Measurements of parameters in electricity grids are frequently average values over some time interval. In scenarios of distributed measurements such as in distribution grids, offsets of local clocks can result in the averaging interval being misaligned. This paper investigates the properties of the so-called time alignment error of such measurands that is caused by shifts of the averaging interval. A Markov model is derived that allows for numerically calculating the expected value and other distribution properties of this error. Actual consumption measurements of an office building are used to study the behavior of this time alignment error, and to compare the results from the trace with numerical results and simulations from a fitted Markov model. For increasing averaging interval offset, the time alignment error approaches a normal distribution, whose parameters can be calculated or approximated from the Markov model.

This paper reports a two-part study examining the relationship between fear of missing out (FoMO) and maladaptive behaviors in college students. This project used a cross-sectional study to examine whether college student FoMO predicts maladaptive behaviors across a range of domains (e.g., alcohol and drug use, academic misconduct, illegal behavior). Participants (N = 472) completed hard copy questionnaire packets assessing trait FoMO levels and questions pertaining to unethical and illegal behavior while in college. Part 1 utilized traditional statistical analyses (i.e., hierarchical regression modeling) to identify any relationships between FoMO, demographic variables (socioeconomic status, living situation, and gender) and the behavioral outcomes of interest. Part 2 looked to quantify the predictive power of FoMO, and demographic variables used in Part 1 through the convergent approach of supervised machine learning. Results from Part 1 indicate that college student FoMO is indeed related to many diverse maladaptive behaviors spanning the legal and illegal spectrum. Part 2, using various techniques such as recursive feature elimination (RFE) and principal component analysis (PCA) and models such as logistic regression, random forest, and Support Vector Machine (SVM), showcased the predictive power of implementing machine learning. Class membership for these behaviors (offender vs. non-offender) was predicted at rates well above baseline (e.g., 50% at baseline vs 87% accuracy for academic misconduct with just three input variables). This study demonstrated FoMO’s relationships with these behaviors as well as how machine learning can provide additional predictive insights that would not be possible through inferential statistical modeling approaches typically employed in psychology, and more broadly, the social sciences. Research in the social sciences stands to gain from regularly utilizing the more traditional statistical approaches in tandem with machine learning.

Measurements of parameters in electricity grids are frequently captured as average values over some time interval. In scenarios of distributed measurements such as in distribution grids, offsets of local clocks can result in misaligned averaging intervals. This paper investigates the properties of the so-called time alignment error of such measurands that is caused by shifts of the averaging interval. We extend a previously derived Markov-modulated model and provide an approximation of the variance of the time alignment error. The model accounts for slow-decaying correlation structure found in actual traces of electrical measures. We compare results of three electrical measures for 20 traces with numerical results and simulations from the the fitted Markov model.

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