scholarly journals New structure-based models for the prediction of normal boiling point temperature of ternary azeotropes

2021 ◽  
pp. 35-35
Author(s):  
Zohreh Faramarzi ◽  
Fatemeh Abbasitabar ◽  
Jalali Jahromi ◽  
Maziar Noei
Keyword(s):  
Statistical Tests ◽  
Model Performance ◽  
Absolute Error ◽  
Qsar Model ◽  
Qspr Model ◽  
Boiling Points ◽  
Azeotropic Mixtures ◽  
Qspr Modeling ◽  
Mathematical Equations ◽  
The Individual

Recently, development of the QSPR models for mixtures has received much attention. The QSPR modeling of mixtures requires the use of appropriate mixture descriptors. In this study, 12 mathematical equations were considered to compute mixture descriptors from the individual components for the prediction of normal boiling points of 78 ternary azeotropic mixtures. Multiple linear regression (MLR) was employed to build all QSPR models. Memorized_ACO algorithm was employed for subset variable selection. An ensemble model was also constructed using averaging strategy to improve the predictability of the final QSAR model. The models have been validated by a test set comprised of 24 ternary azeotropes and by different statistical tests. The resulted ensemble QSPR model had R2training, R2test, and q2 of 0.97, 0.95, and 0.96, respectively. Mean absolute error (MAE) as a good indicator of model performance were found to be 3.06 and 3.52 for training and testing sets, respectively.

scholarly journals Seeking Consensus: A New Approach

2010 ◽  
Vol 138 (12) ◽  
pp. 4402-4415 ◽  
Author(s):  
Paul J. Roebber
Keyword(s):  
Minimum Temperature ◽  
Forecast Accuracy ◽  
Cost Savings ◽  
Absolute Error ◽  
Electricity Production ◽  
Forecast Errors ◽  
Consensus Forecasts ◽  
Temperature Forecast ◽  
Model Output Statistics ◽  
The Individual

Abstract Simulated evolution is used to generate consensus forecasts of next-day minimum temperature for a site in Ohio. The evolved forecast algorithm logic is interpretable in terms of physics that might be accounted for by experienced forecasters, but the logic of the individual algorithms that form the consensus is unique. As a result, evolved program consensus forecasts produce substantial increases in forecast accuracy relative to forecast benchmarks such as model output statistics (MOS) and those from the National Weather Service (NWS). The best consensus produces a mean absolute error (MAE) of 2.98°F on an independent test dataset, representing a 27% improvement relative to MOS. These results translate to potential annual cost savings for electricity production in the state of Ohio of the order of $2 million relative to the NWS forecasts. Perfect forecasts provide nearly $6 million in additional annual electricity production cost savings relative to the evolved program consensus. The frequency of outlier events (forecast busts) falls from 24% using NWS to 16% using the evolved program consensus. Information on when busts are most likely can be provided through a logistic regression equation with two variables: forecast wind speed and the deviation of the NWS minimum temperature forecast from persistence. A forecast of a bust is 4 times more likely to be correct than wrong, suggesting some utility in anticipating the most egregious forecast errors. Discussion concerning the probabilistic applications of evolved programs, the application of this technique to other forecast problems, and the relevance of these findings to the future role of human forecasting is provided.

scholarly journals When Resources Are Scarce - Feasibility of Emergency Ventilation of Two Patients With One Ventilator

2020 ◽  
Author(s):  
Peter C. Reinacher ◽  
Thomas E. Schlaepfer ◽  
Martin A. Schick ◽  
Jürgen Beck ◽  
Hartmut Bürkle ◽  
...  
Keyword(s):  
Tidal Volume ◽  
Respiratory System ◽  
Breathing Circuit ◽  
Absolute Error ◽  
Physical Models ◽  
Respiratory System Mechanics ◽  
Emergency Ventilation ◽  
Artificial Lungs ◽  
Circuit Components ◽  
The Individual

AbstractA potential shortage of intensive care ventilators has led to the idea to ventilate more than one patient with a single ventilator. Besides other problems, this is associated with the lack of knowledge concerning distribution of tidal volume and the patients’ individual respiratory system mechanics.In this study we used two simple hand-manufactured adaptors to connect physical models of two adult respiratory systems to one ventilator. The artificial lungs were ventilated in the pressure-controlled mode and we investigated if disconnecting one lung from the ventilation circuit for several breaths would allow to determine reliably the other lung’s tidal volume and compliance.Compliances and volumes were measured both with the ventilator and external sensors corresponded well. However, tidal volumes measured via the ventilator were smaller compared to the tidal volumes measured via the external sensors with an absolute error of 5.3 ± 2.5%. The tidal volumes of the individual artificial lungs were distributed in proportion to the compliances and did not differ relevantly when both artificial lungs were connected to when one was disconnected.We conclude that in case of emergency, ventilation of two patients with one ventilator requires two simple hand-crafted tubes as adaptors and available standard breathing circuit components. In such a setting, respiratory system mechanics and tidal volume of each individual patient can be reliably measured during short term clamping of the tracheal tube of the respective other patient.

scholarly journals Analysis of Nurse Characteristics with Work Motivation in Wamena Hospital

2021 ◽  
Vol 5 (1) ◽  
pp. 35-41
Author(s):  
Moses Lamere ◽  
Ratna Wardani
Keyword(s):  
Marital Status ◽  
Work Motivation ◽  
Statistical Tests ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Hospital Inpatient ◽  
Meaningful Relationship ◽  
Gender Education ◽  
The Individual ◽  
Relationship Of

Lately there has been attention to work dissatisfaction and declining quality. Most people find it difficult to motivate themselves, therefore it is not surprising that motivating others is a difficult and complicated task. Motivation indicates the process of movement, including the encouraging situation that arises within the individual, the behavior caused by the situation and the purpose or end of the movement or action. The purpose of this study was to analyze the relationship of nurse characteristics with work motivation in Wamena Hospital inpatient room. This type of research is quantitative with an observational approach. The population is the entire nursing plant in Wamena Hospital with 186 people. Large samples were taken as many as 64 respondents. Sampling techniques used in this study is a simple random sampling technique. Based on the results of the study it is known that there is a meaningful relationship between age, working period and position with work motivation. While there are several factors that are not related to work motivation, namely gender, education and marital status. The statistical test used is path analysis. Statistical tests can be concluded that there is a meaningful relationship to variables as follows: the characteristics of nurses that affect work motivation are age, years of service and position, while the characteristics of gender, education and marital status do not have a significant relationship with work motivation. Based on the results of the study is expected to improve the ability and insight of nursing and motivation of work so that their productivity does not decrease. For example, give remuneration, promotion and periodic reward for outstanding nurses will encourage the motivation of nurse work to develop.

scholarly journals Estimation of Leaf Area of Jackfruit Through Non-destructive Method

2019 ◽  
Vol 11 (6) ◽  
pp. 77
Author(s):  
Vinicius de Souza Oliveira ◽  
Leonardo Raasch Hell ◽  
Karina Tiemi Hassuda dos Santos ◽  
Hugo Rebonato Pelegrini ◽  
Jéssica Sayuri Hassuda Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Linear Equations ◽  
Absolute Error ◽  
Order Model ◽  
Sanitary Condition ◽  
Federal Institute ◽  
Independent Variable ◽  
Mathematical Equations ◽  
The Mean ◽  
Non Destructive

The objective of this study was to determine mathematical equations that estimate the leaf area of jackfruit (Artocarpus heterophyllus) in an easy and non-destructive way based on linear dimensions. In this way, 300 leaves of different sizes and in good sanitary condition of adult plants were collected at the Federal Institute of Espírito Santo, Campus Itapina, located in Colatina, municipality north of the State of Espírito Santo, Brazil. Were measured The length (L) along the midrib and the maximum leaf width (W), observed leaf area (OLA), besides the product of the multiplication of length with width (LW), length with length (LL) and width with width (WW). The models of linear equations of first degree, quadratic and power and their respective R2 were adjusted using OLA as dependent variable in function of L, W and LW, LL and WW as independent variable. The data were validated and the estimated leaf area (ELA) was obtained. The means of ELA and OLA were compared by Student’s t test (5% probability) and were evaluated by the mean absolute error (MAE) and root mean square error (RMSE) criteria. The choice of the best model was based on non-significant comparative values of ELA and OLA, in addition to the closest values of zero of EAM and RQME. The jackfruit leaf area estimate can be determined quickly, accurately and non-destructively by the linear first-order model with LW as the independent variable by equation ELA = 1.07451 + 0.71181(LW).

scholarly journals Semi-Siamese U-Net for separation of lung and heart bioimpedance images: A simulation study of thorax EIT

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246071
Author(s):  
Yen-Fen Ko ◽  
Kuo-Sheng Cheng
Keyword(s):  
Mean Absolute Error ◽  
Lung Ventilation ◽  
Semantic Segmentation ◽  
Absolute Error ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Linear Transformations ◽  
Impedance Tomography ◽  
Performance Improvements ◽  
The Individual

Electrical impedance tomography (EIT) is widely used for bedside monitoring of lung ventilation status. Its goal is to reflect the internal conductivity changes and estimate the electrical properties of the tissues in the thorax. However, poor spatial resolution affects EIT image reconstruction to the extent that the heart and lung-related impedance images are barely distinguishable. Several studies have attempted to tackle this problem, and approaches based on decomposition of EIT images using linear transformations have been developed, and recently, U-Net has become a prominent architecture for semantic segmentation. In this paper, we propose a novel semi-Siamese U-Net specifically tailored for EIT application. It is based on the state-of-the-art U-Net, whose structure is modified and extended, forming shared encoder with parallel decoders and has multi-task weighted losses added to adapt to the individual separation tasks. The trained semi-Siamese U-Net model was evaluated with a test dataset, and the results were compared with those of the classical U-Net in terms of Dice similarity coefficient and mean absolute error. Results showed that compared with the classical U-Net, semi-Siamese U-Net exhibited performance improvements of 11.37% and 3.2% in Dice similarity coefficient, and 3.16% and 5.54% in mean absolute error, in terms of heart and lung-impedance image separation, respectively.

scholarly journals Hubungan Kepuasan Kerja dengan Kinerja Perawat di Rumah Sakit

2021 ◽  
Vol 21 (3) ◽  
pp. 1135
Author(s):  
Raja Syafrizal ◽  
Yulihasri Yulihasri ◽  
Zifriyanthi Minanda Putri
Keyword(s):  
Job Satisfaction ◽  
Work Performance ◽  
Statistical Tests ◽  
Sampling Technique ◽  
Cross Sectional Study ◽  
P Value ◽  
Chi Square ◽  
Cross Sectional ◽  
Low Performance ◽  
The Individual

The performance of nurses can be seen from several cases that occur in hospitals. The incidence of falls in patients, nosocomial infections, inadequate documentation is the result of nurses' low performance. Factors that affect the performance of nurses in hospitals are job satisfaction. So this study aims to describe the relationship between job satisfaction and nurse performance. This study used a cross-sectional study design with a sample of 85 nurses at Arosuka Hospital using proportional sampling technique. The research instrument used a job satisfaction survey questionnaire and the Individual Work Performance quasi (IWPQ) and statistical tests used frequency distribution and chi-square tests. The results showed that the majority of nurses' job satisfaction was in the satisfied category as much as 56.5% and the majority of nurses' performance in the high category was 51.8%. Then obtained a significant relationship between job satisfaction and nurse performance with a p-value of 0.000. So it is expected that hospitals can pay attention to aspects of nurse job satisfaction in making policies

Forecasting Of Covid-19 Cases Using Machine Learning Approach

2021 ◽  
Vol 17 ◽  
Author(s):  
Sachin Kumar ◽  
Karan Veer
Keyword(s):  
Machine Learning ◽  
Regression Model ◽  
Model Performance ◽  
Real Data ◽  
Absolute Error ◽  
Viral Disease ◽  
Support Vector ◽  
Family Welfare ◽  
Accuracy Score ◽  
Learning Approaches

Aims: The objective of this research is to predict the covid-19 cases in India based on the machine learning approaches. Background: Covid-19, a respiratory disease caused by one of the coronavirus family members, has led to a pandemic situation worldwide in 2020. This virus was detected firstly in Wuhan city of China in December 2019. This viral disease has taken less than three months to spread across the globe. Objective: In this paper, we proposed a regression model based on the Support vector machine (SVM) to forecast the number of deaths, the number of recovered cases, and total confirmed cases for the next 30 days. Method: For prediction, the data is collected from Github and the ministry of India's health and family welfare from March 14, 2020, to December 3, 2020. The model has been designed in Python 3.6 in Anaconda to forecast the forecasting value of corona trends until September 21, 2020. The proposed methodology is based on the prediction of values using SVM based regression model with polynomial, linear, rbf kernel. The dataset has been divided into train and test datasets with 40% and 60% test size and verified with real data. The model performance parameters are evaluated as a mean square error, mean absolute error, and percentage accuracy. Results and Conclusion: The results show that the polynomial model has obtained 95 % above accuracy score, linear scored above 90%, and rbf scored above 85% in predicting cumulative death, conformed cases, and recovered cases.

scholarly journals Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1786
Author(s):  
Linh T. T. Ho ◽  
Laurent Dubus ◽  
Matteo De Felice ◽  
Alberto Troccoli
Keyword(s):  
Power Generation ◽  
Random Forest ◽  
Model Performance ◽  
Absolute Error ◽  
Random Forest Model ◽  
Low Carbon ◽  
Climate Data ◽  
Hydro Power ◽  
Forest Model ◽  
Continental Scale

Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments.

scholarly journals A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

scholarly journals Ideal point error for model assessment in data-driven river flow forecasting

2012 ◽  
Vol 16 (8) ◽  
pp. 3049-3060 ◽  
Author(s):  
C. W. Dawson ◽  
N. J. Mount ◽  
R. J. Abrahart ◽  
A. Y. Shamseldin
Keyword(s):  
River Flow ◽  
Ideal Point ◽  
Model Performance ◽  
Model Assessment ◽  
River Flow Forecasting ◽  
The Individual ◽  
Measure Of Performance ◽  
Individual Metrics ◽  
Naive Model ◽  
The Ideal

Abstract. When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consistency in evaluation, making studies undertaken by different authors or performed at different locations difficult to compare in a meaningful manner. Moreover, even within individual reported case studies, substantial contradictions are found to occur between one measure of performance and another. In this paper we examine the ideal point error (IPE) metric – a recently introduced measure of model performance that integrates a number of recognised metrics in a logical way. Having a single, integrated measure of performance is appealing as it should permit more straightforward model inter-comparisons. However, this is reliant on a transferrable standardisation of the individual metrics that are combined to form the IPE. This paper examines one potential option for standardisation: the use of naive model benchmarking.

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