Review on Carbonation Study of Reinforcement Concrete Incorporating with Bacteria as Self-Healing Approach.

This study carried out a comprehensive review to determine the carbonation process that causes the most deterioration and destruction of concrete. The carbonation mechanism involved using carbon dioxide (CO2) to penetrate the concrete pore system into the atmosphere and reduce the alkalinity by decreasing the pH level around the reinforcement and initiation of the corrosion process. The use of bacteria in the concrete was to increase the pH of the concrete by producing urease enzyme. This technique may help to maintain concrete alkalinity in high levels, even when the carbonation process occurs, because the CO2 accelerates to the concrete and then converts directly to calcium carbonate, CaCO3. Consequently, the self-healing of the cracks and the pores occurred as a result of the carbonation process and bacteria enzyme reaction. As a result of these reactions, the concrete steel is protected, and the concrete properties and durability may improve. However, there are several factors that control carbonation which have been grouped into internal and external factors. Many studies on carbonation have been carried out to explore the effect of bacteria to improve durability and concrete strength. However, an in-depth literature review revealed that the use of bacteria as a self-healing mechanism can still be improved upon. This review aimed to highlight and discuss the possibility of applying bacteria in concrete to improve reinforcement concrete.

Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete.

The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers' addition in HSC results in bridging behavior which improves its post-cracking behavior, provides cracks arresting and stresses transfer in concrete. Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost. Therefore, sophisticated ML approaches are applied in this study to predict the compressive strength of steel fiber reinforced HSC (SFRHSC). To fulfil this purpose, a standalone ML model called Multiple-Layer Perceptron Neural Network (MLPNN) and ensembled ML algorithms named Bagging and Adaptive Boosting (AdaBoost) were employed in this study. The considered parameters were cement content, fly ash content, slag content, silica fume content, nano-silica content, limestone powder content, sand content, coarse aggregate content, maximum aggregate size, water content, super-plasticizer content, steel fiber content, steel fiber diameter, steel fiber length, and curing time. The application of statistical checks, i.e., root mean square error (RMSE), determination coefficient (R2), and mean absolute error (MAE), was also performed for the assessment of algorithms' performance. The study demonstrated the suitability of the Bagging technique in the prediction of SFRHSC compressive strength. Compared to other models, the Bagging approach was more accurate as it produced higher, i.e., 0.94, R2, and lower error values. It was revealed from the SHAP analysis that curing time and super-plasticizer content have the most significant influence on the compressive strength of SFRHSC. The outcomes of this study will be beneficial for researchers in civil engineering for the timely and effective evaluation of SFRHSC compressive strength.

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming.

Rice husk ash (RHA) is a significant pollutant produced by agricultural sectors that cause a malignant outcome to the environment. To encourage the re-use of RHA, this work used multi expression programming (MEP) to construct an empirical model for forecasting the compressive nature of concrete made with RHA (CRHA) as a cement substitute. Thus, the compressive strength of CRHA was developed comprising of 192 findings from the broad and trustworthy database obtained from literature review. The most significant characteristics, namely the specimen's age, the percentage of RHA, the amount of cement, superplasticizer, aggregates, and the amount of water, were used as input for the modeling of CRHA. External validation, sensitivity analysis, statistical checks, and Shapley Additive Explanations (SHAP) analysis were used to evaluate the models' performance. It was discovered that the most significant factors impacting the compressive strength of CRHA are the age of the concrete sample (AS), the amount of cement (C) and the amount of aggregate (A). The findings of this study have the potential to increase the re-use of RHA in the production of green concrete, hence promoting environmental protection and financial gain.

Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches.

The entraining and distribution of air voids in the concrete matrix is a complex process that makes the mechanical properties of lightweight foamed concrete (LFC) highly unpredictable. To study the complex nature of aerated concrete, a reliable and robust prediction model is required, employing different machine learning (ML) techniques. This study aims to predict the compressive strength of LFC by using a support vector machine (SVM) as an individual learner along with bagging, boosting, and random forest (RF) as a modified ensemble learner. For that purpose, a database of 191 data points was collected from published literature, where the mix design ingredients, i.e., cement content, sand content, water to cement ratio, and foam volume, were chosen to predict the compressive strength of LFC. The 10-K fold cross-validation method and different statistical error and regression tools, i.e., mean absolute error (MAE), root means square error (RMSE), and coefficient of determinant (R2), were used to evaluate the performance of the developed ML models. The modified ensemble learner (RF) outperforms all models by yielding a strong correlation of R2 = 0.96 along with the lowest statistical error values of MAE = 1.84 MPa and RMSE = 2.52 MPa. Overall, the result suggests that the ensemble learners would significantly enhance the performance and robustness of ML models.

Angiopoietin-like proteins 3, 4 and 8 are linked to cardiovascular function in naïve sub-clinical and overt hypothyroid patients receiving levothyroxine therapy.

OBJECTIVE: Angiopoietin-like proteins (ANGPTL) 3, 4 and 8 are upcoming cardiovascular biomarkers. Experimental studies showed that thyroid hormones altered their levels. We assessed ANGPTL3, 4 and 8 as predictors of cardiovascular functions among naïve subclinical and naïve overt hypothyroidism (SCH and OH) and altered ANGPTL levels with levothyroxine replacement (LT4) and their association with improved cardiovascular risk factors and cardiovascular function. DESIGN AND METHODS: The study was a prospective follow-up study that assessed ANGPTL3, 4 and 8 levels, vascular status (flow-mediated dilation% of brachial artery (FMD%), carotid intima-media thickness (CIMT), aortic stiffness index (ASI)), left ventricle (LV) parameters (ejection fraction (EF), myocardial performance index (MPI), and LV mass), well-known cardiovascular risk factors and homeostatic model for the assessment of insulin resistance, at two time points, that is, among naïve SCH, naïve OH, and healthy subjects groups; and at 6 months after achieving the euthyroid state with LT4 by calculating their increased or decreased delta changes (∆↑ or ∆↓) in longitudinal arm among LT4-hypothyroid groups. RESULTS: Significantly elevated levels of ANGPTL3, 4 and 8 among hypothyroid groups than the healthy subjects were reduced with LT4. Multivariate analysis revealed ANGPTLs as independent predictors of cardiovascular functions and the contributors for ANGPTL level included ANGPTL3 and 4 for impaired FMD%, and ANGPTL8 for LV mass among naïve SCH; ANGPTL3 for EF% and ANGPTL8 for CIMT in naïve OH; ∆↓ANGPTL3 for ∆↓ASI meanwhile ∆↑freeT4 for ∆↓ANGPTL3, ∆↓fasting glucose, ∆↓triglyceride, and ∆↓thyroid peroxidase antibody for ∆↓ANGPTL4 among LT4-SCH. ∆↓ANGPTL4 for ∆↓MPI and ∆↓LV mass, meanwhile ∆↓TSH and ∆↓triglyceride for ∆↓ANGPTL3, ∆↑free T3 and ∆↓HOMA-IR for ∆↓ANGPTL4, and systolic blood pressure and waist circumference for ∆↓ANGPTL8 among LT4-OH. CONCLUSION: Elevated ANGPTL3, 4 and 8 levels are differentially independent predictors of endothelial and cardiac function and are reduced with LT4 in SCH and OH.

Lateral and Frontal Cephalometric Measurements in a Cohort With Saethre-Chotzen Syndrome.

OBJECTIVE: Descriptions of the craniofacial morphology in Saethre-Chotzen syndrome (SCS) are primarily based on case reports or visual assessments of affected families. The aim of this study was to compare cephalometric measurements of the craniofacial skeleton in a cohort of individuals with SCS and age- and sex-matched individuals without craniofacial anomalies. DESIGN: Retrospective case series. PATIENTS: Eight girls and 4 boys with SCS (age range, 7.0-19.2 years). METHODS: Cephalometric measurements were performed using lateral and frontal cephalograms. RESULTS: Most of the individuals with Saethre-Chotzen syndrome exhibited lower values for SNA, SNB, s-n and s-ar, while their NSL/NL, NSL/ML, NL/ML, and n-s-ba values were higher than the respective mean reference values for healthy individuals. In comparison with age- and sex-matched individuals without craniofacial anomalies, the individuals with SCS showed higher values for the maxillary and mandibular angular measurements, as well as for the menton midline angle. CONCLUSIONS: This sample of 12 unrelated individuals with SCS is the largest collected to date for cephalometric measurements. We found that the syndrome is associated with bimaxillary retrognathism, posterior maxillary and mandibular inclination, neutral sagittal relation as well as a tendency toward an open vertical skeletal relation, a short and flattened skull base, and facial asymmetry, as compared to individuals without the syndrome.

Mutational Profile of 10 Afflicted Egyptian Families with 17-ß-HSD-3 Deficiency.

This study aimed at the detection of HSD17B3 gene mutations in Egyptian patients with suspected diagnosis of 46,XY DSD due to 17-ß-HSD-3 deficiency and at evaluation of phenotype/genotype relationship of these mutations. The study was conducted on 11 patients of 10 families which were provisionally diagnosed to have 17-ß-HSD-3 enzyme deficiency. Karyotyping, hormonal evaluation of testosterone, x0394;4-androstenedione, and dihydrotestosterone, and sequencing analysis of the 11 exons of the HSD17B3 gene were done. Mutations in HSD17B3 were detected in exons 2, 7, 8, 10, and 11, and 6 novel mutations were determined in exons 1, 2, 7, and 8. Two patients showed compound heterozygous mutations, while 8 families had probands with homozygous mutations. The current study shows that 17-ß-HSD-3 deficiency is not an uncommon disorder among Egyptian DSD cases. It was evidenced that the mutational profile of the disease is rather heterogeneous, relatively different from those reported in other populations, and has a high degree of novel genetic defects.