Deterministic Models: Preliminaries

Physically-based, deterministic models, are considered in this paper. Physically-based, in that the models have a theoretical structure based primarily on the laws of conservation of mass, energy, or momentum; deterministic in the sense that when initial and boundary conditions and inputs are specified, the output is known with certainty. This type of model attempts to describe the structure of a particular hydrologic process and is therefore helpful in predicting what will happen when some change occurs in the system.

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Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190311125256 ◽

2019 ◽

Vol 19 (6) ◽

pp. 413-425 ◽

Cited By ~ 3

Author(s):

Athanasios Alexiou ◽

Stylianos Chatzichronis ◽

Asma Perveen ◽

Abdul Hafeez ◽

Ghulam Md. Ashraf

Keyword(s):

Protein Interactions ◽

Biological Networks ◽

Regulatory Networks ◽

Review Paper ◽

Boolean Networks ◽

Diagnostic Tools ◽

Complex Nature ◽

Cellular Interactions ◽

Protein Protein Interactions ◽

Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

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Ancient Philosophy, Quantum Reality and Management

Purushartha - A Journal of Management Ethics and Spirituality ◽

10.21844/pajmes.v10i1.7794 ◽

2017 ◽

Vol 10 (1) ◽

Author(s):

Anindo Bhattacharjee

Keyword(s):

Uncertainty Principle ◽

Quantum Physics ◽

Ancient Philosophy ◽

Physical World ◽

Scientific Management ◽

Werner Heisenberg ◽

Deterministic Models ◽

Late 19Th Century ◽

Quantum Reality ◽

New View

The romanticism of management for numbers, metrics and deterministic models driven by mathematics, is not new. It still exists. This is exactly the problem which classical physicists had in the late 19th century until Werner Heisenberg brought the uncertainty principle and opened the doors of quantum physics that challenged the deterministic view of the physical world mostly driven by the Newtonian view. In this paper, we propose an uncertainty principle of management and then list a set of factors which capture this uncertainty quite well and arrive at a new view of scientific management thought. The new view which we call as the Quantum view of Management (QVM) will be based on the major tenets from the ancient philosophical traditions viz., Jainism, Taoism, Advaita Vedanta, Buddhism, Greek philosophers (like Hereclitus) etc.

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Temperature and its measurement

10.1093/oso/9780199551668.003.0003 ◽

2017 ◽

Author(s):

Andrew Clarke

Keyword(s):

Thermal Equilibrium ◽

Isotope Fractionation ◽

Thermodynamic Temperature ◽

The Other ◽

Deterministic Models ◽

Triple Point Of Water ◽

Temperature Scales ◽

Classical Thermodynamics ◽

The Relationship ◽

Unit Of Temperature

Temperature is that property of a body which determines whether it gains or loses energy in a particular environment. In classical thermodynamics temperature is defined by the relationship between energy and entropy. Temperature can be defined only for a body that is in thermodynamic and thermal equilibrium; whilst organisms do not conform to these criteria, the errors in assuming that they do are generally small. The Celsius and Fahrenheit temperature scales are arbitrary because they require two fixed points, one to define the zero and the other to set the scale. The thermodynamic (absolute) scale of temperature has a natural zero (absolute zero) and is defined by the triple point of water. Its unit of temperature is the Kelvin. The Celsius scale is convenient for much ecological and physiological work, but where temperature is included in statistical or deterministic models, only thermodynamic temperature should be used. Past temperatures can only be reconstructed with the use of proxies, the most important of which are based on isotope fractionation.

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Operational Forecasting of Tropical Cyclone Rapid Intensification at the National Hurricane Center

Atmosphere ◽

10.3390/atmos12060683 ◽

2021 ◽

Vol 12 (6) ◽

pp. 683

Author(s):

Mark DeMaria ◽

James L. Franklin ◽

Matthew J. Onderlinde ◽

John Kaplan

Keyword(s):

Tropical Cyclone ◽

Absolute Error ◽

Baseline Period ◽

Probability Of Detection ◽

Rapid Intensification ◽

Improvement Program ◽

Deterministic Models ◽

Operational Forecasting ◽

National Hurricane Center ◽

Made In

Although some recent progress has been made in operational tropical cyclone (TC) intensity forecasting, the prediction of rapid intensification (RI) remains a challenging problem. To document RI forecast progress, deterministic and probabilistic operational intensity models used by the National Hurricane Center (NHC) are briefly reviewed. Results show that none of the deterministic models had RI utility from 1991 to about 2015 due to very low probability of detection, very high false alarm ratio, or both. Some ability to forecast RI has emerged since 2015, with dynamical models being the best guidance for the Atlantic and statistical models the best RI guidance for the eastern North Pacific. The first probabilistic RI guidance became available in 2001, with several upgrades since then leading to modest skill in recent years. A tool introduced in 2018 (DTOPS) is currently the most skillful among NHC’s probabilistic RI guidance. To measure programmatic progress in forecasting RI, the Hurricane Forecast Improvement Program has introduced a new RI metric that uses the traditional mean absolute error but restricts the sample to only those cases where RI occurred in the verifying best track or was forecast. By this metric, RI forecasts have improved by ~20–25% since the 2015–2017 baseline period.

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On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models

Sensors ◽

10.3390/s21113837 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3837

Author(s):

Rafael Orellana ◽

Rodrigo Carvajal ◽

Pedro Escárate ◽

Juan C. Agüero

Keyword(s):

Probability Density Function ◽

Probability Density ◽

Density Function ◽

Dynamic Systems ◽

Gaussian Mixture ◽

Error Model ◽

Fault Detection And Diagnosis ◽

Deterministic Models ◽

Linear Dynamic Systems ◽

Linear Dynamic

In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and able to describe the entire dynamics of an aircraft. In addition, the complexity of modern real systems has turned deterministic models impractical, since they cannot adequately represent the behavior of disturbances in sensors and actuators, and tool and machine wear, to name a few. Thus, it is necessary to deal with model uncertainties in the dynamics of the plant by incorporating a stochastic behavior. These uncertainties could also affect the effectiveness of fault diagnosis methodologies used to increment the safety and reliability in real-world systems. Determining suitable dynamic system models of real processes is essential to obtain effective process control strategies and accurate fault detection and diagnosis methodologies that deliver good performance. In this paper, a maximum likelihood estimation algorithm for the uncertainty modeling in linear dynamic systems is developed utilizing a stochastic embedding approach. In this approach, system uncertainties are accounted for as a stochastic error term in a transfer function. In this paper, we model the error-model probability density function as a finite Gaussian mixture model. For the estimation of the nominal model and the probability density function of the parameters of the error-model, we develop an iterative algorithm based on the Expectation-Maximization algorithm using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.

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Response to perturbation during quiet standing resembles delayed state feedback optimized for performance and robustness

Scientific Reports ◽

10.1038/s41598-021-90305-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ambrus Zelei ◽

John Milton ◽

Gabor Stepan ◽

Tamas Insperger

Keyword(s):

State Feedback ◽

Postural Sway ◽

Fast Response ◽

Neural System ◽

Feedback Mechanism ◽

Quiet Standing ◽

Deterministic Models ◽

Measured Time ◽

Delayed State Feedback ◽

Damped Oscillator

AbstractPostural sway is a result of a complex action–reaction feedback mechanism generated by the interplay between the environment, the sensory perception, the neural system and the musculation. Postural oscillations are complex, possibly even chaotic. Therefore fitting deterministic models on measured time signals is ambiguous. Here we analyse the response to large enough perturbations during quiet standing such that the resulting responses can clearly be distinguished from the local postural sway. Measurements show that typical responses very closely resemble those of a critically damped oscillator. The recovery dynamics are modelled by an inverted pendulum subject to delayed state feedback and is described in the space of the control parameters. We hypothesize that the control gains are tuned such that (H1) the response is at the border of oscillatory and nonoscillatory motion similarly to the critically damped oscillator; (H2) the response is the fastest possible; (H3) the response is a result of a combined optimization of fast response and robustness to sensory perturbations. Parameter fitting shows that H1 and H3 are accepted while H2 is rejected. Thus, the responses of human postural balance to “large” perturbations matches a delayed feedback mechanism that is optimized for a combination of performance and robustness.

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