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A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, was not originally designed for COVID19. We used the simple, commonly used SEIR model to retrospectively analyse the initial pandemic data from Singapore. Here, the SEIR model was combined with the actual published Singapore pandemic data, and the key parameters were determined by maximizing the nonlinear goodness of fit R2 and minimizing the root mean square error. These parameters served for the fast and directional convergence of the parameters of an improved model. To cover the quarantine and asymptomatic variables, the existing SEIR model was extended to an infectious disease model with a greater number of population compartments, and with parameter values that were tuned adaptively by solving the nonlinear dynamics equations over the available pandemic data, as well as referring to previous experience with SARS. The contribution presented in this paper is a new model called the adaptive SEAIRD model; it considers the new characteristics of COVID19 and is therefore applicable to a population including asymptomatic carriers. The predictive value is enhanced by tuning of the optimal parameters, whose values better reflect the current pandemic.
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The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in history, and the most recent one has unique characteristics, which are tightly connected to the current society’s lifestyle and beliefs, creating an environment of uncertainty. Because of that, the development of mathematical/computational models to forecast the pandemic behavior since its beginning, i.e., with a restricted amount of data collected, is necessary. This chapter focuses on the analysis of different data mining techniques to allow the pandemic prediction with a small amount of data. A case study is presented considering the data from Wuhan, the Chinese city where the virus was first detected, and the place where the major outbreak occurred. The PNN + CF method (Polynomial Neural Network with Corrective Feedback) is presented as the technique with the best prediction performance. This is a promising method that might be considered in future eventual waves of the current pandemic or event to have a suitable model for future epidemic outbreaks around the world.
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There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of time series under analysis from the available data. The establishment of multiple correlations and causality between the data allows modeling the variables and probabilistic distributions and subsequently obtaining also probabilistic results for time series forecasting. To improve the predictor efficiency, computational intelligence techniques are proposed, including a fuzzy inference system and an Artificial Neural Network architecture. This type of model is suitable to be considered not only for the disease monitoring and compartmental classes, but also for managerial data such as clinical resources, medical and health team allocation, and bed management, which are data related to complex decision-making challenges.
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The application of different tools for predicting COVID19 cases spreading has been widely considered during the pandemic. Comparing different approaches is essential to analyze performance and the practical support they can provide for the current pandemic management. This work proposes using the susceptible-exposed-asymptomatic but infectious-symptomatic and infectious-recovered-deceased (SEAIRD) model for different learning models. The first analysis considers an unsupervised prediction, based directly on the epidemiologic compartmental model. After that, two supervised learning models are considered integrating computational intelligence techniques and control engineering: the fuzzy-PID and the wavelet-ANN-PID models. The purpose is to compare different predictor strategies to validate a viable predictive control system for the COVID19 relevant epidemiologic time series. For each model, after setting the initial conditions for each parameter, the prediction performance is calculated based on the presented data. The use of PID controllers is justified to avoid divergence in the system when the learning process is conducted. The wavelet neural network solution is considered here because of its rapid convergence rate. The proposed solutions are dynamic and can be adjusted and corrected in real time, according to the output error. The results are presented in each subsection of the chapter.
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Examining consumer perceptions of scent marketing in Macau's retail setting is the goal of this study. The study focuses on the following two primary uses of scent marketing in retail environments: subliminal application and application as a component of branding strategy. Qualitative research methodology is used. A total of ten consumer interviews produced the data. The findings show that consumers are in favor of scent marketing's use in retail settings. The use of scent marketing as a tool for establishing brands is preferred, and consumers find this approach to scent marketing to be more acceptable than its subliminal application. Although consumers believe that other factors, such as price, are typically more significant than scent when making purchases, the use of subliminal scents was not always evaluated negatively. Unless occasionally when making an unplanned purchase, consumers do not think that a subliminal scent can significantly impact their purchasing behavior
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OBJECTIVE: A wide range of factors can influence help-seeking attitudes when individuals experience a mental disorder. The current study investigated the relationship between traditional Chinese beliefs related to the aetiology of mental disorders and help-seeking attitudes among elderly participants in Macao. METHODS: In order to ensure the suitability of participants for inclusion in this study, the participants were required to complete an initial screening test using the Short Portable Mental Status Questionnaire (SPMSQ). Participants who successfully passed the test (n = 183) completed a questionnaire that included the Inventory of Attitudes Toward Seeking Mental Health Services (IASMHS), a set of 9 items related to traditional Chinese beliefs about aetiology of mental disorders and demographic items. RESULTS: The IASMHS scores were higher for female participants and for participants who had completed high school compared with those who had never attended school or who had only completed primary education. Endorsement of traditional Chinese beliefs about the aetiology of mental disorders was higher for male participants. There was a negative correlation between traditional Chinese aetiology beliefs related to mental disorders and help-seeking attitudes. CONCLUSION: Traditional Chinese beliefs related to the aetiology of mental disorders are a negative factor that inhibits help seeking. Implications for efforts to increase the utilisation of mental health services by the elderly are discussed.
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OBJECTIVES: To investigate the effects of different Chinese terms for mental illness and related symptoms on the attitudes of adolescents towards sufferers of a mental illness. METHODS: A survey of 578 secondary school students attending 4 schools in Macao was conducted. Each student read a short passage about a new student with a mental illness joining their class. Different versions used different labels to refer to the illness of the new student. The symptoms describing the new student also varied: either describing positive symptoms of schizophrenia or mild negative symptoms only. The attitudes of participants to the new student described were measured. RESULTS: There were significantly more negative attitudes towards the sufferer of a mental illness referred to with a psychiatric label, compared with a general label 'illness'. Participants also expressed significantly more negative attitudes when positive symptoms of schizophrenia were used to describe the new student. The results are discussed in terms of the influence of labels and symptoms on attitudes towards mental illness. CONCLUSIONS: These results supported the existence of 2 additive costs in terms of negative attitudes towards sufferers of mental illness, one associated with the label and the other associated with the symptoms.
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BACKGROUND: Public beliefs concerning the causes of mental disorders are important in their effective treatment. The relative importance of public beliefs related to the etiology of mental disorders among Chinese populations and their relationship to other attitudes to mental disorders are poorly understood. AIMS: To investigate the endorsement of beliefs related to the etiology of mental disorders in Macau, in particular, traditional Chinese beliefs, and to explore their relationship to general attitudes towards mental disorders. METHODS: A survey of 361 members of the public measured attitudes towards 32 possible causes of mental disorders as well as contact with and social distance from individuals with a mental disorder. RESULTS: The results indicated that traditional Chinese beliefs were ranked with relatively low importance compared with psychosocial causes. Traditional beliefs related to two underlying factors and were significantly higher in participants with lower education levels. There was a significant negative correlation between endorsement of traditional beliefs and contact with individuals with a mental disorder and a significant positive correlation between endorsement of traditional beliefs and desired social distance from individuals with a mental disorder. CONCLUSION: Factors underlying traditional Chinese beliefs concerning the causes of mental disorders are associated with more negative attitudes towards individuals with a mental disorder, with such beliefs more strongly endorsed by those with relatively lower education levels.
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Differences in parental expectations related to cultural background, gender and sibling position were investigated among Chinese college students in Macau. Three-hundred and forty-four Chinese college students completed a questionnaire which included measures of parental expectations and psychological distress, as well as information about their gender, cultural background (Mainland China or Macau) and sibling position. Participants born in mainland China reported significantly higher perceived parental expectations (PPE) compared with students from Macau. Contrary to predictions, there was no evidence of a difference in PPE in relation to gender or being the first-born sibling. These findings are discussed in terms of changes in family values and parental attitudes within Chinese society over the past few decades.
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