Subjective Return Expectations, Perceptions, and Portfolio Choice

Exploiting a representative sample of the French population by age, wealth, and asset classes, we document novel facts about their expectations and perceptions of stock market returns. Both expectations and perceptions of returns are very dispersed, significantly lower than their data counterparts, and a substantial portion of the variation in the former is explained by dispersion in the latter. Consistent with portfolio choice models under incomplete information, a conditional risk-return trade-off explains the intensive margin, while at the extensive margin, only expected returns matter. Despite accounting for survey measurement error in subjective return expectations, ’muted sensitivities’ at both portfolio choice margins obtain, getting consistently (i) bigger when excluding informed non-participants, and (ii) smaller, for inertial and professionally delegated portfolios.

Safety risk assessment intelligent system for on-foot construction worker using fuzzy fault tree

A risk assessment model is developed to estimate the potential combined influence of concurrent safety risks facing on-foot construction worker at a certain point in space or instant of time. The model is based on a holistic approach that comprehensively systemizes principal types and subjective values of possible safety risk events. Fuzzy fault tree is built using a deductive approach to identify possible concurrent basic and conditional risk events, not risk symptoms, from the major subgroups of triggering, enabling and environment-related risks. The inclusive risk breakdown structure helps in combating assessment underestimation related to overlooking influential risks. Adequate logic gates are suggested at tree junctions to overcome assessment overestimation related to accumulating the effect of dependent, redundant, and non-concurrent risks, and ignoring the effectiveness of safety precautions and measures that may reduce or eliminate risks. Operational logic gates are applied to properly combine the residual risk of static (non-moving) events and dynamic (moving) events that can concurrently influence safety. The model is programmed into an interactive interfaced intelligent system to simulate cases of risk assessment input, computations, and output. The system shows the advantages of using the model as a prognostic or diagnostic tool to estimate top risk event. Subjective linguistic risk values can be induced for basic risk events at the bottom of the tree, and conditional risk events controlling residual risk values can be induced at different levels of the tree. Fuzzy logic plays a key role in hosting subjective risk evaluation into computational truth values to generate realistic and meaningful assessment values that are helpful for risk control.

Intelligent Condition Monitoring Models For Rotating

Condition-based maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on the changes in machine condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, and debris content). A newly developed condition monitoring model (CMM) is developed based on Bayesian decision theory, which takes vibration signals from a rotating machine and classifies them to either the normal or abnormal state. A conditional risk function is defined, which is calculated based on a loss table and the posterior probabilities. Using the conditional risk funciton, the machine condition can be classified to either the normal or abnormal condition. The developed model can efficiently avoid unnecessary maintenance and take timely actions through analyzing the received vibration signals from the machine. However, the vibration signals sometimes may not be sensed, transmitted, or received precisely due to unexpected situations. Therefore, a fuzzy Bayesian model for condition monitoring of a system is proposed. A program is coded in visual basic to run the models. Illustrative examples are demonstrated to present the application of both models.

Intelligent Condition Monitoring Models For Rotating

Condition-based maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on the changes in machine condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, and debris content). A newly developed condition monitoring model (CMM) is developed based on Bayesian decision theory, which takes vibration signals from a rotating machine and classifies them to either the normal or abnormal state. A conditional risk function is defined, which is calculated based on a loss table and the posterior probabilities. Using the conditional risk funciton, the machine condition can be classified to either the normal or abnormal condition. The developed model can efficiently avoid unnecessary maintenance and take timely actions through analyzing the received vibration signals from the machine. However, the vibration signals sometimes may not be sensed, transmitted, or received precisely due to unexpected situations. Therefore, a fuzzy Bayesian model for condition monitoring of a system is proposed. A program is coded in visual basic to run the models. Illustrative examples are demonstrated to present the application of both models.

Estimating the Lifetime and Age-Conditional Risk of an HIV Diagnosis in Iran, 2011-2015

Background: According to the importance of infectious diseases, especially HIV, the purpose of this study was to estimate lifetime and age-conditional risks of HIV diagnosis in Iran. Methods: We used vital statistics, HIV surveillance and census data for 2011-2015 to calculate Age-specific HIV diagnosis and non-HIV death rates. These rates then converted to the probability of an HIV diagnosis considering the competing risk. Finally, the probabilities were applied to a hypothetical cohort of 10 million live births. The lifetime and age-conditional risk of HIV diagnosis in the total and general population of Iran were calculated by Dev Can software (version 6.7.4). Results: Lifetime risk was 0.084% (95% CI: 0.081-0.088) or one in 1183 for females and 0.21% (95% CI: 0.201- 0.211) or one in 483 for males in the total population. In the general population lifetime risk for men was 0.069% (95% CI: 0.066-0.072) or 1 in 1454 men and 0.066% (95%CI: 0.063-0.069) or one in 1523 for women. In the total and general population, the 10-yr age-conditional risk of HIV diagnosis showed that the highest risk of an HIV diagnosis is related to 30-yr -olds. Conclusion: The estimated risks differed based on gender, age, and type of population. Paying close attention to these differences is critical for infection control planning and policies.