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Instrumented measurements of postural control provide a more accurate insight into the motor development of children with autism. This study aimed to identify postural control deficits in autistic children during quiet standing before and after transient locomotor task. It was hypothesized that the parameters that characterize the trajectory of center of foot pressure (COP) displacement would be higher in autistic children compared to typically developing children. Sixteen autistic children aged 6–10 but without a comorbidity diagnosis, were enrolled in the study group. The control group comprised 16 typically developing peers. The assessment of the transitional task comprised four different conditions: unperturbed and perturbed transition, stepping up, and stepping down tasks. Analysis of the COP signal was carried out for three distinct phases, i.e., phase 1 – quiet standing before step initiation, phase 2 – transit, and phase 3 - quiet standing until measurement completion. The two-way ANOVA with a 2 × 4 factorial design (group × testing condition) revealed a group effect on all posturographic variables in the antero-posterior and medio-lateral directions of phase 1 and in the antero-posterior direction of phase 3. The Bonferroni post-hoc test showed the means of all those variables were significantly higher for the autistic than for typically developing children. Group allocation also had an effect on the time of transit and step length, which turned out to be significantly longer in autistic children compared to healthy peers. Autistic children show increased postural sway before and after transitional locomotor tasks compared to typically developing children. The trial was prospectively registered in the Australian and New Zealand Clinical Trials Registry (no. ACTRN12621001113842; date registered: 23.08.2021). • Autistic children show postural control deficits before and after step initiation. • Autistic children take longer to complete a step than typically developing peers. • Posturography can be used to assess postural control in autistic children. [ABSTRACT FROM AUTHOR]

In this study, we present an empirical comparison of statistical models and machine learning models for daily electricity price forecasting in the New Zealand electricity market. We demonstrate the effectiveness of GARCH and SV models and their t -distribution variants when paired with feature selection techniques, including LASSO, mutual information, and recursive feature elimination. A key aspect of our study is the inclusion of a diverse set of explanatory variables in all models. We compare these models against a range of popular machine learning models, including LSTM, GRU, XGBoost, LEAR, and a four-layer DNN, where the latter two are considered benchmarks. Our results reveal that GARCH and SV models, particularly their t variants, perform exceptionally well when paired with feature selection techniques and explanatory variables. In most scenarios considered, these models outperform machine learning models when coupled with LASSO feature selection. This contribution provides a comprehensive evaluation of the performance of different models and feature selection techniques for electricity price forecasting in the New Zealand electricity market. Our best-performing model improves the symmetric mean absolute percentage error (sMAPE) and mean absolute scaled error (MASE) by 2% to 3% over the LEAR benchmark model, highlighting the practical relevance of our findings. • Statistical and machine learning models to forecast New Zealand electricity prices are compared. • GARCH and SV models with t distribution excel with explanatory variables and feature selection. • LASSO feature selection outperforms base models in most cases. • GARCH and SV models with LASSO outperform the benchmark LEAR and DNN models. [ABSTRACT FROM AUTHOR]

Dietary pattern analysis considers the overall dietary intake and combinations of foods eaten. Valid and reproducible tools for determining dietary patterns are necessary to assess diet–disease relationships. This study evaluated the relative validity and reproducibility of the Researching Eating, Activity, and Cognitive Health (REACH) Study food frequency questionnaire (FFQ) specifically designed to identify dietary patterns in older adults. A subset of participants from the REACH study completed two identical 109-item FFQs 1 month apart (FFQ1 and FFQ2) to assess reproducibility and a 4-day food record between FFQ administrations to assess relative validity. Foods from each dietary assessment tool were assigned to 57 food groups. Principal component analysis was applied to the food group consumption reported in each dietary assessment tool to derive dietary patterns. Dietary data were collected (2018 and 2019) from a subset of the REACH study (n = 294, 37% men) aged 65 to 74 years, living in Auckland, New Zealand. Daily intakes of 57 food groups and dietary patterns of older adults participating in REACH living in New Zealand. Agreement of dietary pattern loadings were assessed using Tucker's congruence coefficient. Agreement of dietary pattern scores and food group intakes were assessed using Spearman correlation coefficients (acceptable correlation rho = 0.20 to 0.49), weighted kappa statistic (acceptable statistic κ w = 0.20 to 0.60), and Bland-Altman analysis, including mean difference, limits of agreement, plots, and slope of bias. Three similar dietary patterns were identified from each dietary assessment tool: Mediterranean style, Western, and prudent. Congruence coefficients between factor loadings ranged from 0.54 to 0.80. Correlations of dietary pattern scores ranged from 0.47 to 0.59 (reproducibility) and 0.33 to 0.43 (validity) (all P values < 0.001); weighted kappa scores from 0.40 to 0.48 (reproducibility) and 0.27 to 0.37 (validity); limits of agreement from ± 1.79 to ± 2.09 (reproducibility) and ± 2.09 to ± 2.27 (validity); a negative slope of bias was seen in the prudent pattern for reproducibility and validity (P < 0.001). The REACH FFQ generated dietary patterns with acceptable reproducibility and relative validity and therefore can be used to examine associations between dietary patterns and health outcomes in older New Zealand adults. [ABSTRACT FROM AUTHOR]