Forecasting of Hydrographs to Simulate Long Term Recharge From Rivers in Numerical Models of Mining Reservoirs; Application to A Coal Mine in NW Spain

Although the natural permeability of rocks in the Asturian Central Coal Basin (NW Spain) is generally low, intensive mining activities over many decades has given rise to fracture flow paths that are far more permeable than those associated with the natural lithology. Abandoned and flooded mining works set up artificial ‘pseudo‐karst’ aquifers, which can act as underground reservoirs, with many potential applications. In particular, a mining reservoir shaped by two connected mining shafts within the River Turón basin has been studied. A runoff model was used to produce accurate simulations of streamflow in three different gauging stations during a monitored period of 2 years. The purpose was to use this model in the forecast situation, in order to predict long‐term situations in a hydrogeological FEFLOW model. It was necessary to develop depletion curves for each gauging station based on the available daily effective rainfall and measured flow data, as well as the knowledge of the basin characteristics. The resulting simulated hydrographs were very similar to the measured hydrographs during the monitored period, so the adequate adjusting allows input of the flow for forecast purposes. The model produced forecast hydrographs that had a r = 0.8 and difference in annual volume ranging from 1.6% to 5%. The defined model was applied to a rainfall data set of 30 years, and the average recharge from the river to the mining reservoir could be adequately estimated. The method developed needs to be refined and tested on additional years, but the approach appears to be applicable to operational runoff forecasting for numerical models input data. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

Privacy protection vs. utility in visual data

Multimedia Tools and Applications - Ubiquitous and networked sensors impose a huge challenge for privacy protection which has become an emerging problem of modern society. Protecting the privacy of...     

Optimization of Sesame Oil Extraction by Screw-Pressing at Low Temperature

Box-Behnken designs were used to optimize a process for sesame oil extraction by screw-pressing at low temperature (50 °C). Experimental designs included seed moisture content (SMC), pressing speed (PS), and restriction die (RD) as the main processing parameters. Extractions at pilot plant scale showed a peak in oil recovery (OR, 71.1 ± 2.80%) at 12.3% SMC, 4 mm RD, and 20 rpm PS. Theoretical models were scanned against experimental data in order to scale up the proposed oil extraction process to industrial scale. A fitted model for OR showed a maximum predicted value similar to the highest experimental value (74.4 ± 1.23%) under the following conditions: 8.03% SMC, 10 mm RD, and 20 rpm PS. Chemical quality parameters of oils obtained at both pilot and industrial scales were in the ranges stated in Codex (FAO/WHO) standards for non-refined sesame oil.     

Coordinating control of production-inventory systems

In most cases real world subsystems for inventory and production control are designed according to very elementary control principles. In order to control the total system, some kind of coordinating control of the subsystems is needed. The approach pursued in this paper is to control the over-all-system by varying different control-parameters of the subsystems. The control system is therefore regarded as a two-level hierarchical system. Coordinating control is carried out with the aid of simple dynamic models based on aggregate terms. Problems concerning the identification of inventory-dynamics ore highlighted. In order to show how the models developed can be used efficiently for coordinating the control of an integrated production-inventory system, a simple control principle is applied to a simulated system.     

Automatic word naming recognition for an on-line aphasia treatment system

One of the most common effects among aphasia patients is the difficulty to recall names or words. Typically, word retrieval problems can be treated through word naming therapeutic exercises. In fact, the frequency and the intensity of speech therapy are key factors in the recovery of lost communication functionalities. In this sense, speech and language technology can have a relevant contribution in the development of automatic therapy methods. In this work, we present an on-line system designed to behave as a virtual therapist incorporating automatic speech recognition technology that permits aphasia patients to perform word naming training exercises. We focus on the study of the automatic word naming detector module and on its utility for both global evaluation and treatment. For that purpose, a database consisting of word naming therapy sessions of aphasic Portuguese native speakers has been collected. In spite of the different patient characteristics and speech quality conditions of the collected data, encouraging results have been obtained thanks to a calibration method that makes use of the patients’ word naming ability to automatically adapt to the patients’ speech particularities.