Results 5 resources
Zuev, D., & Hannam, K. (2020). Anxious immobilities: an ethnography of coping with contagion (Covid-19) in Macau. Mobilities, 1–16. https://doi.org/10.1080/17450101.2020.1827361
In February 2020, Macau became one of the first regions where the pandemic of coronavirus or Covid-19 affected the totality of social and economic life leading to increased anxieties over movement and distance. Although Macau has had very few actual cases of the virus – 46 in total –and no deaths from it, the Macau government rapidly instituted a lock down. The aim of this article is to reflect on how the social experience of being in lockdown can provide insights into understanding the type of experience or condition that we provisionally term ‘anxious immobility.’ Such a condition is characterized by a total disruption of everyday rhythms and specifically anxious waiting for the normalization of activity while being the subject of biosocial narratives of quarantine and socially responsible. The paper is based upon 3 months of ethnographic research conducted by two researchers based in Macau. We also reflect upon some aspects of the politics of mobilities in the light of disruptions and friction points between Hong Kong, Macau, mainland China, and the rest of the world.
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.
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.
Liem, A., Renzaho, A. M. N., Hannam, K., Lam, A. I. F., & Hall, B. J. (2021). Acculturative stress and coping among migrant workers: A global mixed-methods systematic review. Applied Psychology: Health and Well-Being, 13(3), 491–517. https://doi.org/https://doi.org/10.1111/aphw.12271
No existing review has synthesized key questions about acculturation experiences among international migrant workers. This review aimed to explore (1) What are global migrant workers’ experiences with acculturation and acculturative stress? (2) What are acculturative stress coping strategies used by migrant workers? And (3) how effective are these strategies for migrant workers in assisting their acculturation in the host countries? Peer-reviewed and gray literature, without time limitation, were searched in six databases and included if the study: focused on acculturative stress and coping strategies; was conducted with international migrant workers; was published in English; and was empirical. Eleven studies met the inclusion criteria. Three-layered themes of acculturation process and acculturative stress were identified as: individual layer; work-related layer; and social layer. Three key coping strategies were identified: emotion-focused; problem-focused; and appraisal-focused. These coping strategies were used flexibly to increase coping effectiveness and evidence emerged that a particular type of acculturative stress might be solved more effectively by a specific coping strategy. Migrant workers faced numerous challenges in their acculturative process. Understanding this process and their coping strategies could be used in developing research and interventions to improve the well-being of migrant workers.
Bernardo Gois, F. N., Lima, A., Santos, K., Oliveira, R., Santiago, V., Melo, S., Costa, R., Oliveira, M., Henrique, F. das C. D. M., Neto, J. X., Martins Rodrigues Sobrinho, C. R., & Lôbo Marques, J. A. (2021). Predictive models to the COVID-19. In U. Kose, D. Gupta, V. H. C. de Albuquerque, & A. Khanna (Eds.), Data Science for COVID-19 (pp. 1–24). Academic Press. https://doi.org/10.1016/B978-0-12-824536-1.00023-X
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.