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This dataset contains aggregated prevalence estimates regarding alcohol consumption among adolescents in the Czech Republic, based on three waves of the Health Behaviour in School-aged Children (HBSC)…

This dataset contains aggregated prevalence estimates regarding alcohol consumption among adolescents in the Czech Republic, based on three waves of the Health Behaviour in School-aged Children (HBSC) study conducted in 2014, 2018, and 2022. The data represents a total sample of 29,525 respondents (14,761 boys and 13,764 girls) across three target age groups: 11, 13, and 15 years old.

Abstract Background Sensory Processing Sensitivity (SPS) is a trait linked to deeper processing of stimuli and heightened emotional reactivity. These characteristics suggest a potential link to more …

Abstract Background Sensory Processing Sensitivity (SPS) is a trait linked to deeper processing of stimuli and heightened emotional reactivity. These characteristics suggest a potential link to more intense self-conscious emotions. The objective of this study was to investigate the associations between SPS, feelings of guilt, shame, and self-esteem, and to test whether these relationships are independent of the influence of neuroticism. Methods We conducted a cross-sectional study using data from an online survey of Czech adults (n =&thinsp;1012; 49.3 ± 16.7 years, 50.4% female). Participants completed measures of SPS (Sensory Processing Sensitivity Questionnaire, SPSQ; Highly Sensitive Person Scale), feelings of guilt and shame (Guilt and Shame Experience Scale), self-esteem (Rosenberg Self-Esteem Scale), and neuroticism (the neuroticism subscale of the Big Five Inventory). The associations were examined using linear and logistic regression models, with adjustments for neuroticism and key demographic variables. Results Linear regression analyses showed that higher SPS was significantly associated with increased feelings of guilt and shame, and with lower self-esteem. After adjustment for neuroticism, the association between SPS and self-esteem was no longer significant (β ≈ 0.03, p > 0.05), whereas the β-coefficients for feelings of guilt and shame were reduced but remained significant. Logistic regression analyses comparing low, medium, and high SPS groups and, separately, the equivalent levels on the Sensory Sensitivity subscale of the SPSQ, indicated that highly sensitive individuals were more likely to report feelings of guilt alone and combined guilt and shame, with odds ratios (ORs) ranging from 4.42 (95% CI 2.17–8.99, p < 0.001) to 6.38 (95% CI 3.08–13.25, p < 0.001). No significant associations emerged between SPS and feelings of shame alone or low self-esteem. Analyses using alternative SPS measures yielded broadly similar results, with the Highly Sensitive Person Scale showing even stronger associations with feelings of guilt and shame, while again no effect was found for self-esteem. Conclusions Highly sensitive individuals appear to be more prone to experiencing heightened feelings of guilt and, to a lesser degree, shame. However, the initially observed negative association between SPS and self-esteem was no longer significant after neuroticism was included in the model.  

Abstract Digital Behaviour Change Interventions (DBCIs) aim at improving individual health by engaging various means of Information and Communication Technology (ICT), including mobile apps and weara…

Abstract Digital Behaviour Change Interventions (DBCIs) aim at improving individual health by engaging various means of Information and Communication Technology (ICT), including mobile apps and wearables. Participant intervention fatigue may happen when DBCIs become too frequent, repetitive, demanding, or lack perceived relevance, and this may result in participants’ reduced motivation and adherence over time. Advancing technology-supported engagement mechanisms is therefore of utmost importance. To address this problem, we present a reference and solution architecture based on open-source technologies and open Application Programming Interfaces (Open APIs). First, we integrated a Large Language Model (LLM) component into the DBCI design. Second, to support context-awareness, we enhanced this integration by adding a Geographic Information Systems (GIS) element. Our pilot implemented AI4Motion platform targets both personalization and contextualization aspects of DBCIs. Our work contributes to the emerging discussion on LLM/GIS-related system design patterns for digital platforms supporting Ecological Momentary Assessment (EMA), Experience Sampling Method (ESM), and Just-in-Time Adaptive Interventions (JITAIs).  

ABSTRACT This study illustrates how organizations reconcile their information processing capabilities with uncertainty within the supply chain (SC) through generative artificial intelligence (GAI) to…

ABSTRACT This study illustrates how organizations reconcile their information processing capabilities with uncertainty within the supply chain (SC) through generative artificial intelligence (GAI) to achieve carbon performance (CP). A quantitative research methodology is applied, and 155 responses from manufacturing firms are analyzed through structural equation modeling (SEM) for hypothesis testing. The findings suggest that GAI for process automation and cognitive engagement has a positive influence on business intelligence (BI), whereas end-of-life (EOL) product management mediates the relationship between green product innovation (GPI) and CP. This study contributes to the SC context, focusing on GAI and BI in mitigating uncertainties within SCs to foster GPI and improve CP. This study highlights actionable frameworks for leveraging digital technologies in sustainable SCs by addressing technological challenges and integrating green innovation practices

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.