Heteroskedasticity between GNSS time-series repeatabilities and noise magnitudes

<p>In the analysis of GNSS time series, when the sampling frequency and time-series lengths are almost identical, it is possible to highlight a linear relationship between the series repeatabilities (i.e. WRMS) and noise magnitudes. In the literature, linear equations as a function of WRMSs allowed many researchers to estimate the noise magnitudes. However, this was built upon homoskedasticity. We experienced the higher WRMSs, the more erroneous analysis results using the noise magnitudes from the linear equations stated. We hence studied whether or not homoscedasticity clearly describes the modeling errors. To test that, we used the published results of GPS baseline components from the previous work in the literature and realized here that each component forms part of the totality. We introduced all baseline component results as a whole into statistical analysis to check heteroskedasticity. We established null and alternative hypotheses on the residuals which are homoscedastic (H0) or heteroskedastic (HA). We adopted both the Breusch-Pagan test and the Goldfeld-Quandt test to prove heteroskedasticity and obtained p-values for both methods. The p-value, which is the probability measure, equals to almost zero for both test methods, that is, we fail to accept the null hypothesis. Consequently, we can confidently state that the relationship between the WRMSs and the noise magnitudes is heteroskedastic.</p><p><strong>Keywords:</strong> Noise magnitudes, repeatabilities, heteroskedasticity, time-series analysis</p>

DYADIC FACTORS THAT ASSOCIATE WITH INSOMNIA IN CAREGIVERS OF PERSONS LIVING WITH DEMENTIA

Insomnia is prevalent in caregivers of persons living with dementia (PLWD); however, more research is needed to identify which dyadic factors most impact caregiver sleep. This study aimed to identify the factors associated with caregiver insomnia in the baseline component of a randomized clinical trial. A linear regression was conducted with caregiver variables (e.g., depression), and PLWD variables (e.g., disruptive nighttime behaviors) as independent variables in relation to insomnia, as assessed with Insomnia Severity Index (ISI). Caregivers (n=49) were on average 63 years, mostly female (65.3%), White (69.4%), and spouses (65.3%). Mean ISI was 6.8, indicating mild-to-moderately disturbed sleep. Multiple linear regression (F(11,32) = 13.4, p<.001) showed that both caregiver-based measures (depression, p<.023) and PLWD-based measures (disruptive nighttime behaviors, p<.001) were independently associated with ISI. Ideas about needed multicomponent dyadic interventions which target both PLWD nighttime behaviors, caregiver depression, and sleep disturbances will be discussed.

The Most Preferred Route of the Car Navigation System: A Systems Engineering Approach

The typical route planning of on-board car navigation systems (CNS) attempts to find the shortest route without considering users preferences and driving contexts. However it is more effective for a user to find the most preferred route rather than the shortest one. We propose a systems engineering approach for finding the most preferred route by considering and tracking the requirements of CNS route planning from the business point of view. Our approach consists of 4 baselines: customer baseline, system baseline component baseline, and design baseline. The architecture of a route planning engine is suggested according to the baselines.