A novel heuristic algorithm for the modeling and risk assessment of the covid-19 pandemic phenomenon

The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the quality of the health system in each area, the age distribution of population, geographical and environmental factors as well as other conditions. Based on derived predicted epidemic curves, a new 3D-epidemic surface is proposed to assess the epidemic phenomenon at any time of its evolution. This research highlights the potential of the proposed model as a tool which can assist in the risk assessment of the COVID-19. Mapping its development through 3D-epidemic surface can assist in revealing its dynamic nature as well as differences and similarities among different districts

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.- Pfizer Pharmaceuticals(undefined

Prediction of cement-based mortars compressive strength using machine learning techniques

The application of artificial neural networks in mapping the mechanical characteristics of the cement-based materials is underlined in previous investigations. However, this machine learning technique includes several major deficiencies highlighted in the literature, such as the overfitting problem and the inability to explain the decisions. Hence, the present study investigates the applicability of other common machine learning techniques, i.e., support vector machine, random forest (RF), decision tree, AdaBoost and k-nearest neighbors in mapping the behavior of the compressive strength (CS) of cement-based mortars. To this end, a big experimental database has been compiled based on experimental data available in the literature considering, namely the cement grade, which is an important parameter for the modeling of mortar's CS. Other important parameters are namely the age, the water-to-binder ratio, the particle size distribution of the sand and the amount of plasticizer. Many models based on the influential factors affecting machine learning techniques have been developed, and their prediction capacities have been assessed using performance indexes. The present research highlights the potential of AdaBoost and RF models as useful tools which can assist in mortar design and/or optimization. In addition, mapping the development of mortar characteristics can assist in revealing the influence of the different mortar mix parameters on the compressive strength.- (undefined

Mapping and holistic design of natural hydraulic lime mortars

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cemconres.2020.106167.In recent years, the study of high hydraulicity natural hydraulic lime (NHL5) mortars has been in the focus of many researchers, as it is considered a compatible, eco-friendly binding material, which can be used both for the restoration of culturally and historically significant structures, as well as for the construction of contemporary buildings. In the present study, artificial neural networks (ANNs) are used, aiming to simulate and map the development of NHL5 mortars' characteristics, such as compressive strength (CS), ratio of compressive to flexural strength (CS/FL) and consistency (CO), for selected mortar mix parameters, namely the binder to sand ratio (B/S), the water to binder ratio (W/B) and the maximum diameter of the aggregate (MDA) for different mortar specimen ages (AS). To this purpose, databases were developed, integrating experimental data from the international literature. Experimental verification of the developed ANN models revealed satisfactory fitting between theoretical and experimental results. This research highlights the potential of ANNs as a tool which can assist in mortar design and/or optimization, while mapping the development of mortar characteristics can assist in revealing the influence of the different mortar mix parameters on each characteristic. Furthermore, by combining the results of the three developed ANNs (CS, CO, CS/FL) targeted multi-parametric design of mortars can be assisted through a novel approach.The authors would like to thank Jose Ignacio Alvarez Galindo, Professor at the University of Navarra, Spainand Binh Thai Pham, Professor at University of Transport Technology, Hanoi, Vietnam, for their valuable comments and discussions. The authors would also like to express their acknowledgement to graduate students Chrysoula Karamani, Athanasia Skentou and Ioanna Zoumpoulaki for their assistance on the computational implementation of the ANN models

Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

In this study, a model for the estimation of the compressive strength of concretes incorporating metakaolin is developed and parametrically evaluated, using soft computing techniques. Metakaolin is a component extensively employed in recent decades as a means to reduce the requirement for cement in concrete. For the proposed models, six parameters are accounted for as input data. These are the age at testing, the metakaolin percentage in relation to the total binder, the water-to-binder ratio, the percentage of superplasticizer, the binder to sand ratio and the coarse to fine aggregate ratio. For training and verification of the developed models a database of 867 experimental specimens has been compiled, following a broad survey of the relevant published literature. A robust evaluation process has been utilized for the selection of the optimum model, which manages to estimate the concrete compressive strength, accounting for metakaolin usage, with remarkable accuracy. Using the developed model, a number of diagrams is produced that reveal the highly non-linear influence of mix components to the resulting concrete compressive strength.ZU - Zagazig University(undefined