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Objectives: The present study aims to examine trends in adolescent alcohol use over the period from 2014 to 2022.Methods: Data from the last three Health Behaviour in School-aged Children (H…

Objectives: The present study aims to examine trends in adolescent alcohol use over the period from 2014 to 2022.Methods: Data from the last three Health Behaviour in School-aged Children (HBSC) surveys conducted in 2014, 2018 and 2022 were used for this study. Three measures of adolescent alcohol use have been chosen for analyses: lifetime alcohol use, last 30 days alcohol use, and repeated lifetime drunkenness. The analyses comprised calculation of period-specific prevalence estimates and testing of the significance of between-period changes using survey-adjusted logistic regression models.Results: Comparing prevalence rates between the periods, consistent decrease in adolescent alcohol use becomes apparent, particularly for drop of rates in 2018 compared to those in 2014. The corresponding data on the prevalence of lifetime alcohol use among 13-year-old boys was 59.7% in 2014 and 44.2% in 2018; and among 15-year-old boys 80.4% in 2014 and 74.9% in 2018. For 13-year-old girls, the estimated prevalence was 46.9% in 2014 and 41.1% in 2018; and for 15-year-old girls 83.7% in 2014 and 75.9% in 2018. This is the case for repeated lifetime drunkenness, and the decrease is consistent across boys and girls, as well as the respective age groups. In survey waves 2018 and 2022, we do not see a statistically significant decline, but rather a stabilisation of assessed prevalence at a level from the previous wave of the study.Conclusions: The decline in alcohol use among Czech adolescents is part of a global trend of reducing alcohol drinking among young people, on the background of social mechanisms including the change of cultural status of alcohol and changes in young people's leisure preferences.

Objectives: Regular monitoring of health-related behaviours among vulnerable populations is of public health importance. This study examines recent trends in adolescent cigarette smoking in Czech…

Objectives: Regular monitoring of health-related behaviours among vulnerable populations is of public health importance. This study examines recent trends in adolescent cigarette smoking in Czechia following the marked changes reported in the mid-2010s.Methods: Data from three recent rounds of the Health Behaviour in School-aged Children (HBSC) study conducted in Czechia in 2014, 2018 and 2022 were analysed. Temporal trends were assessed for two indicators of adolescent cigarette use: lifetime cigarette use and cigarette use in the last 30 days. Survey-adjusted binary logistic regression models were used to test changes between survey periods. In 2022, the prevalence of electronic cigarette use was additionally estimated using the same indicators.Results: A continued decline in adolescent cigarette use was observed for both indicators, extending the downward trends reported in the mid-2010s into the 2020s. The decline was most pronounced between 2014 and 2018, with smaller but persistent decreases thereafter, particularly among older adolescents. However, the findings also highlight the substantial prevalence of electronic cigarette use. In 2022, more than one-third of 15-year-olds in Czechia reported lifetime electronic cigarette use (35.1% among boys and 36.6% among girls), and approximately one in five reported use in the last 30 days (19.6% among boys and 23.0% among girls).Conclusions: While conventional cigarette use among adolescents continues to decline, electronic cigarette use represents an important component of contemporary adolescent smoking-related behaviour. In the long term, the phenomenon of electronic cigarettes may counteract intended trends in nicotine-related harms. These findings underscore the need for continued surveillance and prevention efforts in Czechia that address both conventional and emerging smoking-related products.

This directory contains R scripts, data files, and analysis reports relatedto the analysis of adherence. FILE OVERVIEW DATA FILES* data.xlsx  Main subject-level dataset. * data-fitbit.xlsx  …

This directory contains R scripts, data files, and analysis reports relatedto the analysis of adherence. FILE OVERVIEW DATA FILES* data.xlsx  Main subject-level dataset. * data-fitbit.xlsx  Dataset with detailed Fitbit-derived measures. DATA LOADING AND PREPROCESSING* data.R  R script for loading and preprocessing the main dataset. * data-fitbit.R  R script for loading and preprocessing the detailed Fitbit dataset. DESCRIPTIVE AND BASELINE ANALYSES* summary.Rmd  R Markdown document providing a basic description of the datasets. * baseline.Rmd  R Markdown document with baseline characteristics and missing-value  analysis, including evaluation and imputation of missing values in SWL  (new variable SWLlm.predicted). * baseline-by-burst.Rmd  R Markdown document with baseline characteristics stratified by burst  number. MODELING AND VARIABLE IMPORTANCE* importance.Rmd  R Markdown analysis template for evaluating variable importance using  stepwise regression and random forest models. PROJECT CONFIGURATION AND BUILD SYSTEM* Makefile.R  Project definition for the rmake package; generates the GNU Makefile. * Makefile  File dependencies and compilation commands used by GNU Make to generate  all project results. * Rproject.Rproj  RStudio project file. ------------------------------------------------------------------------------REQUIREMENTS------------------------------------------------------------------------------ 1) INSTALL REQUIRED R PACKAGES Run the following commands in R:   install.packages(c(    "tidyverse", "knitr", "rmake", "caret", "randomForest",    "fastDummies", "broom", "devtools"  ))  devtools::install_github("beerda/hammer")  devtools::install_github("beerda/mbrtools") 2) GENERATE PROJECT ANALYSES Run the analysis pipeline using rmake:   make() This command generates all results defined in the project Makefile.------------------------------------------------------------------------------  

Abstract This paper presents a structured overview and novel insights into the universal approximation property offeedforward neural networks. We categorize existing results based on the characteristi…

Abstract This paper presents a structured overview and novel insights into the universal approximation property offeedforward neural networks. We categorize existing results based on the characteristics of activation functions— ranging from strictly monotonic to weakly monotonic and continuous almost everywhere — and examinetheir implications under architectural constraints such as bounded depth and width. Building on classical resultsby Cybenko [1], Hornik [2], and Maiorov [3], we introduce new activation functions that enable even simplerneural network architectures to retain universal approximation capabilities. Notably, we demonstrate thatsingle-layer networks with only two neurons and fixed weights can approximate any continuous univariatefunction, and that two-layer networks can extend this capability to multivariate functions. These findings refinethe known lower bounds of neural network complexity and offer constructive approaches that preserve strictmonotonicity, improving upon prior work that relied on relaxed monotonicity conditions. Our results contributeto the theoretical foundation of neural networks and open pathways for designing minimal yet expressivearchitectures.

Abstract In this paper, we explore the use of General Unary Hypotheses Automaton quantifiers and provide representations for their specific subclasses. Furthermore, we focus explicitly on implication…

Abstract In this paper, we explore the use of General Unary Hypotheses Automaton quantifiers and provide representations for their specific subclasses. Furthermore, we focus explicitly on implicational quantifiers for analyzing specific relational dependencies. We discuss their suitability in fuzzy modeling and demonstrate their integration with appropriate fuzzy rules to create a new class of weighted fuzzy rules. This study contributes to the advancement of fuzzy modeling and offers a framework for further research and practical applications.  

Abstract This paper investigates the use of fuzzy set theory in approximating solutions to differential equations under uncertainty. By defining lower and upper bounds, a robust framework is develope…

Abstract This paper investigates the use of fuzzy set theory in approximating solutions to differential equations under uncertainty. By defining lower and upper bounds, a robust framework is developed for modeling transitions between function extrema, extending classical methods to fuzzy initial value problems. The study demonstrates universal approximation properties and proposes numerical techniques that ensure stability and convergence. Applications highlight the practical utility of this approach in engineering and computational mathematics, solidifying fuzzy set theory as a powerful tool for addressing uncertainty in mathematical modeling.  

Abstract This paper introduces an enhanced framework for performing division operations on interactive fuzzy numbers characterized by complete correlation. Unlike traditional methods reliant on the i…

Abstract This paper introduces an enhanced framework for performing division operations on interactive fuzzy numbers characterized by complete correlation. Unlike traditional methods reliant on the independence assumption, we build on the sup-J extension framework to support correlated input fuzzy values. The proposed method establishes precise conditions under which the result aligns with, diverges from, or subsumes conventional divisions such as Zadeh’s and the generalized Hukuhara division. Additionally, we investigate invertibility conditions for the proposed division with respect to multiplication. These refinements offer valuable theoretical insights and have implications for models involving uncertainty, including difference equations.  

In this study, we present an analysis, driven by the Critical Discourse Studies, Frame Analysis, and Narrative Analysis, of how truancy is represented in the Czech media. Based on our findings, we ass…

In this study, we present an analysis, driven by the Critical Discourse Studies, Frame Analysis, and Narrative Analysis, of how truancy is represented in the Czech media. Based on our findings, we assert that Czech media employ three frames (gender bias; moralisation and individualisation; and repression/retribution) for representing truancy, effectively depicting it as a criminal problem created by irresponsible mothers (and children they neglected) who must be punished to address the issue. Employing the cultural and feminist criminology framework, as a combination that is scarcely used in studies, we argue that media (re)produce criminalisation of mothers by amplifying deeply rooted and routinised gendered cultural stereotypes. In this sense, we show that cultural criminology is useful not only for analysing adrenaline/“exciting” transgression, subcultures, or edgework, but, especially when combined with feminist criminology, also for analysing possibilities of criminalisation rooted in mainstream culture. Furthermore, our analysis shows that Czech media representation of truancy is damaging both for society and for addressing the issue, reducing it to a mere neoliberal individual choice of irresponsible mothers, even though truancy is a much more complex phenomenon with communal and structural levels and can be seen rather as a product of advanced marginality.

We observe an unknown function of d variables f(t), t ∈ [0, 1]^d, in the Gaussian white noise model of intensity ε > 0. We assume that the function f is regular and that it is a sum of…

We observe an unknown function of d variables f(t), t ∈ [0, 1]^d, in the Gaussian white noise model of intensity ε > 0. We assume that the function f is regular and that it is a sum of k-variate functions, where k varies from 1 to s (1 ≤ s ≤ d). These functions are unknown to us and only a few of them are nonzero. In this article, we address the problem of identifying the nonzero components of f in the case when d = d_ε → ∞ as ε → 0 and s is either fixed or s = s_ε → ∞, s = o(d) as ε → ∞. This may be viewed as a variable selection problem. We derive the conditions when exact variable selection in the model at hand is possible and provide a selection procedure that achieves this type of selection. The procedure is adaptive to a degree of model sparsity described by the sparsity parameter β ∈ (0, 1). We also derive conditions that make the exact variable selection impossible. Our results augment previous work in this area.