Functional Forms | ScienceGate

AbstractOne of the main factors that determines habitat suitability for sessile and territorial organisms is the presence or absence of another competing individual in that habitat. This type of competition arises in populations occupying patches in a metacommunity. Previous studies have looked at this process using a continuous-time modeling framework, where colonizations and extinctions occur simultaneously. However, different colonization processes may be performed by different species, which may affect the metacommunity dynamics. We address this issue by developing a discrete-time framework that describes these kinds of metacommunity interactions, and we consider different colonization dynamics. To understand potential dynamics, we consider specific functional forms that characterize the colonization and extinction processes of metapopulations competing for space as their limiting factor. We then provide a mathematical analysis of the models generated by this framework, and we compare these results to what is seen in nature and in previous models.

Download Full-text

The contribution of resident physicians to hospital productivity

The European Journal of Health Economics ◽

10.1007/s10198-021-01368-z ◽

2021 ◽

Author(s):

Maria J. Perez-Villadóniga ◽

Ana Rodriguez-Alvarez ◽

David Roibas

Keyword(s):

Production Functions ◽

Weighted Sum ◽

Main Hypothesis ◽

Resident Physicians ◽

Suitable Parameter ◽

Functional Forms ◽

The One ◽

Definition Of ◽

Hospital Productivity ◽

Natural Hypothesis

AbstractResident physicians play a double role in hospital activity. They participate in medical practices and thus, on the one hand, they should be considered as an input. Also, they are medical staff in training and, on the other hand, must be considered as an output. The net effect on hospital activities should therefore be empirically determined. Additionally, when considering their role as active physicians, a natural hypothesis is that resident physicians are not more productive than senior ones. This is a property that standard logarithmic production functions (including Cobb–Douglas and Translog functional forms) cannot verify for the whole technology set. Our main contribution is the development of a Translog modification, which implies the definition of the input “doctors” as a weighted sum of senior and resident physicians, where the weights are estimated from the empirical application. This modification of the standard Translog is able, under suitable parameter restrictions, to verify our main hypothesis across the whole technology set while determining if the net effect of resident physicians in hospitals’ production should be associated to an output or to an input. We estimate the resulting output distance function frontier with a sample of Spanish hospitals. Our findings show that the overall contribution of resident physicians to hospitals’ production allows considering them as an input in most cases. In particular, their average productivity is around 37% of that corresponding to senior physicians.

Download Full-text

Functional forms for lattice correlators at small times

Nuclear Physics B - Proceedings Supplements ◽

10.1016/s0920-5632(01)01690-5 ◽

2002 ◽

Vol 106-107 ◽

pp. 284-286

Author(s):

Danielle Blythe

Keyword(s):

Small Times ◽

Functional Forms

Download Full-text

Cyclic AMP-dependent functional forms of cyclic AMP receptor protein from Vibrio cholerae

Archives of Biochemistry and Biophysics ◽

10.1016/j.abb.2006.01.001 ◽

2006 ◽

Vol 447 (1) ◽

pp. 80-86 ◽

Cited By ~ 6

Author(s):

Rima Chattopadhyay ◽

Pradeep Parrack

Keyword(s):

Cyclic Amp ◽

Vibrio Cholerae ◽

Receptor Protein ◽

Cyclic Amp Receptor Protein ◽

Functional Forms

Download Full-text

Constraints in models of production and cost via slack-based measures

Empirical Economics ◽

10.1007/s00181-020-02013-z ◽

2021 ◽

Author(s):

Caroline Khan ◽

Mike G. Tsionas

Keyword(s):

Power Generation ◽

Stochastic Frontier ◽

Inequality Constraints ◽

Order Conditions ◽

Flexible Functional Forms ◽

Cost Share ◽

First Order ◽

Stochastic Frontier Models ◽

Endogenous Variables ◽

Functional Forms

AbstractIn this paper, we propose the use of stochastic frontier models to impose theoretical regularity constraints (like monotonicity and concavity) on flexible functional forms. These constraints take the form of inequalities involving the data and the parameters of the model. We address a major concern when statistically endogenous variables are present in these inequalities. We present results with and without endogeneity in the inequality constraints. In the system case (e.g., cost-share equations) or more generally, in production function-first-order conditions case, we detect an econometric problem which we solve successfully. We provide an empirical application to US electric power generation plants during 1986–1997, previously used by several authors.

Download Full-text

Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽

10.3390/en14040930 ◽

2021 ◽

Vol 14 (4) ◽

pp. 930

Author(s):

Fahimeh Hadavimoghaddam ◽

Mehdi Ostadhassan ◽

Ehsan Heidaryan ◽

Mohammad Ali Sadri ◽

Inna Chapanova ◽

...

Keyword(s):

Machine Learning ◽

Viscosity Data ◽

Oil Viscosity ◽

Pvt Data ◽

Proposed Model ◽

Wide Range ◽

Engineering Problems ◽

Functional Forms ◽

Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

Download Full-text