carried out through intense and indiscriminate use of anthelmintics.Boletim de Indústria Animal, v. 74

Nanostructured Thermoelectric Chalcogenides

Thermoelectric materials are outstanding to transform temperature differences directly and reversibly into electrical voltage. Exploiting waste heat recovery as a source of power generation could help towards energy sustainability. Recently, the SnSe semiconductor was identified, in single-crystal form, as a mid-temperature thermoelectric material with record high figure of merit, high power factor and surprisingly low thermal conductivity. We describe the preparation of polycrystals of alloys of SnSe obtained by arc-melting; a rapid synthesis that results in strongly nanostructured samples with low thermal conductivity, advantageous for thermoelectricity, approaching the amorphous limit, around 0.3–0.5 W/mK. An initial screening of novel samples Sn1−xMxSe, by alloying with 3d and 4d transition metals such as M = Mn, Y, Ag, Mo, Cd or Au, provides for a means to optimize the power factor. M=Mo, Ag, with excellent values, are described in detail with characterization by x-ray powder diffraction (XRD), scanning electron microscopy (SEM), and electronic and thermal transport measurements. Rietveld analysis of XRD data demonstrates near-perfect stoichiometries of the above-mentioned alloys. SEM analysis shows stacking of nanosized sheets, with large surfaces parallel to layered slabs. An apparatus was developed for the simultaneous measurement of the Seebeck coefficient and electric conductivity at elevated temperatures

Efeito deletério de extratos de acácia sobre o desenvolvimento larvar de Haemonchus contortus.

Haemonchus contortus, endoparasita responsável por danos fisiológicos e nutricionais em pequenos ruminantes, costuma causar diversos problemas, dentre eles anemia, por se tratar de um parasita hematófago, diminuição da resistência imunológica, e consequente queda de produtividade quantitativa e qualitativa de carne, leite e lã

Latent Patient Network Learning for Automatic Diagnosis

Recently, Graph Convolutional Networks (GCNs) has proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key component in these models is to build a population graph, where the graph adjacency matrix represents pair-wise patient similarities. Until now, the similarity metrics have been defined manually, usually based on meta-features like demographics or clinical scores. The definition of the metric, however, needs careful tuning, as GCNs are very sensitive to the graph structure. In this paper, we demonstrate for the first time in the CADx domain that it is possible to learn a single, optimal graph towards the GCN's downstream task of disease classification. To this end, we propose a novel, end-to-end trainable graph learning architecture for dynamic and localized graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well. We demonstrate significant classification improvements with our learned graph on two CADx problems in medicine. We further explain and visualize this result using an artificial dataset, underlining the importance of graph learning for more accurate and robust inference with GCNs in medical applications

Diagnóstico laboratorial da resistência de nematoides gastrintestinais de propriedades ovinas aos anti-helmínticos.

O monitoramento da resistência parasitária,por meio do teste de desenvolvimento larvar (TDL), pode ser uma ferramenta para preservar os anti-helmínticos