BACKGROUNDTests that classify English ability, such as the Test of English as a Foreign Language (TOEFL), are often the only application metric common to international applicants from a wide variety of academic backgrounds. As such, these test results are sometimes used beyond their intended scope to predict student academic success. PURPOSE (HYPOTHESIS)This study evaluated relationships between TOEFL scores and several measures of academic success for students at an American university abroad. Characterizing these relationships helps assess the scope of the TOEFL score's use in admissions decision making. DESIGN/METHODLinear and logistic regression were used to evaluate TOEFL score relative to overall grade point average (GPA), GPA for courses in engineering and in humanities, rate of passing a Comprehensive Assessment Examination (CAE), and graduation rate. High school GPA, gender, and nationality were also included as independent variables. RESULTSA positive, statistically significant relationship was identified between TOEFL score and GPA, although weaker for engineering students than students in other fields, and for engineering courses than non-engineering courses. TOEFL score was also statistically significant in logistic regressions of CAE pass rate and graduation rate, indicating increasing probability of success with increasing TOEFL score. However, model goodness-of-fit measures were relatively low, indicating many students whose performance defies general trends. CONCLUSIONSIn spite of correlations between TOEFL score and academic performance, TOEFL scores should not be used in admissions beyond assessing individual students' English proficiency. Additional research is warranted to investigate trends that were identified related to gender effects and engineering-specific student learning styles.

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Health condition monitoring through comprehensive monitoring, incipient fault diagnosis, and the prediction of impending faults allows for the promotion of the long-term performance of wind turbines, particularly those in harsh environments such as cold regions. The condition monitoring of wind turbines is characterized by the difficulties associated with the lack of measured data and the nonstationary, stochastic, and complicated nature of vibration responses. This article presents a characterization of the vibrations of an operational wind turbine by spectrogram, scalogram, and bi-spectrum analyses. The results reveal varied nonstationary stochastic properties and mode-coupling instability in the vibrations of the tested wind turbine tower. The analysis illustrates that the wind turbine system vibrations exhibit certain non-Gaussian stochastic properties. An analytical model is used to evaluate the nonstationary, stochastic phenomena and mode-coupling phenomena observed in the experimental results. These results are of significance for the fault diagnosis of wind turbine system in operation as well as for improving fatigue designs beyond the wind turbulence spectral models recommended in the standards.