Express diagnostics of accelerated human aging using the questionnaire

An express method of diagnosing accelerated aging of a person has been developed with the help of a questionnaire, which includes 15 indications of somatic aging, 12 indications of psychological aging and 10 objective indicators. The Bayesian method is used for the calculation of biological age. The accuracy of the definition for the biological age was 8.3 years. The method developed is notable for its simplicity, ease and accessibility for use in the outpatient and inpatient conditions. This method can be used for pre-selection of the patients with the risk of accelerated aging for the further in-depth studies. Keywords: biological age; accelerated aging; indicators of aging; Bayesian formula

Express diagnostics of accelerated human aging using the questionnaire

An express method of diagnosing accelerated aging of a person has been developed with the help of a questionnaire, which includes 15 indications of somatic aging, 12 indications of psychological aging and 10 objective indicators. The Bayesian method is used for the calculation of biological age. The accuracy of the definition for the biological age was 8.3 years. The method developed is notable for its simplicity, ease and accessibility for use in the outpatient and inpatient conditions. This method can be used for pre-selection of the patients with the risk of accelerated aging for the further in-depth studies. Keywords: biological age; accelerated aging; indicators of aging; Bayesian formula

Fault Diagnosis of CNC Machine Tool Based on Bayesian Formula

Bayesian formula is used to determine diagnosis sequence when several fault trees meet requirements. Bayesian prior probability is usually determined through expert or the user's subjective judgment and historical experience. If there is lack of expert experience, the determination of priori probability is very difficult. A real-time priori probability calculation method is proposed, which needn’t any priori-knowledge and can regulate automatic on the monitoring parameters. It takes into account the multiple diagnosis impact and more flexible than fixed priori probability according application.

A Theoretical Framework for Estimating Swarm Success Probability Using Scouts

This paper presents a novel theoretical framework for swarms of agents. Before deploying a swarm for a task, it is advantageous to predict whether a desired percentage of the swarm will succeed. The authors present a framework that uses a small group of expendable “scout” agents to predict the success probability of the entire swarm, thereby preventing many agent losses. The scouts apply one of two formulas to predict – the standard Bernoulli trials formula or the new Bayesian formula. For experimental evaluation, the framework is applied to simulated agents navigating around obstacles to reach a goal location. Extensive experimental results compare the mean-squared error of the predictions of both formulas with ground truth, under varying circumstances. Results indicate the accuracy and robustness of the Bayesian approach. The framework also yields an intriguing result, namely, that both formulas usually predict better in the presence of (Lennard-Jones) inter-agent forces than when their independence assumptions hold.

A Theoretical Framework for Estimating Swarm Success Probability Using Scouts

This paper presents a novel theoretical framework for swarms of agents. Before deploying a swarm for a task, it is advantageous to predict whether a desired percentage of the swarm will succeed. The authors present a framework that uses a small group of expendable “scout” agents to predict the success probability of the entire swarm, thereby preventing many agent losses. The scouts apply one of two formulas to predict – the standard Bernoulli trials formula or the new Bayesian formula. For experimental evaluation, the framework is applied to simulated agents navigating around obstacles to reach a goal location. Extensive experimental results compare the mean-squared error of the predictions of both formulas with ground truth, under varying circumstances. Results indicate the accuracy and robustness of the Bayesian approach. The framework also yields an intriguing result, namely, that both formulas usually predict better in the presence of (Lennard-Jones) inter-agent forces than when their independence assumptions hold.