0433 Targeting Light Sensitivity Parameters to Optimize Circadian Phase Predictions

Abstract Introduction Existing models of the human circadian clock accurately predict phase at group-level but not at individual-level. Interindividual variability in light sensitivity is not currently accounted for in these models and may be a practical approach to improving individual-level predictions. Using the gold-standard predictive model, we (i) identified whether varying light sensitivity parameters produces meaningful changes in predicted phase in field conditions; and (ii) tested whether optimizing parameters can significantly improve accuracy of circadian phase prediction. Methods Healthy participants (n=12, 7 women, aged 18-26) underwent continuous light and activity monitoring for 3 weeks (Actiwatch Spectrum). Salivary dim light melatonin onset (DLMO) was measured each week. A model of the human circadian clock and its response to light was used to predict the three weekly DLMO times using the individual’s light data. A sensitivity analysis was performed varying three model parameters within physiological ranges: (i) amplitude of the light response [p]; (ii) advance vs. delay bias of the light response [K]; and (iii) intrinsic circadian period [tau]. These parameters were then fitted using least squares estimation to obtain optimal predictions of DLMO for each individual. Accuracy was compared between optimized parameters and default parameters. Results The default model predicted DLMO with mean absolute error of 1.02h. Sensitivity analysis showed the average range of variation in predicted DLMOs across participants was 0.65h for p, 4.28h for K and 3.26h for tau. Fitting parameters independently, we found mean absolute error of 0.85h for p, 0.71h for K and 0.75h for tau. Fitting p and K together reduced mean absolute error to 0.57h. Conclusion Light sensitivity parameters capture similar or greater variability in phase as intrinsic circadian period, indicating they are a viable option for individualising circadian phase predictions. Future prospective work is needed using measures of light sensitivity to validate this approach. Support N/A

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The Role of Light Sensitivity and Intrinsic Circadian Period in Predicting Individual Circadian Timing

Journal of Biological Rhythms ◽

10.1177/0748730420962598 ◽

2020 ◽

Vol 35 (6) ◽

pp. 628-640 ◽

Cited By ~ 1

Author(s):

Julia E. Stone ◽

Elise M. McGlashan ◽

Nina Quin ◽

Kayan Skinner ◽

Jessica J. Stephenson ◽

...

Keyword(s):

Sensitivity Analysis ◽

Circadian Clock ◽

Response Curve ◽

Mean Absolute Error ◽

Absolute Error ◽

Light Sensitivity ◽

Circadian Period ◽

Interindividual Differences ◽

Circadian Timing ◽

The Mean

There is large interindividual variability in circadian timing, which is underestimated by mathematical models of the circadian clock. Interindividual differences in timing have traditionally been modeled by changing the intrinsic circadian period, but recent findings reveal an additional potential source of variability: large interindividual differences in light sensitivity. Using an established model of the human circadian clock with real-world light recordings, we investigated whether changes in light sensitivity parameters or intrinsic circadian period could capture variability in circadian timing between and within individuals. Healthy participants ( n = 12, aged 18-26 years) underwent continuous light monitoring for 3 weeks (Actiwatch Spectrum). Salivary dim-light melatonin onset (DLMO) was measured each week. Using the recorded light patterns, a sensitivity analysis for predicted DLMO times was performed, varying 3 model parameters within physiological ranges: (1) a parameter determining the steepness of the dose-response curve to light ( p), (2) a parameter determining the shape of the phase-response curve to light ( K), and (3) the intrinsic circadian period ( tau). These parameters were then fitted to obtain optimal predictions of the three DLMO times for each individual. The sensitivity analysis showed that the range of variation in the average predicted DLMO times across participants was 0.65 h for p, 4.28 h for K, and 3.26 h for tau. The default model predicted the DLMO times with a mean absolute error of 1.02 h, whereas fitting all 3 parameters reduced the mean absolute error to 0.28 h. Fitting the parameters independently, we found mean absolute errors of 0.83 h for p, 0.53 h for K, and 0.42 h for tau. Fitting p and K together reduced the mean absolute error to 0.44 h. Light sensitivity parameters captured similar variability in phase compared with intrinsic circadian period, indicating they are viable targets for individualizing circadian phase predictions. Future prospective work is needed that uses measures of light sensitivity to validate this approach.

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Explanation Plus Prediction—The Logical Focus of Project Management Research

Project Management Journal ◽

10.1177/8756972821999945 ◽

2021 ◽

pp. 875697282199994

Author(s):

Joseph F. Hair ◽

Marko Sarstedt

Keyword(s):

Project Management ◽

Statistical Models ◽

Predictive Power ◽

Mean Absolute Error ◽

Explanatory Power ◽

Absolute Error ◽

Model Parameters ◽

Mean Square ◽

Management Research ◽

The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

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A Maximum Entropy Production Evaporation – Transpiration Product for Australia

10.5194/essd-2019-70 ◽

2019 ◽

Author(s):

Olanrewaju Abiodun ◽

Okke Batelaan ◽

Huade Guan ◽

Jingfeng Wang

Keyword(s):

Entropy Production ◽

Maximum Entropy ◽

Mean Absolute Error ◽

Absolute Error ◽

Production Model ◽

Model Parameters ◽

Drought Period ◽

Maximum Entropy Production ◽

Millennium Drought ◽

Mean Square Errors

Abstract. The aim of this research is to develop evaporation and transpiration products for Australia based on the maximum entropy production model (MEP). We introduce a method into the MEP algorithm of estimating the required model parameters over the entire Australia through the use of pedotransfer function, soil properties and remotely sensed soil moisture data. Our algorithm calculates the evaporation and transpiration over Australia on daily timescales at the 5 km2 resolution for 2003–2013. The MEP evapotranspiration (ET) estimates are validated using observed ET data from 20 Eddy Covariance (EC) flux towers across 8 land cover types in Australia. We also compare the MEP ET at the EC flux towers with two other ET products over Australia; MOD16 and AWRA-L products. The MEP model outperforms the MOD16 and AWRA-L across the 20 EC flux sites, with average root mean square errors (RMSE), 8.21, 9.87 and 9.22 mm/8 days respectively. The average mean absolute error (MAE) for the MEP, MOD16 and AWRA-L are 6.21, 7.29 and 6.52 mm/8 days, the average correlations are 0.64, 0.57 and 0.61, respectively. The percentage Bias of the MEP ET was within 20 % of the observed ET at 12 of the 20 EC flux sites while the MOD16 and AWRA-L ET were within 20 % of the observed ET at 4 and 10 sites respectively. Our analysis shows that evaporation and transpiration contribute 38 % and 62 %, respectively, to the total ET across the study period which includes a significant part of the “millennium drought” period (2003–2009) in Australia. The data (Abiodun et al., 2019) is available at https://doi.org/10.25901/5ce795d313db8.

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A functional context for heterogeneity of the circadian clock in cells

PLoS Biology ◽

10.1371/journal.pbio.3000927 ◽

2020 ◽

Vol 18 (10) ◽

pp. e3000927

Author(s):

Martha Merrow ◽

Mary Harrington

Keyword(s):

Circadian Clock ◽

Building Blocks ◽

Circadian Period ◽

Top Down ◽

Bottom Up ◽

Stochastic Generation ◽

Circadian Phase ◽

Functional Context ◽

Definition Of

Characterization of circadian systems at the organism level—a top-down approach—has led to definition of unifying properties, a hallmark of the science of chronobiology. The next challenge is to use a bottom-up approach to show how the molecular workings of the cellular circadian clock work as building blocks of those properties. We review new studies, including a recently published PLOS Biology paper by Nikhil and colleagues, that show how programmed but also stochastic generation of variation in cellular circadian period explain important adaptive features of entrained circadian phase.

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Parameterization dependence of the hydrological cycle in a general circulation model of intermediate complexity

10.5194/egusphere-egu21-1328 ◽

2021 ◽

Author(s):

Oliver Mehling ◽

Elisa Ziegler ◽

Heather Andres ◽

Martin Werner ◽

Kira Rehfeld

Keyword(s):

General Circulation Model ◽

General Circulation ◽

Hydrological Cycle ◽

Mean Absolute Error ◽

Circulation Model ◽

Absolute Error ◽

Bayesian Optimization ◽

Model Parameters ◽

Climate Projections ◽

Intermediate Complexity

The global hydrological cycle is of crucial importance for life on Earth. Hence, it is a focus of both future climate projections and paleoclimate modeling. The latter typically requires long integrations or large ensembles of simulations, and therefore models of reduced complexity are needed to reduce the computational cost. Here, we study the hydrological cycle of the the Planet Simulator (PlaSim) [1], a general circulation model (GCM) of intermediate complexity, which includes evaporation, precipitation, soil hydrology, and river advection.Using published parameter configurations for T21 resolution [2, 3], PlaSim strongly underestimates precipitation in the mid-latitudes as well as global atmospheric water compared to ERA5 reanalysis data [4]. However, the tuning of PlaSim has been limited to optimizing atmospheric temperatures and net radiative fluxes so far [3].Here, we present a different approach by tuning the model&#8217;s atmospheric energy balance and water budget simultaneously. We argue for the use of the globally averaged mean absolute error (MAE) for 2 m temperature, net radiation, and evaporation in the objective function. To select relevant model parameters, especially with respect to radiation and the hydrological cycle, we perform a sensitivity analysis and evaluate the feature importance using a Random Forest regressor. An optimal set of parameters is obtained via Bayesian optimization.Using the optimized set of parameters, the mean absolute error of temperature and cloud cover is reduced on most model levels, and mid-latitude precipitation patterns are improved. In addition to annual zonal-mean patterns, we examine the agreement with the seasonal cycle and discuss regions in which the bias remains considerable, such as the monsoon region over the Pacific.We discuss the robustness of this tuning with regards to resolution (T21, T31, and T42), and compare the atmosphere-only results to simulations with a mixed-layer ocean. Finally, we provide an outlook on the applicability of our parametrization to climate states other than present-day conditions.[1] K. Fraedrich et al., Meteorol. Z. 14, 299&#8211;304 (2005) [2] F. Lunkeit et al., Planet Simulator User&#8217;s Guide Version 16.0 (University of Hamburg, 2016) [3] G. Lyu et al., J. Adv. Model. Earth Syst. 10, 207&#8211;222 (2018) [4] H. Hersbach et al., Q. J. R. Meteorol. Soc. 146, 1999&#8211;2049 (2020)

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Forecasting elections results via the voter model with stubborn nodes

Applied Network Science ◽

10.1007/s41109-020-00342-7 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Antoine Vendeville ◽

Benjamin Guedj ◽

Shi Zhou

Keyword(s):

Mean Absolute Error ◽

Absolute Error ◽

Voter Model ◽

Model Parameters ◽

Popular Vote ◽

Political Landscape ◽

The Us ◽

Novel Method ◽

The Uk ◽

Theoretical Results

AbstractIn this paper we propose a novel method to forecast the result of elections using only official results of previous ones. It is based on the voter model with stubborn nodes and uses theoretical results developed in a previous work of ours. We look at popular vote shares for the Conservative and Labour parties in the UK and the Republican and Democrat parties in the US. We are able to perform time-evolving estimates of the model parameters and use these to forecast the vote shares for each party in any election. We obtain a mean absolute error of 4.74%. As a side product, our parameters estimates provide meaningful insight on the political landscape, informing us on the proportion of voters that are strong supporters of each of the considered parties.

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Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions

Wind Energy Science ◽

10.5194/wes-5-1315-2020 ◽

2020 ◽

Vol 5 (4) ◽

pp. 1315-1338 ◽

Cited By ~ 2

Author(s):

Michael F. Howland ◽

Aditya S. Ghate ◽

Sanjiva K. Lele ◽

John O. Dabiri

Keyword(s):

Boundary Layer ◽

Atmospheric Boundary Layer ◽

State Estimation ◽

Closed Loop ◽

Mean Absolute Error ◽

Absolute Error ◽

Power Production ◽

Power Reduction ◽

Model Parameters ◽

Wake Model

Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions in which the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven ensemble Kalman filter (EnKF) state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six-turbine array embedded in a statistically quasi-stationary, conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller statistically significantly increases power production compared to the baseline, greedy, yaw-aligned control provided that the EnKF estimation is constrained and informed with a physics-based prior belief of the wake model parameters. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that underperform the baseline yaw-aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over the greedy operation, while underestimating the power reduction leads to decreased power; therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. The EnKF-augmented wake model predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02P1, with P1 as the power of the leading turbine at the farm. A standard wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11P1, demonstrating that state estimation improves the predictive capabilities of simplified wake models.

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GA-optimized model predicts dispersion coefficient in natural channels

Hydrology Research ◽

10.2166/nh.2009.010 ◽

2009 ◽

Vol 40 (1) ◽

pp. 65-78 ◽

Cited By ~ 14

Author(s):

Gokmen Tayfur

Keyword(s):

Objective Function ◽

Dispersion Coefficient ◽

Mean Absolute Error ◽

Nonlinear Models ◽

Error Function ◽

Model Performance ◽

Absolute Error ◽

Model Parameters ◽

Longitudinal Dispersion Coefficient ◽

Optimal Values

Models whose parameters were optimized by genetic algorithm (GA) were developed to predict the longitudinal dispersion coefficient in natural channels. Following the existing equations in the literature, ten different linear and nonlinear models were first constructed. The models relate the dispersion coefficient to flow and channel characteristics. The GA model was then employed to find the optimal values of the constructed model parameters by minimizing the mean absolute error function (objective function). The GA model utilized an 80% cross-over rate and 4% mutation rate. It started each computation with a population of 100 chromosomes in the gene pool. For each model, while minimizing the objective function, the values of the model parameters were constrained between [−10, +10] at each iteration. The optimal values of the model parameters were obtained using a calibration set of 54 out of 80 sets of measured data. The minimum error was obtained for the case where the model was a linear equation relating dispersion coefficient to flow discharge. The model performance was then satisfactorily tested against the remaining 26 measured validation datasets. It performed better than the existing equations. It yielded minimum errors of MAE = 21.4 m2/s (mean absolute error) and RMSE = 28.5 m2/s (root mean-squares error) and a maximum accuracy rate of 81%.

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COMPARAÇÃO DE MÉTODOS DE ESTIMATIVA DE ETO E ANÁLISE DE SENSIBILIDADE PARA DIFERENTES CLIMAS BRASILEIROS

Irriga ◽

10.15809/irriga.2019v24n3p538-551 ◽

2019 ◽

Vol 24 (3) ◽

pp. 538-551

Author(s):

João Guilherme Araújo Lima ◽

Paula Carneiro Viana ◽

José Espínola Sobrinho ◽

João Paulo Chaves Couto

Keyword(s):

Sensitivity Analysis ◽

Mean Absolute Error ◽

Irrigation Management ◽

Global Radiation ◽

Absolute Error ◽

Climatic Conditions ◽

Estimation Methods ◽

Coefficient Of Determination ◽

The One ◽

E Mail

COMPARAÇÃO DE MÉTODOS DE ESTIMATIVA DE ETO E ANÁLISE DE SENSIBILIDADE PARA DIFERENTES CLIMAS BRASILEIROS JOÃO GUILHERME ARAÚJO LIMA1; PAULA CARNEIRO VIANA1; JOSÉ ESPÍNOLA SOBRINHO2 E JOÃO PAULO CHAVES COUTO3 1Departamento de Engenharia Civil, UNINASSAU, BR 104, KM 68, N° 1215, Agamenon Magalhães, 55000-000, Caruaru, Pernambuco, Brasil. [email protected]; [email protected]; 2Departamento de Ciências Ambientais e Tecnológicas, UFERSA, Rua Francisco Mota, N° 572, Presidente Costa e Silva, 59625-900 Mossoró, Rio Grande do Norte, Brasil. [email protected]; 3Programa de Pós-Graduação em Engenharia Agrícola, Núcleo de Engenharia de Água e Solo, UFRB, Rua Rui Barbosa, N° 710, Centro, 44380-000, Cruz das Almas, Bahia, Brasil. E-mail: [email protected]. 1 RESUMO A estimativa da evapotranspiração de referência (ETo) tem grande importância para a agricultura e manejo da irrigação. O método Penman-Monteith é considerado padrão para estimativa da ETo. No entanto, por ser completo, o método padrão apresenta como desvantagem a necessidade de uma gama de variáveis meteorológicas. O objetivo dessa pesquisa foi, em escala diária, avaliar o desempenho dos métodos de Hargreaves-Samani, Makkink, Priestley-Taylor, Turc, Radiação FAO-24 e Blaney-Criddle, para as condições climáticas das seis regiões do Brasil. A verificação do desempenho desses modelos foi por meio da comparação ao método de Penman-Monteith. Para avaliar o desempenho dos métodos foi utilizada a raiz quadrada do quadrado médio do erro (RQME), erro absoluto médio (EAM), erro de estimativa (PE) e coeficiente de determinação (R2). Entre os métodos estudados, o de Turc foi o que apresentou melhores resultados para todos os climas do Brasil, exceto para o clima Tropical litorâneo. O método de Makkink foi o que apresentou melhor resultado para o clima Tropical litorâneo. A análise de sensibilidade revelou que a temperatura do ar e a radiação global são as variáveis mais importantes para o método do método Penman-Monteith, exceto para o município BL, em que a variável umidade relativa do ar foi a mais importante. Palavras-Chave: irrigação, consumo de água, evapotranspiração. LIMA, J. G. A.; VIANA, P. C.; SOBRINHO, J. E.; COUTO, J. P. C. COMPARISON OF ETO ESTIMATION METHODS AND SENSITIVITY ANALYSIS FOR DIFFERENT BRAZILIAN CLIMATES 2 ABSTRACT Estimation of reference evapotranspiration (ETo) is of great importance for agriculture and irrigation management. The Penman-Monteith method is considered standard for estimating ETo. However, because it is complete, the standard method presents as a disadvantage the need for a range of meteorological variables. The objective of this research was to evaluate the performance of Hargreaves-Samani, Makkink, Priestley-Taylor, Turc, FAO-24 and Blaney-Criddle methods for the climatic conditions of the six Brazilian regions. The verification of the performance of these models was made by comparison to the Penman-Monteith method. To evaluate the performance of the methods, the square root of mean-square error (MSE), mean absolute error (MAE), error of estimate (EE) and coefficient of determination (R2) were used. Among the methods studied, that of Turc was the one that presented the best results for all the climates of Brazil, except for the tropical coastal climate. The Makkink method was the one that presented the best result for the coastal tropical climate. Sensitivity analysis revealed that air temperature and global radiation are the most important variables for the Penman-Monteith method, except for BL municipality, where the variable relative humidity was the most important. Keywords: irrigation, water consumption, evapotranspiration.

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Contactless Core-Temperature Monitoring by Infrared Thermal Sensor using Mean Absolute Error Analysis

Recent Patents on Engineering ◽

10.2174/1872212115666201230091420 ◽

2020 ◽

Vol 15 ◽

Author(s):

Fahad Layth Malallah ◽

Baraa T. Shareef ◽

Mustafah Ghanem Saeed ◽

Khaled N. Yasen

Keyword(s):

Body Temperature ◽

Embedded System ◽

Core Temperature ◽

Integrated Circuit ◽

Mean Absolute Error ◽

Absolute Error ◽

Thermal Sensor ◽

Human Face ◽

Body Contact ◽

Arduino Microcontroller

Aims: Normally, the temperature increase of individuals leads to the possibility of getting a type of disease, which might be risky to other people such as coronavirus. Traditional techniques for tracking core-temperature require body contact either by oral, rectum, axillary, or tympanic, which are unfortunately considered intrusive in nature as well as causes of contagion. Therefore, sensing human core-temperature non-intrusively and remotely is the objective of this research. Background: Nowadays, increasing level of medical sectors is a necessary targets for the research operations, especially with the development of the integrated circuit, sensors and cameras that made the normal life easier. Methods: The solution is by proposing an embedded system consisting of the Arduino microcontroller, which is trained with a model of Mean Absolute Error (MAE) analysis for predicting Contactless Core-Temperature (CCT), which is the real body temperature. Results: The Arduino is connected to an Infrared-Thermal sensor named MLX90614 as input signal, and connected to the LCD to display the CCT. To evaluate the proposed system, experiments are conducted by participating 31-subject sensing contactless temperature from the three face sub-regions: forehead, nose, and cheek. Conclusion: Experimental results approved that CCT can be measured remotely depending on the human face, in which the forehead region is better to be dependent, rather than nose and cheek regions for CCT measurement due to the smallest

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