scholarly journals Construction of a statistical learning tool based on ordinary differential equations to model the digestive behaviour of ross chickens

2020 ◽  
Vol 67 ◽  
pp. 61-71
Author(s):  
Nicolas Bloyet ◽  
Hélène Flourent ◽  
Emmanuel Frénod ◽  
Marouan Handa ◽  
Harold Moundoyi ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Data Model ◽  
Field Data ◽  
Feed Consumption ◽  
Model Parameters ◽  
Model Coupling ◽  
Learning Tool ◽  
Coupling Approach ◽  
Animal Performances ◽  
Over Time

Being able to monitor and forecast farm animal performances is a strategic problem in the agronomy industry. We use a Data-Model Coupling approach to build a biomimetic Statistical Learning tool taking into account some aspects of the biological dynamics of the animal body. The objective is to build a tool which is able to assimilate data about daily feed consumption and measured performances. The model encompasses several sub-models corresponding to compartments and permitting to mimic a kinetic process divided into several steps. Each sub-model contains parameters which can be learnt by using an optimization algorithm and data. The goal of the first application of the model on field data was to simulate and predict the growth of chickens. An experiment was performed during 70 days to collect every day the feed consumption and the weight gain of a male and a female chickens. After the learning of the model parameters, the model shows a very good approximation of the chicken’s weight evolution over time.

scholarly journals Partial differential equations for oceanic artificial intelligence

2021 ◽  
Vol 70 ◽  
pp. 137-146
Author(s):  
Jules Guillot ◽  
Guillaume Koenig ◽  
Kadi Minbashian ◽  
Emmanuel Frénod ◽  
Héléne Flourent ◽  
...  
Keyword(s):  
Data Model ◽  
Stokes Equations ◽  
Weather Forecasting ◽  
Navier Stokes ◽  
Model Coupling ◽  
Navier Stokes Equations ◽  
Coupling Approach ◽  
Prediction Systems ◽  
Applications Of Machine Learning ◽  
The One

The Sea Surface Temperature (SST) plays a significant role in analyzing and assessing the dynamics of weather and also biological systems. It has various applications such as weather forecasting or planning of coastal activities. On the one hand, standard physical methods for forecasting SST use coupled ocean- atmosphere prediction systems, based on the Navier-Stokes equations. These models rely on multiple physical hypotheses and do not optimally exploit the information available in the data. On the other hand, despite the availability of large amounts of data, direct applications of machine learning methods do not always lead to competitive state of the art results. Another approach is to combine these two methods: this is data-model coupling. The aim of this paper is to use a model in another domain. This model is based on a data-model coupling approach to simulate and predict SST. We first introduce the original model. Then, the modified model is described, to finish with some numerical results.

1D and 2D model coupling approach for the development of operational spatial flood early warning system

2021 ◽  
pp. 1-16
Author(s):  
Sindhu Kalimisetty ◽  
Amanpreet Singh ◽  
Durga Rao Korada Hari Venkata ◽  
Venkateshwar Rao V ◽  
Vazeer Mahammood
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Model Coupling ◽  
Coupling Approach ◽  
2D Model

Inversion of helicopter electromagnetic data to a magnetic conductive layered earth

Geophysics ◽  
2003 ◽  
Vol 68 (4) ◽  
pp. 1211-1223 ◽  
Author(s):  
Haoping Huang ◽  
Douglas C. Fraser
Keyword(s):  
Magnetic Permeability ◽  
Field Data ◽  
Model Parameters ◽  
Relative Importance ◽  
Magnetic Polarization ◽  
Inversion Algorithm ◽  
Layered Earth ◽  
Electromagnetic Data ◽  
Airborne Electromagnetic ◽  
True Values

Inversion of airborne electromagnetic (EM) data for a layered earth has been commonly performed under the assumption that the magnetic permeability of the layers is the same as that of free space. The resistivity inverted from helicopter EM data in this way is not reliable in highly magnetic areas because magnetic polarization currents occur in addition to conduction currents, causing the inverted resistivity to be erroneously high. A new algorithm for inverting for the resistivity, magnetic permeability, and thickness of a layered model has been developed for a magnetic conductive layered earth. It is based on traditional inversion methodologies for solving nonlinear inverse problems and minimizes an objective function subject to fitting the data in a least‐squares sense. Studies using synthetic helicopter EM data indicate that the inversion technique is reasonably dependable and provides fast convergence. When six synthetic in‐phase and quadrature data from three frequencies are used, the model parameters for two‐ and three‐layer models are estimated to within a few percent of their true values after several iterations. The analysis of partial derivatives with respect to the model parameters contributes to a better understanding of the relative importance of the model parameters and the reliability of their determination. The inversion algorithm is tested on field data obtained with a Dighem helicopter EM system at Mt. Milligan, British Columbia, Canada. The output magnetic susceptibility‐depth section compares favorably with that of Zhang and Oldenburg who inverted for the susceptibility on the assumption that the resistivity distribution was known.

Projection Bias in Catalog Orders

2007 ◽  
Vol 97 (4) ◽  
pp. 1217-1249 ◽  
Author(s):  
Michael Conlin ◽  
Ted O'Donoghue ◽  
Timothy J Vogelsang
Keyword(s):  
Field Data ◽  
Structural Model ◽  
Cold Weather ◽  
Change Over Time ◽  
Laboratory Evidence ◽  
Return Probability ◽  
Projection Bias ◽  
Using Data ◽  
Over Time

Evidence suggests that people understand qualitatively how tastes change over time, but underestimate the magnitudes. This evidence is limited, however, to laboratory evidence or surveys of reported happiness. We test for such projection bias in field data. Using data on catalog orders of cold-weather items, we find evidence of projection bias over the weather—specifically, people's decisions are overinfluenced by the current weather. Our estimates suggest that if the order-date temperature declines by 30°F, the return probability increases by 3.95 percent. We also estimate a structural model to measure the magnitude of the bias. (JEL D12, L81)

scholarly journals Dynamic Detection of Delayed Cerebral Ischemia Using Machine Learning

2020 ◽  
Author(s):  
Murad Megjhani ◽  
Kalijah Terilli ◽  
Ayham Alkhachroum ◽  
David J. Roh ◽  
Sachin Agarwal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Ischemia ◽  
Characteristic Curve ◽  
Delayed Cerebral Ischemia ◽  
Risk Scores ◽  
Support Vector ◽  
Model Parameters ◽  
Learning Approaches ◽  
Physiologic Data ◽  
Over Time

AbstractObjectiveTo develop a machine learning based tool, using routine vital signs, to assess delayed cerebral ischemia (DCI) risk over time.MethodsIn this retrospective analysis, physiologic data for 540 consecutive acute subarachnoid hemorrhage patients were collected and annotated as part of a prospective observational cohort study between May 2006 and December 2014. Patients were excluded if (i) no physiologic data was available, (ii) they expired prior to the DCI onset window (< post bleed day 3) or (iii) early angiographic vasospasm was detected on admitting angiogram. DCI was prospectively labeled by consensus of treating physicians. Occurrence of DCI was classified using various machine learning approaches including logistic regression, random forest, support vector machine (linear and kernel), and an ensemble classifier, trained on vitals and subject characteristic features. Hourly risk scores were generated as the posterior probability at time t. We performed five-fold nested cross validation to tune the model parameters and to report the accuracy. All classifiers were evaluated for good discrimination using the area under the receiver operating characteristic curve (AU-ROC) and confusion matrices.ResultsOf 310 patients included in our final analysis, 101 (32.6%) patients developed DCI. We achieved maximal classification of 0.81 [0.75-0.82] AU-ROC. We also predicted 74.7 % of all DCI events 12 hours before typical clinical detection with a ratio of 3 true alerts for every 2 false alerts.ConclusionA data-driven machine learning based detection tool offered hourly assessments of DCI risk and incorporated new physiologic information over time.

scholarly journals Learning Personalized Itemset Mapping for Cross-Domain Recommendation

2020 ◽  
Author(s):  
Yinan Zhang ◽  
Yong Liu ◽  
Peng Han ◽  
Chunyan Miao ◽  
Lizhen Cui ◽  
...  
Keyword(s):  
Single Domain ◽  
Model Parameters ◽  
The Real ◽  
Cross Domain ◽  
Proposed Model ◽  
Latent Space ◽  
Learning Parameter ◽  
Generation Procedure ◽  
Over Time

Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i.e. interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle-consistent loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.

scholarly journals KETEGARAN UPAH NOMINAL UNTUK TURUN: KASUS UPAH NOMINAL PEKERJA PRODUKSI DI BAWAH MANDOR PADA INDUSTRI BESAR DAN SEDANG MAKANAN JADI, BAHAN PAKAIAN, KARET, DAN PLASTIK

2009 ◽  
Vol 10 (1) ◽  
pp. 15
Author(s):  
Joko Susanto
Keyword(s):  
Regression Analysis ◽  
Data Model ◽  
Estimation Method ◽  
Capital Intensity ◽  
Dynamic Panel Data ◽  
Nominal Wage ◽  
Production Worker ◽  
Dynamic Panel ◽  
Regression Parameters ◽  
Over Time

This aim of the research is to test whether the decreasing productivity of the workers results in decreasing of the nominal wage of the production worker under the supervisor. Statistical data of BPS was used in this research. The research data is consist of the nominal base and over time wage of the production worker under the supervisor, productivity of workers, and capital intensity. Furthermore, this research used regression analysis with OLS estimation method. This regression analysis was based on the dynamic panel data model. Finally, this study used redundant coefficient test to reduce several insignificant regression parameters in order to get a parsimony model. The results of the research as follow: (1). the decreasing productivity of the workers does not result in decreasing the nominal base wages of the production workers under the supervisor. (2). the decreasing productivity of the workers results in decreasing of the over time wages of the production workers under the supervisor.

scholarly journals Bayesian B-spline mapping for dynamic quantitative traits

2012 ◽  
Vol 94 (2) ◽  
pp. 85-95 ◽  
Author(s):  
JUN XING ◽  
JIAHAN LI ◽  
RUNQING YANG ◽  
XIAOJING ZHOU ◽  
SHIZHONG XU
Keyword(s):  
Legendre Polynomial ◽  
Legendre Polynomials ◽  
Simulated Dataset ◽  
Model Parameters ◽  
B Spline ◽  
B Splines ◽  
Bayesian Mapping ◽  
Bayesian Shrinkage ◽  
Dynamic Traits ◽  
Over Time

SummaryOwing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.

scholarly journals Deformation Modeling of Flexible Pavement in Expansive Subgrade in Texas

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 446
Author(s):  
Asif Ahmed ◽  
MD Sahadat Hossain ◽  
Pratibha Pandey ◽  
Anuja Sapkota ◽  
Boon Thian
Keyword(s):  
Prediction Model ◽  
Field Data ◽  
Temperature Sensors ◽  
Stress Concentrations ◽  
Deformation Prediction ◽  
Rainfall Events ◽  
Rain Gauges ◽  
State Highway ◽  
Subgrade Soil ◽  
Over Time

The tendency of expansive subgrade soil to undergo swelling and shrinkage with the change in moisture has a significant impact on the performance of the pavement. The repeated cycles of wet and dry periods throughout a year lead to considerable stress concentration in the pavement subgrade soil. Such stress concentrations leads to the formation of severe pavement cracks. The objective of the research is to develop a prediction model to estimate the deformation of pavement over expansive subgrade. Two pavement sites—one farm to market road and one state highway—were monitored regularly using moisture and temperature sensors along with rain gauges. Additionally, geophysical testing was performed to obtain a continuous profile of the subgrade soil over time. Topographical surveying and horizontal inclinometer readings were taken to determine pavement deformation. The field monitoring data resulted in a maximum movement up to 80 mm in the farm to market road, and almost 38 mm in the state highway. The field data were statistically evaluated to develop a deformation prediction model. The validation of the model indicated that only a fraction of the deformation was reflected by seasonal variation, while inclusion of rainfall events in the equation significantly improved the model. Furthermore, the prediction model also incorporated the effects of change in temperature and resistivity values. The generated model could find its application in predicting pavement deformation with respect to rainfall at any time of the year.

scholarly journals  Influence of Lippia citriodora verbascoside on growth performance, antioxidant status, and serum immunoglobulins content in piglets

2012 ◽  
Vol 57 (No. 7) ◽  
pp. 312-322 ◽  
Author(s):  
G. Pastorelli ◽  
R. Rossi ◽  
C. Corino
Keyword(s):  
Growth Performance ◽  
Plant Extract ◽  
Antioxidant Status ◽  
Density Lipoprotein ◽  
Feed Additives ◽  
Lipoprotein Cholesterol ◽  
Feed Consumption ◽  
Reactive Oxygen Metabolites ◽  
Lippia Citriodora ◽  
Over Time

Phytogenic feed additives are used in animal feeding to improve livestock performance. The aim of this study was to determine whether a dietary plant extract from Lippia citriodora, standardized for verbascoside, can modify various immunological, oxidative, and biochemical serum parameters in weaned piglets. A total of 144 piglets, half female and half barrows (7.99 &plusmn; 1.40 kg BW), were allocated to three dietary treatments with six replicates per treatment (pens of 8 piglets each). Piglets were supplemented with the following levels of plant extract standardized for verbascoside: 0 (CON = control group), 5 (LV = low verbascoside group), or 10 (HV = high verbascoside group) mg/kg of diet for 56 days. Body weight (BW) and feed consumption were recorded at days 0 and 56 to determine the average daily gain and gain : feed. Twelve piglets from each treatment were randomly selected, and blood was collected by anterior vena cava puncture on days 0, 14, and 56 for glucose, triglycerides, cholesterol, urea, and oxidative status, and on days 0 and 56 for IgG and IgA concentrations. The HV group grew more (P &lt; 0.05) than the CON and LV groups. No significant differences were observed for any of the biochemical parameters between the groups; urea, high density lipoprotein cholesterol, total cholesterol, and low density lipoprotein cholesterol increased significantly over time. Reactive oxygen metabolites (ROM) showed significant time, time &times; treatment, and treatment effects (P &lt; 0.001). Both serum Igs increased (P &lt; 0.005, P &lt; 0.001 for IgG and IgA respectively) over time in all groups; treatment (P &lt; 0.05), and time &times; treatment (P = 0.056) effects were found for serum IgA concentration. The Lippia citriodora verbascoside positively influenced antioxidant status and IgA content with a tendential effect on growth performance. &nbsp;

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