Embedded Discrete Fracture Networks to Analyze Groundwater Inflows during Tunnel Drilling

Tunnels commonly go through fracture zones that used to be analyzed as an equivalent porous medium with homogeneous permeability. However, it is a rough simplification that overlooks the connection triggered by underground works in fractured massifs. This study introduces the use of synthetic discrete fracture networks (DFN) to analyze groundwater inflows through tunnel excavation in a fractured zone considering the daily advance of the drilling front. First, a hypothetical case with six different settings varying the fracture density, the fracture length, and the aperture distribution is analyzed. Each setting has about 100 iterations. DFN hydraulic properties were estimated and compared with previous DFN studies, displaying the same behavior even though the magnitude of the estimated parameters differs. As an application example, structural measurements of the Alaska fault zone in the La Linea massif (Colombia) are used to obtain the statistical parameters of the fracture length and aperture distributions to generate the DFN. Five settings varying the fracture density are built, obtaining measured and simulated groundwater inflows of the same order of magnitude. These results highlight the potential of the synthetic DFN to analyze tunnels’ effects on groundwater flow.

Empirical Models to Predict Compaction Parameters for Soils in the State of Ceará, Northeastern Brazil

This work developed prediction models for maximum dry unit weight (γd,max) and optimum moisture content (OMC) for compacted soils in Ceará, Brazil, ba M Winnie the Pooh sed on index and physical properties. The methodology included data from soils used in the construction of 15 dams in Ceará, with available information regarding laboratory tests of interest. Correlations were developed using non-linear regression, from 169 laboratory results (83 for training and 86 for validating the models), which presented a R2 of 0,763 for MoPesm (prediction model for γd,max) and 0,761 for MoTuo (model for OMC). A posteriori, the same physical indexes used to train and validate MoPesm and MoTuo were used as inputs of other prediction models available in the literature, whose outputs differed considerably from laboratory results for the evaluated soils. MoPesm and MoTuo were able to satisfactorily predict compaction parameters, with outputs close to those obtained in the laboratory for tested soil samples. Their performance justifies their use for predicting compaction parameters in geotechnical structures that use compacted soils when there are financial restraints, short timeframes, or unavailability of test equipment, particularly in early design stages and preliminary studies, before appropriate soil sampling and field investigation can be conducted, thus saving substantial time and financial resources.

Electrochemical Behavior of a Stainless Steel Superficially Modified with Nitrogen by Three-dimensional Ion Implantation

Martensitic-grade stainless steels are widely used in diverse industrial and surgical applications, despite their natural tendency to suffer local and uniform corrosion when continuously exposed to aggressive operation conditions. In order to enhance their surface properties, this paper characterized the performance, in saline solutions, of AISI 420 stainless steel, which was surface-modified by three-dimensional ion implantation using electrochemical techniques. The surface of the samples was implanted with ionized nitrogen particles with an energy of 10 keV, varying the implantation time between 30 and 90 minutes. After the surface treatment, the samples were exposed to a NaCl 3% (w/w) aqueous solution for 21 days. Tafel extrapolation, linear polarization resistance, and electrochemical impedance spectroscopy tests were performed, with the purpose of quantifying the effect of the ion implantation technique against electrochemical corrosion. To establish a comparison, the same tests were also performed on non-treated samples. The results indicated an increase in the corrosion potential, polarization resistance, and a decrease in the current density of implanted samples, thus demonstrating that, by delaying corrosive activity, traditional ion implantation offers better protection against electrochemical corrosion in AISI 420 stainless steel samples implanted with nitrogen.

COVID-19 is in the air: Why are we still ignoring the importance of ventilation?

The main transmission mechanism of the SARS-CoV-2 virus is airborne, particularly in poorly ventilated indoor environments. Recognizing the importance of this mechanism has taken a long time, despite the evidence generated by aerosol scientists from an early stage of the pandemic. Hence, measures applied more widely by the population have focused on the disinfection of surfaces, often in an exaggerated way, while measures focused on reducing the concentration of aerosols in indoor environments, such as adequate ventilation and air filtration, have been timidly promoted. In addition to the progress of the National Vaccination Plan, it is necessary to intensify transmission prevention measures for a safer reopening of the economy. It is therefore urgent, to educate and generate clear guidelines for the evaluation and improvement of ventilation in indoor spaces.

Comparison of Treatments by Mercerization and Plasma Glow Discharge on Residues of the Amazon Chestnut Shell (Bertholletia Excelsa)

The chestnut shell from the Amazon region shared between Colombia, Brazil, and Perú is an abundant residue of the walnut used for obtaining food and cosmetic products. This residue is not yet usable due to the lack of knowledge of its properties and the environmental impact generated by its treatment through methods such as mercerization. This work presents the results of the characterization of Amazon chestnut shell residues treated by two methods, mercerization with NaOH solution and intense plasma discharge (Glow Discharge Plasma), in a reactor with argon gas in a 0,3-bar vacuum and discharge conditions of 80 mA and 600 s. The microstructural, morphological, topographic, and nanomechanical changes of the chestnut residues without treatment and with the two proposed treatments were evaluated by means of the µRaman, scanning electron microscopy, and atomic force microscopy techniques. The results showed the effectiveness of the plasma method over the mercerization method at obtaining more crystalline cellulose structures due to the reduction of hemicellulose, lignin, and the aqueous phase of walnut shell waste.

Removal of Sulfate Ions by Precipitation and Flotation

The removal of sulfate ions from natural waters, as well as from industrial effluents of different origins, is a problem, considering that most of the proposed processes are inefficient and have a high cost, mainly when reducing the sulfate ion concentration to values below 500 mg.L−1 is required. The flotation technique, combined with precipitation, has proven to be efficient for the removal of heavy metal ions. However, there is not enough research to confirm its efficiency for the removal of sulfate ions. This article presents the results of sulfate ion removal from synthetic solutions prepared in an acidic medium, applying the co-precipitation techniques with polyaluminum chloride (PAC) and solid/liquid separation by dissolved air flotation (DAF). The effect of the pH, the [PAC: sulfate ions] ratio, the effect of saturated water flow with air, and the flocculant and collector doses were studied. The achieved results confirm that it is possible to reduce the concentration of sulfate ions from 1 800 to 350 mg.L−1 (80% removal) from synthetic solutions by applying the flotation technique combined with precipitation.

Overview of the Constitutive Model and Numerical Calibration by FEM to Compute Bearing Capacity and Embankment-Core Deformability

Numerical modeling is a powerful tool to determine the stress-strain relationships of structures. However, for a reliable application, physical and mathematical models must be calibrated and validated. This paper presents an overview of numerical calibration through the finite element method and plate-load tests in an embankment. Additionally, an analysis of the constitutive models used in soils is performed, and the elastic-plastic constitutive model of Mohr-Coulomb was selected since it is the best suited for this study. The results from three test areas within a refinery project that the Cuban government undertook in the province of Cienfuegos are used. The numerical model used in this study was calibrated by means of the error theory and the non-parametric hypothesis tests from Mann-Whitney U. From the practical point of view, this study gives two procedures to calibrate the numerical model with experimental results.

Effect of Diesel Oil and Mixture of Alcohol-Glycol Ether on Colombian Ultrafine Coal Cleaning Using a Test-Rig Closed-Loop Flotation Column

A test-rig closed-loop flotation column was used to observe the effect of diesel oil (collector) and Flomin F-425 (frother) on mass yield and ash content for two Colombian coals: Caypa (northern zone) and Guachinte (southwestern zone). The coal samples of less than 38 µm (-400 M) were processed in a collector concentration range of 0,32 to 1,60 kg/ton of coal, as well as a frother concentration range of 10 to 50 ppm. The response surface methodology was used for the experimental test runs. The results showed that the maximum mass yield obtained by Caypa coal was 98,39% at 1,28 kg of collector/ton of coal and 40 ppm of frother concentration, whereas Guachinte coal obtained a maximum mass yield of 94,71% at 0,96 kg of collector/ton of coal and 30 ppm of frother concentration. In general, for Caypa coal, the mass yield tends to increase (low ash removal) with the collector and frother concentration increase; while the mass yield tends to decrease (high ash removal) for Guachinte coal when the collector concentration increases (low ash removal) at high frother concentrations. It is worth highlighting that the ash content of 0,65% obtained for Caypa coal is the lowest value reported in the literature while employing a test-rig loop flotation column in a single stage, which is considered to be an ultra-clean coal obtained by a physical cleaning process.

COVID-19 Diagnosis with Deep Learning

The coronavirus disease 2019 (COVID-19) is fatal and spreading rapidly. Early detection and diagnosis of the COVID-19 infection will prevent rapid spread. This study aims to automatically detect COVID-19 through a chest computed tomography (CT) dataset. The standard models for automatic COVID-19 detection using raw chest CT images are presented. This study uses convolutional neural network (CNN), Zeiler and Fergus network (ZFNet), and dense convolutional network-121 (DenseNet121) architectures of deep convolutional neural network models. The proposed models are presented to provide accurate diagnosis for binary classification. The datasets were obtained from a public database. This retrospective study included 757 chest CT images (360 confirmed COVID-19 and 397 non-COVID-19 chest CT images). The algorithms were coded using the Python programming language. The performance metrics used were accuracy, precision, recall, F1-score, and ROC-AUC. Comparative analyses are presented between the three models by considering hyper-parameter factors to find the best model. We obtained the best performance, with an accuracy of 94,7%, a recall of 90%, a precision of 100%, and an F1-score of 94,7% from the CNN model. As a result, the CNN algorithm is more accurate and precise than the ZFNet and DenseNet121 models. This study can present a second point of view to medical staff.

Detection of COVID-19 and Other Pneumonia Cases Using Convolutional Neural Networks and X-ray Images

Given that it is fundamental to detect positive COVID-19 cases and treat affected patients quickly to mitigate the impact of the virus, X-ray images have been subjected to research regarding COVID-19, together with deep learning models, eliminating disadvantages such as the scarcity of RT-PCR test kits, their elevated costs, and the long wait for results. The contribution of this paper is to present new models for detecting COVID-19 and other cases of pneumonia using chest X-ray images and convolutional neural networks, thus providing accurate diagnostics in binary and 4-classes classification scenarios. Classification accuracy was improved, and overfitting was prevented by following 2 actions: (1) increasing the data set size while the classification scenarios were balanced; and (2) adding regularization techniques and performing hyperparameter optimization. Additionally, the network capacity and size in the models were reduced as much as possible, making the final models a perfect option to be deployed locally on devices with limited capacities and without the need for Internet access. The impact of key hyperparameters was tested using modern deep learning packages. The final models obtained a classification accuracy of 99,17 and 94,03% for the binary and categorical scenarios, respectively, achieving superior performance compared to other studies in the literature, and requiring a significantly lower number of parameters. The models can also be placed on a digital platform to provide instantaneous diagnostics and surpass the shortage of experts and radiologists.