Post COVID-19 Conditions and the Cardiovascular System

One out of four patients affected by COVID-19 will experience persistent (>3-4 weeks) signs and symptoms (Post COVID-19 conditions or Post-Acute Sequelae of SARS-CoV-2 – PASC) and this fact will have a major significance for the healthcare and economic systems in the upcoming years. The cardiovascular system is one of the key targets for the Post COVID-19 syndrome, given the pathogenesis of the virus and prevalence of ACE-2 receptors. According to our initial personal experience via the campaign “Life after COVID” of the Bulgarian Cardiac Institute, a substantial proportion of patients having suffered from COVID-19 develop long-term cardiovascular consequences. They could range from rhythm disorder and blood pressure variation, through impairment of myocardial mechanics and heart failure, and to acute vascular manifestations of Post COVID-19 conditions, such as acute coronary syndrome, acute pulmonary embolism, and acute limb ischemia. These cardiovascular complications require special and dedicated medical attention, and we could share our personal experience on the matter.

Success prediction of crowdfunding campaigns: a two-phase modeling

Purpose This study aims to analyze Kickstarter data along with social media data from a data mining perspective. Kickstarter is a crowdfunding financing plataform and is a form of fundraising and is increasingly being adopted as a source for achieving the viability of projects. Despite its importance and adoption growth, the success rate of crowdfunding campaigns was 47% in 2017, and it has decreased over the years. A way of increasing the chances of success of campaigns would be to predict, by using machine learning techniques, if a campaign would be successful. By applying classification models, it is possible to estimate if whether or not a campaign will achieve success, and by applying regression models, the authors can forecast the amount of money to be funded. Design/methodology/approach The authors propose a solution in two phases, namely, launching and campaigning. As a result, models better suited for each point in time of a campaign life cycle. Findings The authors produced a static predictor capable of classifying the campaigns with an accuracy of 71%. The regression method for phase one achieved a 6.45 of root mean squared error. The dynamic classifier was able to achieve 85% of accuracy before 10% of campaign duration, the equivalent of 3 days, given a campaign with 30 days of length. At this same period time, it was able to achieve a forecasting performance of 2.5 of root mean squared error. Originality/value The authors carry out this research presenting the results with a set of real data from a crowdfunding platform. The results are discussed according to the existing literature. This provides a comprehensive review, detailing important research instructions for advancing this field of literature.

CFD Simulation for Heat Transfer In Blast Furnace

Iron blast furnace is used in the metallurgical field to extract molten pig iron from its ore through a reduction mechanism. The furnace is a vertical shaft with circular cross section. It has five main parts: stack, belly, bosh, tuyeres and hearth. Amongst these regions, hearth is the most important one for the asset life of a furnace. Erosion of refractory lining of the hearth reduces the furnace’s campaign life. So it is necessary to understand the interactions occurring between the slag, molten metal and the refractories. But the severe operating conditions and very high temperature inside the hearth make it impossible to practically observe the processes taking place within it. In order to overcome this problem, the hearth is modeled by using various Computational Fluid Dynamics (CFD) soft-wares such as ANSYS Fluent, ANSYS-CFX, FLUENT for CATIA V5, ANSYS CFD-Flo etc. The numerical model is then supplied with data which are already known from practical situations as boundary conditions. Proper physical properties of the materials are also used as input. The software runs several simulations and provides us with the result that can validate the experimental observations up to the most accurate level. In this study, temperature distribution profile inside a blast furnace hearth has been shown by modeling a simple hearth with the help of ANSYS 15.0 Workbench. The model is simulated by changing some parameters and making several assumptions. The discrepancy in the calculated and the observed temperature opens up new scope for further improvement.

Study on High Temperature Air Combustion of Hot Blast Stove

NOx is the major technical barrier to increase hot blast temperature and prolong campaign life of hot blast stove (HBS) at present. In order to restrain the amount of NOx formation during combustion process in the HBS, the paper studies and analyses the generation mechanism of NOx production, and calculates NOx generation rate and amount in HBS by means of thermodynamic generation model. A new dome combustion stove is developed based on high temperature air combustion (HTAC) technology. A comparison on the combustion process and characteristic of conventional HBS and HTAC HBS is performed by application of CFD numerical simulation model. Temperature and concentration field distribution, flame shape and NOx concentration distribution of two kinds of stove are calculated. The result shows quite symmetrical HTAC stove temperature field distribution. Under the same dome temperature, NOx generation is 80ppm only, reduced by approximate 76% in comparison with conventional stove. HTAC HBS can obtain higher temperature, energy-saving, emission-reducing, and decrease NOx emission efficiently, as well as realize long campaign life of HBS.