This study aims to identify the optimal forecasting model for predicting Türkiye's tourism income, a crucial factor for economic planning and development. This study employs different forecasting techniques, including the seasonal Autoregressive Integrated Moving Average (SARIMA), the additive and multiplicative Holt-Winters methods, the Exponential Smoothing State Space (ETS), Artificial Neural Networks (ANNs) and seasonal-trend decomposition procedure based on the loess (STL)-ANN hybrid model and evaluates their performance. The methodology involves analyzing monthly tourism income data from January 2012 to December 2023, incorporating additional economic indicators such as the economic confidence index, number of visitors, consumer price index, industrial production index, and USD exchange rate, which serve as input for ANN models. The findings reveal that ANNs, particularly the model that incorporates tourism income alongside other economic indicators, outperform traditional models with the lowest Mean Absolute Scaled Error (MASE) and Root Mean Squared Scaled Error (RMSSE). Specifically, the ANN model with additional predictors demonstrates the highest forecasting accuracy. These results suggest that advanced machine learning techniques provide superior predictive capabilities compared to traditional linear models. The study underscores the importance of integrating complex models to achieve more accurate forecasting, offering valuable insights for policymakers and practitioners in the tourism sector.

In this article, we proposed two-layer median ranked set sampling (TMRSS) design that combines median ranked set sampling (MRSS) and two-layer ranked set sampling (TRSS). Ranked set sampling (RSS) is an alternative sampling method that can improve the efficiency of estimators when exact measurement of response variable is either difficult, time consuming or expensive. Evaluation of the TMRSS performance for different distributions, set, and cycle sizes regarding mean and regression estimators was carried out using Monte Carlo simulation study and real data application. The results indicated that estimators of TMRSS yields are either equivalent to or better than MRSS.

Objectives: To derive sex-specific Relative Body Mass Index (rBMI) cut-offs for classifying adolescents into four adiposity categories defined by dual-energy X-ray absorptiometry (DXA)-based body fat percentage (BF%), quantify their stability, and evaluate their classification performance against current BMI%-based classification and previously published ROC-derived rBMI cut-offs. Materials and Methods: Data from 567 observations in adolescents aged 11–19 years were analyzed. Adiposity categories (normal, mildly elevated, moderately elevated, severely elevated) were defined by sex-specific BF% thresholds. Classification and Regression Tree models with inverse class-frequency weighting were fitted in two formulations: sex-stratified, and a pooled-threshold model with shared lower boundaries and sex-specific upper boundaries. Threshold stability was assessed by bootstrap resampling. Performance was estimated by repeated nested cross-validation, with the full derivation pipeline repeated within each training fold; the previously published ROC-derived thresholds were re-derived within each fold so that both approaches carried the same correction for optimism. Results: Sex-stratified thresholds were 103, 118, and 162 in males and 100, 118, and 176 in females; three of six agreed with the previously published ROC-derived values to within 0.5 units and a fourth to within 3.2 units. Five of the six published values fell within the corresponding bootstrap intervals, the exception being the female moderately/severely elevated boundary (160.0; 95% CI: 161.4–218.7). The male boundary separating normal from mildly elevated adiposity was identified in 76.0% of bootstrap resamples; estimating the lower boundaries from the pooled sample raised this to 99.9% and improved ordinal agreement among males (quadratic-weighted kappa 0.812 vs. 0.759). Out-of-fold accuracy was 0.697, 0.694, and 0.694 for the sex-stratified, pooled-threshold, and ROC-derived approaches, respectively, with all paired differences including zero in the overall sample. All three exceeded BMI% classification (accuracy 0.608), which identified only 15.5% of adolescents with mildly elevated adiposity and produced more than twice the proportion of errors spanning two or more categories (6.0% vs. 1.8–2.1%). Conclusions: rBMI cut-offs aligned more closely with DXA-defined adiposity than BMI% classification, particularly in the intermediate categories. Tree-derived and ROC-derived thresholds converged and performed equivalently, indicating that threshold location does not depend on the derivation procedure. A pooled-threshold formulation provided a simpler and more stable rule. These are derivation-stage estimates requiring external validation before clinical adoption.

This study examined associations of perceived restedness, sleep duration, and emotion regulation difficulties with mental health outcomes among university students. Data from 1192 Canadian college students were analyzed using multiple regression models. Mental health outcomes included distress (DASS), perceived stress (PSS), anxiety (GAD), and depression (PHQ), alongside indicators of emotion regulation, physical activity, mindfulness, and social support. Hierarchical models assessed the incremental value of these predictors. Across models, perceived restedness was consistently associated with better mental health after accounting for sleep duration and other covariates, whereas sleep duration showed less consistent associations across outcomes. Difficulties in emotion regulation were the strongest and most consistent correlates across outcomes, while physical activity and mindfulness showed inconsistent effects once other variables were considered. Adding health behavior variables improved model performance, whereas sociodemographic factors contributed little additional explanatory value. Interaction effects were generally small and inconsistent, although students who felt somewhat rested appeared to benefit more from stronger social support. These findings suggest perceived restedness may serve as a practical and efficient indicator of mental health screening, but results should be interpreted as hypothesis-generating given measurement asymmetry, self-report design, and pandemic-era data.