Forecasting the Long-Term Trends of Coronavirus Disease 2019 (COVID-19) Epidemic Using the Susceptible-Infectious-Recovered (SIR) Model

A simple model for predicting Coronavirus Disease 2019 (COVID-19) epidemic is presented in this study. The prediction model is presented based on the classic Susceptible-Infectious-Recovered (SIR) model, which has been widely used to describe the epidemic time evolution of infectious diseases. The original version of the Kermack and McKendrick model is used in this study. This included the daily rates of infection spread by infected individuals when these individuals interact with a susceptible population, which is denoted by the parameter β, while the recovery rates to determine the number of recovered individuals is expressed by the parameter γ. The parameters estimation of the three-compartment SIR model is determined through using a mathematical sequential reduction process from the logistic growth model equation. As the parameters are the basic characteristics of epidemic time evolution, the model is always tested and applied to the latest actual data of confirmed COVID-19 cases. It seems that this simple model is still reliable enough to describe the dynamics of the COVID-19 epidemic, not only qualitatively but also quantitatively with a high degree of correlation between actual data and prediction results. Therefore, it is possible to apply this model to predict cases of COVID-19 in several countries. In addition, the parameter characteristics of the classic SIR model can provide information on how these parameters reflect the efforts by each country to prevent the spread of the COVID-19 outbreak. This is clearly seen from the changes of the parameters shown by the classic SIR model.

Computational Generation of Virtual Concrete Mesostructures

Concrete is a heterogeneous material with a disordered material morphology that strongly governs the behaviour of the material. In this contribution, we present a computational tool called the Concrete Mesostructure Generator (CMG) for the generation of ultra-realistic virtual concrete morphologies for mesoscale and multiscale computational modelling and the simulation of concrete. Given an aggregate size distribution, realistic generic concrete aggregates are generated by a sequential reduction of a cuboid to generate a polyhedron with multiple faces. Thereafter, concave depressions are introduced in the polyhedron using Gaussian surfaces. The generated aggregates are assembled into the mesostructure using a hierarchic random sequential adsorption algorithm. The virtual mesostructures are first calibrated using laboratory measurements of aggregate distributions. The model is validated by comparing the elastic properties obtained from laboratory testing of concrete specimens with the elastic properties obtained using computational homogenisation of virtual concrete mesostructures. Finally, a 3D-convolutional neural network is trained to directly generate elastic properties from voxel data.

Computational Generation of Virtual Concrete Mesostructures

Concrete is a heterogeneous material with a disordered material morphology that strongly governs the behavior of the material. In this contribution, we present a computational tool called the Concrete Mesostructure Generator (CMG) for the generation of ultra-realistic virtual concrete morphologies for mesoscale and multiscale computational modeling and simulation of concrete. Given an aggregate size distribution, realistic generic concrete aggregates are generated by a sequential reduction of a cuboid to generate a polyhedron with multiple faces. Thereafter, concave depressions are introduced in the polyhedron using gaussian surfaces. The generated aggregates are assembled into the mesostructure using a hierarchic random sequential adsorption algorithm. The virtual mesostructures are first calibrated using laboratory measurements of aggregate distributions. The model is validated by comparing the elastic properties obtained from laboratory testing of concrete specimens with the elastic properties obtained using computational homogenisation of virtual concrete mesostructures. Finally, a 3D-convolutional neural network is trained to directly generate elastic properties from voxel data.

Case of vaso-occlusive retinopathy in Kikuchi-Fujimoto and lupus overlap syndrome

A 35-year-old woman presented with a constellation of systemic symptoms: rashes, weight loss, arthralgia and mouth ulcers. Six months afterwards, she experienced bilateral and sequential reduction in vision, and was found to have bilateral vaso-occlusive retinopathy, with critical macular ischaemia in the left eye. Her serological markers were consistent with a diagnosis of lupus. A lymph node biopsy confirmed Kikuchi-Fujimoto disease, a benign condition of unknown cause characterised by fever, cervical and axillary lymphadenopathy. Given that this overlap syndrome was associated with a number of systemic features and had affected the eyes, an immunosuppressive regime with rituximab was considered prudent. This rendered her vasculitis stable and non-progressive, and there were signs of partial retinal microvasculature recovery on optical coherence tomography angiography. There is increasing evidence of an overlap between Kikuchi-Fujimoto disease and systemic lupus erythematosus, which is associated with vaso-occlusive retinopathy. In these instances, a multidisciplinary approach is warranted, with consideration of appropriate treatment in order to prevent harmful sequelae of vasculitis. Our treatment with rituximab abated the disease process, although close follow-up is paramount to monitor results and side-effects of treatment.

Significance of Inorganic Nitrate Supplement in Cardiovascular Health

Background amp; Objectives: Nitric Oxide (NO) is frequently produced by the enzyme Nitric Oxide Synthase (NOS) and is crucial to the control and effective ness of the cardiovascular system. However, there is substantial reduction in NOS activity with aging that can lead to the development of hypertension and other cardiovascular obstacles. Fortunately, NO can also being produced by sequential reduction of inorganic nitrates supplementation. This proves that NO from inorganic nitrate supplements can provide compensation when NOS activity is inadequate and cardio protective benefits and beyond that provided by healthy NOS system. Discussion: This review focus on the general information about Nitrous oxide, types, mechanism of action of NO & overview of NOS activity is inadequate and cardio protective benefits and beyond that provided by healthy NOS system were often studied for cardiovascular treatments. Conclusion: We concluded that the Natural plant NO is the essential for cardiovascular activity to target site with desired concentration. Moreover, the researchers were focused on Evidence suggested that nitrate supplementation can help regulate blood pressure, limit progression of atherosclerosis, and improve myocardial contractility in both healthy individuals and those with cardiovascular disease.

Dynamic reduction in the calculation of bridge structures

In modern bridge construction, on the one hand, there are increasing trends towards increasing bridge spans, which requires reducing the weight of structures. On the other hand, the use of structural elements made of various composite materials is expanding, which allows to significantly reduce the weight of the bridge structures as a whole. However, the creation of new forms of span structures of bridges requires more detailed calculations in order to optimize such forms, in particular the role of calculating the dynamic impact, because with increasing spans and weight loss, increases design flexibility and sensitivity to dynamic loads. In the present paper, the problems of solving an incomplete algebraic problem of eigenvalues and eigenvectors are considered. To increase the accuracy of the calculation and exclude the use of high-order matrices, a method of sequential reduction of the stiffness and equivalent mass matrices is proposed. The method is based on the construction of partial systems using a static transformation, followed by the solution of its own problem for the partial system. In the process of solving this problem through the eigenvectors of the system, the minor unknowns are reduced to the main ones. Dynamic reduction showed high calculation accuracy..

Catalytic Enantioselective Synthesis of Benzocyclobutenols and Cyclobutanols via A Sequential Reduction/C-H Functionalization

We report here a sequential enantioselective reduction/C-H functionalization to install contiguous stereogenic carbon centers of benzocyclobutenols and cyclobutanols. This strategy features a practical enantioselective reduction of ketone and a diastereospecific...

The association between general practitioner regularity of care and ‘high use’ hospitalisation

Background In Australia, as in many high income countries, there has been a movement to improve out-of-hospital care. If primary care improvements can yield appropriately lower hospital use, this would improve productive efficiency. This is especially important among ‘high cost users’, a small group of patients accounting for disproportionately high hospitalisation costs. This study aimed to assess the association between regularity of general practitioner (GP) care and ‘high use’ hospitalisation. Methods This retrospective, cohort study used linked administrative and survey data from the 45 and Up Study, conducted in New South Wales, Australia. The exposure was regularity of GP care between 1 July 2005 and 30 June 2009, categorised by quintile (lowest to highest). Outcomes were ‘high use’ of hospitalisation (defined as ≥3 and ≥ 5 admissions within 12 months), extended length of stay (LOS, ≥30 days), a combined metric (≥3 hospitalisations in a 12 month period where ≥1 hospitalisation was ≥30 days) and 30-day readmission between 1 July 2009 and 31 December 2017. Associations were assessed using multivariable logistic regression. Potential for outcome prevention in a hypothetical scenario where all individuals attain the highest GP regularity was estimated via the population attributable fraction (PAF). Results Of 253,500 eligible participants, 15% had ≥3 and 7% had ≥5 hospitalisations in a 12-month period. Five percent of the cohort had a hospitalisation lasting ≥30 days and 25% had a readmission within 30 days. Compared with lowest regularity, highest regularity was associated with between 6% (p < 0.001) and 11% (p = 0.027) lower odds of ‘high use’. There was a 7–8% reduction in odds for all regularity levels above ‘low’ regularity for LOS ≥30 days. Otherwise, there was no clear sequential reduction in ‘high use’ with increasing regularity. The PAF associated with a move to highest regularity ranged from 0.05 to 0.13. The number of individuals who could have had an outcome prevented was estimated to be between 269 and 2784, depending on outcome. Conclusions High GP regularity is associated with a decreased likelihood of ‘high use’ hospitalisation, though for most outcomes there was not an apparent linear association with regularity.