Superconducting Vortices: Chapman Full Model

2009 ◽  
pp. 41-55 ◽  
Author(s):  
S. N. Antontsev ◽  
N. V. Chemetov
Keyword(s):  
Full Model ◽  
Superconducting Vortices

Structural Validity and Reliability of The Urdu Translated Version of IPIP - NEO - 300 Web - Based Inventory

2020 ◽  
Author(s):  
Minhaaj Rehman ◽  
John Anthony Johnson
Keyword(s):  
Data Collection ◽  
Measurement Invariance ◽  
Internal Consistency ◽  
Ocean Model ◽  
Full Model ◽  
Validity And Reliability ◽  
Personality Tests ◽  
Personality Preferences ◽  
Web Based ◽  
Invariance Test

The NEO-IPIP-300 is a 300-item version scale of freely available personality tests based on the OCEAN Model of 30 distinctive personality traits. The scale measures human personality preferences and groups them into five distinct factors, namely Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The scale has been translated into many languages before, but there was no translation and norms available for the Urdu language.Paper reports the translation, creation of web version, data collection (N=869), and reliability of Urdu version of NEO-IPIP-300. We also did a CFA Analysis and Measurement Invariance test as part of the paper. Full measurement invariance was met for the full model, and partial measurement invariance was met for neuroticism (metric and scalar) and extraversion (metric). In general, all models fit well and suggest that the Urdu IPIP-300-NEO aligns well with the English IPIP-300-NEO. In some cases, the Urdu inventory performed better (e.g., higher internal consistency) than the English inventory.

scholarly journals Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e047121
Author(s):  
Devin Incerti ◽  
Shemra Rizzo ◽  
Xiao Li ◽  
Lisa Lindsay ◽  
Vincent Yau ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prognostic Model ◽  
Vital Signs ◽  
Saturation Temperature ◽  
Full Model ◽  
Good Discrimination ◽  
Hospitalised Patients ◽  
Laboratory Results ◽  
All Cause Mortality ◽  
The Usa

ObjectivesTo develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19.DesignRetrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores and calibration plots in the test set.SettingOptum de-identified COVID-19 Electronic Health Record dataset including over 700 hospitals and 7000 clinics in the USA.Participants17 086 patients hospitalised with COVID-19 between 20 February 2020 and 5 June 2020.Main outcome measureAll-cause mortality while hospitalised.ResultsThe full model that included information on demographics, comorbidities, laboratory results, and vital signs had good discrimination (C-index=0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index=0.79) was only slightly better than a model that only included age (C-index=0.76). Across the study period, predicted mortality was 1.3% for patients aged 18 years old, 8.9% for 55 years old and 28.7% for 85 years old. Predicted mortality across all ages declined over the study period from 22.4% by March to 14.0% by May.ConclusionAge was the most important predictor of all-cause mortality, although vital signs and laboratory results added considerable prognostic information, with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase and white cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The full model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.

scholarly journals Mechanical Control of Individual Superconducting Vortices

Nano Letters ◽  
2016 ◽  
Vol 16 (3) ◽  
pp. 1626-1630 ◽  
Author(s):  
Anna Kremen ◽  
Shai Wissberg ◽  
Noam Haham ◽  
Eylon Persky ◽  
Yiftach Frenkel ◽  
...  
Keyword(s):  
Mechanical Control ◽  
Superconducting Vortices

Investigation of Train Dynamics in Passing Through Curves Using a Full Model

Joint Rail ◽  
2004 ◽  
Author(s):  
Mohammad Durali ◽  
Mohammad Mehdi Jalili Bahabadi
Keyword(s):  
Degrees Of Freedom ◽  
Equations Of Motion ◽  
Three Dimensional ◽  
Full Model ◽  
Bogie Frame ◽  
Nonlinear Characteristics ◽  
Train Derailment ◽  
Train Dynamics

In this article a train model is developed for studying train derailment in passing through bends. The model is three dimensional, nonlinear, and considers 43 degrees of freedom for each wagon. All nonlinear characteristics of suspension elements as well as flexibilities of wagon body and bogie frame, and the effect of coupler forces are included in the model. The equations of motion for the train are solved numerically for different train conditions. A neural network was constructed as an element in solution loop for determination of wheel-rail contact geometry. Derailment factor was calculated for each case. The results are presented and show the major role of coupler forces on possible train derailment.

Tilting Pad Journal Bearings: A Discussion on Stability Calculation, Frequency Dependence, and Pad and Pivot Flexibility

2012 ◽  
Author(s):  
Jason C. Wilkes ◽  
Dara W. Childs
Keyword(s):  
Degrees Of Freedom ◽  
Journal Bearing ◽  
Operating Temperature ◽  
Excitation Frequency ◽  
Full Model ◽  
Stability Calculation ◽  
Damping Coefficients ◽  
Tilting Pad ◽  
Stiffness And Damping ◽  
Tilting Pad Journal Bearing

For several years, researchers have presented predictions showing that using a full tilting-pad journal bearing (TPJB) model (retaining all of the pad degrees of freedom) is necessary to accurately perform stability calculations for a shaft operating on TPJBs. This paper will discuss this issue, discuss the importance of pad and pivot flexibility in predicting impedance coefficients for the tilting-pad journal bearing, present measured changes in bearing clearance with operating temperature, and summarize the differences between measured and predicted frequency dependence of dynamic impedance coefficients. The current work presents recent test data for a 100 mm (4 in) five-pad TPJB tested in load on pad (LOP) configuration. Measured results include bearing clearance as a function of operating temperature, pad clearance and radial displacement of the loaded pad (the pad having the static load vector directed through its pivot), and frequency dependent stiffness and damping. Measured hot bearing clearances are approximately 30% smaller than measured cold bearing clearances and are inversely proportional to pad surface temperature; predicting bearing impedances with a rigid pad and pivot model using these reduced clearances results in overpredicted stiffness and damping coefficients that are several times larger than previous comparisons. The effect of employing a full bearing model versus a reduced bearing model (where only journal degrees of freedom are retained) in a stability calculation for a realistic rotor-bearing system is assessed. For the bearing tested, the bearing coefficients reduced at the frequency of the unstable eigenvalue (subsynchronously reduced) predicted a destabilizing cross-coupled stiffness coefficient at the onset of instability within 1% of the full model, while synchronously reduced coefficients for the lightly loaded bearing required 25% more destabilizing cross-coupled stiffness than the full model to cause system instability. The same stability calculation was performed using measured stiffness and damping coefficients at synchronous and subsynchronous frequencies. These predictions showed that both the synchronously measured stiffness and damping and predictions using the full bearing model were more conservative than the model using subsynchronously measured stiffness and damping, an outcome that is completely opposite from conclusions reached by comparing different prediction models. This contrasting outcome results from a predicted increase in damping with increasing excitation frequency at all speeds and loads; however, this increase in damping with increasing excitation frequency was only measured at the most heavily loaded conditions.

Full Model Selection issue in temporal data through evolutionary algorithms: A brief review

2017 ◽  
Author(s):  
Nancy Perez-Castro ◽  
Aldo Marquez-Grajales ◽  
Hector Gabriel Acosta-Mesa ◽  
Efren Mezura-Montes
Keyword(s):  
Model Selection ◽  
Evolutionary Algorithms ◽  
Temporal Data ◽  
Full Model

scholarly journals Global Sensitivity Analysis and Adaptive Stochastic Sampling of a Subsurface-Flow Model Using Active Subspaces

2019 ◽  
Author(s):  
Daniel Erdal ◽  
Olaf A. Cirpka
Keyword(s):  
Sensitivity Analysis ◽  
Flow Model ◽  
Global Sensitivity Analysis ◽  
Subsurface Flow ◽  
Full Model ◽  
Stochastic Sampling ◽  
Global Sensitivity ◽  
Parameter Combination ◽  
Active Subspaces ◽  
Active Subspace

Abstract. Integrated hydrological modelling of domains with complex subsurface features requires many highly uncertain parameters. Performing a global uncertainty analysis using an ensemble of model runs can help bring clarity which of these parameters really influence system behavior, and for which high parameter uncertainty does not result in similarly high uncertainty of model predictions. However, already creating a sufficiently large ensemble of model simulation for the global sensitivity analysis can be challenging, as many combinations of model parameters can lead to unrealistic model behavior. In this work we use the method of active subspaces to perform a global sensitivity analysis. While building-up the ensemble, we use the already existing ensemble members to construct low-order meta-models based on the first two active subspace dimensions. The meta-models are used to pre-determine whether a random parameter combination in the stochastic sampling is likely to result in unrealistic behavior, so that such a parameter combination is excluded without running the computationally expensive full model. An important reason for choosing the active subspace method is that both the activity score of the global sensitivity analysis and the meta-models can easily be understood and visualized. We test the approach on a subsurface flow model including uncertain hydraulic parameter, uncertain boundary conditions, and uncertain geological structure. We show that sufficiently detailed active subspaces exist for most observations of interest. The pre-selection by the meta-model significantly reduces the number of full model runs that must be rejected due to unrealistic behavior. An essential but difficult part in active subspace sampling using complex models is approximating the gradient of the simulated observation with respect to all parameters. We show that this can effectively and meaningful be done with second-order polynomials.

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