Cooke Monahan (deathbirch86)

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. 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. BI4020 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