Changing Managerial Beliefs: Toward a Conversion Model

2010 ◽  
Author(s):  
Mzamo P. Mangaliso ◽  
Stanley J. Young
Keyword(s):  
Conversion Model ◽  
Managerial Beliefs

The Micro and Macro of Managerial Beliefs

2020 ◽  
Author(s):  
Jose Maria Barrero
Keyword(s):  
Rational Expectations ◽  
Asset Price ◽  
Heterogeneous Firms ◽  
Adjustment Costs ◽  
Consumer Welfare ◽  
Sales Growth ◽  
Firm Level ◽  
Managerial Decisions ◽  
Managerial Beliefs ◽  
Growth Volatility

This paper studies how biases in managerial beliefs affect managerial decisions, firm performance, and the macroeconomy. Using a new survey of US managers I establish three facts. (1) Managers are not over-optimistic: sales growth forecasts on average do not exceed realizations. (2) Managers are overprecise (overconfident): they underestimate future sales growth volatility. (3) Managers overextrapolate: their forecasts are too optimistic after positive shocks and too pessimistic after negative shocks. To quantify the implications of these facts, I estimate a dynamic general equilibrium model in which managers of heterogeneous firms use a subjective beliefs process to make forward-looking hiring decisions. Overprecision and overextrapolation lead managers to overreact to firm-level shocks and overspend on adjustment costs, destroying 2.1 percent of the typical firm’s value. Pervasive overreaction leads to excess volatility and reallocation, lowering consumer welfare by 0.5 to 2.3 percent relative to the rational expectations equilibrium. These findings suggest overreaction may amplify asset-price and business cycle fluctuations.

scholarly journals Thermogravimetry of the Steam Gasification of Calluna vulgaris: Kinetic Study

Catalysts ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 657
Author(s):  
José María Encinar ◽  
Juan Félix González ◽  
Sergio Nogales-Delgado
Keyword(s):  
Kinetic Study ◽  
Raw Materials ◽  
Biomass Conversion ◽  
Calluna Vulgaris ◽  
Thermal Conversion ◽  
Positive Influence ◽  
Steam Gasification ◽  
Conversion Model ◽  
Oil Reserves ◽  
Shrinking Core

On account of the continuous decrease in oil reserves, as well as the promotion of sustainable policies, there is an increasing interest in biomass conversion processes, which imply the search for new raw materials as energy sources, like forestry and agricultural wastes. On the other hand, gasification seems to be a suitable thermal conversion process for this purpose. This work studied the thermogravimetry of the steam gasification of charcoal from heather (Calluna vulgaris) in order to determine the kinetics of the process under controlled reaction conditions. The variables studied were temperature (from 750 to 900 °C), steam partial pressure (from 0.26 to 0.82 atm), initial charcoal mass (from 50 to 106 mg), particle size (from 0.4 to 2.0 mm), N2 and steam volumetric flows (from 142 to 446 mL·min−1) and catalyst (K2CO3) concentration (from 0 to 10% w/w). The use of the shrinking core model and uniform conversion model allowed us to determine the kinetic parameters of the process. As a result, a positive influence of catalyst concentration was found up to 7.5% w/w. The kinetic study of the catalytic steam gasification showed activation energies of 99.5 and 114.8 kJ·mol−1 and order of reactions (for steam) of 1/2 and 2/3.

scholarly journals Applying MRI Intensity Normalization on Non-Bone Tissues to Facilitate Pseudo-CT Synthesis from MRI

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 816
Author(s):  
Kuei-Yuan Hou ◽  
Hao-Yuan Lu ◽  
Ching-Ching Yang
Keyword(s):  
Mean Value ◽  
Tissue Type ◽  
Routine Practice ◽  
Intensity Level ◽  
Mean Values ◽  
Conversion Model ◽  
Ratio Image ◽  
Abdominal Mri ◽  
High Level ◽  
Intensity Normalization

This study aimed to facilitate pseudo-CT synthesis from MRI by normalizing MRI intensity of the same tissue type to a similar intensity level. MRI intensity normalization was conducted through dividing MRI by a shading map, which is a smoothed ratio image between MRI and a three-intensity mask. Regarding pseudo-CT synthesis from MRI, a conversion model based on a three-layer convolutional neural network was trained and validated. Before MRI intensity normalization, the mean value ± standard deviation of fat tissue in 0.35 T chest MRI was 297 ± 73 (coefficient of variation (CV) = 24.58%), which was 533 ± 91 (CV = 17.07%) in 1.5 T abdominal MRI. The corresponding results were 149 ± 32 (CV = 21.48%) and 148 ± 28 (CV = 18.92%) after intensity normalization. With regards to pseudo-CT synthesis from MRI, the differences in mean values between pseudo-CT and real CT were 3, 15, and 12 HU for soft tissue, fat, and lung/air in 0.35 T chest imaging, respectively, while the corresponding results were 3, 14, and 15 HU in 1.5 T abdominal imaging. Overall, the proposed workflow is reliable in pseudo-CT synthesis from MRI and is more practicable in clinical routine practice compared with deep learning methods, which demand a high level of resources for building a conversion model.

scholarly journals What Does Neuroscience Tell Us About the Conversion Model of Functional Neurological Disorders?

2020 ◽  
Vol 32 (1) ◽  
pp. 24-32 ◽  
Author(s):  
Alexandre Cretton ◽  
Richard J. Brown ◽  
W. Curt LaFrance ◽  
Selma Aybek
Keyword(s):  
Neurological Disorders ◽  
Conversion Model ◽  
Functional Neurological Disorders

Investigations of Tail and Defect States in a-Si0.6Ge0.4:H Alloys by PDS and ESR - Implications for the Interaction of Weak and Dangling Bonds

1995 ◽  
Vol 377 ◽  
Author(s):  
Tilo P. Drüsedau ◽  
Andreas N. Panckow ◽  
Bernd Schröder
Keyword(s):  
Silicon Germanium ◽  
Defect Density ◽  
State Density ◽  
Model Parameters ◽  
Defect States ◽  
Conversion Model ◽  
Order Of Magnitude ◽  
Underlying Mechanisms ◽  
Amorphous Silicon Germanium ◽  
Excess Absorption

ABSTRACTInvestigations on the gap state density were performed on a variety of samples of hydrogenated amorphous silicon germanium alloys (Ge fraction around 40 at%) containing different amounts of hydrogen. From subgap absorption measurements the values of the “integrated excess absorption” and the “defect absorption” were determined. Using a calibration constant, which is well established for the determination of the defect density from the integrated excess absorption of a-Si:H and a-Ge:H, it was found that the defect density is underestimated by nearly one order of magnitude. The underlying mechanisms for this discrepancy are discussed. The calibration constants for the present alloys are determined to 8.3×1016 eV−1 cnr2 and 1.7×1016 cm−2 for the excess and defect absorption, respectively. The defect density of the films was found to depend on the Urbach energy according to the law derived from Stutzmann's dangling bond - weak bond conversion model for a-Si:H. However, the model parameters - the density of states at the onset of the exponential tails N*=27×1020 eV−1 cm−3 and the position of the demarcation energy Edb-E*=0.1 eV are considerably smaller than in a-Si:H.

Runoff Forecast and Analysis of the Probability of Dry and Wet Transition in the Hanjiang River Basin

2021 ◽  
Author(s):  
Haoyu Jin ◽  
Xiaohong Chen ◽  
Ruida Zhong
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Transition Probability ◽  
Conversion Model ◽  
Drought And Flood ◽  
Runoff Prediction ◽  
Runoff Forecast ◽  
Hanjiang River ◽  
Basin Area ◽  
Regional Water

Abstract Runoff prediction has an important guiding role in the planning and management of regional water resources, flood prevention and drought resistance, and can effectively predict the risk of changes in regional water resources. This study used 12 runoff prediction methods to predict the runoff of four hydrological stations in the Hanjiang River Basin (HRB). Through the MCMC method, the HRB runoff probability conversion model from low to high (high to low) is constructed. The study found that the runoff of the HRB had a decreasing trend. In the mid-1980s, the runoff had a significant decreasing trend. The smoother the runoff changes, the easier it is to make accurate prediction. On the whole, the QS-MFM, MFM, MA-MFM, CES and DNN methods have strong generalization ability and can more accurately predict the runoff of the HRB. The Logistic model can accurately simulate the change of runoff status in the HRB. Among them, the HLT station has the fastest conversion rate of drought and flood, and the flow that generates floods is 6 times that of drought. The smaller the basin area, the larger the gap between drought and flood discharge. Overall, this research provides important technical support for the prediction of change in water resources and the transition probability from drought to flood in the HRB.

The conversion model of low dose rate effect in bipolar transistors

2009 ◽  
Author(s):  
V. S. Pershenkov ◽  
D. V. Savchenkov ◽  
A. S. Bakerenkov ◽  
V. N. Ulimov ◽  
A. Y. Nikiforov ◽  
...  
Keyword(s):  
Dose Rate ◽  
Low Dose ◽  
Bipolar Transistors ◽  
Rate Effect ◽  
Conversion Model ◽  
Low Dose Rate ◽  
Dose Rate Effect

Theoretical Analysis on the Constitution of Calorific Values of Biomass Fuels

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Fengqi Yao ◽  
Haihui Wang
Keyword(s):  
Chemical Species ◽  
Energy Utilization ◽  
Chemical Compositions ◽  
Chemical Bonds ◽  
Biomass Fuels ◽  
Conversion Model ◽  
Negative Role ◽  
First Time ◽  
Mass Increase ◽  
Heat Released

The present work explored the constitution of the calorific values of biomass fuels and the mechanism by which basic chemical compositions affect the fuel calorific data. For the first time, an energy conversion model was developed for the functional groups stored in biomass fuels by combustion. Validation of the model was performed by testing with various types of substances. By analyzing the effect of mass increase of individual chemical species on the amount of heat released by a fuel, it was confirmed that for ligno-cellulosic fuels, the species containing C–H, C–C and C=C bonds positively affect the fuel calorific values, whereas the species containing O–H, C–N, C–O, and C=O bonds have negative role in the increase of the fuel calorific values. A ratio parameter was then developed to quantitatively evaluate the potential of individual chemical bonds to contribute to the calorific values of biomass fuels, which well explained the existing techniques for treating biomass as fuels. The outcomes of this work serve as a theoretical basis for improving the efficiency in energy utilization of biomass fuels.

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