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Morgan, S. (2021). Liturgical Text as Mandorla: windows from the Everyday to the Sacred, from the Sacred to the Everyday. In S. Morgan (Ed.), Sacred and the Everyday: Comparative Approaches to Literature, Religious and Secular (pp. 66–82). USJ Press.

dos Santos Silva, B. R., Cesar Cortez, P., Gomes Aguiar, R., Rodrigues Ribeiro, T., Pereira Teixeira, A., Bernardo Gois, F. N., & Lobo Marques, J. A. (2023). Lung Segmentation of Chest X-Rays Using Unet Convolutional Networks. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 15–28). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_2

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%.

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Soares, N., & Simões, F. (2017). Macao Shaped by Use – Formalizing the Vernacular Customization of the City. In H. H. Choi & H. Pai (Eds.), Imminent Commons: Commoning Cities: Seoul Biennale of Architecture and Urbanism 2017 (pp. 66–67). Actar Publishers.

Soares, N., & Simões, F. (2017). Macao Shaped by Use –Formalizing the Vernacular Customization of the City in Choi. In Imminent Commons: Commoning Cities: Seoul Biennale of Architecture and Urbanism. Actar Publishers.

Soares, N., & F., S. (2017). Macao Shaped by Use –Formalizing the Vernacular Customization of the City in Choi, H. & Pai, H. (ed.). In Imminent commons: commoning cities. Actar Publishers.

Simoes, J. M. (2022). Macau: 7 desenhos, 7 artistas, 7 perfis, 7 pontes entre a China e o mundo lusófono. In F. Ilharco (Ed.), Em português: falar, viver e pensar no século XXI (p. 317). Universidade Católica Editora.

Simoes, J. M. (2022). Macau: Carlos Marreiros, macaense, luso-descendente – um testemunho de uma carreira internacional de um arquiteto e artista. In F. Ilharco (Ed.), Em português: falar, viver e pensar no século XXI (p. 335). Universidade Católica Editora.

Morais, I. (2018). Macau entre a ‘China Tropical’ e a Lusofonia a Oriente: Algumas achegas sobre os contextos colonial e pós-colonial. In M. Cahen & P. F. de Matos (Eds.), Novas perspetivas sobre o luso-tropicalismo, Portuguese Studies Review PSR (pp. 63–96). Bayworlf Press. http://www.maproom44.com/psr/26_1.html

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Davey, G., & Rato, R. (2014). Macau, Quality of Life. In A. C. Michalos (Ed.), Encyclopedia of Quality of Life and Well-Being Research (pp. 3737–3740). Springer Netherlands. https://doi.org/10.1007/978-94-007-0753-5_3953

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Choi, H. H., & Pai, H. (Eds.). (2017). Macau Shaped by Use - Vernacular Customization of the City. In Imminent Commons: Commoning Cities", Seoul Biennale of Srchitecture and Urbanism. Actar.

Gois, F. N. B., Marques, J. A. L., de Oliveira Dantas, A. B., Santos, M. C., Neto, J. V. S., de Macêdo, J. A. F., Du, W., & Li, Y. (2023). Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures. In R. Lee (Ed.), Computer and Information Science (pp. 197–212). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_14

Fast and efficient malaria diagnostics are essential in efforts to detect and treat the disease in a proper time. The standard approach to diagnose malaria is a microscope exam, which is submitted to a subjective interpretation. Thus, the automating of the diagnosis process with the use of an intelligent system capable of recognizing malaria parasites could aid in the early treatment of the disease. Usually, laboratories capture a minimum set of images in low quality using a system of microscopes based on mobile devices. Due to the poor quality of such data, conventional algorithms do not process those images properly. This paper presents the application of deep learning techniques to improve the accuracy of malaria plasmodium detection in the presented context. In order to increase the number of training sets, deep convolutional generative adversarial networks (DCGAN) were used to generate reliable training data that were introduced in our deep learning model to improve accuracy. A total of 6 experiments were performed and a synthesized dataset of 2.200 images was generated by the DCGAN for the training phase. For a real image database with 600 blood smears with malaria plasmodium, the proposed Deep Learning architecture obtained the accuracy of 100% for the plasmodium detection. The results are promising and the solution could be employed to support a mass medical diagnosis system.

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Gois, F. N. B., Marques, J. A. L., De Oliveira Dantas, A. B., Santos, M. C., Neto, J. V. S., De Macêdo, J. A. F., Du, W., & Li, Y. (2023). Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures. In R. Lee (Ed.), Computer and Information Science (Vol. 1055, pp. 197–212). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_14

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Tagulao, K. (2022). Mangroves as a nature-based solution for water and climate change issues in Macao: from scientific research to environmental education. In S. Naumann, M. Davis, R. Noebel, & N. Burgos Cuevas, Fostering applied research on the synergies between biodiversity and climate. Federal Agency for Nature Conservation. https://bfn.bsz-bw.de/frontdoor/index/index/docId/1090

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Singh, P., Chaudhary, G., & Lobo Marques, J. A. (2023). Medical Information Extraction of Clinical Notes and Pictorial Visualisation of Electronic Medical Records Summary Interface. In Smart Distributed Embedded Systems for Healthcare Applications (pp. 29–40). CRC Press. https://www.routledge.com/Smart-Distributed-Embedded-Systems-for-Healthcare-Applications/Nagrath-Alzubi-Singla-Rodrigues-Verma/p/book/9781032183473

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Rafael, Â. P., & Wei, D. (2024). Modelos de Incorporação do Desenvolvimento Sustentável nos Acordos de Comércio. In Uma viagem Jurídica Entre o Rio das Pérolas e a Pérola do Índico: Publicação Comemorativa dos 25 Anos da Cooperação entre a Faculdade de Direito da Universidade Eduardo Mondlane (FDUEM) e a Faculdade de Direito da Universidade de Macau (FDUM) (1st ed., pp. 751–781). Inter Escolar Editora.

Tagulao, K. A. (2022). Nature-Based Solutions for Environmental Education in the East Asian Context. In C. Vasconcelos & C. S. C. Calheiros (Eds.), Enhancing Environmental Education Through Nature-Based Solutions (pp. 361–370). Springer International Publishing. https://doi.org/10.1007/978-3-030-91843-9_21

Environmental education (EE) has long been practiced worldwide, while Nature-based solutions (NBS) is a relatively new concept. This chapter aims to provide an overview of the EE and NBS practices in East Asia and evaluate how these two valuable applications can be used concurrently. East Asia has a well developed environmental education (EE) programs and activities, both in formal and informal education. These ranges from developing green schools and campuses to establishing policies and acts. While EE has been actively practiced for decades in the region, the adoption of NBS to address environmental and societal challenges is limited. The educational benefits and opportunities from NBS are also lacking. Although there are some projects that can be classified as NBS, like the use of wetlands for wastewater treatment, they are not clearly categorized as one. These projects are also not integrated into environmental education programs. Considering this, the region should develop innovative environmental education programs for schools, universities and communities, that integrate NBS projects. Integrating the two together will boost the effectiveness of environmental education in raising environmental awareness and changing the environmental attitude and behavior of people, which will also help address societal issues.

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Gonçalves, D., Félix, A. S., & Oliveira, R. F. (2017). Neuroendocrinology of Social Behavior in Teleost Fish. In Hormones, Brain and Behavior (pp. 3–18). Elsevier. https://doi.org/10.1016/B978-0-12-803592-4.00020-1

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Caldas, W. L., Do Vale Madeiro, J. P., Pedrosa, R. C., Gomes, J. P. P., Du, W., & Marques, J. A. L. (2023). Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks. In R. Lee (Ed.), Computer and Information Science (Vol. 1055, pp. 117–129). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_8

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Caldas, W. L., do Vale Madeiro, J. P., Pedrosa, R. C., Gomes, J. P. P., Du, W., & Marques, J. A. L. (2023). Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks. In R. Lee (Ed.), Computer and Information Science (pp. 117–129). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_8

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.

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 55–68). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_4

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