Wallaceharmon9855

Results point to redefining the way humanity has related, functioned and conceptualised realities. There is need to go beyond prevention from infection as majority of urban dwellers are in the informal sector or unemployed. NSC 178886 For the urban poor, strategies for social distancing may not be possible or effective. People are being asked to make choices between being hungry and risk of getting infected. The paper recommends planning response at national, regional and local level bearing in mind informal settlements, high densities and forms of overcrowding which have been placed as hotspots for the virus. There is need for rebuilding societies, during and beyond COVID-19 calling for immediate disaster risk planning adaptation and transformation to promote resilience.How do guests feel during their stay at quarantine lodging? This study draws on terror management theory and social exclusion theory to synthesize a model that highlights guests' perceptions about their experience under enforced isolation. The model articulates guests' feeling of anxiety and loneliness, whereas quality of service presents warmth and care that activates an anxiety buffer mechanism that mitigates the effect of anxiety. In turn guests' level of anxiety is further explained by an interaction between their health status and the length of stay. Results point to a conduit for studying the dark side of hospitality, opening up research avenues that could help assess broader social behavioral changes during the global pandemic, while offering operators revelations for lodging management during a crisis.Evaluation studies of youth employment programs prioritize employment and earnings outcomes and use these indicators to determine what labor market interventions are most successful. Evidence from pre and post data of a cluster randomized controlled longitudinal study, consisting of 1 892 youth between 18 and 25 years who participated in Youth Employability Programs (YEPs) in South Africa, confirms the importance of the inclusion of non-economic indicators to measure success for youth. This study provides evidence that non-economic markers of success such as job-search resilience, self-esteem, self-efficacy and future orientation are potentially important in the transition to employment in the longer term and points to the need for more evaluations that use these markers to predict youth's success in employment. The findings further suggest that these non-economic outcomes, which were conceptualized as intermediary outcomes, can influence how young people manage the increasingly protracted and difficult transition to work. The study enlarges our understanding of the non-linear and protracted pathways of youth transitions to work in a development context, and how to best support youth in this transition period. These findings have implications for rethinking YEP evaluation outcomes that could lead to adaptive programming and management of interventions.In view of recent global pandemic the 3-alkynyl substituted 2-chloroquinoxaline framework has been explored as a potential template for the design of molecules targeting COVID-19. Initial in silico studies of representative compounds to assess their binding affinities via docking into the N-terminal RNA-binding domain (NTD) of N-protein of SARS-CoV-2 prompted further study of these molecules. Thus building of a small library of molecules based on the said template became essential for this purpose. Accordingly, a convenient and environmentally safer method has been developed for the rapid synthesis of 3-alkynyl substituted 2-chloroquinoxaline derivatives under Cu-catalysis assisted by ultrasound. This simple and straightforward method involved the coupling of 2,3-dichloroquinoxaline with commercially available terminal alkynes in the presence of CuI, PPh3 and K2CO3 in PEG-400. Further in silico studies revealed some remarkable observations and established a virtual SAR (Structure Activity Relationship) within the series. Three compounds appeared as potential agents for further studies.The COVID-19 outbreak continues to threaten the health and life of people worldwide. It is an immediate priority to develop and test a computer-aided detection (CAD) scheme based on deep learning (DL) to automatically localize and differentiate COVID-19 from community-acquired pneumonia (CAP) on chest X-rays. Therefore, this study aims to develop and test an efficient and accurate deep learning scheme that assists radiologists in automatically recognizing and localizing COVID-19. A retrospective chest X-ray image dataset was collected from open image data and the Xiangya Hospital, which was divided into a training group and a testing group. The proposed CAD framework is composed of two steps with DLs the Discrimination-DL and the Localization-DL. The first DL was developed to extract lung features from chest X-ray radiographs for COVID-19 discrimination and trained using 3548 chest X-ray radiographs. The second DL was trained with 406-pixel patches and applied to the recognized X-ray radiographs to localize and assign them into the left lung, right lung or bipulmonary. X-ray radiographs of CAP and healthy controls were enrolled to evaluate the robustness of the model. Compared to the radiologists' discrimination and localization results, the accuracy of COVID-19 discrimination using the Discrimination-DL yielded 98.71%, while the accuracy of localization using the Localization-DL was 93.03%. This work represents the feasibility of using a novel deep learning-based CAD scheme to efficiently and accurately distinguish COVID-19 from CAP and detect localization with high accuracy and agreement with radiologists.After several decades' development of retrieval techniques in aerosol remote sensing, no fast and accurate analytical Radiative Transfer Model (RTM) has been developed and applied to create global aerosol products for non-polarimetric instruments such as Ocean and Land Colour Instrument/Sentinel-3 (OLCI/Sentinel-3) and Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). Global aerosol retrieval algorithms are typically based on a Look-Up-Table (LUT) technique, requiring high-performance computers. The current eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm also utilizes the LUT method. In order to have a near-real time retrieval and achieve a quick and accurate "FIRST-LOOK" aerosol product without high-demand of computing resource, we have developed a Fast and Accurate Semi-analytical Model of Atmosphere-surface Reflectance (FASMAR) for aerosol remote sensing. The FASMAR is developed based on a successive order of scattering technique. In FASMAR, the first three orders of scattering are calculated exactly.