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Diakite, A. D., & Phillips, J. O. L. (2019). Motives of Traditional and Emerging Donors in Aid Giving: Comparative Study between China and France. Journal of Social and Political Sciences, 2(4), 1026–1037. https://doi.org/10.31014/aior.1991.02.04.140

Since the beginning of bilateral aid giving in the aftermath of the Second World War, the motives for aid giving have changed from being purely political and humanitarian to a mix of different interests. While poverty reduction is frequently stated as the goal of aid giving, it is commonplace for donors to use aid to advance their national interests. The rise of new, emerging donors is creating discussion in both the political and academic fields of aid giving. Traditional or western donors see emerging donors, such as China’s efforts in aid-giving as seeking the natural resources of the recipient countries. This paper provides a historical analysis of the aid-giving motivations underlying an emerging donor, China, and a traditional donor, France. The motives for China’s and France’s aid giving to African countries, with special focus on Guinea, show a great number of similarities.

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de Aguiar, A. W. O., Bezerra, J. D. H., Marques, J. A. L., & de Alexandria, A. R. (2019). Lead: An iOS Application to Help in the Construction of New Habits. International Journal of Information and Electronics Engineering, 9(4). http://www.ijiee.org/index.php?m=content&c=index&a=show&catid=87&id=820

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 69–87). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_5

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 15–39). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_2

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Forecasting COVID-19 Time Series Based on an Autoregressive Model. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 41–54). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_3

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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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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 89–98). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_6

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Prediction for Decision Support During the COVID-19 Pandemic. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 1–13). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_1

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Lan, K., Liu, L., Li, T., Chen, Y., Fong, S., Marques, J. A. L., Wong, R. K., & Tang, R. (2020). Multi-view convolutional neural network with leader and long-tail particle swarm optimizer for enhancing heart disease and breast cancer detection. Neural Computing and Applications, Neural Computing and Applications. https://doi.org/10.1007/s00521-020-04769-y

Marques, J. A. L., Cortez, P. C., Madeiro, J. P. V., Albuquerque, V. H. C., Fong, S. J., & Schlindwein, F. S. (2020). Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures. Journal of Supercomputing, Journal of Supercomputing, 76(2), 1305–1320. https://doi.org/10.1007/s11227-018-2570-8

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Armando Carlos Hombo Nogueira, Luis Miguel Pacheco, & Marcus Antonio Almeida Rodrigues. (2019). Influência da nova estrutura fiscal de impostos de Angola na gestão de escolas privadas do municipio do Lobito. Revista Gestao Em Analise. https://doi.org/10.12662/2359-618xregea.v8i2.p11-30.2019

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Do Vale Madeiro, J. P., Barreto, D., Marques, J. A. L., & Salinet, J. L. (2019). Evaluating Mathematical Models for Morphological Classification of the QRS Complex. Computing in Cardiology, Computing in Cardiology, 2019-September. https://doi.org/10.23919/CinC49843.2019.9005790

Lan, K., Li, T., Fong, S., Marques, J. A. L., & Wong, R. K. (2018). Multi-view Convolution Neural Network with Swarm Search Based Hyperparameter Optimization for Enhancing Heart Disease and Breast Cancer Detection. 5th International Conference on Soft Computing and Machine Intelligence, ISCMI 2018, 5th International Conference on Soft Computing and Machine Intelligence, ISCMI 2018, 140–146. https://doi.org/10.1109/ISCMI.2018.8703249

Marques, J. A. L., Cortez, P. C., Madeiro, J. P. D. V., Fong, S. J., Schlindwein, F. S., & Albuquerque, V. H. C. D. (2019). Automatic Cardiotocography Diagnostic System Based on Hilbert Transform and Adaptive Threshold Technique. IEEE Access, 7, 73085–73094. https://doi.org/10.1109/ACCESS.2018.2877933

The visual analysis of cardiotocographic examinations is a very subjective process. The accurate detection and segmentation of the fetal heart rate (FHR) features and their correlation with the uterine contractions in time allow a better diagnostic and the possibility of anticipation of many problems related to fetal distress. This paper presents a computerized diagnostic aid system based on digital signal processing techniques to detect and segment changes in the FHR and the uterine tone signals automatically. After a pre-processing phase, the FHR baseline detection is calculated. An auxiliary signal called detection line is proposed to support the detection and segmentation processes. Then, the Hilbert transform is used with an adaptive threshold for identifying fiducial points on the fetal and maternal signals. For an antepartum (before labor) database, the positive predictivity value (PPV) is 96.80% for the FHR decelerations, and 96.18% for the FHR accelerations. For an intrapartum (during labor) database, the PPV found was 91.31% for the uterine contractions, 94.01% for the FHR decelerations, and 100% for the FHR accelerations. For the whole set of exams, PPV and SE were both 100% for the identification of FHR DIP II and prolonged decelerations.

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Arraut, I., Au, A., Tse, A. C., & Marques, J. A. L. (2020). On the probability flow in the Stock market I: The Black-Scholes case. arXiv.Org, 1, 1–10. https://search.proquest.com/docview/2332255379?pq-origsite=primo

It is known that the probability is not a conserved quantity in the stock market, given the fact that it corresponds to an open system. In this paper we analyze the flow of probability in this system by expressing the ideal Black-Scholes equation in the Hamiltonian form. We then analyze how the non-conservation of probability affects the stability of the prices of the Stocks. Finally, we find the conditions under which the probability might be conserved in the market, challenging in this way the non-Hermitian nature of the Black-Scholes Hamiltonian.

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2021). Predictive Models for Decision Support in the COVID-19 Crisis. Springer International Publishing. https://doi.org/10.1007/978-3-030-61913-8

COVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations. Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.

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Yang, V. (2016). The Criminalization of Verbal Sexual Harassment in Amending Macao’s Criminal Code. Socioeconomic Development Research, (1), 24–35.

Alves, J., & Trimarchi, M. (2016). Analysis of Business Clusters in Macau [Research Report]. University of Saint Joseph.

Leandro, F. J. (2016). Asia-Europe Comparative Studies Research Project(AECSRP) [Research Report]. University of Saint Joseph.

Alves, J. (2015). Macau’s Strategies to Develop Training of Talented People with Expertise in the Cooperation between China and Portuguese Speaking Countries [Research Report]. University of Saint Joseph.