Purpose: Physical activity levels (PA-levels) significantly decline during adolescence, and sport participation during childhood and adolescence is frequently emphasized as protec-tive factors of PA-decline. However, there is a lack of studies which specifically examined sport-related factors and its influence on changes in PA (PA-changes) in adolescence. This study aimed to prospectively observe sport factors as: (i) correlates of PA-levels and (ii) pre-dictors of PA-changes in the period between 16 and 18 years of age among urban adoles-cents from Bosnia and Herzegovina. Methods: The sample of participants comprised 324 adolescents (44% females) who were prospectively observed over two testing waves: (i) baseline, when participants were 16 years old; and (ii) follow-up, 20 months later (18 years of age). The variables were collected by previously validated questionnaires including questions on predictors (sociodemographic variables and various sport factors [current/former/ever participation in individual and team sports, experience in sports, competitive result achieved]), and criteria (PA level obtained at study baseline and follow up, measured by Physical Activity Questionnaire for Adolescents [PAQ-A], and difference between PA-levels at baseline and follow-up). The t-test was used to compare PA-levels. The associations between variables were evidenced by: (i) Spear-man’s rank order correlations (between predictors and PA-levels), and (ii) logistic regression analysis (between predictors, and PA-changes observed as binomial criterion [PA-incline vs. PA-decline] – excluding those participants who reported active sport participation at study baseline). Results: The PA-level significantly declined over the study course (t-test: 6.60, p < 0.01). Sport-related predictors were significantly associated with PA at baseline (Spearman’s R: 0.33–0.45, p < 0.01), and PAat follow-up (Spearman’s R: 0.32-0.45, p < 0.01). Meanwhile, there was no significant correlation between studied predictors and differences in PA-levels between baseline and follow-up. Also, logistic regression did not reveal any significant influ-ence of predictors obtained at study baseline and PA-changes observed as binomial criterion (PA-incline vs PA-decline). Conclusion: While studied sport-related predictors significantly influence the PA-levels in the age of 16 and 18, with the higher level of PA among those adolescents who are actively in-volved in sports, sport-participation do not predict changes in PA-levels over the observed period of life. Knowing the influence of PAon overall health status, future studies should pro-vide additional details on possible predictors of PA-changes in adolescence.

An advanced system for determination of face mask efficiency is developed and presented in this paper It is based on a high-end scanning mobility particle sizer (SMPS), which was able to detect nanoparticles from the diameter of 10 nm up to 1000 nm in 129 equidistant channels Two fitting installations for face masks were used: a mannequin doll head, to simulate realistic use of face mask and a tight-fit system which prevents the air leak The SMPS-based system was able to determine mask efficiency for different particle sizes © 2020 Danube Adria Association for Automation and Manufacturing, DAAAM All rights reserved

Purpose: Change of direction speed (CODS) and reactive agility (RAG) are important qualities in futsal, but studies rarely examined the predictors of these conditioning capacities in players of advanced level. This study aimed to evaluate predictive validity of certain anthropometric and conditioning capacities in evaluation of futsal specific CODS and RAG in top-level players. Methods: The sample comprised 54 male players from Croatia and Bosnia and Herzegovina, members of teams competing at the highest national rank, including national champions for the 2017–2018 competitive season in both countries. The variables comprised set of predictors (body mass, body height, triceps skinfold, reactive strength index [RSI], sprint 10 m [S10M], and broad jump [BJ]; and four criteria: futsal specific CODS and RAG, performed with and without dribbling (CODS_D, CODS_WD, RAG_D, RAG_WD). To identify the association between variables Pearson’s correlation and multiple regressions were calculated. Results: Observed predictors explained statistically significant (p < 0.05) percentage of variance for all four criteria (Rsq: 0.28, 0.30, 0.23 and 0.25, for CODS_WD, CODS_D, RAG_WD, RAG_D, respectively). Body mass was significant predictor for all criteria (Beta: 0.35–0.51), with poorer performances in heavier players. In both performances which involved dribbling, significant predictors was RSI (Beta: −0.27 and −0.31 for CODS_D and RAG_D, respectively), with superior performances in players with better RSI. The S10M and BJ were not identified as being significantly correlated to studied RAG and CODS performances. Conclusion: Study confirmed specific influence of studied predictors of futsal specific CODS and RAG with consistent negative influence of body mass on studied performances. Almost certainly this can be explained by specifics of RAG and CODS execution. Specifically, tests are performed over relatively small distances, with several changes of direction, which clearly mimic the futsal specific performances. Although sprint performance is often observed as important determinant of CODS and RAG, herein we did not confirm its predictive validity in explanation of futsal specific CODS and RAG. Future studies should evaluate other potentially important predictors of these capacities in futsal.