Root temperature and energy consumption at different cable depths in electrically heated substrates

A finite element method-based model of a substrate heated by an electric heating cable buried in a thermal isolated container was experimentally validated with root mean square error values of root zone temperature ranging 0.25 to 0.62 ºC. The two-dimensional transient model allowed variations in the physical properties of the substrate with temperature, water content and depth. The operation of nine configurations of a heating cable buried in sand at different depths (50 to 450 mm, at 50 mm intervals) at 200 mm spacing was simulated and assessed. The validated model was used to perform 24-h simulations applying boundary conditions, and substrate moisture content was experimentally obtained at a mean substrate surface temperature of 13.98 ºC. Such simulations reproduced the operation of the heating system by setting a reference temperature of 20 ºC at the control point in the root zone. Burying the heating cable in the surface layers of the substrate caused large temperature gradients and high heat losses through the substrate surface. Accordingly, average temperature in the root zone increased with heating cable depth, up to the 200 mm depth. For greater depths, temperature in the root zone was constant. The ON/OFF control was most effective with the heating cable buried in the root zone and at control point temperatures of 20 ± 1 ºC. Burying the heating cable in the surface layers required higher energy consumption, up to 28 % at 50 mm. The most efficient heating cable depth was 350 mm, with a daily energy consumption of 6750 kJ m-2.S

Análisis estadístico de mecanismos de eventos asincrónicos basado en la web para la integración de la comunicación en tiempo real

Investigación para el análisis estadístico de mecanismos de eventos asíncronos (Polling, Long Polling y Websocket), basados en la web para la integración de la comunicación en tiempo real en la plataforma web “Rastreo Directo Satelital 2.0”, de la empresa FASTNOTEQ S.A de la ciudad de Ambato. Para desarrollar la investigación se utilizó método científico, para la recopilación de información se usó herramientas de benchmarking que permiten conocer, en tiempo real, el consumo del CPU, de la RAM, de la red y la latencia; para el análisis se aplicó estadística descriptiva e inferencial, el estudio comparativo de mecanismos permitió determinar el de mejor rendimiento, se desarrolló un ambiente simulado con características del escenario que se va a implementar en la Plataforma Web “Rastreo Directo Satelital 2.0. En el análisis estadístico se observó que: el mecanismo de menor latencia, menor uso de Red y menor consumo de memoria RAM Websocket; los mecanismos estudiados, presentan un rendimiento similar en el uso del CPU del servidor. Aplicando análisis de varianza a los resultados de las pruebas realizadas y luego de la respectiva ponderación, se pudo determinar que el mecanismo Websocket es con 100 de 100 puntos, el de mejor rendimiento para implementar la plataforma web “Rastreo Directo Satelital 2.0”. Se recomienda optimizar la base de datos de la empresa para que, con el crecimiento de la misma, los datos no corran ningún riesgo

Long-Term Measurement of Piglet Activity Using Passive Infrared Detectors

Measuring animal activity is useful for monitoring animal welfare in real time. In this regard, passive infrared detectors have been used in recent years to quantify piglet activity because of their robustness and ease of use. This study was conducted on a commercial farm in Northwest Spain during six complete breeding cycles. The hourly average activity of weaned piglets with a body mass of 6–20 kg was recorded and further analyzed by using a multiplicative decomposition of the series followed by a wavelet analysis. Finally, the real series were compared to the theoretical models of activity. Results showed a high level of movement immediately after weaning and a sustained level of activity throughout the cycles. The daily behavior of the piglets followed a clear circadian pattern with several peaks of activity. No differences in behavior were observed between spring–summer cycles and autumn–winter cycles. Single-peak models achieved the best predictive results. In addition, the installed sensors were found to underestimate mild activityThis research was funded by Consellería de Educación, Universidade e Formación Profesional and Consellería de Economía, Emprego e Industria da Xunta de Galicia, grant number ED431B 2018/12-GPCS

New Fertilizer Strategies Combining Manure and Urea for Improved Rice Growth in Mozambique

The cost of chemical fertilizers is increasing and becoming unaffordable for smallholders in Africa. The present study aimed to assess the impact of combined fertilization strategies using urea and animal manure (beef cattle manure and poultry litter manure) on rice yield and nutrient uptake. For this, a field experiment was carried out on a loam sandy soil in the Chókwè Irrigation Scheme. We set seven treatments in a Randomized Complete Block Design (RCBD), namely: T0: no fertilizer, T1: 100% urea, T2: 100% beef cattle manure, T3: 100% poultry litter, T4: 50% urea + 50% beef cattle manure, T5: 50% urea + 50% poultry litter and T6: 40% urea + 30% beef cattle manure + 30% poultry litter, replicated four times each. All treatments, except T0, received an amount of nitrogen (N) equivalent to 100 kgN ha1. Results revealed that the highest yield grain (425 g m2), plant height (115 cm), number of tillers (18) and thousand-grain weight (34g) were observed in treatments combining urea with manure (T4, T5 and T6) indicating that N supply in the mixture (urea + manure) is more efficient than in isolated applications of N (T1, T2 and T3). The data obtained in this study suggest that a combination of fertilizers (T6) lead to competitive yields and is thus recommended for best soil management practicesinfo:eu-repo/semantics/publishedVersio

Evolution of NH3 Concentrations in Weaner Pig Buildings Based on Setpoint Temperature

Ammonia (NH3) concentration has seldom been used for environmental control of weaner buildings despite its impact on environment, animal welfare, and workers’ health. This paper aims to determine the effects of setpoint temperature (ST) on the daily evolution of NH3 concentration in the animal-occupied zone. An experimental test was conducted on a conventional farm, with ST between 23 °C and 26 °C. NH3 concentrations in the animal-occupied zone were dependent on ST insofar as ST controlled the operation of the ventilation system, which effectively removed NH3 from the building. The highest NH3 concentrations occurred at night and the lowest concentrations occurred during the daytime. Data were fitted to a sinusoidal model using the least squares setting (LSS) and fast Fourier transform (FFT), which provided R2 values between 0.71 and 0.93. FFT provided a better fit than LSS, with root mean square errors (RMSEs) between 0.09 ppm for an ST of 23 °C and 0.55 ppm for an ST of 25 °C. A decrease in ST caused a delay in the wave and a decrease in wave amplitude. The proposed equations can be used for modeling NH3 concentrations and implemented in conventional controllers for real-time environmental control of livestock buildings to improve animal welfare and productivityThis research was funded by Xunta de Galicia, grant number GPC-ED431B 2018/012S

Validation of an AutoRegressive Integrated Moving Average model for the prediction of animal zone temperature in a weaned piglet building

An AutoRegressive Integrated Moving Average model was validated for the prediction of temperatures in the animal zone of conventional weaned piglet barn. The validation period covered seven cycles and recorded values at 10-min intervals for 292 days. Average weight was 5.75 ± 0.86 kg at the beginning of the production cycle and 18.41 ± 2.12 kg at the end of the cycle. Mean outdoor air temperatures ranged 6.14 to 17.85 °C with deviations in the range 2.49 °C to 5.24 °C, which involved marked differences in the operation of the ventilation system. The Mean Average Percentage Error was below 4%, with a mean error of ≤1 °C. The Root Mean Square Error was in the range 0.77 °C to 1.19 °C, whereas the coefficient of determination ranged between 0.52 and 0.81. Despite the changes in environmental conditions and in animal weight and management, the accuracy of the model remained stable with low dispersion of values. The model showed good accuracy and reliability covering all the seasons under changing meteorological conditions because it considered the operation of the heating and ventilation systems and changes in animal weight. The residuals obtained from the validation of the seven production cycles were Gaussian distributed, which confirmed the validity of the model. The generated model can be used for more effective environmental control systems that are capable of anticipating events and show a better response, which helps improve energy savings and animal welfareThe authors are grateful to the regional government Xunta de Galicia for funding this research through the “Programme of consolidation and structuring of competitive research units” (GPC2014/072)S

Energy, Production and Environmental Characteristics of a Conventional Weaned Piglet Farm in North West Spain

Postweaning is one of the most sensitive and energy-demanding phases of swine production. The objective of this research was to assess the energy, production and environmental characteristics of a conventional farm with temperature-based environmental control. The selected energy, environmental and production variables were measured on farm, in a high livestock density area of NW Spain, for seven production cycles. The quantification of variables was aimed at obtaining the maximum performance with the lowest possible use of resources, focusing on animal welfare and production efficiency. The Brown–Forsythe, Welch and Games-Howell tests revealed significant differences in terms of temperature, relative humidity and CO2 concentrations among production cycles, and among the critical, postcritical and final periods. Improved humidity management resulted in a 17% reduction of climate control energy, which involved energy savings in the range of 33% to 47% per kg produced at the end of the postweaning cycle. Accordingly, adding humidity as a control variable could result in higher ventilation rates, thereby improving animal welfare, reducing heating energy use and increasing weight gain per unit climate control energy. In addition, the strong correlations found between heating energy and relative humidity (R2 = 0.73) and ventilation energy and CO2 (R2 = 0.99) suggest that these variables could be readily estimated without additional sensor costsThis research has received funding from European Regional Development Fund (ERDF) (2007–2013) under the project Control and automation strategies for energy and production efficiency in weaned farms, included in the research and innovation programme for Galicia, PEME I+D SumaS

Evolution and neural network prediction of CO2 emissions in weaned piglet farms

This paper aims to study the evolution of CO2 concentrations and emissions on a conventional farm with weaned piglets between 6.9 and 17.0 kg live weight based on setpoint temperature, outdoor temperature, and ventilation flow. The experimental trial was conducted during one transition cycle. Generally, the ventilation flow increased with the reduction in setpoint temperature throughout the cycle, which caused a reduction in CO2 concentration and an increase in emissions. The mean CO2 concentration was 3.12 g m–3. Emissions of CO2 had a mean value of 2.21 mg s−1 per animal, which is equivalent to 0.195 mg s−1 kg−1. A potential function was used to describe the interaction between 10 min values of ventilation flow and CO2 concentrations, whereas a linear function was used to describe the interaction between 10 min values of ventilation flow and CO2 emissions, with r values of 0.82 and 0.85, respectively. Using such equations allowed for simple and direct quantification of emissions. Furthermore, two prediction models for CO2 emissions were developed using two neural networks (for 10 min and 60 min predictions), which reached r values of 0.63 and 0.56. These results are limited mainly by the size of the training period, as well as by the differences between the behavior of the series in the training stage and the testing stageThis research was funded by Consellería de Educación, Universidade e Formación Profesional and Consellería de Economía, Emprego e Industria from the Galician Government (Xunta de Galicia). Granted with reference ED431B 2018/12-GPCS

Grammatical inference with bioinformatics criteria

In this paper we describe both the theoretical and practical results of a novel approach that combines hybrid techniques of association analysis and classical sequentiation algorithms of genomics to generate the grammatical structures of a specific language. We used an application of a compiler generator system that allows a practical application to be developed within the area of grammarware, where the concepts of language analysis are applied to other disciplines, such as bioinformatics. The tool allows the complexity of the obtained grammar to be measured automatically from textual data. A technique involving the incremental discovery of sequential patterns is presented to obtain simplified production rules, and compacted with bioinformatics criteria to make up a grammar

Blood Biomarkers to Predict Long-Term Mortality after Ischemic Stroke

Altres ajuts: This work has been funded by La Fundació La Marató (Reg. 84/240 proj. 201702).Stroke is a major cause of disability and death globally, and prediction of mortality represents a crucial challenge. We aimed to identify blood biomarkers measured during acute ischemic stroke that could predict long-term mortality. Nine hundred and forty-one ischemic stroke patients were prospectively recruited in the Stroke-Chip study. Post-stroke mortality was evaluated during a median 4.8-year follow-up. A 14-biomarker panel was analyzed by immunoassays in blood samples obtained at hospital admission. Biomarkers were normalized and standardized using Z -scores. Multiple Cox regression models were used to identify clinical variables and biomarkers independently associated with long-term mortality and mortality due to stroke. In the multivariate analysis, the independent predictors of long-term mortality were age, female sex, hypertension, glycemia, and baseline National Institutes of Health Stroke Scale (NIHSS) score. Independent blood biomarkers predictive of long-term mortality were endostatin > quartile 2, tumor necrosis factor receptor-1 (TNF-R1) > quartile 2, and interleukin (IL)-6 > quartile 2. The risk of mortality when these three biomarkers were combined increased up to 69%. The addition of the biomarkers to clinical predictors improved the discrimination (integrative discriminative improvement (IDI) 0.022 (0.007-0.048), p quartile 3 was an independent predictor of mortality due to stroke. Altogether, endostatin, TNF-R1, and IL-6 circulating levels may aid in long-term mortality prediction after stroke