Evaluating SEM Model Fit with Small Degrees of Freedom.

Research has revealed that the performance of root mean square error of approximation (RMSEA) in assessing structural equation models with small degrees of freedom (df) is suboptimal, often resulting in the rejection of correctly specified or closely fitted models. This study investigates the performance of standardized root mean square residual (SRMR) and comparative fit index (CFI) in small df models with various levels of factor loadings, sample sizes, and model misspecifications. We find that, in comparison with RMSEA, population SRMR and CFI are less susceptible to the effects of df. In small df models, the sample SRMR and CFI could provide more useful information to differentiate models with various levels of misfit. The confidence intervals and p-values of a close fit were generally accurate for all three fit indices. We recommend researchers use caution when interpreting RMSEA for models with small df and to rely more on SRMR and CFI.

Robust optimal control of compartmental models in epidemiology: Application to the COVID-19 pandemic.

In this paper, a spectral approach is used to formulate and solve robust optimal control problems for compartmental epidemic models, allowing the uncertainty propagation through the optimal control model to be represented by a polynomial expansion of its stochastic state variables. More specifically, a statistical moment-based polynomial chaos expansion is employed. The spectral expansion of the stochastic state variables allows the computation of their main statistics to be carried out, resulting in a compact and efficient representation of the variability of the optimal control model with respect to its random parameters. The proposed robust formulation provides the designers of the optimal control strategy of the epidemic model the capability to increase the predictability of the results by simply adding upper bounds on the variability of the state variables. Moreover, this approach yields a way to efficiently estimate the probability distributions of the stochastic state variables and conduct a global sensitivity analysis. To show the practical implementation of the proposed approach, a mathematical model of COVID-19 transmission is considered. The numerical results show that the spectral approach proposed to formulate and solve robust optimal control problems for compartmental epidemic models provides healthcare systems with a valuable tool to mitigate and control the impact of infectious diseases.

Uncertainty quantification of a mathematical model of COVID-19 transmission dynamics with mass vaccination strategy.

In this paper, the uncertainty quantification and sensitivity analysis of a mathematical model of the SARS-CoV-2 virus transmission dynamics with mass vaccination strategy has been carried out. More specifically, a compartmental epidemic model has been considered, in which vaccination, social distance measures, and testing of susceptible individuals have been included. Since the application of these mitigation measures entails a degree of uncertainty, the effects of the uncertainty about the application of social distance actions and testing of susceptible individuals on the disease transmission have been quantified, under the assumption of a mass vaccination program deployment. A spectral approach has been employed, which allows the uncertainty propagation through the epidemic model to be represented by means of the polynomial chaos expansion of the output random variables. In particular, a statistical moment-based polynomial chaos expansion has been implemented, which provides a surrogate model for the compartments of the epidemic model, and allows the statistics, the probability distributions of the interesting output variables of the model at a given time instant to be estimated and the sensitivity analysis to be conducted. The purpose of the sensitivity analysis is to understand which uncertain parameters have most influence on a given output random variable of the model at a given time instant. Several numerical experiments have been conducted whose results show that the proposed spectral approach to uncertainty quantification and sensitivity analysis of epidemic models provides a useful tool to control and mitigate the effects of the COVID-19 pandemic, when it comes to healthcare resource planning.

The impact of summer vacation on children's obesogenic behaviors and body mass index: a natural experiment.

BACKGROUND: Children's BMI gain accelerates during summer. The Structured Days Hypothesis posits that the lack of the school day during summer vacation negatively impacts children's obesogenic behaviors (i.e., physical activity, screen time, diet, sleep). This natural experiment examined the impact of summer vacation on children's obesogenic behaviors and body mass index (BMI). METHODS: Elementary-aged children (n = 285, 5-12 years, 48.7% male, 57.4% African American) attending a year-round (n = 97) and two match-paired traditional schools (n = 188) in the United States participated in this study. Rather than taking a long break from school during the summer like traditional schools, year-round schools take shorter and more frequent breaks from school. This difference in school calendars allowed for obesogenic behaviors to be collected during three conditions: Condition 1) all children attend school, Condition 2) year-round children attend school while traditional children were on summer vacation, and Condition 3) summer vacation for all children. Changes in BMI z-score were collected for the corresponding school years and summers. Multi-level mixed effects regressions estimated obesogenic behaviors and monthly zBMI changes. It was hypothesized that children would experience unhealthy changes in obesogenic behaviors when entering summer vacation because the absence of the school day (i.e., Condition 1 vs. 2 for traditional school children and 2 vs. 3 for year-round school children). RESULTS: From Condition 1 to 2 traditional school children experienced greater unhealthy changes in daily minutes sedentary (∆ = 24.2, 95CI = 10.2, 38.2), screen time minutes (∆ = 33.7, 95CI = 17.2, 50.3), sleep midpoint time (∆ = 73:43, 95CI = 65:33, 81:53), and sleep efficiency percentage (-∆ = 0.7, 95CI = -1.1, - 0.3) when compared to year-round school children. Alternatively, from Condition 2 to 3 year-round school children experienced greater unhealthy changes in daily minutes sedentary (∆ = 54.5, 95CI = 38.0, 70.9), light physical activity minutes (∆ = - 42.2, 95CI = -56.2, - 28.3) MVPA minutes (∆ = - 11.4, 95CI = -3.7, - 19.1), screen time minutes (∆ = 46.5, 95CI = 30.0, 63.0), and sleep midpoint time (∆ = 95:54, 95CI = 85:26, 106:22) when compared to traditional school children. Monthly zBMI gain accelerated during summer for traditional (∆ = 0.033 95CI = 0.019, 0.047) but not year-round school children (∆ = 0.004, 95CI = -0.014, 0.023). CONCLUSIONS: This study suggests that the lack of the school day during summer vacation negatively impacts sedentary behaviors, sleep timing, and screen time. Changes in sedentary behaviors, screen time, and sleep midpoint may contribute to accelerated summer BMI gain. Providing structured programming during summer vacation may positively impact these behaviors, and in turn, mitigate accelerated summer BMI gain. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03397940 . Registered January 12th 2018.

Evaluating Factorial Invariance: An Interval Estimation Approach Using Bayesian Structural Equation Modeling.

In this study, we introduce an interval estimation approach based on Bayesian structural equation modeling to evaluate factorial invariance. For each tested parameter, the size of noninvariance with an uncertainty interval (i.e. highest density interval [HDI]) is assessed via Bayesian parameter estimation. By comparing the most credible values (i.e. 95% HDI) with a region of practical equivalence (ROPE), the Bayesian approach allows researchers to (1) support the null hypothesis of practical invariance, and (2) examine the practical importance of the noninvariant parameter. Compared to the traditional likelihood ratio test, simulation results suggested that the proposed Bayesian approach could offer additional insight into evaluating factorial invariance, thus, leading to more informative conclusions. We provide an empirical example to demonstrate the procedures necessary to implement the proposed method in applied research. The importance of and influences on the choice of an appropriate ROPE are discussed.

The Meaning of Aggression Varies Across Culture: Testing the Measurement Invariance of the Refined Aggression Questionnaire in Samples From Spain, the United States, and Hong Kong.

Cultural differences in aggression are still poorly understood. The purpose of this article is to assess whether a tool for measuring aggression has the same meaning across cultures. Analyzing samples from Spain (n = 262), the United States (n = 344), and Hong Kong (n = 645), we used confirmatory factor analysis to investigate measurement invariance of the refined version of the Aggression Questionnaire (Bryant & Smith, 2001 ). The measurement of aggression was more equivalent between the Chinese and Spanish versions than between these two and the U.S. version. Aggression does not show invariance at the cultural level. Cultural variables such as affective autonomy or individualism could influence the meaning of aggression. Aggressive behavior models can be improved by incorporating cultural variables.

The Relationship Between the Standardized Root Mean Square Residual and Model Misspecification in Factor Analysis Models.

We argue that the definition of close fitting models should embody the notion of substantially ignorable misspecifications (SIM). A SIM model is a misspecified model that might be selected, based on parsimony, over the true model should knowledge of the true model be available. Because in applications the true model (i.e., the data generating mechanism) is unknown, we investigate the relationship between the population standardized root mean square residual (SRMR) values and various model misspecifications in factor analysis models to better understand the magnitudes of the SRMR. Summary effect sizes of misfit such as the SRMR are necessarily insensitive to some non-ignorable localized misspecifications (i.e., the presence of a few large residual correlations in large models). Localized misspecifications may be identified by examining the largest standardized residual covariance. Based on the findings, our population reference values for close fit are based on a two-index strategy: (1) largest absolute value of standardized residual covariance ≤0.10, and (2) SRMR ≤0.05× R¯2 the average R2 of the manifest variables; for acceptable fit our values are 0.15 and 0.10× R¯2 , respectively.

Improving Psychological Measurement: Does It Make a Difference? A Comment on Nesselroade and Molenaar (2016).

Nesselroade and Molenaar advocate the use of an idiographic filter approach. This is a fixed-effects approach, which may limit the number of individuals that can be simultaneously modeled, and it is not clear how to model the presence of subpopulations. Most important, Nesselroade and Molenaar's proposal appears to be best suited for modeling long time series on a few variables for a few individuals. Long time series are not common in psychological applications. Can it be applied to the usual longitudinal data we face? These are characterized by short time series (four to five points in time), hundreds of individuals, and dozens of variables. If so, what do we gain? Applied settings most often involve between-individual decisions. I conjecture that their approach will not outperform common, simpler, methods. However, when intraindividual decisions are involved, their approach may have an edge.

There's More Than Catastrophizing in Chronic Pain: Low Frustration Tolerance and Self-Downing Also Predict Mental Health in Chronic Pain Patients.

Among the potential range of irrational beliefs that could be used as predictors of physical and mental health, catastrophizing is the process that has received most attention in chronic pain research. Other irrational processes such as demandingness, low frustration tolerance, and self-downing have rarely been studied. The goal of this study was to explore whether this wider range of beliefs is associated with health in chronic pain patients beyond catastrophizing. A total of 492 chronic pain patients completed a measure of irrational beliefs, a measure of physical and mental health, and a numerical rating scale designed to assess pain intensity and interference. Irrational processes were more strongly associated with mental than with physical health. Low frustration tolerance and self-downing were found to be significantly related to mental health even after controlling for the effect of catastrophizing. Processes other than catastrophizing appear to have potentially important relationships with the mental health of people with chronic pain. These results may offer new intervention targets for practitioners.

Towards the automatic scanning of indoors with robots.

This paper is framed in both 3D digitization and 3D data intelligent processing research fields. Our objective is focused on developing a set of techniques for the automatic creation of simple three-dimensional indoor models with mobile robots. The document presents the principal steps of the process, the experimental setup and the results achieved. We distinguish between the stages concerning intelligent data acquisition and 3D data processing. This paper is focused on the first stage. We show how the mobile robot, which carries a 3D scanner, is able to, on the one hand, make decisions about the next best scanner position and, on the other hand, navigate autonomously in the scene with the help of the data collected from earlier scans. After this stage, millions of 3D data are converted into a simplified 3D indoor model. The robot imposes a stopping criterion when the whole point cloud covers the essential parts of the scene. This system has been tested under real conditions indoors with promising results. The future is addressed to extend the method in much more complex and larger scenarios.

Acute oral toxicity and anti-inflammatory activity of hydroalcoholic extract from Lampaya medicinalis Phil in rats.

BACKGROUND: Algesia and inflammation are related with several pathological conditions. It is known that many drugs available for the treatment of these problems cause unwanted side effects. This study was aimed at evaluating acute toxicity and anti-inflammatory activity of Lampaya medicinalis Phil. (Verbenaceae) widely used in the folk medicine of Northern Chile against rheumatism, arthritis and body joints pain. RESULTS: Oral administration of hydroalcoholic extract (HAE) at the highest dose of 3000 mg/ Kg body weight resulted in no mortalities or evidence of significant behavioral changes. Histological examination revealed normal architecture and no significant adverse effects were observed on the liver, kidney, heart, lung or ovaries and testicles. The results suggest that the oral administration of hydroalcoholic extract (HAE) from Lampaya medicinalis did not produce any toxic effect in rats. Hydroalcoholic extract (HAE) significantly inhibited the carrageenan-induced rat paw edema in dose - response relationship, at test doses of 37.5, 75, 150 and 300 mg/Kg body weight. Maximum inhibition (61.98 ± 2.69%) was noted at 300 mg/Kg after 2 h of drug treatment carrageenan induced paw edema, whereas indomethacin produced 47.90 ± 1.16% of inhibition. The inhibitory values of edema at 3 h postcarrageenan were 31.04 ± 0.75%, 40.51 ± 2.36%, 48.97 ± 1.14% and 56.87 ± 0.41% for 37.5, 75, 150, and 300 mg/kg of extract respectively. Indomethacin (10 mg/Kg) gave a percentage inhibition of 49.44 ± 1.44. HAE (300 and 150 mg/kg) induced an anti-inflammatory effect greater than (or comparable) with the effect of indomethacin from 2nd to 4th hours of the experiment. CONCLUSIONS: Our results reveal for first time that compounds contained in the hydroalcoholic extract of Lampaya medicinalis Phil exert anti-inflammatory effect and the oral administration is safe and non toxic up to dose level 3000 mg/kg body weight. The anti-inflammatory activity may be associated with the presence of flavonoids. These findings also justify the traditional use of the plant for treating pain.

Assessing Approximate Fit in Categorical Data Analysis.

A family of Root Mean Square Error of Approximation (RMSEA) statistics is proposed for assessing the goodness of approximation in discrete multivariate analysis with applications to item response theory (IRT) models. The family includes RMSEAs to assess the approximation up to any level of association of the discrete variables. Two members of this family are RMSEA2, which uses up to bivariate moments, and the full information RMSEAn. The RMSEA2 is estimated using the M2 statistic of Maydeu-Olivares and Joe (2005, 2006), whereas for maximum likelihood estimation, RMSEAn is estimated using Pearson's X(2) statistic. Using IRT models, we provide cutoff criteria of adequate, good, and excellent fit using the RMSEA2. When the data are ordinal, we find a strong linear relationship between the RMSEA2 and the Standardized Root Mean Squared Residual goodness-of-fit index. We are unable to offer cutoff criteria for the RMSEAn as its population values decrease as the number of variables and categories increase.

Identifying the Source of Misfit in Item Response Theory Models.

When an item response theory model fails to fit adequately, the items for which the model provides a good fit and those for which it does not must be determined. To this end, we compare the performance of several fit statistics for item pairs with known asymptotic distributions under maximum likelihood estimation of the item parameters: (a) a mean and variance adjustment to bivariate Pearson's X(2), (b) a bivariate subtable analog to Reiser's (1996) overall goodness-of-fit test, (c) a z statistic for the bivariate residual cross product, and (d) Maydeu-Olivares and Joe's (2006) M2 statistic applied to bivariate subtables. The unadjusted Pearson's X(2) with heuristically determined degrees of freedom is also included in the comparison. For binary and ordinal data, our simulation results suggest that the z statistic has the best Type I error and power behavior among all the statistics under investigation when the observed information matrix is used in its computation. However, if one has to use the cross-product information, the mean and variance adjusted X(2) is recommended. We illustrate the use of pairwise fit statistics in 2 real-data examples and discuss possible extensions of the current research in various directions.

Environmental risk and protective factors of adolescents' and youths' mental health: differences between parents' appraisal and self-reports.

PURPOSE: We investigated the effect of parents' mental health, life events, and home life (among other factors) on adolescents'/youths' mental health, whether such an effect varies when several variables are assessed jointly, and also whether the informant source of the mental health problem modifies the estimations. METHODS: We studied a representative sample of 454 Spanish adolescents/youths studied longitudinally (2 assessments, 3 years apart). We considered factors associated with adolescents'/youths' mental health (conduct, emotional, and hyperactivity scores [SDQ]): risk factors (parents' mental health and life events) and mediators (social and financial support). Structural equation modeling was applied. We constructed two models: (a) with parents' SDQ responses and (b) with self-reported SDQ responses (in a subsample of N = 260). RESULTS: Model fit was adequate for parents' appraisal. Parents' mental health (p < 0.05) and undesirable life events (p < 0.05) were the most important risk factors. The same model showed poorer fit when self-reported measures were used. Home life exerted a stronger protective effect on adolescents'/youths' mental health when reported by adolescents/youths. The negative effect of parents' mental health was significantly protected by home life in emotional [-0.14 (0.07)] and hyperactivity scores [-0.2 (0.08)]. CONCLUSIONS: Even in the presence of other factors, parents' mental health has an important effect on adolescents'/youths' mental health. Good levels of home life are protective, especially when adolescents'/youths' mental health is self-reported.

Automatic determination of validity of input data used in ellipsoid fitting MARG calibration algorithms.

Ellipsoid fitting algorithms are widely used to calibrate Magnetic Angular Rate and Gravity (MARG) sensors. These algorithms are based on the minimization of an error function that optimizes the parameters of a mathematical sensor model that is subsequently applied to calibrate the raw data. The convergence of this kind of algorithms to a correct solution is very sensitive to input data. Input calibration datasets must be properly distributed in space so data can be accurately fitted to the theoretical ellipsoid model. Gathering a well distributed set is not an easy task as it is difficult for the operator carrying out the maneuvers to keep a visual record of all the positions that have already been covered, as well as the remaining ones. It would be then desirable to have a system that gives feedback to the operator when the dataset is ready, or to enable the calibration process in auto-calibrated systems. In this work, we propose two different algorithms that analyze the goodness of the distributions by computing four different indicators. The first approach is based on a thresholding algorithm that uses only one indicator as its input and the second one is based on a Fuzzy Logic System (FLS) that estimates the calibration error for a given calibration set using a weighted combination of two indicators. Very accurate classification between valid and invalid datasets is achieved with average Area Under Curve (AUC) of up to 0:98.

Ambient assisted living devices interoperability based on OSGi and the X73 standard.

The need for interoperability among devices is an issue of vital importance in current telemedicine systems. Although a completely standardized system is an ideal solution, most commercially available devices include their own software and communication protocols, which cause serious problems and hinder the application of a standard. Patients' telemonitoring at home requires a wide variety of biometric and ambient sensors and devices that usually present a set of very specific features and characteristics. The present article introduces a system based on the Open Services Gateway Initiative architecture, which offers plug-and-play connectivity of ambient assisted living devices. Using a data model inspired by the X73 standard, we describe a set of bundles that reduces the interoperability problem and allows the data stored in the platform to be independent from the connected devices.

G6P[8] Rotavirus a Possessing a Wa-like VP3 Gene from a Child with Acute Gastroenteritis Living in the Northwest Amazon Region.

The introduction of rotavirus A (RVA) vaccines has considerably reduced the RVA-associated mortality among children under 5 years of age worldwide. The ability of RVA to reassort gives rise to different combinations of surface proteins G (glycoprotein, VP7) and P (protease sensitive, VP4) RVA types infecting children. During the epidemiological surveillance of RVA in the Northwest Amazon region, an unusual rotavirus genotype G6P[8] was detected in feces of a 2-year-old child with acute gastroenteritis (AGE) that had been vaccinated with one dose of Rotarix® (RV1). The G6P[8] sample had a DS-1-like constellation with a Wa-like VP3 gene mono-reassortment similar to equine-like G3P[8] that has been frequently detected in Brazil previously. The results presented here reinforce the evolutionary dynamics of RVA and the importance of constant molecular surveillance.

Detection of (in)activity periods in human body motion using inertial sensors: a comparative study.

Determination of (in)activity periods when monitoring human body motion is a mandatory preprocessing step in all human inertial navigation and position analysis applications. Distinction of (in)activity needs to be established in order to allow the system to recompute the calibration parameters of the inertial sensors as well as the Zero Velocity Updates (ZUPT) of inertial navigation. The periodical recomputation of these parameters allows the application to maintain a constant degree of precision. This work presents a comparative study among different well known inertial magnitude-based detectors and proposes a new approach by applying spectrum-based detectors and memory-based detectors. A robust statistical comparison is carried out by the use of an accelerometer and angular rate signal synthesizer that mimics the output of accelerometers and gyroscopes when subjects are performing basic activities of daily life. Theoretical results are verified by testing the algorithms over signals gathered using an Inertial Measurement Unit (IMU). Detection accuracy rates of up to 97% are achieved.