GaiaDoc： A Tool Focused on Business Requirements for Code Documentation with a CMMI Compliant and RUP Based on Requirement Flow

Aiming to fill a gap in traditional methods of source code documentation, which focus mainly on the API （application programming interface） documentation for other programmers, this article presents a new approach for business requirements, mapping them through a set of annotations. These annotations, in turn, are interpreted by the GaiaDoc tool, which is specified in this paper and is able to generate documentation in form of use case specifications in a language and format easily understandable by the project stakeholders. Along with the GaiaDoc tool proposal, a RUP （rational unified process） based requirements flow is developed to fit with its needs and it is validated through CMMI （capability maturity model integration） requirement process areas and a case study of the proposed methodology＇s application is presented before the final considerations.     

Modelization of hybrid systems with hydrogen and renewable energy oriented to electric propulsion in sailboats

This paper presents a conceptual model of a hybrid electric sailboat in which energy from electric grid is stored in batteries and energy from renewable energies (eolic, solar and hydro) is stored as hydrogen. The main objective of this model is to study the viability of electrifying traditional sailboats with internal combustion engines into hybrid systems with batteries and fuel cell. The most important advantage of this design is the possibility to reduce up to zero emissions of traditional sailboat. Conversion of renewable energy to hydrogen is performed through an electrolyzer and post conversion to energy is carried out by a fuel cell. The fuel cell with the batteries forms the hybrid system (batteries-fuel cell) for propulsion electrical energy supply. In order to model the boat dynamic and energy systems, modular mathematical models were developed under Matlab^®-Simulink^®, using a fixed-step solver for the simulation of global model. A simulated logic controller manages the global model. In this paper, many models have been used: some of them are based in literature models and others were developed from experimental data. A control strategy has also been developed to manage energy flows and then it has been embedded to Matlab^® language. The global model permits test the performance of the sailboat.     

Short‐term load forecasting based on support vector regression and load profiling

The article proposes a methodology to forecast the electric load for the 24 h of the following day based on support vector regression. The study considers 24 distinct models, one for each predicted hour, where each individual model is treated independently. Its objective is to find the optimal combination of support vector machine parameters that could generalize low forecasting errors, using simulated annealing as a metaheuristic. The adopted methodology is compared to concurrent methods based on neural networks when applied to a simulated load diagram (to illustrate a distribution feeder supplying a sample of 740 consumers). The results have proven its effectiveness with mean absolute percentage errors being less than 5% for testing samples. The study also focuses on evaluating the potential benefits of adopting load profiling information as input in support vector regression, giving a consistent proof of its importance. Copyright © 2013 John Wiley & Sons, Ltd.     

Theoretical considerations on the modelling of transport in a three-phase electrode and application to a proton conducting solid oxide electrolysis cell

This paper presents a numerical model for fuel cells and electrolysis cells that use cermets as electrodes. The mass and charge continuity equations were demonstrated inside the electrodes and in the divergence term, surface ratios were used instead of more usual volumetric ratios. The Butler–Volmer equation for electrokinetics was used with concentration coefficients in order to predict correctly the concentration effects on the value of the transfer current density in the cermets. In addition, it was considered that the reaction takes place inside all of cermet's volume inste1 ad of a thin layer near the electrolyte. The model was tested for the proton-conducting SOEC technology on a generic cell design and the calculations were performed with COMSOL Multiphysics 4.1™. A parametric analysis was carried out on a proton-conducting SOEC in galvanostatic mode in order to evaluate the influence of parameters on oxygen production across the electrode in the anodic compartment. This analysis showed that the structural parameters of the cermets such as grain radius or volumetric ratios of the conductors play a major role in the distribution of reaction kinetics.     

Membrane treatment of side-stream cooling tower water for reduction of water usage

A pilot study was conducted to determine whether membrane treatment on a side stream of recirculating cooling-tower water could reduce overall water usage and discharge. The treated permeate was returned to the cooling tower while the concentrate was discharged to the sanitary sewer. Flow rates, pressures and water chemistry were monitored. The pilot demonstrated potential substantial water savings. Maximum make-up water and discharge reduction were 16% and 49%, respectively. As high as possible permeate recovery is needed to maximize water conservation. Silica scaling on the membranes limited water savings in this pilot. Development of membranes with a solute-rejection capacity less than the 92% average of the membranes used in the pilot would assist in optimizing water savings. Decreased water outlays compensated for the additional energy used by membrane treatment. Scaling control is critical for economic operation.     

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from ?0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.