Post-Cracking Properties of Concrete Reinforced with Polypropylene Fibers through the Barcelona Test.

The Barcelona method was developed as an alternative to other tests for assessing the post-cracking behavior of fiber-reinforced concrete, with the main advantage being that it uses significantly smaller specimens compared to other methods. For this reason, it can provide a solution for characterizing concrete in hard-to-reach constructions such as roads and tunnels. On the other hand, polypropylene (PP) fibers have gained increased attention in recent years within the scientific community due to their high tensile strength and cost-effectiveness. This research aimed to understand the influence of PP fiber volume, slenderness (l/d), and reinforcement index on post-cracking properties of concrete, including toughness and residual strength (f_res), using the Barcelona method. Three fiber volumes, 0.4%, 0.8%, and 1.2%, and three slenderness ratios, 46.5, 58.1, and 69.8, were employed in normal-strength concrete. In addition to the reference mixture without fibers, 10 mixtures were prepared with 10 specimens each, resulting in a total of 100 specimens. Pearson's hypothesis test was employed to determine the existence of correlations between variables, followed by scatter plots to generate predictive equations between post-cracking properties and fiber attributes. The results indicated no direct correlation between fiber slenderness and post-cracking properties. Regarding fiber volume, there was a correlation with residual strength but not with toughness. However, the combined effect of volume and slenderness, the reinforcement index, correlates with the post-cracking properties of concrete. Finally, four predictive equations for toughness and residual strength were derived based on the reinforcement index. These equations can prove valuable for designing structures made of polypropylene fiber-reinforced concrete.

Influence of Synthetic Fibers on the Flexural Properties of Concrete: Prediction of Toughness as a Function of Volume, Slenderness Ratio and Elastic Modulus of Fibers.

The construction industry requires concrete with adequate post-cracking behavior for applications such as tunnels, bridges, and pavements. For this reason, polypropylene macrofibers are used, which are synthetic fibers that fulfill the function of providing residual strength to concrete. In this study, an experimental plan is carried out to evaluate the bending behavior of concrete reinforced with polypropylene fibers using the four-point bending test according to ASTM C1609. Three fiber dosages (3.6, 7.2 and 10.8 kg/m3) and three fiber lengths (40, 50, and 60 mm) were used. The use of macro polypropylene fibers increased the post-cracking behavior of concrete. In addition, based on the experimentally obtained results and available literature data, a multivariable equation was developed to predict the concrete toughness as a function of the volume, slenderness, and modulus of elasticity of the fibers. A Pearson's correlation coefficient, r of 0.90, showed a strong correlation between the developed equation and the experimental data. From this equation, it was possible to determine the participation of the following parameters in calculating toughness. The participation or weight of the fiber's modulus of elasticity on the concrete's tenacity is 26%, the volume of the fiber is 39%, the slenderness is 19%, and the reinforcement index is 16%.

Dataset of manually classified images obtained from a construction site.

A manually classified dataset of images obtained by four static cameras located around a construction site is presented. Eight object classes, typically found in a construction environment, were considered. The dataset consists of 1046 images selected from video footage by a frame extraction algorithm and txt files containing the objects' class and coordinates information. These data can be used to develop computer vision techniques in the engineering and construction fields.

Geo-environmental parametric 3D models of SARS-CoV-2 virus circulation in hospital ventilation systems.

The novel coronavirus, SARS-CoV-2, has the potential to cause natural ventilation systems in hospital environments to be rendered inadequate, not only for workers but also for people who transit through these environments even for a limited duration. Studies in of the fields of geosciences and engineering, when combined with appropriate technologies, allow for the possibility of reducing the impacts of the SARS-CoV-2 virus in the environment, including those of hospitals which are critical centers for healthcare. In this work, we build parametric 3D models to assess the possible circulation of the SARS-CoV-2 virus in the natural ventilation system of a hospital built to care infected patients during the COVID-19 pandemic. Building Information Modeling (BIM) was performed, generating 3D models of hospital environments utilizing Revit software for Autodesk CFD 2021. The evaluation considered dimensional analyses of 0°, 45°, 90° and 180°. The analysis of natural ventilation patterns on both internal and external surfaces and the distribution of windows in relation to the displacement dynamics of the SARS-CoV-2 virus through the air were considered. The results showed that in the external area of the hospital, the wind speed reached velocities up to 2.1 m/s when entering the building through open windows. In contact with the furniture, this value decreased to 0.78 m/s. In some internal isolation wards that house patients with COVID-19, areas that should be equipped with negative room pressure, air velocity was null. Our study provides insights into the possibility of SARS-CoV-2 contamination in internal hospital environments as well as external areas surrounding hospitals, both of which encounter high pedestrian traffic in cities worldwide.

Integrity of Neurocognitive Networks in Dementing Disorders as Measured with Simultaneous PET/Functional MRI.

Functional MRI (fMRI) studies have reported altered integrity of large-scale neurocognitive networks (NCNs) in dementing disorders. However, findings on the specificity of these alterations in patients with Alzheimer disease (AD) and behavioral-variant frontotemporal dementia (bvFTD) are still limited. Recently, NCNs have been successfully captured using PET with 18F-FDG. Methods: Network integrity was measured in 72 individuals (38 male) with mild AD or bvFTD, and in healthy controls, using a simultaneous resting-state fMRI and 18F-FDG PET. Indices of network integrity were calculated for each subject, network, and imaging modality. Results: In either modality, independent-component analysis revealed 4 major NCNs: anterior default-mode network (DMN), posterior DMN, salience network, and right central executive network (CEN). In fMRI data, the integrity of the posterior DMN was found to be significantly reduced in both patient groups relative to controls. In the AD group the anterior DMN and CEN appeared to be additionally affected. In PET data, only the integrity of the posterior DMN in patients with AD was reduced, whereas 3 remaining networks appeared to be affected only in patients with bvFTD. In a logistic regression analysis, the integrity of the anterior DMN as measured with PET alone accurately differentiated between the patient groups. A correlation between indices of 2 imaging modalities was low overall. Conclusion: FMRI and 18F-FDG PET capture partly different aspects of network integrity. A higher disease specificity for NCNs as derived from PET data supports metabolic connectivity imaging as a promising diagnostic tool.

Resting-State Networks as Simultaneously Measured with Functional MRI and PET.

Functional MRI (fMRI) studies reported disruption of resting-state networks (RSNs) in several neuropsychiatric disorders. PET with 18F-FDG captures neuronal activity that is in steady state at a longer time span and is less dependent on neurovascular coupling. Methods: In the present study, we aimed to identify RSNs in 18F-FDG PET data and compare their spatial pattern with those obtained from simultaneously acquired resting-state fMRI data in 22 middle-aged healthy subjects. Results: Thirteen and 17 meaningful RSNs could be identified in PET and fMRI data, respectively. Spatial overlap was fair to moderate for the default mode, left central executive, primary and secondary visual, sensorimotor, cerebellar, and auditory networks. Despite recording different aspects of neural activity, similar RSNs were detected by both imaging modalities. Conclusion: The results argue for the common neural substrate of RSNs and encourage testing of the clinical utility of resting-state connectivity in PET data.

Eigenanatomy on Fractional Anisotropy Imaging Provides White Matter Anatomical Features Discriminating Between Alzheimer's Disease and Late Onset Bipolar Disorder.

BACKGROUND: Late Onset Bipolar Disorder (LOBD) is the arousal of Bipolar Disorder (BD) at old age (>60) without any previous history of disorders. LOBD is often difficult to distinguish from degenerative dementias, such as Alzheimer Disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence is increasing due to population aging. Biomarkers extracted from blood plasma are not discriminant because both pathologies share pathophysiological features related to neuroinflammation, therefore we look for anatomical features highly correlated with blood biomarkers that allow accurate diagnosis prediction. This may shed some light on the basic biological mechanisms leading to one or another disease. Moreover, accurate diagnosis is needed to select the best personalized treatment. OBJECTIVE: We look for white matter features which are correlated with blood plasma biomarkers (inflammatory and neurotrophic) discriminating LOBD from AD. MATERIALS: A sample of healthy controls (HC) (n=19), AD patients (n=35), and BD patients (n=24) has been recruited at the Alava University Hospital. Plasma biomarkers have been obtained at recruitment time. Diffusion weighted (DWI) magnetic resonance imaging (MRI) are obtained for each subject. METHODS: DWI is preprocessed to obtain diffusion tensor imaging (DTI) data, which is reduced to fractional anisotropy (FA) data. In the selection phase, eigenanatomy finds FA eigenvolumes maximally correlated with plasma biomarkers by partial sparse canonical correlation analysis (PSCCAN). In the analysis phase, we take the eigenvolume projection coefficients as the classification features, carrying out cross-validation of support vector machine (SVM) to obtain discrimination power of each biomarker effects. The John Hopkins Universtiy white matter atlas is used to provide anatomical localizations of the detected feature clusters. RESULTS: Classification results show that one specific biomarker of oxidative stress (malondialdehyde MDA) gives the best classification performance ( accuracy 85%, F-score 86%, sensitivity, and specificity 87%, ) in the discrimination of AD and LOBD. Discriminating features appear to be localized in the posterior limb of the internal capsule and superior corona radiata. CONCLUSION: It is feasible to support contrast diagnosis among LOBD and AD by means of predictive classifiers based on eigenanatomy features computed from FA imaging correlated to plasma biomarkers. In addition, white matter eigenanatomy localizations offer some new avenues to assess the differential pathophysiology of LOBD and AD.

Discrimination between Alzheimer's Disease and Late Onset Bipolar Disorder Using Multivariate Analysis.

BACKGROUND: Late onset bipolar disorder (LOBD) is often difficult to distinguish from degenerative dementias, such as Alzheimer disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence in the elder population is not negligible and it is increasing. Both pathologies share pathophysiological neuroinflammation features. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment. OBJECTIVE: The aim of this study is to assess the relative significance of clinical observations, neuropsychological tests, and specific blood plasma biomarkers (inflammatory and neurotrophic), separately and combined, in the differential diagnosis of LOBD versus AD. It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis (CAD) systems based on these variables. MATERIALS: A sample of healthy controls (HC) (n = 26), AD patients (n = 37), and LOBD patients (n = 32) was recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers were measured at recruitment time. METHODS: We applied multivariate machine learning classification methods to discriminate subjects from HC, AD, and LOBD populations in the study. We analyzed, for each classification contrast, feature sets combining clinical observations, neuropsychological measures, and biological markers, including inflammation biomarkers. Furthermore, we analyzed reduced feature sets containing variables with significative differences determined by a Welch's t-test. Furthermore, a battery of classifier architectures were applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance was evaluated in a leave-one-out (LOO) cross-validation scheme. Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance. RESULTS: Welch's t-test found one biomarker (Malondialdehyde) with significative differences (p < 0.001) in LOBD vs. AD contrast. Classification results with the best features are as follows: discrimination of HC vs. AD patients reaches accuracy 97.21% and AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26% and AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76% and AUC 88.46%. CONCLUSION: It is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables. Clinical observations provide the greatest discrimination. Neuropsychological tests are improved by the addition of biomarkers, and both contribute significantly to improve the overall predictive performance.

Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM.

Resting state functional Magnetic Resonance Imaging (rs-fMRI) is increasingly used for the identification of image biomarkers of brain diseases or psychiatric conditions such as schizophrenia. This paper deals with the application of ensembles of Extreme Learning Machines (ELM) to build Computer Aided Diagnosis systems on the basis of features extracted from the activity measures computed over rs-fMRI data. The power of ELM to provide quick but near optimal solutions to the training of Single Layer Feedforward Networks (SLFN) allows extensive exploration of discriminative power of feature spaces in affordable time with off-the-shelf computational resources. Exploration is performed in this paper by an evolutionary search approach that has found functional activity map features allowing to achieve quite successful classification experiments, providing biologically plausible voxel-site localizations.