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We develop a new mathematical model by including the resistive class together with quarantine class and use it to investigate the transmission dynamics of the novel corona virus disease (COVID-19). Our developed model consists of four compartments, namely the susceptible class, S ( t ) , the healthy (resistive) class, H ( t ) , the infected class, I ( t ) and the quarantine class, Q ( t ) . We derive basic properties like, boundedness and positivity, of our proposed model in a biologically feasible region. To discuss the local as well as the global behaviour of the possible equilibria of the model, we compute the threshold quantity. The linearization and Lyapunov function theory are used to derive conditions for the stability analysis of the possible equilibrium states. We present numerical simulations to support our investigations. The simulations are compared with the available real data for Wuhan city in China, where the infection was initially originated.Nitrogen dioxide (NO2) can have harmful effects on human health and can act as a precursor for the formation of other air pollutants in urban environment such as secondary PM2.5 and ozone. The lockdown measures for CoViD-19 allowed to simulate on a large scale the massive and prolonged reduction of road traffic (the main source for NO2 in urban environment). This work aims to selectively assess the maximum impact that total traffic blocking measures can have on NO2. For this reason, three megacities (London, Milan and Paris) were chosen which had similar characteristics in terms of climatic conditions, population, policies of urban traffic management and lockdown measures. 52 air quality control units have been used to compare data measured in lockdown and in the same periods of previous years, highlighting a significant decrease in NO2 concentration due to traffic (London 71.1 % - 80.8 %; Milan 8.6 % - 42.4 %; Paris 65.7 % - 79.8 %). In 2020 the contribution of traffic in London, Milan and Paris dropped to 3.3 ± 1.3 μg m-3, 6.1 ± 0.8 μg m-3, and 13.4 ± 1.5 μg m-3, respectively. Despite the significant reduction in the NO2 concentration, in UT stations average NO2 concentrations higher than 40 μg m-3 were registered for several days. In order to reduce the pollution, the limitation of road traffic could be not enough, but a vision also aimed at rethink the vehicles and their polluting effects should be developed.Sweetpotato purée processing is new to Kenya and a rapidly growing value addition activity among informal, small, and medium-sized food enterprises (SMEs) in sub-Saharan Africa (SSA). Inadequate knowledge of food safety and poor hygiene practices by food handlers, low level of compliance with Good Manufacturing Practices (GMPs), and microbial contamination are major food safety challenges in orange-fleshed sweetpotato (OFSP) purée processing in Kenya. The extent of food safety training in enhancing food safety in rural-based SMEs and food processing environments has not been fully investigated. This study aimed at evaluating the impact of food safety training on food safety knowledge and hygiene practices of food handlers and in control of microbial contamination in OFSP purée processing in Kenya. Pre- and post-food safety training assessments were conducted to determine food handler's (N = 14) knowledge and practices on food safety. Food, water, and swab samples (n = 62) from the processing environment were ge and hygiene practices as well as enhancing microbial safety and quality of processed foods in SMEs if necessary food safety support resources are provided.The widely spreading COVID-19 has caused thousands of hundreds of mortalities over the world in the past few months. Early diagnosis of the virus is of great significance for both of infected patients and doctors providing treatments. Chest Computerized tomography (CT) screening is one of the most straightforward techniques to detect pneumonia which was caused by the virus and thus to make the diagnosis. To facilitate the process of diagnosing COVID-19, we therefore developed a graph convolutional neural network ResGNet-C under ResGNet framework to automatically classify lung CT images into normal and confirmed pneumonia caused by COVID-19. In ResGNet-C, two by-products named NNet-C, ResNet101-C that showed high performance on detection of COVID-19 are simultaneously generated as well. Our best model ResGNet-C achieved an averaged accuracy at 0.9662 with an averaged sensitivity at 0.9733 and an averaged specificity at 0.9591 using five cross-validations on the dataset, which is comprised of 296 CT images. To our best knowledge, this is the first attempt at integrating graph knowledge into the COVID-19 classification task. Graphs are constructed according to the Euclidean distance between features extracted by our proposed ResNet101-C and then are encoded with the features to give the prediction results of CT images. [https://www.selleckchem.com/products/ml792.html ML792 chemical structure] Besides the high-performance system, which surpassed all state-of-the-art methods, our proposed graph construction method is simple, transferrable yet quite helpful for improving the performance of classifiers, as can be justified by the experimental results.Understanding the complex relationships amongst Water, Energy, Food and Ecosystems (WEFE nexus) together with the achievement of Sustainable Development Goals (SDGs) is critical for the development of a sustainable and secure future in the Mediterranean area. In this study, we analysed 29 case studies across the Mediterranean region which describe potential success stories for the implementation of good nexus practices. We developed an analytical framework for investigating the impacts on 15 SDG targets and we also explicitly quantified the magnitude of interconnection of nexus pillars with SDGs. Our findings showed that renewable energies have a predominant role on sustainability. Moreover, to achieve the highest positive impacts on economy, environment and society, it is necessary to ensure that both people and ecosystems benefit from a minimum amount of goods/qualities as expected by specific targets like SDG 6.1-4 (clean water and sanitation) and 15.1-3 (life on land), as well as 7.2-3 (affordable and clean energy) that are strongly linked with 13.