Aldridgemarshall4439

The growing and pervasive presence of plastic pollution has attracted considerable interest in recent years, especially small ( less then 5 mm) plastic particles known as 'microplastics' (MPs). Their widespread presence may pose a threat to marine organisms globally. Most of the nano and microplastic (N&MP) pollution in marine environments is assumed to originate from land-based sources, but their sources, transport routes, and transformations are uncertain. Information on freshwater and terrestrial systems is lacking, and data on nanoplastic pollution are particularly sparse. The shortage of systematic studies of freshwater and terrestrial systems is a critical research gap because estimates of plastic release into these systems are much higher than those for oceans. As most plastic pollution originates in urban environments, studies of urban watersheds, particularly those with high population densities and industrial activities, are especially relevant with respect to source apportionment. Released plastic debris is transported in water, soil, and air. It can be exchanged between environmental compartments, adsorb toxic compounds, and ultimately be carried long distances, with potential to cause both physical and chemical harm to a multitude of species. Measurement challenges and a lack of standardized methods has slowed progress in determining the environmental prevalence and impacts of N&MPs. An overall aim of this review is to report the sources and abundances of N&MPs in urban watersheds. We focus on urban watersheds, and summarize monitoring methods and their limitations, knowing that identifying N&MPs and their urban/industrial sources is necessary to reduce their presence in all environments.Why are in-kind transfers a prominent feature of the U.S. social safety net, and why is such a significant fraction of these benefits given to individuals who do not actively supply labor in the market? This paper presents home production as a novel rationalization for such transfers. It first shows that in a broad class of dynamic Mirrleesian models that include only market production, the optimal allocation features undistorted marginal rates of substitution between goods whenever agents are not working, and thus in-kind benefits provided to these agents do not help decentralize an optimal allocation. However, adding home production drastically changes the nature of the optimal allocation. In particular, if goods and labor are substitutes in home production and home and market productivity are positively correlated, in-kind benefits in the form of goods used in home production, such as groceries, energy, and housing capital, should be provided to agents who do not work. A numerical simulation shows that the optimal in-kind program for disabled workers in a plausibly calibrated version of the home production model is consistent with the scale of SNAP and other programs that provide home production goods in the U.S.The complexity of systems now under consideration (be they biological, physical, chemical, social, etc), together with the technicalities of experimentation in the real-world and the non-linear nature of system dynamics, means that computational modelling is indispensible in the pursuit of furthering our understanding of complex systems. Agent-based modelling and simulation is rapidly increasing in its popularity, in part due to the increased appreciation of the paradigm by the non-computer science community, but also due to the increase in the usability, sophistication and number of modelling frameworks that use the approach. find more The Flexible Large-scale Agent-based Modelling Environment (FLAME) is a relatively recent addition to the list. FLAME was designed and developed from the outset to deal with massive simulations, and to ensure that the simulation code is portable across different scales of computing and across different operating systems. In this study, we report our experiences when using FLAME to model the development and propagation of conflict within large multi-partner enterprise system implementations, which acts as an example of a complex dynamical social system. We believe FLAME is an excellent choice for experienced modellers, who will be able to fully harness the capabilities that it has to offer, and also be competent in diagnosing and solving any limitations that are encountered. Conversely, because FLAME requires considerable development of instrumentation tools, along with development of statistical analysis scripts, we believe that it is not suitable for the novice modeller, who may be better suited to using a graphical user interface driven framework until their experience with modelling and competence in programming increases.COVID-19, the highly contagious novel disease caused by SARS-CoV-2, has become a major international concern as it has spread quickly all over the globe. However, scientific knowledge and therapeutic treatment options for this new coronavirus remain limited. Although previous outbreaks of human coronaviruses (CoVs) such as SARS and MERS stimulated research, there are, to date, no antiviral therapeutics available that specifically target these kinds of viruses. Natural compounds with a great diversity of chemical structures may provide an alternative approach for the discovery of new antivirals. In fact, numerous flavonoids were found to have antiviral effects against SARS-and MERS-CoV by mainly inhibiting the enzymes 3-chymotrypsin-like protease (3CLpro) and papain-like protease (PLpro). In this review, we specifically focused on the search for flavonoids, polyphenolic compounds, which are proven to be effective against human CoVs. We therefore summarized and analyzed the latest progress in research to identify flavonoids for antiviral therapy and proposed strategies for future work on medicinal plants against coronaviruses such as SARS-CoV-2. We discovered quercetin, herbacetin, and isobavachalcone as the most promising flavonoids with anti-CoV potential.In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set.