The benefits of physical activity for the physical health of individuals are well documented. Less is known about the benefits of physical activity for mental health. This paper explores the associations between physical activity and positive mental health and mental health problems. The paper utilises data collected from a representative sample of 10-17-year-old adolescents in Ireland. Physical activity in the study is measured using moderate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA). Mental health was measured using the Cantril Leader of Life Satisfaction, the WHO-5 index, Mental Health Inventory (MHI-5) and the Health Behaviour in School-Aged Children (HBSC) Symptom Checklist (HBSC-SCL). Data were analysed using bivariate (Pearson Correlation, t-test, one-way ANOVA) and multivariate (two-way ANOVA, ordinary least squares (OLS) regressions) analyses. In total, 8636 adolescents were included in this analysis. Higher participation in physical activity was associated with higher scores on the positive mental health indicators and lower scores on the mental health problems indicators. When modelled together, VPA was a stronger predictor of mental health than MVPA, especially in girls. For example, standardised beta coefficients for predicting MHI-5 were -0.09 for MVPA (p less then 0.001) and -0.13 for VPA (p less then 0.001) To our knowledge, this is the first study that looks at levels of physical activity as well as both positive mental health and mental health problems. The study highlights the need to encourage and enable adolescents, and especially girls, to participate in vigorous exercising as way of promoting positive mental health.High crystalline ZnO nanorods (NRs) on Zn pre-deposited graphene/Cu sheet without graphene transfer process have been fabricated by self-catalyzed vapor-phase transport synthesis. Here, the pre-deposited Zn metal on graphene not only serves as a seed to grow the ZnO NRs, but also passivates the graphene underneath. The temperature-dependent photoluminescence spectra of the fabricated ZnO NRs reveal a dominant peak of 3.88 eV at 10 K associated with the neutral-donor bound exciton, while the redshifted peak by bandgap shrinkage with temperature and electron-lattice interactions leads a strong emission at 382 nm at room temperature. The optical absorption of the ZnO NRs/graphene hetero-nanostructure at this ultraviolet (UV) emission is then theoretically analyzed to quantify the absorption amount depending on the ZnO NR distribution. By simply covering the ZnO NR/graphene/Cu structure with the graphene/glass as a top electrode, it is observed that the current-voltage characteristic of the ZnO NR/graphene hetero-nanojunction device exhibits a photocurrent of 1.03 mA at 3 V under a light illumination of 100 μW/cm2. In particular, the suggested graphene/ZnO NRs/graphene hybrid-nanostructure-based devices reveal comparable photocurrents at a bidirectional bias, which can be a promising platform to integrate 1D and 2D nanomaterials without complex patterning process for UV device applications.Microbial biomass concentration is a key bioprocess parameter, estimated using various labor, operator and process cross-sensitive techniques, analyzed in a broad context and therefore the subject of correct interpretation. In this paper, the authors present the results of P. pastoris cell density estimation based on off-line (optical density, wet/dry cell weight concentration), in-situ (turbidity, permittivity), and soft-sensor (off-gas O2/CO2, alkali consumption) techniques. Cultivations were performed in a 5 L oxygen-enriched stirred tank bioreactor. The experimental plan determined varying aeration rates/levels, glycerol or methanol substrates, residual methanol levels, and temperature. In total, results from 13 up to 150 g (dry cell weight)/L cultivation runs were analyzed. Linear and exponential correlation models were identified for the turbidity sensor signal and dry cell weight concentration (DCW). Evaluated linear correlation between permittivity and DCW in the glycerol consumption phase ( less then 60 g/L) and medium (for Mut+ strain) to significant (for MutS strain) linearity decline for methanol consumption phase. DCW and permittivity-based biomass estimates used for soft-sensor parameters identification. Dataset consisting from 4 Mut+ strain cultivation experiments used for estimation quality (expressed in NRMSE) comparison for turbidity-based (8%), permittivity-based (11%), O2 uptake-based (10%), CO2 production-based (13%), and alkali consumption-based (8%) biomass estimates. Fasiglifam cell line Additionally, the authors present a novel solution (algorithm) for uncommon in-situ turbidity and permittivity sensor signal shift (caused by the intensive stirrer rate change and antifoam agent addition) on-line identification and minimization. The sensor signal filtering method leads to about 5-fold and 2-fold minimized biomass estimate drifts for turbidity- and permittivity-based biomass estimates, respectively.Pregnancy and the postpartum period represent a condition characterized by a thrombotic predisposition. The majority of pregnant women do not face acute or severe thrombotic events. In general, mild inconveniences such as leg swelling or moderately painful thrombotic events (phlebitis) are encountered. However, when pregnancy is associated with inherited or acquired deficits that affect homeostasis, the risk of acute or even life-threatening events can increase significantly. The major consequence is the loss of the fetus or the venous thromboembolism that endangers the mother’s life. Venous thromboembolism is caused by deep vein thrombosis, therefore timely detection and especially the assessment of the extent of the thrombotic event are crucial. In this paper we have summarized the most important paraclinical investigations. The study emphasizes the importance of selecting the methods of investigation. The right choice allows establishing a correct diagnosis and individualizing the treatment.We outline in this article a hybrid intelligent fuzzy fractal approach for classification of countries based on a mixture of fractal theoretical concepts and fuzzy logic mathematical constructs. The mathematical definition of the fractal dimension provides a way to estimate the complexity of the non-linear dynamic behavior exhibited by the time series of the countries. Fuzzy logic offers a way to represent and handle the inherent uncertainty of the classification problem. The hybrid intelligent approach is composed of a fuzzy system formed by a set of fuzzy rules that uses the fractal dimensions of the data as inputs and produce as a final output the classification of countries. The hybrid approach calculations are based on the COVID-19 data of confirmed and death cases. The main contribution is the proposed hybrid approach composed of the fractal dimension definition and fuzzy logic concepts for achieving an accurate classification of countries based on the complexity of the COVID-19 time series data. Publicly available datasets of 11 countries have been the basis to construct the fuzzy system and 15 different countries were considered in the validation of the proposed classification approach.