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This Practice Note provides an overview of the powers of tribunals and courts to issue interim remedies including an anti-suit injunction pursuant to the Arbitration Law and the Civil Procedure Code of Macau and provisions dealing with emergency arbitrator appointments pursuant to the Macau Arbitration Law.

In any physical system, when we move from short to large scales, new spacetime symmetries emerge which help us to simplify the dynamics of the system. In this letter we demonstrate that certain variations on the symmetries of general relativity at large scales generate the effects equivalent to dark matter ones. In particular, we reproduce the Tully-Fisher law, consistent with the predictions proposed by MOND. Additionally, we demonstrate that the dark matter effects derived in this way are consistent with the predictions suggested by MOND, without modifying gravity.

Colloquially, Guangzhou is known as the ‘Factory of China’,and, in many respects, it may be one of the great factories of the world. Located in the Guangdong Province in Southern China, Guangzhou is an exemplar of twenty-first century metropolis. It is home to over 14 million residents and is in a state of extreme growth. Old farmlands encircling the city are incentivised, through government subsidies, to build apartment complexes that can accommodate the rapid growth as the city develops. The city is home to migrant population—from the rural areas of China that is larger than the entire population of Perth. The middle classin Guangzhou is outpacing the growth of middle classes in Australia and the US. Factories, shipping ports, apartment blocks, malls, and urban farms are mixed in a tightly knit tapestry across the city. ‘Guangzhou Places’ is a series of short videos that present viewers with a glimpse of urbanisation that is akin to app development. Guangzhou is a ‘beta city’, an environment

Sustainability takes priority with architect Matthew Barnett Howland. His house in England is made entirely of cork: 100 percent natural, 100 percent recyclable, with almost zero carbon emissions.

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.