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Smartphone use provides a significant amount of screen-time for youth, and there have been growing concerns regarding its impact on their mental health. While time spent in a passive manner on the device is frequently considered deleterious, more active engagement with the phone might be protective for mental health. Recent developments in mobile sensing technology provide a unique opportunity to examine behaviour in a naturalistic manner. The present study sought to investigate, in a sample of 451 individuals (mean age 20.97 years old, 83% female), whether the amount of time spent on the device, an indicator of passive smartphone use, would be associated with worse mental health in youth and whether an active form of smartphone use, namely frequent checking of the device, would be associated with better outcomes. The findings highlight that overall time spent on the smartphone was associated with more pronounced internalizing and externalizing symptoms in youth, while the number of unlocks was associated with fewer internalizing symptoms. For externalizing symptoms, there was also a significant interaction between the two types of smartphone use observed. Using objective measures, our results suggest interventions targeting passive smartphone use may contribute to improving the mental health of youth., Competing Interests: Declaration of Competing Interest All authors have no conflicts of interest to declare., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The projection of several ultrasonic pulse velocity (UPV) measurements is one of the common procedures for the generation of ultrasonic tomographic images. However, only the time of arrival of ultrasonic waves or the first amplitude of the waveform after onset is evaluated. This paper presents a methodology for the generation of tomographic images from the analysis of the time-domain propagation spectrum. Nonlinear optimization techniques were applied with the use of heuristic algorithms, and the methodology effectiveness was demonstrated in simple UPV applications extended to the tomography generation. The approach provided reliable values and images in all numerical applications and showed the analysis of time spectrum significantly reduces the number of measurements required for the generation of a tomogram maintaining the quality of the results., (Copyright © 2021. Published by Elsevier B.V.)

Titanium dioxide (TiO 2 ) is an advantageous material in catalytic photodegradation due to its low cost, high stability, and considerably higher efficiency when compared to other semiconductors. However, the need for artificial radiation sources in the UV range is a limitation to its use in wastewater remediation. In this context, Localized Surface Plasmon Resonance (LSPR) has been shown to enhance the photoexcitation of charge carriers in the semiconductor. In the present work, the investigation of catalytic photodegradation of phenol solution under distinct excitation by UV-visible or just visible radiation, employing three TiO 2 based plasmonic catalysts, was conducted. Spherical silver nanoparticles which present LSPR along the TiO 2 bandgap energy and electrically insulated silver nanoparticles were employed. Gold nanoparticles, which present low energy LSPR, were also employed in order to compare the excitation efficiency. Discrete dipole approximation simulations were carried out in order to verify the electric field enhancement and penetration at the semiconductor surface of each plasmonic catalyst. The results presented here may help to shed some light with respect to the contribution of plasmonic photocatalysts and the charge transfer mechanism in catalysts containing plasmonic structures., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

This work used L-tartaric acid as a model molecule to evaluate how the use of inert and oxidizing atmospheres during pyrolysis affected the physical and optical properties of the resulting carbon dots (CDs). Pyrolysis revealed to be a simple procedure that afforded CDs in a single step, dismissed the addition of organic solvents, and involved only one extraction stage that employed water. By X-ray diffraction a dependency between the structure of the CDs and the atmosphere (oxidizing or inert) used during the pyrolysis was found. Potentiometric titration demonstrated that the CDs were largely soluble in water; it also aided characterization of the various groups that contained sp(3) -hybridized carbon atoms on the surface of the dots. Raman spectroscopy suggested that different amounts of sp(2)- and sp(3)-hybridized carbon atoms emerged on the CDs depending on the pyrolysis atmosphere. In conclusion, the pyrolysis atmosphere influenced the physical properties, such as the composition and the final structure., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.