Treatment efficiency was lower in the UV-AHP than in the UV-APS treatment system and was attributed to greater aqueous and solid phase scavenging rates. The cost of commercially available H2O2 ($0.031 mol-1) and PS ($0.24 mol-1) was used in conjunction with the overall treatment efficiency to assess specific cost of treatment. The specific cost to treat the probe compound with UV-AHP was greater than UV-APS and was attributed to the much lower treatment efficiency with UV-AHP. The much-desired high reaction rate constants between •OH and environmental contaminants, relative to SO4 •-, may come at the cost of greater combined scavenging rates, and consequently lower treatment efficiency.Procrastination is a maladaptive behaviour that students often experience in academic activities and can result in negative consequences to mental health.  https://www.selleckchem.com/products/ly2157299.html  The challenges imposed by the COVID-19 pandemic can contribute to increase procrastination behaviors in academic activities that the student does not like and in those he/she is passionate. The main objective of this research was to test an integrative model of passion, procrastination, satisfaction with life and psychological distress in students during pandemic. The sample was comprised of 416 university students aged between 18 and 57 years (M age  = 24.81 ± 7.02, 78.1% women). Structural Equation Modeling results revealed that academic procrastination is negatively linked to harmonious passion, and positively linked to obsessive passion. Academic procrastination in turn is negatively linked to satisfaction with life and positively linked to psychological distress. Harmonious passion also was directly positively associated to satisfaction with life and negatively associated to psychological distress. These results suggest that students' harmonious passion for their studies plays a protective role against academic procrastination and mental health indicators, while obsessive passion represents a risk factor.The unavailability of appropriate mechanisms for timely detection of diseases and successive treatment causes the death of a large number of people around the globe. The timely diagnosis of grave diseases like different forms of cancer and other life-threatening diseases can save a valuable life or at least extend the life span of an afflicted individual. The advancement of the Internet of Medical Things (IoMT) enabled healthcare technologies can provide effective medical facilities to the population and contribute greatly towards the recuperation of patients. The usage of IoMT in the diagnosis and study of histopathological images can enable real-time identification of diseases and corresponding remedial actions can be taken to save an affected individual. This can be achieved by the use of imaging apparatus with the capacity of auto-analysis of captured images. However, most deep learning-based image classifying models are bulk in size and are inappropriate for use in IoT based imaging devices. The objective of this research work is to design a deep learning-based lightweight model suitable for histopathological image analysis with appreciable accuracy. This paper presents a novel lightweight deep learning-based model "ReducedFireNet", for auto-classification of histopathological images. The proposed method attained a mean accuracy of 96.88% and an F1 score of 0.968 on evaluating an actual histopathological image data set. The results are encouraging, considering the complexity of histopathological images. In addition to the high accuracy the lightweight design (size in few KBs) of the ReducedFireNet model, makes it suitable for IoMT imaging equipment. The simulation results show the proposed model has computational requirement of 0.201 GFLOPS and has a mere size of only 0.391 MB.A major focus of current research is understanding why people fall for and share fake news on social media. While much research focuses on understanding the role of personality-level traits for those who share the news, such as partisanship and analytic thinking, characteristics of the articles themselves have not been studied. Across two pre-registered studies, we examined whether character-deprecation headlines - headlines designed to deprecate someone's character, but which have no impact on policy or legislation - increased the likelihood of self-reported sharing on social media. In Study 1 we harvested fake news items from online sources and compared sharing intentions between Republicans and Democrats. Results showed that, compared to Democrats, Republicans had greater intention to share character-deprecation headlines compared to news with policy implications. We then applied these findings experimentally. In Study 2 we developed a set of fake news items that was matched for content across pro-Democratic and pro-Republican headlines and across news focusing on a specific person (e.g., Trump) versus a generic person (e.g., a Republican). We found that, contrary to Study 1, Republicans were no more inclined toward character deprecation than Democrats. However, these findings suggest that while character assassination may be a feature of pro-Republican news, it is not more attractive to Republicans versus Democrats. News with policy implications, whether fake or real, seems consistently more attractive to members of both parties regardless of whether it attempts to deprecate an opponent's character. Thus, character deprecation in fake news may in be in supply, but not in demand.Incidental detection of species of concern (e.g., invasive species, pathogens, threatened and endangered species) during biodiversity assessments based on high-throughput DNA sequencing holds significant risks in the absence of rigorous, fit-for-purpose data quality and reporting standards. Molecular biodiversity data are predominantly collected for ecological studies and thus are generated to common quality assurance standards. However, the detection of certain species of concern in these data would likely elicit interest from end users working in biosecurity or other surveillance contexts (e.g., pathogen detection in health-related fields), for which more stringent quality control standards are essential to ensure that data are suitable for informing decision-making and can withstand legal or political challenges. We suggest here that data quality and reporting criteria are urgently needed to enable clear identification of those studies that may be appropriately applied to surveillance contexts. In the interim, more pointed disclaimers on uncertainties associated with the detection and identification of species of concern may be warranted in published studies.