Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.

Liver Injury in Patients with COVID-19 without Underlying Liver Disease

SARS-CoV-2 shows a high affinity for the ACE-2 receptor, present on the epithelial cells of the upper and lower respiratory tract, within the intestine, kidneys, heart, testes, biliary epithelium, and—where it is particularly challenging—on vascular endothelial cells. Liver involvement is a rare manifestation of COVID-19. Material and Methods: We reviewed 450 patients admitted due to the fact of SARS-CoV-2 infection (COVID-19) including 88 with liver injury. Based on medical history and previous laboratory test results, we excluded cases of underlying liver disease. The analysis involved a clinical course of COVID-19 in patients without underlying liver disease as well as the type and course of liver injury. Results: Signs and symptoms of liver injury were present in 20% of patients, mostly presenting as a mixed-type pattern of injury with less common cases of standalone hepatocellular (parenchymal) or cholestatic injury. The liver injury symptoms resolved at the end of inpatient treatment in 20% of cases. Sixteen patients died with no cases where liver injury would be deemed a cause of death. Conclusions: (1) Liver injury secondary to COVID-19 was mild, and in in 20%, the signs and symptoms of liver injury resolved by the end of hospitalization. (2) It seems that liver injury in patients with COVID-19 was not associated with a higher risk of mortality. (3) The underlying mechanism of liver injury as well as its sequelae are not fully known. Therefore, caution and further monitoring are advised, especially in patients whose liver function tests have not returned to normal values.

ISWFoam: a numerical model for internal solitary wave simulation in continuously stratified fluids

A numerical model, ISWFoam, for simulating internal solitary waves (ISWs) in continuously stratified, incompressible, viscous fluids is developed based on a fully three-dimensional (3D) Navier–Stokes equation using the open-source code OpenFOAM®. This model combines the density transport equation with the Reynolds-averaged Navier–Stokes equation with the Coriolis force, and the model discrete equation adopts the finite-volume method. The k–ω SST turbulence model has also been modified according to the variable density field. ISWFoam provides two initial wave generation methods to generate an ISW in continuously stratified fluids, including solving the weakly nonlinear models of the extended Korteweg–de Vries (eKdV) equation and the fully nonlinear models of the Dubreil–Jacotin–Long (DJL) equation. Grid independence tests for ISWFoam are performed, and considering the accuracy and computing efficiency, the appropriate grid size of the ISW simulation is recommended to be 1/150th of the characteristic length and 1/25th of the ISW amplitude. Model verifications are conducted through comparisons between the simulated and experimental data for ISW propagation examples over a flat bottom section, including laboratory scale and actual ocean scale, a submerged triangular ridge, a Gaussian ridge, and slope. The laboratory test results, including the ISW profile, wave breaking location, ISW arrival time, and the spatial and temporal changes in the mixture region, are well reproduced by ISWFoam. The ISWFoam model with unstructured grids and local mesh refinement can effectively simulate the evolution of ISWs, the ISW breaking phenomenon, waveform inversion of ISWs, and the interaction between ISWs and complex topography.

Neural Network Based Estimation of Service Life of Different Metal Culverts in Arkansas

The Arkansas Department of Transportation (ARDOT) uses different types of metal culverts and cross-drains. Service lives of these culverts are largely influenced by the corrosion of the metals used in these culverts. Corrosion of metallic parts in any soil-water environment is governed by geochemical and electrochemical properties of the soils and waters. Many transportation agencies including ARDOT primarily focus on investigating the physical and mechanical properties of soils rather than their chemical aspects. The main objective of this study is to analyze the geotechnical and geochemical properties of soils in Arkansas to estimate the service lives of different metal pipes in different conditions. Soil resistivity values were predicted after analyzing the United States Department of Agriculture (USDA) soil survey data using neural network (NN) models. The developed NN models were trained and verified by using laboratory test results of soil samples collected from ARDOT, and survey data were obtained from the USDA. The service lives of metal culverts were then estimated based on the predicted soil properties and water quality parameters extracted from the data acquired from the Arkansas Department of Environmental Quality (ADEQ). Finally, Geographic Information System-based corrosion risk maps of three different types of metal pipes were developed based on their estimated service lives. The developed maps will help ARDOT engineers to assess the corrosion potential of the metal pipes before starting the new construction and repair projects and will allow using proper culvert materials to maximize their life spans.

An opportunity for using constructed wetland technology in hospital wastewater treatment: a preliminary study

The problem faced by many hospitals in Indonesia is the low efficiency of Waste Water Treatment Plant (WWTP) hospitals. If it does not get the attention, it will have an impact on health and environmental pollution. Various technologies have been used but experienced many obstacles. Constructed wetlands based on aquatic plants that have been used in developed countries are very prospects of being developed in regions such as Indonesia with a tropical climate. A preliminary study is needed with the first step to investigate the quality of hospital wastewater in Palu City, Indonesia, as a sample. This study aims to obtain a description of hospital wastewater characteristics and to evaluate the performance of the hospital WWTP in Palu. Data collection was done by taking data indirectly through laboratory test results during 2015-2019. Tabulating data using Excel software to illustrate statistics, then presented in the form of bar charts, interpreting according to the quality standards. Investigation results showed that the characteristics of hospital inlet wastewater in Palu are parameter values varying with four high concentration parameters: total coliform, TSS, Ammonia Nitrogen, and COD. The efficiency level of WWTP hospitals in Palu is relatively low in removing pollutants.

Integration of Multiple Surveillance Systems to Track COVID-19 in the U.S. Army Population

ABSTRACT Introduction The coronavirus disease (COVID-19) pandemic presented unique challenges for surveillance of the military population, which include active component service members and their family members. Through integrating multiple Department of Defense surveillance systems, the Army Public Health Center can provide near real-time case counts to Army leadership on a daily basis. Materials and Methods The incidence of COVID-19 was tracked by incorporating data from the Disease Reporting System Internet, laboratory test results, Commanders’ Critical Incidence Reports, reports from the Centers for Disease Control and Prevention military liaison, and media reports. Cases were validated via a medical record review for all Army beneficiaries. Descriptive analyses were performed using Microsoft Excel and SAS 9.4 to measure demographic frequencies. Results In the first year of the pandemic from February 1, 2020 to February 28, 2021, a total of 96,315 COVID-19 cases were reported to the Disease Reporting System internet, the Army’s passive surveillance system, of which 95,429 (99%) were confirmed and 886 (1%) were probable. A total of 76 outbreak reports were submitted from 14 Army installations. The proportion of Army beneficiaries with severe illness was low: 2,271 (2.4%) individuals required hospitalization and 269 (0.3%) died. Installations in Texas reported the highest proportion of confirmed—not hospitalized cases (n = 19,246, 20.7%), confirmed—hospitalized cases (n = 1,037, 45.7%), and deaths (n = 137, 50.9%) as compared to other states with Army installations. Conclusions The pandemic has demonstrated the need for a robust public health enterprise with a focus on data collection, validation, and analysis, allowing leaders to make informed decisions that may impact the health of the Army.

K-means Based Soil Classification System Applicable to a Brazilian Mineral Province

This article presents a geotechnical soil classification system proposed for application on soils of a tropical mineral province, located in Minas Gerais state, Brazil. The system was constructed using data mining techniques, i.e., principal component analysis and k-means cluster analysis, which were applied to a dataset composed of 101 geotechnical characterization laboratory test results of soils from the Province of Quadrilátero Ferrífero. The main objective of the proposed soil classification method was to establish a regional soil classification system, which encompass the interpretability of the main geotechnical parameters of soils by means of the classification, given the little explanatory capacity of the Unified Soil Classification System classification system for the performance of such task. It was possible to establish a chart for soil classification capable of explaining 81.68% of the variability of the analyzed parameters, being established the soil classes A, B and C for the studied soils.

Personalized reference intervals – statistical approaches and considerations

For many measurands, physicians depend on population-based reference intervals (popRI), when assessing laboratory test results. The availability of personalized reference intervals (prRI) may provide a means to improve the interpretation of laboratory test results for an individual. prRI can be calculated using estimates of biological and analytical variation and previous test results obtained in a steady-state situation. In this study, we aim to outline statistical approaches and considerations required when establishing and implementing prRI in clinical practice. Data quality assessment, including analysis for outliers and trends, is required prior to using previous test results to estimate the homeostatic set point. To calculate the prRI limits, two different statistical models based on ‘prediction intervals’ can be applied. The first model utilizes estimates of ‘within-person biological variation’ which are based on an individual’s own data. This model requires a minimum of five previous test results to generate the prRI. The second model is based on estimates of ‘within-subject biological variation’, which represents an average estimate for a population and can be found, for most measurands, in the EFLM Biological Variation Database. This model can be applied also when there are lower numbers of previous test results available. The prRI offers physicians the opportunity to improve interpretation of individuals’ test results, though studies are required to demonstrate if using prRI leads to better clinical outcomes. We recommend that both popRIs and prRIs are included in laboratory reports to aid in evaluating laboratory test results in the follow-up of patients.