Application of Finger Span Cross Modality Matching Method (FSCM) by Naive Assessors for Olfactometric Discrimination of Gewürztraminer Wines

A gas-chromatography-olfactometric detection (GCO) using a direct estimation of odor intensities with the finger span (FSCM) was used to establish aroma profiles of three wines of gewürztraminer from different ‘terroirs’ of the Alsace region. The GCO-FSCM analyses of their hydroalcoholic wine extracts were performed using a group of untrained assessors. Forty-four olfactory signals were perceived by at least four assessors out of six and their intensities were scored in triplicate in each of the three wine extracts, amongst which ten of them were found discriminant between wines. The reliability of each assessor was studied on the basis of the finger span estimation repeatability, from the number of discriminant variables and the mean finger span values found for the assessor. These untrained assessors appear to be able to achieve self-calibration from odor intensities without using any internal of external quantitative reference. The results suggest that GCO-FSCM may be performed by naive assessors to analyse and compare odor profiles of complex extracts without time-consuming training periods.     

Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems: The case of wind generation in northeastern Brazil

Several studies have proposed different tools for analyzing the integration of variable renewable energy into power grids. This study applies an optimization tool to model the expansion of the electric power system in northeastern Brazil, enabling the most efficient dispatch of the variable output of the wind farms that will be built in the region over the next 20 years. The expected combined expansion of wind generation with conventional inflexible generation facilities, such as nuclear plants and run-of-the-river hydropower plants, poses risks of future mismatch between supply and demand in northeastern Brazil. Therefore, this article evaluates the possibility of using a fleet of plug-in hybrid electric vehicles (PHEVs) to regularize possible energy imbalances. Findings indicate that a dedicated fleet of 500 thousand PHEVs in 2015, and a further 1.5 million in 2030, could be recharged overnight to take advantage of the surplus power generated by wind farms. To avoid the initial costs of smart grids, this article suggests, as a first step, the use of a governmental PHEV fleet that allows fleet managers to control battery charging times. Finally, the study demonstrates the advantages of optimizing simultaneously the power and transport sectors to test the strategy suggested here.     

Estimates of genetic parameters for single- and multiple-country test-day models

Single- and multiple-country random regression models were applied to estimate genetic parameters for first-lactation test-day milk yield of cows from four countries: Australia, Canada, Italy, and New Zealand. Selected countries represented a wide range of production systems and environments. Milk production in Canada and Italy is based mainly on intensive management systems, while Australia and New Zealand are largely based on rotational grazing. Legendre polynomials with five coefficients were used to model genetic and environmental lactation curves. Covariance components of lactation curve coefficients within and across countries, and selected functions of those, were estimated by Bayesian methods with Gibbs sampling, on selected subsets of data. Countries differed in both phenotypic and genetic parameters of lactation curves between d 5 and 305 of lactation. Principal component analysis of single-trait genetic and environmental covariance matrices showed, however, that the pattern of variability in test-day milk yield was very similar between countries. General level of milk production in lactation and persistency components accounted for more than 90% of the total variance. Estimated genetic correlations between countries for total yield in lactation ranged from 0.65 (Italy and New Zealand) to 0.83 (Australia and New Zealand), indicating a possibility of genotype by environment interaction for some pairs of countries.     

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.     

Seasonal patterns in hydrochemical mixing in three Great Lakes rivermouth ecosystems

Rivermouth ecosystems in the Laurentian Great Lakes represent complex hydrologic mixing zones where lake and river water combine to form biologically productive areas that are functionally similar to marine estuaries. As urban, industrial, shipping, and recreational centers, rivermouths are the focus of human interactions with the Great Lakes and, likewise, may represent critical habitat for larval fish and other biota. The hydrology and related geomorphology in these deltaic systems form the basis for ecosystem processes and wetland habitat structure but are poorly understood. To this end, we examined hydrogeomorphic structure and lake-tributary mixing in three rivermouths of intermediate size using water chemistry, stable isotopes, and current profiling over a five-month period. In rivermouths of this size, the maximum depth of the rivermouth ecosystem influenced water mixing, with temperature-related, density-dependent wedging and layering that isolated lake water below river water occurring in deeper systems. The inherent size of the rivermouth ecosystem, local geomorphology, and human modifications such as shoreline armoring and dredging influenced mixing by altering the propensity for density differences to occur. The improved scientific understanding and framework for characterizing hydrogeomorphic processes in Great Lakes rivermouths across a disturbance gradient is useful for conservation, management, restoration, and protection of critical habitats needed by native species.     

The impact of CO2 taxation on the configuration of new refineries: An application to Brazil

This article evaluates the impact of pricing CO₂ emissions over the configuration of new refinery complexes in their conceptual phase. Two refineries’ schemes were simulated through a linear programming optimization model in order to compare the optimum configuration obtained before and after the input of different CO₂ prices. The cases analyzed represent refining projects to be located in Brazil, a growing market for fuels and petrochemical feedstocks, as well as an oil producing country with rising crude exports. After 2012, emerging countries, such as Brazil, may adopt carbon emission reduction targets. Therefore, it is worth analyzing the impact of pricing CO₂ emissions in these countries, where the majority of new refining projects will be located. Our findings indicate that the initial refinery configurations proposed are quite rigid technologically for CO₂ prices up to US$ 100/t CO₂. For CO₂ prices higher than US$ 100/t CO₂, refineries reduced their emissions by increasing the consumption of natural gas used to produce hydrogen, and through changes in the original configurations towards less-energy consuming process units. Promising technological advances, such as carbon capture and storage (CCS), can also diminish the rigidity of the model and facilitate actions to curb carbon emissions.     

DBC Technology for Extremely Thin Flat Heat Pipes

This paper presents the concept and experimental demonstration of flat copper heat pipe (HP) embedded in extremely thin 3-D packaging substrates. Since conventional machining for this application is very complicated and expensive, the direct bonded copper (DBC) technology proved to be interesting for mass production. The experimental results demonstrated that the DBC HP is an excellent solution to enhance the heat transfer within the 3-D packaging.     

Formation and Pore Structure of Boron Nitride Aerogels

Gels containing a poly(borazinyl amine) and tetrahydrofuran were processed by CO₂ supercritical drying techniques followed by pyrolysis. The resulting BN ceramic aerogels are highly porous, and the microstructure, porosity, and surface area characteristics have been examined. The aerogels show excellent thermal stability exhibiting surface areas in excess of 350 m²/g and porosities greater than 0.8 even when heated in argon at 1500°C for 8 h. By removing solvent via evaporation before supercritical drying, the mean pore radius can be varied between 3.6 and 10 nm.     

Peelfit: An Integrated Computer Program for Pharmacokinetic Analysis

An Integrated program PEELFIT is reported for the Apple IIe microcomputer, which performs pharmacokinetic analysis for intravenous and first order absorption (oral or intramuscular) data. PEELFIT, written in basic language, uses plasma concentration and time for input. Upon the user's choice, it carries out non-linear regression on the data for curve fitting, according to a one or two compartment model. the curve fitting can be done with three weighting schemes. Parameters of the plasma concentration equations are obtained from the regression analysis and further, these are employed to calculate the relevant pharmacokinetic parameters. Finally, the percentage of dose absorbed (absorbable fraction) is calculated as a function of time. various known data were analysed with PEELFIT and the results obtained confirmed its accuracy.     

Image Analysis for Mapping Immeasurable Phenotypes in Maize [Life Sciences]

This work will allow bio-informaticians to analyze the ever-increasing gene sequence data, discover valuable knowledge in maize biology and related plant; development, and understand subtle variations among different phenotypes. Furthermore, successful measuring of visual phenotypes will advance plant research by finding the genes and/or environmental factors that cause a given visual phenotype. In what follows, the field of plant genetics is introduced (particularly quantitative trait loci and disease scoring) to the signal processing community, discuss the challenges involved, and present an image analysis system for precisely quantifying and mapping immeasurable phenotypes in maize