Worklife and Unemployment: A New Consideration

2021 ◽  
Author(s):  
David I. Rosenbaum ◽  
Kalana Jayanetti
Keyword(s):  
Initial Data ◽  
State Model ◽  
Inactive State ◽  
Three State Model ◽  
Good Proxy ◽  
Two State Model

Abstract Do traditional two-state worklife estimates need adjustment for unemployment? To answer, an augmented three-state model classifies individuals as either 1) employed; 2) unemployed; or 3) inactive but not marginally attached. Periods of unemployment may reduce worklives; however, removal of those marginally attached or discouraged from the inactive state raises worklives. The three-state model results are compared to worklife estimates from the same initial data using the traditional two-state model. Results show that in many cases, the two-state model results are a good proxy for the three-state results that control for unemployment.

scholarly journals SIMULATIONS OF FINANCIAL MARKETS IN A POTTS-LIKE MODEL

2005 ◽  
Vol 16 (08) ◽  
pp. 1311-1317 ◽  
Author(s):  
TETSUYA TAKAISHI
Keyword(s):  
Financial Markets ◽  
Financial Market ◽  
Potts Model ◽  
State Model ◽  
Exponential Distributions ◽  
Stylized Facts ◽  
Time Correlations ◽  
Long Time ◽  
Three State Model ◽  
Two State Model

A three-state model based on the Potts model is proposed to simulate financial markets. The three states are assigned to "buy", "sell" and "inactive" states. The model shows the main stylized facts observed in the financial market: fat-tailed distributions of returns and long time correlations in the absolute returns. At low inactivity rate, the model effectively reduces to the two-state model of Bornholdt and shows similar results to the Bornholdt model. As the inactivity increases, we observe the exponential distributions of returns.

Three timescales in prism adaptation

2015 ◽  
Vol 113 (1) ◽  
pp. 328-338 ◽  
Author(s):  
Masato Inoue ◽  
Motoaki Uchimura ◽  
Ayaka Karibe ◽  
Jacinta O'Shea ◽  
Yves Rossetti ◽  
...  
Keyword(s):  
Visual Feedback ◽  
Learning System ◽  
Prism Adaptation ◽  
State Model ◽  
Prism Exposure ◽  
The Difference ◽  
Three State Model ◽  
Two State Model ◽  
Adaptation Task ◽  
Plos Biol

It has been proposed that motor adaptation depends on at least two learning systems, one that learns fast but with poor retention and another that learns slowly but with better retention (Smith MA, Ghazizadeh A, Shadmehr R. PLoS Biol 4: e179, 2006). This two-state model has been shown to account for a range of behavior in the force field adaptation task. In the present study, we examined whether such a two-state model could also account for behavior arising from adaptation to a prismatic displacement of the visual field. We first confirmed that an “adaptation rebound,” a critical prediction of the two-state model, occurred when visual feedback was deprived after an adaptation-extinction episode. We then examined the speed of decay of the prism aftereffect (without any visual feedback) after repetitions of 30, 150, and 500 trials of prism exposure. The speed of decay decreased with the number of exposure trials, a phenomenon that was best explained by assuming an “ultraslow” system, in addition to the fast and slow systems. Finally, we compared retention of aftereffects 24 h after 150 or 500 trials of exposure: retention was significantly greater after 500 than 150 trials. This difference in retention could not be explained by the two-state model but was well explained by the three-state model as arising from the difference in the amount of adaptation of the “ultraslow process.” These results suggest that there are not only fast and slow systems but also an ultraslow learning system in prism adaptation that is activated by prolonged prism exposure of 150–500 trials.

scholarly journals A Novel Approach for Calculating Exact Forms of mRNA Distribution in Single-Cell Measurements

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 27
Author(s):  
Jiaxin Chen ◽  
Feng Jiao
Keyword(s):  
Steady State ◽  
Single Cell ◽  
Gene Activation ◽  
State Model ◽  
Distribution Data ◽  
Mrna Levels ◽  
Novel Approach ◽  
Kinetic Rates ◽  
Three State Model ◽  
Two State Model

Gene transcription is a stochastic process manifested by fluctuations in mRNA copy numbers in individual isogenic cells. Together with mathematical models of stochastic transcription, the massive mRNA distribution data that can be used to quantify fluctuations in mRNA levels can be fitted by Pm(t), which is the probability of producing m mRNA molecules at time t in a single cell. Tremendous efforts have been made to derive analytical forms of Pm(t), which rely on solving infinite arrays of the master equations of models. However, current approaches focus on the steady-state (t→∞) or require several parameters to be zero or infinity. Here, we present an approach for calculating Pm(t) with time, where all parameters are positive and finite. Our approach was successfully implemented for the classical two-state model and the widely used three-state model and may be further developed for different models with constant kinetic rates of transcription. Furthermore, the direct computations of Pm(t) for the two-state model and three-state model showed that the different regulations of gene activation can generate discriminated dynamical bimodal features of mRNA distribution under the same kinetic rates and similar steady-state mRNA distribution.

scholarly journals The mechanism of thin filament regulation: Models in conflict?

2019 ◽  
Vol 151 (11) ◽  
pp. 1265-1271 ◽  
Author(s):  
Michael A. Geeves ◽  
Sherwin S. Lehrer ◽  
William Lehman
Keyword(s):  
Rate Constant ◽  
Binding Sites ◽  
Thin Filament ◽  
State Model ◽  
Muscle Contractility ◽  
Thin Filaments ◽  
Thin Filament Regulation ◽  
Three State Model ◽  
State Models ◽  
Two State Model

In a recent JGP article, Heeley et al. (2019. J. Gen. Physiol. https://doi.org/10.1085/jgp.201812198) reopened the debate about two- versus three-state models of thin filament regulation. The authors review their work, which measures the rate constant of Pi release from myosin.ADP.Pi activated by actin or thin filaments under a variety of conditions. They conclude that their data can be described by a two-state model and raise doubts about the generally accepted three-state model as originally formulated by McKillop and Geeves (1993. Biophys. J. https://doi.org/10.1016/S0006-3495(93)81110-X). However, in the following article, we follow Plato’s dictum that “twice and thrice over, as they say, good it is to repeat and review what is good.” We have therefore reviewed the evidence for the three- and two-state models and present our view that the evidence is overwhelmingly in favor of three structural states of the thin filament, which regulate access of myosin to its binding sites on actin and, hence, muscle contractility.

Polarographic studies on renaturation of urea-denatured human serum albumin

1989 ◽  
Vol 54 (2) ◽  
pp. 536-543 ◽  
Author(s):  
Josef Chmelík ◽  
Pavel Anzenbacher ◽  
Vítěz Kalous
Keyword(s):  
Human Serum Albumin ◽  
Serum Albumin ◽  
Human Serum ◽  
Irreversible Process ◽  
State Model ◽  
Native Protein ◽  
Main Components ◽  
Two State Model ◽  
Kinetics Of

The renaturation of the two main components of human serum albumin, i.e. of mercaptalbumin and nonmercaptalbumin, was studied polarographically. It has been demonstrated that renaturation of both proteins after 1-min denaturation in 8M urea is reversible. By contrast, renaturation after 200 min denaturation in 8M urea is an irreversible process; the characteristics of renatured mercaptalbumin differ more from the properties of the native protein than the characteristics of nonmercaptalbumin. The studies of the kinetics of renaturation of both proteins have shown that the renaturation can be represented by a two-state model. This means that the existence of stable intermediary products during the renaturation process was not determined polarographically.

scholarly journals Three state model of actin-tropomyosin complex

2000 ◽  
Vol 40 (supplement) ◽  
pp. S139
Author(s):  
K-I. Sano ◽  
K. Maeda ◽  
Y. Maeda
Keyword(s):  
State Model ◽  
Three State Model
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