scholarly journals The effect of winter weather on timber truck tare weights

Silva Fennica ◽  
2020 ◽  
Vol 54 (4) ◽  
Author(s):  
Perttu Anttila ◽  
Tuomas Nummelin ◽  
Kari Väätäinen ◽  
Juha Laitila
Keyword(s):  
Regression Models ◽  
Capacity Utilization ◽  
Weather Data ◽  
Weather Variables ◽  
Monthly Basis ◽  
Maximum Loss ◽  
Payload Capacity ◽  
One Year ◽  
Increasing Precipitation ◽  
Very High

In wintertime, the payload capacity of a timber truck is reduced by snow that accumulates on the structures of the truck. The aim of this study was to quantify the potential payload loss due to snow and winter accessories and to predict the loss with weather variables. Tare weights of eight timber trucks were collected at mill receptions in Finland over a one-year period. Monthly and annual loss of potential payload was estimated using the tare measurements in summer months as a reference. Each load was also connected with weather data at the location and time of delivery and payload loss explained by the weather data with the aid of regression models. The maximum loss of payload varied between 1560 kg and 3100 kg. On a monthly basis, the highest losses occurred in January, when the median values varied between 760 kg and 2180 kg. Over the year, the payload loss ranged between the trucks from 0.5% to 1.5% (from 1.9% and 5.1% in January) of the total number of loads in the study. Payload loss was found to increase with decreasing temperature, increasing relative humidity and increasing precipitation. Although the average payload loss was not very high, the biggest losses occur just during the season of highest capacity utilization. Big differences were also found in the tare weights between the trucks. The results of the study give incentive to develop truck and trailer structures that reduce the adherence of snow.

Evaluation of the Hargreaves-Samani Method for Estimating Reference Evapotranspiration with Ground and Gridded Weather Data Sources

2019 ◽  
Vol 35 (5) ◽  
pp. 823-835
Author(s):  
Rogério De Souza Nóia Júnior ◽  
Clyde William Fraisse ◽  
Vinicius Andrei Cerbaro ◽  
Mauricio Alex Z. Karrei ◽  
Noemi Guindin
Keyword(s):  
United States ◽  
Production Systems ◽  
Reference Evapotranspiration ◽  
The United States ◽  
Weather Data ◽  
Data Sets ◽  
Weather Variables ◽  
Method Performance ◽  
Main Input ◽  
One Year

Abstract. Methods of estimating evapotranspiration require weather variables as their main input data. Thus, the lack of full weather data sets is one of the main challenges for evaluating and mitigating the effects of climate variability and climate change on agricultural production systems. The Hargreaves-Samani method (HS) is one of the ways to estimate reference evapotranspiration (ETo) when only temperature observations are available, which is a common situation in many agricultural enterprises. Another possible option for regions not served by weather stations is the use of gridded weather data (GWD). Based on that, the main objective of this study was to evaluate the performance of the HS method to estimate ETo in different regions of the United States, as well as to assess the suitability of two gridded weather data (PRISM and NOAA-RTMA) sources to estimate ETo by comparing the results obtained to ETo estimated by the Penman-Monteith (FAO-PM) method, which is the recommended methodology by FAO Irrigation and Drainage Paper 56 when all weather variables are available. Weather observations were obtained for 17 locations across the United States representing regions with subtropical humid and semi-arid continental climates, considering the period of one year (2017). These observations were used to estimate daily ETo with the HS and Penman-Monteith methods. Our results demonstrated that the HS method performance varied according to the location and month of the year. Due to the high relative humidity (RH) during the winter, and high air temperature (Ta) during the summer, the locations selected in the state of Florida, presented the worst performance. The HS method performed well in many other locations such as Froid – MT. Also, the estimation of ETo by HS method and by using PRISM and NOAA-RTMA gridded weather databases showed a good agreement with the ETo estimated by FAO-PM based on weather station observations. Keywords: Penman-Monteith, PRISM, RTMA, Water Management.

scholarly journals Modeling and Syndromic Surveillance for Estimating Weather-Induced Heat-Related Illness

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Alexander G. Perry ◽  
Michael J. Korenberg ◽  
Geoffrey G. Hall ◽  
Kieran M. Moore
Keyword(s):  
Time Series ◽  
Syndromic Surveillance ◽  
Regression Models ◽  
Weather Data ◽  
Hospital Data ◽  
Weather Variables ◽  
Syndromic Surveillance System ◽  
Fast Orthogonal Search ◽  
Ed Visits ◽  
Highly Correlated

This paper compares syndromic surveillance and predictive weather-based models for estimating emergency department (ED) visits for Heat-Related Illness (HRI). A retrospective time-series analysis of weather station observations and ICD-coded HRI ED visits to ten hospitals in south eastern Ontario, Canada, was performed from April 2003 to December 2008 using hospital data from the National Ambulatory Care Reporting System (NACRS) database, ED patient chief complaint data collected by a syndromic surveillance system, and weather data from Environment Canada. Poisson regression and Fast Orthogonal Search (FOS), a nonlinear time series modeling technique, were used to construct models for the expected number of HRI ED visits using weather predictor variables (temperature, humidity, and wind speed). Estimates of HRI visits from regression models using both weather variables and visit counts captured by syndromic surveillance as predictors were slightly more highly correlated with NACRS HRI ED visits than either regression models using only weather predictors or syndromic surveillance counts.

General practitioners and clinical pharmacology

1989 ◽  
Vol 4 (4) ◽  
pp. 241-244
Author(s):  
P. Lemoine
Keyword(s):  
Clinical Pharmacology ◽  
General Practitioners ◽  
Field Studies ◽  
Discussion Group ◽  
Group Method ◽  
Study Group ◽  
Test Drug ◽  
Monthly Basis ◽  
One Year ◽  
The One

SummaryIt is difficult to undertake field studies with non marketed psychotropic drugs because of two apparently contradictory conditions : on the one hand, the methodology has to be rigorously controlled, and on the other hand, such studies have to be carried out in their future environment by general practitioners (GPs). Bearing in mind the lack of training and experience regarding this kind of approach, the author adopted a discussion group method according to the techniques developed by M. Balint. The study group comprised five GPs, a clinical pharmacology expert and a doctor from the pharmaceutical laboratory which had developed the test drug. These persons met on a monthly basis over a one year period. In the present paper, the author indicates the benefits of such a methodology, based on six years’ experience and several trials, with special emphasis placed on the pedagogical aspects.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Predicting emergency department visits in a large teaching hospital

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nathan Singh Erkamp ◽  
Dirk Hendrikus van Dalen ◽  
Esther de Vries
Keyword(s):  
Emergency Department ◽  
The Netherlands ◽  
Teaching Hospital ◽  
Time Trend ◽  
Prediction Models ◽  
Emergency Department Visits ◽  
Weather Data ◽  
Negative Consequences ◽  
Weather Variables ◽  
Ed Visits

Abstract Background Emergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors. Methods Data on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit. Results The number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%). Conclusions Because of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

scholarly journals Incidence of bloodstream infection among patients on hemodialysis by central venous catheter

2010 ◽  
Vol 18 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Cibele Grothe ◽  
Angélica Gonçalves da Silva Belasco ◽  
Ana Rita de Cássia Bittencourt ◽  
Lucila Amaral Carneiro Vianna ◽  
Ricardo de Castro Cintra Sesso ◽  
...  
Keyword(s):  
Central Venous Catheter ◽  
Bloodstream Infection ◽  
Severe Infection ◽  
Venous Catheter ◽  
Central Venous Catheter Insertion ◽  
Central Venous ◽  
Double Lumen ◽  
One Year ◽  
Very High

This study evaluated the incidence and risk factors of bloodstream infection (BSI) among patients with a double-lumen central venous catheter (CVC) for hemodialysis (HD) and identified the microorganisms isolated from the bloodstream. A follow-up included all patients (n=156) who underwent hemodialysis by double-lumen CVC at the Federal University of São Paulo - UNIFESP, Brazil, over a one-year period. From the group of patients, 94 presented BSI, of whom 39 had positive cultures at the central venous catheter insertion location. Of the 128 microorganisms isolated from the bloodstream, 53 were S. aureus, 30 were methicillin-sensitive and 23 were methicillin-resistant. Complications related to BSI included 35 cases of septicemia and 27 cases of endocarditis, of which 15 cases progressed to death. The incidence of BSI among these patients was shown to be very high, and this BSI progressed rapidly to the condition of severe infection with a high mortality rate.

Decision Support System Based on Fuzzy Logic for Assessment of Expected Corporate Income Performance

2021 ◽  
pp. 2150009
Author(s):  
Arthur Yosef ◽  
Eli Shnaider ◽  
Rimona Palas ◽  
Amos Baranes
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Operating Income ◽  
Explanatory Variables ◽  
Section Model ◽  
Evaluation Mechanism ◽  
One Year ◽  
High Degree ◽  
Decision Support Method ◽  
Very High

This study presents a decision-support method to estimate the next year performance of corporate Operating Income Margin (OIM). It is based on a unique combination of cross-section model and the rules-based evaluation mechanism. The estimate is done in terms of broad categories, and not precise numerical values. The model is constructed as follows: its dependent variable (OIM) is one year ahead vs. the corresponding explanatory variables. This structure of the model allows us to view explanatory variables as reflecting financial potential of corporations. The evaluation component consists of a set of rules designed to identify the companies whose “potential” clearly points to an opportunity to invest. For the method presented here to succeed, it is necessary to utilize a highly reliable modeling method, even if it is “Fuzzy”. We apply Soft Regression (SR), which is a Soft Computing modeling tool based on Fuzzy Logic, and utilize all available proxy variables by creating intervals of values. Advantages of utilizing SR, and the intervals’-based modeling are extensively discussed. Modeling results for five consecutive years are consistent and stable, thus indicating high degree of reliability. Testing indicates very high success rate for the stock market related domain, the lowest being 87.9%.

scholarly journals Analysis of reproductive biology and spawning season of the pink ear emperor Lethrinus lentjan, from marine ecosystem

2020 ◽  
Vol 37 ◽  
pp. 1-10
Author(s):  
Elsayed M. Younis ◽  
Nasser A. Al-Asgah ◽  
Abdel-Wahab A. Abdel-Warith ◽  
Mohamed H. Gabr ◽  
Fozi S. Shamlol
Keyword(s):  
Fish Species ◽  
Gonadosomatic Index ◽  
Marine Ecosystem ◽  
Local Population ◽  
Spawning Season ◽  
Monthly Basis ◽  
Population Sampling ◽  
Maturation Rate ◽  
Maturation Index ◽  
One Year

A total of 593 samples of Lethrinus lentjan (Lacepede, 1802) were collected from the Red Sea, Jeddah, Saudi Arabia, to study their productive biology and spawning season of the local population. Sampling was carried out on a monthly basis for a period of one year. The monthly sex ratios indicated that females were dominant throughout the study period, with an overall male:female sex ratio of 1:7.98, although males were larger than females. The highest monthly performance maturation index (PMI), as well as the male and female gonadosomatic index (GSI) and ovarian maturation rate (OMR) were observed in February and March. Histological examination of the gonads confirmed the process of sexual transformation in this fish species, wherein individuals mature first as female, and then change sex to male (protogynous hermaphroditism). Histological sections also showed that the sexual maturation of males of L. lenjtan comprised three main stages, while the sexual development of females could be classified into four main stages. Extended spawning in the form of batches released during different months throughout the year were recorded for this fish species, with the main spawning season in February and March, and an additional, shorter spawning season in September.

scholarly journals Evaluation of determinants of the serological response to the split-inactivated influenza vaccine

2021 ◽  
Author(s):  
Shaohuan Wu ◽  
Ted M. Ross ◽  
Michael A. Carlock ◽  
Elodie Ghedin ◽  
Hyungwon Choi ◽  
...  
Keyword(s):  
Influenza Vaccine ◽  
Regression Models ◽  
Seasonal Influenza ◽  
Vaccine Dose ◽  
Serological Response ◽  
Linear Regression Models ◽  
Negative Effects ◽  
Categorical Response ◽  
One Year ◽  
The Impact

AbstractThe seasonal influenza vaccine is only effective in half of the vaccinated population. To identify determinants of vaccine efficacy, we used data from >1,300 vaccination events to predict the response to vaccination measured as seroconversion as well as hemagglutination inhibition (HAI) levels one year after. We evaluated the predictive capabilities of age, body mass index (BMI), sex, race, comorbidities, prevaccination history, and baseline HAI titers, as well as vaccination month and vaccine dose in multiple linear regression models. The models predicted the categorical response for >75% of the cases in all subsets with one exception. Prior vaccination, baseline titer level, and age were the strongest determinants on seroconversion, all of which had negative effects. Further, we identified a gender effect in older participants, and an effect of vaccination month. BMI played a surprisingly small role, likely due to its correlation with age. Comorbidities, vaccine dose, and race had negligible effects. Our models can generate a new seroconversion score that is corrected for the impact of these factors which can facilitate future biomarker identification.

Study of some parameters of electrocardiography of skiers-riders

2021 ◽  
pp. 68-75
Author(s):  
Sergey V. Saykin ◽  
Valery N. Yakovlev
Keyword(s):  
Physical Activity ◽  
Cardiovascular System ◽  
Motor Skills ◽  
The Body ◽  
Young Athletes ◽  
Functional Diagnosis ◽  
Electrocardiographic Examination ◽  
One Year ◽  
The One ◽  
Very High

Very high results, the achievement of which is possible only with long systematic train-ing with the use of large and sometimes excessive physical activity characterizes modern sports. The preparation process from beginner to master of sports takes an average of 5–10 years. During this time, the athlete must develop and improve special physical and mental qualities, as well as master certain motor skills specific to this sport. Therefore, children's and youth's organisms of athletes are subject to increased loads, especially in classes that develop endurance. But not always physical activity contributes to the strengthening of the body, sometimes excessive loads, especially with the wrong approach, lead to complications from the cardiovascular system, in particular, to changes in heart rate. Therefore, the issue of adapting the functions of the heart of young athletes to muscle loads becomes increasingly important. The purpose of the work was to study the activities of the cardiovascular system of skiers-riders in the preparatory period of the one-year cycle. Currently, various methods of functional diagnosis of the cardiovascular system are used. We considered the results obtained during electrocardiographic examination of skiers-riders. We investigated electrical activity of the heart and presented model characteristics according to the considered indicators.

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