Documentation of a daily mean stream temperature module—An enhancement to the Precipitation-Runoff Modeling System

Science.gov (United States)

Sanders, Michael J.; Markstrom, Steven L.; Regan, R. Steven; Atkinson, R. Dwight

2017-09-15

A module for simulation of daily mean water temperature in a network of stream segments has been developed as an enhancement to the U.S. Geological Survey Precipitation Runoff Modeling System (PRMS). This new module is based on the U.S. Fish and Wildlife Service Stream Network Temperature model, a mechanistic, one-dimensional heat transport model. The new module is integrated in PRMS. Stream-water temperature simulation is activated by selection of the appropriate input flags in the PRMS Control File and by providing the necessary additional inputs in standard PRMS input files.This report includes a comprehensive discussion of the methods relevant to the stream temperature calculations and detailed instructions for model input preparation.

NOAA Daily Optimum Interpolation Sea Surface Temperature

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The NOAA 1/4° daily Optimum Interpolation Sea Surface Temperature (or daily OISST) is an analysis constructed by combining observations from different platforms...

Daily temperature records from a mesonet in the foothills of the Canadian Rocky Mountains, 2005–2010

Directory of Open Access Journals (Sweden)

W. H. Wood

2018-03-01

Full Text Available Near-surface air temperatures were monitored from 2005 to 2010 in a mesoscale network of 230 sites in the foothills of the Rocky Mountains in southwestern Alberta, Canada. The monitoring network covers a range of elevations from 890 to 2880 m above sea level and an area of about 18 000 km2, sampling a variety of topographic settings and surface environments with an average spatial density of one station per 78 km2. This paper presents the multiyear temperature dataset from this study, with minimum, maximum, and mean daily temperature data available at https://doi.org/10.1594/PANGAEA.880611. In this paper, we describe the quality control and processing methods used to clean and filter the data and assess its accuracy. Overall data coverage for the study period is 91 %. We introduce a weather-system-dependent gap-filling technique to estimate the missing 9 % of data. Monthly and seasonal distributions of minimum, maximum, and mean daily temperature lapse rates are shown for the region.

Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

Science.gov (United States)

Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

2012-04-01

The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the

Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

Science.gov (United States)

Hengl, Tomislav; Heuvelink, Gerard B. M.; Perčec Tadić, Melita; Pebesma, Edzer J.

2012-01-01

A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

Science.gov (United States)

Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal

2013-11-01

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.

Neural network based daily precipitation generator (NNGEN-P)

Energy Technology Data Exchange (ETDEWEB)

Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Paris (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Penalba, Olga [University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)

2007-02-15

Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. (orig.)

Daily extreme temperature multifractals in Catalonia (NE Spain)

International Nuclear Information System (INIS)

Burgueño, A.; Lana, X.; Serra, C.; Martínez, M.D.

2014-01-01

The multifractal character of the daily extreme temperatures in Catalonia (NE Spain) is analyzed by means of the multifractal detrended fluctuation analysis (MF-DFA) applied to 65 thermometric records covering years 1950–2004. Although no clear spatial patterns of the multifractal spectrum parameters appear, factor scores deduced from Principal Component analysis indicate some signs of spatial gradients. Additionally, the daily extreme temperature series are classified depending on their complex time behavior, through four multifractal parameters (Hurst exponent, Hölder exponent with maximum spectrum, spectrum asymmetry and spectrum width). As a synthesis of the three last parameters, a basic measure of complexity is proposed through a normalized Complexity Index. Its regional behavior is found to be free of geographical dependences. This index represents a new step towards the description of the daily extreme temperatures complexity.

Daily extreme temperature multifractals in Catalonia (NE Spain)

Energy Technology Data Exchange (ETDEWEB)

Burgueño, A. [Departament d' Astronomia i Meteorologia, Universitat de Barcelona, Barcelona (Spain); Lana, X., E-mail: francisco.javier.lana@upc.edu [Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Barcelona (Spain); Serra, C. [Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Barcelona (Spain); Martínez, M.D. [Departament de Física Aplicada, Universitat Politècnica de Catalunya, Barcelona (Spain)

2014-02-01

The multifractal character of the daily extreme temperatures in Catalonia (NE Spain) is analyzed by means of the multifractal detrended fluctuation analysis (MF-DFA) applied to 65 thermometric records covering years 1950–2004. Although no clear spatial patterns of the multifractal spectrum parameters appear, factor scores deduced from Principal Component analysis indicate some signs of spatial gradients. Additionally, the daily extreme temperature series are classified depending on their complex time behavior, through four multifractal parameters (Hurst exponent, Hölder exponent with maximum spectrum, spectrum asymmetry and spectrum width). As a synthesis of the three last parameters, a basic measure of complexity is proposed through a normalized Complexity Index. Its regional behavior is found to be free of geographical dependences. This index represents a new step towards the description of the daily extreme temperatures complexity.

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Directory of Open Access Journals (Sweden)

Xike Zhang

2018-05-01

Full Text Available Daily land surface temperature (LST forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD coupled with Machine Learning (ML algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs and a single residue item. Then, the Partial Autocorrelation Function (PACF is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE, Mean Absolute Error (MAE, Mean Absolute Percentage Error (MAPE, Root Mean Square Error (RMSE, Pearson Correlation Coefficient (CC and Nash-Sutcliffe Coefficient of Efficiency (NSCE. To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN, LSTM and Empirical Mode Decomposition (EMD coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

Science.gov (United States)

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-05-21

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five

Daily global solar radiation modelling using multi-layer perceptron neural networks in semi-arid region

Directory of Open Access Journals (Sweden)

Mawloud GUERMOUI

2016-07-01

Full Text Available Accurate estimation of Daily Global Solar Radiation (DGSR has been a major goal for solar energy application. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly of the search for relationships between weather variables, such as temperature, humidity, sunshine duration, etc. In this respect, the present study focuses on the development of artificial neural network (ANN model for estimation of daily global solar radiation on horizontal surface in Ghardaia city (South Algeria. In this analysis back-propagation algorithm is applied. Daily mean air temperature, relative humidity and sunshine duration was used as climatic inputs parameters, while the daily global solar radiation (DGSR was the only output of the ANN. We have evaluated Multi-Layer Perceptron (MLP models to estimate DGSR using three year of measurement (2005-2008. It was found that MLP-model based on sunshine duration and mean air temperature give accurate results in term of Mean Absolute Bias Error, Root Mean Square Error, Relative Square Error and Correlation Coefficient. The obtained values of these indicators are 0.67 MJ/m², 1.28 MJ/m², 6.12%and 98.18%, respectively which shows that MLP is highly qualified for DGSR estimation in semi-arid climates.

Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

International Nuclear Information System (INIS)

Xu, Bo; Dan, Han-Cheng; Li, Liang

2017-01-01

Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.

Monthly Summaries of the Global Historical Climatology Network - Daily (GHCN-D)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Monthly Summaries of Global Historical Climatology Network (GHCN)-Daily is a dataset derived from GHCN-Daily. The data are produced by computing simple averages or...

Social Network Characteristics and Daily Smoking among Young Adults in Sweden

Directory of Open Access Journals (Sweden)

Mikael Rostila

2013-11-01

Full Text Available A large number of studies have shown that friends’ smoking behavior is strongly associated with an individual’s own risk for smoking. However, few studies have examined whether other features of social networks, independently or conjointly with friends’ smoking behavior, may influence the risk for smoking. Because it is characterized by the growing importance of friendship networks, the transition from adolescence to young adulthood may constitute a particularly relevant period on which to focus our investigation of network influences on smoking behavior. The aim of this study was therefore to examine the consequences of peer smoking as well as other network characteristics (friends’ other health behaviors, relationship content, and structural aspects of the network on the risk for smoking among young adults. The data was based on a cross-sectional survey of Swedish 19-year-olds carried out in 2009 (n = 5,695 with a response rate of 51.6%. Logistic regression was the primary method of analysis. The results show that having a large percentage of smokers in one’s network was by far the most important risk factor for daily smoking. The risk of daily smoking was 21.20 (CI 14.24. 31.54 if 76%–100% of the network members smoked. Having a high percentage of physically active friends was inversely associated with daily smoking. The risk of smoking was 0.65 (CI 0.42. 1.00 if 76%–100% of the network members were physically active. No main associations between the other network characteristics (relationship content and structural aspects of the network and smoking were found. However, there was an interaction between the percentage of smokers in the network and relationship content (i.e., trust, relationship quality and propensity to discuss problems: positive relationship content in combination with peer smoking may increase the risk of smoking. Women with a high percentage of smokers in their networks were also at higher risk of daily

The EUSTACE project: delivering global, daily information on surface air temperature

Science.gov (United States)

Ghent, D.; Rayner, N. A.

2017-12-01

Day-to-day variations in surface air temperature affect society in many ways; however, daily surface air temperature measurements are not available everywhere. A global daily analysis cannot be achieved with measurements made in situ alone, so incorporation of satellite retrievals is needed. To achieve this, in the EUSTACE project (2015-2018, https://www.eustaceproject.eu) we have developed an understanding of the relationships between traditional (land and marine) surface air temperature measurements and retrievals of surface skin temperature from satellite measurements, i.e. Land Surface Temperature, Ice Surface Temperature, Sea Surface Temperature and Lake Surface Water Temperature. Here we discuss the science needed to produce a fully-global daily analysis (or ensemble of analyses) of surface air temperature on the centennial scale, integrating different ground-based and satellite-borne data types. Information contained in the satellite retrievals is used to create globally-complete fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place. This includes developing new "Big Data" analysis methods as the data volumes involved are considerable. We will present recent progress along this road in the EUSTACE project, i.e.: • identifying inhomogeneities in daily surface air temperature measurement series from weather stations and correcting for these over Europe; • estimating surface air temperature over all surfaces of Earth from surface skin temperature retrievals; • using new statistical techniques to provide information on higher spatial and temporal scales than currently available, making optimum use of information in data-rich eras. Information will also be given on how interested users can become involved.

Forecasting the daily electricity consumption in the Moscow region using artificial neural networks

Science.gov (United States)

Ivanov, V. V.; Kryanev, A. V.; Osetrov, E. S.

2017-07-01

In [1] we demonstrated the possibility in principle for short-term forecasting of daily volumes of passenger traffic in the Moscow metro with the help of artificial neural networks. During training and predicting, a set of the factors that affect the daily passenger traffic in the subway is passed to the input of the neural network. One of these factors is the daily power consumption in the Moscow region. Therefore, to predict the volume of the passenger traffic in the subway, we must first to solve the problem of forecasting the daily energy consumption in the Moscow region.

Daily temperature and precipitation data for 223 USSR Stations

Energy Technology Data Exchange (ETDEWEB)

Razuvaev, V.N.; Apasova, E.G.; Martuganov, R.A. [Research Institute of Hydrometeorological Information, Obninsk (Russian Federation); Vose, R.S. [Univ. of Tennessee, Knoxville, TN (United States); Steurer, P.M. [National Climatic Data Center, Asheville, NC (United States)

1993-11-01

On- May 23, 1972, the United States and the USSR established a bilateral initiative known as the Agreement on Protection of the Environment. Given recent interest in possible greenhouse gas-induced climate change, Working Group VIII (Influence of Environmental Changes on Climate) has become particularly useful to the scientific communities of both nations. Among its many achievements, Working Group VIII has been instrumental in the exchange of climatological information between the principal climate data centers of each country [i.e., the National Climatic Data Center (NCDC) in Asheville, North Carolina, and the Research Institute of Hydrometeorological Information in Obninsk, Russia]. Considering the relative lack of climate records previously available for the USSR, data obtained via this bilateral exchange are particularly valuable to researchers outside the former Soviet Union. To expedite the dissemination of these data, NOAA`s Climate and Global Change Program funded the Carbon Dioxide Information Analysis Center (CDIAC) and NCDC to distribute one of the more useful archives acquired through this exchange: a 223-station daily data set covering the period 1881-1989. This data set contains: (1) daily mean, minimum, and maximum temperature data; (2) daily precipitation data; (3) station inventory information (WMO No., name, coordinates, and elevation); (4) station history information (station relocation and rain gauge replacement dates); and (5) quality assurance information (i.e., flag codes that were assigned as a result of various data checks). The data set is available, free of charge, as a Numeric Data Package (NDP) from CDIAC. The NDP consists of 18 data files and a printed document which describes both the data files and the 223-station network in detail.

Daily Air Temperature and Electricity Load in Spain.

Science.gov (United States)

Valor, Enric; Meneu, Vicente; Caselles, Vicente

2001-08-01

Weather has a significant impact on different sectors of the economy. One of the most sensitive is the electricity market, because power demand is linked to several weather variables, mainly the air temperature. This work analyzes the relationship between electricity load and daily air temperature in Spain, using a population-weighted temperature index. The electricity demand shows a significant trend due to socioeconomic factors, in addition to daily and monthly seasonal effects that have been taken into account to isolate the weather influence on electricity load. The results indicate that the relationship is nonlinear, showing a `comfort interval' of ±3°C around 18°C and two saturation points beyond which the electricity load no longer increases. The analysis has also revealed that the sensitivity of electricity load to daily air temperature has increased along time, in a higher degree for summer than for winter, although the sensitivity in the cold season is always more significant than in the warm season. Two different temperature-derived variables that allow a better characterization of the observed relationship have been used: the heating and cooling degree-days. The regression of electricity data on them defines the heating and cooling demand functions, which show correlation coefficients of 0.79 and 0.87, and predicts electricity load with standard errors of estimate of ±4% and ±2%, respectively. The maximum elasticity of electricity demand is observed at 7 cooling degree-days and 9 heating degree-days, and the saturation points are reached at 11 cooling degree-days and 13 heating degree-days, respectively. These results are helpful in modeling electricity load behavior for predictive purposes.

A comparison of climatological observing windows and their impact on detecting daily temperature extrema

Science.gov (United States)

Žaknić-Ćatović, Ana; Gough, William A.

2018-04-01

Climatological observing window (COW) is defined as a time frame over which continuous or extreme air temperature measurements are collected. A 24-h time interval, ending at 00UTC or shifted to end at 06UTC, has been associated with difficulties in characterizing daily temperature extrema. A fixed 24-h COW used to obtain the temperature minima leads to potential misidentification due to fragmentation of "nighttime" into two subsequent nighttime periods due to the time discretization interval. The correct identification of air temperature extrema is achievable using a COW that identifies daily minimum over a single nighttime period and maximum over a single daytime period, as determined by sunrise and sunset. Due to a common absence of hourly air temperature observations, the accuracy of the mean temperature estimation is dependent on the accuracy of determination of diurnal air temperature extrema. Qualitative and quantitative criteria were used to examine the impact of the COW on detecting daily air temperature extrema. The timing of the 24-h observing window occasionally affects the determination of daily extrema through a mischaracterization of the diurnal minima and by extension can lead to errors in determining daily mean temperature. Hourly air temperature data for the time period from year 1987 to 2014, obtained from Toronto Buttonville Municipal Airport weather station, were used in analysis of COW impacts on detection of daily temperature extrema and calculation of annual temperature averages based on such extrema.

Enhancing Extreme Heat Health-Related Intervention and Preparedness Activities Using Remote Sensing Analysis of Daily Surface Temperature, Surface Observation Networks and Ecmwf Reanalysis

Science.gov (United States)

Garcia, R. L.; Booth, J.; Hondula, D.; Ross, K. W.; Stuyvesant, A.; Alm, G.; Baghel, E.

2015-12-01

Extreme heat causes more human fatalities in the United States than any other natural disaster, elevating the concern of heat-related mortality. Maricopa County Arizona is known for its high heat index and its sprawling metropolitan complex which makes this region a perfect candidate for human health research. Individuals at higher risk are unequally spatially distributed, leaving the poor, homeless, non-native English speakers, elderly, and the socially isolated vulnerable to heat events. The Arizona Department of Health Services, Arizona State University and NASA DEVELOP LaRC are working to establish a more effective method of placing hydration and cooling centers in addition to enhancing the heat warning system to aid those with the highest exposure. Using NASA's Earth Observation Systems from Aqua and Terra satellites, the daily spatial variability within the UHI was quantified over the summer heat seasons from 2005 - 2014, effectively establishing a remotely sensed surface temperature climatology for the county. A series of One-way Analysis of Variance revealed significant differences between daily surface temperature averages of the top 30% of census tracts within the study period. Furthermore, synoptic upper tropospheric circulation patterns were classified to relate surface weather types and heat index. The surface weather observation networks were also reviewed for analyzing the veracity of the other methods. The results provide detailed information regarding nuances within the UHI effect and will allow pertinent recommendations regarding the health department's adaptive capacity. They also hold essential components for future policy decision-making regarding appropriate locations for cooling centers and efficient warning systems.

Meteorological Reference Years of Daily Mean Temperature during the Slighting Time

International Nuclear Information System (INIS)

Marchante Jimenez, M.; Ramirez Santigosa, L.; Navarro Fernandez, A.; Mora Lopez, L.; Sidrach de Cardona Ortin, M.

2002-01-01

In this work the characterization of the daily mean temperature during the sunlight time has been analyzed. An algorithm for the hourly series generation from extreme daily values has been applied to evaluate the daily mean temperature during the sunlight time. A generic algorithm has been enhanced as a function of the sunrise time. This algorithm allows taking into account the fractions related to the sunrise and sunset hours. This methodology has been applied in data from 45 Spanish stations, uniformly distributed in the Iberian Peninsula. Data for a period of 14 years has been used in most of locations, and once the interest variable has been calculated, the meteorological reference year of the daily mean temperature during the sunlight time has been evaluated in each stations. The next step is the evaluation of the daily mean temperature during the sunlight time in any point into the zone of evaluation, not only in the measured stations. From the result data in each measured station, an geographic information system has been used in order to calculate the interpolation, obtaining maps with a data each 5 km. for each of the 365 days of the year. Then, this results can be superposed with the solar radiation evaluation obtaining the input data for the sizing of the photovoltaic grid connected system in any point of the Spanish geography. (Author) 64 refs

Determination of daily solar ultraviolet radiation using statistical models and artificial neural networks

Directory of Open Access Journals (Sweden)

F. J. Barbero

2006-09-01

Full Text Available In this study, two different methodologies are used to develop two models for estimating daily solar UV radiation. The first is based on traditional statistical techniques whereas the second is based on artificial neural network methods. Both models use daily solar global broadband radiation as the only measured input. The statistical model is derived from a relationship between the daily UV and the global clearness indices but modulated by the relative optical air mass. The inputs to the neural network model were determined from a large number of radiometric and atmospheric parameters using the automatic relevance determination method, although only the daily solar global irradiation, daily global clearness index and relative optical air mass were shown to be the optimal input variables. Both statistical and neural network models were developed using data measured at Almería (Spain, a semiarid and coastal climate, and tested against data from Table Mountain (Golden, CO, USA, a mountainous and dry environment. Results show that the statistical model performs adequately in both sites for all weather conditions, especially when only snow-free days at Golden were considered (RMSE=4.6%, MBE= –0.1%. The neural network based model provides the best overall estimates in the site where it has been trained, but presents an inadequate performance for the Golden site when snow-covered days are included (RMSE=6.5%, MBE= –3.0%. This result confirms that the neural network model does not adequately respond on those ranges of the input parameters which were not used for its development.

Modeling maximum daily temperature using a varying coefficient regression model

Science.gov (United States)

Han Li; Xinwei Deng; Dong-Yum Kim; Eric P. Smith

2014-01-01

Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature...

Gridded 5km GHCN-Daily Temperature and Precipitation Dataset, Version 1

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Gridded 5km GHCN-Daily Temperature and Precipitation Dataset (nClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature,...

Comparing various artificial neural network types for water temperature prediction in rivers

Science.gov (United States)

Piotrowski, Adam P.; Napiorkowski, Maciej J.; Napiorkowski, Jaroslaw J.; Osuch, Marzena

2015-10-01

A number of methods have been proposed for the prediction of streamwater temperature based on various meteorological and hydrological variables. The present study shows a comparison of few types of data-driven neural networks (multi-layer perceptron, product-units, adaptive-network-based fuzzy inference systems and wavelet neural networks) and nearest neighbour approach for short time streamwater temperature predictions in two natural catchments (mountainous and lowland) located in temperate climate zone, with snowy winters and hot summers. To allow wide applicability of such models, autoregressive inputs are not used and only easily available measurements are considered. Each neural network type is calibrated independently 100 times and the mean, median and standard deviation of the results are used for the comparison. Finally, the ensemble aggregation approach is tested. The results show that simple and popular multi-layer perceptron neural networks are in most cases not outperformed by more complex and advanced models. The choice of neural network is dependent on the way the models are compared. This may be a warning for anyone who wish to promote own models, that their superiority should be verified in different ways. The best results are obtained when mean, maximum and minimum daily air temperatures from the previous days are used as inputs, together with the current runoff and declination of the Sun from two recent days. The ensemble aggregation approach allows reducing the mean square error up to several percent, depending on the case, and noticeably diminishes differences in modelling performance obtained by various neural network types.

Statistical downscaling of daily temperature in Central Europe

Czech Academy of Sciences Publication Activity Database

Huth, Radan

2002-01-01

Roč. 15, - (2002), s. 1731-1742 ISSN 0894-8755 R&D Projects: GA ČR GA205/99/1561; GA AV ČR IAA3042903 Institutional research plan: CEZ:AV0Z3042911 Keywords : statistical downscaling * daily temperature * Central Europe Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 3.250, year: 2002

Application of Markov chain model to daily maximum temperature for thermal comfort in Malaysia

International Nuclear Information System (INIS)

Nordin, Muhamad Asyraf bin Che; Hassan, Husna

2015-01-01

The Markov chain’s first order principle has been widely used to model various meteorological fields, for prediction purposes. In this study, a 14-year (2000-2013) data of daily maximum temperatures in Bayan Lepas were used. Earlier studies showed that the outdoor thermal comfort range based on physiologically equivalent temperature (PET) index in Malaysia is less than 34°C, thus the data obtained were classified into two state: normal state (within thermal comfort range) and hot state (above thermal comfort range). The long-run results show the probability of daily temperature exceed TCR will be only 2.2%. On the other hand, the probability daily temperature within TCR will be 97.8%

Association between daily environmental temperature and suicide mortality in Korea (2001-2005).

Science.gov (United States)

Kim, Yoonhee; Kim, Ho; Kim, Dong-Sik

2011-04-30

Little attention has been paid to whether temperature is associated with suicide and to whether suicide seasonality appears in Asian countries as shown in Western countries, even though suicide rates in Korea have increased steadily. The goal of the present study was to examine the association between daily temperature and daily suicide rate in Korea, taking gender, age, and education level into account. Data were analyzed using a generalized additive model, adjusting for confounding factors such as sunshine, relative humidity, holidays, and long-term trends. Suicide rates were higher in spring and summer than other seasons. We observed a 1.4% increase (95% confidence interval=1.0-1.7%) in suicide with each 1°C-increase in daily mean temperature. The suicide risks related to the temperature for males, elderly people, and those with less education were higher than for females, younger people, and those with more education, respectively. These findings have confirmed that temperature is associated with suicide in Korea and further our understanding of more susceptible groups, the effects of gender, age, and education level. Therefore, temperature, one of the meteorological factors, is an important risk factor on suicide. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Testing the performance of three nonlinear methods of time seriesanalysis for prediction and downscaling of European daily temperatures

Directory of Open Access Journals (Sweden)

J. Miksovsky

2005-01-01

Full Text Available We investigated the usability of the method of local linear models (LLM, multilayer perceptron neural network (MLP NN and radial basis function neural network (RBF NN for the construction of temporal and spatial transfer functions between different meteorological quantities, and compared the obtained results both mutually and to the results of multiple linear regression (MLR. The tested methods were applied for the short-term prediction of daily mean temperatures and for the downscaling of NCEP/NCAR reanalysis data, using series of daily mean, minimum and maximum temperatures from 25 European stations as predictands. None of the tested nonlinear methods was recognized to be distinctly superior to the others, but all nonlinear techniques proved to be better than linear regression in the majority of the cases. It is also discussed that the most frequently used nonlinear method, the MLP neural network, may not be the best choice for processing the climatic time series - LLM method or RBF NNs can offer a comparable or slightly better performance and they do not suffer from some of the practical disadvantages of MLPs. Aside from comparing the performance of different methods, we paid attention to geographical and seasonal variations of the results. The forecasting results showed that the nonlinear character of relations between climate variables is well apparent over most of Europe, in contrast to rather weak nonlinearity in the Mediterranean and North Africa. No clear large-scale geographical structure of nonlinearity was identified in the case of downscaling. Nonlinearity also seems to be noticeably stronger in winter than in summer in most locations, for both forecasting and downscaling.

A space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature

KAUST Repository

Jha, Sanjeev Kumar

2015-07-21

A geostatistical framework is proposed to downscale daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here, the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 km and 10 km resolution for a twenty year period ranging from 1985 to 2004. The data are used to predict downscaled climate variables for the year 2005. The result, for each downscaled pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference dataset indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple-point geostatistics approach is an efficient tool to be used for statistical downscaling to obtain local scale estimates of precipitation and temperature from General Circulation Models. This article is protected by copyright. All rights reserved.

Daily Cycle of Air Temperature and Surface Temperature in Stone Forest

Science.gov (United States)

Wang, K.; Li, Y.; Wang, X.; Yuan, M.

2013-12-01

Urbanization is one of the most profound human activities that impact on climate change. In cities, where are highly artificial areas, the conflict between human activity and natural climate is particularly prominent. Urban areas always have the larger area of impervious land, the higher consumption of greenhouse gases, more emissions of anthropogenic heat and air pollution, all contribute to the urban warming phenomena. Understanding the mechanisms causing a variety of phenomena involved in the urban warming is critical to distinguish the anthropogenic effect and natural variation in the climate change. However, the exact dynamics of urban warming were poorly understood, and effective control strategies are not available. Here we present a study of the daily cycle of air temperature and surface temperature in Stone Forest. The specific heat of the stones in the Stone Forest and concrete of the man-made structures within the cities are approximate. Besides, the height of the Stone Forest and the height of buildings within the city are also similar. As a scenic area, the Stone Forest is being preserved and only opened for sightseeing. There is no anthropogenic heat, as well air pollution within the Stone Forest. The thermal environment in Stone Forest can be considered to be a simulation of thermal environment in the city, which can reveal the effect of man-made structures on urban thermal environment. We conducted the field studies and numerical analysis in the Stone Forest for 4 typical urban morphology and environment scenarios, including high-rise compact cities, low-rise sparse cities, garden cities and isolated single stone. Air temperature and relative humidity were measured every half an hour in 15 different locations, which within different spatial distribution of stones and can represent the four urban scenarios respectively. At the same time, an infrared camera was used to take thermal images and get the hourly surface temperatures of stones and

Near-Real-Time DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures

Data.gov (United States)

National Aeronautics and Space Administration — The Near-Real-Time DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperature product provides near-real-time brightness temperatures for both the Northern and...

A space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature

KAUST Repository

Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason; McCabe, Matthew; Sharma, Ashish

2015-01-01

precipitation and daily temperature over several years. Here, the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 km and 10 km resolution for a twenty year period ranging from 1985

Estimating the daily global solar radiation spatial distribution from diurnal temperature ranges over the Tibetan Plateau in China

International Nuclear Information System (INIS)

Pan, Tao; Wu, Shaohong; Dai, Erfu; Liu, Yujie

2013-01-01

Highlights: ► Bristow–Campbell model was calibrated and validated over the Tibetan Plateau. ► Develop a simple method to rasterise the daily global solar radiation and get gridded information. ► The daily global solar radiation spatial distribution over the Tibetan Plateau was estimated. - Abstract: Daily global solar radiation is fundamental to most ecological and biophysical processes because it plays a key role in the local and global energy budget. However, gridded information about the spatial distribution of solar radiation is limited. This study aims to parameterise the Bristow–Campbell model for the daily global solar radiation estimation in the Tibetan Plateau and propose a method to rasterise the daily global solar radiation. Observed daily solar radiation and diurnal temperature data from eleven stations over the Tibetan Plateau during 1971–2010 were used to calibrate and validate the Bristow–Campbell radiation model. The extra-terrestrial radiation and clear sky atmospheric transmittance were calculated on a Geographic Information System (GIS) platform. Results show that the Bristow–Campbell model performs well after adjusting the parameters, the average Pearson’s correlation coefficients (r), Nash–Sutcliffe equation (NSE), ratio of the root mean square error to the standard deviation of measured data (RSR), and root mean-square error (RMSE) of 11 stations are 0.85, 2.81 MJ m −2 day −1 , 0.3 and 0.77 respectively. Gridded maximum and minimum average temperature data were obtained using Parameter-elevation Regressions on Independent Slopes Model (PRISM) and validated by the Chinese Ecosystem Research Network (CERN) stations’ data. The spatial daily global solar radiation distribution pattern was estimated and analysed by combining the solar radiation model (Bristow–Campbell model) and meteorological interpolation model (PRISM). Based on the overall results, it can be concluded that a calibrated Bristow–Campbell performs well

Evaluation of empirical relationships between extreme rainfall and daily maximum temperature in Australia

Science.gov (United States)

Herath, Sujeewa Malwila; Sarukkalige, Ranjan; Nguyen, Van Thanh Van

2018-01-01

Understanding the relationships between extreme daily and sub-daily rainfall events and their governing factors is important in order to analyse the properties of extreme rainfall events in a changing climate. Atmospheric temperature is one of the dominant climate variables which has a strong relationship with extreme rainfall events. In this study, a temperature-rainfall binning technique is used to evaluate the dependency of extreme rainfall on daily maximum temperature. The Clausius-Clapeyron (C-C) relation was found to describe the relationship between daily maximum temperature and a range of rainfall durations from 6 min up to 24 h for seven Australian weather stations, the stations being located in Adelaide, Brisbane, Canberra, Darwin, Melbourne, Perth and Sydney. The analysis shows that the rainfall - temperature scaling varies with location, temperature and rainfall duration. The Darwin Airport station shows a negative scaling relationship, while the other six stations show a positive relationship. To identify the trend in scaling relationship over time the same analysis is conducted using data covering 10 year periods. Results indicate that the dependency of extreme rainfall on temperature also varies with the analysis period. Further, this dependency shows an increasing trend for more extreme short duration rainfall and a decreasing trend for average long duration rainfall events at most stations. Seasonal variations of the scale changing trends were analysed by categorizing the summer and autumn seasons in one group and the winter and spring seasons in another group. Most of 99th percentile of 6 min, 1 h and 24 h rain durations at Perth, Melbourne and Sydney stations show increasing trend for both groups while Adelaide and Darwin show decreasing trend. Furthermore, majority of scaling trend of 50th percentile are decreasing for both groups.

Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks

International Nuclear Information System (INIS)

Cao Jiacong; Lin Xingchun

2008-01-01

An accurate forecast of solar irradiation is required for various solar energy applications and environmental impact analyses in recent years. Comparatively, various irradiation forecast models based on artificial neural networks (ANN) perform much better in accuracy than many conventional prediction models. However, the forecast precision of most existing ANN based forecast models has not been satisfactory to researchers and engineers so far, and the generalization capability of these networks needs further improving. Combining the prominent dynamic properties of a recurrent neural network (RNN) with the enhanced ability of a wavelet neural network (WNN) in mapping nonlinear functions, a diagonal recurrent wavelet neural network (DRWNN) is newly established in this paper to perform fine forecasting of hourly and daily global solar irradiance. Some additional steps, e.g. applying historical information of cloud cover to sample data sets and the cloud cover from the weather forecast to network input, are adopted to help enhance the forecast precision. Besides, a specially scheduled two phase training algorithm is adopted. As examples, both hourly and daily irradiance forecasts are completed using sample data sets in Shanghai and Macau, and comparisons between irradiation models show that the DRWNN models are definitely more accurate

The Multi-Layered Perceptrons Neural Networks for the Prediction of Daily Solar Radiation

OpenAIRE

Radouane Iqdour; Abdelouhab Zeroual

2007-01-01

The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in term of the statistical indicators, with a linear model most used in literature, is also perfo...

Daily number of fractures is associated with road temperature in an urban area

DEFF Research Database (Denmark)

Jantzen, Christopher; Jørgensen, Henrik L; Thomsen, Morten

2014-01-01

winters. MATERIAL AND METHODS: Retrospective data collection was conducted on all patients treated at Bispebjerg Hospital, Denmark, for a humeral, ankle, distal radius or hip fracture during the periods October to April 2009/2010 and 2010/2011. Patients were grouped according to age into the following......,938 fractures) were treated during the study periods. The daily number of distal radius, humeral and ankle fractures increased significantly with decreasing road surface temperature and the presence of IA. For hip fractures no significant association was found. Decreasing temperature was associated......INTRODUCTION: Different factors related to winter are known to influence the fracture incidence, but little is known about the effect of road surface temperature. This study examines the association between road surface temperature and the daily number of fractures in an urban area during two...

Climate Prediction Center(CPC)Daily U.S. Precipitation and Temperature Summary

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Daily U.S. minimum and maximum temperatures in whole degrees Fahrenheit and reported and estimated precipitation amounts in hundredths of inches(ex 100 is 1.00...

Reconstructing missing daily precipitation data using regression trees and artificial neural networks

Science.gov (United States)

Incomplete meteorological data has been a problem in environmental modeling studies. The objective of this work was to develop a technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN)....

New England observed and predicted August stream/river temperature daily range points

Data.gov (United States)

U.S. Environmental Protection Agency — The shapefile contains points with associated observed and predicted August stream/river temperature daily ranges in New England based on a spatial statistical...

Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution

NARCIS (Netherlands)

Kilibarda, M.; Hengl, T.; Heuvelink, G.B.M.; Graler, B.; Pebesma, E.; Tadic, M.P.; Bajat, B.

2014-01-01

Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1km for the global land mass. Predictions in

Operational forecasting of daily temperatures in the Valencia Region. Part I: maximum temperatures in summer.

Science.gov (United States)

Gómez, I.; Estrela, M.

2009-09-01

Extreme temperature events have a great impact on human society. Knowledge of summer maximum temperatures is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, summer maximum daily temperatures are considered a parameter of interest and concern since persistent heat-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict heat-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily maximum temperatures during summer over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the summer forecast period of 1 June - 30 September, 2007. The results obtained are encouraging and indicate a good agreement between the observed and simulated maximum temperatures. Moreover, the model captures quite well the temperatures in the extreme heat episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia, Spain).

Influence of temperature on daily locomotor activity in the crab Uca pugilator.

Directory of Open Access Journals (Sweden)

Audrey M Mat

Full Text Available Animals living in the intertidal zone are exposed to prominent temperature changes. To cope with the energetic demands of environmental thermal challenges, ectotherms rely mainly on behavioral responses, which may change depending on the time of the day and seasonally. Here, we analyze how temperature shapes crabs' behavior at 2 different times of the year and show that a transition from constant cold (13.5°C to constant warm (17.5°C water temperature leads to increased locomotor activity levels throughout the day in fiddler crabs (Uca pugilator collected during the summer. In contrast, the same transition in environmental temperature leads to a decrease in the amplitude of the daily locomotor activity rhythm in crabs collected during the winter. In other words, colder temperatures during the cold season favor a more prominent diurnal behavior. We interpret this winter-summer difference in the response of daily locomotor activity to temperature changes within the framework of the circadian thermoenergetics hypothesis, which predicts that a less favorable energetic balance would promote a more diurnal activity pattern. During the winter, when the energetic balance is likely less favorable, crabs would save energy by being more active during the expected high-temperature phase of the day-light phase-and less during the expected low-temperature phase of the day-dark phase. Our results suggest that endogenous rhythms in intertidal ectotherms generate adaptive behavioral programs to cope with thermoregulatory demands of the intertidal habitat.

Hour-Glass Neural Network Based Daily Money Flow Estimation for Automatic Teller Machines

Science.gov (United States)

Karungaru, Stephen; Akashi, Takuya; Nakano, Miyoko; Fukumi, Minoru

Monetary transactions using Automated Teller Machines (ATMs) have become a normal part of our daily lives. At ATMs, one can withdraw, send or debit money and even update passbooks among many other possible functions. ATMs are turning the banking sector into a ubiquitous service. However, while the advantages for the ATM users (financial institution customers) are many, the financial institution side faces an uphill task in management and maintaining the cash flow in the ATMs. On one hand, too much money in a rarely used ATM is wasteful, while on the other, insufficient amounts would adversely affect the customers and may result in a lost business opportunity for the financial institution. Therefore, in this paper, we propose a daily cash flow estimation system using neural networks that enables better daily forecasting of the money required at the ATMs. The neural network used in this work is a five layered hour glass shaped structure that achieves fast learning, even for the time series data for which seasonality and trend feature extraction is difficult. Feature extraction is carried out using the Akamatsu Integral and Differential transforms. This work achieves an average estimation accuracy of 92.6%.

Chronic air pollution and social deprivation as modifiers of the association between high temperature and daily mortality.

Science.gov (United States)

Benmarhnia, Tarik; Oulhote, Youssef; Petit, Claire; Lapostolle, Annabelle; Chauvin, Pierre; Zmirou-Navier, Denis; Deguen, Séverine

2014-06-18

Heat and air pollution are both associated with increases in mortality. However, the interactive effect of temperature and air pollution on mortality remains unsettled. Similarly, the relationship between air pollution, air temperature, and social deprivation has never been explored. We used daily mortality data from 2004 to 2009, daily mean temperature variables and relative humidity, for Paris, France. Estimates of chronic exposure to air pollution and social deprivation at a small spatial scale were calculated and split into three strata. We developed a stratified Poisson regression models to assess daily temperature and mortality associations, and tested the heterogeneity of the regression coefficients of the different strata. Deaths due to ambient temperature were calculated from attributable fractions and mortality rates were estimated. We found that chronic air pollution exposure and social deprivation are effect modifiers of the association between daily temperature and mortality. We found a potential interactive effect between social deprivation and chronic exposure with regards to air pollution in the mortality-temperature relationship. Our results may have implications in considering chronically polluted areas as vulnerable in heat action plans and in the long-term measures to reduce the burden of heat stress especially in the context of climate change.

TAO/TRITON, RAMA, and PIRATA Buoys, Daily, 1977-present, Sea Surface Temperature

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has daily Sea Surface Temperature (SST) data from the TAO/TRITON (Pacific Ocean, https://www.pmel.noaa.gov/gtmba/ ), RAMA (Indian Ocean,...

Investigation of Breakpoint and Trend of Daily Air Temperature Range for Mashhad, Iran

Directory of Open Access Journals (Sweden)

shideh shams

2017-01-01

Full Text Available Introduction: Air temperature as an important climatic factor can influence variability and distribution of other climatic parameters. Therefore, tracking the changes in air temperature is a popular procedure in climate change studies.. According to the national academy in the last decade, global temperature has raised 0.4 to 0.8⁰C. Instrumental records show that, with the exception of 1998, the 10 warmest year (during the last 150 years, occurred since 2000, and 2014 was the warmest year. Investigation of maximum and minimum air temperature temporal trend indicates that these two parameters behave differently over time. It has been shown that the minimum air temperature raises noticeably more than the maximum air temperature, which causes a reduction in the difference of maximum and minimum daily air temperature (daily temperature range, DTR. There are several factors that have an influence on reducing DTR such as: Urban development, farms’ irrigation and desertification. It has been shown that DTR reduction occurs mostly during winter and is less frequent during summer, which shows the season’s effect on the temperature trend. Considering the significant effects of the climatological factors on economic and agricultural management issues, the aim of this study is to investigate daily air temperature range for yearly, seasonal and monthly time scales, using available statistical methods. Materials and Methods: Daily maximum and minimum air temperature records (from 1950 to 2010 were obtained from Mashhad Meteorological Organization. In order to control the quality of daily Tmax and Tmin data, four different types of quality controls were applied. First of all, gross errors were checked. In this step maximum and minimum air temperature data exceeding unlikely air temperature values, were eliminated from data series. Second, data tolerance was checked by searching for periods longer than a certain number of consecutive days with exactly the

Neural networks to predict exosphere temperature corrections

Science.gov (United States)

Choury, Anna; Bruinsma, Sean; Schaeffer, Philippe

2013-10-01

Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low-orbiting satellite Gravity Field and Steady-State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.

Automatic Control of Reactor Temperature and Power Distribution for a Daily Load following Operation

Energy Technology Data Exchange (ETDEWEB)

Yu, Keuk Jong; Kim, Han Gon [Korea Hydro and Nuclear Power Institute, Daejeon (Korea, Republic of)

2010-10-15

An automatic control method of reactor power and power distribution was developed for a daily load following operation of APR1400. This method used a model predictive control (MPC) methodology having second-order plant data. And it utilized a reactor power ratio and axial shape index as control variables. However, the reactor regulating system of APR1400 is operated by the difference between the average temperature of the reactor core and the reference temperature, which is proportional to the turbine load. Thus, this paper reports on the model predictive control methodology using fourth-order plant data and a reactor temperature instead of the reactor power shape. The purpose of this study is to develop a revised automatic controller and analyze the behavior of the nuclear reactor temperature (Tavg) and the axial shape index (ASI) using the MPC method during a daily load following operation

Estimation of Daily Air Temperature Based on MODIS Land Surface Temperature Products over the Corn Belt in the US

Directory of Open Access Journals (Sweden)

Linglin Zeng

2015-01-01

Full Text Available Air temperature (Ta is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS Ts (Land Surface Temperature (LST products are widely used to estimate daily Ta. However, only daytime LST (Ts-day or nighttime LST (Ts-night data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature, respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May–September from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19–1.85, 0.37–1.12 and 0.26–0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc.

Comparing daily temperature averaging methods: the role of surface and atmosphere variables in determining spatial and seasonal variability

Science.gov (United States)

Bernhardt, Jase; Carleton, Andrew M.

2018-05-01

The two main methods for determining the average daily near-surface air temperature, twice-daily averaging (i.e., [Tmax+Tmin]/2) and hourly averaging (i.e., the average of 24 hourly temperature measurements), typically show differences associated with the asymmetry of the daily temperature curve. To quantify the relative influence of several land surface and atmosphere variables on the two temperature averaging methods, we correlate data for 215 weather stations across the Contiguous United States (CONUS) for the period 1981-2010 with the differences between the two temperature-averaging methods. The variables are land use-land cover (LULC) type, soil moisture, snow cover, cloud cover, atmospheric moisture (i.e., specific humidity, dew point temperature), and precipitation. Multiple linear regression models explain the spatial and monthly variations in the difference between the two temperature-averaging methods. We find statistically significant correlations between both the land surface and atmosphere variables studied with the difference between temperature-averaging methods, especially for the extreme (i.e., summer, winter) seasons (adjusted R2 > 0.50). Models considering stations with certain LULC types, particularly forest and developed land, have adjusted R2 values > 0.70, indicating that both surface and atmosphere variables control the daily temperature curve and its asymmetry. This study improves our understanding of the role of surface and near-surface conditions in modifying thermal climates of the CONUS for a wide range of environments, and their likely importance as anthropogenic forcings—notably LULC changes and greenhouse gas emissions—continues.

Daily Weather Records

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is...

Gap Filling of Daily Sea Levels by Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Lyubka Pashova

2013-06-01

Full Text Available In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN architectures - Feed-Forward Backpropagation (FFBP and recurrent Echo state network (ESN. In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5-years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real-time interpolation of missing data in the time series.

Energy and exergy analysis of low temperature district heating network

International Nuclear Information System (INIS)

Li, Hongwei; Svendsen, Svend

2012-01-01

Low temperature district heating with reduced network supply and return temperature provides better match of the low quality building heating demand and the low quality heating supply from waste heat or renewable energy. In this paper, a hypothetical low temperature district heating network is designed to supply heating for 30 low energy detached residential houses. The network operational supply/return temperature is set as 55 °C/25 °C, which is in line with a pilot project carried out in Denmark. Two types of in-house substations are analyzed to supply the consumer domestic hot water demand. The space heating demand is supplied through floor heating in the bathroom and low temperature radiators in the rest of rooms. The network thermal and hydraulic conditions are simulated under steady state. A district heating network design and simulation code is developed to incorporate the network optimization procedure and the network simultaneous factor. Through the simulation, the overall system energy and exergy efficiencies are calculated and the exergy losses for the major district heating system components are identified. Based on the results, suggestions are given to further reduce the system energy/exergy losses and increase the quality match between the consumer heating demand and the district heating supply. -- Highlights: ► Exergy and energy analysis for low and medium temperature district heating systems. ► Different district heating network dimensioning methods are analyzed. ► Major exergy losses are identified in the district heating network and the in-house substations. ► Advantages to apply low temperature district heating are highlighted through exergy analysis. ► The influence of thermal by-pass on system exergy/energy performance is analyzed.

Global Daily Climatology Network: Kazakhstan subset

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset is a compilation of in situ daily meteorological observations for Kazakhstan within the framework of joint efforts to create Global Daily Climatology...

Effects of temperature and photoperiod on daily activity rhythms of Lutzomyia longipalpis (Diptera: Psychodidae).

Science.gov (United States)

Rivas, Gustavo B S; de Souza, Nataly Araujo; Peixoto, Alexandre A; Bruno, Rafaela V

2014-06-19

Insect vectors have been established as models in Chronobiology for many decades, and recent studies have demonstrated a close relationship between the circadian clock machinery, daily rhythms of activity and vectorial capacity. Lutzomyia longipalpis, the primary vector of Leishmania (Leishmania) infantum in the New World, is reported to have crepuscular/nocturnal activity in the wild. However, most of these studies applied hourly CDC trap captures, which is a good indicative of L. longipalpis behaviour, but has limited accuracy due to the inability to record the daily activity of a single insect during consecutive days. In addition, very little is known about the activity pattern of L. longipalpis under seasonal variations of average temperature and day length in controlled laboratory conditions. We recorded the locomotor activity of L. longipalpis males under different artificial regimes of temperature and photoperiod. First, in order to test the effects of temperature on the activity, sandflies were submitted to regimes of light/dark cycles similar to the equinox photoperiod (LD 12:12) combined with different constant temperatures (20°C, 25°C and 30°C). In addition, we recorded sandfly locomotor activity under a mild constant temperature (25°C with different day length regimes: 8 hours, 12 hours and 16 hours). L. longipalpis exhibited more activity at night, initiating dusk-related activity (onset time) at higher rather than lower temperatures. In parallel, changes of photoperiod affected anticipation as well as all the patterns of activity (onset, peak and offset time). However, under LD 16:08, sandflies presented the earliest values of maximum peak and offset times, contrary to other regimes. Herein, we showed that light and temperature modulate L. longipalpis behaviour under controlled laboratory conditions, suggesting that sandflies might use environmental information to sustain their crepuscular/nocturnal activity, as well as other important aspects as

The daily rhythm of body temperature, heart and respiratory rate in newborn dogs.

Science.gov (United States)

Piccione, Giuseppe; Giudice, Elisabetta; Fazio, Francesco; Mortola, Jacopo P

2010-08-01

We asked whether, during the postnatal period, the daily patterns of body temperature (Tb), heart rate (HR) and breathing frequency (f) begin and develop in synchrony. To this end, measurements of HR, f and Tb were performed weekly, on two consecutive days, for the first two postnatal months on puppies of three breeds of dogs (Rottweiler, Cocker Spaniel and Carlino dogs) with very different birth weights and postnatal growth patterns. Ambient conditions and feeding habits were constant for all puppies. The results indicated that (1) the 24-h average Tb increased and average HR and f decreased with growth, (2) the daily rhythms in Tb were apparent by 4 weeks, irrespective of the puppy's growth pattern, (3) the daily rhythm of Tb in the puppy was not necessarily following that of the mother; in fact, it could anticipate it. (4) The daily rhythms in HR and f were not apparent for the whole study period. We conclude that in neonatal dogs the onset of the daily rhythms of Tb has no obvious relationship with body size or rate of growth and is not cued by the maternal Tb rhythm. The daily rhythms of HR and f do not appear before 2 months of age. Hence, they are not in synchrony with those of Tb.

Trends in extreme daily temperatures and humidex index in the United Arab Emirates over 1948-2014.

Science.gov (United States)

Yang, H. W.; Ouarda, T.

2015-12-01

This study deals with the analysis of the characteristics of extreme temperature events in the Middle East, using NCEP reanalysis gridded data, for the summer (May-October) and winter (November-April) seasons. Trends in the occurrences of three types of heat spells during 1948-2014 are studied by both Linear Regression (LR) and Mann-Kendall (MK) test. Changes in the diurnal temperature range (DTR) are also investigated. To better understand the effects of heat spells on public health, the Humidex, a combination index of ambient temperature and relative humidity, is also used. Using percentile threshold, temperature (Humidex) Type-A and Type-B heat spells are defined respectively by daily maximum and minimum temperature (Humidex). Type-C heat spells are defined as the joint occurrence of Type-A and Type-B heat spells at the same time. In the Middle East, it is found that no coherent trend in temperature Type-A heat spells is observed. However, the occurrences of temperature Type-B and C heat spells have consistently increased since 1948. For Humidex heat spells, coherently increased activities of all three types of heat spells are observed in the area. During the summer, the magnitude of the positive trends in Humidex heat spells are generally stronger than temperature heat spells. More than half of the locations in the area show significantly negative DTR trends in the summer, but the trends vary according to the region in the winter. Annual mean temperature has increased an average by 0.5°C, but it is mainly associated with the daily minimum temperature which has warmed up by 0.84°C.Daily maximum temperature showed no significant trends. The warming is hence stronger in minimum temperatures than in maximum temperatures resulting in a decrease in DTR by 0.16 °C per decade. This study indicates hence that the UAE has not become hotter, but it has become less cold during 1948 to 2014.

Application of Wireless Sensor Networks for Indoor Temperature Regulation

DEFF Research Database (Denmark)

Stojkoska, Biljana Risteska; Popovska Avramova, Andrijana; Chatzimisios, Periklis

2014-01-01

Wireless sensor networks take a major part in our everyday lives by enhancing systems for home automation, healthcare, temperature control, energy consumption monitoring, and so forth. In this paper we focus on a system used for temperature regulation for residential, educational, industrial...... energy savings by reducing the amount of data transmissions through the network. Furthermore, the framework explores techniques for localization, such that the location of the nodes can be used by algorithms that regulate temperature settings......., and commercial premises, and so forth. We propose a framework for indoor temperature regulation and optimization using wireless sensor networks based on ZigBee platform. This paper considers architectural design of the system, as well as implementation guidelines. The proposed system favors methods that provide...

Evaluation and projection of daily temperature percentiles from statistical and dynamical downscaling methods

Directory of Open Access Journals (Sweden)

A. Casanueva

2013-08-01

Full Text Available The study of extreme events has become of great interest in recent years due to their direct impact on society. Extremes are usually evaluated by using extreme indicators, based on order statistics on the tail of the probability distribution function (typically percentiles. In this study, we focus on the tail of the distribution of daily maximum and minimum temperatures. For this purpose, we analyse high (95th and low (5th percentiles in daily maximum and minimum temperatures on the Iberian Peninsula, respectively, derived from different downscaling methods (statistical and dynamical. First, we analyse the performance of reanalysis-driven downscaling methods in present climate conditions. The comparison among the different methods is performed in terms of the bias of seasonal percentiles, considering as observations the public gridded data sets E-OBS and Spain02, and obtaining an estimation of both the mean and spatial percentile errors. Secondly, we analyse the increments of future percentile projections under the SRES A1B scenario and compare them with those corresponding to the mean temperature, showing that their relative importance depends on the method, and stressing the need to consider an ensemble of methodologies.

Exergy and Energy Analysis of Low Temperature District Heating Network

DEFF Research Database (Denmark)

Li, Hongwei; Svendsen, Svend

is in line with a pilot project that is carrying out in Denmark with network supply/return temperature at 55oC/25 oC. The consumer domestic hot water (DHW) demand is supplied with a special designed district heating (DH) storage tank. The space heating (SH) demand is supplied with a low temperature radiator......Low temperature district heating (LTDH) with reduced network supply and return temperature provides better match of the low quality building thermal demand and the low quality waste heat supply. In this paper, an exemplary LTDH network was designed for 30 low energy demand residential houses, which....... The network thermal and hydraulic conditions were simulated under steady state with an in-house district heating network design and simulation code. Through simulation, the overall system energetic and exergetic efficiencies were calculated and the exergy losses for the major district heating system...

[Risk of deaths from cardiovascular diseases in Polish urban population associated with changes in maximal daily temperature].

Science.gov (United States)

Rabczenko, Daniel; Wojtyniak, Bogdan; Kuchcik, Magdalena; Seroka, Wojciech

2009-01-01

The paper presents results of analysis of short-term effect of changes in maximal daily temperature on daily mortality from cardiovascular diseases in warm season in years 1999-2006. Analysis was carried out in six large Polish cities--Katowice, Kraków, Łódź, Poznań, Warszawa and Wrocław. Generalized additive models were used in the analysis. Potential confounding factors--long term changes of mortality, day of week and other meteorological factors (atmospheric pressure, humidity, mean wind speed) were taken into account during model building process. Analysis was done for two age groups--0-69 and 70 years and older. Significant, positive association between daily maximal temperature and risk of death from cardiovascular diseases was found only in older age group.

AMSR-E/Aqua Daily L3 6.25 km 89 GHz Brightness Temperature (Tb) Polar Grids V002

Data.gov (United States)

National Aeronautics and Space Administration — The AMSR-E/Aqua Level-3 6.25 km daily sea ice product includes 89.0 GHz brightness temperature averages (daily, ascending, and descending) on a 6.25 km polar...

Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Chun-tian Cheng

2015-07-01

Full Text Available Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO, ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.

Wireless Sensor Networks Framework for Indoor Temperature Regulation

DEFF Research Database (Denmark)

Stojkoska, Biljana; Popovska Avramova, Andrijana

2013-01-01

Wireless Sensor Networks take a major part in our everyday lives by enhancing systems for home automation, health-care, temperature control, energy consumption monitoring etc. In this paper we focus on a system used for temperature regulation for homes, educational, industrial, commercial premises...... etc. We propose a framework for indoor regulation and optimization of temperature using wireless sensor networks based on ZigBee. Methods for optimal temperature regulation are suggested and discussed. The framework is based on methods that provide energy savings by reducing the amount of data...... transmissions through prediction methods. Additionally the framework explores techniques for localization, such that the location of the nodes is used for optimization of the temperature settings. Information on node location is used to provide the most optimal tradeo between the time it takes to reach...

Energetic and Exergetic Analysis of Low and Medium Temperature District Heating Network Integration

DEFF Research Database (Denmark)

Li, Hongwei; Svendsen, Svend

In this paper, energetic and exergetic approaches were applied to an exemplary low temperature district heating (LTDH) network with supply/return water temperature at 55oC/25 oC. The small LTDH network is annexed to a large medium temperature district heating (MTDH) network. The LTDH network can ...... will reduce the amount of water supply from the MTDH network and improve the system energy conversion efficiency. Through the simulation, the system energetic and exergetic efficiencies based on the two network integration approaches were calculated and evaluated.......In this paper, energetic and exergetic approaches were applied to an exemplary low temperature district heating (LTDH) network with supply/return water temperature at 55oC/25 oC. The small LTDH network is annexed to a large medium temperature district heating (MTDH) network. The LTDH network can...... be supplied through upgrading the return water from the MTDH network with a small centralized heat pump. Alternatively, the supply and return water from the MTDH network can be mixed with a shunt at the junction point to supply the LTDH network. Comparing with the second approach, the heat pump system...

Fluid temperatures: Modeling the thermal regime of a river network

Science.gov (United States)

Rhonda Mazza; Ashley Steel

2017-01-01

Water temperature drives the complex food web of a river network. Aquatic organisms hatch, feed, and reproduce in thermal niches within the tributaries and mainstem that comprise the river network. Changes in water temperature can synchronize or asynchronize the timing of their life stages throughout the year. The water temperature fluctuates over time and place,...

Trends and variability of daily temperature extremes during 1960-2012 in the Yangtze River Basin, China

Science.gov (United States)

The variability of temperature extremes has been the focus of attention during the past few decades, and may exert a great influence on the global hydrologic cycle and energy balance through thermal forcing. Based on daily minimum and maximum temperature observed by the China Meteorological Administ...

Identifying the optimal supply temperature in district heating networks - A modelling approach

DEFF Research Database (Denmark)

Mohammadi, Soma; Bojesen, Carsten

2014-01-01

of this study is to develop a model for thermo-hydraulic calculation of low temperature DH system. The modelling is performed with emphasis on transient heat transfer in pipe networks. The pseudo-dynamic approach is adopted to model the District Heating Network [DHN] behaviour which estimates the temperature...... dynamically while the flow and pressure are calculated on the basis of steady state conditions. The implicit finite element method is applied to simulate the transient temperature behaviour in the network. Pipe network heat losses, pressure drop in the network and return temperature to the plant...... are calculated in the developed model. The model will serve eventually as a basis to find out the optimal supply temperature in an existing DHN in later work. The modelling results are used as decision support for existing DHN; proposing possible modifications to operate at optimal supply temperature....

Evaluation of thermal network correction program using test temperature data

Science.gov (United States)

Ishimoto, T.; Fink, L. C.

1972-01-01

An evaluation process to determine the accuracy of a computer program for thermal network correction is discussed. The evaluation is required since factors such as inaccuracies of temperatures, insufficient number of temperature points over a specified time period, lack of one-to-one correlation between temperature sensor and nodal locations, and incomplete temperature measurements are not present in the computer-generated information. The mathematical models used in the evaluation are those that describe a physical system composed of both a conventional and a heat pipe platform. A description of the models used, the results of the evaluation of the thermal network correction, and input instructions for the thermal network correction program are presented.

Energy and exergy analysis of low temperature district heating network

DEFF Research Database (Denmark)

Li, Hongwei; Svendsen, Svend

2012-01-01

is designed to supply heating for 30 low energy detached residential houses. The network operational supply/return temperature is set as 55 °C/25 °C, which is in line with a pilot project carried out in Denmark. Two types of in-house substations are analyzed to supply the consumer domestic hot water demand...... energy/exergy losses and increase the quality match between the consumer heating demand and the district heating supply.......Low temperature district heating with reduced network supply and return temperature provides better match of the low quality building heating demand and the low quality heating supply from waste heat or renewable energy. In this paper, a hypothetical low temperature district heating network...

Artificial neural network optimisation for monthly average daily global solar radiation prediction

International Nuclear Information System (INIS)

Alsina, Emanuel Federico; Bortolini, Marco; Gamberi, Mauro; Regattieri, Alberto

2016-01-01

Highlights: • Prediction of the monthly average daily global solar radiation over Italy. • Multi-location Artificial Neural Network (ANN) model: 45 locations considered. • Optimal ANN configuration with 7 input climatologic/geographical parameters. • Statistical indicators: MAPE, NRMSE, MPBE. - Abstract: The availability of reliable climatologic data is essential for multiple purposes in a wide set of anthropic activities and operative sectors. Frequently direct measures present spatial and temporal lacks so that predictive approaches become of interest. This paper focuses on the prediction of the Monthly Average Daily Global Solar Radiation (MADGSR) over Italy using Artificial Neural Networks (ANNs). Data from 45 locations compose the multi-location ANN training and testing sets. For each location, 13 input parameters are considered, including the geographical coordinates and the monthly values for the most frequently adopted climatologic parameters. A subset of 17 locations is used for ANN training, while the testing step is against data from the remaining 28 locations. Furthermore, the Automatic Relevance Determination method (ARD) is used to point out the most relevant input for the accurate MADGSR prediction. The ANN best configuration includes 7 parameters, only, i.e. Top of Atmosphere (TOA) radiation, day length, number of rainy days and average rainfall, latitude and altitude. The correlation performances, expressed through statistical indicators as the Mean Absolute Percentage Error (MAPE), range between 1.67% and 4.25%, depending on the number and type of the chosen input, representing a good solution compared to the current standards.

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

Directory of Open Access Journals (Sweden)

Ran Huang

2015-07-01

Full Text Available Air temperature is one of the most important factors in crop growth monitoring and simulation. In the present study, we estimated and mapped daily mean air temperature using daytime and nighttime land surface temperatures (LSTs derived from TERRA and AQUA MODIS data. Linear regression models were calibrated using LSTs from 2003 to 2011 and validated using LST data from 2012 to 2013, combined with meteorological station data. The results show that these models can provide a robust estimation of measured daily mean air temperature and that models that only accounted for meteorological data from rural regions performed best. Daily mean air temperature maps were generated from each of four MODIS LST products and merged using different strategies that combined the four MODIS products in different orders when data from one product was unavailable for a pixel. The annual average spatial coverage increased from 20.28% to 55.46% in 2012 and 28.31% to 44.92% in 2013.The root-mean-square and mean absolute errors (RMSE and MAE for the optimal image merging strategy were 2.41 and 1.84, respectively. Compared with the least-effective strategy, the RMSE and MAE decreased by 17.2% and 17.8%, respectively. The interpolation algorithm uses the available pixels from images with consecutive dates in a sliding-window mode. The most appropriate window size was selected based on the absolute spatial bias in the study area. With an optimal window size of 33 × 33 pixels, this approach increased data coverage by up to 76.99% in 2012 and 89.67% in 2013.

Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

Science.gov (United States)

Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty

2017-09-01

In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

Temperature dependence of the multistability of lactose utilization network of Escherichia coli

Science.gov (United States)

Nepal, Sudip; Kumar, Pradeep

Biological systems are capable of producing multiple states out of a single set of inputs. Multistability acts like a biological switch that allows organisms to respond differently to different environmental conditions and hence plays an important role in adaptation to changing environment. One of the widely studied gene regulatory networks underlying the metabolism of bacteria is the lactose utilization network, which exhibits a multistable behavior as a function of lactose concentration. We have studied the effect of temperature on multistability of the lactose utilization network at various concentrations of thio-methylgalactoside (TMG), a synthetic lactose. We find that while the lactose utilization network exhibits a bistable behavior for temperature T >20° C , a graded response arises for temperature T lactose utilization network as a function of temperature and TMG concentration. Our results suggest that environmental conditions, in this case temperature, can alter the nature of cellular regulation of metabolism.

Temperature-dependent daily variability of precipitable water in special sensor microwave/imager observations

Science.gov (United States)

Gutowski, William J.; Lindemulder, Elizabeth A.; Jovaag, Kari

1995-01-01

We use retrievals of atmospheric precipitable water from satellite microwave observations and analyses of near-surface temperature to examine the relationship between these two fields on daily and longer time scales. The retrieval technique producing the data used here is most effective over the open ocean, so the analysis focuses on the southern hemisphere's extratropics, which have an extensive ocean surface. For both the total and the eddy precipitable water fields, there is a close correspondence between local variations in the precipitable water and near-surface temperature. The correspondence appears particularly strong for synoptic and planetary scale transient eddies. More specifically, the results support a typical modeling assumption that transient eddy moisture fields are proportional to transient eddy temperature fields under the assumption f constant relative humidity.

Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

International Nuclear Information System (INIS)

Schulze, P; Schmidl, E; Grund, T; Lampke, T

2016-01-01

For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels. (paper)

Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

Science.gov (United States)

Schulze, P.; Schmidl, E.; Grund, T.; Lampke, T.

2016-03-01

For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.

New England observed and predicted August stream/river temperature maximum positive daily rate of change points

Data.gov (United States)

U.S. Environmental Protection Agency — The shapefile contains points with associated observed and predicted August stream/river temperature maximum positive daily rate of change in New England based on a...

New England observed and predicted July stream/river temperature maximum positive daily rate of change points

Data.gov (United States)

U.S. Environmental Protection Agency — The shapefile contains points with associated observed and predicted July stream/river temperature maximum positive daily rate of change in New England based on a...

New England observed and predicted July maximum negative stream/river temperature daily rate of change points

Data.gov (United States)

U.S. Environmental Protection Agency — The shapefile contains points with associated observed and predicted July stream/river temperature maximum negative daily rate of change in New England based on a...

Daily rhythms of blood pressure, heart rate, and body temperature in fed and fasted male dogs.

Science.gov (United States)

Piccione, G; Caola, G; Refinetti, R

2005-10-01

Daily or circadian rhythmicity in physiological processes has been described in a large number of species of birds and mammals. However, in dogs, most studies have either failed to detect rhythmicity or have found that rhythmicity reflects merely an acute exogenous effect of feeding rather than an autonomous rhythmic process. In the present study, we investigated the rhythmicity of body temperature, blood pressure, and heart rate in dogs fed daily as well as in dogs deprived of food for 60 h. Our results document clear rhythmicity in all three parameters and demonstrate that the rhythmicity is independent of the feeding schedule. The failure of various previous investigations to document daily rhythmicity in dogs is probably due to lack of experimental rigour rather than to weakness of daily rhythmicity in dogs.

Satellite Estimation of Daily Land Surface Water Vapor Pressure Deficit from AMSR- E

Science.gov (United States)

Jones, L. A.; Kimball, J. S.; McDonald, K. C.; Chan, S. K.; Njoku, E. G.; Oechel, W. C.

2007-12-01

Vapor pressure deficit (VPD) is a key variable for monitoring land surface water and energy exchanges, and estimating plant water stress. Multi-frequency day/night brightness temperatures from the Advanced Microwave Scanning Radiometer on EOS Aqua (AMSR-E) were used to estimate daily minimum and average near surface (2 m) air temperatures across a North American boreal-Arctic transect. A simple method for determining daily mean VPD (Pa) from AMSR-E air temperature retrievals was developed and validated against observations across a regional network of eight study sites ranging from boreal grassland and forest to arctic tundra. The method assumes that the dew point and minimum daily air temperatures tend to equilibrate in areas with low night time temperatures and relatively moist conditions. This assumption was tested by comparing the VPD algorithm results derived from site daily temperature observations against results derived from AMSR-E retrieved temperatures alone. An error analysis was conducted to determine the amount of error introduced in VPD estimates given known levels of error in satellite retrieved temperatures. Results indicate that the assumption generally holds for the high latitude study sites except for arid locations in mid-summer. VPD estimates using the method with AMSR-E retrieved temperatures compare favorably with site observations. The method can be applied to land surface temperature retrievals from any sensor with day and night surface or near-surface thermal measurements and shows potential for inferring near-surface wetness conditions where dense vegetation may hinder surface soil moisture retrievals from low-frequency microwave sensors. This work was carried out at The University of Montana, at San Diego State University, and at the Jet Propulsion Laboratory, California Institute of Technology, under contract to the National Aeronautics and Space Administration.

Hot metal temperature prediction by neural networks in the blast furnace

International Nuclear Information System (INIS)

Cantera, C.; Jimenez, J.; Varela, I.; Formoso, A.

2002-01-01

Based on a simplified model, the underlying temperature criteria is proposed as a method to study the temperature trends in a blast furnace. As an application, a neural network able to forecast hot metal temperatures from 2 to 16 h in advance (with decreasing precision) has been built. This neural network has been designed to work at real time in a production plant. (Author)

Operational forecasting of daily temperatures in the Valencia Region. Part II: minimum temperatures in winter.

Science.gov (United States)

Gómez, I.; Estrela, M.

2009-09-01

Extreme temperature events have a great impact on human society. Knowledge of minimum temperatures during winter is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, winter minimum temperatures are considered a parameter of interest and concern since persistent cold-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict cold-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that low temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily minimum temperatures during winter over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the winter forecast period from 1 December 2007 - 31 March 2008. The results obtained are encouraging and indicate a good agreement between the observed and simulated minimum temperatures. Moreover, the model captures quite well the temperatures in the extreme cold episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia

A European daily high-resolution gridded dataset of surface temperature and precipitation for 1950-2006

NARCIS (Netherlands)

Haylock, M.; Hofstra, N.; Klein Tank, A.; Klok, L.; Jones, P.; New, M.

2008-01-01

We present a European land-only daily high-resolution gridded data set for precipitation and minimum, maximum, and mean surface temperature for the period 1950–2006. This data set improves on previous products in its spatial resolution and extent, time period, number of contributing stations, and

Increasing minimum daily temperatures are associated with enhanced pesticide use in cultivated soybean along a latitudinal gradient in the mid-western United States.

Directory of Open Access Journals (Sweden)

Lewis H Ziska

Full Text Available Assessments of climate change and food security often do not consider changes to crop production as a function of altered pest pressures. Evaluation of potential changes may be difficult, in part, because management practices are routinely utilized in situ to minimize pest injury. If so, then such practices, should, in theory, also change with climate, although this has never been quantified. Chemical (pesticide applications remain the primary means of managing pests in industrialized countries. While a wide range of climate variables can influence chemical use, minimum daily temperature (lowest 24 h recorded temperature in a given year can be associated with the distribution and thermal survival of many agricultural pests in temperate regions. The current study quantifies average pesticide applications since 1999 for commercial soybean grown over a 2100 km North-South latitudinal transect for seven states that varied in minimum daily temperature (1999-2013 from -28.6°C (Minnesota to -5.1°C (Louisiana. Although soybean yields (per hectare did not vary by state, total pesticide applications (kg of active ingredient, ai, per hectare increased from 4.3 to 6.5 over this temperature range. Significant correlations were observed between minimum daily temperatures and kg of ai for all pesticide classes. This suggested that minimum daily temperature could serve as a proxy for pesticide application. Longer term temperature data (1977-2013 indicated greater relative increases in minimum daily temperatures for northern relative to southern states. Using these longer-term trends to determine short-term projections of pesticide use (to 2023 showed a greater comparative increase in herbicide use for soybean in northern; but a greater increase in insecticide and fungicide use for southern states in a warmer climate. Overall, these data suggest that increases in pesticide application rates may be a means to maintain soybean production in response to rising

Effect of daily fluctuations in ambient temperature on reproductive failure traits of Landrace and Yorkshire sows under Thai tropical environmental conditions.

Science.gov (United States)

Jaichansukkit, Teerapong; Suwanasopee, Thanathip; Koonawootrittriron, Skorn; Tummaruk, Padet; Elzo, Mauricio A

2017-03-01

The aim of this study was to determine the effects of daily ranges and maximum ambient temperatures, and other risk factors on reproductive failure of Landrace (L) and Yorkshire (Y) sows under an open-house system in Thailand. Daily ambient temperatures were added to information on 35,579 litters from 5929 L sows and 1057 Y sows from three commercial herds. The average daily temperature ranges (ADT) and the average daily maximum temperatures (PEAK) in three gestation periods from the 35th day of gestation to parturition were classified. The considered reproductive failure traits were the occurrences of mummified fetuses (MM), stillborn piglets (STB), and piglet death losses (PDL) and an indicator trait for number of piglets born alive below the population mean (LBA). A multiple logistic regression model included farrowing herd-year-season (HYS), breed group of sow (BG), parity group (PAR), number of total piglets born (NTB), ADT1, ADT2, ADT3, PEAK1, PEAK2, and PEAK3 as fixed effects, while random effects were animal, repeated observations, and residual. Yorkshire sows had a higher occurrence of LBA than L sows (P = 0.01). The second to fifth parities sows had lower reproductive failures than other parities. The NTB regression coefficients of log-odds were positive (P reproductive failures, particularly late in gestation, producers would need to closely monitor their temperature management strategies.

MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Day V006

Data.gov (United States)

National Aeronautics and Space Administration — MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Day (MOD21A1D.006). A new suite of MODIS Land Surface Temperature (LST) and...

MODIS/Aqua Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Day V006

Data.gov (United States)

National Aeronautics and Space Administration — MODIS/Aqua Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Day (MYD21A1D.006). A new suite of MODIS Land Surface Temperature (LST) and...

MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Night V006

Data.gov (United States)

National Aeronautics and Space Administration — MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Night (MOD21A1N.006). A new suite of MODIS Land Surface Temperature (LST) and...

MODIS/Aqua Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Night V006

Data.gov (United States)

National Aeronautics and Space Administration — MODIS/Aqua Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Night (MYD21A1N.006). A new suite of MODIS Land Surface Temperature (LST) and...

Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

Directory of Open Access Journals (Sweden)

Vitor Badiale Furlong

2013-02-01

Full Text Available In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days, number of clusters (10, 30 and 50 clusters and internal weight softening parameter (Sigma (0.30, 0.45 and 0.60. These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A and 18 (B days of culture growth. The validations demonstrated that in long-term experiments (Validation A the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B, Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.

Design of a low temperature district heating network with supply recirculation

DEFF Research Database (Denmark)

Li, Hongwei; Dalla Rosa, Alessandro; Svendsen, Svend

2010-01-01

The focus on continuing improving building energy efficiency and reducing building energy consumption brings the key impetus for the development of the new generation district heating (DH) system. In the new generation DH network, the supply and return temperature are designed low in order to sig...... calculates the heat loss in the twin pipe as that in the single pipe. The influence of this simplification on the supply/return water temperature prediction was analyzed by solving the coupled differential energy equations.......-pass system starts to function. The aim of this paper is to investigate the influence of by-pass water on the network return temperature and introduce the concept of supply water recirculation into the network design so that the traditional by-pass system can be avoided. Instead of mixing the by-pass water......The focus on continuing improving building energy efficiency and reducing building energy consumption brings the key impetus for the development of the new generation district heating (DH) system. In the new generation DH network, the supply and return temperature are designed low in order...

Meteorological Reference Years of Daily Mean Temperature during the Sunlight Time; Anos Tipos de Temperaturas Medias Diarias durante las Horas de Sol

Energy Technology Data Exchange (ETDEWEB)

Marchante Jimenez, M.; Ramirez Santigosa, L.N.; Mora Lopez, L.; Sidrach de Cardona Ortin, M.

2002-07-01

In this work the characterization of the daily mean temperature during the sunlight time has been analyzed. An algorithm for the hourly series generation from extreme daily values has been applied to evaluate the daily mean temperature during the sunlight time. A generic algorithm has been enounced as a function of the sunrise time. This algorithm allows taking into account the fractions related to the sunrise and sunset hours. This methodology has been applied in data from 45 Spanish stations, uniformly distributed in the Iberian Peninsula. Data for a period of 14 years has been used in most of locations, and once the interest variable has been calculated, the meteorological reference year of the daily mean temperature during the sunlight time has been evaluated in each stations. The next step is the evaluation of the daily mean temperature during the sunlight time in any point into the zone of evaluation, not only in the measured stations. >From the result data in each measured station, an geographic information system has been used in order to calculate the interpolation, obtaining maps with a data each 5 km for each of the 365 days of the year. Then, this results can be superposed with the solar radiation evaluation obtaining the input data for the sizing of the photovoltaic grid connected system in any point of the Spanish geography. (Author) 8 refs.

Temperature impact on yeast metabolism : Insights from experimental and modeling approaches

NARCIS (Netherlands)

Braga da Cruz, A.L.

2013-01-01

Temperature is an environmental parameter that greatly affects the growth of microorganisms, due to its impact on the activity of all enzymes in the network. This is particularly relevant in habitats where there are large temperature changes, either daily or seasonal. Understanding how organisms

Comparison of Gene Expression Programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain)

International Nuclear Information System (INIS)

Landeras, Gorka; López, José Javier; Kisi, Ozgur; Shiri, Jalal

2012-01-01

Highlights: ► Solar radiation estimation based on Gene Expression Programming is unexplored. ► This approach is evaluated for the first time in this study. ► Other artificial intelligence models (ANN and ANFIS) are also included in the study. ► New alternatives for solar radiation estimation based on temperatures are provided. - Abstract: Surface incoming solar radiation is a key variable for many agricultural, meteorological and solar energy conversion related applications. In absence of the required meteorological sensors for the detection of global solar radiation it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). A comparison was also made among these techniques and traditional temperature based global solar radiation estimation equations. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SS RMSE ), MAE-based skill score (SS MAE ) and r 2 criterion of Nash and Sutcliffe criteria were used to assess the models’ performances. An ANN (a four-input multilayer perceptron with 10 neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m −2 d −1 of RMSE). The ability of GEP approach to model global solar radiation based on daily atmospheric variables was found to be satisfactory.

Accuracy assessment of NOAA gridded daily reference evapotranspiration for the Texas High Plains

Science.gov (United States)

Moorhead, Jerry; Gowda, Prasanna H.; Hobbins, Michael; Senay, Gabriel; Paul, George; Marek, Thomas; Porter, Dana

2015-01-01

The National Oceanic and Atmospheric Administration (NOAA) provides daily reference evapotranspiration (ETref) maps for the contiguous United States using climatic data from North American Land Data Assimilation System (NLDAS). This data provides large-scale spatial representation of ETref, which is essential for regional scale water resources management. Data used in the development of NOAA daily ETref maps are derived from observations over surfaces that are different from short (grass — ETos) or tall (alfalfa — ETrs) reference crops, often in nonagricultural settings, which carries an unknown discrepancy between assumed and actual conditions. In this study, NOAA daily ETos and ETrs maps were evaluated for accuracy, using observed data from the Texas High Plains Evapotranspiration (TXHPET) network. Daily ETos, ETrs and the climatic data (air temperature, wind speed, and solar radiation) used for calculating ETref were extracted from the NOAA maps for TXHPET locations and compared against ground measurements on reference grass surfaces. NOAA ETrefmaps generally overestimated the TXHPET observations (1.4 and 2.2 mm/day ETos and ETrs, respectively), which may be attributed to errors in the NLDAS modeled air temperature and wind speed, to which reference ETref is most sensitive. Therefore, a bias correction to NLDAS modeled air temperature and wind speed data, or adjustment to the resulting NOAA ETref, may be needed to improve the accuracy of NOAA ETref maps.

The estimation of phenological thresholds of Saffron cultivation in Isfahan province based on the daily temperature statistics

Directory of Open Access Journals (Sweden)

Gholamabbas Fallahghalhary

2015-04-01

Full Text Available In this research, the statistics of daily temperature of meteorology stations for estimating the probable occurrence of the first and last frost temperature, the phonological thresholds of saffron and its flowering and irrigation dates were explored. Furthermore, by applying different methods, the probabilities of the attained dates in different probability levels were fit in Smada software. The estimated probability level of 95 percent, as the optimum date, was donated for the entire Isfahan province in ArcGIS9/3 software environment using the interpolation method of Cokriging. The time of occurrence of minimum temperatures is under the influence of the geographical and height condition of each region and the first fall season frost occurs in high regions of the eastern half of Isfahan province at the beginning of November and in the low-lying eastern regions in the late December. The occurrence of daily temperature is changeable from the first half of October and the second half of November. From the west to the east of Isfahan, the time of occurrence of this threshold has a delay of about one month. The optimum flowering date of saffron, based on climatic conditions, is from the first half of October to the late of November. By considering the flowering date and daily temperature requirement of saffron, the irrigation date prior to flowering continues from the second half of September in the western parts and the beginning of November in the eastern regions. From the perspective of thermal condition, the western, central, northern and eastern parts of Isfahan province are more suited to the cultivation and development of saffron product. The amount of water requirement in the study area based on evapotranspiration and crop coefficients of saffron occurred in the mid-season, late season and Initial.

Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel.

Science.gov (United States)

Rosenfeld, Adar; Dorman, Michael; Schwartz, Joel; Novack, Victor; Just, Allan C; Kloog, Itai

2017-11-01

Meteorological stations measure air temperature (Ta) accurately with high temporal resolution, but usually suffer from limited spatial resolution due to their sparse distribution across rural, undeveloped or less populated areas. Remote sensing satellite-based measurements provide daily surface temperature (Ts) data in high spatial and temporal resolution and can improve the estimation of daily Ta. In this study we developed spatiotemporally resolved models which allow us to predict three daily parameters: Ta Max (day time), 24h mean, and Ta Min (night time) on a fine 1km grid across the state of Israel. We used and compared both the Aqua and Terra MODIS satellites. We used linear mixed effect models, IDW (inverse distance weighted) interpolations and thin plate splines (using a smooth nonparametric function of longitude and latitude) to first calibrate between Ts and Ta in those locations where we have available data for both and used that calibration to fill in neighboring cells without surface monitors or missing Ts. Out-of-sample ten-fold cross validation (CV) was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with and without available Ts observations for both Aqua and Terra (CV Aqua R 2 results for min 0.966, mean 0.986, and max 0.967; CV Terra R 2 results for min 0.965, mean 0.987, and max 0.968). Our research shows that daily min, mean and max Ta can be reliably predicted using daily MODIS Ts data even across Israel, with high accuracy even for days without Ta or Ts data. These predictions can be used as three separate Ta exposures in epidemiology studies for better diurnal exposure assessment. Copyright © 2017 Elsevier Inc. All rights reserved.

Clarifying the role of fire heat and daily temperature fluctuations as germination cues for Mediterranean Basin obligate seeders.

Science.gov (United States)

Santana, Victor M; Baeza, M Jaime; Blanes, M Carmen

2013-01-01

This study aims to determine the role that both direct effects of fire and subsequent daily temperature fluctuations play in the seed bank dynamics of obligate seeders from the Mediterranean Basin. The short yet high soil temperatures experienced due to passage of fire are conflated with the lower, but longer, temperatures experienced by daily fluctuations which occur after removing vegetation. These germination cues are able to break seed dormancy, but it is difficult to assess their specific level of influence because they occur consecutively after summer fires, just before the flush of germination in the wet season (autumn). By applying experimental fires, seed treatments were imposed that combined fire exposure/non-fire exposure with exposure to microhabitats under a gradient of disturbance (i.e. gaps opened by fire, mechanical brushing and intact vegetation). The seeds used were representative of the main families of obligate seeders (Ulex parviflorus, Cistus albidus and Rosmarinus officinalis). Specifically, an assessment was made of (1) the proportion of seeds killed by fire, (2) seedling emergence under field conditions and (3) seeds which remained ungerminated in soil. For the three species studied, the factors that most influenced seedling emergence and seeds remaining ungerminated were microhabitats with higher temperature fluctuations after fire (gaps opened by fire and brushing treatments). The direct effect of fire decreased the seedling emergence of U. parviflorus and reduced the proportion of seeds of R. officinalis remaining ungerminated. The relevance of depleting vegetation (and subsequent daily temperature fluctuation in summer) suggests that studies focusing on lower temperature thresholds for breaking seed dormancy are required. This fact also supports the hypothesis that the seeding capacity in Mediterranean Basin obligate seeders may have evolved as a response to a wide range of disturbances, and not exclusively to fire.

A Gridded Daily Min/Max Temperature Dataset With 0.1° Resolution for the Yangtze River Valley and its Error Estimation

Science.gov (United States)

Xiong, Qiufen; Hu, Jianglin

2013-05-01

The minimum/maximum (Min/Max) temperature in the Yangtze River valley is decomposed into the climatic mean and anomaly component. A spatial interpolation is developed which combines the 3D thin-plate spline scheme for climatological mean and the 2D Barnes scheme for the anomaly component to create a daily Min/Max temperature dataset. The climatic mean field is obtained by the 3D thin-plate spline scheme because the relationship between the decreases in Min/Max temperature with elevation is robust and reliable on a long time-scale. The characteristics of the anomaly field tend to be related to elevation variation weakly, and the anomaly component is adequately analyzed by the 2D Barnes procedure, which is computationally efficient and readily tunable. With this hybridized interpolation method, a daily Min/Max temperature dataset that covers the domain from 99°E to 123°E and from 24°N to 36°N with 0.1° longitudinal and latitudinal resolution is obtained by utilizing daily Min/Max temperature data from three kinds of station observations, which are national reference climatological stations, the basic meteorological observing stations and the ordinary meteorological observing stations in 15 provinces and municipalities in the Yangtze River valley from 1971 to 2005. The error estimation of the gridded dataset is assessed by examining cross-validation statistics. The results show that the statistics of daily Min/Max temperature interpolation not only have high correlation coefficient (0.99) and interpolation efficiency (0.98), but also the mean bias error is 0.00 °C. For the maximum temperature, the root mean square error is 1.1 °C and the mean absolute error is 0.85 °C. For the minimum temperature, the root mean square error is 0.89 °C and the mean absolute error is 0.67 °C. Thus, the new dataset provides the distribution of Min/Max temperature over the Yangtze River valley with realistic, successive gridded data with 0.1° × 0.1° spatial resolution and

Direct and indirect toxicity of the fungicide pyraclostrobin to Hyalella azteca and effects on leaf processing under realistic daily temperature regimes

International Nuclear Information System (INIS)

Willming, Morgan M.; Maul, Jonathan D.

2016-01-01

Fungicides in aquatic environments can impact non-target bacterial and fungal communities and the invertebrate detritivores responsible for the decomposition of allochthonous organic matter. Additionally, in some aquatic systems daily water temperature fluctuations may influence these processes and alter contaminant toxicity, but such temperature fluctuations are rarely examined in conjunction with contaminants. In this study, the shredding amphipod Hyalella azteca was exposed to the fungicide pyraclostrobin in three experiments. Endpoints included mortality, organism growth, and leaf processing. One experiment was conducted at a constant temperature (23 °C), a fluctuating temperature regime (18–25 °C) based on field-collected data from the S. Llano River, Texas, or an adjusted fluctuating temperature regime (20–26 °C) based on possible climate change predictions. Pyraclostrobin significantly reduced leaf shredding and increased H. azteca mortality at concentrations of 40 μg/L or greater at a constant 23 °C and decreased leaf shredding at concentrations of 15 μg/L or greater in the fluctuating temperatures. There was a significant interaction between temperature treatment and pyraclostrobin concentration on H. azteca mortality, body length, and dry mass under direct aqueous exposure conditions. In an indirect exposure scenario in which only leaf material was exposed to pyraclostrobin, H. azteca did not preferentially feed on or avoid treated leaf disks compared to controls. This study describes the influence of realistic temperature variation on fungicide toxicity to shredding invertebrates, which is important for understanding how future alterations in daily temperature regimes due to climate change may influence the assessment of ecological risk of contaminants in aquatic ecosystems. - Highlights: • Pyraclostrobin was directly toxic to Hyalella azteca and reduced leaf processing. • Indirect exposure via leaf material did not change H

Foreground removal from CMB temperature maps using an MLP neural network

DEFF Research Database (Denmark)

Nørgaard-Nielsen, Hans Ulrik; Jørgensen, H.E.

2008-01-01

the CMB temperature signal from the combined signal CMB and the foregrounds has been investigated. As a specific example, we have analysed simulated data, as expected from the ESA Planck CMB mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates over...... CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the Galactic foregrounds simple power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting...

Smooth Information Flow in Temperature Climate Network Reflects Mass Transport

Czech Academy of Sciences Publication Activity Database

Hlinka, Jaroslav; Jajcay, Nikola; Hartman, David; Paluš, Milan

2017-01-01

Roč. 27, č. 3 (2017), č. článku 035811. ISSN 1054-1500 R&D Projects: GA ČR GCP103/11/J068; GA MŠk LH14001 Institutional support: RVO:67985807 Keywords : directed network * causal network * Granger causality * climate network * information flow * temperature network Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.283, year: 2016

A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

OpenAIRE

Khatib, Tamer; Elmenreich, Wilfried

2015-01-01

This paper presents a model for predicting hourly solar radiation data using daily solar radiation averages. The proposed model is a generalized regression artificial neural network. This model has three inputs, namely, mean daily solar radiation, hour angle, and sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. The results show that...

Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data

Science.gov (United States)

Yoo, Cheolhee; Im, Jungho; Park, Seonyoung; Quackenbush, Lindi J.

2018-03-01

Urban air temperature is considered a significant variable for a variety of urban issues, and analyzing the spatial patterns of air temperature is important for urban planning and management. However, insufficient weather stations limit accurate spatial representation of temperature within a heterogeneous city. This study used a random forest machine learning approach to estimate daily maximum and minimum air temperatures (Tmax and Tmin) for two megacities with different climate characteristics: Los Angeles, USA, and Seoul, South Korea. This study used eight time-series land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS), with seven auxiliary variables: elevation, solar radiation, normalized difference vegetation index, latitude, longitude, aspect, and the percentage of impervious area. We found different relationships between the eight time-series LSTs with Tmax/Tmin for the two cities, and designed eight schemes with different input LST variables. The schemes were evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE) from 10-fold cross-validation. The best schemes produced R2 of 0.850 and 0.777 and RMSE of 1.7 °C and 1.2 °C for Tmax and Tmin in Los Angeles, and R2 of 0.728 and 0.767 and RMSE of 1.1 °C and 1.2 °C for Tmax and Tmin in Seoul, respectively. LSTs obtained the day before were crucial for estimating daily urban air temperature. Estimated air temperature patterns showed that Tmax was highly dependent on the geographic factors (e.g., sea breeze, mountains) of the two cities, while Tmin showed marginally distinct temperature differences between built-up and vegetated areas in the two cities.

Simulating daily water temperatures of the Klamath River under dam removal and climate change scenarios

Science.gov (United States)

Perry, Russell W.; Risley, John C.; Brewer, Scott J.; Jones, Edward C.; Rondorf, Dennis W.

2011-01-01

A one-dimensional daily averaged water temperature model was used to simulate Klamath River temperatures for two management alternatives under historical climate conditions and six future climate scenarios. The analysis was conducted for the Secretarial Determination on removal of four hydroelectric dams on the Klamath River. In 2012, the Secretary of the Interior will determine if dam removal and implementation of the Klamath Basin Restoration Agreement (KBRA) (Klamath Basin Restoration Agreement, 2010) will advance restoration of salmonid fisheries and is in the public interest. If the Secretary decides dam removal is appropriate, then the four dams are scheduled for removal in 2020.

Comparison of the Gen Expression Programming, Nonlinear Time Series and Artificial Neural Network in Estimating the River Daily Flow (Case Study: The Karun River

Directory of Open Access Journals (Sweden)

R. Zamani

2015-06-01

Full Text Available Today, the daily flow forecasting of rivers is an important issue in hydrology and water resources and thus can be used the results of daily river flow modeling in water resources management, droughts and floods monitoring. In this study, due to the importance of this issue, using nonlinear time series models and artificial intelligence (Artificial Neural Network and Gen Expression Programming, the daily flow modeling has been at the time interval (1981-2012 in the Armand hydrometric station on the Karun River. Armand station upstream basin is one of the most basins in the North Karun basin and includes four sub basins (Vanak, Middle Karun, Beheshtabad and Kohrang.The results of this study shown that artificial intelligence models have superior than nonlinear time series in flow daily simulation in the Karun River. As well as, modeling and comparison of artificial intelligence models showed that the Gen Expression Programming have evaluation criteria better than artificial neural network.

Temperature and extreme rainfalls on France in a climatic change scenario

International Nuclear Information System (INIS)

Deque, M.

2007-01-01

Impact of an anthropogenic climate change scenario on the frequency distribution of temperature and precipitation over France is studied with a numerical simulation calibrated with observed daily data from the synoptic network. (author)

Climate applications for NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature

Science.gov (United States)

Boyer, T.; Banzon, P. V. F.; Liu, G.; Saha, K.; Wilson, C.; Stachniewicz, J. S.

2015-12-01

Few sea surface temperature (SST) datasets from satellites have the long temporal span needed for climate studies. The NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) on a 1/4° grid, produced at National Centers for Environmental Information, is based primarily on SSTs from the Advanced Very High Resolution Radiometer (AVHRR), available from 1981 to the present. AVHRR data can contain biases, particularly when aerosols are present. Over the three decade span, the largest departure of AVHRR SSTs from buoy temperatures occurred during the Mt Pinatubo and El Chichon eruptions. Therefore, in DOISST, AVHRR SSTs are bias-adjusted to match in situ SSTs prior to interpolation. This produces a consistent time series of complete SST fields that is suitable for modelling and investigating local climate phenomena like El Nino or the Pacific warm blob in a long term context. Because many biological processes and animal distributions are temperature dependent, there are also many ecological uses of DOISST (e.g., coral bleaching thermal stress, fish and marine mammal distributions), thereby providing insights into resource management in a changing ocean. The advantages and limitations of using DOISST for different applications will be discussed.

The influence of cold temperature on cellular excitability of hippocampal networks.

Science.gov (United States)

de la Peña, Elvira; Mälkiä, Annika; Vara, Hugo; Caires, Rebeca; Ballesta, Juan J; Belmonte, Carlos; Viana, Felix

2012-01-01

The hippocampus plays an important role in short term memory, learning and spatial navigation. A characteristic feature of the hippocampal region is its expression of different electrical population rhythms and activities during different brain states. Physiological fluctuations in brain temperature affect the activity patterns in hippocampus, but the underlying cellular mechanisms are poorly understood. In this work, we investigated the thermal modulation of hippocampal activity at the cellular network level. Primary cell cultures of mouse E17 hippocampus displayed robust network activation upon light cooling of the extracellular solution from baseline physiological temperatures. The activity generated was dependent on action potential firing and excitatory glutamatergic synaptic transmission. Involvement of thermosensitive channels from the transient receptor potential (TRP) family in network activation by temperature changes was ruled out, whereas pharmacological and immunochemical experiments strongly pointed towards the involvement of temperature-sensitive two-pore-domain potassium channels (K(2P)), TREK/TRAAK family. In hippocampal slices we could show an increase in evoked and spontaneous synaptic activity produced by mild cooling in the physiological range that was prevented by chloroform, a K(2P) channel opener. We propose that cold-induced closure of background TREK/TRAAK family channels increases the excitability of some hippocampal neurons, acting as a temperature-sensitive gate of network activation. Our findings in the hippocampus open the possibility that small temperature variations in the brain in vivo, associated with metabolism or blood flow oscillations, act as a switch mechanism of neuronal activity and determination of firing patterns through regulation of thermosensitive background potassium channel activity.

Branched carbon nanofiber network synthesis at room temperature using radio frequency supported microwave plasmas

International Nuclear Information System (INIS)

Boskovic, Bojan O.; Stolojan, Vlad; Zeze, Dagou A.; Forrest, Roy D.; Silva, S. Ravi P.; Haq, Sajad

2004-01-01

Carbon nanofibers have been grown at room temperature using a combination of radio frequency and microwave assisted plasma-enhanced chemical vapor deposition. The nanofibers were grown, using Ni powder catalyst, onto substrates kept at room temperature by using a purposely designed water-cooled sample holder. Branched carbon nanofiber growth was obtained without using a template resulting in interconnected carbon nanofiber network formation on substrates held at room temperature. This method would allow room-temperature direct synthesized nanofiber networks over relatively large areas, for a range of temperature sensitive substrates, such as organic materials, plastics, and other polymers of interest for nanoelectronic two-dimensional networks, nanoelectromechanical devices, nanoactuators, and composite materials

A model for evaluating stream temperature response to climate change in Wisconsin

Science.gov (United States)

Stewart, Jana S.; Westenbroek, Stephen M.; Mitro, Matthew G.; Lyons, John D.; Kammel, Leah E.; Buchwald, Cheryl A.

2015-01-01

Expected climatic changes in air temperature and precipitation patterns across the State of Wisconsin may alter future stream temperature and flow regimes. As a consequence of flow and temperature changes, the composition and distribution of fish species assemblages are expected to change. In an effort to gain a better understanding of how climatic changes may affect stream temperature, an approach was developed to predict and project daily summertime stream temperature under current and future climate conditions for 94,341 stream kilometers across Wisconsin. The approach uses a combination of static landscape characteristics and dynamic time-series climatic variables as input for an Artificial Neural Network (ANN) Model integrated with a Soil-Water-Balance (SWB) Model. Future climate scenarios are based on output from downscaled General Circulation Models (GCMs). The SWB model provided a means to estimate the temporal variability in groundwater recharge and provided a mechanism to evaluate the effect of changing air temperature and precipitation on groundwater recharge and soil moisture. The Integrated Soil-Water-Balance and Artificial Neural Network version 1 (SWB-ANNv1) Model was used to simulate daily summertime stream temperature under current (1990–2008) climate and explained 76 percent of the variation in the daily mean based on validation at 67 independent sites. Results were summarized as July mean water temperature, and individual stream segments were classified by thermal class (cold, cold transition, warm transition, and warm) for comparison of current (1990–2008) with future climate conditions.

Generating daily weather data for ecosystem modelling in the Congo River Basin

Science.gov (United States)

Petritsch, Richard; Pietsch, Stephan A.

2010-05-01

Daily weather data are an important constraint for diverse applications in ecosystem research. In particular, temperature and precipitation are the main drivers for forest ecosystem productivity. Mechanistic modelling theory heavily relies on daily values for minimum and maximum temperatures, precipitation, incident solar radiation and vapour pressure deficit. Although the number of climate measurement stations increased during the last centuries, there are still regions with limited climate data. For example, in the WMO database there are only 16 stations located in Gabon with daily weather measurements. Additionally, the available time series are heavily affected by measurement errors or missing values. In the WMO record for Gabon, on average every second day is missing. Monthly means are more robust and may be estimated over larger areas. Therefore, a good alternative is to interpolate monthly mean values using a sparse network of measurement stations, and based on these monthly data generate daily weather data with defined characteristics. The weather generator MarkSim was developed to produce climatological time series for crop modelling in the tropics. It provides daily values for maximum and minimum temperature, precipitation and solar radiation. The monthly means can either be derived from the internal climate surfaces or prescribed as additional inputs. We compared the generated outputs observations from three climate stations in Gabon (Lastourville, Moanda and Mouilla) and found that maximum temperature and solar radiation were heavily overestimated during the long dry season. This is due to the internal dependency of the solar radiation estimates to precipitation. With no precipitation a cloudless sky is assumed and thus high incident solar radiation and a large diurnal temperature range. However, in reality it is cloudy in the Congo River Basin during the long dry season. Therefore, we applied a correction factor to solar radiation and temperature range

Optimized district heating supply temperature for large networks; Optimerad framledningstemperatur foer stora fjaerrvaermenaet

Energy Technology Data Exchange (ETDEWEB)

Saarinen, Lisa; Boman, Katarina

2012-02-15

The supply temperature of the Uppsala district heating network was optimized using a model-based control strategy. Simulation of the network showed that the supply temperature could be decreased by in average 8 deg and the electricity production of the plants supplying the network could be increased with 2.5 % during the period January- April, giving an extra income of 1.2 MSEK due to increased income from electricity sales

The Benefits of Using Dense Temperature Sensor Networks to Monitor Urban Warming

Science.gov (United States)

Twine, T. E.; Snyder, P. K.; Kucharik, C. J.; Schatz, J.

2015-12-01

Urban heat islands (UHIs) occur when urban and suburban areas experience temperatures that are elevated relative to their rural surroundings because of differences in the fraction of gray and green infrastructure. Studies have shown that communities most at risk for impacts from climate-related disasters (i.e., lower median incomes, higher poverty, lower education, and minorities) tend to live in the hottest areas of cities. Development of adequate climate adaptation tools for cities relies on knowledge of how temperature varies across space and time. Traditionally, a city's urban heat island has been quantified using near-surface air temperature measurements from a few sites. This methodology assumes (1) that the UHI can be characterized by the difference in air temperature from a small number of points, and (2) that these few points represent the urban and rural signatures of the region. This methodology ignores the rich information that could be gained from measurements across the urban to rural transect. This transect could traverse elevations, water bodies, vegetation fraction, and other land surface properties. Two temperature sensor networks were designed and implemented in the Minneapolis-Saint Paul, MN and Madison, WI metropolitan areas beginning in 2011 and 2012, respectively. Both networks use the same model sensor and record temperature every 15 minutes from ~150 sensors. Data from each network has produced new knowledge of how temperature varies diurnally and seasonally across the cities and how the UHI magnitude is influenced by weather phenomena (e.g., wind, snow cover, heat waves) and land surface characteristics such as proximity to inland lakes. However, the two metropolitan areas differ in size, population, structure, and orientation to water bodies. In addition, the sensor networks were established in very different manners. We describe these differences and present lessons learned from the design and ongoing efforts of these two dense networks

Better Off Alone: Daily Solitude Is Associated With Lower Negative Affect in More Conflictual Social Networks.

Science.gov (United States)

Birditt, Kira S; Manalel, Jasmine A; Sommers, Heidi; Luong, Gloria; Fingerman, Karen L

2018-06-19

Older adults are often considered at risk for social isolation. Little is known, however, about how often older adults lack social contact (in person, phone, electronic) throughout the day, the implications of lacking contact (i.e., solitude), and whether the effects of solitude vary by the broader social context. Participants were from the Daily Experiences and Well-being Study (DEWS) which included 313 older adults (aged 65+) who completed baseline interviews followed by 5-6 days of ecological momentary assessments approximately every 3 hr. Individuals reported having no social contact (i.e., solitude) on 11% of the occasions. Solitude predicted lower negative and positive affect on those occasions. The solitude-negative affect link varied by social network quality. Solitude predicted lower negative affect among individuals with more conflictual social networks but not among those with less conflictual networks. Overall, solitude may serve as an adaptive strategy for individuals embedded in demanding or irritating social contexts.

Short-term PV/T module temperature prediction based on PCA-RBF neural network

Science.gov (United States)

Li, Jiyong; Zhao, Zhendong; Li, Yisheng; Xiao, Jing; Tang, Yunfeng

2018-02-01

Aiming at the non-linearity and large inertia of temperature control in PV/T system, short-term temperature prediction of PV/T module is proposed, to make the PV/T system controller run forward according to the short-term forecasting situation to optimize control effect. Based on the analysis of the correlation between PV/T module temperature and meteorological factors, and the temperature of adjacent time series, the principal component analysis (PCA) method is used to pre-process the original input sample data. Combined with the RBF neural network theory, the simulation results show that the PCA method makes the prediction accuracy of the network model higher and the generalization performance stronger than that of the RBF neural network without the main component extraction.

Comparative Study of Regional Estimation Methods for Daily Maximum Temperature (A Case Study of the Isfahan Province

Directory of Open Access Journals (Sweden)

Ghamar Fadavi

2016-02-01

Full Text Available Introduction: As the statistical time series are in short period and the meteorological station are not distributed well in mountainous area determining of climatic criteria are complex. Therefore, in recent years interpolation methods for establishment of continuous climatic data have been considered. Continuous daily maximum temperature data are a key factor for climate-crop modeling which is fundamental for water resources management, drought, and optimal use from climatic potentials of different regions. The main objective of this study is to evaluate different interpolation methods for estimation of regional maximum temperature in the Isfahan province. Materials and Methods: Isfahan province has about 937,105 square kilometers, between 30 degree and 43 minutes to 34 degree and 27 minutes North latitude equator line and 49 degree and 36 minutes to 55 degree and 31 minutes east longitude Greenwich. It is located in the center of Iran and it's western part extend to eastern footage of the Zagros mountain range. It should be mentioned that elevation range of meteorological stations are between 845 to 2490 in the study area. This study was done using daily maximum temperature data of 1992 and 2007 years of synoptic and climatology stations of I.R. of Iran meteorological organization (IRIMO. In order to interpolate temperature data, two years including 1992 and 2007 with different number of meteorological stations have been selected the temperature data of thirty meteorological stations (17 synoptic and 13 climatologically stations for 1992 year and fifty four meteorological stations (31 synoptic and 23 climatologically stations for 2007 year were used from Isfahan province and neighboring provinces. In order to regionalize the point data of daily maximum temperature, the interpolation methods, including inverse distance weighted (IDW, Kriging, Co-Kriging, Kriging-Regression, multiple regression and Spline were used. Therefore, for this allocated

Impact of automatization in temperature series in Spain and comparison with the POST-AWS dataset

Science.gov (United States)

Aguilar, Enric; López-Díaz, José Antonio; Prohom Duran, Marc; Gilabert, Alba; Luna Rico, Yolanda; Venema, Victor; Auchmann, Renate; Stepanek, Petr; Brandsma, Theo

2016-04-01

Climate data records are most of the times affected by inhomogeneities. Especially inhomogeneities introducing network-wide biases are sometimes related to changes happening almost simultaneously in an entire network. Relative homogenization is difficult in these cases, especially at the daily scale. A good example of this is the substitution of manual observations (MAN) by automatic weather stations (AWS). Parallel measurements (i.e. records taken at the same time with the old (MAN) and new (AWS) sensors can provide an idea of the bias introduced and help to evaluate the suitability of different correction approaches. We present here a quality controlled dataset compiled under the DAAMEC Project, comprising 46 stations across Spain and over 85,000 parallel measurements (AWS-MAN) of daily maximum and minimum temperature. We study the differences between both sensors and compare it with the available metadata to account for internal inhomogeneities. The differences between both systems vary much across stations, with patterns more related to their particular settings than to climatic/geographical reasons. The typical median biases (AWS-MAN) by station (comprised between the interquartile range) oscillate between -0.2°C and 0.4 in daily maximum temperature and between -0.4°C and 0.2°C in daily minimum temperature. These and other results are compared with a larger network, the Parallel Observations Scientific Team, a working group of the International Surface Temperatures Initiative (ISTI-POST) dataset, which comprises our stations, as well as others from different countries in America, Asia and Europe.

Foreground removal from WMAP 5 yr temperature maps using an MLP neural network

DEFF Research Database (Denmark)

Nørgaard-Nielsen, Hans Ulrik

2010-01-01

CMB signal makes it essential to minimize the systematic errors in the CMB temperature determinations. Methods. The feasibility of using simple neural networks to extract the CMB signal from detailed simulated data has already been demonstrated. Here, simple neural networks are applied to the WMAP 5...... yr temperature data without using any auxiliary data. Results. A simple multilayer perceptron neural network with two hidden layers provides temperature estimates over more than 75 per cent of the sky with random errors significantly below those previously extracted from these data. Also......, the systematic errors, i.e. errors correlated with the Galactic foregrounds, are very small. Conclusions. With these results the neural network method is well prepared for dealing with the high-quality CMB data from the ESA Planck Surveyor satellite. © ESO, 2010....

Estimating Daily Global Evapotranspiration Using Penman–Monteith Equation and Remotely Sensed Land Surface Temperature

Directory of Open Access Journals (Sweden)

Roozbeh Raoufi

2017-11-01

Full Text Available Daily evapotranspiration (ET is modeled globally for the period 2000–2013 based on the Penman–Monteith equation with radiation and vapor pressures derived using remotely sensed Land Surface Temperature (LST from the MODerate resolution Imaging Spectroradiometer (MODIS on the Aqua and Terra satellites. The ET for a given land area is based on four surface conditions: wet/dry and vegetated/non-vegetated. For each, the ET resistance terms are based on land cover, leaf area index (LAI and literature values. The vegetated/non-vegetated fractions of the land surface are estimated using land cover, LAI, a simplified version of the Beer–Lambert law for describing light transition through vegetation and newly derived light extension coefficients for each MODIS land cover type. The wet/dry fractions of the land surface are nonlinear functions of LST derived humidity calibrated using in-situ ET measurements. Results are compared to in-situ measurements (average of the root mean squared errors and mean absolute errors for 39 sites are 0.81 mm day−1 and 0.59 mm day−1, respectively and the MODIS ET product, MOD16, (mean bias during 2001–2013 is −0.2 mm day−1. Although the mean global difference between MOD16 and ET estimates is only 0.2 mm day−1, local temperature derived vapor pressures are the likely contributor to differences, especially in energy and water limited regions. The intended application for the presented model is simulating ET based on long-term climate forecasts (e.g., using only minimum, maximum and mean daily or monthly temperatures.

Comparison of the Spatiotemporal Variability of Temperature, Precipitation, and Maximum Daily Spring Flows in Two Watersheds in Quebec Characterized by Different Land Use

Directory of Open Access Journals (Sweden)

Ali A. Assani

2016-01-01

Full Text Available We compared the spatiotemporal variability of temperatures and precipitation with that of the magnitude and timing of maximum daily spring flows in the geographically adjacent L’Assomption River (agricultural and Matawin River (forested watersheds during the period from 1932 to 2013. With regard to spatial variability, fall, winter, and spring temperatures as well as total precipitation are higher in the agricultural watershed than in the forested one. The magnitude of maximum daily spring flows is also higher in the first watershed as compared with the second, owing to substantial runoff, given that the amount of snow that gives rise to these flows is not significantly different in the two watersheds. These flows occur early in the season in the agricultural watershed because of the relatively high temperatures. With regard to temporal variability, minimum temperatures increased over time in both watersheds. Maximum temperatures in the fall only increased in the agricultural watershed. The amount of spring rain increased over time in both watersheds, whereas total precipitation increased significantly in the agricultural watershed only. However, the amount of snow decreased in the forested watershed. The magnitude of maximum daily spring flows increased over time in the forested watershed.

Effect of daily temperature range on respiratory health in Argentina and its modification by impaired socio-economic conditions and PM_1_0 exposures

International Nuclear Information System (INIS)

Carreras, Hebe; Zanobetti, Antonella; Koutrakis, Petros

2015-01-01

Epidemiological investigations regarding temperature influence on human health have focused on mortality rather than morbidity. In addition, most information comes from developed countries despite the increasing evidence that climate change will have devastating impacts on disadvantaged populations living in developing countries. In the present study, we assessed the impact of daily temperature range on upper and lower respiratory infections in Cordoba, Argentina, and explored the effect modification of socio-economic factors and influence of airborne particles We found that temperature range is a strong risk factor for admissions due to both upper and lower respiratory infections, particularly in elderly individuals, and that these effects are more pronounced in sub-populations with low education level or in poor living conditions. These results indicate that socio-economic factors are strong modifiers of the association between temperature variability and respiratory morbidity, thus they should be considered in risk assessments. - Highlights: • Daily temperature range is a strong risk factor for respiratory infections. • Low education level and poor living conditions are strong modifiers of this relationship. • In Cordoba city higher risk for respiratory infections were observed during summertime. - Daily temperature range is a strong risk factor for respiratory infections, particularly for populations with low educational level or poor living conditions.

Oral temperatures of the elderly in nursing homes in summer and winter in relation to activities of daily living

Science.gov (United States)

Nakamura, K.; Tanaka, Masatoshi; Motohashi, Yutaka; Maeda, Akira

This study was conducted to clarify the seasonal difference in body temperature in summer and winter, and to document the thermal environment of the elderly living in nursing homes. The subjects were 57 healthy elderly people aged >=63 years living in two nursing homes in Japan. One of the homes was characterized by subjects with low levels of activities of daily living (ADL). Oral temperatures were measured in the morning and afternoon, with simultaneous recording of ambient temperature and relative humidity. Oral temperatures in summer were higher than in winter, with statistically significant differences (Pchanges in ambient temperature.

Core Body and Skin Temperature in Type 1 Narcolepsy in Daily Life; Effects of Sodium Oxybate and Prediction of Sleep Attacks

NARCIS (Netherlands)

van der Heide, Astrid; Werth, Esther; Donjacour, Claire E H M; Reijntjes, Robert H A M; Lammers, Gert Jan; Van Someren, Eus J W; Baumann, Christian R; Fronczek, Rolf

2016-01-01

STUDY OBJECTIVES: Previous laboratory studies in narcolepsy patients showed altered core body and skin temperatures, which are hypothesised to be related to a disturbed sleep wake regulation. In this ambulatory study we assessed temperature profiles in normal daily life, and whether sleep attacks

Evaluation of daily maximum and minimum 2-m temperatures as simulated with the Regional Climate Model COSMO-CLM over Africa

Directory of Open Access Journals (Sweden)

Stefan Krähenmann

2013-07-01

Full Text Available The representation of the diurnal 2-m temperature cycle is challenging because of the many processes involved, particularly land-atmosphere interactions. This study examines the ability of the regional climate model COSMO-CLM (version 4.8 to capture the statistics of daily maximum and minimum 2-m temperatures (Tmin/Tmax over Africa. The simulations are carried out at two different horizontal grid-spacings (0.22° and 0.44°, and are driven by ECMWF ERA-Interim reanalyses as near-perfect lateral boundary conditions. As evaluation reference, a high-resolution gridded dataset of daily maximum and minimum temperatures (Tmin/Tmax for Africa (covering the period 2008–2010 is created using the regression-kriging-regression-kriging (RKRK algorithm. RKRK applies, among other predictors, the remotely sensed predictors land surface temperature and cloud cover to compensate for the missing information about the temperature pattern due to the low station density over Africa. This dataset allows the evaluation of temperature characteristics like the frequencies of Tmin/Tmax, the diurnal temperature range, and the 90th percentile of Tmax. Although the large-scale patterns of temperature are reproduced well, COSMO-CLM shows significant under- and overestimation of temperature at regional scales. The hemispheric summers are generally too warm and the day-to-day temperature variability is overestimated over northern and southern extra-tropical Africa. The average diurnal temperature range is underestimated by about 2°C across arid areas, yet overestimated by around 2°C over the African tropics. An evaluation based on frequency distributions shows good model performance for simulated Tmin (the simulated frequency distributions capture more than 80% of the observed ones, but less well performance for Tmax (capture below 70%. Further, over wide parts of Africa a too large fraction of daily Tmax values exceeds the observed 90th percentile of Tmax, particularly

Evaluation of daily maximum and minimum 2-m temperatures as simulated with the regional climate model COSMO-CLM over Africa

Energy Technology Data Exchange (ETDEWEB)

Kraehenmann, Stefan; Kothe, Steffen; Ahrens, Bodo [Frankfurt Univ. (Germany). Inst. for Atmospheric and Environmental Sciences; Panitz, Hans-Juergen [Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen (Germany)

2013-10-15

The representation of the diurnal 2-m temperature cycle is challenging because of the many processes involved, particularly land-atmosphere interactions. This study examines the ability of the regional climate model COSMO-CLM (version 4.8) to capture the statistics of daily maximum and minimum 2-m temperatures (Tmin/Tmax) over Africa. The simulations are carried out at two different horizontal grid-spacings (0.22 and 0.44 ), and are driven by ECMWF ERA-Interim reanalyses as near-perfect lateral boundary conditions. As evaluation reference, a high-resolution gridded dataset of daily maximum and minimum temperatures (Tmin/Tmax) for Africa (covering the period 2008-2010) is created using the regression-kriging-regression-kriging (RKRK) algorithm. RKRK applies, among other predictors, the remotely sensed predictors land surface temperature and cloud cover to compensate for the missing information about the temperature pattern due to the low station density over Africa. This dataset allows the evaluation of temperature characteristics like the frequencies of Tmin/Tmax, the diurnal temperature range, and the 90{sup th} percentile of Tmax. Although the large-scale patterns of temperature are reproduced well, COSMO-CLM shows significant under- and overestimation of temperature at regional scales. The hemispheric summers are generally too warm and the day-to-day temperature variability is overestimated over northern and southern extra-tropical Africa. The average diurnal temperature range is underestimated by about 2 C across arid areas, yet overestimated by around 2 C over the African tropics. An evaluation based on frequency distributions shows good model performance for simulated Tmin (the simulated frequency distributions capture more than 80% of the observed ones), but less well performance for Tmax (capture below 70%). Further, over wide parts of Africa a too large fraction of daily Tmax values exceeds the observed 90{sup th} percentile of Tmax, particularly across

Understanding metropolitan patterns of daily encounters.

Science.gov (United States)

Sun, Lijun; Axhausen, Kay W; Lee, Der-Horng; Huang, Xianfeng

2013-08-20

Understanding of the mechanisms driving our daily face-to-face encounters is still limited; the field lacks large-scale datasets describing both individual behaviors and their collective interactions. However, here, with the help of travel smart card data, we uncover such encounter mechanisms and structures by constructing a time-resolved in-vehicle social encounter network on public buses in a city (about 5 million residents). Using a population scale dataset, we find physical encounters display reproducible temporal patterns, indicating that repeated encounters are regular and identical. On an individual scale, we find that collective regularities dominate distinct encounters' bounded nature. An individual's encounter capability is rooted in his/her daily behavioral regularity, explaining the emergence of "familiar strangers" in daily life. Strikingly, we find individuals with repeated encounters are not grouped into small communities, but become strongly connected over time, resulting in a large, but imperceptible, small-world contact network or "structure of co-presence" across the whole metropolitan area. Revealing the encounter pattern and identifying this large-scale contact network are crucial to understanding the dynamics in patterns of social acquaintances, collective human behaviors, and--particularly--disclosing the impact of human behavior on various diffusion/spreading processes.

Everyday politics, social practices and movement networks: daily life in Barcelona's social centres.

Science.gov (United States)

Yates, Luke

2015-06-01

The relations between everyday life and political participation are of interest for much contemporary social science. Yet studies of social movement protest still pay disproportionate attention to moments of mobilization, and to movements with clear organizational boundaries, tactics and goals. Exceptions have explored collective identity, 'free spaces' and prefigurative politics, but such processes are framed as important only in accounting for movements in abeyance, or in explaining movement persistence. This article focuses on the social practices taking place in and around social movement spaces, showing that political meanings, knowledge and alternative forms of social organization are continually being developed and cultivated. Social centres in Barcelona, Spain, autonomous political spaces hosting cultural and educational events, protest campaigns and alternative living arrangements, are used as empirical case studies. Daily practices of food provisioning, distributing space and dividing labour are politicized and politicizing as they unfold and develop over time and through diverse networks around social centres. Following Melucci, such latent processes set the conditions for social movements and mobilization to occur. However, they not only underpin mobilization, but are themselves politically expressive and prefigurative, with multiple layers of latency and visibility identifiable in performances of practices. The variety of political forms - adversarial, expressive, theoretical, and routinized everyday practices, allow diverse identities, materialities and meanings to overlap in movement spaces, and help explain networks of mutual support between loosely knit networks of activists and non-activists. An approach which focuses on practices and networks rather than mobilization and collective actors, it is argued, helps show how everyday life and political protest are mutually constitutive. © London School of Economics and Political Science 2014.

DAILY RAINFALL-RUNOFF MODELLING BY NEURAL NETWORKS ...

African Journals Online (AJOL)

K. Benzineb, M. Remaoun

2016-09-01

Sep 1, 2016 ... The hydrologic behaviour modelling of w. Journal of ... i Ouahrane's basin from rainfall-runoff relation which is non-linea networks ... will allow checking efficiency of formal neural networks for flows simulation in semi-arid zone.

A highly crystalline single Au wire network as a high temperature transparent heater

Science.gov (United States)

Rao, K. D. M.; Kulkarni, Giridhar U.

2014-05-01

A transparent conductor which can generate high temperatures finds important applications in optoelectronics. In this article, a wire network made of Au on quartz is shown to serve as an effective high temperature transparent heater. The heater has been fabricated by depositing Au onto a cracked sacrificial template. The highly interconnected Au wire network thus formed exhibited a transmittance of ~87% in a wide spectral range with a sheet resistance of 5.4 Ω □-1. By passing current through the network, it could be joule heated to ~600 °C within a few seconds. The extraordinary thermal performance and stability owe much to the seamless junctions present in the wire network. Furthermore, the wire network gets self-annealed through joule heating as seen from its increased crystallinity. Interestingly, both transmittance and sheet resistance improved following annealing to 92% and 3.2 Ω □-1, respectively. A transparent conductor which can generate high temperatures finds important applications in optoelectronics. In this article, a wire network made of Au on quartz is shown to serve as an effective high temperature transparent heater. The heater has been fabricated by depositing Au onto a cracked sacrificial template. The highly interconnected Au wire network thus formed exhibited a transmittance of ~87% in a wide spectral range with a sheet resistance of 5.4 Ω □-1. By passing current through the network, it could be joule heated to ~600 °C within a few seconds. The extraordinary thermal performance and stability owe much to the seamless junctions present in the wire network. Furthermore, the wire network gets self-annealed through joule heating as seen from its increased crystallinity. Interestingly, both transmittance and sheet resistance improved following annealing to 92% and 3.2 Ω □-1, respectively. Electronic supplementary information (ESI) available: Optical micrographs, EDAX, XRD, SEM and TEM images of Au metal wires. See DOI: 10.1039/c4nr00869c

Time response of temperature sensors using neural networks

International Nuclear Information System (INIS)

Santos, Roberto Carlos dos

2010-01-01

In a PWR nuclear power plant, the primary coolant temperature and feedwater temperature are measured using RTDs (Resistance Temperature Detectors). These RTDs typically feed the plant's control and safety systems and must, therefore, be very accurate and have good dynamic performance. The response time of RTDs is characterized by a single parameter called the Plunge Time Constant defined as the time it takes the sensor output to achieve 63.2 percent of its final value after a step change in temperature. Nuclear reactor service conditions are difficult to reproduce in the laboratory, and an in-situ test method called LCSR (Loop Current Step Response) test was developed to measure remotely the response time of RTDs. >From this test, the time constant of the sensor is identified by means of the LCSR transformation that involves the dynamic response modal time constants determination using a nodal heat-transfer model. This calculation is not simple and requires specialized personnel. For this reason an Artificial Neural Network has been developed to predict the time constant of RTD from LCSR test transient. It eliminates the transformations involved in the LCSR application. A series of LCSR tests on RTDs generates the response transients of the sensors, the input data of the networks. Plunge tests are used to determine the time constants of the RTDs, the desired output of the ANN, trained using these sets of input/output data. This methodology was firstly applied to theoretical data simulating 10 RTDs with different time constant values, resulting in an average error of about 0.74 %. Experimental data from three different RTDs was used to predict time constant resulting in a maximum error of 3,34 %. The time constants values predicted from ANN were compared with those obtained from traditional way resulting in an average error of about 18 % and that shows the network is able to predict accurately the sensor time constant. (author)

Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation

International Nuclear Information System (INIS)

Voyant, Cyril; Muselli, Marc; Paoli, Christophe; Nivet, Marie-Laure

2011-01-01

This paper presents an application of Artificial Neural Networks (ANNs) to predict daily solar radiation. We look at the Multi-Layer Perceptron (MLP) network which is the most used of ANNs architectures. In previous studies, we have developed an ad-hoc time series preprocessing and optimized a MLP with endogenous inputs in order to forecast the solar radiation on a horizontal surface. We propose in this paper to study the contribution of exogenous meteorological data (multivariate method) as time series to our optimized MLP and compare with different forecasting methods: a naive forecaster (persistence), ARIMA reference predictor, an ANN with preprocessing using only endogenous inputs (univariate method) and an ANN with preprocessing using endogenous and exogenous inputs. The use of exogenous data generates an nRMSE decrease between 0.5% and 1% for two stations during 2006 and 2007 (Corsica Island, France). The prediction results are also relevant for the concrete case of a tilted PV wall (1.175 kWp). The addition of endogenous and exogenous data allows a 1% decrease of the nRMSE over a 6 months-cloudy period for the power production. While the use of exogenous data shows an interest in winter, endogenous data as inputs on a preprocessed ANN seem sufficient in summer. -- Research highlights: → Use of exogenous data as ANN inputs to forecast horizontal daily global irradiation data. → New methodology allowing to choice the adequate exogenous data - a systematic method comparing endogenous and exogenous data. → Different referenced mathematical predictors allows to conclude about the pertinence of the proposed methodology.

Foreground removal from CMB temperature maps using an MLP neural network

Science.gov (United States)

Nørgaard-Nielsen, H. U.; Jørgensen, H. E.

2008-12-01

One of the main obstacles for extracting the Cosmic Microwave Background (CMB) signal from observations in the mm-submm range is the foreground contamination by emission from Galactic components: mainly synchrotron, free-free and thermal dust emission. Due to the statistical nature of the intrinsic CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the Galactic foregrounds simple power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting the CMB temperature signal from the combined signal CMB and the foregrounds has been investigated. As a specific example, we have analysed simulated data, as expected from the ESA Planck CMB mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates over more than 80 per cent of the sky that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky.

Equivalent network for resistance and temperature coefficient of resistance versus temperature and composition of thick resistive films

International Nuclear Information System (INIS)

Kusy, A.

1987-01-01

Two types of elementary resistances in thick resistive films have been considered: (i) constriction resistance R/sub C/ determined by the bulk properties of conducting material and by the geometry of constriction, and (ii) barrier resistance R/sub B/ determined by the parameters of a thermally activated type of tunneling process and by the geometry of the metal-insulator-metal unit. On this basis a resistance network composed of a large number of the two types of resistances has been defined. The network has been considered as being equivalent to thick resistive film (TRF) from the point of view of the resistance and temperature coefficient of resistance (TCR). The parameters of this network have been evaluated by the computer-aided approximation of the experimental data found for RuO 2 -based TRFs. On the basis of the equations derived for the network as well as the results of the approximation process, it can be concluded that the small values of the network TCR result from the superposition of the TCR of the conducting component β/sub C/ and of the temperature coefficient of barrier resistance α/sub B/. In this superposition β/sub C/ is attenuated (by 1--2 orders of magnitude), while α/sub B/ is attenuated by only few percentages. The network has been found to be strongly barrier dominated

Soil temperature synchronisation improves estimation of daily variation of ecosystem respiration in Sphagnum peatlands

Science.gov (United States)

D'Angelo, Benoît; Gogo, Sébastien; Le Moing, Franck; Jégou, Fabrice; Guimbaud, Christophe; Laggoun, Fatima

2015-04-01

Ecosystem respiration (ER) is a key process in the global C cycle and thus, plays an important role in the climate regulation. Peatlands contain a third of the world soil C in spite of their relatively low global area (3% of land area). Although these ecosystems represent potentially a significant source of C under global change, they are still not taken into account accordingly in global climatic models. Therefore, ER variations have to be accounted for, especially by estimating its dependence to temperature.s The relationship between ER and temperature often relies only on one soil temperature depth and the latter is generally taken in the first 10 centimetres. Previous studies showed that the temperature dependence of ER depends on the depth at which the temperature is recorded. The depth selection for temperature measurement is thus a predominant issue. A way to deal with this is to analyse the time-delay between ER and temperature. The aim of this work is to assess whether using synchronised data in models leads to a better ER daily variation estimation than using non-synchronised data. ER measurements were undertaken in 2013 in 4 Sphagnum peatlands across France: La Guette (N 47°19'44', E 2°17'04', 154m) in July, Landemarais (N 48°26'30', E -1°10'54', 145m) in August, Frasne (N 46°49'35', E 6°10'20', 836m) in September, and Bernadouze (N 42°48'09', E 1°25'24', 1500m) in October. A closed method chamber was used to measure ER hourly during 72 hours in each of the 4 replicates installed in each site. Average ER ranged from 1.75 μmol m-2 s-1 to 6.13 μmol m-2 s-1. A weather station was used to record meteorological data and soil temperature profiles (5, 10, 20 and 30 cm). Synchronised data were determined for each depth by selecting the time-delay leading to the best correlation between ER and soil temperature. The data were used to simulate ER according to commonly used equations: linear, exponential with Q10, Arrhenius, Lloyd and Taylor. Models

Uniformity factor of temperature difference in heat exchanger networks

International Nuclear Information System (INIS)

Chen, Shang; Cui, Guo-min

2016-01-01

Highlights: • A uniformity factor of temperature (UFTD) is proposed to heat exchanger network (HEN). • A novel stage-wise superstructure with inner utilities is presented based on UFTD. • New model and DE method is combined as an optimization method. • Optimal HEN structures with inner utilities can be obtained with new method. - Abstract: A uniformity factor of temperature difference (UFTD) is proposed and set up to guide the optimization of Heat exchanger network (HEN). At first, the factor is presented to evaluate the whole enhancement of HEN by handling the logical mean temperature difference as two-dimensional discrete temperature field in system. Then, the factor is applied to different HENs, of which the comparison indicates that a more uniform discrete temperature field leads to a lower UFTD which correlated with a better whole enhancement to improve the optimization level of HEN. A novel stage-wise superstructure model where inner utility can be generated is presented for further analysis of correlation between UFTD and the efficiency of HEN, and more optimal HEN structures can be obtained as inner utility added. Inner utility appears to violate the thermodynamic law, but it makes the discrete temperature field more uniform and improves the heat transfer efficiency of the whole HEN, which brings much more profit than the side effect of inner utility. In sum, the UFTD can not only evaluate the optimization level of the HEN, but also be an optimization object to design new HEN with higher efficiency of energy utilization and lower total annual cost.

Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

Directory of Open Access Journals (Sweden)

Ani Shabri

2014-01-01

Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

Stability of Intelligent Transportation Network Dynamics: A Daily Path Flow Adjustment considering Travel Time Differentiation

Directory of Open Access Journals (Sweden)

Ming-Chorng Hwang

2015-01-01

Full Text Available A theoretic formulation on how traffic time information distributed by ITS operations influences the trajectory of network flows is presented in this paper. The interactions between users and ITS operator are decomposed into three parts: (i travel time induced path flow dynamics (PFDTT; (ii demand induced path flow dynamics (PFDD; and (iii predicted travel time dynamics for an origin-destination (OD pair (PTTDOD. PFDTT describes the collective results of user’s daily route selection by pairwise comparison of path travel time provided by ITS services. The other two components, PTTDOD and PFDD, are concentrated on the evolutions of system variables which are predicted and observed, respectively, by ITS operators to act as a benchmark in guiding the target system towards an expected status faster. In addition to the delivered modelings, the stability theorem of the equilibrium solution in the sense of Lyapunov stability is also provided. A Lyapunov function is developed and employed to the proof of stability theorem to show the asymptotic behavior of the aimed system. The information of network flow dynamics plays a key role in traffic control policy-making. The evaluation of ITS-based strategies will not be reasonable without a well-established modeling of network flow evolutions.

Daily temperature variations on Mars

Science.gov (United States)

Ditteon, R.

1982-01-01

It is noted that for approximately 32% of the Martian surface area no values of thermal inertia or albedo can fit the thermal observations. These temperature anomalies do not correlate with elevation, geologic units, morphology, or atmospheric dust content. All regions having a Lambert albedo less than 0.18 can be well fit with the standard thermal model, but all areas with albedo greater than 0.28 are anomalous. A strong inverse correlation is seen between the magnitude of the anomaly and the thermal inertia. This correlation is seen as indicating that some surface property is responsible for the anomaly. In the anomalous region the temperatures are observed to be warmer in the morning and cooler late in the afternoon and to decrease more slowly during the night than the Viking model temperatures. It is believed that of all the physical processes likely to occur on Mars but not included in the Viking thermal model, only a layered soil can explain the observations. A possible explanation of the layering deduced from the infrared thermal mapper observations is a layer of aeolian deposited dust about one thermal skin depth thick (1 to 4 cm), covering a duricrust.

The Threshold Temperature and Lag Effects on Daily Excess Mortality in Harbin, China: A Time Series Analysis

Directory of Open Access Journals (Sweden)

Hanlu Gao

2017-04-01

Full Text Available Background: A large number of studies have reported the relationship between ambient temperature and mortality. However, few studies have focused on the effects of high temperatures on cardio-cerebrovascular diseases mortality (CCVDM and their acute events (ACCVDM. Objective: To assess the threshold temperature and time lag effects on daily excess mortality in Harbin, China. Methods: A generalized additive model (GAM with a Poisson distribution was used to investigate the relative risk of mortality for each 1 °C increase above the threshold temperature and their time lag effects in Harbin, China. Results: High temperature threshold was 26 °C in Harbin. Heat effects were immediate and lasted for 0–6 and 0–4 days for CCVDM and ACCVDM, respectively. The acute cardiovascular disease mortality (ACVDM seemed to be more sensitive to temperature than cardiovascular disease mortality (CVDM with higher death risk and shorter time lag effects. The lag effects lasted longer for cerebrovascular disease mortality (CBDM than CVDM; so did ACBDM compared to ACVDM. Conclusion: Hot temperatures increased CCVDM and ACCVDM in Harbin, China. Public health intervention strategies for hot temperatures adaptation should be concerned.

Foreground removal from WMAP 5 yr temperature maps using an MLP neural network

Science.gov (United States)

Nørgaard-Nielsen, H. U.

2010-09-01

Aims: One of the main obstacles for extracting the cosmic microwave background (CMB) signal from observations in the mm/sub-mm range is the foreground contamination by emission from Galactic component: mainly synchrotron, free-free, and thermal dust emission. The statistical nature of the intrinsic CMB signal makes it essential to minimize the systematic errors in the CMB temperature determinations. Methods: The feasibility of using simple neural networks to extract the CMB signal from detailed simulated data has already been demonstrated. Here, simple neural networks are applied to the WMAP 5 yr temperature data without using any auxiliary data. Results: A simple multilayer perceptron neural network with two hidden layers provides temperature estimates over more than 75 per cent of the sky with random errors significantly below those previously extracted from these data. Also, the systematic errors, i.e. errors correlated with the Galactic foregrounds, are very small. Conclusions: With these results the neural network method is well prepared for dealing with the high - quality CMB data from the ESA Planck Surveyor satellite. unknown author type, collab

Improving thermal performance of an existing UK district heat network: a case for temperature optimization

DEFF Research Database (Denmark)

Tunzi, Michele; Boukhanouf, Rabah; Li, Hongwei

2018-01-01

This paper presents results of a research study into improving energy performance of small-scale district heat network through water supply and return temperature optimization technique. The case study involves establishing the baseline heat demand of the estate’s buildings, benchmarking...... the existing heat network operating parameters, and defining the optimum supply and return temperature. A stepwise temperature optimization technique of plate radiators heat emitters was applied to control the buildings indoor thermal comfort using night set back temperature strategy of 21/18 °C....... It was established that the heat network return temperature could be lowered from the current measured average of 55 °C to 35.6 °C, resulting in overall reduction of heat distribution losses and fuel consumption of 10% and 9% respectively. Hence, the study demonstrates the potential of operating existing heat...

Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction

Science.gov (United States)

Deng, Ziwang; Liu, Jinliang; Qiu, Xin; Zhou, Xiaolan; Zhu, Huaiping

2017-10-01

A novel method for daily temperature and precipitation downscaling is proposed in this study which combines the Ensemble Optimal Interpolation (EnOI) and bias correction techniques. For downscaling temperature, the day to day seasonal cycle of high resolution temperature of the NCEP climate forecast system reanalysis (CFSR) is used as background state. An enlarged ensemble of daily temperature anomaly relative to this seasonal cycle and information from global climate models (GCMs) are used to construct a gain matrix for each calendar day. Consequently, the relationship between large and local-scale processes represented by the gain matrix will change accordingly. The gain matrix contains information of realistic spatial correlation of temperature between different CFSR grid points, between CFSR grid points and GCM grid points, and between different GCM grid points. Therefore, this downscaling method keeps spatial consistency and reflects the interaction between local geographic and atmospheric conditions. Maximum and minimum temperatures are downscaled using the same method. For precipitation, because of the non-Gaussianity issue, a logarithmic transformation is used to daily total precipitation prior to conducting downscaling. Cross validation and independent data validation are used to evaluate this algorithm. Finally, data from a 29-member ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) GCMs are downscaled to CFSR grid points in Ontario for the period from 1981 to 2100. The results show that this method is capable of generating high resolution details without changing large scale characteristics. It results in much lower absolute errors in local scale details at most grid points than simple spatial downscaling methods. Biases in the downscaled data inherited from GCMs are corrected with a linear method for temperatures and distribution mapping for precipitation. The downscaled ensemble projects significant warming with amplitudes of 3

Changing world extreme temperature statistics

Science.gov (United States)

Finkel, J. M.; Katz, J. I.

2018-04-01

We use the Global Historical Climatology Network--daily database to calculate a nonparametric statistic that describes the rate at which all-time daily high and low temperature records have been set in nine geographic regions (continents or major portions of continents) during periods mostly from the mid-20th Century to the present. This statistic was defined in our earlier work on temperature records in the 48 contiguous United States. In contrast to this earlier work, we find that in every region except North America all-time high records were set at a rate significantly (at least $3\\sigma$) higher than in the null hypothesis of a stationary climate. Except in Antarctica, all-time low records were set at a rate significantly lower than in the null hypothesis. In Europe, North Africa and North Asia the rate of setting new all-time highs increased suddenly in the 1990's, suggesting a change in regional climate regime; in most other regions there was a steadier increase.

A Statistical Method to Predict Flow Permanence in Dryland Streams from Time Series of Stream Temperature

Directory of Open Access Journals (Sweden)

Ivan Arismendi

2017-12-01

Full Text Available Intermittent and ephemeral streams represent more than half of the length of the global river network. Dryland freshwater ecosystems are especially vulnerable to changes in human-related water uses as well as shifts in terrestrial climates. Yet, the description and quantification of patterns of flow permanence in these systems is challenging mostly due to difficulties in instrumentation. Here, we took advantage of existing stream temperature datasets in dryland streams in the northwest Great Basin desert, USA, to extract critical information on climate-sensitive patterns of flow permanence. We used a signal detection technique, Hidden Markov Models (HMMs, to extract information from daily time series of stream temperature to diagnose patterns of stream drying. Specifically, we applied HMMs to time series of daily standard deviation (SD of stream temperature (i.e., dry stream channels typically display highly variable daily temperature records compared to wet stream channels between April and August (2015–2016. We used information from paired stream and air temperature data loggers as well as co-located stream temperature data loggers with electrical resistors as confirmatory sources of the timing of stream drying. We expanded our approach to an entire stream network to illustrate the utility of the method to detect patterns of flow permanence over a broader spatial extent. We successfully identified and separated signals characteristic of wet and dry stream conditions and their shifts over time. Most of our study sites within the entire stream network exhibited a single state over the entire season (80%, but a portion of them showed one or more shifts among states (17%. We provide recommendations to use this approach based on a series of simple steps. Our findings illustrate a successful method that can be used to rigorously quantify flow permanence regimes in streams using existing records of stream temperature.

A statistical method to predict flow permanence in dryland streams from time series of stream temperature

Science.gov (United States)

Arismendi, Ivan; Dunham, Jason B.; Heck, Michael; Schultz, Luke; Hockman-Wert, David

2017-01-01

Intermittent and ephemeral streams represent more than half of the length of the global river network. Dryland freshwater ecosystems are especially vulnerable to changes in human-related water uses as well as shifts in terrestrial climates. Yet, the description and quantification of patterns of flow permanence in these systems is challenging mostly due to difficulties in instrumentation. Here, we took advantage of existing stream temperature datasets in dryland streams in the northwest Great Basin desert, USA, to extract critical information on climate-sensitive patterns of flow permanence. We used a signal detection technique, Hidden Markov Models (HMMs), to extract information from daily time series of stream temperature to diagnose patterns of stream drying. Specifically, we applied HMMs to time series of daily standard deviation (SD) of stream temperature (i.e., dry stream channels typically display highly variable daily temperature records compared to wet stream channels) between April and August (2015–2016). We used information from paired stream and air temperature data loggers as well as co-located stream temperature data loggers with electrical resistors as confirmatory sources of the timing of stream drying. We expanded our approach to an entire stream network to illustrate the utility of the method to detect patterns of flow permanence over a broader spatial extent. We successfully identified and separated signals characteristic of wet and dry stream conditions and their shifts over time. Most of our study sites within the entire stream network exhibited a single state over the entire season (80%), but a portion of them showed one or more shifts among states (17%). We provide recommendations to use this approach based on a series of simple steps. Our findings illustrate a successful method that can be used to rigorously quantify flow permanence regimes in streams using existing records of stream temperature.

Automated general temperature correction method for dielectric soil moisture sensors

Science.gov (United States)

Kapilaratne, R. G. C. Jeewantinie; Lu, Minjiao

2017-08-01

An effective temperature correction method for dielectric sensors is important to ensure the accuracy of soil water content (SWC) measurements of local to regional-scale soil moisture monitoring networks. These networks are extensively using highly temperature sensitive dielectric sensors due to their low cost, ease of use and less power consumption. Yet there is no general temperature correction method for dielectric sensors, instead sensor or site dependent correction algorithms are employed. Such methods become ineffective at soil moisture monitoring networks with different sensor setups and those that cover diverse climatic conditions and soil types. This study attempted to develop a general temperature correction method for dielectric sensors which can be commonly used regardless of the differences in sensor type, climatic conditions and soil type without rainfall data. In this work an automated general temperature correction method was developed by adopting previously developed temperature correction algorithms using time domain reflectometry (TDR) measurements to ThetaProbe ML2X, Stevens Hydra probe II and Decagon Devices EC-TM sensor measurements. The rainy day effects removal procedure from SWC data was automated by incorporating a statistical inference technique with temperature correction algorithms. The temperature correction method was evaluated using 34 stations from the International Soil Moisture Monitoring Network and another nine stations from a local soil moisture monitoring network in Mongolia. Soil moisture monitoring networks used in this study cover four major climates and six major soil types. Results indicated that the automated temperature correction algorithms developed in this study can eliminate temperature effects from dielectric sensor measurements successfully even without on-site rainfall data. Furthermore, it has been found that actual daily average of SWC has been changed due to temperature effects of dielectric sensors with a

Artificial neural networks in prediction of mechanical behavior of concrete at high temperature

International Nuclear Information System (INIS)

Mukherjee, A.; Nag Biswas, S.

1997-01-01

The behavior of concrete structures that are exposed to extreme thermo-mechanical loading is an issue of great importance in nuclear engineering. The mechanical behavior of concrete at high temperature is non-linear. The properties that regulate its response are highly temperature dependent and extremely complex. In addition, the constituent materials, e.g. aggregates, influence the response significantly. Attempts have been made to trace the stress-strain curve through mathematical models and rheological models. However, it has been difficult to include all the contributing factors in the mathematical model. This paper examines a new programming paradigm, artificial neural networks, for the problem. Implementing a feedforward network and backpropagation algorithm the stress-strain relationship of the material is captured. The neural networks for the prediction of uniaxial behavior of concrete at high temperature has been presented here. The results of the present investigation are very encouraging. (orig.)

Asymetric change of daily temperature range: Proceedings

Energy Technology Data Exchange (ETDEWEB)

Kukla, G. [ed.] [Columbia Univ., Palisades, NY (United States). Lamont-Doherty Earth Observatory; Karl, T.R. [ed.] [National Climatic Data Center, Asheville, NC (United States); Riches, M.R. [ed.] [USDOE, Washington, DC (United States)

1994-04-01

This report is a compilation of abstracts of papers presented at the MINIMAX workshop. Topics include; temperature fluxes, influence of clouds on temperature, anthropogenic influences on temperature flux, and carbon dioxide and aerosol induced greenhouse effect.

Asymetric change of daily temperature range: Proceedings

International Nuclear Information System (INIS)

Kukla, G.; Riches, M.R.

1994-04-01

This report is a compilation of abstracts of papers presented at the MINIMAX workshop. Topics include; temperature fluxes, influence of clouds on temperature, anthropogenic influences on temperature flux, and carbon dioxide and aerosol induced greenhouse effect

Climate Reference Network Daily01 Product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The U.S. Climate Reference Network is designed specifically to monitor national climate change with best scientific practice and adherence to the accepted principles...

Daily temperature and precipitation extremes in the Baltic Sea region derived from the BaltAn65+ reanalysis

Science.gov (United States)

Toll, Velle; Post, Piia

2018-04-01

Daily 2-m temperature and precipitation extremes in the Baltic Sea region for the time period of 1965-2005 is studied based on data from the BaltAn65 + high resolution atmospheric reanalysis. Moreover, the ability of regional reanalysis to capture extremes is analysed by comparing the reanalysis data to gridded observations. The shortcomings in the simulation of the minimum temperatures over the northern part of the region and in the simulation of the extreme precipitation over the Scandinavian mountains in the BaltAn65+ reanalysis data are detected and analysed. Temporal trends in the temperature and precipitation extremes in the Baltic Sea region, with the largest increases in temperature and precipitation in winter, are detected based on both gridded observations and the BaltAn65+ reanalysis data. However, the reanalysis is not able to capture all of the regional trends in the extremes in the observations due to the shortcomings in the simulation of the extremes.

Combined IR imaging-neural network method for the estimation of internal temperature in cooked chicken meat

Science.gov (United States)

Ibarra, Juan G.; Tao, Yang; Xin, Hongwei

2000-11-01

A noninvasive method for the estimation of internal temperature in chicken meat immediately following cooking is proposed. The external temperature from IR images was correlated with measured internal temperature through a multilayer neural network. To provide inputs for the network, time series experiments were conducted to obtain simultaneous observations of internal and external temperatures immediately after cooking during the cooling process. An IR camera working at the spectral band of 3.4 to 5.0 micrometers registered external temperature distributions without the interference of close-to-oven environment, while conventional thermocouples registered internal temperatures. For an internal temperature at a given time, simultaneous and lagged external temperature observations were used as the input of the neural network. Based on practical and statistical considerations, a criterion is established to reduce the nodes in the neural network input. The combined method was able to estimate internal temperature for times between 0 and 540 s within a standard error of +/- 1.01 degree(s)C, and within an error of +/- 1.07 degree(s)C for short times after cooking (3 min), with two thermograms at times t and t+30s. The method has great potential for monitoring of doneness of chicken meat in conveyor belt type cooking and can be used as a platform for similar studies in other food products.

Daily changes of radon concentration in soil gas under influence of atmospheric factors: room temperature, soil surface temperature and relative humidity

International Nuclear Information System (INIS)

Lara, Evelise G.; Oliveira, Arno Heeren de

2015-01-01

This work aims at relating the daily change in the radon concentration in soil gas in a Red Yellow Acrisol (SiBCS) under influence of atmospheric factors: room temperature, soil surface temperature and relative humidity. The 226 Ra, 232 Th, U content and permeability were also performed. The measurements of radon soil gas were carried out by using an AlphaGUARD monitor. The 226 Ra activity concentration was made by Gamma Spectrometry (HPGe); the permeability was carried out using the RADON-JOK permeameter and ICP-MS analysis to 232 Th and U content. The soil permeability is 5.0 x 10 -12 , which is considered average. The 226 Ra (22.2 ± 0.3 Bq.m -3 ); U content (73.4 ± 3.6 Bq.kg -1 ) and 232 Th content (55.3 ± 4.0 Bq.kg -1 ) were considered above of average concentrations, according to mean values for soils typical (~ 35.0 Bq.kg -1 ) by UNSCEAR. The results showed a difference of 26.0% between the highest and the lowest concentration of radon in soil gas: at midnight (15.5 ± 1.0 kBq.m -3 ) and 3:00 pm, the highest mean radon concentration (21.0 ± 1.0 kBq.m -3 ). The room temperature and surface soil temperature showed equivalent behavior and the surface soil temperature slightly below room temperature during the entire monitoring time. Nevertheless, the relative humidity showed the highest cyclical behavior, showing a higher relationship with the radon concentration in soil gas. (author)

Monitoring and Modeling Temperature Variations Inside Silage Stack Using Novel Wireless Sensor Networks

DEFF Research Database (Denmark)

Green, Ole; Shahrak Nadimi, Esmaeil; Blanes-Vidal, Victoria

2009-01-01

the sensor nodes were successfully delivered to the gateway. The reliable performance of the network confirmed the correct choice of network characteristics (i.e., frequency range of 433 MHz, a handshaking communication protocol and 10 mW transmission power). The designed sensor housings were capable......Abstract: By monitoring silage temperature at different locations inside silage stacks, it is possible to detect any significant increases in temperature occurring during silage decomposition. The objectives of this study were: (1) to develop novel noninvasive wireless sensor nodes for measuring...... the temperature inside silage stacks; (2) to design a suitable sensor protection housing that prevents physical and chemical damage to the sensor; and (3) to mathematically model temperature variations inside a silage stack, using system identification techniques. The designed wireless nodes were used to monitor...

Monitoring and modeling temperature variations inside silage stacks using novel wireless sensor networks

DEFF Research Database (Denmark)

Green, O.; Nadimi, E.S.; Blanes-Vidal, V.

2009-01-01

the sensor nodes were successfully delivered to the gateway. The reliable performance of the network confirmed the correct choice of network characteristics (i.e., frequency range of 433 MHz, a handshaking communication protocol, and 10 mW transmission power). The designed sensor housings were capable......By monitoring silage temperature at different locations inside silage stacks, it is possible to detect any significant increases in temperature occurring during silage decomposition. The objectives of this study were: (1) to develop novel noninvasive wireless sensor nodes for measuring...... the temperature inside silage stacks; (2) to design a suitable sensor protection housing that prevents physical and chemical damage to the sensor: and (3) to mathematically model temperature variations inside a silage stack, using system identification techniques. The designed wireless nodes were used to monitor...

Direct and indirect toxicity of the fungicide pyraclostrobin to Hyalella azteca and effects on leaf processing under realistic daily temperature regimes.

Science.gov (United States)

Willming, Morgan M; Maul, Jonathan D

2016-04-01

Fungicides in aquatic environments can impact non-target bacterial and fungal communities and the invertebrate detritivores responsible for the decomposition of allochthonous organic matter. Additionally, in some aquatic systems daily water temperature fluctuations may influence these processes and alter contaminant toxicity, but such temperature fluctuations are rarely examined in conjunction with contaminants. In this study, the shredding amphipod Hyalella azteca was exposed to the fungicide pyraclostrobin in three experiments. Endpoints included mortality, organism growth, and leaf processing. One experiment was conducted at a constant temperature (23 °C), a fluctuating temperature regime (18-25 °C) based on field-collected data from the S. Llano River, Texas, or an adjusted fluctuating temperature regime (20-26 °C) based on possible climate change predictions. Pyraclostrobin significantly reduced leaf shredding and increased H. azteca mortality at concentrations of 40 μg/L or greater at a constant 23 °C and decreased leaf shredding at concentrations of 15 μg/L or greater in the fluctuating temperatures. There was a significant interaction between temperature treatment and pyraclostrobin concentration on H. azteca mortality, body length, and dry mass under direct aqueous exposure conditions. In an indirect exposure scenario in which only leaf material was exposed to pyraclostrobin, H. azteca did not preferentially feed on or avoid treated leaf disks compared to controls. This study describes the influence of realistic temperature variation on fungicide toxicity to shredding invertebrates, which is important for understanding how future alterations in daily temperature regimes due to climate change may influence the assessment of ecological risk of contaminants in aquatic ecosystems. Copyright © 2016. Published by Elsevier Ltd.

A fuzzy recommendation system for daily water intake

Directory of Open Access Journals (Sweden)

Bin Dai

2016-05-01

Full Text Available Water is one of the most important constituents of the human body. Daily consumption of water is thus necessary to protect human health. Daily water consumption is related to several factors such as age, ambient temperature, and degree of physical activity. These factors are generally difficult to express with exact numerical values. The main objective of this article is to build a daily water intake recommendation system using fuzzy methods. This system will use age, physical activity, and ambient temperature as the input factors and daily water intake values as the output factor. The reasoning mechanism of the fuzzy system can calculate the recommended value of daily water intake. Finally, the system will compare the actual recommended values with our system to determine the usefulness. The experimental results show that this recommendation system is effective in actual application.

Curvature and temperature of complex networks.

Science.gov (United States)

Krioukov, Dmitri; Papadopoulos, Fragkiskos; Vahdat, Amin; Boguñá, Marián

2009-09-01

We show that heterogeneous degree distributions in observed scale-free topologies of complex networks can emerge as a consequence of the exponential expansion of hidden hyperbolic space. Fermi-Dirac statistics provides a physical interpretation of hyperbolic distances as energies of links. The hidden space curvature affects the heterogeneity of the degree distribution, while clustering is a function of temperature. We embed the internet into the hyperbolic plane and find a remarkable congruency between the embedding and our hyperbolic model. Besides proving our model realistic, this embedding may be used for routing with only local information, which holds significant promise for improving the performance of internet routing.

Research on Daily Objects Detection Based on Deep Neural Network

Science.gov (United States)

Ding, Sheng; Zhao, Kun

2018-03-01

With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.

Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature

Directory of Open Access Journals (Sweden)

Ding Fang

2013-06-01

Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.

A soil moisture and temperature network for SMOS validation in Western Denmark

DEFF Research Database (Denmark)

Bircher, Simone; Skou, Niels; Jensen, K. H.

2011-01-01

The Soil Moisture and Ocean Salinity Mission (SMOS) acquires surface soil moisture data globally, and thus product validation for a range of climate and environmental conditions across continents is a crucial step. For this purpose, a soil moisture and temperature network of Decagon ECH2O 5TE...... SMOS pixel (44 × 44 km), which is representative of the land surface conditions of the catchment and with minimal impact from open water (2) arrangement of three network clusters along the precipitation gradient, and (3) distribution of the stations according to respective fractions of classes...... representing the prevailing environmental conditions. Overall, measured moisture and temperature patterns could be related to the respective land cover and soil conditions. Texture-dependency of the 0–5 cm soil moisture measurements was demonstrated. Regional differences in 0–5 cm soil moisture, temperature...

Daily changes of radon concentration in soil gas under influence of atmospheric factors: room temperature, soil surface temperature and relative humidity

Energy Technology Data Exchange (ETDEWEB)

Lara, Evelise G.; Oliveira, Arno Heeren de, E-mail: evelise.lara@gmail.com, E-mail: heeren@nuclear.ufmg.br [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Departamento de Engenharia Nuclear; Rocha, Zildete; Rios, Francisco Javier, E-mail: rochaz@cdtn.br, E-mail: javier@cdtn.br [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

2015-07-01

This work aims at relating the daily change in the radon concentration in soil gas in a Red Yellow Acrisol (SiBCS) under influence of atmospheric factors: room temperature, soil surface temperature and relative humidity. The {sup 226}Ra, {sup 232}Th, U content and permeability were also performed. The measurements of radon soil gas were carried out by using an AlphaGUARD monitor. The {sup 226}Ra activity concentration was made by Gamma Spectrometry (HPGe); the permeability was carried out using the RADON-JOK permeameter and ICP-MS analysis to {sup 232}Th and U content. The soil permeability is 5.0 x 10{sup -12}, which is considered average. The {sup 226}Ra (22.2 ± 0.3 Bq.m{sup -3}); U content (73.4 ± 3.6 Bq.kg{sup -1}) and {sup 232}Th content (55.3 ± 4.0 Bq.kg{sup -1}) were considered above of average concentrations, according to mean values for soils typical (~ 35.0 Bq.kg{sup -1}) by UNSCEAR. The results showed a difference of 26.0% between the highest and the lowest concentration of radon in soil gas: at midnight (15.5 ± 1.0 kBq.m{sup -3}) and 3:00 pm, the highest mean radon concentration (21.0 ± 1.0 kBq.m{sup -3}). The room temperature and surface soil temperature showed equivalent behavior and the surface soil temperature slightly below room temperature during the entire monitoring time. Nevertheless, the relative humidity showed the highest cyclical behavior, showing a higher relationship with the radon concentration in soil gas. (author)

Short term load forecasting using neuro-fuzzy networks

Energy Technology Data Exchange (ETDEWEB)

Hoffman, M.; Hassan, A. [South Dakota School of Mines and Technology, Rapid City, SD (United States); Martinez, D. [Black Hills Power and Light, Rapid City, SD (United States)

2005-07-01

Details of a neuro-fuzzy network-based short term load forecasting system for power utilities were presented. The fuzzy logic controller was used to fuzzify inputs representing historical temperature and load curves. The fuzzified inputs were then used to develop the fuzzy rules matrix. Output membership function values were determined by evaluating the fuzzified inputs with the fuzzy rules. Output membership function values were used as inputs for the neural network portion of the system. The training process used a back propagation gradient descent algorithm to adjust the weight values of the neural network in order to reduce the error between the neural network output and the desired output. The neural network was then used to predict future load values. Sample data were taken from a local power company's daily load curve to validate the system. A 10 per cent forecast error was introduced in the temperature values to determine the effect on load prediction. Results of the study suggest that the combined use of fuzzy logic and neural networks provide greater accuracy than studies where either approach is used alone. 6 refs., 6 figs.

Hubungan antara Perubahan Suhu Udara Harian, Perilaku Petani dan Keankeragaman Serangga Penyerbuk di Desa Serang Kecamatan Karangreja, Kabupaten Purbalingga Jawa Tengah (The relationship between the Air Temperature Change Daily, Farmer Behavior, and Diver

Directory of Open Access Journals (Sweden)

Dwi YULIANI

2015-10-01

Full Text Available Global warming has threatened Indonesian’s agricultural sector and put the sectorvulnerable to climate change. The changes affect the daily air temperature changes, farmerbehavior, and the diversity of insect pollinators. The study aims to determine the daily changes inair temperature, farmer behavior, and diversity of pollinating insects in the village of Serang,Karangreja, Purbalingga, Central Java; and to analyze the relationship between daily airtemperature changes, farmer behavior, and insect pollinators diversity. The research employedsurvey methods. Samples for the daily air temperature measurement were taken purposeviley. Thediversity of insect pollinators on the three farming type and respondens were selected radmonlywith total respondent 99. The results showed that the average daily air temperature in chili farms ishigher than that in tomato and strawberry farm; farmers have a good knowledge about theenvironmental degradation of agriculture land, good attitude and awareness in maintaining andimproving the quality of agriculture, but they have negative behaviour in the use of excessiveinsecticides. Species richness of insect pollinators in tomato farm is higher than that in chili andstrawberry farm. A good knowledge, good attitude, and bad behaviour are closely related to thedaily air temperature and insect pollinators.

Seasonal variation of daily physical activity in individuals with heart failure

Directory of Open Access Journals (Sweden)

Michael J. Shoemaker

2016-09-01

Full Text Available BackgroundPrevious studies indicate that seasonal variation affects daily physical activity (PA, but none have investigated this relationship in individuals with heart failure (HF who also have implanted cardioverter defibrillators and cardiac resynchronization therapy (ICD/CRT devices. The purpose of the present study was to determine if seasonal variation in temperature affects daily PA in patients with HF and ICD/CRTs.Patients and methodsSecondary analysis of data from 16 subjects with HF and Medtronic® ICD/CRT devices enrolled in a randomized trial investigating interventions to improve daily PA. Due to the rolling study enrollment, daily PA data for all subjects were not available for the entire time frame and were divided into two groups. Determination of seasonal variation of daily PA was determined using visual analysis of daily PA plotted with average temperature, autocorrelation, visual analysis of seasonal subseries plots and boxplot analysis, as well as Wilcoxon signed-rank tests.ResultsSubjects 1-8 demonstrated the greatest differences in daily PA during periods of seasonal transition whereas subjects 9-16 demonstrated the greatest variation in daily PA with greatest seasonal temperature difference. Wilcoxon signed-rank testing of the lowest and highest months for daily PA revealed median differences of 0.30 (p = 0.050 and 0.36 hours (p = 0.036 for subjects 1-8 and 9-16, with effect sizes of 0.69 and 0.74, respectively.ConclusionsSeasonal variation in mean temperature appears to affect daily PA in individuals with HF and ICD/CRT devices by a magnitude of 0.30-0.36 hours, which may need to be accounted for in future research investigating interventions to improve daily PA.

An Operational In Situ Soil Moisture & Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network.

Science.gov (United States)

Petropoulos, George P; McCalmont, Jon P

2017-06-23

This paper describes a soil moisture dataset that has been collecting ground measurements of soil moisture, soil temperature and related parameters for west Wales, United Kingdom. Already acquired in situ data have been archived to the autonomous Wales Soil Moisture Network (WSMN) since its foundation in July 2011. The sites from which measurements are being collected represent a range of conditions typical of the Welsh environment, with climate ranging from oceanic to temperate and a range of the most typical land use/cover types found in Wales. At present, WSMN consists of a total of nine monitoring sites across the area with a concentration of sites in three sub-areas around the region of Aberystwyth located in Mid-Wales. The dataset of composed of 0-5 (or 0-10) cm soil moisture, soil temperature, precipitation, and other ancillary data. WSMN data are provided openly to the public via the International Soil Moisture Network (ISMN) platform. At present, WSMN is also rapidly expanding thanks to funding obtained recently which allows more monitoring sites to be added to the network to the wider community interested in using its data.

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

Science.gov (United States)

Pryor, Sara C.; Sullivan, Ryan C.; Schoof, Justin T.

2017-12-01

The static energy content of the atmosphere is increasing on a global scale, but exhibits important subglobal and subregional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called warming holes (i.e., locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New nonlinear statistical models for summertime daily maximum and minimum θe are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily global mean temperature, daily indices of the synoptic-scale meteorology derived from T and specific humidity (Q) at 850 and 500 hPa geopotential heights (Z), and spatiotemporally averaged soil moisture (text">SM). text">SM is particularly important in determining the magnitude of θe over regions that have previously been identified as exhibiting warming holes, confirming the key importance of text">SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using artificial neural networks (ANNs) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and text">SM). This is particularly marked in regions with high variability in minimum and maximum θe, where

Analysis of a resistance-energy balance method for estimating daily evaporation from wheat plots using one-time-of-day infrared temperature observations

Science.gov (United States)

Choudhury, B. J.; Idso, S. B.; Reginato, R. J.

1986-01-01

Accurate estimates of evaporation over field-scale or larger areas are needed in hydrologic studies, irrigation scheduling, and meteorology. Remotely sensed surface temperature might be used in a model to calculate evaporation. A resistance-energy balance model, which combines an energy balance equation, the Penman-Monteith (1981) evaporation equation, and van den Honert's (1948) equation for water extraction by plant roots, is analyzed for estimating daily evaporation from wheat using postnoon canopy temperature measurements. Additional data requirements are half-hourly averages of solar radiation, air and dew point temperatures, and wind speed, along with reasonable estimates of canopy emissivity, albedo, height, and leaf area index. Evaporation fluxes were measured in the field by precision weighing lysimeters for well-watered and water-stressed wheat. Errors in computed daily evaporation were generally less than 10 percent, while errors in cumulative evaporation for 10 clear sky days were less than 5 percent for both well-watered and water-stressed wheat. Some results from sensitivity analysis of the model are also given.

Depression of Glass Transition Temperatures of Polymer Networks by Diluents

NARCIS (Netherlands)

Brinke, Gerrit ten; Karasz, Frank E.; Ellis, Thomas S.

1983-01-01

A classical thermodynamic theory is used to derive expressions for the depression of the glass transition temperature Tg of a polymer network by a diluent. The enhanced sensitivity of Tg in cross-linked systems to small amounts of diluent is explained. Predictions of the theory are in satisfactory

Discriminating low frequency components from long range persistent fluctuations in daily atmospheric temperature variability

Directory of Open Access Journals (Sweden)

V. Cuomo

2009-07-01

Full Text Available This study originated from recent results reported in literature, which support the existence of long-range (power-law persistence in atmospheric temperature fluctuations on monthly and inter-annual scales. We investigated the results of Detrended Fluctuation Analysis (DFA carried out on twenty-two historical daily time series recorded in Europe in order to evaluate the reliability of such findings in depth. More detailed inspections emphasized systematic deviations from power-law and high statistical confidence for functional form misspecification. Rigorous analyses did not support scale-free correlation as an operative concept for Climate modelling, as instead suggested in literature. In order to understand the physical implications of our results better, we designed a bivariate Markov process, parameterised on the basis of the atmospheric observational data by introducing a slow dummy variable. The time series generated by this model, analysed both in time and frequency domains, tallied with the real ones very well. They accounted for both the deceptive scaling found in literature and the correlation details enhanced by our analysis. Our results seem to evidence the presence of slow fluctuations from another climatic sub-system such as ocean, which inflates temperature variance up to several months. They advise more precise re-analyses of temperature time series before suggesting dynamical paradigms useful for Climate modelling and for the assessment of Climate Change.

A study of the daily life of modern teenagers: the presence in social networks as an integral component of communication

Directory of Open Access Journals (Sweden)

Koroleva D.O.

2016-06-01

Full Text Available The article introduces the methodologies of the study of the everyday life of modern teenagers whiсh can simultaneously be used to traceevents occurring in real and virtual space: “Online and offline diary" and "15 minutes". The data of pilot studies showed that the teenager’s online performance is mainly presented in a form of communication in social networks. We have discovered that the on- and offline convergence of space for growth is inseparable from respondents themselves. Through the active use of social networks, a modern teenager is able to be present in different socialenvironments simultaneously. Constant checking news and posts in social networks is a new, peculiar to teenage daily ritual. The so-called "red zones", where a teenager consciously waives the possibility of "escape" into a parallel reality, are linked to significant events in everyday life, while a feeling of boredom brings to life the compensation through "getting about" in virtual space.

Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network

Directory of Open Access Journals (Sweden)

Miao Suzhen

2016-01-01

Full Text Available Coal ash is the residual generated from combustion of coal. The ash fusion temperature (AFT of coal gives detail information on the suitability of a coal source for gasification procedures, and specifically to which extent ash agglomeration or clinkering is likely to occur within the gasifier. To investigate the contribution of oxides in coal ash to AFT, data of coal ash chemical compositions and Softening Temperature (ST in different regions of China were collected in this work and a BP neural network model was established by XD-APC PLATFORM. In the BP model, the inputs were the ash compositions and the output was the ST. In addition, the ash fusion temperature prediction model was obtained by industrial data and the model was generalized by different industrial data. Compared to empirical formulas, the BP neural network obtained better results. By different tests, the best result and the best configurations for the model were obtained: hidden layer nodes of the BP network was setted as three, the component contents (SiO2, Al2O3, Fe2O3, CaO, MgO were used as inputs and ST was used as output of the model.

Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas

Directory of Open Access Journals (Sweden)

R. K. Tiwari

2011-08-01

Full Text Available A novel technique based on the Bayesian neural network (BNN theory is developed and employed to model the temperature variation record from the Western Himalayas. In order to estimate an a posteriori probability function, the BNN is trained with the Hybrid Monte Carlo (HMC/Markov Chain Monte Carlo (MCMC simulations algorithm. The efficacy of the new algorithm is tested on the well known chaotic, first order autoregressive (AR and random models and then applied to model the temperature variation record decoded from the tree-ring widths of the Western Himalayas for the period spanning over 1226–2000 AD. For modeling the actual tree-ring temperature data, optimum network parameters are chosen appropriately and then cross-validation test is performed to ensure the generalization skill of the network on the new data set. Finally, prediction result based on the BNN model is compared with the conventional artificial neural network (ANN and the AR linear models results. The comparative results show that the BNN based analysis makes better prediction than the ANN and the AR models. The new BNN modeling approach provides a viable tool for climate studies and could also be exploited for modeling other kinds of environmental data.

Effects of Resveratrol on Daily Rhythms of Locomotor Activity and Body Temperature in Young and Aged Grey Mouse Lemurs

Directory of Open Access Journals (Sweden)

Fabien Pifferi

2013-01-01

Full Text Available In several species, resveratrol, a polyphenolic compound, activates sirtuin proteins implicated in the regulation of energy balance and biological clock processes. To demonstrate the effect of resveratrol on clock function in an aged primate, young and aged mouse lemurs (Microcebus murinus were studied over a 4-week dietary supplementation with resveratrol. Spontaneous locomotor activity and daily variations in body temperature were continuously recorded. Reduction in locomotor activity onset and changes in body temperature rhythm in resveratrol-supplemented aged animals suggest an improved synchronisation on the light-dark cycle. Resveratrol could be a good candidate to restore the circadian rhythms in the elderly.

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Science.gov (United States)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

On effective temperature in network models of collective behavior

International Nuclear Information System (INIS)

Porfiri, Maurizio; Ariel, Gil

2016-01-01

Collective behavior of self-propelled units is studied analytically within the Vectorial Network Model (VNM), a mean-field approximation of the well-known Vicsek model. We propose a dynamical systems framework to study the stochastic dynamics of the VNM in the presence of general additive noise. We establish that a single parameter, which is a linear function of the circular mean of the noise, controls the macroscopic phase of the system—ordered or disordered. By establishing a fluctuation–dissipation relation, we posit that this parameter can be regarded as an effective temperature of collective behavior. The exact critical temperature is obtained analytically for systems with small connectivity, equivalent to low-density ensembles of self-propelled units. Numerical simulations are conducted to demonstrate the applicability of this new notion of effective temperature to the Vicsek model. The identification of an effective temperature of collective behavior is an important step toward understanding order–disorder phase transitions, informing consistent coarse-graining techniques and explaining the physics underlying the emergence of collective phenomena.

Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration

DEFF Research Database (Denmark)

Jannesar, Mohammad Rasol; Sedighi, Alireza; Savaghebi, Mehdi

2018-01-01

when photovoltaic penetration is increased in low voltage distribution network. Local battery energy storage system can mitigate these disadvantages and as a result, improve the system operation. For this purpose, battery energy storage system is charged when production of photovoltaic is more than...... consumers’ demands and discharged when consumers’ demands are increased. Since the price of battery energy storage system is high, economic, environmental, and technical objectives should be considered together for its placement and sizing. In this paper, optimal placement, sizing, and daily (24 h) charge......Proper installation of rooftop photovoltaic generation in distribution networks can improve voltage profile, reduce energy losses, and enhance the reliability. But, on the other hand, some problems regarding harmonic distortion, voltage magnitude, reverse power flow, and energy losses can arise...

Daily torpor and hibernation in birds and mammals

Science.gov (United States)

RUF, THOMAS; GEISER, FRITZ

2014-01-01

Many birds and mammals drastically reduce their energy expenditure during times of cold exposure, food shortage, or drought, by temporarily abandoning euthermia, i.e., the maintenance of high body temperatures. Traditionally, two different types of heterothermy, i.e., hypometabolic states associated with low body temperatures (torpor), have been distinguished: Daily torpor, which lasts less than 24 h and is accompanied by continued foraging, versus hibernation, with torpor bouts lasting consecutive days to several weeks in animals that usually do not forage but rely on energy stores, either food caches or body energy reserves. This classification of torpor types has been challenged however, suggesting that these phenotypes may merely represent the extremes in a continuum of traits. Here, we investigate whether variables of torpor in 214 species, 43 birds and 171 mammals form a continuum or a bimodal distribution. We use Gaussian-mixture cluster analysis as well as phylogenetically informed regressions to quantitatively assess the distinction between hibernation and daily torpor and to evaluate the impact of body mass and geographical distribution of species on torpor traits. Cluster analysis clearly confirmed the classical distinction between daily torpor and hibernation. Overall, heterothermic endotherms are small on average, but hibernators are significantly heavier than daily heterotherms and also are distributed at higher average latitudes (~35°) than daily heterotherms (~25°). Variables of torpor for an average 30-g heterotherm differed significantly between daily heterotherms and hibernators. Average maximum torpor bout duration was >30-fold longer, and mean torpor bout duration >25-fold longer in hibernators. Mean minimum body temperature differed by ~13°C, and the mean minimum torpor metabolic rate was ~35% of the BMR in daily heterotherms but only 6% of basal metabolic rate in hibernators. Consequently, our analysis strongly supports the view that

Exergetic evaluation of heat pump booster configurations in a low temperature district heating network

DEFF Research Database (Denmark)

Ommen, Torben Schmidt; Elmegaard, Brian

2012-01-01

In order to minimise losses in a district heating network, one approach is to lower the temperature difference between working media and soil. Considering only direct heat exchange, the minimum forward temperature level is determined by the demand side, as energy services are required at a certai...

Crowdsourcing urban air temperatures from smartphone battery temperatures

Science.gov (United States)

Overeem, Aart; Robinson, James C. R.; Leijnse, Hidde; Steeneveld, Gert-Jan; Horn, Berthold K. P.; Uijlenhoet, Remko

2014-05-01

Accurate air temperature observations in urban areas are important for meteorology and energy demand planning. They are indispensable to study the urban heat island effect and the adverse effects of high temperatures on human health. However, the availability of temperature observations in cities is often limited. Here we show that relatively accurate air temperature information for the urban canopy layer can be obtained from an alternative, nowadays omnipresent source: smartphones. In this study, battery temperatures were collected by an Android application for smartphones. It has been shown that a straightforward heat transfer model can be employed to estimate daily mean air temperatures from smartphone battery temperatures for eight major cities around the world. The results demonstrate the enormous potential of this crowdsourcing application for real-time temperature monitoring in densely populated areas. Battery temperature data were collected by users of an Android application for cell phones (opensignal.com). The application automatically sends battery temperature data to a server for storage. In this study, battery temperatures are averaged in space and time to obtain daily averaged battery temperatures for each city separately. A regression model, which can be related to a physical model, is employed to retrieve daily air temperatures from battery temperatures. The model is calibrated with observed air temperatures from a meteorological station of an airport located in or near the city. Time series of air temperatures are obtained for each city for a period of several months, where 50% of the data is for independent verification. The methodology has been applied to Buenos Aires, London, Los Angeles, Paris, Mexico City, Moscow, Rome, and Sao Paulo. The evolution of the retrieved air temperatures often correspond well with the observed ones. The mean absolute error of daily air temperatures is less than 2 degrees Celsius, and the bias is within 1 degree

Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

Science.gov (United States)

Lu, Thomas; Pham, Timothy; Liao, Jason

2011-01-01

This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

Adaptation of the pituitary-adrenal axis to daily repeated forced swim exposure in rats is dependent on the temperature of water.

Science.gov (United States)

Rabasa, Cristina; Delgado-Morales, Raúl; Gómez-Román, Almudena; Nadal, Roser; Armario, Antonio

2013-11-01

Comparison of exposure to certain predominantly emotional stressors reveals a qualitatively similar neuroendocrine response profile as well as a reduction of physiological responses after daily repeated exposure (adaptation). However, particular physical components of the stressor may interfere with adaptation. As defective adaptation to stress can enhance the probability to develop pathologies, we studied in adult male rats (n = 10/group) swimming behavior (struggling, immobility and mild swim) and physiological responses (ACTH, corticosterone and rectal temperature) to daily repeated exposure to forced swim (20 min, 13 d) at 25 or 36 °C (swim25 or swim36). Rats were repeatedly blood-sampled by tail-nick and hormones measured by radioimmunoassay. Some differences were observed between the two swim temperature groups after the first exposure to forced swim: (a) active behaviors were greater in swim25 than swim36 groups; (b) swim25 but not swim36 caused hypothermia; and (c) swim36 elicited the same ACTH response as swim25, but plasma corticosterone concentration was lower for swim36 at 30 min post-swim. After daily repeated exposure, adaptation in ACTH secretion was observed with swim36 already on day 4, whereas with swim25 adaptation was not observed until day 13 and was of lower magnitude. Nevertheless, after repeated exposure to swim25 a partial protection from hypothermia was observed and the two swim conditions resulted in progressive reduction of active behaviors. Thus, daily repeated swim at 25 °C impairs adaptation of the hypothalamic-pituitary-adrenal axis as compared to swim at 36 °C, supporting the hypothesis that certain physical components of predominantly emotional stressors can interfere with the process of adaptation.

Seasonal microbial and nutrient responses during a 5-year reduction in the daily temperature range of soil in a Chihuahuan Desert ecosystem.

Science.gov (United States)

van Gestel, Natasja C; Dhungana, Nirmala; Tissue, David T; Zak, John C

2016-01-01

High daily temperature range of soil (DTRsoil) negatively affects soil microbial biomass and activity, but its interaction with seasonal soil moisture in regulating ecosystem function remains unclear. For our 5-year field study in the Chihuahuan Desert, we suspended shade cloth 15 cm above the soil surface to reduce daytime temperature and increase nighttime soil temperature compared to unshaded plots, thereby reducing DTRsoil (by 5 ºC at 0.2 cm depth) without altering mean temperatures. Microbial biomass production was primarily regulated by seasonal precipitation with the magnitude of the response dependent on DTRsoil. Reduced DTRsoil more consistently increased microbial biomass nitrogen (MBN; +38%) than microbial biomass carbon (MBC) with treatment responses being similar in spring and summer. Soil respiration depended primarily on soil moisture with responses to reduced DTRsoil evident only in wetter summer soils (+53%) and not in dry spring soils. Reduced DTRsoil had no effect on concentrations of dissolved organic C, soil organic matter (SOM), nor soil inorganic N (extractable NO3 (-)-N + NH4 (+)-N). Higher MBN without changes in soil inorganic N suggests faster N cycling rates or alternate sources of N. If N cycling rates increased without a change to external N inputs (atmospheric N deposition or N fixation), then productivity in this desert system, which is N-poor and low in SOM, could be negatively impacted with continued decreases in daily temperature range. Thus, the future N balance in arid ecosystems, under conditions of lower DTR, seems linked to future precipitation regimes through N deposition and regulation of soil heat load dynamics.

A fuzzy recommendation system for daily water intake

OpenAIRE

Bin Dai; Rung-Ching Chen; Shun-Zhi Zhu; Chung-Yi Huang

2016-01-01

Water is one of the most important constituents of the human body. Daily consumption of water is thus necessary to protect human health. Daily water consumption is related to several factors such as age, ambient temperature, and degree of physical activity. These factors are generally difficult to express with exact numerical values. The main objective of this article is to build a daily water intake recommendation system using fuzzy methods. This system will use age, physical activity, and a...

Design and Optimization of the VideoWeb Wireless Camera Network

Directory of Open Access Journals (Sweden)

Nguyen HoangThanh

2010-01-01

Full Text Available Sensor networks have been a very active area of research in recent years. However, most of the sensors used in the development of these networks have been local and nonimaging sensors such as acoustics, seismic, vibration, temperature, humidity. The emerging development of video sensor networks poses its own set of unique challenges, including high-bandwidth and low latency requirements for real-time processing and control. This paper presents a systematic approach by detailing the design, implementation, and evaluation of a large-scale wireless camera network, suitable for a variety of practical real-time applications. We take into consideration issues related to hardware, software, control, architecture, network connectivity, performance evaluation, and data-processing strategies for the network. We also perform multiobjective optimization on settings such as video resolution and compression quality to provide insight into the performance trade-offs when configuring such a network and present lessons learned in the building and daily usage of the network.

Branched carbon nanofiber network synthesis at room temperature using radio frequency supported microwave plasmas

OpenAIRE

Boskovic, BO; Stolojan, V; Zeze, DA; Forrest, RD; Silva, SRP; Haq, S

2004-01-01

Carbon nanofibers have been grown at room temperature using a combination of radio frequency and microwave assisted plasma-enhanced chemical vapor deposition. The nanofibers were grown, using Ni powder catalyst, onto substrates kept at room temperature by using a purposely designed water-cooled sample holder. Branched carbon nanofiber growth was obtained without using a template resulting in interconnected carbon nanofiber network formation on substrates held at room temperatur...

Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas

Science.gov (United States)

Diaconescu, Emilia Paula; Mailhot, Alain; Brown, Ross; Chaumont, Diane

2018-03-01

This study focuses on the evaluation of daily precipitation and temperature climate indices and extremes simulated by an ensemble of 12 Regional Climate Model (RCM) simulations from the ARCTIC-CORDEX experiment with surface observations in the Canadian Arctic from the Adjusted Historical Canadian Climate Dataset. Five global reanalyses products (ERA-Interim, JRA55, MERRA, CFSR and GMFD) are also included in the evaluation to assess their potential for RCM evaluation in data sparse regions. The study evaluated the means and annual anomaly distributions of indices over the 1980-2004 dataset overlap period. The results showed that RCM and reanalysis performance varied with the climate variables being evaluated. Most RCMs and reanalyses were able to simulate well climate indices related to mean air temperature and hot extremes over most of the Canadian Arctic, with the exception of the Yukon region where models displayed the largest biases related to topographic effects. Overall performance was generally poor for indices related to cold extremes. Likewise, only a few RCM simulations and reanalyses were able to provide realistic simulations of precipitation extreme indicators. The multi-reanalysis ensemble provided superior results to individual datasets for climate indicators related to mean air temperature and hot extremes, but not for other indicators. These results support the use of reanalyses as reference datasets for the evaluation of RCM mean air temperature and hot extremes over northern Canada, but not for cold extremes and precipitation indices.

Application of artificial neural networks in fault diagnosis for 10MW high-temperature gas-cooled reactor

International Nuclear Information System (INIS)

Li Hui; Wang Ruipian; Hu Shouyin

2003-01-01

This paper makes researches on 10 MW High-Temperature Gas-Cooled Reactor fault diagnosis system using Artificial Neural Network, and uses the tendency value and real value of the data under the accidents to train and test two BP networks respectively. The final diagnostic result is the combination of the results of the two networks. The compound system can enhance the accuracy and adaptability of the diagnosis compared to the single network system

Climate Prediction Center (CPC) U.S. Daily Precipitation Observations

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Observational reports of daily precipitation (1200 UTC to 1200 UTC) are made by members of the NWS Automated Surface Observing Systems (ASOS) network; NWS...

Fundamental statistical relationships between monthly and daily meteorological variables: Temporal downscaling of weather based on a global observational dataset

Science.gov (United States)

Sommer, Philipp; Kaplan, Jed

2016-04-01

Accurate modelling of large-scale vegetation dynamics, hydrology, and other environmental processes requires meteorological forcing on daily timescales. While meteorological data with high temporal resolution is becoming increasingly available, simulations for the future or distant past are limited by lack of data and poor performance of climate models, e.g., in simulating daily precipitation. To overcome these limitations, we may temporally downscale monthly summary data to a daily time step using a weather generator. Parameterization of such statistical models has traditionally been based on a limited number of observations. Recent developments in the archiving, distribution, and analysis of "big data" datasets provide new opportunities for the parameterization of a temporal downscaling model that is applicable over a wide range of climates. Here we parameterize a WGEN-type weather generator using more than 50 million individual daily meteorological observations, from over 10'000 stations covering all continents, based on the Global Historical Climatology Network (GHCN) and Synoptic Cloud Reports (EECRA) databases. Using the resulting "universal" parameterization and driven by monthly summaries, we downscale mean temperature (minimum and maximum), cloud cover, and total precipitation, to daily estimates. We apply a hybrid gamma-generalized Pareto distribution to calculate daily precipitation amounts, which overcomes much of the inability of earlier weather generators to simulate high amounts of daily precipitation. Our globally parameterized weather generator has numerous applications, including vegetation and crop modelling for paleoenvironmental studies.

Effects of boosting the supply temperature on pipe dimensions of low-energy district heating networks

DEFF Research Database (Denmark)

Tol, Hakan; Svendsen, Svend

2015-01-01

This paper presents a method for the dimensioning of the low-energy District Heating (DH) piping networks operating with a control philosophy of supplying heat in low-temperature such as 55 °C in supply and 25°C in return regularly while the supply temperature levels are being boosted in cold...... winter periods. The performance of the existing radiators that were formerly sized with over-dimensions was analyzed, its results being used as input data for the performance evaluation of the piping network of the low-energy DH system operating with the control philosophy in question. The optimization...

Thermo-economic optimization of secondary distribution network of low temperature district heating network under local conditions of South Korea

DEFF Research Database (Denmark)

Park, Byung Sik; Imran, Muhammad; Hoon, Im-Yong

2017-01-01

. The corresponding heat loss from secondary network, pumping power and area of domestic hot water heat exchanger unit for each supply temperature and temperature difference for required heating load of the apartment complex are calculated. Results indicate that when supply temperature is decreased from 65 °C to 45...... apartment. The Apartment complex has 15 floors, 4 apartments on each floor and each apartment has heating surface area of 85 m2. The supply temperature of the hot water is reduced from 65 °C to 45 °C and the temperature difference between supply and return line is varied from 18 °C to 27 °C...... °C, area of heat exchanger is increased by 68.2%, pumping power is also increased by 9.8% and heat loss is reduced by 15.6%. These results correspond to a temperature difference of 20 °C, the standard temperature difference in South Korea residential heating system. Economic assessment...

Efficient room temperature hydrogen sensor based on UV-activated ZnO nano-network

Science.gov (United States)

Kumar, Mohit; Kumar, Rahul; Rajamani, Saravanan; Ranwa, Sapana; Fanetti, Mattia; Valant, Matjaz; Kumar, Mahesh

2017-09-01

Room temperature hydrogen sensors were fabricated from Au embedded ZnO nano-networks using a 30 mW GaN ultraviolet LED. The Au-decorated ZnO nano-networks were deposited on a SiO2/Si substrate by a chemical vapour deposition process. X-ray diffraction (XRD) spectrum analysis revealed a hexagonal wurtzite structure of ZnO and presence of Au. The ZnO nanoparticles were interconnected, forming nano-network structures. Au nanoparticles were uniformly distributed on ZnO surfaces, as confirmed by FESEM imaging. Interdigitated electrodes (IDEs) were fabricated on the ZnO nano-networks using optical lithography. Sensor performances were measured with and without UV illumination, at room temperate, with concentrations of hydrogen varying from 5 ppm to 1%. The sensor response was found to be ˜21.5% under UV illumination and 0% without UV at room temperature for low hydrogen concentration of 5 ppm. The UV-photoactivated mode enhanced the adsorption of photo-induced O- and O2- ions, and the d-band electron transition from the Au nanoparticles to ZnO—which increased the chemisorbed reaction between hydrogen and oxygen. The sensor response was also measured at 150 °C (without UV illumination) and found to be ˜18% at 5 ppm. Energy efficient low cost hydrogen sensors can be designed and fabricated with the combination of GaN UV LEDs and ZnO nanostructures.

Daily MUR SST, Interim near-real-time (nrt) product

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — A daily, global Sea Surface Temperature (SST) data set is produced at 1-km (MUR, or Multi-scale ultra-high resolution Temperature) by the JPL sciengists Drs. Mike...

Trends and periodicity of daily temperature and precipitation extremes during 1960-2013 in Hunan Province, central south China

Science.gov (United States)

Chen, Ajiao; He, Xinguang; Guan, Huade; Cai, Yi

2018-04-01

In this study, the trends and periodicity in climate extremes are examined in Hunan Province over the period 1960-2013 on the basis of 27 extreme climate indices calculated from daily temperature and precipitation records at 89 meteorological stations. The results show that in the whole province, temperature extremes exhibit a warming trend with more than 50% stations being statistically significant for 7 out of 16 temperature indices, and the nighttime temperature increases faster than the daytime temperature at the annual scale. The changes in most extreme temperature indices show strongly coherent spatial patterns. Moreover, the change rates of almost all temperature indices in north Hunan are greater than those of other regions. However, the statistically significant changes in indices of extreme precipitation are observed at fewer stations than in extreme temperature indices, forming less spatially coherent patterns. Positive trends in indices of extreme precipitation show that the amount and intensity of extreme precipitation events are generally increasing in both annual and seasonal scales, whereas the significant downward trend in consecutive wet days indicates that the precipitation becomes more even over the study period. Analysis of changes in probability distributions of extreme indices for 1960-1986 and 1987-2013 also demonstrates a remarkable shift toward warmer condition and increasing tendency in the amount and intensity of extreme precipitation during the past decades. The variations in extreme climate indices exhibit inconstant frequencies in the wavelet power spectrum. Among the 16 temperature indices, 2 of them show significant 1-year periodic oscillation and 7 of them exhibit significant 4-year cycle during some certain periods. However, significant periodic oscillations can be found in all of the precipitation indices. Wet-day precipitation and three absolute precipitation indices show significant 1-year cycle and other seven provide

Comparative long-term trend analysis of daily weather conditions with daily pollen concentrations in Brussels, Belgium

Science.gov (United States)

Bruffaerts, Nicolas; De Smedt, Tom; Delcloo, Andy; Simons, Koen; Hoebeke, Lucie; Verstraeten, Caroline; Van Nieuwenhuyse, An; Packeu, Ann; Hendrickx, Marijke

2018-03-01

A clear rise in seasonal and annual temperatures, a gradual increase of total radiation, and a relative trend of change in seasonal precipitation have been observed for the last four decades in Brussels (Belgium). These local modifications may have a direct and indirect public health impact by altering the timing and intensity of allergenic pollen seasons. In this study, we assessed the statistical correlations (Spearman's test) between pollen concentration and meteorological conditions by using long-term daily datasets of 11 pollen types (8 trees and 3 herbaceous plants) and 10 meteorological parameters observed in Brussels between 1982 and 2015. Furthermore, we analyzed the rate of change in the annual cycle of the same selected pollen types by the Mann-Kendall test. We revealed an overall trend of increase in daily airborne tree pollen (except for the European beech tree) and an overall trend of decrease in daily airborne pollen from herbaceous plants (except for Urticaceae). These results revealed an earlier onset of the flowering period for birch, oak, ash, plane, grasses, and Urticaceae. Finally, the rates of change in pollen annual cycles were shown to be associated with the rates of change in the annual cycles of several meteorological parameters such as temperature, radiation, humidity, and rainfall.

Comparative long-term trend analysis of daily weather conditions with daily pollen concentrations in Brussels, Belgium.

Science.gov (United States)

Bruffaerts, Nicolas; De Smedt, Tom; Delcloo, Andy; Simons, Koen; Hoebeke, Lucie; Verstraeten, Caroline; Van Nieuwenhuyse, An; Packeu, Ann; Hendrickx, Marijke

2018-03-01

A clear rise in seasonal and annual temperatures, a gradual increase of total radiation, and a relative trend of change in seasonal precipitation have been observed for the last four decades in Brussels (Belgium). These local modifications may have a direct and indirect public health impact by altering the timing and intensity of allergenic pollen seasons. In this study, we assessed the statistical correlations (Spearman's test) between pollen concentration and meteorological conditions by using long-term daily datasets of 11 pollen types (8 trees and 3 herbaceous plants) and 10 meteorological parameters observed in Brussels between 1982 and 2015. Furthermore, we analyzed the rate of change in the annual cycle of the same selected pollen types by the Mann-Kendall test. We revealed an overall trend of increase in daily airborne tree pollen (except for the European beech tree) and an overall trend of decrease in daily airborne pollen from herbaceous plants (except for Urticaceae). These results revealed an earlier onset of the flowering period for birch, oak, ash, plane, grasses, and Urticaceae. Finally, the rates of change in pollen annual cycles were shown to be associated with the rates of change in the annual cycles of several meteorological parameters such as temperature, radiation, humidity, and rainfall.

A global gridded dataset of daily precipitation going back to 1950, ideal for analysing precipitation extremes

Science.gov (United States)

Contractor, S.; Donat, M.; Alexander, L. V.

2017-12-01

Reliable observations of precipitation are necessary to determine past changes in precipitation and validate models, allowing for reliable future projections. Existing gauge based gridded datasets of daily precipitation and satellite based observations contain artefacts and have a short length of record, making them unsuitable to analyse precipitation extremes. The largest limiting factor for the gauge based datasets is a dense and reliable station network. Currently, there are two major data archives of global in situ daily rainfall data, first is Global Historical Station Network (GHCN-Daily) hosted by National Oceanic and Atmospheric Administration (NOAA) and the other by Global Precipitation Climatology Centre (GPCC) part of the Deutsche Wetterdienst (DWD). We combine the two data archives and use automated quality control techniques to create a reliable long term network of raw station data, which we then interpolate using block kriging to create a global gridded dataset of daily precipitation going back to 1950. We compare our interpolated dataset with existing global gridded data of daily precipitation: NOAA Climate Prediction Centre (CPC) Global V1.0 and GPCC Full Data Daily Version 1.0, as well as various regional datasets. We find that our raw station density is much higher than other datasets. To avoid artefacts due to station network variability, we provide multiple versions of our dataset based on various completeness criteria, as well as provide the standard deviation, kriging error and number of stations for each grid cell and timestep to encourage responsible use of our dataset. Despite our efforts to increase the raw data density, the in situ station network remains sparse in India after the 1960s and in Africa throughout the timespan of the dataset. Our dataset would allow for more reliable global analyses of rainfall including its extremes and pave the way for better global precipitation observations with lower and more transparent uncertainties.

Reciprocal associations between positive emotions and motivation in daily life: Network analyses in anhedonic individuals and healthy controls.

Science.gov (United States)

van Roekel, Eeske; Heininga, Vera E; Vrijen, Charlotte; Snippe, Evelien; Oldehinkel, Albertine J

2018-04-19

Anhedonia reflects a dysfunction in the reward system, which can be manifested in an inability to enjoy pleasurable situations (i.e., lack of positive emotions), but also by a lack of motivation to engage in pleasurable activities (i.e., lack of motivation). Little is known about the interrelations between positive emotions and motivation in daily life, and whether these associations are altered in anhedonic individuals. In the present study, we used a network approach to explore the reciprocal, lagged associations between positive emotions and motivation in anhedonic individuals (N = 66) and controls (N = 68). Participants (aged between 18 and 24 years) filled out momentary assessments of affect 3 times per day for 30 consecutive days. Our results showed that (a) anhedonic individuals and controls had similar moment-to-moment transfer of positive emotions; (b) in the anhedonic network feeling cheerful was the node with the highest outstrength, both within this group and compared with the control group; (c) feeling relaxed had the highest outstrength in the control network, and (d) anhedonic individuals had stronger pathways from positive emotions to motivation than controls. Taken together, our findings suggest that low levels of positive emotions lead to decreased motivation in the anhedonic group, which could instigate a negative spiral of low pleasure and low motivation. On a more positive note, we showed that cheerfulness had the highest outstrength in the network of anhedonic participants. Hence, interventions may focus on increasing cheerfulness in anhedonic individuals, as this will likely have the greatest impact on other positive emotions and motivations. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Evaluation of the Global Land Data Assimilation System (GLDAS) air temperature data products

Science.gov (United States)

Ji, Lei; Senay, Gabriel B.; Verdin, James P.

2015-01-01

There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

Retrieving Single Scattering Albedos and Temperatures from CRISM Hyperspectral Data Using Neural Networks

Science.gov (United States)

He, L.; Arvidson, R. E.; O'Sullivan, J. A.

2018-04-01

We use a neural network (NN) approach to simultaneously retrieve surface single scattering albedos and temperature maps for CRISM data from 1.40 to 3.85 µm. It approximates the inverse of DISORT which simulates solar and emission radiative streams.

Intelligent Data Transfer for Multiple Sensor Networks over a Broad Temperature Range

Science.gov (United States)

Krasowski, Michael (Inventor)

2018-01-01

A sensor network may be configured to operate in extreme temperature environments. A sensor may be configured to generate a frequency carrier, and transmit the frequency carrier to a node. The node may be configured to amplitude modulate the frequency carrier, and transmit the amplitude modulated frequency carrier to a receiver.

Estimation of daily minimum land surface air temperature using MODIS data in southern Iran

Science.gov (United States)

Didari, Shohreh; Norouzi, Hamidreza; Zand-Parsa, Shahrokh; Khanbilvardi, Reza

2017-11-01

Land surface air temperature (LSAT) is a key variable in agricultural, climatological, hydrological, and environmental studies. Many of their processes are affected by LSAT at about 5 cm from the ground surface (LSAT5cm). Most of the previous studies tried to find statistical models to estimate LSAT at 2 m height (LSAT2m) which is considered as a standardized height, and there is not enough study for LSAT5cm estimation models. Accurate measurements of LSAT5cm are generally acquired from meteorological stations, which are sparse in remote areas. Nonetheless, remote sensing data by providing rather extensive spatial coverage can complement the spatiotemporal shortcomings of meteorological stations. The main objective of this study was to find a statistical model from the previous day to accurately estimate spatial daily minimum LSAT5cm, which is very important in agricultural frost, in Fars province in southern Iran. Land surface temperature (LST) data were obtained using the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites at daytime and nighttime periods with normalized difference vegetation index (NDVI) data. These data along with geometric temperature and elevation information were used in a stepwise linear model to estimate minimum LSAT5cm during 2003-2011. The results revealed that utilization of MODIS Aqua nighttime data of previous day provides the most applicable and accurate model. According to the validation results, the accuracy of the proposed model was suitable during 2012 (root mean square difference ( RMSD) = 3.07 °C, {R}_{adj}^2 = 87 %). The model underestimated (overestimated) high (low) minimum LSAT5cm. The accuracy of estimation in the winter time was found to be lower than the other seasons ( RMSD = 3.55 °C), and in summer and winter, the errors were larger than in the remaining seasons.

International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 2 Daily

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global land surface temperature databank contains monthly timescale mean, max, and min temperature for approximately 40,000 stations globally. It was developed...

International Surface Temperature Initiative (ISTI) Global Land Surface Temperature Databank - Stage 1 Daily

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global land surface temperature databank contains monthly timescale mean, max, and min temperature for approximately 40,000 stations globally. It was developed...

Estimating Daily Maximum and Minimum Land Air Surface Temperature Using MODIS Land Surface Temperature Data and Ground Truth Data in Northern Vietnam

Directory of Open Access Journals (Sweden)

Phan Thanh Noi

2016-12-01

Full Text Available This study aims to evaluate quantitatively the land surface temperature (LST derived from MODIS (Moderate Resolution Imaging Spectroradiometer MOD11A1 and MYD11A1 Collection 5 products for daily land air surface temperature (Ta estimation over a mountainous region in northern Vietnam. The main objective is to estimate maximum and minimum Ta (Ta-max and Ta-min using both TERRA and AQUA MODIS LST products (daytime and nighttime and auxiliary data, solving the discontinuity problem of ground measurements. There exist no studies about Vietnam that have integrated both TERRA and AQUA LST of daytime and nighttime for Ta estimation (using four MODIS LST datasets. In addition, to find out which variables are the most effective to describe the differences between LST and Ta, we have tested several popular methods, such as: the Pearson correlation coefficient, stepwise, Bayesian information criterion (BIC, adjusted R-squared and the principal component analysis (PCA of 14 variables (including: LST products (four variables, NDVI, elevation, latitude, longitude, day length in hours, Julian day and four variables of the view zenith angle, and then, we applied nine models for Ta-max estimation and nine models for Ta-min estimation. The results showed that the differences between MODIS LST and ground truth temperature derived from 15 climate stations are time and regional topography dependent. The best results for Ta-max and Ta-min estimation were achieved when we combined both LST daytime and nighttime of TERRA and AQUA and data from the topography analysis.

Effect of temperature and temperature fluctuation on thermophilic anaerobic digestion of cattle manure.

Science.gov (United States)

El-Mashad, Hamed M; Zeeman, Grietje; van Loon, Wilko K P; Bot, Gerard P A; Lettinga, Gatze

2004-11-01

The influence of temperature, 50 and 60 degrees C, at hydraulic retention times (HRTs) of 20 and 10 days, on the performance of anaerobic digestion of cow manure has been investigated in completely stirred tank reactors (CSTRs). Furthermore, the effect of both daily downward and daily upward temperature fluctuations has been studied. In the daily downward temperature fluctuation regime the temperatures of each reactor was reduced by 10 degrees C for 10 h while in the daily upward fluctuation regime the temperature of each reactor was increased 10 degrees C for 5 h. The results show that the methane production rate at 60 degrees C is lower than that at 50 degrees C at all experimental conditions of imposed HRT except when downward temperature fluctuations were applied at an HRT of 10 days. It also was found that the free ammonia concentration not only affects the acetate-utilising bacteria but also the hydrolysis and acidification process. The upward temperature fluctuation affects the maximum specific methanogenesis activity more severely as compared to imposed downward temperature fluctuations. The results clearly reveal the possibility of using available solar energy at daytime to heat up the reactor(s) without the need of heat storage during nights, especially at an operational temperature of 50 degrees C and at a 20 days HRT, and without the jeopardising of the overheating.

Effect of temperature and temperature fluctuation on thermophilic anaerobic digestion of cattle manure

Energy Technology Data Exchange (ETDEWEB)

El-Mashad, H.M. [Mansoura University, El-Mansoura (Egypt). Faculty of Agriculture, Department of Agricultural Engineering; Zeeman, G.; Van Loon, W.K.P.; Bot, G.P.A.; Lettinga, G. [Wageningen University Agrotechnion (Netherlands). Department of Agrotechnology and Food Sciences

2004-11-01

The influence of temperature, 50 and 60 {sup o}C, at hydraulic retention times (HRTs) of 20 and 10 days, on the performance of anaerobic digestion of cow manure has been investigated in completely stirred tank reactors (CSTRs). Furthermore, the effect of both daily downward and daily upward temperature fluctuations has been studied. In the daily downward temperature fluctuation regime the temperatures of each reactor was reduced by 10 {sup o}C for 10 h while in the daily upward fluctuation regime the temperature of each reactor was increased 10 {sup o}C for 5 h. The results show that the methane production rate at 60 {sup o}C is lower than that at 50 {sup o}C at all experimental conditions of imposed HRT except when downward temperature fluctuations were applied at an HRT of 10 days. It also was found that the free ammonia concentration not only affects the acetate-utilising bacteria but also the hydrolysis and acidification process. The upward temperature fluctuation affects the maximum specific methanogenesis activity more severely as compared to imposed downward temperature fluctuations. The results clearly reveal the possibility of using available solar energy at daytime to heat up the reactor(s) without the need of heat storage during nights, especially at an operational temperature of 50 {sup o}C and at a 20 days HRT, and without the jeopardising of the overheating. (author)

Foreground removal from Planck Sky Model temperature maps using a MLP neural network

DEFF Research Database (Denmark)

Nørgaard-Nielsen, Hans Ulrik; Hebert, K.

2009-01-01

with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them...... in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80% of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors....

Women's experiences of daily life after anterior cervical decompression and fusion surgery: A qualitative interview study.

Science.gov (United States)

Hermansen, Anna; Peolsson, Anneli; Kammerlind, Ann-Sofi; Hjelm, Katarina

2016-04-01

To explore and describe women's experiences of daily life after anterior cervical decompression and fusion surgery. Qualitative explorative design. Fourteen women aged 39-62 years (median 52 years) were included 1.5-3 years after anterior cervical decompression and fusion for cervical disc disease. Individual semi-structured interviews were analysed by qualitative content analysis with an inductive approach. The women described their experiences of daily life in 5 different ways: being recovered to various extents; impact of remaining symptoms on thoughts and feelings; making daily life work; receiving support from social and occupational networks; and physical and behavioural changes due to interventions and encounters with healthcare professionals. This interview study provides insight into women's daily life after anterior cervical decompression and fusion. Whilst the subjects improved after surgery, they also experienced remaining symptoms and limitations in daily life. A variety of mostly active coping strategies were used to manage daily life. Social support from family, friends, occupational networks and healthcare professionals positively influenced daily life. These findings provide knowledge about aspects of daily life that should be considered in individualized postoperative care and rehabilitation in an attempt to provide better outcomes in women after anterior cervical decompression and fusion.

Modeling of High Temperature Oxidation Behavior of FeCrAl Alloy by using Artificial Neural Network

Energy Technology Data Exchange (ETDEWEB)

Kim, Jae Joon; Ryu, Ho Jin [KAIST, Daejeon (Korea, Republic of)

2016-10-15

Refractory alloys are candidate materials for replacing current zirconium-base cladding of light water reactors and they retain significant creep resistance and mechanical strength at high temperatures up to 1500 ℃ due to their high melting temperature. Thermal neutron cross sections of refractory metals are higher than that of zirconium, however the loss of neutron can be overcome by reducing cladding thickness which can be facilitated with enhanced mechanical properties. However, most refractory metals show the poor oxidation resistance at a high temperature. Oxidation behaviors of the various compositions of FeCrAl alloys in high temperature conditions were modeled by using Bayesian neural network. The automatic relevance determination (ARD) technique represented the influence of the composition of alloying elements on the oxidation resistance of FeCrAl alloys. This model can be utilized to understand the tendency of oxidation behavior along the composition of each element and prove the applicability of neural network modeling for the development of new cladding material of light water reactors.

Generalized additive model of air pollution to daily mortality

International Nuclear Information System (INIS)

Kim, J.; Yang, H.E.

2005-01-01

The association of air pollution with daily mortality due to cardiovascular disease, respiratory disease, and old age (65 or older) in Seoul, Korea was investigated in 1999 using daily values of TSP, PM10, O 3 , SO 2 , NO 2 , and CO. Generalized additive Poisson models were applied to allow for the highly flexible fitting of daily trends in air pollution as well as nonlinear association with meteorological variables such as temperature, humidity, and wind speed. To estimate the effect of air pollution and weather on mortality, LOESS smoothing was used in generalized additive models. The findings suggest that air pollution levels affect significantly the daily mortality. (orig.)

Local Social Networks

DEFF Research Database (Denmark)

Sapuppo, Antonio; Sørensen, Lene Tolstrup

2011-01-01

Online social networks have become essential for many users in their daily communication. Through a combination of the online social networks with opportunistic networks, a new concept arises: Local Social Networks. The target of local social networks is to promote social networking benefits...... in physical environment in order to leverage personal affinities in the users' surroundings. The purpose of this paper is to present and discuss the concept of local social networks as a new social communication system. Particularly, the preliminary architecture and the prototype of local social networks...

Climate Prediction Center (CPC) U.S. Daily Snow Fall Observations

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Observational reports of daily snow fall (1200 UTC to 1200 UTC) are made by members of the NWS Automated Surface Observing Systems (ASOS) network and NWS Cooperative...

Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean.

Science.gov (United States)

de Gennaro, Gianluigi; Trizio, Livia; Di Gilio, Alessia; Pey, Jorge; Pérez, Noemi; Cusack, Michael; Alastuey, Andrés; Querol, Xavier

2013-10-01

An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANN's forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies. © 2013 Elsevier B.V. All rights reserved.

Estimation of daily solar radiation from routinely observed meteorological data in Chongqing, China

International Nuclear Information System (INIS)

Li Maofen; Liu Hongbin; Guo Pengtao; Wu Wei

2010-01-01

Solar radiation is a very important and major variable in crop simulation models. However, it is measured at a very limited number of meteorological stations worldwide. Models were developed to estimate daily solar radiation in Chongqing, one of the most important agricultural areas in China. Several routinely observed meteorological variables including daily maximum and minimum temperatures, daily mean dew point temperature, fog and rainfall had been obtained, investigated and analyzed from 1986 to 2000 for Chongqing. The monthly mean daily solar radiation at this location ranged from a maximum of 15.082 MJ m -2 day -1 in August and a minimum of 3.042 MJ m -2 day -1 in December. A newly developed model that included all selected variables proved the best method with a RMSE value of 2.522 MJ m -2 day -1 . The best performed models for different seasons were further evaluated according to divide-and-conquer principle. The model using all selected variables provided the best estimates of daily solar radiation in winter and autumn with RMSE values of 1.491 and 2.037 MJ m -2 day -1 , respectively. The method involving temperatures and rainfall information could be used to estimate daily solar radiation in summer with a RMSE value of 3.163 MJ m -2 day -1 . The model using temperature, rainfall and dew point data performed better than other models in spring with a RMSE value of 2.910 MJ m -2 day -1 .

A collection of sub-daily pressure and temperature observations for the early instrumental period with a focus on the “year without a summer” 1816

Czech Academy of Sciences Publication Activity Database

Brugnara, Y.; Auchmann, R.; Broennimann, S.; Allan, R. J.; Auer, I.; Barriendos, M.; Bergström, H.; Bhend, J.; Brázdil, Rudolf; Compo, G. S.; Cornes, R. C.; Dominguez-Castro, F.; van Engelen, A. F. V.; Filipiak, J.; Holopainen, J.; Jourdain, S.; Kunz, M.; Luterbacher, J.; Maugeri, M.; Mercalli, L.; Moberg, A.; Mock, C. J.; Pichard, G.; Řezníčková, Ladislava; van der Schrier, G.; Slonosky, V.; Ustrnul, Z.; Valente, M. A.; Wypych, A.; Yin, X.

2015-01-01

Roč. 11, č. 8 (2015), s. 1027-1047 ISSN 1814-9324 R&D Projects: GA MŠk(CZ) LO1415 Institutional support: RVO:67179843 Keywords : daily-air temperature * sea-level pressure * volcanic eruptions * climate Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 3.638, year: 2015

Effect of temperature and temperature fluctuation on thermophilic anaerobic digestion of cattle manure

NARCIS (Netherlands)

Mashad, El H.; Zeeman, G.; Loon, van W.K.P.; Bot, G.P.A.; Lettinga, G.

2004-01-01

The influence of temperature, 50 and 60 °C, at hydraulic retention times (HRTs) of 20 and 10 days, on the performance of anaerobic digestion of cow manure has been investigated in completely stirred tank reactors (CSTRs). Furthermore, the effect of both daily downward and daily upward temperature

A NEW NETWORK FOR HIGHER-TEMPERATURE GAS-PHASE CHEMISTRY. I. A PRELIMINARY STUDY OF ACCRETION DISKS IN ACTIVE GALACTIC NUCLEI

International Nuclear Information System (INIS)

Harada, Nanase; Herbst, Eric; Wakelam, Valentine

2010-01-01

We present a new interstellar chemical gas-phase reaction network for time-dependent kinetics that can be used for modeling high-temperature sources up to ∼800 K. This network contains an extended set of reactions based on the Ohio State University (OSU) gas-phase chemical network. The additional reactions include processes with significant activation energies, reverse reactions, proton exchange reactions, charge exchange reactions, and collisional dissociation. Rate coefficients already in the OSU network are modified for H 2 formation on grains, ion-neutral dipole reactions, and some radiative association reactions. The abundance of H 2 O is enhanced at high temperature by hydrogenation of atomic O. Much of the elemental oxygen is in the form of water at T ≥ 300 K, leading to effective carbon-rich conditions, which can efficiently produce carbon-chain species such as C 2 H 2 . At higher temperatures, HCN and NH 3 are also produced much more efficiently. We have applied the extended network to a simplified model of the accretion disk of an active galactic nucleus.

Developing cyber security architecture for military networks using cognitive networking

OpenAIRE

Kärkkäinen, Anssi

2015-01-01

In recent years, the importance of cyber security has increased. Cyber security has not become a critical issue only for governmental or business actors, but also for armed forces that nowadays rely on national or even global networks in their daily activities. The Network Centric Warfare (NCW) paradigm has increased the significance of networking during last decades as it enables information superiority in which military combat power increased by networking the battlefield actors from perspe...

Arctic daily temperature and precipitation extremes: Observed and simulated physical behavior

Science.gov (United States)

Glisan, Justin Michael

Simulations using a six-member ensemble of Pan-Arctic WRF (PAW) were produced on two Arctic domains with 50-km resolution to analyze precipitation and temperature extremes for various periods. The first study used a domain developed for the Regional Arctic Climate Model (RACM). Initial simulations revealed deep atmospheric circulation biases over the northern Pacific Ocean, manifested in pressure, geopotential height, and temperature fields. Possible remedies to correct these large biases, such as modifying the physical domain or using different initial/boundary conditions, were unsuccessful. Spectral (interior) nudging was introduced as a way of constraining the model to be more consistent with observed behavior. However, such control over numerical model behavior raises concerns over how much nudging may affect unforced variability and extremes. Strong nudging may reduce or filter out extreme events, since the nudging pushes the model toward a relatively smooth, large-scale state. The question then becomes---what is the minimum spectral nudging needed to correct biases while not limiting the simulation of extreme events? To determine this, we use varying degrees of spectral nudging, using WRF's standard nudging as a reference point during January and July 2007. Results suggest that there is a marked lack of sensitivity to varying degrees of nudging. Moreover, given that nudging is an artificial forcing applied in the model, an important outcome of this work is that nudging strength apparently can be considerably smaller than WRF's standard strength and still produce reliable simulations. In the remaining studies, we used the same PAW setup to analyze daily precipitation extremes simulated over a 19-year period on the CORDEX Arctic domain for winter and summer. We defined these seasons as the three-month period leading up to and including the climatological sea ice maximum and minimum, respectively. Analysis focused on four North American regions defined using

Merging daily sea surface temperature data from multiple satellites using a Bayesian maximum entropy method

Science.gov (United States)

Tang, Shaolei; Yang, Xiaofeng; Dong, Di; Li, Ziwei

2015-12-01

Sea surface temperature (SST) is an important variable for understanding interactions between the ocean and the atmosphere. SST fusion is crucial for acquiring SST products of high spatial resolution and coverage. This study introduces a Bayesian maximum entropy (BME) method for blending daily SSTs from multiple satellite sensors. A new spatiotemporal covariance model of an SST field is built to integrate not only single-day SSTs but also time-adjacent SSTs. In addition, AVHRR 30-year SST climatology data are introduced as soft data at the estimation points to improve the accuracy of blended results within the BME framework. The merged SSTs, with a spatial resolution of 4 km and a temporal resolution of 24 hours, are produced in the Western Pacific Ocean region to demonstrate and evaluate the proposed methodology. Comparisons with in situ drifting buoy observations show that the merged SSTs are accurate and the bias and root-mean-square errors for the comparison are 0.15°C and 0.72°C, respectively.

Effect modification of the association between temperature variability and daily cardiovascular mortality by air pollutants in three Chinese cities.

Science.gov (United States)

Luo, Kai; Li, Runkui; Wang, Zongshuang; Zhang, Ruiming; Xu, Qun

2017-11-01

There is limited evidence showing the mortality effects of temperature variability (TV) on cardiovascular diseases. The joint effects between TV and air pollutants are also less well-established. This study aims to assess the effect modification of TV-cardiovascular mortality by air pollutants in three Chinese cities (Beijing, Nanjing and Chengdu). Data of daily mortality, air pollutants and meteorological factors from 2008 to 2011 was collected from each city. TV was calculated as the standard deviation of daily maximum and minimum temperatures over exposure days. The city-specific effect estimates of TV on cardiovascular mortality were calculated using a quasi-Poisson regression model, adjusting for potential confounders (e.g., seasonality and temperature). An interaction term of TV and a three-level air pollutants stratum indicator was included in the models. Effect modifications by air pollutants were assessed by comparing the estimates of TV's effect between pollutant stratums and calculating the corresponding 95% confidential interval of the differences. Multivariate meta-analysis was conducted to obtain the pooled estimates. The data showed that TV was associated with increased risk of cardiovascular mortality, especially for longer TV exposure days (0-8 days, TV08). This association was still observed after adjusting for air pollutants on current day or the previous two days. Stronger estimates were observed in females, but no significant difference between males and females was detected, indicating the absence of evidence of effect modification by gender. Estimates of TV-cardiovascular mortality varied across two season periods (warm and cool season) and age groups, but the evidence of effect modification by age and seasons was absent. Regarding the effect modification of TV-cardiovascular mortality association by air pollutants, a significant effect modification was identified for PM 10, but not for NO 2 and SO 2 in the whole population for all TV

Daily Living Functioning, Social Engagement and Wellness of Older Adults

Directory of Open Access Journals (Sweden)

Noor Zainab

2017-08-01

Full Text Available AimThe present study aim to investigate the contributing role of daily living functioning and social engagement in enhancing wellness and various dimensions of wellness in older adults.MethodA correlational research was designed. Socio-demographic data was collected. Lawton Instrumental Activities of Daily Living, Lubben Social Network Scale, and Perceived Wellness Survey were administered on a sample of 112 participants, including 56 men and 56 women.ResultsA correlation analysis found positive correlations between daily living functioning, social engagement and wellness of older adults. The results of regression analysis concluded that both the daily living functioning and social engagement predicted wellness and domains of wellness as well.ConclusionThe obtained results indicate that older adults who are self-reliant lead a more satisfied life in old age and demonstrate to be more adjusted to the effects of aging.

Forecasting daily patient volumes in the emergency department.

Science.gov (United States)

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by

Silver nanowires network encapsulated by low temperature sol-gel ZnO for transparent flexible electrodes with ambient stability

Science.gov (United States)

Shin, Wonjung; Cho, Wonki; Baik, Seung Jae

2018-01-01

As a geometrically engineered realization of transparent electrode, Ag nanowires network is promising for its superior characteristics both on electrical conductivity and optical transmittance. However, for a potential commercialization of Ag nanowires network, further investigations on encapsulation materials are necessary to prevent degradation caused by ambient aging. In addition, the temperature range of the coating process for the encapsulation material needs to be low enough to prevent degradation of polymer substrates during the film coating processes, when considering emerging flexible device application of transparent electrodes. We present experimental results showing that low temperature sol-gel ZnO processed under 130 °C is an effective encapsulation material preventing ambient oxidation of Ag nanowires network without degrading electrical, optical, and mechanical properties.

Overcoming the sign problem at finite temperature: Quantum tensor network for the orbital eg model on an infinite square lattice

Science.gov (United States)

Czarnik, Piotr; Dziarmaga, Jacek; Oleś, Andrzej M.

2017-07-01

The variational tensor network renormalization approach to two-dimensional (2D) quantum systems at finite temperature is applied to a model suffering the notorious quantum Monte Carlo sign problem—the orbital eg model with spatially highly anisotropic orbital interactions. Coarse graining of the tensor network along the inverse temperature β yields a numerically tractable 2D tensor network representing the Gibbs state. Its bond dimension D —limiting the amount of entanglement—is a natural refinement parameter. Increasing D we obtain a converged order parameter and its linear susceptibility close to the critical point. They confirm the existence of finite order parameter below the critical temperature Tc, provide a numerically exact estimate of Tc, and give the critical exponents within 1 % of the 2D Ising universality class.

Changes in Indices of Daily Temperature and Precipitation Extremes ...

African Journals Online (AJOL)

It's a known fact that climate change will bring about increases in the occurrence of weather extreme events such as elevated temperature, drought, and floods; most especially in areas classified as hotspots to climate change – such as northwest Nigeria. This study investigates trends in extreme temperature and ...

Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation

Science.gov (United States)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2016-08-01

In the present research, three artificial intelligence methods including Gene Expression Programming (GEP), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as, 48 empirical equations (10, 12 and 26 equations were temperature-based, sunshine-based and meteorological parameters-based, respectively) were used to estimate daily solar radiation in Kerman, Iran in the period of 1992-2009. To develop the GEP, ANN and ANFIS models, depending on the used empirical equations, various combinations of minimum air temperature, maximum air temperature, mean air temperature, extraterrestrial radiation, actual sunshine duration, maximum possible sunshine duration, sunshine duration ratio, relative humidity and precipitation were considered as inputs in the mentioned intelligent methods. To compare the accuracy of empirical equations and intelligent models, root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE) and determination coefficient (R2) indices were used. The results showed that in general, sunshine-based and meteorological parameters-based scenarios in ANN and ANFIS models presented high accuracy than mentioned empirical equations. Moreover, the most accurate method in the studied region was ANN11 scenario with five inputs. The values of RMSE, MAE, MARE and R2 indices for the mentioned model were 1.850 MJ m-2 day-1, 1.184 MJ m-2 day-1, 9.58% and 0.935, respectively.

The GOCF/AWAP system - forecasting temperature extremes

International Nuclear Information System (INIS)

Fawcett, Robert; Hume, Timothy

2010-01-01

Gridded hourly temperature forecasts from the Bureau of Meteorology's Gridded Operational Consensus Forecasting (GOCF) system are combined in real time with the Australian Water Availability Project (AWAP) gridded daily temperature analyses to produce gridded daily maximum and minimum temperature forecasts with lead times from one to five days. These forecasts are compared against the historical record of AWAP daily temperature analyses (1911 to present), to identify regions where record or near-record temperatures are predicted to occur. This paper describes the GOCF/AWAP system, showing how the daily maximum and minimum temperature forecasts are prepared from the hourly forecasts, and how they are bias-corrected in real time using the AWAP analyses, against which they are subsequently verified. Using monthly climatologies of long-term daily mean, standard deviation and all-time highest and lowest on record, derived forecast products (for both maximum and minimum temperature) include ordinary and standardised anomalies, 'forecast - highest on record' and 'forecast - lowest on record'. Compensation for the climatological variation across the country is achieved in these last two products, which provide the necessary guidance as to whether or not record-breaking temperatures are expected, by expressing the forecast departure from the previous record in both 0 C and standard deviations.

Use intensity of social networks in southern Brazil

Directory of Open Access Journals (Sweden)

Rafaele Matte Wojahn

2017-08-01

Full Text Available A social network implies in connect people. This article aims to identify the use intensity of social network in Southern Brazil. The research was characterized by quantitative approach, descriptive, cross-sectional and survey, with a sample of 372 respondents. To data analysis was used descriptive analysis to characterize the sample, verify the access frequency of social networks and the daily access time, and Pearson’s Correlation to identify the daily access time and the social networks. The results indicated the social network used in more intensity is the Facebook and then Whatsapp, and the access occurs at home. However, all the social networks promote interactions toward users.

Monitoring daily and sub-daily variations in crustal strain with seismic arrays

Science.gov (United States)

Mao, S.; Campillo, M.; van der Hilst, R. D.; Brenguier, F.; Hillers, G.

2017-12-01

We demonstrate that we can monitor deformation of the shallow crust (with hourly temporal resolution) directly with seismic waves, by measuring relative seismic wave speed changes (dv/v) due to relatively known periodical forcing (tides and changes in atmospheric temperature) at Piton de la Fournaise Volcano (PdF), La Réunion. We use ambient seismic noise recorded (for one month) at VolcArray, an experiment with three arrays of 49 vertical-component geophones deployed on a 7x7 grid of approximately 80 m spacing. Through noise-based coda wave interferometry we infer for each array the average relative changes in propagation speed of seismic waves (dv/v) as a function of time, which relate to temporal changes in medium properties within 100m depth. The variations in dv/v ( 0.05%) on time-scales longer than a day are best explained by effects of precipitation on pore pressure. In contrast, the (weaker) daily and sub-daily fluctuations of dv/v ( 0.01%) are likely to be caused by tidal and thermal effects. We verify that the inferred variations of dv/v are unrelated to spatiotemporal changes of noise wavefields. We further compare the power spectrum of dv/v with spectra of simulated tide-induced volumetric strain, temperature records, very broadband (VBB) seismograms, and borehole tilt records. In all five types of data, dominant peaks are found at around diurnal, semi-diurnal, and ter-diurnal frequencies. A comparison of phase and spectra of the data suggests that the tidal and thermal effects on dv/v are of similar magnitude but vary with frequency. Theoretical modeling of tide- and temperature-induced strain in different frequency bands agrees with the relative magnitude of the two effects on dv/v from passive monitoring.

Network structure and thermal stability study of high temperature seal glass

Science.gov (United States)

Lu, K.; Mahapatra, M. K.

2008-10-01

High temperature seal glass has stringent requirement on glass thermal stability, which is dictated by glass network structures. In this study, a SrO-La2O3-Al2O3-B2O3-SiO2 based glass system was studied using nuclear magnetic resonance, Raman spectroscopy, and x-ray diffraction for solid oxide cell application purpose. Glass structural unit neighboring environment and local ordering were evaluated. Glass network connectivity as well as silicon and boron glass former coordination were calculated for different B2O3:SiO2 ratios. Thermal stability of the borosilicate glasses was studied after thermal treatment at 850 °C. The study shows that high B2O3 content induces BO4 and SiO4 structural unit ordering, increases glass localized inhomogeneity, decreases glass network connectivity, and causes devitrification. Glass modifiers interact with either silicon- or boron-containing structural units and form different devitrified phases at different B2O3:SiO2 ratios. B2O3-free glass shows the best thermal stability among the studied compositions, remaining stable after thermal treatment for 200 h at 850 °C.

Recent Intensified Winter Coldness in the Mid-High Latitudes of Eurasia and Its Relationship with Daily Extreme Low Temperature Variability

Directory of Open Access Journals (Sweden)

Chuhan Lu

2016-01-01

Full Text Available Observational records in recent decades show a large-scale decrease in the cold-season temperature variance in the Northern Hemisphere midlatitudes under continuous global warming. However, severe low temperature events in winter frequently occurred in midlatitude Eurasia (MEA in the last decade. Here, we define a new coldness intensity (CI index for the near-surface based on the amplitude of daily anomalously cold temperatures in winter to demonstrate the CI of the variability of low temperature extremes. The results show that a sign-consistent mode dominates the CI variation in MEA, with a marked intensification during the last decade via empirical orthogonal function (EOF analysis. This leading mode is significantly related to the frequency of winter extreme events. The associated circulations are characterized by a remarkable anomalous anticyclone in Northwest Eurasia, which induced substantial cold advection in MEA. The widespread intensified CI in MEA is closely linked with strong surface anticyclones and synoptic blocking in the mid-high latitudes (25°E–85°E. Coincidently, positive phase shifts of the first two leading modes of the extratropical circulation, which feature similar blocking-like anomalies in the northwestern Eurasian subarctic, jointly play an important role in the recent frequency of severe winters.

The effect of simulated heat-shock and daily temperature fluctuations on seed germination of four species from fire-prone ecosystems

Directory of Open Access Journals (Sweden)

Talita Zupo

2016-01-01

Full Text Available ABSTRACT Seed germination in many species from fire-prone ecosystems may be triggered by heat shock and/or temperature fluctuation, and how species respond to such fire-related cues is important to understand post-fire regeneration strategies. Thus, we tested how heat shock and daily temperature fluctuations affect the germination of four species from fire-prone ecosystems; two from the Cerrado and two from the Mediterranean Basin. Seeds of all four species were subjected to four treatments: Fire (F, temperature fluctuations (TF, fire+temperature fluctuations (F+TF and control (C. After treatments, seeds were put to germinate for 60 days at 25ºC (dark. Responses differed according to species and native ecosystem. Germination percentage for the Cerrado species did not increase with any of the treatments, while germination of one Mediterranean species increased with all treatments and the other only with treatments that included fire. Although the Cerrado species did not respond to the treatments used in this study, their seeds survived the exposure to heat shock, which suggests they possess tolerance to fire. Fire frequency in the Cerrado is higher than that in Mediterranean ecosystems, thus traits related to fire-resistance would be more advantageous than traits related to post-fire recruitment, which are widespread among Mediterranean species.

Home Network Security

NARCIS (Netherlands)

Scholten, Hans; van Dijk, Hylke

2008-01-01

Service discovery and secure and safe service usage are essential elements in the deployment of home and personal networks. Because no system administrator is present, setup and daily operation of such a network has to be automated as much as possible with a high degree of user friendliness. To

Systematic investigation of gridding-related scaling effects on annual statistics of daily temperature and precipitation maxima: A case study for south-east Australia

OpenAIRE

Francia B. Avila; Siyan Dong; Kaah P. Menang; Jan Rajczak; Madeleine Renom; Markus G. Donat; Lisa V. Alexander

2015-01-01

Using daily station observations over the period 1951–2013 in a region of south-east Australia, we systematically compare how the horizontal resolution, interpolation method and order of operation in generating gridded data sets affect estimates of annual extreme indices of temperature and precipitation maxima (hottest and wettest days). Three interpolation methods (natural neighbors, cubic spline and angular distance weighting) are used to calculate grids at five different horizontal gridded...

Opinion formation in a social network: The role of human activity

Science.gov (United States)

Grabowski, Andrzej

2009-03-01

The model of opinion formation in human population based on social impact theory is investigated numerically. On the basis of a database received from the on-line game server, we examine the structure of social network and human dynamics. We calculate the activity of individuals, i.e. the relative time devoted daily to interactions with others in the artificial society. We study the influence of correlation between the activity of an individual and its connectivity on the process of opinion formation. We find that such correlations have a significant influence on the temperature of the phase transition and the effect of the mass media, modeled as an external stimulation acting on the social network.

Glass Transition Temperature Measurement for Undercured Cyanate Ester Networks: Challenges, Tips, and Tricks (Briefing Charts)

Science.gov (United States)

2014-01-29

DISTRIBUTION A: Approved for public release; distribution is unlimited. Thermosetting Polymers Have a TG Envelope – Not Just a TG 4 • The glass transition...glass transition temperature of a thermosetting polymer can vary over a wide range of temperatures depending on how the polymer is processed • A... thermosetting polymer with only one kind of network formation and negligible side reactions, the conversion may be determined at every point in the scan. • By

Network performance of a wireless sensor network for temperature monitoring in vineyards

DEFF Research Database (Denmark)

Liscano, Ramiro; Jacoub, John Khalil; Dersingh, Anand

2011-01-01

Wireless sensor networks (WSNs) are an emerging technology which can be used for outdoor environmental monitoring. This paper presents challenges that arose from the development and deployment of a WSN for environmental monitoring as well as network performance analysis of this network. Different...... components in our sensor network architecture are presented like the physical nodes, the sensor node code, and two messaging protocols; one for collecting sensor and network values and the other for sensor node commands. An information model for sensor nodes to support plug-and-play capabilities in sensor...... networks is also presented....

Prospects of real-time ion temperature and rotation profiles based on neural-network charge exchange analysis

Energy Technology Data Exchange (ETDEWEB)

Koenig, R W.T.; Von Hellermann, M [Commission of the European Communities, Abingdon (United Kingdom). JET Joint Undertaking; Svensson, J [Royal Inst. of Tech., Stockholm (Sweden)

1994-07-01

A back-propagation neural network technique is used at JET to extract plasma parameters like ion temperature, rotation velocities or spectral line intensities from charge exchange (CX) spectra. It is shown that in the case of the C VI CX spectra, neural networks can give a good estimation (better than +-20% accuracy) for the main plasma parameters (Ti, V{sub rot}). Since the neural network approach involves no iterations or initial guesses the speed with which a spectrum is processed is so high (0.2 ms/spectrum) that real time analysis will be achieved in the near future. 4 refs., 8 figs.

Prospects of real-time ion temperature and rotation profiles based on neural-network charge exchange analysis

International Nuclear Information System (INIS)

Koenig, R.W.T.; Von Hellermann, M.

1994-01-01

A back-propagation neural network technique is used at JET to extract plasma parameters like ion temperature, rotation velocities or spectral line intensities from charge exchange (CX) spectra. It is shown that in the case of the C VI CX spectra, neural networks can give a good estimation (better than +-20% accuracy) for the main plasma parameters (Ti, V rot ). Since the neural network approach involves no iterations or initial guesses the speed with which a spectrum is processed is so high (0.2 ms/spectrum) that real time analysis will be achieved in the near future. 4 refs., 8 figs

Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature.

Science.gov (United States)

Bhandari, Siddhartha; Bergmann, Neil; Jurdak, Raja; Kusy, Branislav

2017-05-26

Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of measured temperature time series. Unlike previous works which have concentrated on suppressing the transmission of some data samples by time-series analysis but still maintaining high sampling rates, this work investigates reducing the sampling rate (and sensor wake up rate) and looks at the effects on accuracy. Results show that the sampling period of the sensor can be increased up to one hour while still allowing intermediate and future states to be estimated with interpolation RMSE less than 0.2 °C and forecasting RMSE less than 1 °C.

Analysis of the structure of climate networks under El Niño and La Niña conditions

Science.gov (United States)

Graciosa, Juan Carlos; Pastor, Marissa

The El Niño-Southern Oscillation (ENSO) is the most important driver of natural climate variability and is characterized by anomalies in the sea surface temperatures (SST) over the tropical Pacific ocean. It has three phases: neutral, a warming phase or El Niño, and a cooling phase called La Niña. In this research, we modeled the climate under the three phases as a network and characterized its properties. We utilized the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily surface temperature reanalysis data from January 1950 to December 2016. A network associated to a month was created using the temperature spanning from the previous month to the succeeding month, for a total of three months worth of data for each network. Each site of the included data was a potential node in the network and the existence of links were determined by the strength of their relationship, which was based on mutual information. Interestingly, we found that climate networks exhibit small-world properties and these are found to be more prominent from October to April, coinciding with observations that El Niño occurrences peak from December to March. During these months, the temperature of a relatively large part of the Pacific ocean and its surrounding areas increase and the anomaly values become synchronized. This synchronization in the temperature anomalies forms links around the Pacific, increasing the clustering in the region and in effect, that of the entire network.

Making Daily Mobility

DEFF Research Database (Denmark)

Jensen, Ole B.; Wind, Simon

elucidate aspects of urban everyday mobility that can be utilized in policy and planning perspectives. This knowledge can aid construction of generalized qualitative scenarios that together with quantitative transport models can serve as wider knowledge foundation in decision making process.......In 2012 the average daily transportation distance for every Dane were 40 km (TU Data). Realising how much of life is spend thinking about, planning and performing mobility practices it becomes evident that it is much more than an instrumental physical phenomenon – it has great repercussions on life......, social networks, understanding of places and ultimately ourselves and others. To successfully accomplish everyday life, households have to cope with large number of different activities and mobility in relation to their children, work, social life, obligations, expectations, needs and wishes. Drawing...

Neural network-based preprocessing to estimate the parameters of the X-ray emission of a single-temperature thermal plasma

Science.gov (United States)

Ichinohe, Y.; Yamada, S.; Miyazaki, N.; Saito, S.

2018-04-01

We present data preprocessing based on an artificial neural network to estimate the parameters of the X-ray emission spectra of a single-temperature thermal plasma. The method finds appropriate parameters close to the global optimum. The neural network is designed to learn the parameters of the thermal plasma (temperature, abundance, normalization and redshift) of the input spectra. After training using 9000 simulated X-ray spectra, the network has grown to predict all the unknown parameters with uncertainties of about a few per cent. The performance dependence on the network structure has been studied. We applied the neural network to an actual high-resolution spectrum obtained with Hitomi. The predicted plasma parameters agree with the known best-fitting parameters of the Perseus cluster within uncertainties of ≲10 per cent. The result shows that neural networks trained by simulated data might possibly be used to extract a feature built in the data. This would reduce human-intensive preprocessing costs before detailed spectral analysis, and would help us make the best use of the large quantities of spectral data that will be available in the coming decades.

Wood Modification at High Temperature and Pressurized Steam: a Relational Model of Mechanical Properties Based on a Neural Network

Directory of Open Access Journals (Sweden)

Hong Yang

2015-07-01

Full Text Available Thermally modified wood has high dimensional stability and biological durability.But if the process parameters of thermal modification are not appropriate, then there will be a decline in the physical properties of wood.A neural network algorithm was employed in this study to establish the relationship between the process parameters of high-temperature and high-pressure thermal modification and the mechanical properties of the wood. Three important parameters: temperature, relative humidity, and treatment time, were considered as the inputs to the neural network. Back propagation (BP neural network and radial basis function (RBF neural network models for prediction were built and compared. The comparison showed that the RBF neural network model had advantages in network structure, convergence speed, and generalization capacity. On this basis, the inverse model, reflecting the relationship between the process parameters and the mechanical properties of wood, was established. Given the desired mechanical properties of the wood, the thermal modification process parameters could be inversely optimized and predicted. The results indicated that the model has good learning ability and generalization capacity. This is of great importance for the theoretical and applicational studies of the thermal modification of wood.

Daily dynamics of emerging pollutants in a sewer network (région Centre, France)

Science.gov (United States)

Thiebault, Thomas; Réty, Maxime; Jacob, Jérémy; Destandau, Emilie; Fougère, Laetitia; Morio, Cédric

2017-04-01

As any catchment, cities are characterized by important flux of various materials. The specificity of urban socio-ecosystems lies in the nature of these materials that mainly result from human activities or are man-made. An important issue concerns emerging pollutants for which an understanding of their temporal dynamics is crucial to better forecast flux and adapt remediation treatments before waters are released in the environment. This study aims at better understanding the socio-economic drivers of emerging pollutants temporal dynamics by monitoring, on a daily basis during 85 consecutive days, a series of fifty illicit drugs and pharmaceuticals as well as their metabolites in a sewer network that collects wastewater from 90,000 inhabitants and upstream a wastewater plant. Flow-enslaved composite samples were automatically collected over 24h, then filtered, and target compounds were concentrated by solid-phase extraction before quantitation by HPLC-MS. Concentrations were converted into mass loads per population equivalent by several corrections (i) the flow, (ii) the solid/liquid partition and the molar ratio between target residue and parent-compound (iii) the number of population-equivalent on the catchment and (iv) the excretion rate of target residue. The large dataset obtained, combined to a literature survey, allows us discussing several issues. (1) Calculated daily mass loads of pharmaceuticals are in agreement with literature data for comparable cities except for to anti-inflammatory drugs: ketoprofen that shows the highest values and ibuprofen that displays the lowest values. This would attest to local therapeutic preference that remains to be explained. Daily mass loads for illicit drugs are lower than those measured in more populated cities, except for tetrahydrocannabinol that exceeds the highest reported values. (2) Consumption estimates of pharmaceutical based on our approach are very close to theoretical values from the literature. Additionally

Daily rainfall-runoff modelling by neural networks in semi-arid zone ...

African Journals Online (AJOL)

This research work will allow checking efficiency of formal neural networks for flows' modelling of wadi Ouahrane's basin from rainfall-runoff relation which is non-linear. Two models of neural networks were optimized through supervised learning and compared in order to achieve this goal, the first model with input rain, and ...

Artificial Intelligence Can Predict Daily Trauma Volume and Average Acuity.

Science.gov (United States)

Stonko, David P; Dennis, Bradley M; Betzold, Richard D; Peetz, Allan B; Gunter, Oliver L; Guillamondegui, Oscar D

2018-04-19

The goal of this study was to integrate temporal and weather data in order to create an artificial neural network (ANN) to predict trauma volume, the number of emergent operative cases, and average daily acuity at a level 1 trauma center. Trauma admission data from TRACS and weather data from the National Oceanic and Atmospheric Administration (NOAA) was collected for all adult trauma patients from July 2013-June 2016. The ANN was constructed using temporal (time, day of week), and weather factors (daily high, active precipitation) to predict four points of daily trauma activity: number of traumas, number of penetrating traumas, average ISS, and number of immediate OR cases per day. We trained a two-layer feed-forward network with 10 sigmoid hidden neurons via the Levenberg-Marquardt backpropagation algorithm, and performed k-fold cross validation and accuracy calculations on 100 randomly generated partitions. 10,612 patients over 1,096 days were identified. The ANN accurately predicted the daily trauma distribution in terms of number of traumas, number of penetrating traumas, number of OR cases, and average daily ISS (combined training correlation coefficient r = 0.9018+/-0.002; validation r = 0.8899+/- 0.005; testing r = 0.8940+/-0.006). We were able to successfully predict trauma and emergent operative volume, and acuity using an ANN by integrating local weather and trauma admission data from a level 1 center. As an example, for June 30, 2016, it predicted 9.93 traumas (actual: 10), and a mean ISS score of 15.99 (actual: 13.12); see figure 3. This may prove useful for predicting trauma needs across the system and hospital administration when allocating limited resources. Level III STUDY TYPE: Prognostic/Epidemiological.

Impact of daily cooling treatment on skin inflammation in patients with chronic venous disease.

Science.gov (United States)

Kelechi, Teresa J; Mueller, Martina; King, Dana E; Madisetti, Mohan; Prentice, Margie

2015-05-01

People with chronic venous disease are at high risk for developing venous leg ulcers. Inflammation is posited as a pathological factor for this chronic condition as evidenced by persistently elevated skin temperature. As part of a larger trial to test the effects of a cooling regimen on leg ulcer prevention, the objective of this preliminary study was to evaluate the first 30 days of intense daily cooling. Compared to a placebo control cuff, a gel cuff applied to the most severely affected lower leg skin for 30 min daily showed no statistically significant differences between temperatures taken in the home at baseline compared to those measured at the 1 month follow up visit. There were also no differences in temperatures noted between the two groups, although the temperatures in the treatment group were lower 30 min after treatment, an indication of adherence. There was no discernable decrease or increase in temperature at a given time point during the 30 day treatment period compared to the control group. It may be better to have patients monitor skin temperature on a daily basis and then apply the cuff as necessary, rather than requiring daily cooling based on baseline measurement. This "prn" approach may provide a sufficient cooling milieu to prevent escalation of inflammation and thwart ulcer occurrence or recurrence. Clinical trials registration #NCT01509599. Copyright © 2015 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.

Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

Energy Technology Data Exchange (ETDEWEB)

Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

2009-09-15

Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

Sensor monitoring to measure and support daily functioning for independently living older people: A systematic review and road map for further development

NARCIS (Netherlands)

Pol, M.C.; Poerbodipoero, S.; Robben, S.; Daams, J.; van Hartingsveldt, M.; de Vos, R.; de Rooij, S.E.; Kröse, B.; Buurman, B.M.

2013-01-01

Objectives To study sensor monitoring (use of a sensor network placed in the home environment to observe individuals' daily functioning (activities of daily living and instrumental activities of daily living)) as a method to measure and support daily functioning for older people living independently

Sensor monitoring to measure and support daily functioning for independently living older people: a systematic review and road map for further development

NARCIS (Netherlands)

Pol, Margriet C.; Poerbodipoero, Soemitro; Robben, Saskia; Daams, Joost; van Hartingsveldt, Margo; de Vos, Rien; de Rooij, Sophia E.; Kröse, Ben; Buurman, Bianca M.

2013-01-01

To study sensor monitoring (use of a sensor network placed in the home environment to observe individuals' daily functioning (activities of daily living and instrumental activities of daily living)) as a method to measure and support daily functioning for older people living independently at home.

Temperature- and supply voltage-independent time references for wireless sensor networks

CERN Document Server

De Smedt, Valentijn; Dehaene, Wim

2015-01-01

This book investigates the possible circuit solutions to overcome the temperature- and supply voltage-sensitivity of fully-integrated time references for ultra-low-power communication in wireless sensor networks. The authors provide an elaborate theoretical introduction and literature study to enable full understanding of the design challenges and shortcomings of current oscillator implementations.  Furthermore, a closer look to the short-term as well as the long-term frequency stability of integrated oscillators is taken. Next, a design strategy is developed and applied to 5 different oscillator topologies and 1 sensor interface.All 6 implementations are subject to an elaborate study of frequency stability, phase noise, and power consumption. In the final chapter all blocks are compared to the state of the art. The main goals of this book are: • to provide a comprehensive overview of timing issues and solutions in wireless sensor networks; • to gain understanding of all underlying mechanisms by starti...

Retrieval of air temperatures from crowd-sourced battery temperatures of cell phones

Science.gov (United States)

Overeem, Aart; Robinson, James; Leijnse, Hidde; Uijlenhoet, Remko; Steeneveld, Gert-Jan; Horn, Berthold K. P.

2013-04-01

Accurate air temperature observations are important for urban meteorology, for example to study the urban heat island and adverse effects of high temperatures on human health. The number of available temperature observations is often relatively limited. A new development is presented to derive temperature information for the urban canopy from an alternative source: cell phones. Battery temperature data were collected by users of an Android application for cell phones (opensignal.com). The application automatically sends battery temperature data to a server for storage. In this study, battery temperatures are averaged in space and time to obtain daily averaged battery temperatures for each city separately. A regression model, which can be related to a physical model, is employed to retrieve daily air temperatures from battery temperatures. The model is calibrated with observed air temperatures from a meteorological station of an airport located in or near the city. Time series of air temperatures are obtained for each city for a period of several months, where 50% of the data is for independent verification. Results are presented for Buenos Aires, London, Los Angeles, Paris, Mexico City, Moscow, Rome, and Sao Paulo. The evolution of the retrieved air temperatures often correspond well with the observed ones. The mean absolute error of daily air temperatures is less than 2 degrees Celsius, and the bias is within 1 degree Celsius. This shows that monitoring air temperatures employing an Android application holds great promise. Since 75% of the world's population has a cell phone, 20% of the land surface of the earth has cellular telephone coverage, and 500 million devices use the Android operating system, there is a huge potential for measuring air temperatures employing cell phones. This could eventually lead to real-time world-wide temperature maps.

Noise analysis method for monitoring the moderator temperature coefficient of pressurized water reactors: Neural network calibration

International Nuclear Information System (INIS)

Thomas, J.R. Jr.; Adams, J.T.

1994-01-01

A neural network was trained with data for the frequency response function between in-core neutron noise and core-exit thermocouple noise in a pressurized water reactor, with the moderator temperature coefficient (MTC) as target. The trained network was subsequently used to predict the MTC at other points in the same fuel cycle. Results support use of the method for operating pressurized water reactors provided noise data can be accumulated for several fuel cycles to provide a training base

Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques

Science.gov (United States)

Delbari, Masoomeh; Sharifazari, Salman; Mohammadi, Ehsan

2018-02-01

The knowledge of soil temperature at different depths is important for agricultural industry and for understanding climate change. The aim of this study is to evaluate the performance of a support vector regression (SVR)-based model in estimating daily soil temperature at 10, 30 and 100 cm depth at different climate conditions over Iran. The obtained results were compared to those obtained from a more classical multiple linear regression (MLR) model. The correlation sensitivity for the input combinations and periodicity effect were also investigated. Climatic data used as inputs to the models were minimum and maximum air temperature, solar radiation, relative humidity, dew point, and the atmospheric pressure (reduced to see level), collected from five synoptic stations Kerman, Ahvaz, Tabriz, Saghez, and Rasht located respectively in the hyper-arid, arid, semi-arid, Mediterranean, and hyper-humid climate conditions. According to the results, the performance of both MLR and SVR models was quite well at surface layer, i.e., 10-cm depth. However, SVR performed better than MLR in estimating soil temperature at deeper layers especially 100 cm depth. Moreover, both models performed better in humid climate condition than arid and hyper-arid areas. Further, adding a periodicity component into the modeling process considerably improved the models' performance especially in the case of SVR.

Nonlinearity and chaos in wireless network traffic

International Nuclear Information System (INIS)

Mukherjee, Somenath; Ray, Rajdeep; Samanta, Rajkumar; Khondekar, Mofazzal H.; Sanyal, Goutam

2017-01-01

The natural complexity of wireless mobile network traffic dynamics has been assessed in this article by tracing the presence of nonlinearity and chaos in the profile of daily peak hour call arrival and daily call drop of a sub-urban local mobile switching centre. The tools like Recurrence Plot and Recurrence Quantification Analysis (RQA) has been used to reveal the probable presence of non-stationarity, nonlinearity and chaosity in the network traffic. Information Entropy (IE) and 0–1 test have been employed to provide the quantitative support to the findings. Both the daily peak hour call arrival profile and the daily call drop profile exhibit non-stationarity, determinism and nonlinearity with the former one being more regular while the later one is chaotic.

Environmental Influences on Daily Emergency Admissions in Sickle-Cell Disease Patients

Science.gov (United States)

Mekontso Dessap, Armand; Contou, Damien; Dandine-Roulland, Claire; Hemery, François; Habibi, Anoosha; Charles-Nelson, Anaïs; Galacteros, Frederic; Brun-Buisson, Christian; Maitre, Bernard; Katsahian, Sandrine

2014-01-01

Abstract Previous reports have suggested a role for weather conditions and air pollution on the variability of sickle cell disease (SCD) severity, but large-scale comprehensive epidemiological studies are lacking. In order to evaluate the influence of air pollution and climatic factors on emergency hospital admissions (EHA) in SCD patients, we conducted an 8-year observational retrospective study in 22 French university hospitals in Paris conurbation, using distributed lag non-linear models, a methodology able to flexibly describe simultaneously non-linear and delayed associations, with a multivariable approach. During the 2922 days of the study, there were 17,710 EHA, with a mean daily number of 6.1 ± 2.8. Most environmental factors were significantly correlated to each other. The risk of EHA was significantly associated with higher values of nitrogen dioxide, atmospheric particulate matters, and daily mean wind speed; and with lower values of carbon monoxide, ozone, sulfur dioxide, daily temperature (minimal, maximal, mean, and range), day-to-day mean temperature change, daily bright sunshine, and occurrence of storm. There was a lag effect for 12 of 15 environmental factors influencing hospitalization rate. Multivariate analysis identified carbon monoxide, day-to-day temperature change, and mean wind speed, along with calendar factors (weekend, summer season, and year) as independent factors associated with EHA. In conclusion, most weather conditions and air pollutants assessed were correlated to each other and influenced the rate of EHA in SCD patients. In multivariate analysis, lower carbon monoxide concentrations, day-to-day mean temperature drop and higher wind speed were associated with increased risk of EHA. PMID:25546672

Effect of the Network Structure and Programming Temperature on the Shape-Memory Response of Thiol-Epoxy “Click” Systems

Directory of Open Access Journals (Sweden)

Alberto Belmonte

2015-10-01

Full Text Available This paper presents a new methodology to develop “thiol-epoxy” shape-memory polymers (SMPs with enhanced mechanical properties in a simple and efficient manner via “click” chemistry by using thermal latent initiators. The shape-memory response (SMR, defined by the mechanical capabilities of the SMP (high ultimate strength and strain, the shape-fixation and the recovery of the original shape (shape-recovery, was analyzed on thiol-epoxy systems by varying the network structure and programming temperature. The glass transition temperature (Tg and crosslinking density were modified using 3- or 4- functional thiol curing agents and different amounts of a rigid triglycidyl isocyanurate compound. The relationship between the thermo-mechanical properties, network structure and the SMR was evidenced by means of qualitative and quantitative analysis. The influence of the programming temperature (Tprog on the SMR was also analyzed in detail. The results demonstrate the possibility of tailoring SMPs with enhanced mechanical capabilities and excellent SMR, and intend to provide a better insight into the relationship between the network structure properties, programming temperature and the SMR of unconstrained (stress-free systems; thus, making it easier to decide between different SMP and to define the operative parameters in the useful life.

Hard-Wired Dopant Networks and the Prediction of High Transition Temperatures in Ceramic Superconductors

International Nuclear Information System (INIS)

Phillips, J.C.

2010-01-01

The review multiple successes of the discrete hard-wired dopant network model ZZIP, and comment on the equally numerous failures of continuum models, in describing and predicting the properties of ceramic superconductors. The prediction of transition temperatures can be regarded in several ways, either as an exacting test of theory, or as a tool for identifying theoretical rules for defining new homology models. Popular first principle methods for predicting transition temperatures in conventional crystalline superconductors have failed for cuprate HTSC, as have parameterized models based on CuO2 planes (with or without apical oxygen). Following a path suggested by Bayesian probability, it was found that the glassy, self-organized dopant network percolative model is so successful that it defines a new homology class appropriate to ceramic superconductors. The reasons for this success in an exponentially complex (non-polynomial complete, NPC) problem are discussed, and a critical comparison is made with previous polynomial (PC) theories. The predictions are successful for the superfamily of all ceramics, including new non-cuprates based on FeAs in place of CuO2.

Identification of Gene Modules Associated with Low Temperatures Response in Bambara Groundnut by Network-Based Analysis.

Directory of Open Access Journals (Sweden)

Venkata Suresh Bonthala

Full Text Available Bambara groundnut (Vigna subterranea (L. Verdc. is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01 under the sub-optimal (23°C and very sub-optimal (18°C temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties.

Room temperature synthesis of heptazine-based microporous polymer networks as photocatalysts for hydrogen evolution.

Science.gov (United States)

Kailasam, Kamalakannan; Schmidt, Johannes; Bildirir, Hakan; Zhang, Guigang; Blechert, Siegfried; Wang, Xinchen; Thomas, Arne

2013-06-25

Two emerging material classes are combined in this work, namely polymeric carbon nitrides and microporous polymer networks. The former, polymeric carbon nitrides, are composed of amine-bridged heptazine moieties and showed interesting performance as a metal-free photocatalyst. These materials have, however, to be prepared at high temperatures, making control of their chemical structure difficult. The latter, microporous polymer networks have received increasing interest due to their high surface area, giving rise to interesting applications in gas storage or catalysis. Here, the central building block of carbon nitrides, a functionalized heptazine as monomer, and tecton are used to create microporous polymer networks. The resulting heptazine-based microporous polymers show high porosity, while their chemical structure resembles the ones of carbon nitrides. The polymers show activity for the photocatalytic production of hydrogen from water, even under visible light illumination. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Calculating activation energies for temperature compensation in circadian rhythms

International Nuclear Information System (INIS)

Bodenstein, C; Heiland, I; Schuster, S

2011-01-01

Many biological species possess a circadian clock, which helps them anticipate daily variations in the environment. In the absence of external stimuli, the rhythm persists autonomously with a period of approximately 24 h. However, single pulses of light, nutrients, chemicals or temperature can shift the clock phase. In the case of light- and temperature-cycles, this allows entrainment of the clock to cycles of exactly 24 h. Circadian clocks have the remarkable property of temperature compensation, that is, the period of the circadian rhythm remains relatively constant within a physiological range of temperatures. For several organisms, temperature-regulated processes within the circadian clock have been identified in recent years. However, how these processes contribute to temperature compensation is not fully understood. Here, we theoretically investigate temperature compensation in general oscillatory systems. It is known that every oscillator can be locally temperature compensated around a reference temperature, if reactions are appropriately balanced. A balancing is always possible if the control coefficient with respect to the oscillation period of at least one reaction in the oscillator network is positive. However, for global temperature compensation, the whole physiological temperature range is relevant. Here, we use an approach which leads to an optimization problem subject to the local balancing principle. We use this approach to analyse different circadian clock models proposed in the literature and calculate activation energies that lead to temperature compensation

Spatial Epidemic Modelling in Social Networks

Science.gov (United States)

Simoes, Joana Margarida

2005-06-01

The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.

Real-Time Alpine Measurement System Using Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Sami A. Malek

2017-11-01

Full Text Available Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

Real-Time Alpine Measurement System Using Wireless Sensor Networks.

Science.gov (United States)

Malek, Sami A; Avanzi, Francesco; Brun-Laguna, Keoma; Maurer, Tessa; Oroza, Carlos A; Hartsough, Peter C; Watteyne, Thomas; Glaser, Steven D

2017-11-09

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra's wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

Simulating the effect of climate change on stream temperature in the Trout Lake Watershed, Wisconsin

Energy Technology Data Exchange (ETDEWEB)

Selbig, William R., E-mail: wrselbig@usgs.gov

2015-07-15

The potential for increases in stream temperature across many spatial and temporal scales as a result of climate change can pose a difficult challenge for environmental managers, especially when addressing thermal requirements for sensitive aquatic species. This study evaluates simulated changes to the thermal regime of three northern Wisconsin streams in response to a projected changing climate using a modeling framework and considers implications of thermal stresses to the fish community. The Stream Network Temperature Model (SNTEMP) was used in combination with a coupled groundwater and surface water flow model to assess forecasts in climate from six global circulation models and three emission scenarios. Model results suggest that annual average stream temperature will steadily increase approximately 1.1 to 3.2 °C (varying by stream) by the year 2100 with differences in magnitude between emission scenarios. Daily mean stream temperature during the months of July and August, a period when cold-water fish communities are most sensitive, showed excursions from optimal temperatures with increased frequency compared to current conditions. Projections of daily mean stream temperature, in some cases, were no longer in the range necessary to sustain a cold water fishery. - Highlights: • A stream temperature model was calibrated for three streams in northern Wisconsin. • The effect of climate change on stream temperature was simulated in each stream. • Annual average stream temperature was projected to rise from 1 to 3 °C by 2100. • Forecasts of stream temperature exceeded optimal ranges for brook trout.

Privacy in Online Social Networks

NARCIS (Netherlands)

Beye, Michael; Jeckmans, Arjan; Erkin, Zekeriya; Erkin, Zekeriya; Hartel, Pieter H.; Lagendijk, Reginald; Tang, Qiang; Abraham, A.

Online Social Networks (OSNs) have become part of daily life for millions of users. Users building explicit networks that represent their social relationships and often share a wealth of personal information to their own benefit. The potential privacy risks of such behavior are often underestimated

Glacier Melt Detection in Complex Terrain Using New AMSR-E Calibrated Enhanced Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record

Science.gov (United States)

Ramage, J. M.; Brodzik, M. J.; Hardman, M.

2016-12-01

Passive microwave (PM) 18 GHz and 36 GHz horizontally- and vertically-polarized brightness temperatures (Tb) channels from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) have been important sources of information about snow melt status in glacial environments, particularly at high latitudes. PM data are sensitive to the changes in near-surface liquid water that accompany melt onset, melt intensification, and refreezing. Overpasses are frequent enough that in most areas multiple (2-8) observations per day are possible, yielding the potential for determining the dynamic state of the snow pack during transition seasons. AMSR-E Tb data have been used effectively to determine melt onset and melt intensification using daily Tb and diurnal amplitude variation (DAV) thresholds. Due to mixed pixels in historically coarse spatial resolution Tb data, melt analysis has been impractical in ice-marginal zones where pixels may be only fractionally snow/ice covered, and in areas where the glacier is near large bodies of water: even small regions of open water in a pixel severely impact the microwave signal. We use the new enhanced-resolution Calibrated Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record product's twice daily obserations to test and update existing snow melt algorithms by determining appropriate melt thresholds for both Tb and DAV for the CETB 18 and 36 GHz channels. We use the enhanced resolution data to evaluate melt characteristics along glacier margins and melt transition zones during the melt seasons in locations spanning a wide range of melt scenarios, including the Patagonian Andes, the Alaskan Coast Range, and the Russian High Arctic icecaps. We quantify how improvement of spatial resolution from the original 12.5 - 25 km-scale pixels to the enhanced resolution of 3.125 - 6.25 km improves the ability to evaluate melt timing across boundaries and transition zones in diverse glacial environments.

Foreground removal from Planck Sky Model temperature maps using a MLP neural network

Science.gov (United States)

Nørgaard-Nielsen, H. U.; Hebert, K.

2009-08-01

Unfortunately, the Cosmic Microwave Background (CMB) radiation is contaminated by emission originating in the Milky Way (synchrotron, free-free and dust emission). Since the cosmological information is statistically in nature, it is essential to remove this foreground emission and leave the CMB with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80 % of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors.

Short-term, daily exposure to cold temperature may be an efficient way to prevent muscle atrophy and bone loss in a microgravity environment

Science.gov (United States)

Deng, Claudia; Wang, Ping; Zhang, Xiangming; Wang, Ya

2015-04-01

Microgravity induces less pressure on muscle/bone, which is a major reason for muscle atrophy as well as bone loss. Currently, physical exercise is the only countermeasure used consistently in the U.S. human space program to counteract the microgravity-induced skeletal muscle atrophy and bone loss. However, the routinely almost daily time commitment is significant and represents a potential risk to the accomplishment of other mission operational tasks. Therefore, development of more efficient exercise programs (with less time) to prevent astronauts from muscle atrophy and bone loss are needed. Consider the two types of muscle contraction: exercising forces muscle contraction and prevents microgravity-induced muscle atrophy/bone loss, which is a voluntary response through the motor nervous system; and cold temperature exposure-induced muscle contraction is an involuntary response through the vegetative nervous system, we formed a new hypothesis. The main purpose of this pilot study was to test our hypothesis that exercise at 4 °C is more efficient than at room temperature to prevent microgravity-induced muscle atrophy/bone loss and, consequently reduces physical exercise time. Twenty mice were divided into two groups with or without daily short-term (10 min × 2, at 12 h interval) cold temperature (4 °C) exposure for 30 days. The whole bodyweight, muscle strength and bone density were measured after terminating the experiments. The results from the one-month pilot study support our hypothesis and suggest that it would be reasonable to use more mice, in a microgravity environment and observe for a longer period to obtain a conclusion. We believe that the results from such a study will help to develop efficient exercise, which will finally benefit astronauts' heath and NASA's missions.

Building a Smartphone Seismic Network

Science.gov (United States)

Kong, Q.; Allen, R. M.

2013-12-01

We are exploring to build a new type of seismic network by using the smartphones. The accelerometers in smartphones can be used to record earthquakes, the GPS unit can give an accurate location, and the built-in communication unit makes the communication easier for this network. In the future, these smartphones may work as a supplement network to the current traditional network for scientific research and real-time applications. In order to build this network, we developed an application for android phones and server to record the acceleration in real time. These records can be sent back to a server in real time, and analyzed at the server. We evaluated the performance of the smartphone as a seismic recording instrument by comparing them with high quality accelerometer while located on controlled shake tables for a variety of tests, and also the noise floor test. Based on the daily human activity data recorded by the volunteers and the shake table tests data, we also developed algorithm for the smartphones to detect earthquakes from daily human activities. These all form the basis of setting up a new prototype smartphone seismic network in the near future.

An efficient transmission power control scheme for temperature variation in wireless sensor networks.

Science.gov (United States)

Lee, Jungwook; Chung, Kwangsue

2011-01-01

Wireless sensor networks collect data from several nodes dispersed at remote sites. Sensor nodes can be installed in harsh environments such as deserts, cities, and indoors, where the link quality changes considerably over time. Particularly, changes in transmission power may be caused by temperature, humidity, and other factors. In order to compensate for link quality changes, existing schemes detect the link quality changes between nodes and control transmission power through a series of feedback processes, but these approaches can cause heavy overhead with the additional control packets needed. In this paper, the change of the link quality according to temperature is examined through empirical experimentation. A new power control scheme combining both temperature-aware link quality compensation and a closed-loop feedback process to adapt to link quality changes is proposed. We prove that the proposed scheme effectively adapts the transmission power to the changing link quality with less control overhead and energy consumption.

An Efficient Transmission Power Control Scheme for Temperature Variation in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Jungwook Lee

2011-03-01

Full Text Available Wireless sensor networks collect data from several nodes dispersed at remote sites. Sensor nodes can be installed in harsh environments such as deserts, cities, and indoors, where the link quality changes considerably over time. Particularly, changes in transmission power may be caused by temperature, humidity, and other factors. In order to compensate for link quality changes, existing schemes detect the link quality changes between nodes and control transmission power through a series of feedback processes, but these approaches can cause heavy overhead with the additional control packets needed. In this paper, the change of the link quality according to temperature is examined through empirical experimentation. A new power control scheme combining both temperature-aware link quality compensation and a closed-loop feedback process to adapt to link quality changes is proposed. We prove that the proposed scheme effectively adapts the transmission power to the changing link quality with less control overhead and energy consumption.

Temperature dependence of the partially localized state in a 2D molecular nanoporous network

Energy Technology Data Exchange (ETDEWEB)

Piquero-Zulaica, Ignacio, E-mail: ipiquerozulaica@gmail.com [Centro de Física de Materiales (CSIC/UPV-EHU)—Materials Physics Center, Manuel Lardizabal 5, 20018 San Sebastián (Spain); Nowakowska, Sylwia [Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel (Switzerland); Ortega, J. Enrique [Centro de Física de Materiales (CSIC/UPV-EHU)—Materials Physics Center, Manuel Lardizabal 5, 20018 San Sebastián (Spain); Donostia International Physics Center (DIPC), Manuel Lardizabal 4, 20018 San Sebastián (Spain); Departamento Física Aplicada I, Universidad del País Vasco, 20018 San Sebastián (Spain); Stöhr, Meike [Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen (Netherlands); Gade, Lutz H. [Anorganisch-Chemisches Institut, Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg (Germany); Jung, Thomas A. [Laboratory for Micro- and Nanotechnology, Paul Scherrer Institute, 5232 Villigen (Switzerland); Lobo-Checa, Jorge, E-mail: jorge.lobo@csic.es [Instituto de Ciencia de Materiales de Aragón (ICMA), CSIC-Universidad de Zaragoza, E-50009 Zaragoza (Spain); Departamento de Física de la Materia Condensada, Universidad de Zaragoza, E-50009 Zaragoza (Spain)

2017-01-01

Highlights: • A state of a 2D porous network is demonstrated to originate from the Shockley state. • The temperature evolution of both states is followed by means of ARPES. • Identical energy shifts are observed for both states, proving their common origin. - Abstract: Two-dimensional organic and metal-organic nanoporous networks can scatter surface electrons, leading to their partial localization. Such quantum states are related to intrinsic surface states of the substrate material. We further corroborate this relation by studying the thermally induced energy shifts of the electronic band stemming from coupled quantum states hosted in a metal-organic array formed by a perylene derivative on Cu(111). We observe by angle-resolved photoemission spectroscopy (ARPES), that both, the Shockley and the partially localized states, shift by the same amount to higher binding energies upon decreasing the sample temperature, providing evidence of their common origin. Our experimental approach and results further support the use of surface states for modelling these systems, which are expected to provide new insight into the physics concerning partially confined electronic states: scattering processes, potential barrier strengths, excited state lifetimes or the influence of guest molecules.

Robust/optimal temperature profile control of a high-speed aerospace vehicle using neural networks.

Science.gov (United States)

Yadav, Vivek; Padhi, Radhakant; Balakrishnan, S N

2007-07-01

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A 1-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

Interpersonal Similarity between Body Movements in Face-To-Face Communication in Daily Life

OpenAIRE

Higo, Naoki; Ogawa, Ken-ichiro; Minemura, Juichi; Xu, Bujie; Nozawa, Takayuki; Ogata, Taiki; Ara, Koji; Yano, Kazuo; Miyake, Yoshihiro

2014-01-01

Individuals are embedded in social networks in which they communicate with others in their daily lives. Because smooth face-to-face communication is the key to maintaining these networks, measuring the smoothness of such communication is an important issue. One indicator of smoothness is the similarity of the body movements of the two individuals concerned. A typical example noted in experimental environments is the interpersonal synchronization of body movements such as nods and gestures dur...

Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States

Energy Technology Data Exchange (ETDEWEB)

Li, Xiaoma; Zhou, Yuyu; Asrar, Ghassem R.; Zhu, Zhengyuan

2018-03-01

High spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. Moderate Resolution Imaging Spectroradiometer (MODIS) LST is one of such high spatiotemporal datasets that are widely used. But, it has large amount of missing values primarily because of clouds. Gapfilling the missing values is an important approach to create high spatiotemporal LST datasets. However current gapfilling methods have limitations in terms of accuracy and time required to assemble the data over large areas (e.g., national and continental levels). In this study, we developed a 3-step hybrid method by integrating a combination of daily merging, spatiotemporal gapfilling, and temporal interpolation methods, to create a high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. We applied this method in urban and surrounding areas for the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates that its root mean squared error (RMSE) to be 3.3K for mid-daytime (1:30 pm) and 2.7K for mid-13 nighttime (1:30 am) observations. The method can be easily extended to other years and regions and is also applicable to other satellite products. This seamless daily (mid-daytime and mid-nighttime) LST product with 1 km spatial resolution is of great value for studying effects of urbanization (e.g., urban heat island) and the related impacts on people, ecosystems, energy systems and other infrastructure for cities.

Predicting forest insect flight activity: A Bayesian network approach.

Directory of Open Access Journals (Sweden)

Stephen M Pawson

Full Text Available Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.

Micro-scale heterogeneity in water temperature | Dallas | Water SA

African Journals Online (AJOL)

Micro-scale heterogeneity in water temperature was examined in 6 upland sites in the Western Cape, South Africa. Hourly water temperature data converted to daily data showed that greatest differences were apparent in daily maximum temperatures between shallow- and deep-water biotopes during the warmest period of ...

Evaluating the Network: A Workflow for Tracking Twitter Interactions Using Social Networking Analysis

Science.gov (United States)

Goodier, Sarah

2018-01-01

Networking plays an important role in research projects to build a community and audience around a research area. Using social media is popular in project communication as it provides the ability to engage with a group of followers daily. Such online networking tools provide the advantage of providing nearrealtime data, which can be used to…

Daily rhythms of cloacal temperature in broiler chickens of different age groups administered with zinc gluconate and probiotic during the hot-dry season.

Science.gov (United States)

Aluwong, Tagang; Sumanu, Victory O; Ayo, Joseph O; Ocheja, Benjamin O; Zakari, Friday O; Minka, Ndazo S

2017-06-01

The aim of the experiment was to evaluate effects of zinc gluconate (ZnGlu) and probiotic administration on the daily rhythm of cloacal temperature ( t cloacal ) in broiler chickens of different age groups during the hot-dry season. One-day-old broiler chicks ( n  = 60) were divided into groups I-IV of 15 chicks per group, and treated for 35 days: Group I (control) was given deionized water; Group II, ZnGlu (50 mg/kg); Group III, probiotic (4.125 × 10 6  cfu/100 mL), and Group IV, ZnGlu (50 mg/kg) + probiotic (4.125 × 10 6  cfu/100 mL). Air dry-bulb temperature ( t db ), relative humidity (RH), and temperature-humidity index (THI) inside the pen, and t cloacal of each broiler chick were obtained bihourly over a 24-h period; on days 21, 28, and 35 of the study. Values of t db (32.10 ± 0.49°C), RH (49.94 ± 1.91%), and THI (38.85 ± 0.42) obtained were outside the thermoneutral zone for broiler chickens, and suggested that the birds were subjected to heat stress. Application of the periodic model showed disruption of daily rhythm of t cloacal in broilers on day 21, which was synchronized by probiotic administration. The administration of probiotics or ZnGlu + probiotics to a greater extent decreased the mesor and amplitude, delayed the acrophases of t cloacal in broilers, especially at day 35, as compared to the controls. Overall, the t cloacal values in broiler chickens administered with probiotic alone (41.25 ± 0.05°C) and ZnGlu + probiotic (41.52 ± 0.05°C) were lower ( P  probiotic alone synchronized t cloacal of the birds at day 21, and, in addition, decreased t cloacal response most, followed by its coadministration with ZnGlu, the antioxidants may be beneficial in modulating daily rhythmicity of t cloacal and alleviating adverse effects of heat stress on broiler chickens during the hot-dry season. © 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the

Prediction of Sea Surface Temperature Using Long Short-Term Memory

Science.gov (United States)

Zhang, Qin; Wang, Hui; Dong, Junyu; Zhong, Guoqiang; Sun, Xin

2017-10-01

This letter adopts long short-term memory(LSTM) to predict sea surface temperature(SST), which is the first attempt, to our knowledge, to use recurrent neural network to solve the problem of SST prediction, and to make one week and one month daily prediction. We formulate the SST prediction problem as a time series regression problem. LSTM is a special kind of recurrent neural network, which introduces gate mechanism into vanilla RNN to prevent the vanished or exploding gradient problem. It has strong ability to model the temporal relationship of time series data and can handle the long-term dependency problem well. The proposed network architecture is composed of two kinds of layers: LSTM layer and full-connected dense layer. LSTM layer is utilized to model the time series relationship. Full-connected layer is utilized to map the output of LSTM layer to a final prediction. We explore the optimal setting of this architecture by experiments and report the accuracy of coastal seas of China to confirm the effectiveness of the proposed method. In addition, we also show its online updated characteristics.

Temperature-induced phase transition in hydrogels of interpenetrating networks poly(N-isopropylmethacrylamide)/poly(N-isopropylacrylamide)

Czech Academy of Sciences Publication Activity Database

Šťastná, J.; Hanyková, L.; Sedláková, Zdeňka; Valentová, H.; Spěváček, Jiří

2013-01-01

Roč. 291, č. 10 (2013), s. 2409-2417 ISSN 0303-402X R&D Projects: GA ČR GA202/09/1281 Institutional support: RVO:61389013 Keywords : temperature-induced volume phase transition * poly (N-isopropylmethacrylamide) poly (Nisopropylacrylamide) interpenetrating network * 1H NMR spectroscopy Subject RIV: CD - Macromolecular Chemistry Impact factor: 2.410, year: 2013

Continental-Scale Temperature Reconstructions from the PAGES 2k Network

Science.gov (United States)

Kaufman, D. S.

2012-12-01

We present a major new synthesis of seven regional temperature reconstructions to elucidate the global pattern of variations and their association with climate-forcing mechanisms over the past two millennia. To coordinate the integration of new and existing data of all proxy types, the Past Global Changes (PAGES) project developed the 2k Network. It comprises nine working groups representing eight continental-scale regions and the oceans. The PAGES 2k Consortium, authoring this paper, presently includes 79 representatives from 25 countries. For this synthesis, each of the PAGES 2k working groups identified the proxy climate records for reconstructing past temperature and associated uncertainty using the data and methodologies that they deemed most appropriate for their region. The datasets are from 973 sites where tree rings, pollen, corals, lake and marine sediment, glacier ice, speleothems, and historical documents record changes in biologically and physically mediated processes that are sensitive to temperature change, among other climatic factors. The proxy records used for this synthesis are available through the NOAA World Data Center for Paleoclimatology. On long time scales, the temperature reconstructions display similarities among regions, and a large part of this common behavior can be explained by known climate forcings. Reconstructed temperatures in all regions show an overall long-term cooling trend until around 1900 C.E., followed by strong warming during the 20th century. On the multi-decadal time scale, we assessed the variability among the temperature reconstructions using principal component (PC) analysis of the standardized decadal mean temperatures over the period of overlap among the reconstructions (1200 to 1980 C.E.). PC1 explains 35% of the total variability and is strongly correlated with temperature reconstructions from the four Northern Hemisphere regions, and with the sum of external forcings including solar, volcanic, and greenhouse

Responses of antennal campaniform sensilla to rapid temperature changes in ground beetles of the tribe platynini with different habitat preferences and daily activity rhythms.

Science.gov (United States)

Must, Anne; Merivee, Enno; Luik, Anne; Mänd, Marika; Heidemaa, Mikk

2006-05-01

Responses of temperature sensitive (cold) cells from the antenna of ground beetles (tribe Platynini) were compared in species with different ecological preferences and daily activity rhythms. Action potential rates were characterized at various temperatures (ranges 23-39 degrees C) and during rapid changes in it (Deltat=0.5-15 degrees C). The stationary firing frequencies were nearly twice as high in eurythermic open field ground beetles Agonum muelleri and Anchomenus dorsalis (firing rates ranging from 22 to 47imp/s) than in a stenothermic forest species Platynus assimilis. In the eurythermic species, the firing rate did not significantly depend on temperature (Anchomenus dorsalis range of 23-27 degrees C and Agonum muelleri range of 23-33 degrees C) but plots of firing rate versus temperature showed rapid declines when lethally high temperatures were approached. In contrast, a nearly linear decline of the firing rate/temperature curve was observed in Platynus assimilis. Responses to rapid temperature decreases were also considerably higher in eurythermic species. Both the peak frequency of the initial burst (maximum 420-650Hz) as well as the sustained discharge in the first 4s of the response were higher than in Platynus assimilis. Long silent periods, lasting up to several seconds, that occurred at the beginning of the response to rapid warming were significantly shorter in Agonum muelleri and Anchomenus dorsalis compared to Platynus assimilis. These findings suggest that the responses of thermoreceptors to temperature changes may be correlated with specific ecological preferences.

Intent to Quit among Daily and Non-Daily College Student Smokers

Science.gov (United States)

Pinsker, E. A.; Berg, C. J.; Nehl, E. J.; Prokhorov, A. V.; Buchanan, T. S.; Ahluwalia, J. S.

2013-01-01

Given the high prevalence of young adult smoking, we examined (i) psychosocial factors and substance use among college students representing five smoking patterns and histories [non-smokers, quitters, native non-daily smokers (i.e. never daily smokers), converted non-daily smokers (i.e. former daily smokers) and daily smokers] and (ii) smoking…

Daily iTBS worsens hand motor training--a combined TMS, fMRI and mirror training study.

Science.gov (United States)

Läppchen, C H; Ringer, T; Blessin, J; Schulz, K; Seidel, G; Lange, R; Hamzei, F

2015-02-15

Repetitive transcranial magnetic stimulation (rTMS) is used to increase regional excitability to improve motor function in combination with training after neurological diseases or events such as stroke. We investigated whether a daily application of intermittent theta burst stimulation (iTBS; a short-duration rTMS that increases regional excitability) improves the training effect compared with sham stimulation in association with a four-day hand training program using a mirror (mirror training, MT). The right dorsal premotor cortex (dPMC right) was chosen as the target region for iTBS because this region has recently been emphasized as a node within a network related to MT. Healthy subjects were randomized into the iTBS group or sham group (control group CG). In the iTBS group, iTBS was applied daily over dPMC right, which was functionally determined in an initial fMRI session prior to starting MT. MT involved 20 min of hand training daily in a mirror over four days. The hand tests, the intracortical excitability and fMRI were evaluated prior to and at the end of MT. The results of the hand training tests of the iTBS group were surprisingly significantly poorer compared with those from the CG group. Both groups showed a different course of excitability in both M1 and a different course of fMRI activation within the supplementary motor area and M1 left. We suggest the inter-regional functional balance was affected by daily iTBS over dPMC right. Maybe an inter-regional connectivity within a network is differentially balanced. An excitability increase within an inhibitory-balanced network would therefore disturb the underlying network. Copyright © 2014 Elsevier Inc. All rights reserved.

Optimization of Residual Stress of High Temperature Treatment Using Genetic Algorithm and Neural Network

Directory of Open Access Journals (Sweden)

M. Susmikanti

2015-12-01

Full Text Available In a nuclear industry area, high temperature treatment of materials is a factor which requires special attention. Assessment needs to be conducted on the properties of the materials used, including the strength of the materials. The measurement of material properties under thermal processes may reflect residual stresses. The use of Genetic Algorithm (GA to determine the optimal residual stress is one way to determine the strength of a material. In residual stress modeling with several parameters, it is sometimes difficult to solve for the optimal value through analytical or numerical calculations. Here, GA is an efficient algorithm which can generate the optimal values, both minima and maxima. The purposes of this research are to obtain the optimization of variable in residual stress models using GA and to predict the center of residual stress distribution, using fuzzy neural network (FNN while the artificial neural network (ANN used for modeling. In this work a single-material 316/316L stainless steel bar is modeled. The minimal residual stresses of the material at high temperatures were obtained with GA and analytical calculations. At a temperature of 6500C, the GA optimal residual stress estimation converged at –711.3689 MPa at adistance of 0.002934 mm from center point, whereas the analytical calculation result at that temperature and position is -975.556 MPa . At a temperature of 8500C, the GA result was -969.868 MPa at 0.002757 mm from the center point, while with analytical result was -1061.13 MPa. The difference in residual stress between GA and analytical results at a temperatureof6500C is about 27 %, while at 8500C it is 8.67 %. The distribution of residual stress showed a grouping concentrated around a coordinate of (-76; 76 MPa. The residuals stress model is a degree-two polynomial with coefficients of 50.33, -76.54, and -55.2, respectively, with a standard deviation of 7.874.

A Survey on Temperature-Aware Routing Protocols in Wireless Body Sensor Networks

Directory of Open Access Journals (Sweden)

Sangman Moh

2013-08-01

Full Text Available The rapid growth of the elderly population in the world and the rising cost of healthcare impose big issues for healthcare and medical monitoring. A Wireless Body Sensor Network (WBSN is comprised of small sensor nodes attached inside, on or around a human body, the main purpose of which is to monitor the functions and surroundings of the human body. However, the heat generated by the node’s circuitry and antenna could cause damage to the human tissue. Therefore, in designing a routing protocol for WBSNs, it is important to reduce the heat by incorporating temperature into the routing metric. The main contribution of this paper is to survey existing temperature-aware routing protocols that have been proposed for WBSNs. In this paper, we present a brief overview of WBSNs, review the existing routing protocols comparatively and discuss challenging open issues in the design of routing protocols.

Daily activities and living at a Therapeutic Residential Care Center

Directory of Open Access Journals (Sweden)

Luciane Prado Kantorki

2014-12-01

Full Text Available This study describes and analyzes day-to-day issues in a Therapeutic Residential Care Center and the daily life characteristics of its residents. This case study was conducted in Caxias do Sul, based on an evaluation of the fourth generation together with a Methodology for Analyzing Everyday Life Networks. The following categories emerged: possibilities in the territory, participation and flexibility in household tasks, situations that mark living, employees who are mediators in conflict resolution, staff committed to the resident, freedom as a therapeutic tool, difficulties in daily life, and building of alliances. This study helped to get to know the structure of everyday life experienced by the residents, identifying some difficulties they face and the mechanisms used to overcome them, in addition to noticing that the professionals can be instrumental in strengthening a daily living that can be pluralized, busy, and enriched, while still respecting the uniqueness of each resident. doi: 10.5216/ree.v16i4.22923.

Artificial neural networks for dynamic monitoring of simulated-operating parameters of high temperature gas cooled engineering test reactor (HTTR)

International Nuclear Information System (INIS)

Seker, Serhat; Tuerkcan, Erdinc; Ayaz, Emine; Barutcu, Burak

2003-01-01

This paper addresses to the problem of utilisation of the artificial neural networks (ANNs) for detecting anomalies as well as physical parameters of a nuclear power plant during power operation in real time. Three different types of neural network algorithms were used namely, feed-forward neural network (back-propagation, BP) and two types of recurrent neural networks (RNN). The data used in this paper were gathered from the simulation of the power operation of the Japan's High Temperature Engineering Testing Reactor (HTTR). For the wide range of power operation, 56 signals were generated by the reactor dynamic simulation code for several hours of normal power operation at different power ramps between 30 and 100% nominal power. Paper will compare the outcomes of different neural networks and presents the neural network system and the determination of physical parameters from the simulated operating data

MSU (Microwave Sounding Unit) Daily Troposphere Temperatures and Precipitation

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of two MSU tropospheric temperatures levels and precipitation which are described in detail below. The NOAA satellites contributing to this...

Healable, Transparent, Room-Temperature Electronic Sensors Based on Carbon Nanotube Network-Coated Polyelectrolyte Multilayers.

Science.gov (United States)

Bai, Shouli; Sun, Chaozheng; Yan, Hong; Sun, Xiaoming; Zhang, Han; Luo, Liang; Lei, Xiaodong; Wan, Pengbo; Chen, Xiaodong

2015-11-18

Transparent and conductive film based electronics have attracted substantial research interest in various wearable and integrated display devices in recent years. The breakdown of transparent electronics prompts the development of transparent electronics integrated with healability. A healable transparent chemical gas sensor device is assembled from layer-by-layer-assembled transparent healable polyelectrolyte multilayer films by developing effective methods to cast transparent carbon nanotube (CNT) networks on healable substrates. The healable CNT network-containing film with transparency and superior network structures on self-healing substrate is obtained by the lateral movement of the underlying self-healing layer to bring the separated areas of the CNT layer back into contact. The as-prepared healable transparent film is assembled into healable transparent chemical gas sensor device for flexible, healable gas sensing at room temperature, due to the 1D confined network structure, relatively high carrier mobility, and large surface-to-volume ratio. The healable transparent chemical gas sensor demonstrates excellent sensing performance, robust healability, reliable flexibility, and good transparency, providing promising opportunities for developing flexible, healable transparent optoelectronic devices with the reduced raw material consumption, decreased maintenance costs, improved lifetime, and robust functional reliability. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network

Directory of Open Access Journals (Sweden)

Li Zhang

2017-12-01

Full Text Available Winding hotspot temperature is the key factor affecting the load capacity and service life of transformers. For the early detection of transformer winding hotspot temperature anomalies, a new prediction model for the hotspot temperature fluctuation range based on fuzzy information granulation (FIG and the chaotic particle swarm optimized wavelet neural network (CPSO-WNN is proposed in this paper. The raw data are firstly processed by FIG to extract useful information from each time window. The extracted information is then used to construct a wavelet neural network (WNN prediction model. Furthermore, the structural parameters of WNN are optimized by chaotic particle swarm optimization (CPSO before it is used to predict the fluctuation range of the hotspot temperature. By analyzing the experimental data with four different prediction models, we find that the proposed method is more effective and is of guiding significance for the operation and maintenance of transformers.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina Desenvolvimento e avaliação de modelos de redes neurais para estimação da irradiação solar diária em Córdoba, Argentina

Directory of Open Access Journals (Sweden)

Mónica Bocco

2006-02-01

Full Text Available The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.O objetivo deste trabalho foi desenvolver modelos de redes neuronais, do tipo retropropagação, para a estimação da irradiação solar, a partir de dados de irradiação solar extraterrestre, amplitude térmica, precipitação, nebulosidade e razão de insolação. O treinamento e a validação foram realizados com dados correspondentes a Córdoba, Argentina. O comportamento e ajuste entre os valores observados e os estimados pelas redes foram avaliados para diferentes combinações das variáveis de entrada, que apresentaram valores do erro quadrático médio entre 3,15 e 3,88 MJ m-2 d-1 . Este último valor corresponde ao modelo que calcula a irradiação somente utilizando precipitação e amplitude térmica diária. Os resultados exibem em todos os modelos um ajuste apropriado ao comportamento sazonal da irradiação solar e permitem concluir a pertinência e o adequado desempenho desse método para estimar fenômenos complexos como a irradiação solar.

Observed Trends in Indices of Daily Precipitation and Temperature Extremes in Rio de Janeiro State (brazil)

Science.gov (United States)

Silva, W. L.; Dereczynski, C. P.; Cavalcanti, I. F.

2013-05-01

One of the main concerns of contemporary society regarding prevailing climate change is related to possible changes in the frequency and intensity of extreme events. Strong heat and cold waves, droughts, severe floods, and other climatic extremes have been of great interest to researchers because of its huge impact on the environment and population, causing high monetary damages and, in some cases, loss of life. The frequency and intensity of extreme events associated with precipitation and air temperature have been increased in several regions of the planet in recent years. These changes produce serious impacts on human activities such as agriculture, health, urban planning and development and management of water resources. In this paper, we analyze the trends in indices of climatic extremes related to daily precipitation and maximum and minimum temperatures at 22 meteorological stations of the National Institute of Meteorology (INMET) in Rio de Janeiro State (Brazil) in the last 50 years. The present trends are evaluated using the software RClimdex (Canadian Meteorological Service) and are also subjected to statistical tests. Preliminary results indicate that periods of drought are getting longer in Rio de Janeiro State, except in the North/Northwest area. In "Vale do Paraíba", "Região Serrana" and "Região dos Lagos" the increase of consecutive dry days is statistically significant. However, we also detected an increase in the total annual rainfall all over the State (taxes varying from +2 to +8 mm/year), which are statistically significant at "Região Serrana". Moreover, the intensity of heavy rainfall is also growing in most of Rio de Janeiro, except in "Costa Verde". The trends of heavy rainfall indices show significant increase in the "Metropolitan Region" and in "Região Serrana", factor that increases the vulnerability to natural disasters in these areas. With respect to temperature, it is found that the frequency of hot (cold) days and nights is

Chronic and Daily Stressors Along With Negative Affect Interact to Predict Daily Tiredness.

Science.gov (United States)

Hartsell, Elizabeth N; Neupert, Shevaun D

2017-11-01

The present study examines the within-person relationship of daily stressors and tiredness and whether this depends on daily negative affect and individual differences in chronic stress. One hundred sixteen older adult participants were recruited via Amazon's Mechanical Turk for a 9-day daily diary study. Daily tiredness, daily stressors, and negative affect were measured each day, and chronic stress was measured at baseline. Daily stressors, daily negative affect, and chronic stress interacted to predict daily tiredness. People with high chronic stress who experienced an increase in daily negative affect were the most reactive to daily stressors in terms of experiencing an increase in daily tiredness. We also found that people with low levels of chronic stress were the most reactive to daily stressors when they experienced low levels of daily negative affect. Our results highlight the need for individualized and contextualized approaches to combating daily tiredness in older adults.

Daily urban systems in function of the spatial organisation in Serbia

Directory of Open Access Journals (Sweden)

Tošić Dragutin

2009-01-01

Full Text Available In the form of theoretical discussion, this paper makes a brief analysis of relevant methodological steps for determination of Daily Urban Systems and the approach for their spatial-functional representation. The potential of using Daily Urban Systems as instruments for regional planning and regional development has been indicated. A model for determining Daily Urban Systems in Serbia has been proposed according to our socio-economic conditions. The experience thus far in reference to research of demographic, spatial and functional components of Daily Urban Systems demonstrates that for definition of their spatial and temporal manifestation and continuity models, the most relevant indicators are those which relate to distribution and functional specialization of work centers and places of living, and those which relate to quantitative-qualitative characteristics of daily migrants. Daily Urban Systems of Serbia have been developed under the conditions of continuous redistribution of population from rural to urban settlements and more or less synchronized processes of deagrarisation, deindustrialisation and urbanization with general socioeconomic flows. According to the dynamics of development in functions of work, living, education, service activities and public-social facilities in the urban regions, there have been formed Daily Urban Systems with appropriate hierarchy. The paper presents results of the latest research of Daily Urban Systems in Serbia, driven by scientific and appreciative reasons (preparation of the Regional spatial plans for municipalities of Južno pomoravlje and for Timočka krajina, and determination of the nodal systems in Zlatibor county. Daily Urban Systems, especially their regional role and implication, are proposed for instruments of rational spatial-functional organization of Serbia. According to the relevant theoretical-methodological approach and presented empirical evidence, the model of Daily Urban Systems

Estimativa do total de horas abaixo de determinada temperatura-base através das medidas diárias da temperatura do ar Number of hours below any base temperature estimated by daily measurements of air temperature

Directory of Open Access Journals (Sweden)

Luiz B. Angelocci

1979-01-01

Full Text Available São propostas equações matemáticas para estimar o total diário de horas abaixo de determinada temperatura-base, em certo período, utilizando-se somente dos valores diários das temperaturas máxima, mínima e das 21 horas (hora local. A comparação entre os totais diários e mensais de "horas de frio" estimados pelas equações propostas e os observados através de termogramas, para várias localidades do Estado de São Paulo, mostrou boa concordância entre os métodos de cálculo. O modelo proposto dispensa o uso de registros contínuos de temperatura. As vantagens de tal estimativa residem na maior disponibilidade de registros de temperaturas máximas, mínimas e das 21 horas, permitindo maior densidade de pontos em trabalhos de zoneamento agroclimático e de cartografia, além da eliminação do processo de cotação de termogramas.Mathematical equations are proposed to estimate the daily number of hours in which the air temperature remains below a determined treshold value. The equations require only daily values of maximum, minimum and 9 p.m. local time temperature, measured inside the meteorological shelter. This technique is suitable for machine computation thus avoiding the tremendous task of quantifying a large number of thermograms. This fact permits the utilization of a greater number of stations in studies of crop zonation and cartography. Good correlations were obtained between estimated and observed data of the daily and monthly total number of hours below 7°C, 13°C and 17°C, for five stations in the State of São Paulo, Brazil, showing relative accuracy of the proposed equations.

Friendly network robotics; Friendly network robotics

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-03-01

This paper summarizes the research results on the friendly network robotics in fiscal 1996. This research assumes an android robot as an ultimate robot and the future robot system utilizing computer network technology. The robot aiming at human daily work activities in factories or under extreme environments is required to work under usual human work environments. The human robot with similar size, shape and functions to human being is desirable. Such robot having a head with two eyes, two ears and mouth can hold a conversation with human being, can walk with two legs by autonomous adaptive control, and has a behavior intelligence. Remote operation of such robot is also possible through high-speed computer network. As a key technology to use this robot under coexistence with human being, establishment of human coexistent robotics was studied. As network based robotics, use of robots connected with computer networks was also studied. In addition, the R-cube (R{sup 3}) plan (realtime remote control robot technology) was proposed. 82 refs., 86 figs., 12 tabs.

The effect of thermal environment on daily milk yield of dairy cows

Directory of Open Access Journals (Sweden)

Metodija Trajchev

2016-10-01

Full Text Available The primary objective of this research was to evaluate the impact of environmental air temperatures on the milk production of dairy cows. Therefore, the experiment was carried out at the height of summer (from 15th of June to 15th of September in three small family dairy farms and one commercial dairy farm. In total, 51 dairy cows from the black-white breed were subjected to the study. The season of research was divided into three periods: the period before the hot season, the period of the hot season and the period after the hot season. In each period there were three test days selected that were used for recording the air temperature inside and outside the barns and cow’s daily milk yield. Unvaried associations between the test day milk yield of cows and independent environmental and cow factors were done using the linear mixed model for repeated measurements. The statistical model showed that the farm management system and the hot season of the year had significant infl uence on daily milk yield per cow at the level of p<0.001. The test day per periods of the season when milk control was performed and the average test day environmental temperature showed statistically significant influence on daily milk yield per cow at level p<0.01. The present results revealed that environmental air temperatures contributes to considerable loss in the milk production of dairy cows.

The effects of daily weather variables on psychosis admissions to psychiatric hospitals.

LENUS (Irish Health Repository)

McWilliams, Stephen

2012-08-02

Several studies have noted seasonal variations in admission rates of patients with psychotic illnesses. However, the changeable daily meteorological patterns within seasons have never been examined in any great depth in the context of admission rates. A handful of small studies have posed interesting questions regarding a potential link between psychiatric admission rates and meteorological variables such as environmental temperature (especially heat waves) and sunshine. In this study, we used simple non-parametric testing and more complex ARIMA and time-series regression analysis to examine whether daily meteorological patterns (wind speed and direction, barometric pressure, rainfall, sunshine, sunlight and temperature) exert an influence on admission rates for psychotic disorders across 12 regions in Ireland. Although there were some weak but interesting trends for temperature, barometric pressure and sunshine, the meteorological patterns ultimately did not exert a clinically significant influence over admissions for psychosis. Further analysis is needed.

The effects of daily weather variables on psychosis admissions to psychiatric hospitals

Science.gov (United States)

McWilliams, Stephen; Kinsella, Anthony; O'Callaghan, Eadbhard

2013-07-01

Several studies have noted seasonal variations in admission rates of patients with psychotic illnesses. However, the changeable daily meteorological patterns within seasons have never been examined in any great depth in the context of admission rates. A handful of small studies have posed interesting questions regarding a potential link between psychiatric admission rates and meteorological variables such as environmental temperature (especially heat waves) and sunshine. In this study, we used simple non-parametric testing and more complex ARIMA and time-series regression analysis to examine whether daily meteorological patterns (wind speed and direction, barometric pressure, rainfall, sunshine, sunlight and temperature) exert an influence on admission rates for psychotic disorders across 12 regions in Ireland. Although there were some weak but interesting trends for temperature, barometric pressure and sunshine, the meteorological patterns ultimately did not exert a clinically significant influence over admissions for psychosis. Further analysis is needed.

Context discovery in ad-hoc networks

NARCIS (Netherlands)

Liu, F.

2011-01-01

With the rapid development of wireless technology and portable devices, mobile ad-hoc networks (MANETs) are more and more present in our daily life. Ad-hoc networks are often composed of mobile and battery-supplied devices, like laptops, mobile phones, and PDAs. With no requirement for

Intent to quit among daily and non-daily college student smokers

OpenAIRE

Pinsker, E. A.; Berg, C. J.; Nehl, E. J.; Prokhorov, A. V.; Buchanan, T. S.; Ahluwalia, J. S.

2012-01-01

Given the high prevalence of young adult smoking, we examined (i) psychosocial factors and substance use among college students representing five smoking patterns and histories [non-smokers, quitters, native non-daily smokers (i.e. never daily smokers), converted non-daily smokers (i.e. former daily smokers) and daily smokers] and (ii) smoking category as it relates to readiness to quit among current smokers. Of the 4438 students at six Southeast colleges who completed an online survey, 69.7%...

Copolymer Networks From Oligo(ε-caprolactone) and n-Butyl Acrylate Enable a Reversible Bidirectional Shape-Memory Effect at Human Body Temperature.

Science.gov (United States)

Saatchi, Mersa; Behl, Marc; Nöchel, Ulrich; Lendlein, Andreas

2015-05-01

Exploiting the tremendous potential of the recently discovered reversible bidirectional shape-memory effect (rbSME) for biomedical applications requires switching temperatures in the physiological range. The recent strategy is based on the reduction of the melting temperature range (ΔT m ) of the actuating oligo(ε-caprolactone) (OCL) domains in copolymer networks from OCL and n-butyl acrylate (BA), where the reversible effect can be adjusted to the human body temperature. In addition, it is investigated whether an rbSME in the temperature range close or even above Tm,offset (end of the melting transition) can be obtained. Two series of networks having mixtures of OCLs reveal broad ΔTm s from 2 °C to 50 °C and from -10 °C to 37 °C, respectively. In cyclic, thermomechanical experiments the rbSME can be tailored to display pronounced actuation in a temperature interval between 20 °C and 37 °C. In this way, the application spectrum of the rbSME can be extended to biomedical applications. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Social Networks in Education

Science.gov (United States)

Klimova, Blanka; Poulova, Petra

2015-01-01

At present social networks are becoming important in all areas of human activities. They are simply part and parcel of everyday life. They are mostly used for advertising, but they have already found their way into education. The future potential of social networks is high as it can be seen from their statistics on a daily, monthly or yearly…

Association between temperature and maternal stress during pregnancy.

Science.gov (United States)

Lin, Yanfen; Hu, Wenjing; Xu, Jian; Luo, Zhongcheng; Ye, Xiaofang; Yan, Chonghuai; Liu, Zhiwei; Tong, Shilu

2017-10-01

Maternal psychological stress during pregnancy has essentially been conceptualized as a teratogen. However, little is known about the effect of temperature on maternal stress during pregnancy. The aim of this study is to investigate the relationship between temperature and maternal stress during pregnancy. In 2010, a total of 1931 eligible pregnant women were enrolled across Shanghai from four prenatal-care clinics during their mid-to-late pregnancy. Maternal life-event stress and emotional stress levels during pregnancy were assessed by the "Life Event Scale for Pregnant Women" (LESPW) and "Symptom Checklist-90-Revised Scale" (SCL-90-R), respectively. Exposure to ambient temperature was evaluated based on daily regional average in different moving average and lag days. The generalized estimating equations were used to evaluate the relationship between daily average temperature/temperature difference and maternal stress. After adjusting for relevant confounders, an U-shaped relationship was observed between daily average temperature and maternal Global-Severity-Index (GSI) of the SCL-90-R. Cumulative exposures to extremely low temperatures (stress during pregnancy. Copyright © 2017 Elsevier Inc. All rights reserved.

Long-term patterns of air temperatures, daily temperature range, precipitation, grass-reference evapotranspiration and aridity index in the USA Great Plains: Part I. Spatial trends

Science.gov (United States)

Kukal, M.; Irmak, S.

2016-11-01

Due to their substantial spatio-temporal behavior, long-term quantification and analyses of important hydrological variables are essential for practical applications in water resources planning, evaluating the water use of agricultural crop production and quantifying crop evapotranspiration patterns and irrigation management vs. hydrologic balance relationships. Observed data at over 800 sites across the Great Plains of USA, comprising of 9 states and 2,307,410 km2 of surface area, which is about 30% of the terrestrial area of the USA, were used to quantify and map large-scale and long-term (1968-2013) spatial trends of air temperatures, daily temperature range (DTR), precipitation, grass-reference evapotranspiration (ETo) and aridity index (AI) at monthly, growing season and annual time steps. Air temperatures had a strong north to south increasing trend, with annual average varying from -1 to 24 °C, and growing season average temperature varying from 8 to 30 °C. DTR gradually decreased from western to eastern parts of the region, with a regional annual and growing season averages of 14.25 °C and 14.79 °C, respectively. Precipitation had a gradual shift towards higher magnitudes from west to east, with the average annual and growing season (May-September) precipitation ranging from 163 to 1486 mm and from 98 to 746 mm, respectively. ETo had a southwest-northeast decreasing trend, with regional annual and growing season averages of 1297 mm and 823 mm, respectively. AI increased from west to east, indicating higher humidity (less arid) towards the east, with regional annual and growing season averages of 0.49 and 0.44, respectively. The spatial datasets and maps for these important climate variables can serve as valuable background for climate change and hydrologic studies in the Great Plains region. Through identification of priority areas from the developed maps, efforts of the concerned personnel and agencies and resources can be diverted towards development

The Effectiveness of Transgovernmental Networks

DEFF Research Database (Denmark)

Martinsen, Dorte Sindbjerg; Hobolth, Mogens

2016-01-01

participating in Solvit, an internal market problem-solving network, this paper investigates the role of transgovernmental networks in enforcing and managing the daily application of EU legislation by national authorities. We show that informal conflict resolution has become an important and effective tool...... for addressing misapplication of EU law. Anchored in national public administrations yet working under the ‘shadow of hierarchy’ (namely the Commission) transgovermental networks are in fact able to improve the compliance of domestic authorities....

Hot metal temperature prediction by neural networks in the blast furnace; Prediccion mediante redes neuronales de la temperatura de arrabio de un horno alto. Temperatura subyacente de arrabio

Energy Technology Data Exchange (ETDEWEB)

Cantera, C.; Jimenez, J.; Varela, I.; Formoso, A.

2002-07-01

Based on a simplified model, the underlying temperature criteria is proposed as a method to study the temperature trends in a blast furnace. As an application, a neural network able to forecast hot metal temperatures from 2 to 16 h in advance (with decreasing precision) has been built. This neural network has been designed to work at real time in a production plant. (Author)

Glass transition temperatures of microphase separated semi-interpenetrating polymer networks of polystyrene-inter-poly(cross)-2-ethylhexyl-methacrylate

NARCIS (Netherlands)

de Graaf, L.A.; de Graaf, Leontine A.; Möller, Martin; Moller, M.

1995-01-01

The glass transition temperature of semi-interpenetrating polymer networks (semi-IPNs) of atactic polystyrene (PS) in crosslinked methacrylates was studied by systematic variation of the morphology, that is domain size, continuity and concentration in the domains. Semi-IPNs were prepared from

STAMMEX high resolution gridded daily precipitation dataset over Germany: a new potential for regional precipitation climate research

Science.gov (United States)

Zolina, Olga; Simmer, Clemens; Kapala, Alice; Mächel, Hermann; Gulev, Sergey; Groisman, Pavel

2014-05-01

We present new high resolution precipitation daily grids developed at Meteorological Institute, University of Bonn and German Weather Service (DWD) under the STAMMEX project (Spatial and Temporal Scales and Mechanisms of Extreme Precipitation Events over Central Europe). Daily precipitation grids have been developed from the daily-observing precipitation network of DWD, which runs one of the World's densest rain gauge networks comprising more than 7500 stations. Several quality-controlled daily gridded products with homogenized sampling were developed covering the periods 1931-onwards (with 0.5 degree resolution), 1951-onwards (0.25 degree and 0.5 degree), and 1971-2000 (0.1 degree). Different methods were tested to select the best gridding methodology that minimizes errors of integral grid estimates over hilly terrain. Besides daily precipitation values with uncertainty estimates (which include standard estimates of the kriging uncertainty as well as error estimates derived by a bootstrapping algorithm), the STAMMEX data sets include a variety of statistics that characterize temporal and spatial dynamics of the precipitation distribution (quantiles, extremes, wet/dry spells, etc.). Comparisons with existing continental-scale daily precipitation grids (e.g., CRU, ECA E-OBS, GCOS) which include considerably less observations compared to those used in STAMMEX, demonstrate the added value of high-resolution grids for extreme rainfall analyses. These data exhibit spatial variability pattern and trends in precipitation extremes, which are missed or incorrectly reproduced over Central Europe from coarser resolution grids based on sparser networks. The STAMMEX dataset can be used for high-quality climate diagnostics of precipitation variability, as a reference for reanalyses and remotely-sensed precipitation products (including the upcoming Global Precipitation Mission products), and for input into regional climate and operational weather forecast models. We will present

Effects of temperature variation between neighbouring days on daily hospital visits for childhood asthma: a time-series analysis.

Science.gov (United States)

Li, K; Ni, H; Yang, Z; Wang, Y; Ding, S; Wen, L; Yang, H; Cheng, J; Su, H

2016-07-01

To identify the relationship between temperature variation between neighbouring days (TVN) and hospital visits for childhood asthma in age- and sex-specific groups. An ecological design was used to explore the effect of TVN on hospital visits for childhood asthma. A Poisson generalised linear regression model combined with a distributed lag non-linear model was used to analyse the association between TVN and hospital visits for childhood asthma. All hospital visits for childhood asthma from June 2010 to July 2013 were included (n = 17,022). Daily climate data were obtained from Hefei Meteorological Bureau. A significant correlation was found between TVN and hospital visits for childhood asthma in age- and sex-specific groups. For different gender groups, the effect of TVN on childhood asthma was the greatest at 3 and 5 days lag for males and females. For different age groups, the effect of TVN on childhood asthma was the greatest at 1 and 5 days lag for 0-4 years and 5-14 years children, respectively. A 1 °C increase in TVN was associated with a 4.2% (95% confidence interval 0.9-7.6%) increase in hospital visits for childhood asthma. TVN is associated with hospital visits for childhood asthma. Once the temperature change rapidly, guardians will be urged to pay more attention to their children's health, which may reduce the morbidity of childhood asthma. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Temperature and Violent Crime in Dallas, Texas: Relationships and Implications of Climate Change

Directory of Open Access Journals (Sweden)

Janet L. Gamble

2012-08-01

Full Text Available Introduction: To investigate relationships between ambient temperatures and violent crimes to determine whether those relationships are consistent across different crime categories and whether they are best described as increasing linear functions, or as curvilinear functions that decrease beyond some temperature threshold. A secondary objective was to consider the implications of the observed relationships for injuries and deaths from violent crimes in the context of a warming climate. To address these questions, we examined the relationship between daily ambient temperatures and daily incidents of violent crime in Dallas, Texas from 1993–1999.Methods: We analyzed the relationships between daily fluctuations in ambient temperature, other meteorological and temporal variables, and rates of daily violent crime using time series piece-wise regression and plots of daily data. Violent crimes, including aggravated assault, homicide, and sexualassault, were analyzed.Results: We found that daily mean ambient temperature is related in a curvilinear fashion to daily rates of violent crime with a positive and increasing relationship between temperature and aggravated crime that moderates beyond temperatures of 80 F and then turns negative beyond 90 F.Conclusion: While some have characterized the relationship between temperature and violent crime as a continually increasing linear function, leaving open the possibility that aggravated crime will increase in a warmer climate, we conclude that the relationship in Dallas is not linear, but moderatesand turns negative at high ambient temperatures. We posit that higher temperatures may encourage people to seek shelter in cooler indoor spaces, and that street crime and other crimes of opportunity are subsequently decreased. This finding suggests that the higher ambient temperatures expected with climate change may result in marginal shifts in violent crime in the short term, but are not likely to be

Simulation of Daily Weather Data Using Theoretical Probability Distributions.

Science.gov (United States)

Bruhn, J. A.; Fry, W. E.; Fick, G. W.

1980-09-01

A computer simulation model was constructed to supply daily weather data to a plant disease management model for potato late blight. In the weather model Monte Carlo techniques were employed to generate daily values of precipitation, maximum temperature, minimum temperature, minimum relative humidity and total solar radiation. Each weather variable is described by a known theoretical probability distribution but the values of the parameters describing each distribution are dependent on the occurrence of rainfall. Precipitation occurrence is described by a first-order Markov chain. The amount of rain, given that rain has occurred, is described by a gamma probability distribution. Maximum and minimum temperature are simulated with a trivariate normal probability distribution involving maximum temperature on the previous day, maximum temperature on the current day and minimum temperature on the current day. Parameter values for this distribution are dependent on the occurrence of rain on the previous day. Both minimum relative humidity and total solar radiation are assumed to be normally distributed. The values of the parameters describing the distribution of minimum relative humidity is dependent on rainfall occurrence on the previous day and current day. Parameter values for total solar radiation are dependent on the occurrence of rain on the current day. The assumptions made during model construction were found to be appropriate for actual weather data from Geneva, New York. The performance of the weather model was evaluated by comparing the cumulative frequency distributions of simulated weather data with the distributions of actual weather data from Geneva, New York and Fort Collins, Colorado. For each location, simulated weather data were similar to actual weather data in terms of mean response, variability and autocorrelation. The possible applications of this model when used with models of other components of the agro-ecosystem are discussed.

Periodic regulation of expression of genes for kisspeptin, gonadotropin-inhibitory hormone and their receptors in the grass puffer: Implications in seasonal, daily and lunar rhythms of reproduction.

Science.gov (United States)

Ando, Hironori; Shahjahan, Md; Kitahashi, Takashi

2018-04-03

The seasonal, daily and lunar control of reproduction involves photoperiodic, circadian and lunar changes in the activity of kisspeptin, gonadotropin-inhibitory hormone (GnIH) and gonadotropin-releasing hormone (GnRH) neurons. These changes are brought through complex networks of light-, time- and non-photic signal-dependent control mechanisms, which are mostly unknown at present. The grass puffer, Takifugu alboplumbeus, a semilunar spawner, provides a unique and excellent animal model to assess this question because its spawning is synchronized with seasonal, daily and lunar cycles. In the diencephalon, the genes for kisspeptin, GnIH and their receptors showed similar expression patterns with clear seasonal and daily oscillations, suggesting that they are regulated by common mechanisms involving melatonin, circadian clock and water temperature. For implications in semilunar-synchronized spawning rhythm, melatonin receptor genes showed ultradian oscillations in expression with the period of 14.0-15.4 h in the pineal gland. This unique ultradian rhythm might be driven by circatidal clock. The possible circatidal clock and circadian clock in the pineal gland may cooperate to drive circasemilunar rhythm to regulate the expression of the kisspeptin, GnIH and their receptor genes. On the other hand, high temperature (over 28 °C) conditions, under which the expression of the kisspeptin and its receptor genes is markedly suppressed, may provide an environmental signal that terminates reproduction at the end of breeding period. Taken together, the periodic regulation of the kisspeptin, GnIH and their receptor genes by melatonin, circadian clock and water temperature may be important in the precisely-timed spawning of the grass puffer. Copyright © 2018 Elsevier Inc. All rights reserved.

Artificial Neural Network-Based Monitoring of the Fuel Assembly Temperature Sensor and FPGA Implementation

Energy Technology Data Exchange (ETDEWEB)

NONE

2015-07-01

Numerous methods have been developed around the world to model the dynamic behavior and detect a faulty operating mode of a temperature sensor. In this context, we present in this study a new method based on the dependence between the fuel assembly temperature profile on control rods positions, and the coolant flow rate in a nuclear reactor. This seems to be possible since the insertion of control rods at different axial positions and variations in flow rate of the reactor coolant results in different produced thermal power in the reactor. This is closely linked to the instant fuel rod temperature profile. In a first step, we selected parameters to be used and confirmed the adequate correlation between the chosen parameters and those to be estimated by the proposed monitoring system. In the next step, we acquired and de-noised the data of corresponding parameters, the qualified data is then used to design and train the artificial neural network. The effective data denoising was done by using the wavelet transform to remove a various kind of artifacts such as inherent noise. With the suitable choice of wavelet level and smoothing method, it was possible for us to remove all the non-required artifacts with a view to verify and analyze the considered signal. In our work, several potential mother wavelet functions (Haar, Daubechies, Bi-orthogonal, Reverse Bi-orthogonal, Discrete Meyer and Symlets) were investigated to find the most similar function with the being processed signals. To implement the proposed monitoring system for the fuel rod temperature sensor (03 wire RTD sensor), we used the Bayesian artificial neural network 'BNN' technique to model the dynamic behavior of the considered sensor, the system correlate the estimated values with the measured for the concretization of the proposed system we propose an FPGA (field programmable gate array) implementation. The monitoring system use the correlation. (authors)

On nonstationarity and antipersistency in global temperature series

Science.gov (United States)

KäRner, O.

2002-10-01

Statistical analysis is carried out for satellite-based global daily tropospheric and stratospheric temperature anomaly and solar irradiance data sets. Behavior of the series appears to be nonstationary with stationary daily increments. Estimating long-range dependence between the increments reveals a remarkable difference between the two temperature series. Global average tropospheric temperature anomaly behaves similarly to the solar irradiance anomaly. Their daily increments show antipersistency for scales longer than 2 months. The property points at a cumulative negative feedback in the Earth climate system governing the tropospheric variability during the last 22 years. The result emphasizes a dominating role of the solar irradiance variability in variations of the tropospheric temperature and gives no support to the theory of anthropogenic climate change. The global average stratospheric temperature anomaly proceeds like a 1-dim random walk at least up to 11 years, allowing good presentation by means of the autoregressive integrated moving average (ARIMA) models for monthly series.

Animal Telemetry Network (ATN)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — These data (updated daily) are from the Animal Telemetry Network (ATN) program. Begun as one of the field projects in the international Census of Marine Life, the...

The circadian rhythm of core body temperature (Part I: The use of modern telemetry systems to monitor core body temperature variability

Directory of Open Access Journals (Sweden)

Słomko Joanna

2016-06-01

Full Text Available The best known daily rhythms in humans include: the sleep-wake rhythm, the circadian core body temperature variability, daily fluctuations in arterial blood pressure and heartbeat frequency, and daily changes in hormone secretion: e.g. melatonin, cortisol, growth hormone, prolactin. The core body temperature in humans has a characteristic sinusoidal course, with the maximum value occurring between 3:00-5:00 pm and the minimum between 3:00-5:00 am. Analysis of literature indicates that the obtained results concerning core body temperature are to a large extent influenced by the type of method applied in the measurement. Depending on test protocols, we may apply various methodologies to measuring core body temperature. One of the newest methods of measuring internal and external body temperature consists in the utilisation of remote temperature sensors transmitting the obtained value via a radio signal. The advantages of this method includes the ability to perform: continuous core temperature measurement, observe dynamic changes in core body temperature occurring in circadian rhythm and the repeatability and credibility of the obtained results, which is presented in numerous scientific reports.

Daily Thermal Predictions of the AGR-1 Experiment with Gas Gaps Varying with Time

Energy Technology Data Exchange (ETDEWEB)

Grant Hawkes; James Sterbentz; John Maki; Binh Pham

2012-06-01

A new daily as-run thermal analysis was performed at the Idaho National Laboratory on the Advanced Gas Reactor (AGR) test experiment number one at the Advanced Test Reactor (ATR). This thermal analysis incorporates gas gaps changing with time during the irradiation experiment. The purpose of this analysis was to calculate the daily average temperatures of each compact to compare with experimental results. Post irradiation examination (PIE) measurements of the graphite holder and fuel compacts showed the gas gaps varying from the beginning of life. The control temperature gas gap and the fuel compact – graphite holder gas gaps were linearly changed from the original fabrication dimensions, to the end of irradiation measurements. A steady-state thermal analysis was performed for each daily calculation. These new thermal predictions more closely match the experimental data taken during the experiment than previous analyses. Results are presented comparing normalized compact average temperatures to normalized log(R/B) Kr-85m. The R/B term is the measured release rate divided by the predicted birth rate for the isotope Kr-85m. Correlations between these two normalized values are presented.

Ambient temperature and emergency room admissions for acute coronary syndrome in Taiwan

Science.gov (United States)

Liang, Wen-Miin; Liu, Wen-Pin; Chou, Sze-Yuan; Kuo, Hsien-Wen

2008-01-01

Acute coronary syndrome (ACS) is an important public health problem around the world. Since there is a considerable seasonal fluctuation in the incidence of ACS, climatic temperature may have an impact on the onset of this disease. The objective of this study was to assess the relationship between the average daily temperature, diurnal temperature range and emergency room (ER) admissions for ACS in an ER in Taichung City, Taiwan. A longitudinal study was conducted which assessed the correlation of the average daily temperature and the diurnal temperature range to ACS admissions to the ER of the city’s largest hospital. Daily ER admissions for ACS and ambient temperature were collected from 1 January 2000 to 31 March 2003. The Poisson regression model was used in the analysis after adjusting for the effects of holiday, season, and air pollutant concentrations. The results showed that there was a negative significant association between the average daily temperature and ER admissions for ACS. ACS admissions to the ER increased 30% to 70% when the average daily temperature was lower than 26.2°C. A positive association between the diurnal temperature range and ACS admissions was also noted. ACS admissions increased 15% when the diurnal temperature range was over 8.3°C. The data indicate that patients suffering from cardiovascular disease must be made aware of the increased risk posed by lower temperatures and larger changes in temperature. Hospitals and ERs should take into account the increased demand of specific facilities during colder weather and wider temperature variations.

Daily weather variables and affective disorder admissions to psychiatric hospitals

Science.gov (United States)

McWilliams, Stephen; Kinsella, Anthony; O'Callaghan, Eadbhard

2014-12-01

Numerous studies have reported that admission rates in patients with affective disorders are subject to seasonal variation. Notwithstanding, there has been limited evaluation of the degree to which changeable daily meteorological patterns influence affective disorder admission rates. A handful of small studies have alluded to a potential link between psychiatric admission rates and meteorological variables such as environmental temperature (heat waves in particular), wind direction and sunshine. We used the Kruskal-Wallis test, ARIMA and time-series regression analyses to examine whether daily meteorological variables—namely wind speed and direction, barometric pressure, rainfall, hours of sunshine, sunlight radiation and temperature—influence admission rates for mania and depression across 12 regions in Ireland over a 31-year period. Although we found some very weak but interesting trends for barometric pressure in relation to mania admissions, daily meteorological patterns did not appear to affect hospital admissions overall for mania or depression. Our results do not support the small number of papers to date that suggest a link between daily meteorological variables and affective disorder admissions. Further study is needed.

Using Wireless Sensor Networks to Achieve Intelligent Monitoring for High-Temperature Gas-Cooled Reactor

Directory of Open Access Journals (Sweden)

Jianghai Li

2017-01-01

Full Text Available High-temperature gas-cooled reactors (HTGR can incorporate wireless sensor network (WSN technology to improve safety and economic competitiveness. WSN has great potential in monitoring the equipment and processes within nuclear power plants (NPPs. This technology not only reduces the cost of regular monitoring but also enables intelligent monitoring. In intelligent monitoring, large sets of heterogeneous data collected by the WSN can be used to optimize the operation and maintenance of the HTGR. In this paper, WSN-based intelligent monitoring schemes that are specific for applications of HTGR are proposed. Three major concerns regarding wireless technology in HTGR are addressed: wireless devices interference, cybersecurity of wireless networks, and wireless standards selected for wireless platform. To process nonlinear and non-Gaussian data obtained by WSN for fault diagnosis, novel algorithms combining Kernel Entropy Component Analysis (KECA and support vector machine (SVM are developed.

Response surface and neural network based predictive models of cutting temperature in hard turning

Directory of Open Access Journals (Sweden)

Mozammel Mia

2016-11-01

Full Text Available The present study aimed to develop the predictive models of average tool-workpiece interface temperature in hard turning of AISI 1060 steels by coated carbide insert. The Response Surface Methodology (RSM and Artificial Neural Network (ANN were employed to predict the temperature in respect of cutting speed, feed rate and material hardness. The number and orientation of the experimental trials, conducted in both dry and high pressure coolant (HPC environments, were planned using full factorial design. The temperature was measured by using the tool-work thermocouple. In RSM model, two quadratic equations of temperature were derived from experimental data. The analysis of variance (ANOVA and mean absolute percentage error (MAPE were performed to suffice the adequacy of the models. In ANN model, 80% data were used to train and 20% data were employed for testing. Like RSM, herein, the error analysis was also conducted. The accuracy of the RSM and ANN model was found to be ⩾99%. The ANN models exhibit an error of ∼5% MAE for testing data. The regression coefficient was found to be greater than 99.9% for both dry and HPC. Both these models are acceptable, although the ANN model demonstrated a higher accuracy. These models, if employed, are expected to provide a better control of cutting temperature in turning of hardened steel.

Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products

International Nuclear Information System (INIS)

Qin, Jun; Chen, Zhuoqi; Yang, Kun; Liang, Shunlin; Tang, Wenjun

2011-01-01

Global solar radiation (GSR) is required in a large number of fields. Many parameterization schemes are developed to estimate it using routinely measured meteorological variables, since GSR is directly measured at a limited number of stations. Even so, meteorological stations are sparse, especially, in remote areas. Satellite signals (radiance at the top of atmosphere in most cases) can be used to estimate continuous GSR in space. However, many existing remote sensing products have a relatively coarse spatial resolution and these inversion algorithms are too complicated to be mastered by experts in other research fields. In this study, the artificial neural network (ANN) is utilized to build the mathematical relationship between measured monthly-mean daily GSR and several high-level remote sensing products available for the public, including Moderate Resolution Imaging Spectroradiometer (MODIS) monthly averaged land surface temperature (LST), the number of days in which the LST retrieval is performed in 1 month, MODIS enhanced vegetation index, Tropical Rainfall Measuring Mission satellite (TRMM) monthly precipitation. After training, GSR estimates from this ANN are verified against ground measurements at 12 radiation stations. Then, comparisons are performed among three GSR estimates, including the one presented in this study, a surface data-based estimate, and a remote sensing product by Japan Aerospace Exploration Agency (JAXA). Validation results indicate that the ANN-based method presented in this study can estimate monthly-mean daily GSR at a spatial resolution of about 5 km with high accuracy.

A spatial model for a stream networks of Citarik River with the environmental variables: potential of hydrogen (PH) and temperature

Science.gov (United States)

Bachrudin, A.; Mohamed, N. B.; Supian, S.; Sukono; Hidayat, Y.

2018-03-01

Application of existing geostatistical theory of stream networks provides a number of interesting and challenging problems. Most of statistical tools in the traditional geostatistics have been based on a Euclidean distance such as autocovariance functions, but for stream data is not permissible since it deals with a stream distance. To overcome this autocovariance developed a model based on the distance the flow with using convolution kernel approach (moving average construction). Spatial model for a stream networks is widely used to monitor environmental on a river networks. In a case study of a river in province of West Java, the objective of this paper is to analyze a capability of a predictive on two environmental variables, potential of hydrogen (PH) and temperature using ordinary kriging. Several the empirical results show: (1) The best fit of autocovariance functions for temperature and potential hydrogen (ph) of Citarik River is linear which also yields the smallest root mean squared prediction error (RMSPE), (2) the spatial correlation values between the locations on upstream and on downstream of Citarik river exhibit decreasingly

The association of air temperature with cardiac arrhythmias

Science.gov (United States)

Čulić, Viktor

2017-11-01

The body response to meteorological influences may activate pathophysiological mechanisms facilitating the occurrence of cardiac arrhythmias in susceptible patients. Putative underlying mechanisms include changes in systemic vascular resistance and blood pressure, as well as a network of proinflammatory and procoagulant processes. Such a chain reaction probably occurs within the time window of several hours, so use of daily average values of meteorological elements do not seem appropriate for investigation in this area. In addition, overall synoptic situation, and season-specific combinations of meteorological elements and air pollutant levels probably cause the overall effect rather than a single atmospheric element. Particularly strong interrelations have been described among wind speed, air pressure and temperature, relative air humidity, and suspended particulate matter. This may be the main reason why studies examining the association between temperature and ventricular arrhythmias have found linear positive, negative, J-shaped or no association. Further understanding of the pathophysiological adaptation to atmospheric environment may help in providing recommendations for protective measures during "bad" weather conditions in patients with cardiac arrhythmias.

Intrusion Detection in Networked Control Systems: From System Knowledge to Network Security

NARCIS (Netherlands)

Caselli, M.

2016-01-01

“Networked control system‿ (NCS) is an umbrella term encompassing a broad variety of infrastructures such as industrial control systems (ICSs) and building automation systems (BASs). Nowadays, all these infrastructures play an important role in several aspects of our daily life, from managing

Daily Rhythms in Mobile Telephone Communication.

Science.gov (United States)

Aledavood, Talayeh; López, Eduardo; Roberts, Sam G B; Reed-Tsochas, Felix; Moro, Esteban; Dunbar, Robin I M; Saramäki, Jari

2015-01-01

Circadian rhythms are known to be important drivers of human activity and the recent availability of electronic records of human behaviour has provided fine-grained data of temporal patterns of activity on a large scale. Further, questionnaire studies have identified important individual differences in circadian rhythms, with people broadly categorised into morning-like or evening-like individuals. However, little is known about the social aspects of these circadian rhythms, or how they vary across individuals. In this study we use a unique 18-month dataset that combines mobile phone calls and questionnaire data to examine individual differences in the daily rhythms of mobile phone activity. We demonstrate clear individual differences in daily patterns of phone calls, and show that these individual differences are persistent despite a high degree of turnover in the individuals' social networks. Further, women's calls were longer than men's calls, especially during the evening and at night, and these calls were typically focused on a small number of emotionally intense relationships. These results demonstrate that individual differences in circadian rhythms are not just related to broad patterns of morningness and eveningness, but have a strong social component, in directing phone calls to specific individuals at specific times of day.

A method of optimized neural network by L-M algorithm to transformer winding hot spot temperature forecasting

Science.gov (United States)

Wei, B. G.; Wu, X. Y.; Yao, Z. F.; Huang, H.

2017-11-01

Transformers are essential devices of the power system. The accurate computation of the highest temperature (HST) of a transformer’s windings is very significant, as for the HST is a fundamental parameter in controlling the load operation mode and influencing the life time of the insulation. Based on the analysis of the heat transfer processes and the thermal characteristics inside transformers, there is taken into consideration the influence of factors like the sunshine, external wind speed etc. on the oil-immersed transformers. Experimental data and the neural network are used for modeling and protesting of the HST, and furthermore, investigations are conducted on the optimization of the structure and algorithms of neutral network are conducted. Comparison is made between the measured values and calculated values by using the recommended algorithm of IEC60076 and by using the neural network algorithm proposed by the authors; comparison that shows that the value computed with the neural network algorithm approximates better the measured value than the value computed with the algorithm proposed by IEC60076.

Daily transactional and transformational leadership and daily employee engament

NARCIS (Netherlands)

Breevaart, K.; Bakker, A.B.; Hetland, Jorn; Demerouti, E.; Olsen, O.K.; Espevik, R.

2014-01-01

This diary study adds to the leadership literature by examining the daily influence of transformational leadership, contingent reward, and active management-by-exception (MBE active) on followers' daily work engagement. We compare the unique contribution of these leadership behaviours and focus on

Summer outdoor temperature and occupational heat-related illnesses in Quebec (Canada)

International Nuclear Information System (INIS)

Adam-Poupart, Ariane; Smargiassi, Audrey; Busque, Marc-Antoine; Duguay, Patrice; Fournier, Michel; Zayed, Joseph; Labrèche, France

2014-01-01

Background: Predicted rise in global mean temperature and intensification of heat waves associated with climate change present an increasing challenge for occupational health and safety. Although important scientific knowledge has been gathered on the health effects of heat, very few studies have focused on quantifying the association between outdoor heat and mortality or morbidity among workers. Objective: To quantify the association between occupational heat-related illnesses and exposure to summer outdoor temperatures. Methods: We modeled 259 heat-related illnesses compensated by the Workers' Compensation Board of Quebec between May and September, from 1998 to 2010, with maximum daily summer outdoor temperatures in 16 health regions of Quebec (Canada) using generalized linear models with negative binomial distributions, and estimated the pooled effect sizes for all regions combined, by sex and age groups, and for different time lags with random-effect models for meta-analyses. Results: The mean daily compensation count was 0.13 for all regions of Quebec combined. The relationship between daily counts of compensations and maximum daily temperatures was log-linear; the pooled incidence rate ratio (IRR) of daily heat-related compensations per 1 °C increase in daily maximum temperatures was 1.419 (95% CI 1.326 to 1.520). Associations were similar for men and women and by age groups. Increases in daily maximum temperatures at lags 1 and 2 and for two and three-day lag averages were also associated with increases in daily counts of compensations (IRRs of 1.206 to 1.471 for every 1 °C increase in temperature). Conclusion: This study is the first to quantify the association between occupational heat-related illnesses and exposure to summer temperatures in Canada. The model (risk function) developed in this study could be useful to improve the assessment of future impacts of predicted summer outdoor temperatures on workers and vulnerable groups, particularly in

Summer outdoor temperature and occupational heat-related illnesses in Quebec (Canada)

Energy Technology Data Exchange (ETDEWEB)

Adam-Poupart, Ariane [Department of Environmental and Occupational Health, School of Public Health, Université de Montréal, Montreal, QC (Canada); Smargiassi, Audrey [Department of Environmental and Occupational Health, School of Public Health, Université de Montréal, Montreal, QC (Canada); Institut national de santé publique du Québec (INSPQ), Montreal, QC (Canada); Busque, Marc-Antoine; Duguay, Patrice [Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), Montreal, QC (Canada); Fournier, Michel [Direction de santé publique, Agence de la santé et des services sociaux de Montréal, Montreal, QC (Canada); Zayed, Joseph [Department of Environmental and Occupational Health, School of Public Health, Université de Montréal, Montreal, QC (Canada); Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), Montreal, QC (Canada); Labrèche, France, E-mail: labreche.france@irsst.qc.ca [Department of Environmental and Occupational Health, School of Public Health, Université de Montréal, Montreal, QC (Canada); Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), Montreal, QC (Canada)

2014-10-15

Background: Predicted rise in global mean temperature and intensification of heat waves associated with climate change present an increasing challenge for occupational health and safety. Although important scientific knowledge has been gathered on the health effects of heat, very few studies have focused on quantifying the association between outdoor heat and mortality or morbidity among workers. Objective: To quantify the association between occupational heat-related illnesses and exposure to summer outdoor temperatures. Methods: We modeled 259 heat-related illnesses compensated by the Workers' Compensation Board of Quebec between May and September, from 1998 to 2010, with maximum daily summer outdoor temperatures in 16 health regions of Quebec (Canada) using generalized linear models with negative binomial distributions, and estimated the pooled effect sizes for all regions combined, by sex and age groups, and for different time lags with random-effect models for meta-analyses. Results: The mean daily compensation count was 0.13 for all regions of Quebec combined. The relationship between daily counts of compensations and maximum daily temperatures was log-linear; the pooled incidence rate ratio (IRR) of daily heat-related compensations per 1 °C increase in daily maximum temperatures was 1.419 (95% CI 1.326 to 1.520). Associations were similar for men and women and by age groups. Increases in daily maximum temperatures at lags 1 and 2 and for two and three-day lag averages were also associated with increases in daily counts of compensations (IRRs of 1.206 to 1.471 for every 1 °C increase in temperature). Conclusion: This study is the first to quantify the association between occupational heat-related illnesses and exposure to summer temperatures in Canada. The model (risk function) developed in this study could be useful to improve the assessment of future impacts of predicted summer outdoor temperatures on workers and vulnerable groups, particularly in

Differences between true mean temperatures and means calculated with four different approaches: a case study from three Croatian stations

Science.gov (United States)

Bonacci, Ognjen; Željković, Ivana

2018-01-01

Different countries use varied methods for daily mean temperature calculation. None of them assesses precisely the true daily mean temperature, which is defined as the integral of continuous temperature measurements in a day. Of special scientific as well as practical importance is to find out how temperatures calculated by different methods and approaches deviate from the true daily mean temperature. Five mean daily temperatures were calculated (T0, T1, T2, T3, T4) using five different equations. The mean of 24-h temperature observations during the calendar day is accepted to represent the true, daily mean T0. The differences Δ i between T0 and four other mean daily temperatures T1, T2, T3, and T4 were calculated and analysed. In the paper, analyses were done with hourly data measured in a period from 1 January 1999 to 31 December 2014 (149,016 h, 192 months and 16 years) at three Croatian meteorological stations. The stations are situated in distinct climatological areas: Zagreb Grič in a mild climate, Zavižan in the cold mountain region and Dubrovnik in the hot Mediterranean. Influence of fog on the temperature is analysed. Special attention is given to analyses of extreme (maximum and minimum) daily differences occurred at three analysed stations. Selection of the fixed local hours, which is in use for calculation of mean daily temperature, plays a crucial role in diminishing of bias from the true daily temperature.

Microorganism-regulated mechanisms of temperature effects on the performance of anaerobic digestion.

Science.gov (United States)

Lin, Qiang; He, Guihua; Rui, Junpeng; Fang, Xiaoyu; Tao, Yong; Li, Jiabao; Li, Xiangzhen

2016-06-03

Temperature is an important factor determining the performance and stability of the anaerobic digestion process. However, the microorganism-regulated mechanisms of temperature effects on the performance of anaerobic digestion systems remain further elusive. To address this issue, we investigated the changes in composition, diversity and activities of microbial communities under temperature gradient from 25 to 55 °C using 16S rRNA gene amplicon sequencing approach based on genomic DNA (refer to as "16S rDNA") and total RNA (refer to as "16S rRNA"). Microbial community structure and activities changed dramatically along the temperature gradient, which corresponded to the variations in digestion performance (e.g., daily CH4 production, total biogas production and volatile fatty acids concentration). The ratios of 16S rRNA to 16S rDNA of microbial taxa, as an indicator of the potentially relative activities in situ, and whole activities of microbial community assessed by the similarity between microbial community based on 16S rDNA and rRNA, varied strongly along the temperature gradient, reflecting different metabolic activities. The daily CH4 production increased with temperature from 25 to 50 °C and declined at 55 °C. Among all the examined microbial properties, the whole activities of microbial community and alpha-diversity indices of both microbial communities and potentially relative activities showed highest correlations to the performance. The whole activities of microbial community and alpha-diversity indices of both microbial communities and potentially relative activities were sensitive indicators for the performance of anaerobic digestion systems under temperature gradient, while beta-diversity could predict functional differences. Microorganism-regulated mechanisms of temperature effects on anaerobic digestion performance were likely realized through increasing alpha-diversity of both microbial communities and potentially relative activities to supply

Future of IoT Networks: A Survey

Directory of Open Access Journals (Sweden)

Suk Kyu Lee

2017-10-01

Full Text Available The introduction of mobile devices has changed our daily lives. They enable users to obtain information even in a nomadic environment and provide information without limitations. A decade after the introduction of this technology, we are now facing the next innovation that will change our daily lives. With the introduction of the Internet of Things (IoT, our communication ability will not be restricted to only mobile devices. Rather, it will expand to all things with which we coexist. Many studies have discussed IoT-related services and platforms. However, there are only limited discussions about the IoT network. In this paper, we will thoroughly analyze the technical details about the IoT network. Based on our survey of papers, we will provide insight about the future IoT network and the crucial components that will enable it.

People as sensors: mass media and local temperature influence climate change discussion on Twitter

Science.gov (United States)

Kirilenko, A.; Molodtsova, T.; Stepchenkova, S.

2014-12-01

We examined whether people living under significant temperature anomalies connect their sensory experiences to climate change and the role that media plays in this process. We used Twitter messages containing words "climate change" and "global warming" as the indicator of attention that public pays to the issue. Specifically, the goals were: (1) to investigate whether people immediately notice significant local weather anomalies and connect them to climate change and (2) to examine the role of mass media in this process. Over 2 million tweets were collected for a two-year period (2012 - 2013) and were assigned to 157 urban areas in the continental USA (Figure 1). Geographical locations of the tweets were identified with a geolocation resolving algorithm based the profile of the users. Daily number of tweets (tweeting rate) was computed for 157 conterminous USA urban areas and adjusted for data acquisition errors. The USHCN daily minimum and maximum temperatures were obtained for the station locations closest to the centers of the urban areas and the 1981-2010 30-year temperature mean and standard deviation were used as the climate normals. For the analysis, we computed the following indices for each day of 2012 - 2013 period: standardized temperature anomaly, absolute standardized temperature anomaly, and extreme cold and hot temperature anomalies for each urban zone. The extreme cold and hot temperature anomalies were then transformed into country-level values that represent the number of people living in extreme temperature conditions. The rate of tweeting on climate change was regressed on the time variables, number of climate change publications in the mass media, and temperature. In the majority of regression models, the mass media and temperature variables were significant at the pmedia acts as a mediator in the relationship between local weather and climate change discourse intensity. Our analysis of Twitter data confirmed that the public is able to

A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

Directory of Open Access Journals (Sweden)

Y. Tulunay

2008-12-01

Full Text Available Atmospheric processes are highly nonlinear. A small group at the METU in Ankara has been working on a fuzzy data driven generic model of nonlinear processes. The model developed is called the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M. The METU-FNN-M consists of a Fuzzy Inference System (METU-FIS, a data driven Neural Network module (METU-FNN of one hidden layer and several neurons, and a mapping module, which employs the Bezier Surface Mapping technique. In this paper, the percent cloud coverage (%CC and cloud top temperatures (CTT are forecast one month ahead of time at 96 grid locations. The probable influence of cosmic rays and sunspot numbers on cloudiness is considered by using the METU-FNN-M.

Daily snow depth measurements from 195 stations in the United States

Energy Technology Data Exchange (ETDEWEB)

Allison, L.J. [ed.] [Oak Ridge National Lab., TN (United States). Carbon Dioxide Information Analysis Center; Easterling, D.R.; Jamason, P.; Bowman, D.P.; Hughes, P.Y.; Mason, E.H. [National Oceanic and Atmospheric Administration, Asheville, NC (United States). National Climatic Data Center

1997-02-01

This document describes a database containing daily measurements of snow depth at 195 National Weather Service (NWS) first-order climatological stations in the United States. The data have been assembled and made available by the National Climatic Data Center (NCDC) in Asheville, North Carolina. The 195 stations encompass 388 unique sampling locations in 48 of the 50 states; no observations from Delaware or Hawaii are included in the database. Station selection criteria emphasized the quality and length of station records while seeking to provide a network with good geographic coverage. Snow depth at the 388 locations was measured once per day on ground open to the sky. The daily snow depth is the total depth of the snow on the ground at measurement time. The time period covered by the database is 1893--1992; however, not all station records encompass the complete period. While a station record ideally should contain daily data for at least the seven winter months (January through April and October through December), not all stations have complete records. Each logical record in the snow depth database contains one station`s daily data values for a period of one month, including data source, measurement, and quality flags.

Daily reservoir inflow forecasting combining QPF into ANNs model

Science.gov (United States)

Zhang, Jun; Cheng, Chun-Tian; Liao, Sheng-Li; Wu, Xin-Yu; Shen, Jian-Jian

2009-01-01

Daily reservoir inflow predictions with lead-times of several days are essential to the operational planning and scheduling of hydroelectric power system. The demand for quantitative precipitation forecasting (QPF) is increasing in hydropower operation with the dramatic advances in the numerical weather prediction (NWP) models. This paper presents a simple and an effective algorithm for daily reservoir inflow predictions which solicits the observed precipitation, forecasted precipitation from QPF as predictors and discharges in following 1 to 6 days as predicted targets for multilayer perceptron artificial neural networks (MLP-ANNs) modeling. An improved error back-propagation algorithm with self-adaptive learning rate and self-adaptive momentum coefficient is used to make the supervised training procedure more efficient in both time saving and search optimization. Several commonly used error measures are employed to evaluate the performance of the proposed model and the results, compared with that of ARIMA model, show that the proposed model is capable of obtaining satisfactory forecasting not only in goodness of fit but also in generalization. Furthermore, the presented algorithm is integrated into a practical software system which has been severed for daily inflow predictions with lead-times varying from 1 to 6 days of more than twenty reservoirs operated by the Fujian Province Grid Company, China.

[Relationship between daily mean temperature and emergency department visits for respiratory diseases: a time-series analysis].

Science.gov (United States)

Mo, Yun-zheng; Zheng, Ya-an; Tao, Hui; Xu, Mei-mei; Li, Guo-xing; Dong, Feng-ming; Liu, Jun-han; Pan, Xiao-chuan

2012-06-18

To quantitatively evaluate the influences of daily mean air temperature (DMT) on Emergency Department Visits (EDVs) for the respiratory diseases. The EDV data from medical records for respiratory diseases in Peking University Third Hospital between January 2004 and June 2009 were collected. The data of the air pollutants (SO(2), NO(2) and PM(10)) and meteorological factors at the same time periods were also collected from the local authorities of Beijing. Time-series analysis and generalized additive models (GAM) were used to explore the exposurrre-response relationship between DMT and EDVs for respiratory diseases. A total of 35 073 patients [males 14 707(41.93%,14 707/35 073), females 19 122(54.52%,19 122/35 073) and gender missing 1 244(3.55%, 1 244/35 073)] EDVs for respiratory diseases were included. The relationship between DMT and EDVs for the respiratory diseases was mainly of "V" shape, the optimum temperature(OT) was about 4 °C and the effect of DMT was significant with a 0-3 day lag structure for most of the models. When DMT≤OT, each 1°C decrease in DMT corresponded to 3.75% (95% CI of RR: 0.938 3-0.965 3), 3.10% (95% CI of RR:0.949 2-0.989 1), 4.09% (95% CI of RR:0.940 7-0.977 8) increase of EDVs for the overall, male, and female, respectively. When DMT>OT, the value caused by each increase in 1°C in DMT was 1.54% (95% CI of RR:1.006 6-1.024 3), 1.80% (95% CI of RR:1.005 3-1.030 9), and 1.51 (95% CI of RR:1.003 2- 1.027 2), respectively. The effect was statistically significant within the 0-3 day lag. When DMT≤OT, the effect was stronger for the older people, while the effect was strongest for the 45-59 years old people. The relationship between DMT and EDVs for respiratory diseases is mainly of "V" type, with an optimum temperature of 4 °C.Both DMT decrease when DMT≤OT and increase when DMT>OT correspond to different increase of EDVs for respiratory diseases. Low DMT has stronger effect than high DMT. Different age group and gender have

Spatial distribution of unidirectional trends in temperature and temperature extremes in Pakistan

Science.gov (United States)

Khan, Najeebullah; Shahid, Shamsuddin; Ismail, Tarmizi bin; Wang, Xiao-Jun

2018-06-01

Pakistan is one of the most vulnerable countries of the world to temperature extremes due to its predominant arid climate and geographic location in the fast temperature rising zone. Spatial distribution of the trends in annual and seasonal temperatures and temperature extremes over Pakistan has been assessed in this study. The gauge-based gridded daily temperature data of Berkeley Earth Surface Temperature (BEST) having a spatial resolution of 1° × 1° was used for the assessment of trends over the period 1960-2013 using modified Mann-Kendall test (MMK), which can discriminate the multi-decadal oscillatory variations from secular trends. The results show an increase in the annual average of daily maximum and minimum temperatures in 92 and 99% area of Pakistan respectively at 95% level of confidence. The annual temperature is increasing faster in southern high-temperature region compared to other parts of the country. The minimum temperature is rising faster (0.17-0.37 °C/decade) compared to maximum temperature (0.17-0.29 °C/decade) and therefore declination of diurnal temperature range (DTR) (- 0.15 to - 0.08 °C/decade) in some regions. The annual numbers of both hot and cold days are increasing in whole Pakistan except in the northern sub-Himalayan region. Heat waves are on the rise, especially in the hot Sindh plains and the Southern coastal region, while the cold waves are becoming lesser in the northern cold region. Obtained results contradict with the findings of previous studies on temperature trends, which indicate the need for reassessment of climatic trends in Pakistan using the MMK test to understand the anthropogenic impacts of climate change.

Equivalent electrical network model approach applied to a double acting low temperature differential Stirling engine

International Nuclear Information System (INIS)

Formosa, Fabien; Badel, Adrien; Lottin, Jacques

2014-01-01

Highlights: • An equivalent electrical network modeling of Stirling engine is proposed. • This model is applied to a membrane low temperate double acting Stirling engine. • The operating conditions (self-startup and steady state behavior) are defined. • An experimental engine is presented and tested. • The model is validated against experimental results. - Abstract: This work presents a network model to simulate the periodic behavior of a double acting free piston type Stirling engine. Each component of the engine is considered independently and its equivalent electrical circuit derived. When assembled in a global electrical network, a global model of the engine is established. Its steady behavior can be obtained by the analysis of the transfer function for one phase from the piston to the expansion chamber. It is then possible to simulate the dynamic (steady state stroke and operation frequency) as well as the thermodynamic performances (output power and efficiency) for given mean pressure, heat source and heat sink temperatures. The motion amplitude especially can be determined by the spring-mass properties of the moving parts and the main nonlinear effects which are taken into account in the model. The thermodynamic features of the model have then been validated using the classical isothermal Schmidt analysis for a given stroke. A three-phase low temperature differential double acting free membrane architecture has been built and tested. The experimental results are compared with the model and a satisfactory agreement is obtained. The stroke and operating frequency are predicted with less than 2% error whereas the output power discrepancy is of about 30%. Finally, some optimization routes are suggested to improve the design and maximize the performances aiming at waste heat recovery applications

Corporate Social Networking: Risks and Opportunities

OpenAIRE

Straumsheim, Jan Henrik Schou

2011-01-01

Social networks have seen an explosive growth over the last few years, with the most popular online services totaling over half a billion users. These networks have started permeating several aspects of our daily lives: for example by changing the ways we communicate with our friends and family, share media and organize events. Popular social networking websites like Facebook and Twitter now account for over half of the content shared on the web. Norwegian businesses are taking note, and are ...

Network attacks and defenses a hands-on approach

CERN Document Server

Trabelsi, Zouheir; Al Braiki, Arwa; Mathew, Sujith Samuel

2012-01-01

The attacks on computers and business networks are growing daily, and the need for security professionals who understand how malfeasants perform attacks and compromise networks is a growing requirement to counter the threat. Network security education generally lacks appropriate textbooks with detailed, hands-on exercises that include both offensive and defensive techniques. Using step-by-step processes to build and generate attacks using offensive techniques, Network Attacks and Defenses: A Hands-on Approach enables students to implement appropriate network security solutions within a laborat

Core body temperature in obesity.

Science.gov (United States)

Heikens, Marc J; Gorbach, Alexander M; Eden, Henry S; Savastano, David M; Chen, Kong Y; Skarulis, Monica C; Yanovski, Jack A

2011-05-01

A lower core body temperature set point has been suggested to be a factor that could potentially predispose humans to develop obesity. We tested the hypothesis that obese individuals have lower core temperatures than those in normal-weight individuals. In study 1, nonobese [body mass index (BMI; in kg/m(2)) temperature-sensing capsules, and we measured core temperatures continuously for 24 h. In study 2, normal-weight (BMI of 18-25) and obese subjects swallowed temperature-sensing capsules to measure core temperatures continuously for ≥48 h and kept activity logs. We constructed daily, 24-h core temperature profiles for analysis. Mean (±SE) daily core body temperature did not differ significantly between the 35 nonobese and 46 obese subjects (36.92 ± 0.03°C compared with 36.89 ± 0.03°C; P = 0.44). Core temperature 24-h profiles did not differ significantly between 11 normal-weight and 19 obese subjects (P = 0.274). Women had a mean core body temperature ≈0.23°C greater than that of men (36.99 ± 0.03°C compared with 36.76 ± 0.03°C; P body temperature. It may be necessary to study individuals with function-altering mutations in core temperature-regulating genes to determine whether differences in the core body temperature set point affect the regulation of human body weight. These trials were registered at clinicaltrials.gov as NCT00428987 and NCT00266500.

Chronic daily headaches

Directory of Open Access Journals (Sweden)

Fayyaz Ahmed

2012-01-01

Full Text Available Chronic Daily Headache is a descriptive term that includes disorders with headaches on more days than not and affects 4% of the general population. The condition has a debilitating effect on individuals and society through direct cost to healthcare and indirectly to the economy in general. To successfully manage chronic daily headache syndromes it is important to exclude secondary causes with comprehensive history and relevant investigations; identify risk factors that predict its development and recognise its sub-types to appropriately manage the condition. Chronic migraine, chronic tension-type headache, new daily persistent headache and medication overuse headache accounts for the vast majority of chronic daily headaches. The scope of this article is to review the primary headache disorders. Secondary headaches are not discussed except medication overuse headache that often accompanies primary headache disorders. The article critically reviews the literature on the current understanding of daily headache disorders focusing in particular on recent developments in the treatment of frequent headaches.

On the daily cycle of the climatological variables in the Municipality of Quibdo

International Nuclear Information System (INIS)

Pabon, Jose Daniel; Reiner Palomino Lemus; William Murillo Lopez

2005-01-01

We analyzed the daily cycle of the climatological variables (solar radiation, air temperature, atmospheric pressure, precipitation and wind) in Quibdo, (5.45 degrades N and 76.39 degrades W, 53m), department of Choco. The analysis is based on hourly data recorded during the period 2000-2004 at the meteorological and radiometric station of the technological university of Choco, Diego Luis Cordoba (UTCH). It is shown that the daily cycle of the climatological variables in the city of Quibdo is typical of the tropical areas and the particularities of the daily pattern of precipitation were found: most of the rains occur during the evening. The seasonal variation of the daily cycle was also established. Finally, an approximation was made to the analyses of the effect of extreme phases of inter annual climate variability associated to El Nino and la Nina

The global SMOS Level 3 daily soil moisture and brightness temperature maps

Directory of Open Access Journals (Sweden)

A. Al Bitar

2017-06-01

Full Text Available The objective of this paper is to present the multi-orbit (MO surface soil moisture (SM and angle-binned brightness temperature (TB products for the SMOS (Soil Moisture and Ocean Salinity mission based on a new multi-orbit algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des Données SMOS makes use of MO retrieval to enhance the robustness and quality of SM retrievals. The motivation of the approach is to make use of the longer temporal autocorrelation length of the vegetation optical depth (VOD compared to the corresponding SM autocorrelation in order to enhance the retrievals when an acquisition occurs at the border of the swath. The retrieval algorithm is implemented in a unique operational processor delivering multiple parameters (e.g. SM and VOD using multi-angular dual-polarisation TB from MO. A subsidiary angle-binned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive TB. The Level 3 SM V300 product is compared to the single-orbit (SO retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.5 days, which can be a limiting factor for applications like flood forecast but reasonable for drought monitoring and climate change studies. The SMOS L3 soil moisture and L3 brightness temperature products are delivered using an

A physically based model of global freshwater surface temperature

Science.gov (United States)

van Beek, Ludovicus P. H.; Eikelboom, Tessa; van Vliet, Michelle T. H.; Bierkens, Marc F. P.

2012-09-01

Temperature determines a range of physical properties of water and exerts a strong control on surface water biogeochemistry. Thus, in freshwater ecosystems the thermal regime directly affects the geographical distribution of aquatic species through their growth and metabolism and indirectly through their tolerance to parasites and diseases. Models used to predict surface water temperature range between physically based deterministic models and statistical approaches. Here we present the initial results of a physically based deterministic model of global freshwater surface temperature. The model adds a surface water energy balance to river discharge modeled by the global hydrological model PCR-GLOBWB. In addition to advection of energy from direct precipitation, runoff, and lateral exchange along the drainage network, energy is exchanged between the water body and the atmosphere by shortwave and longwave radiation and sensible and latent heat fluxes. Also included are ice formation and its effect on heat storage and river hydraulics. We use the coupled surface water and energy balance model to simulate global freshwater surface temperature at daily time steps with a spatial resolution of 0.5° on a regular grid for the period 1976-2000. We opt to parameterize the model with globally available data and apply it without calibration in order to preserve its physical basis with the outlook of evaluating the effects of atmospheric warming on freshwater surface temperature. We validate our simulation results with daily temperature data from rivers and lakes (U.S. Geological Survey (USGS), limited to the USA) and compare mean monthly temperatures with those recorded in the Global Environment Monitoring System (GEMS) data set. Results show that the model is able to capture the mean monthly surface temperature for the majority of the GEMS stations, while the interannual variability as derived from the USGS and NOAA data was captured reasonably well. Results are poorest for

Challenges in Smart Low-Temperature District Heating Development

DEFF Research Database (Denmark)

Li, Hongwei; Wang, Stephen Jia

2014-01-01

Previous research and development shows that low temperature district heating (LTDH) system is economic feasible for low energy buildings and buildings at sparse areas. Coupling with reduced network temperature and well-designed district heating (DH) networks, LTDH can reduce network heat loss by...

Modeling of solar radiation using remote sensing and artificial neural network in Turkey

International Nuclear Information System (INIS)

Senkal, Ozan

2010-01-01

Artificial neural networks (ANNs) were used to estimate solar radiation in Turkey (26-45 o E, 36-42 o N) using geographical and satellite-estimated data. In order to train the Generalized regression neural network (GRNN) geographical and satellite-estimated data for the period from January 2002 to December 2002 from 19 stations spread over Turkey were used in training (ten stations) and testing (nine stations) data. Latitude, longitude, altitude, surface emissivity for ε 4, surface emissivity for ε 5, and land surface temperature are used in the input layer of the network. Solar radiation is the output. Root Mean Square Error (RMSE) and correlation coefficient (R 2 ) between the estimated and measured values for monthly mean daily sum with ANN values have been found as 0.1630 MJ/m 2 and 95.34% (training stations), 0.3200 MJ/m 2 and 93.41% (testing stations), respectively. Since these results are good enough it was concluded that the developed GRNN tool can be used to predict the solar radiation in Turkey.

Validation of China-wide interpolated daily climate variables from 1960 to 2011

Science.gov (United States)

Yuan, Wenping; Xu, Bing; Chen, Zhuoqi; Xia, Jiangzhou; Xu, Wenfang; Chen, Yang; Wu, Xiaoxu; Fu, Yang

2015-02-01

Temporally and spatially continuous meteorological variables are increasingly in demand to support many different types of applications related to climate studies. Using measurements from 600 climate stations, a thin-plate spline method was applied to generate daily gridded climate datasets for mean air temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, atmospheric pressure, and precipitation over China for the period 1961-2011. A comprehensive evaluation of interpolated climate was conducted at 150 independent validation sites. The results showed superior performance for most of the estimated variables. Except for wind speed, determination coefficients ( R 2) varied from 0.65 to 0.90, and interpolations showed high consistency with observations. Most of the estimated climate variables showed relatively consistent accuracy among all seasons according to the root mean square error, R 2, and relative predictive error. The interpolated data correctly predicted the occurrence of daily precipitation at validation sites with an accuracy of 83 %. Moreover, the interpolation data successfully explained the interannual variability trend for the eight meteorological variables at most validation sites. Consistent interannual variability trends were observed at 66-95 % of the sites for the eight meteorological variables. Accuracy in distinguishing extreme weather events differed substantially among the meteorological variables. The interpolated data identified extreme events for the three temperature variables, relative humidity, and sunshine duration with an accuracy ranging from 63 to 77 %. However, for wind speed, air pressure, and precipitation, the interpolation model correctly identified only 41, 48, and 58 % of extreme events, respectively. The validation indicates that the interpolations can be applied with high confidence for the three temperatures variables, as well as relative humidity and sunshine duration based

Strong impacts of daily minimum temperature on the green-up date and summer greenness of the Tibetan Plateau.

Science.gov (United States)

Shen, Miaogen; Piao, Shilong; Chen, Xiaoqiu; An, Shuai; Fu, Yongshuo H; Wang, Shiping; Cong, Nan; Janssens, Ivan A

2016-09-01

Understanding vegetation responses to climate change on the Tibetan Plateau (TP) helps in elucidating the land-atmosphere energy exchange, which affects air mass movement over and around the TP. Although the TP is one of the world's most sensitive regions in terms of climatic warming, little is known about how the vegetation responds. Here, we focus on how spring phenology and summertime greenness respond to the asymmetric warming, that is, stronger warming during nighttime than during daytime. Using both in situ and satellite observations, we found that vegetation green-up date showed a stronger negative partial correlation with daily minimum temperature (Tmin ) than with maximum temperature (Tmax ) before the growing season ('preseason' henceforth). Summer vegetation greenness was strongly positively correlated with summer Tmin , but negatively with Tmax . A 1-K increase in preseason Tmin advanced green-up date by 4 days (P greenness by 3.6% relative to the mean greenness during 2000-2004 (P green-up date (P > 0.10) and higher summer Tmax even reduced greenness by 2.6% K(-1) (P greenness were probably due to the accompanying decline in water availability. The dominant enhancing effect of nighttime warming indicates that climatic warming will probably have stronger impact on TP ecosystems than on apparently similar Arctic ecosystems where vegetation is controlled mainly by Tmax . Our results are crucial for future improvements of dynamic vegetation models embedded in the Earth System Models which are being used to describe the behavior of the Asian monsoon. The results are significant because the state of the vegetation on the TP plays an important role in steering the monsoon. © 2016 John Wiley & Sons Ltd.

Estimating surface longwave radiative fluxes from satellites utilizing artificial neural networks

Science.gov (United States)

Nussbaumer, Eric A.; Pinker, Rachel T.

2012-04-01

A novel approach for calculating downwelling surface longwave (DSLW) radiation under all sky conditions is presented. The DSLW model (hereafter, DSLW/UMD v2) similarly to its predecessor, DSLW/UMD v1, is driven with a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 cloud parameters and information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim model. To compute the clear sky component of DSLW a two layer feed-forward artificial neural network with sigmoid hidden neurons and linear output neurons is implemented; it is trained with simulations derived from runs of the Rapid Radiative Transfer Model (RRTM). When computing the cloud contribution to DSLW, the cloud base temperature is estimated by using an independent artificial neural network approach of similar architecture as previously mentioned, and parameterizations. The cloud base temperature neural network is trained using spatially and temporally co-located MODIS and CloudSat Cloud Profiling Radar (CPR) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. Daily average estimates of DSLW from 2003 to 2009 are compared against ground measurements from the Baseline Surface Radiation Network (BSRN) giving an overall correlation coefficient of 0.98, root mean square error (rmse) of 15.84 W m-2, and a bias of -0.39 W m-2. This is an improvement over an earlier version of the model (DSLW/UMD v1) which for the same time period has an overall correlation coefficient 0.97 rmse of 17.27 W m-2, and bias of 0.73 W m-2.

Local warming: daily temperature change influences belief in global warming.

Science.gov (United States)

Li, Ye; Johnson, Eric J; Zaval, Lisa

2011-04-01

Although people are quite aware of global warming, their beliefs about it may be malleable; specifically, their beliefs may be constructed in response to questions about global warming. Beliefs may reflect irrelevant but salient information, such as the current day's temperature. This replacement of a more complex, less easily accessed judgment with a simple, more accessible one is known as attribute substitution. In three studies, we asked residents of the United States and Australia to report their opinions about global warming and whether the temperature on the day of the study was warmer or cooler than usual. Respondents who thought that day was warmer than usual believed more in and had greater concern about global warming than did respondents who thought that day was colder than usual. They also donated more money to a global-warming charity if they thought that day seemed warmer than usual. We used instrumental variable regression to rule out some alternative explanations.

Evaluation of different methods to estimate daily reference evapotranspiration in ungauged basins in Southern Brazil

Science.gov (United States)

Ribeiro Fontoura, Jessica; Allasia, Daniel; Herbstrith Froemming, Gabriel; Freitas Ferreira, Pedro; Tassi, Rutineia

2016-04-01

Evapotranspiration is a key process of hydrological cycle and a sole term that links land surface water balance and land surface energy balance. Due to the higher information requirements of the Penman-Monteith method and the existing data uncertainty, simplified empirical methods for calculating potential and actual evapotranspiration are widely used in hydrological models. This is especially important in Brazil, where the monitoring of meteorological data is precarious. In this study were compared different methods for estimating evapotranspiration for Rio Grande do Sul, the Southernmost State of Brazil, aiming to suggest alternatives to the recommended method (Penman-Monteith-FAO 56) for estimate daily reference evapotranspiration (ETo) when meteorological data is missing or not available. The input dataset included daily and hourly-observed data from conventional and automatic weather stations respectively maintained by the National Weather Institute of Brazil (INMET) from the period of 1 January 2007 to 31 January 2010. Dataset included maximum temperature (Tmax, °C), minimum temperature (Tmin, °C), mean relative humidity (%), wind speed at 2 m height (u2, m s-1), daily solar radiation (Rs, MJ m- 2) and atmospheric pressure (kPa) that were grouped at daily time-step. Was tested the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith method (PM) at its full form, against PM assuming missing several variables not normally available in Brazil in order to calculate daily reference ETo. Missing variables were estimated as suggested in FAO56 publication or from climatological means. Furthermore, PM was also compared against the following simplified empirical methods: Hargreaves-Samani, Priestley-Taylor, Mccloud, McGuiness-Bordne, Romanenko, Radiation-Temperature, Tanner-Pelton. The statistical analysis indicates that even if just Tmin and Tmax are available, it is better to use PM estimating missing variables from syntetic data than

Thermal performance curves under daily thermal fluctuation: A study in helmeted water toad tadpoles.

Science.gov (United States)

Bartheld, José L; Artacho, Paulina; Bacigalupe, Leonardo

2017-12-01

Most research in physiological ecology has focused on the effects of mean changes in temperature under the classic "hot vs cold" acclimation treatment; however, current evidence suggests that an increment in both the mean and variance of temperature could act synergistically to amplify the negative effects of global temperature increase and how it would affect fitness and performance-related traits in ectothermic organisms. We assessed the effects of acclimation to daily variance of temperature on thermal performance curves of swimming speed in helmeted water toad tadpoles (Calyptocephalella gayi). Acclimation treatments were 20°C ± 0.1 SD (constant) and 20°C ± 1.5 SD (fluctuating). We draw two key findings: first, tadpoles exposed to daily temperature fluctuation had reduced maximal performance (Z max ), and flattened thermal performance curves, thus supporting the "vertical shift or faster-slower" hypothesis, and suggesting that overall swimming performance would be lower through an examination of temperatures under more realistic and ecologically-relevant fluctuating regimens; second, there was significant interindividual variation in performance traits by means of significant repeatability estimates. Our present results suggest that the widespread use of constant acclimation temperatures in laboratory experiments to estimate thermal performance curves (TPCs) may lead to an overestimation of actual organismal performance. We encourage the use of temperature fluctuation acclimation treatments to better understand the variability of physiological traits, which predict ecological and evolutionary responses to global change. Copyright © 2017 Elsevier Ltd. All rights reserved.

Advances and Challenges in Convergent Communication Networks

DEFF Research Database (Denmark)

Toral-Cruz, Homero; Mihovska, Albena

2017-01-01

Welcome to this special issue of Wireless Personal Communications on Advances and Challenges in Convergent Communication Networks. The main purpose of this special issue is to present new progresses and challenges in convergent networks. Communication networks play an important role in our daily...... life because they allow communicating and sharing contents between heterogeneous nodes around the globe. The emergence of multiple network architectures and emerging technologies have resulted in new applications and services over a heterogeneous network. This heterogeneous network has undergone...... significant challenges in recent years, such as the evolution to a converged network with the capability to support multiple services, while maintaining a satisfactory level of QoE/QoS, security, efficiency and trust. The special issue on Advances and Challenges in Convergent Communication Networks...

Profiles of social networking sites users in the Netherlands

NARCIS (Netherlands)

Constantinides, Efthymios; Alarcón del Amo, Maria del Carmen; Lorenzo Romero, Carlota

2010-01-01

Online social networking has become a reality and integral part of the daily personal, social and business life. The extraordinary increase of the user numbers of Social Networking Sites (SNS) and the rampant creation of online communities presents businesses with many challenges and opportunities.

Mobile social networking an innovative approach

CERN Document Server

Zhang, Daqing

2014-01-01

The use of contextually aware, pervasive, distributed computing, and sensor networks to bridge the gap between the physical and online worlds is the basis of mobile social networking. This book shows how applications can be built to provide mobile social networking, the research issues that need to be solved to enable this vision, and how mobile social networking can be used to provide computational intelligence that will improve daily life. With contributions from the fields of sociology, computer science, human-computer interaction and design, this book demonstrates how mobile social networks can be inferred from users' physical interactions both with the environment and with others, as well as how users behave around them and how their behavior differs on mobile vs. traditional online social networks.

Daily variation of urban heat island effect and its correlations to urban greenery: A case study of Adelaide

Directory of Open Access Journals (Sweden)

Ali Soltani

2017-12-01

Full Text Available Urban structure and landscape cause an artificial temperature increase in cities, known as the urban heat island effect. The magnitude of such urban–rural temperature difference varies in daily and seasonal basis. Daily patterns of urban heat accumulation in Adelaide is under investigation. In this paper, East–West air temperature profile of Adelaide metropolitan area was mapped in 60 journeys alongside a straight cross route connecting Adelaide Hills to the West Beach under clear sky between 26 July and 15 August 2013. The most intense urban–rural temperature differences of 5.9 °C occurred during midnight in Adelaide. However, maximum urban heat variation occurred during the late afternoon when the near-surface urban heat fluctuates by 2 °C between the CBD East and Western Parklands. During summer heatwaves, the afternoon heat stress limits public life vibrancy in Adelaide. Increased urban greenery can facilitate resilience to heat by providing shadow and evaporative cooling. A better understanding of daily urban heat variations and the cooling effect of urban greenery assists urban policy making and public life management in the context of climate change.

Silicon microgyroscope temperature prediction and control system based on BP neural network and Fuzzy-PID control method

International Nuclear Information System (INIS)

Xia, Dunzhu; Kong, Lun; Hu, Yiwei; Ni, Peizhen

2015-01-01

We present a novel silicon microgyroscope (SMG) temperature prediction and control system in a narrow space. As the temperature of SMG is closely related to its drive mode frequency and driving voltage, a temperature prediction model can be established based on the BP neural network. The simulation results demonstrate that the established temperature prediction model can estimate the temperature in the range of −40 to 60 °C with an error of less than ±0.05 °C. Then, a temperature control system based on the combination of fuzzy logic controller and the increment PID control method is proposed. The simulation results prove that the Fuzzy-PID controller has a smaller steady state error, less rise time and better robustness than the PID controller. This is validated by experimental results that show the Fuzzy-PID control method can achieve high precision in keeping the SMG temperature stable at 55 °C with an error of less than 0.2 °C. The scale factor can be stabilized at 8.7 mV/°/s with a temperature coefficient of 33 ppm °C −1 . ZRO (zero rate output) instability is decreased from 1.10°/s (9.5 mV) to 0.08°/s (0.7 mV) when the temperature control system is implemented over an ambient temperature range of −40 to 60 °C. (paper)

ARIMA representation for daily solar irradiance and surface air temperature time series

Science.gov (United States)

Kärner, Olavi

2009-06-01

Autoregressive integrated moving average (ARIMA) models are used to compare long-range temporal variability of the total solar irradiance (TSI) at the top of the atmosphere (TOA) and surface air temperature series. The comparison shows that one and the same type of the model is applicable to represent the TSI and air temperature series. In terms of the model type surface air temperature imitates closely that for the TSI. This may mean that currently no other forcing to the climate system is capable to change the random walk type variability established by the varying activity of the rotating Sun. The result should inspire more detailed examination of the dependence of various climate series on short-range fluctuations of TSI.

The effects of disease-related symptoms on daily function in Wolfram Syndrome.

Science.gov (United States)

Doty, Tasha; Foster, Erin R; Marshall, Bess; Ranck, Samantha; Hershey, Tamara

2017-01-01

To investigate daily function among individuals with Wolfram Syndrome (WFS) and examine whether any limitations are related to disease-related symptoms. WFS ( n = 31), Type 1 diabetic (T1DM; n = 25), and healthy control (HC; n = 29) participants completed the Pediatric Quality of Life Questionnaire (PEDSQL) Self and Parent Report. PEDSQL domain scores were compared among these groups and between WFS patients with and without specific disease-related symptoms. Relationships between PEDSQL scores and symptom severity as assessed by the Wolfram Unified Rating Scale (WURS) Physical Scale were also examined. Across most domains, the WFS group had lower PEDSQL Self and Parent Report scores than the T1DM and HC groups. WFS participants with urinary, sleep, and temperature regulation problems had lower PEDSQL scores than those without. The WURS Physical Scale correlated with Self and Parent Report PEDSQL domains. WFS group Self and Parent Reports correlated with each other. The WFS group reported lower daily function compared to T1DM and HC groups. Within WFS, worse symptom severity and the specific symptoms of sleep, temperature regulation, and urinary problems were associated with poorer daily function. These findings provide rationale for an increased emphasis on identifying, treating and understanding these less well-known symptoms of WFS.

Gross anatomy of network security

Science.gov (United States)

Siu, Thomas J.

2002-01-01

Information security involves many branches of effort, including information assurance, host level security, physical security, and network security. Computer network security methods and implementations are given a top-down description to permit a medically focused audience to anchor this information to their daily practice. The depth of detail of network functionality and security measures, like that of the study of human anatomy, can be highly involved. Presented at the level of major gross anatomical systems, this paper will focus on network backbone implementation and perimeter defenses, then diagnostic tools, and finally the user practices (the human element). Physical security measures, though significant, have been defined as beyond the scope of this presentation.

Development of neural network driven fuzzy controller for outlet sodium temperature of DHX

International Nuclear Information System (INIS)

Okusa, Kyoichi; Endou, Akira; Yoshikawa, Shinji; Ozawa, Kenji

1996-01-01

Fuzzy controls are capable to exquisitely control non-linear dynamic systems in wide operating range, using linguistic description to define the control law. However the selection and the definition of the fuzzy rules and sets require a tedious trial and error process based on experience. As a method to overcome this limitation, a neural network driven fuzzy control (NDF), where the learning capability of the neural network (NN) is used to build the fuzzy rules and sets, is presented in this paper. In the NDF control the IF part of a fuzzy control is represented by a multilayer NN while the THEN part is represented by a series of multilayer NNs which calculate the desirable control action. In this work the usual stepwise variable reduction method, used for the selection of the input variable in the THEN part NN, is replaced with a learning algorithm with forgetting mechanism that realizes the automatic reduction of the variables and the tuning up of all the fuzzy control law i.e. the membership function. The NDF has been successfully applied to control the outlet sodium temperature of a dump heat exchanger (DHX) of a FBR plant

The global historical climatology network: Long-term monthly temperature, precipitation, and pressure data

International Nuclear Information System (INIS)

Vose, R.S.; Schmoyer, R.L.; Peterson, T.C.; Steurer, P.M.; Heim, R.R. Jr.; Karl, T.R.; Eischeid, J.K.

1992-01-01

Interest in global climate change has risen dramatically during the past several decades. In a similar fashion, the number of data sets available to study global change has also increased. Unfortunately, many different organizations and researchers have compiled these data sets, making it confusing and time consuming for individuals to acquire the most comprehensive data. In response to this rapid growth in the number of global data sets, DOE's Carbon Dioxide Information Analysis Center (CDIAC) and NOAA's National Climatic Data Center (NCDC) established the Global Historical Climatology Network (GHCN) project. The purpose of this project is to compile an improved data set of long-term monthly mean temperature, precipitation, sea level pressure, and station pressure for as dense a network of global stations as possible. Specifically, the GHCN project seeks to consolidate the numerous preexisting national-, regional-, and global-scale data sets into a single global data base; to subject the data to rigorous quality control; and to update, enhance, and distribute the data set at regular intervals. The purpose of this paper is to describe the compilation and contents of the GHCN data base (i.e., GHCN Version 1.0)

Physics in daily life

CERN Document Server

Hermans, Jo

2012-01-01

This book provides answers to everyday questions that any curious mind would ask, like : Why is water blue ? What makes ice so slippery ? How do we localize sound ? How do we keep our body temperature so nice and constant ? How do we survive the sauna at 90 C ? Why do large raindrops fall faster than small ones, and what exactly is their speed ? The answers are given in an accessible and playful way, and are illustrated with funny cartoons. In this book forty "Physics in Daily Life" columns, which appeared earlier in Europhysics News, are brought together in one inspiring volume. As well as being a source of enjoyment and satisfying insights for anyone with some physics background, it also serves as a very good teaching tool for science students. This booklet is a feast of erudition and humour.

The scheduling of tracking times for interplanetary spacecraft on the Deep Space Network

Science.gov (United States)

Webb, W. A.

1978-01-01

The Deep Space Network (DSN) is a network of tracking stations, located throughout the globe, used to track spacecraft for NASA's interplanetary missions. This paper describes a computer program, DSNTRAK, which provides an optimum daily tracking schedule for the DSN given the view periods at each station for a mission set of n spacecraft, where n is between 2 and 6. The objective function is specified in terms of relative total daily tracking time requirements between the n spacecraft. Linear programming is used to maximize the total daily tracking time and determine an optimal daily tracking schedule consistent with DSN station capabilities. DSNTRAK is used as part of a procedure to provide DSN load forecasting information for proposed future NASA mission sets.

Spatial and temporal multiyear sea ice distributions in the Arctic: A neural network analysis of SSM/I data, 1988-2001

Science.gov (United States)

Belchansky, G.I.; Douglas, D.C.; Alpatsky, I.V.; Platonov, Nikita G.

2004-01-01

Arctic multiyear sea ice concentration maps for January 1988-2001 were generated from SSM/I brightness temperatures (19H, 19V, and 37V) using modified multiple layer perceptron neural networks. Learning data for the neural networks were extracted from ice maps derived from Okean and ERS satellite imagery to capitalize on the stability of active radar multiyear ice signatures. Evaluations of three learning algorithms and several topologies indicated that networks constructed with error back propagation learning and 3-20-1 topology produced the most consistent and physically plausible results. Operational neural networks were developed specifically with January learning data, and then used to estimate daily multiyear ice concentrations from daily-averaged SSM/I brightness temperatures during January. Monthly mean maps were produced for analysis by averaging the respective daily estimates. The 14-year series of January multiyear ice distributions revealed dense and persistent cover in the central Arctic surrounded by expansive regions of highly fluctuating interannual cover. Estimates of total multiyear ice area by the neural network were intermediate to those of other passive microwave algorithms, but annual fluctuations and trends were similar among all algorithms. When compared to Radarsat estimates of multiyear ice concentration in the Beaufort and Chukchi Seas (1997-1999), average discrepancies were small (0.9-2.5%) and spatial coherency was reasonable, indicating the neural network's Okean and ERS learning data facilitated passive microwave inversion that emulated backscatter signatures. During 1988-2001, total January multiyear ice area declined at a significant linear rate of -54.3 x 103 km2/yr-1 (-1.4%/yr-1). The most persistent and extensive decline in multiyear ice concentration (-3.3%/yr-1) occurred in the southern Beaufort and Chukchi Seas. In autumn 1996, a large multiyear ice recruitment of over 106 km2 (mostly in the Siberian Arctic) fully replenished

The influences of ambient temperature and crude protein levels on performance and serum biochemical parameters in broilers.

Science.gov (United States)

Liu, Q W; Feng, J H; Chao, Z; Chen, Y; Wei, L M; Wang, F; Sun, R P; Zhang, M H

2016-04-01

This study was undertaken to investigate the effects of ambient temperature, crude protein levels and their interaction on performance and serum biochemical parameters of broiler chickens. A total of 216 Arbor Acre broiler chickens (108 males and 108 females) were used in a 2 × 3 factorial arrangement and randomly reared at two temperatures (normal temperature: 23 °C; daily cyclic high temperature: 28-32 °C) and fed on three diets with different crude protein levels (153.3, 183.3 or 213.3 g/kg, with constant essential amino acids) from 28 to 42 days of age. Daily cyclic high ambient temperature decreased final body weight, average daily weight gain, average daily feed intake and serum total protein contents (p chickens was interacted by daily cyclic high ambient temperature and dietary crude protein levels (p = 0.003). These results indicated that daily cyclic high ambient temperature had a great effect on performance and serum biochemical parameters in broiler chickens, whereas dietary crude protein levels affected them partially. Journal of Animal Physiology and Animal Nutrition © 2015 Blackwell Verlag GmbH.

Responses of Yield Characteristics to High Temperature During Flowering Stage in Hybrid Rice Guodao 6

Directory of Open Access Journals (Sweden)

Guan-fu FU

2008-09-01

Full Text Available By sowing at different dates during 2005 and 2006 both in paddy fields and greenhouse, a super hybrid rice combination Guodao 6 and a conventional hybrid rice combination Xieyou 46 (as control were used to analyze the differences in heat injury index, seed setting rate, grain yield and its components. Guodao 6 showed more stable yield and spikelet fertility, and lower heat injury index than Xieyou 46. Further studies indicated that the spikelet sterility is positively correlated with the average daily temperature and the maximum daily temperature, with the coefficients of 0.8604 and 0.9850 (P<0.05 respectively in Guodao 6. The effect of high temperature injury on seed setting caused by maximum daily temperature was lower than that by average daily temperature during the grain filling stage.

Identification of tipping elements of the Indian Summer Monsoon using climate network approach

Science.gov (United States)

Stolbova, Veronika; Surovyatkina, Elena; Kurths, Jurgen

2015-04-01

Spatial and temporal variability of the rainfall is a vital question for more than one billion of people inhabiting the Indian subcontinent. Indian Summer Monsoon (ISM) rainfall is crucial for India's economy, social welfare, and environment and large efforts are being put into predicting the Indian Summer Monsoon. For predictability of the ISM, it is crucial to identify tipping elements - regions over the Indian subcontinent which play a key role in the spatial organization of the Indian monsoon system. Here, we use climate network approach for identification of such tipping elements of the ISM. First, we build climate networks of the extreme rainfall, surface air temperature and pressure over the Indian subcontinent for pre-monsoon, monsoon and post-monsoon seasons. We construct network of extreme rainfall event using observational satellite data from 1998 to 2012 from the Tropical Rainfall Measuring Mission (TRMM 3B42V7) and reanalysis gridded daily rainfall data for a time period of 57 years (1951-2007) (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE). For the network of surface air temperature and pressure fields, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). Second, we filter out data by coarse-graining the network through network measures, and identify tipping regions of the ISM. Finally, we compare obtained results of the network analysis with surface wind fields and show that occurrence of the tipping elements is mostly caused by monsoonal wind circulation, migration of the Intertropical Convergence Zone (ITCZ) and Westerlies. We conclude that climate network approach enables to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to identify tipping regions of the ISM. Obtained tipping elements deserve a

Managing Daily Life

Science.gov (United States)

... Duchenne / Managing Daily Life Print Email Managing Daily Life Environmental accessibility As the person with Duchenne starts ... such as wider doorways and ramps, can make life easier once the person with Duchenne cannot climb ...

Potential of a smartphone as a stress-free sensor of daily human behaviour.

Science.gov (United States)

Mimura, Koki; Kishino, Hirohisa; Karino, Genta; Nitta, Etsuko; Senoo, Aya; Ikegami, Kentaro; Kunikata, Tetsuya; Yamanouchi, Hideo; Nakamura, Shun; Sato, Kan; Koshiba, Mamiko

2015-01-01

Behaviour is one of the most powerful objective signals that connotes psychological functions regulated by neuronal network systems. This study searched for simple behaviours using smartphone sensors with three axes for measuring acceleration, angular speed and direction. We used quantitative analytic methodology of pattern recognition for work contexts, individual workers and seasonal effects in our own longitudinally recorded data. Our 13 laboratory members were involved in the care of common marmosets and domestic chicks, which lived in separate rooms. They attached a smartphone to their front waist-belts during feeding and cleaning in five care tasks. Behavioural characteristics such as speed, acceleration and azimuth, pitch, and roll angles were monitored. Afterwards, participants noted subjective scores of warmth sensation and work efficiency. The multivariate time series behavioral data were characterized by the subjective scores and environmental factors such as room temperature, season, and humidity, using the linear mixed model. In contrast to high-precision but stress-inducing sensors, the mobile sensors measuring daily behaviours allowed us to quantify the effects of the psychological states and environmental factors on the behavioural traits. Copyright © 2014 Elsevier B.V. All rights reserved.

Daily and seasonal temperatures in the burrows of African rodent ...

African Journals Online (AJOL)

1987-03-02

Mar 2, 1987 ... temperature extremes, containing a nest and a bolt-hole. (Jarvis & Sale 1971; Davies & Jarvis 1986; Lovegrove &. Painting 1987). In the burrow systems of Cryptomys damarensis we have found nests as deep as 2,5 m below ground. Dissimilarities in ..... The live-trap in which it was confined was shaded ...

Extreme summer temperatures in Iberia: health impacts and associated synoptic conditions

OpenAIRE

Garcia-Herrera , R.; Diaz , J.; Trigo , R. M.; Hernández , E.

2005-01-01

This paper examines the effect of extreme summer temperatures on daily mortality in two large cities of Iberia: Lisbon (Portugal) and Madrid (Spain). Daily mortality and meteorological variables are analysed using the same methodology based on Box-Jenkins models. Results reveal that in both cases there is a triggering effect on mortality when maximum daily temperature exceeds a given threshold (34°C in Lisbon and 36°C in Madrid). The impact of most intense heat events is very ...

Self-critical perfectionism, daily stress, and disclosure of daily emotional events.

Science.gov (United States)

Richardson, Clarissa M E; Rice, Kenneth G

2015-10-01

Although disclosure of stressful events can alleviate distress, self-critical perfectionism may pose an especially strong impediment to disclosure during stress, likely contributing to poorer psychological well-being. In the current study, after completing a measure of self-critical perfectionism (the Discrepancy subscale of the Almost Perfect Scale--Revised; Slaney, Rice, Mobley, Trippi, & Ashby, 2001), 396 undergraduates completed measures of stress and disclosure at the end of each day for 1 week. Consistent with hypotheses and previous research, multilevel modeling results indicated significant intraindividual coupling of daily stress and daily disclosure where disclosure was more likely when experiencing high stress than low stress. As hypothesized, Discrepancy moderated the relationship between daily stress and daily disclosure. Individuals higher in self-critical perfectionism (Discrepancy) were less likely to engage in disclosure under high stress, when disclosure is often most beneficial, than those with lower Discrepancy scores. These results have implications for understanding the role of stress and coping in the daily lives of self-critical perfectionists. (c) 2015 APA, all rights reserved).

Selective Narrowing of Social Networks across Adulthood is Associated with Improved Emotional Experience in Daily Life

Science.gov (United States)

English, Tammy; Carstensen, Laura L.

2014-01-01

Past research has documented age differences in the size and composition of social networks that suggest that networks grow smaller with age and include an increasingly greater proportion of well-known social partners. According to socioemotional selectivity theory, such changes in social network composition serve an antecedent emotion regulatory…

Spatial patterns of stream temperatures and electric conductivity in a mesoscale catchment

Science.gov (United States)

Lieder, Ernestine; Weiler, Markus; Blume, Theresa

2017-04-01

Stream temperature and electric conductivity (EC) are both relatively easily measured and can provide valuable information on runoff generation processes and catchment storage.This study investigates the spatial variability of stream temperature and EC in a mesoscale basin. We focus on the mesoscale (sub-catchments and reach scale), and long term (seasonal / annual) stream temperature and EC patterns. Our study basin is the Attert catchment in Luxembourg (288km2), which contains multiple sub-catchments of different geology, topography and land use patterns. We installed 90 stream temperature and EC sensors at sites across the basin in summer 2015. The collected data is complemented by land use and discharge data and an extensive climate data set. Thermal sensitivity was calculated as the slope of daily air temperature-water-temperature regression line and describes the sensitivity of stream temperature to long term environmental change. Amplitude sensitivity was calculated as slope of the daily air and water temperature amplitude regression and describes the short term warming capacity of the stream. We found that groups with similar long term thermal and EC patterns are strongly related to different geological units. The sandstone reaches show the coldest temperatures and lowest annual thermal sensitivity to air temperature. The slate reaches are characterized by comparably low EC and high daily temperature amplitudes and amplitude sensitivity. Furthermore, mean annual temperatures and thermal sensitivities increase exponentially with drainage area, which can be attributed to the accumulation of heat throughout the system. On the reach scale, daily stream temperature fluctuations or sensitivities were strongly influenced by land cover distribution, stream shading and runoff volume. Daily thermal sensitivities were low for headwater streams; peaked for intermediate reaches in the middle of the catchment and then decreased again further downstream with increasing