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We quantified the joint evolution of sleep duration and screen time between 2018 and 2022 in a large sample of adolescents from Quebec, Canada, to ascertain changes that occurred during the COVID-19 pandemic. A natural experiment design was used to compare variations from year to year and in association with the pandemic outbreak. Using structural equation modeling on data collected between 2018 and 2022 among adolescents attending 63 high schools, we analyzed the joint evolution of sleep duration and screen time while adjusting for previous year values, concurrent flourishing score, sex, age, and family level of material deprivation. A total of 28,307 adolescents, aged on average 14.9 years, were included in the analyses. Between 2019 and 2022, sleep duration increased by 9.6 (5.7, 13.5) minutes and screen time by 129.2 (120.5, 138.0) minutes on average. In 2022, the adolescents spent almost equal amounts of time sleeping and using screens. Lower flourishing scores were associated with shorter sleep duration and lengthier screen time. Girls' screen time became similar to boys' over time. Adolescents now spend almost equal amounts of time sleeping and using screens, a situation that calls for urgent public health actions. These findings highlight the importance of tracking changes in adolescents' behaviours over time, to design and implement interventions adapted to the changing health needs of different groups. [ABSTRACT FROM AUTHOR]

In the domain of machine learning and predictive analytics, classifiers hold significant importance as they are instrumental in extracting valuable patterns and enabling precise predictions from data. However, data distributions in streams are rarely stable in real-world circumstances and may evolve over time. Concept drifts, or dynamic changes in the data, have a major impact on how well classifiers predict outcomes. Furthermore, virtual concept drifts have become equally essential when dealing with the development of virtual environments and synthetic data production tools. This study employs a range of conventional classifiers, and ensemble models to examine the effects of real and virtual concept drifts on the predictive performance of classifiers. In order to ensure the conclusions are applicable to a wide range of domains, the study will be undertaken on a variety of datasets. The generation of incremental data streams from historical records with drifting patterns will replicate real concept drifts. Synthetic data will be incorporated into the training and testing sets to introduce virtual concept drifts. The classifier models are then assessed using the benchmark metrics for performance on these datasets. Comparative evaluations are conducted to evaluate the classifiers' robustness and adaptability in the face of real and virtual concept drifts. The results of this study will shed light on the performance of various classifiers and models in dynamic data environments while offering helpful insights to businesses for developing machine learning applications that are more resilient. The research examined the impact of real and virtual concept drifts on classifier performance, using three models: Streaming Random Patches (SRP), Adaptive Windowing (ADWIN), and Streaming Random Patches with Adaptive Windowing (SRP-ADWIN). The study found that the SRP-ADWIN ensemble model outperformed SRP and ADWIN individually, effectively adapting to changing data distributions, and showed improved predictive accuracy of 98.63% for virtual drift and 86.46% for real drift. [ABSTRACT FROM AUTHOR]

The enhancement of dehydrogenation activity in liquid organic hydrogen carriers (LOHC), such as perhydro-benzyltoluene (H 12 -BT), has been attributed to the addition of various promoters and support modifications into platinum (Pt)-based heterogeneous catalysts. Comprehensive understanding of surface sites predominantly catalyzing the dehydrogenation process is crucial for further enhancing catalytic performance. In this contribution, an in-depth quantitative CO diffuse reflectance infrared Fourier transform spectroscopy (CO-DRIFTS) study on Pt-based catalysts with varying contents of sulfur, known to be effective in dehydrogenation reactions, is performed to unveil the active sites involved in the H 12 -BT dehydrogenation. Furthermore, while challenges associated with the adsorption step of reactants and products potentially deteriorate catalytic activity, these processes remain open questions for H 12 -BT dehydrogenation. To shed light on these aspects, we conducted in situ DRIFTS experiments utilizing H 12 -BT and H 0 -BT, its dehydrogenated counterpart, to determine the adsorption strength between these molecules and the different catalyst surfaces, influenced by the electron-deficiency effect due to the addition of sulfur. Additionally, our study investigates the correlation between sulfur-induced catalyst surface characteristics and the formation of methane through the demethylation process. [Display omitted] • Sulfur deposition increases metal dispersion by altering the particle distribution across the catalyst beads. • At a sulfur content of 0.13 wt%, the degree of dehydrogenation reaches 97 % over 50 h. • CO-DRIFTS suggest a rigorous correlation between dehydrogenation activity and the density of step Pt sites. • Sulfur addition boosts perhydro-benzyltoluene adsorption while suppressing benzyltoluene adsorption. • Demethylation reaction is restricted as sulfur loading increases due to the decline in strong Lewis acidic sites. [ABSTRACT FROM AUTHOR]