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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.

Current global shifts in education towards inclusive early childhood education are deeply engineered by the crisis of educational exclusion. In responding to exclusion, teachers have mainly utilized dominant western theories to plan and implement inclusive teaching. In this chapter, we draw on a non-western philosophy, a Nichiren Buddhist (Soka) philosophy, to provide a ‘kaleidoscopic’ lens through which to create inclusive educational learning spaces that engender full participation of all children. The Soka education philosophy is a humanist concept which can guide teachers when preparing to create inclusive education. The aims of this chapter are threefold: The first is an exploration of the Nichiren Buddhist (Soka) philosophy. The second aim is to highlight how this philosophy can enable teachers to unleash the unlimited potential of children in inclusive learning settings. Thirdly, we argue that grounding early childhood teacher education in this philosophy can help improve the effectiveness of inclusive educational experience for all children.

Fish acoustic signals associated with mating behaviour are typically low-frequency sounds produced by males when in close proximity to females. However, some species make sounds that serve the function and follow the design of advertisement calls, well known in insects, anurans, and birds. Close-range courtship acoustic signals may be used by females in mate assessment as they contain information of male quality such as size and condition. For example, sound-dominant frequency, amplitude, and fatigue resistance may signal body size whereas pulse period (i.e. muscle contraction rate) and calling activity are related with body condition in some species. Some signal features, such as sound pulse number, may carry multiple messages including size and condition. Playback experiments on mate choice of a restricted number of species suggest that females prefer vocal to silent males and may use sound frequency, amplitude, and mainly calling rateCalling ratewhen assessing males. The assessment of males by females becomes more challenging when males engage in choruses or when sounds are otherwise masked by anthropogenic noise but almost nothing is known about how these aspects affect mating decisions and fish reproductive success.

In this chapter, a mathematical model explaining generically the propagation of a pandemic is proposed, helping in this way to identify the fundamental parameters related to the outbreak in general. Three free parameters for the pandemic are identified, which can be finally reduced to only two independent parameters. The model is inspired in the concept of spontaneous symmetry breaking, used normally in quantum field theory, and it provides the possibility of analyzing the complex data of the pandemic in a compact way. Data from 12 different countries are considered and the results presented. The application of nonlinear quantum physics equations to model epidemiologic time series is an innovative and promising approach.

Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease's behavior and the progression of the virus's natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with R2 score of 0.99 to short-term predictions and 0.93 to long-term predictions.

The use of computational tools for medical image processing are promising tools to effectively detect COVID-19 as an alternative to expensive and time-consuming RT-PCR tests. For this specific task, CXR (Chest X-Ray) and CCT (Chest CT Scans) are the most common examinations to support diagnosis through radiology analysis. With these images, it is possible to support diagnosis and determine the disease’s severity stage. Computerized COVID-19 quantification and evaluation require an efficient segmentation process. Essential tasks for automatic segmentation tools are precisely identifying the lungs, lobes, bronchopulmonary segments, and infected regions or lesions. Segmented areas can provide handcrafted or self-learned diagnostic criteria for various applications. This Chapter presents different techniques applied for Chest CT Scans segmentation, considering the state of the art of UNet networks to segment COVID-19 CT scans and a segmentation experiment for network evaluation. Along 200 epochs, a dice coefficient of 0.83 was obtained.

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.

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.

Maria Celeste Natário, Renato Epifânio, Carlos Ascenso André, Gonçalo Cordeiro, Inocência Mata, Jorge Rangel, Maria Antónia Espadinha

The design thinking methodology is a problem-solving approach that involves empathising with end-users, (re)defining problems, brainstorming solutions creatively, and experimenting with prototypes and testing. It has been widely adopted in education to help students develop critical thinking, creativity, and problem-solving skills in design. On the other hand, text-to-image artificial intelligence is a method used to generate images from natural language descriptors (usually referred to as prompts). Design thinking methodology can teach students to think creatively and critically about real-world problems when applied in the classroom. In the context of design teaching at the University of Saint Joseph, Macao, students use the design thinking methodology to develop innovative proposals for furniture design solutions. Combining design thinking methodologies with text-to-image artificial intelligence can further enhance the learning experience by allowing students to generate visual representations of their ideas during the ideation phase. The authors developed a systematic approach to generate images for ideation on furniture design based on prompting text-to-image (PTI). The analysis related students’ results who applied the design thinking methodology without using AI tools and the results generated using a standard text-to-image programme. By combining both methods, teachers can help students develop critical thinking, creativity, and problem-solving skills, while also allowing them to generate visual representations in a different paradigm and, by so, being able to communicate their ideas with the most appropriate support for them.

Continuous cardiac monitoring has been increasingly adopted to prevent heart diseases, especially the case of Chagas disease, a chronic condition that can degrade the heart condition, leading to sudden cardiac death. Unfortunately, a common challenge for these systems is the low-quality and high level of noise in ECG signal collection. Also, generic techniques to assess the ECG quality can discard useful information in these so-called chagasic ECG signals. To mitigate this issue, this work proposes a 1D CNN network to assess the quality of the ECG signal for chagasic patients and compare it to the state of art techniques. Segments of 10 s were extracted from 200 1-lead ECG Holter signals. Different feature extractions were considered such as morphological fiducial points, interval duration, and statistical features, aiming to classify 400 segments into four signal quality types: Acceptable ECG, Non-ECG, Wandering Baseline (WB), and AC Interference (ACI) segments. The proposed CNN architecture achieves a $$0.90 \pm 0.02$$accuracy in the multi-classification experiment and also $$0.94 \pm 0.01$$when considering only acceptable ECG against the other three classes. Also, we presented a complementary experiment showing that, after removing noisy segments, we improved morphological recognition (based on QRS wave) by 33% of the entire ECG data. The proposed noise detector may be applied as a useful tool for pre-processing chagasic ECG signals.