Sentence examples for predict training from inspiring English sources

Conscientiousness is positively related to job performance, training proficiency and personnel data (such as salary or promotions), while Openness and Extraversion predict training proficiency (Barrick and Mount 1991).

Since then, a few studies have addressed anxiety, age, cognitive skill or level of impairment, personality, education, and depression as potential individual difference factors that could predict training success (for reviews, see Hill et al 2000).

Because lactate acid level serves as a diagnostically important oxidizable substrate during exercise, it has been used to access, plan, and predict training and performance endurance programs (Cabrera et al., 1999).

Section 3 focuses on the train dispatcher support systems that predict train movements, detect potential conflicts and show the dispatcher the possibilities available to solve the detected problems.

This paper presents a validation of EnVib-01, an empirical model to predict train-induced ground vibrations in a preliminary design phase.

Ms. Geller said that the advertisements — intended to be displayed beside actual clocks, not the countdown clocks that predict train arrivals — cost roughly $320 each for a four-week placement — about $70,000 for all the clocks.

The results suggest that, within a sample of existing brain trainers, openness to experience, need for cognition, and age predicted training discontinuation.

Overall, results indicated that subjective similarity is more important in predicting training outcomes than objective similarity, and that surface-level similarity is less important than deep-level similarity.

(D) Observed and predicted training set error rate distributions for Model logP3-1b.

Training Load: This feature within polarpersonaltrainer.com displays a color-coded graph of a user's cumulative training load, notifying a person when he or she has recovered enough for the next session, and accurately predicting training developments to prevent overtraining.

In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays.