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Agglomerated cork is a known material by its contribution to the sustainment of the environment, not only because it is a wholly natural material, without chemical additives, but also because its industrial process of production results from the lowest quality residues of cork or industrial waste material, unsuitable for other applications. It is a reusable material, which means, the cork facade elements can be converted into a new agglomerated material, demonstrating a huge potential for adaptation to existing buildings following a reversible process. It is durable, lightweight, water resistant, low-cost material, some of the properties that may qualify it as suitable for application in large surfaces of vertical construction façades. The aim of this article is to analyze the mechanical, thermal and acoustic characteristics of cork composites against site-specific climatic conditions of subtropical climates and its suitability as an external coating system for residential buildings with the goal to reduce the energy consumption for cooling the inner environment. In high-density cities like Guangzhou, Shenzhen and Hong Kong the majority of the buildings starting from the 1960s until early 21st century (Brach & Song 2006), did not integrate thermal insulation systems into external walls, producing a high level of heat transfer through the external façade from the outside environment during spring and summer seasons. Due to the extremely fast urban growth of the modern Chinese city, little importance is given to the quality of the external walls in current residential building construction. For at least during six months each year the consumption of energy due to air conditioning in Guangdong province is extremely high. The study concluded that substantial energy could be saved by implementing an external coating upgrade to existing buildings. Additionally, this study details the result obtained through software for energy simulations (Design Builder, ENVI-met) demonstrating the potential of this project to produce homogeneous and comfortable inside temperatures, which cools the indoor ambient temperature in summer time.

This study is an attempt to understand and describe the compositional principles of Chaoshan traditional houses (CTH) through a computational space syntax. In this approach, the space syntax is used to describe and verify the compositional rules of Chaoshan houses. Chaoshan rural residence is a classical Lingnan style building in Chaoshan area of eastern Guangdong province, associated with Teo-Swa people, a Han Chinese minority. This study takes the example the prototypes existing in the village of Zhupu, Haojiang District, Shantou city as a case study, to analyse the spatial form of the residences. The Zhupu village houses date from the Qing Dynasty - Qianlong period, around 1700 AD. The hypothesis of this study is that CTH buildings are a result of a space compositional rule system that can be described and replicated through a computational design methodology. This study will establish a computational architectural syntax, and is the first stage of an extended research work on the evolution of Chaoshan residential types. The understanding of this evolution may help, as future work, to develop urban strategies for adaptation of the CTH heritage buildings to the contemporary living conditions. As the result of this study is a computational 3D graphics modelling algorithm, the ability of the system to generate the house layouts is not limited to the reconstruction of existing typologies of CTH and its variations. The same algorithm will allow the generation of new housing schemes, with adaptation to design variables extracted from a particular site and region.

The pathophysiological mechanisms of arterial hypertension during hemodialysis (HD) in patients with end-stage renal disease (ESRD) are still poorly understood. The aim of this study is to investigate physiological, cardiovascular and neuroendocrine changes in patients with ESRD and its correlation with changes in blood pressure (BP) during the HD session. The present study included 21 patients with ESRD undergoing chronic HD treatment. Group A (study) consisted of patients who had BP increase and group B (control) consisted of those who had BP reduction during HD session. Echocardiograms were performed during the HD session to evaluate cardiac output (CO) and systemic vascular resistance (SVR). Before and after the HD session, blood samples were collected to measure brain natriuretic peptide (BNP), catecholamines, endothelin-1 (ET-1), nitric oxide (NO), electrolytes, hematocrit, albumin and nitrogen substances. The mean age of the studied patients was 43±4.9 years, and 54.6% were males. SVR significantly increased in group A (P<0.001). There were no differences in the values of BNP, NO, adrenalin, dopamin and noradrenalin, before and after dialysis, between the two groups. The mean value of ET-1, post HD, was 25.9 pg ml−1 in group A and 13.3 pg ml−1 in group B (P=<0.001). Patients with ESRD showed different hemodynamic patterns during the HD session, with significant BP increase in group A, caused by an increase in SVR possibly due to endothelial dysfunction, evidenced by an increase in serum ET-1 levels.

The Black-Scholes equation is famous for predicting values for the prices of Options inside the stock market scenario. However, it has the limitation of depending on the estimated value for the volatility. On the other hand, several Machine learning techniques have been employed for predicting the values of the same quantity. In this paper we analyze some fundamental properties of the Black-Scholes equation and we then propose a way to train its free-parameters, the volatility in particular. This with the purpose of using this parameter as the fundamental one to be learned by a Machine Learning system and then improve the predictions in the stock market.

Even with more than 12 billion vaccine doses administered globally, the Covid-19 pandemic has caused several global economic, social, environmental, and healthcare impacts. Computer Aided Diagnostic (CAD) systems can serve as a complementary method to aid doctors in identifying regions of interest in images and help detect diseases. In addition, these systems can help doctors analyze the status of the disease and check for their progress or regression. To analyze the viability of using CNNs for differentiating Covid-19 CT positive images from Covid-19 CT negative images, we used a dataset collected by Union Hospital (HUST-UH) and Liyuan Hospital (HUST-LH) and made available at the Kaggle platform. The main objective of this chapter is to present results from applying two state-of-the-art CNNs on a Covid-19 CT Scan images database to evaluate the possibility of differentiating images with imaging features associated with Covid-19 pneumonia from images with imaging features irrelevant to Covid-19 pneumonia. Two pre-trained neural networks, ResNet50 and MobileNet, were fine-tuned for the datasets under analysis. Both CNNs obtained promising results, with the ResNet50 network achieving a Precision of 0.97, a Recall of 0.96, an F1-score of 0.96, and 39 false negatives. The MobileNet classifier obtained a Precision of 0.94, a Recall of 0.94, an F1-score of 0.94, and a total of 20 false negatives.

The gold standard to detect SARS-CoV-2 infection consider testing methods based on Polymerase Chain Reaction (PCR). Still, the time necessary to confirm patient infection can be lengthy, and the process is expensive. On the other hand, X-Ray and CT scans play a vital role in the auxiliary diagnosis process. Hence, a trusted automated technique for identifying and quantifying the infected lung regions would be advantageous. Chest X-rays are two-dimensional images of the patient’s chest and provide lung morphological information and other characteristics, like ground-glass opacities (GGO), horizontal linear opacities, or consolidations, which are characteristics of pneumonia caused by COVID-19. But before the computerized diagnostic support system can classify a medical image, a segmentation task should usually be performed to identify relevant areas to be analyzed and reduce the risk of noise and misinterpretation caused by other structures eventually present in the images. This chapter presents an AI-based system for lung segmentation in X-ray images using a U-net CNN model. The system’s performance was evaluated using metrics such as cross-entropy, dice coefficient, and Mean IoU on unseen data. Our study divided the data into training and evaluation sets using an 80/20 train-test split method. The training set was used to train the model, and the evaluation test set was used to evaluate the performance of the trained model. The results of the evaluation showed that the model achieved a Dice Similarity Coefficient (DSC) of 95%, Cross entropy of 97%, and Mean IoU of 86%.

In 2020, the World Health Organization declared the Coronavirus Disease 19 a global pandemic. While detecting COVID-19 is essential in controlling the disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a CT-Scan room. In other cases, the devices are not available. The adoption of ambulatory monitoring examinations, such as Electrocardiography (ECG), can be considered a viable tool to address the patient’s cardiovascular condition and to act as a predictor for future disease outcomes. In this investigation, ten non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) extracted from 2 ECG signals (collected from 2 different patient’s positions). Windows of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested:  (Low vs. Moderate, Low vs. Severe, Moderate vs. Severe) and 1 Multi-class comparison  (All vs. All)). The results indicate that some statistically significant parameter distributions were found for all comparisons.  (Low vs. Moderate—Approximate Entropy p-value = 0.0067 < 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 < 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 < 0.05, All vs. All—Correlation Dimension p-value = 0.0185 < 0.05. The non-linear analysis of the time-frequency representation of the ECG signal can be considered a promising tool for describing and distinguishing the COVID-19 severity activity along its different stages.

The scientific literature indicates that pregnant women with COVID-19 are at an increased risk for developing more severe illness conditions when compared with non-pregnant women. The risk of admission to an ICU (Intensive Care Unit) and the need for mechanical ventilator support is three times higher. More significantly, statistics indicate that these patients are also at 70% increased risk of evolving to severe states or even death. In addition, other previous illnesses and age greater than 35 years old increase the risk for the mother and the fetus, including a higher number of cesarean sections, higher systolic and diastolic maternal blood pressure, increasing the risk of eclampsia, and, in some cases, preterm birth. Additionally, pregnant women have more Emotional lability/fluctuations (between positive and negative feelings) during the entire pregnancy. The emotional instability and brain fog that takes place during gestation may open vulnerability for neuropsychiatric symptoms of long COVID, which this population was not studied in depth. The present Chapter characterizes the database presented in this work with clinical and survey data collected about emotions and feelings using the Coronavirus Perinatal Experiences—Impact Survey (COPE-IS). Pregnant women with or without COVID-19 symptoms who gave birth at the Assis Chateaubriand Maternity Hospital (MEAC), a public maternity of the Federal University of Ceara, Brazil, were recruited. In total, 72 mother-infant dyads were included in the study and are considered in this exploratory analysis. The participants have undergone serological tests for SARS-CoV-2 antibody detection and a nasopharyngeal swab test for COVID-19 diagnoses by RT-PCR. A comprehensive Exploratory Data Analysis (EDA) is performed using frequency distribution analysis of multiple types of variables generated from numerical data, multiple-choice, categorized, and Likert-scale questions.

The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.

This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity.

COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94.

The Covid-19 pandemic evidenced the need Computer Aided Diagnostic Systems to analyze medical images, such as CT and MRI scans and X-rays, to assist specialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the spread of the virus. There are some types of CAD systems, such as Machine and Deep Learning-based and Transfer learning-based. This chapter proposes a pipeline for feature extraction and classification of Covid-19 in X-ray images using transfer learning for feature extraction with VGG-16 CNN and machine learning classifiers. Five classifiers were evaluated: Accuracy, Specificity, Sensitivity, Geometric mean, and Area under the curve. The SVM Classifier presented the best performance metrics for Covid-19 classification, achieving 90% accuracy, 97.5% of Specificity, 82.5% of Sensitivity, 89.6% of Geometric mean, and 90% for the AUC metric. On the other hand, the Nearest Centroid (NC) classifier presented poor sensitivity and geometric mean results, achieving 33.9% and 54.07%, respectively.