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Information contained within documents is an essential ingredient of any office operation. A content management application (CMA) is an organization plan for the conception, use, retention, disposal and selective preservation of its data. Using an appropriate CMA framework can greatly help Macao Government agencies, for instance, that are increasingly using electronic means to create, exchange and store a major variety of records daily. By definition, a record is information, in whatever form, for government functions, activities, decisions and other important transactions. As expected, as the volume of electronic information increases, so does the complexity of managing electronic records. This project goal was to evaluate the software capabilities of the Alfresco© Enterprise Content Management (ECM) against a set of functional requirements, aimed by the Macao Government agency. Drawing on the results of this evaluation, the present analysis concludes that Alfresco© ECM is capable of supporting an entire agency needs related to the management of its records content.
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The knowledge of spatial distribution of grasshoppers can be very relevant for agricultural planning purposes. On the other hand, the comparison of spatial interpolators for efficiency and reliability reasons is also a key factor to understand interpolation maps outcomes (versus reality). At last, but not least, the use of open Web geographical tools to disseminate true spatial inferential methods to address spatial issues is still quite limited (if none) in high schools and universities, particularly in Geography subjects. If the latter can be addressed with myGeoffice©, the first issue will use the Utah, USA, dataset (58 samples) to layout the spatial distribution of grasshoppers and understand the counties that are more pro to this kind of agriculture infestation. Inverse Distance Weighted (IDW), Moving Average (MA), Multi-quadratic, Inverse Multi-quadratic and Nearest Neighbor (NN) will produce interpolated surfaces of grasshopper’s properties. Efficiency of spatial interpolators was assessed in this writing based on the prediction error’s statistics derived from the difference between the estimation and the real samples on a cross-validation procedure. Remarkably, results show that NN was the most accurate one when compared with the remaining deterministic approaches at sample’s locations.
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In 2020, the World Health Organization declared the Coronavirus Disease 19 a global pandemic. While detecting COVID-19 is essential in controlling the disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a CT-Scan room. In other cases, the devices are not available. The adoption of ambulatory monitoring examinations, such as Electrocardiography (ECG), can be considered a viable tool to address the patient’s cardiovascular condition and to act as a predictor for future disease outcomes. In this investigation, ten non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) extracted from 2 ECG signals (collected from 2 different patient’s positions). Windows of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested: (Low vs. Moderate, Low vs. Severe, Moderate vs. Severe) and 1 Multi-class comparison (All vs. All)). The results indicate that some statistically significant parameter distributions were found for all comparisons. (Low vs. Moderate—Approximate Entropy p-value = 0.0067 < 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 < 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 < 0.05, All vs. All—Correlation Dimension p-value = 0.0185 < 0.05. The non-linear analysis of the time-frequency representation of the ECG signal can be considered a promising tool for describing and distinguishing the COVID-19 severity activity along its different stages.
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It is plausible to assume that the component waves in ECG signals constitute a unique human characteristic because morphology and amplitudes of recorded beats are governed by multiple individual factors. According to the best of our knowledge, the issue of automatically classifying different ’identities’ of QRS morphology has not been explored within the literature. This work proposes five alternative mathematical models for representing different QRS morphologies providing the extraction of a set of features related to QRS shape. The technique incorporates mechanisms of combining the mathematical functions Gaussian, Mexican-Hat and Rayleigh probability density function and also a mechanism for clipping the waveform of those functions. The searching for the optimal parameters which minimize the normalized RMS error between each mathematical model and a given QRS search window enables to find an optimal model. Such modeling behaves as a robust alternative for delineating heartbeats, classifying beat morphologies, detecting subtle and anomalous changes, compression of QRS complex windows among others. The validation process evaluates the ability of each model to represent different QRS morphology classes within 159 full ECG signal records from QT database and 584 QRS search windows from MIT-BIH Arrhythmia database. From the experimental results, we rank the winning rates for which each mathematical model best models and also discriminates the most predominant QRS morphologies Rs, rS, RS, qR, qRs, R, rR’s and QS. Furthermore, the average time errors computed for QRS onset and offset locations when using the corresponding winner mathematical models for delineation purposes were, respectively, 12.87±8.5 ms and 1.47±10.06 ms.
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In government studies, electronic government has become a hot topic in recent decades. Many scholars believe that soon, the government might not be able to operate smoothly without the help of ICTs as the Internet has been overwhelming people's daily lives already. In analyzing people's behavioral factors towards adopting e-government services, most studies targeted the adult population, while those in the hard-to-reach groups are minimal. This study was designed especially to understand the behavioral factors of the younger generation aged between 18 and 24 and the senior citizens above 60 on their adoption of e-government services in Macao SAR. Sixteen in-depth interviews were conducted based on the semi-structured interview questions developed from the prior literature on the Theory of Planned Behavior and e-government studies. Six significant findings are yielded, which could serve as an important reference for policymakers designing e-government policy and promoting its implementation strategy. These behavioral factors also contribute empirical data to support the theoretical framework of TPB in the context of Macao SAR e-government services.
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The scientific literature indicates that pregnant women with COVID-19 are at an increased risk for developing more severe illness conditions when compared with non-pregnant women. The risk of admission to an ICU (Intensive Care Unit) and the need for mechanical ventilator support is three times higher. More significantly, statistics indicate that these patients are also at 70% increased risk of evolving to severe states or even death. In addition, other previous illnesses and age greater than 35 years old increase the risk for the mother and the fetus, including a higher number of cesarean sections, higher systolic and diastolic maternal blood pressure, increasing the risk of eclampsia, and, in some cases, preterm birth. Additionally, pregnant women have more Emotional lability/fluctuations (between positive and negative feelings) during the entire pregnancy. The emotional instability and brain fog that takes place during gestation may open vulnerability for neuropsychiatric symptoms of long COVID, which this population was not studied in depth. The present Chapter characterizes the database presented in this work with clinical and survey data collected about emotions and feelings using the Coronavirus Perinatal Experiences—Impact Survey (COPE-IS). Pregnant women with or without COVID-19 symptoms who gave birth at the Assis Chateaubriand Maternity Hospital (MEAC), a public maternity of the Federal University of Ceara, Brazil, were recruited. In total, 72 mother-infant dyads were included in the study and are considered in this exploratory analysis. The participants have undergone serological tests for SARS-CoV-2 antibody detection and a nasopharyngeal swab test for COVID-19 diagnoses by RT-PCR. A comprehensive Exploratory Data Analysis (EDA) is performed using frequency distribution analysis of multiple types of variables generated from numerical data, multiple-choice, categorized, and Likert-scale questions.
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The classification of emotions based on facial expressions have been a new topic of research in recent years, especially in marketing and consumer behavior areas. However, there is lack of studies to understand how the research topic is developed in terms of bibliometric data. Therefore, the purpose of this work is to provide a bibliometric analysis of the research on the analysis of facial expressions for marketing and consumer behavior, identifying the state of the art, the latest research direction, and other indicators. We extracted data from Web of Science (WOS) platform, considering its core database, resulting in a total of 117 articles. The software Vosviewer was used to analyze the data and graphically visualize the results. This study indicates some of the most influential authors citations and coupling analysis in this specific field, identifies journals with the most published articles, and provide trends of the research area based on the analysis of keywords and corresponding number of articles per year. The results shows that 11 articles (9.4%) were cited more than 100 times, and the two most prolific authors published 5 articles, and the two most influential authors are Bouaziz Sofien and Pauly mark(270 citations) in this field. Of the 117 articles retrieved by WOS, more than 70% were published in high impact journals. The bibliometric analysis of the existing work in this study provides a valuable and reliable reference for researchers in this field and makes a reasonable prediction of the research direction trends.
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Digital inclusion in Macao is in the very beginning stage, and disability inclusion practice on social media in producing and promoting accessible social media content and needs is hard to find. This study aims to analyse the factors that influence communication staff's practice of disability inclusion on social media by using the combined employee behaviour and communication process model to provide suggestions to management who wants to promote disability inclusion practice on social media.The Service Centre for the Deaf of the Macau Deaf Association (MDA) was selected as the object for this case study. The online social media used for MDA's communication was analysed, and semi-structured in-depth interviews with members of the Association were conducted. The research findings showed that, except for the reward structure, factors examined from the work environment in terms of organisation, supervision and co-workers; communication staff themselves; outcomes of accessible social media communication; audience and feedbacks show relations with disability inclusion practices on social media. The delivery of inclusive culture, the influential power of disability stakeholders and the positioning of social media platforms are the key influencing factors.The interesting part of this study is that people without disabilities seem to be excluded from the disability inclusion practice carried out on MDA's Facebook. Their social media content is highly accessible to deaf and hard-of-hearing audiences yet seems not to involve the general public. The study object is a good model for producing accessible content, yet the optimisation of promoting social media accessibility needs further exploration.
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Association Rule Mining by Aprior method has been one of the popular data mining techniques for decades, where knowledge in the form of item-association rules is harvested from a dataset. The quality of item-association rules nevertheless depends on the concentration of frequent items from the input dataset. When the dataset becomes large, the items are scattered far apart. It is known from previous literature that clustering helps produce some data groups which are concentrated with frequent items. Among all the data clusters generated by a clustering algorithm, there must be one or more clusters which contain suitable and frequent items. In turn, the association rules that are mined from such clusters would be assured of better qualities in terms of high confidence than those mined from the whole dataset. However, it is not known in advance which cluster is the suitable one until all the clusters are tried by association rule mining. It is time consuming if they were to be tested by brute-force. In this paper, a statistical property called prior probability is investigated with respect to selecting the best out of many clusters by a clustering algorithm as a pre-processing step before association rule mining. Experiment results indicate that there is correlation between prior probability of the best cluster and the relatively high quality of association rules generated from that cluster. The results are significant as it is possible to know which cluster should be best used for association rule mining instead of testing them all out exhaustively.
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If Australia has been subject to major influences by the United States and European countries, why is its economy healthier than their counter partners? What are the economic foundations that underline this anti-counter cycle of financial worldwide crisis from Australia? What are some of the lessons that countries from Europe that have not fared during the current financial worldwide crisis should learn from Australia? The purpose of this paper is to review the present Australian management system. Four changes are identified including embracement of corporate governance, a shift to adopt more R&D activities, a shift to adopt environmental sustainability practices and emerging corporate social responsibility. On the conclusions settings, a recap and recommendation on how Portugal, a member of the PIGS (Portugal, Italy, Greece and Spain) Southern European Countries club forgot to embrace directives that have been applied in Australia, to avoid the actual financial and identity crisis.
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Intended as an economic and development hub, the Hengqin Cooperation Zone aims to foster collaboration and integration between mainland China, Hong Kong, and Macao, serving as a platform for economic development and innovation among the three regions. The zone's development has increased demand for financial services, often offered through fintech. There is, however, a lack of interoperability between the fintech services currently used in Macao and Hengqin. This may hinder Macao users' adoption of the technology. Thus, our research objective is to identify the factors determining Macao residents' adoption of fintech services in the area and provide insights for service providers, developers, and policymakers. A framework based on the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) was used for this purpose. The responses of 103 Macao residents provided evidence that ease of use significantly and positively impacts the usefulness of the technology. This in turn influences attitudes towards fintech usage. Subjective norms and perceived behavioral control positively impact fintech adoption intentions. The fintech industry and the governments of Macao and Hengqin can work on improving technology's ease of use and usefulness. They can also promote them to Macao users, and provide the resources required for better access to fintech in the zone
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By using the Hamiltonian formulation, we demonstrate that the Merton-Garman equation emerges naturally from the Black-Scholes equation after imposing invariance (symmetry) under local (gauge) transformations over changes in the stock price. This is the case because imposing gauge symmetry implies the appearance of an additional field, which corresponds to the stochastic volatility. The gauge symmetry then imposes some constraints over the free-parameters of the Merton-Garman Hamiltonian. Finally, we analyze how the stochastic volatility gets massive dynamically via Higgs mechanism.
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By using the Hamiltonian formulation, we demonstrate that the Merton-Garman equation emerges naturally from the Black-Scholes equation after imposing invariance (symmetry) under local (gauge) transformations over changes in the stock price. This is the case because imposing gauge symmetry implies the appearance of an additional field, which corresponds to the stochastic volatility. The gauge symmetry then imposes some constraints over the free parameters of the Merton-Garman Hamiltonian. Finally, we analyze how the stochastic volatility gets massive dynamically via Higgs mechanism.
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The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and th...
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