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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.

Abstract With its large population and natural resources, Africa needs investors who can sustain its development. At the same time, foreign investors expect returns on their investments. In ...

The adoption of IoT for smart health applications is a relevant tool for distributed and intelligent automatic diagnostic systems. This work proposes the development of an integrated solution to monitor maternal and fetal signals for high-risk pregnancies based on IoT sensors, feature extraction based on data analytics, and an intelligent diagnostic aid system based on a 1-D convolutional neural network (CNN) classifier. The fetal heart rate and a group of maternal clinical indicators, such as the uterine tonus activity, blood pressure, heart rate, temperature, and oxygen saturation are monitored. Multiple data sources generate a significant amount of data in different formats and rates. An emergency diagnostic subsystem is proposed based on a fog computing layer and the best accuracy was 92.59% for both maternal and fetal emergency. A smart health analytics system is proposed for multiple feature extraction and the calculation of linear and nonlinear measures. Finally, a classification technique is proposed as a prediction system for maternal, fetal, and simultaneous health status classification, considering six possible outputs. Different classifiers are evaluated and a proposed CNN presented the best results, with the F1-score ranging from 0.74 to 0.91. The results are validated based on the diagnosis provided by two specialists. The results show that the proposed system is a viable solution for maternal and fetal ambulatory monitoring based on IoT.

Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.

Background and objective Intrauterine Growth Restriction (IUGR) is a condition in which a fetus does not grow to the expected weight during pregnancy. There are several well documented causes in the literature for this issue, such as maternal disorder, and genetic influences. Nevertheless, besides the risk during pregnancy and labour periods, in a long term perspective, the impact of IUGR condition during the child development is an area of research itself. The main objective of this work is to propose a machine learning solution to identify the most significant features of importance based on physiological, clinical or socioeconomic factors correlated with previous IUGR condition after 10 years of birth. Methods In this work, 41 IUGR (18 male) and 34 Non-IUGR (22 male) children were followed up 9 years after the birth, in average (9.1786 ± 0.6784 years old). A group of machine learning algorithms is proposed to classify children previously identified as born under IUGR condition based on 24-hours monitoring of ECG (Holter) and blood pressure (ABPM), and other clinical and socioeconomic attributes. In additional, an algorithm of relevance determination based on the classifier is also proposed, to determine the level of importance of the considered features. Results The proposed classification solution achieved accuracy up to 94.73%, and better performance than seven state-of-the-art machine learning algorithms. Also, relevant latent factors related to HRV and BP monitoring are proposed, such as: day-time heart rate (day-time HR), day-night systolic blood pressure (day-night SBP), 24-hour standard deviation (SD) of SBP, dropped, morning cortisol creatinine, 24-hour mean of SDs of all NN intervals for each 5 minutes segment (24-hour SDNNi), among others. Conclusion With outstanding accuracy of our proposed solutions, the classification system and the indication of relevant attributes may support medical teams on the clinical monitoring of IUGR children during their childhood development.

The spontaneous symmetry breaking phenomena applied to Quantum Finance considers that the martingale state in the stock market corresponds to a ground (vacuum) state if we express the financial equations in the Hamiltonian form. The original analysis for this phenomena completely ignores the kinetic terms in the neighborhood of the minimal of the potential terms. This is correct in most of the cases. However, when we deal with the martingale condition, it comes out that the kinetic terms can also behave as potential terms and then reproduce a shift on the effective location of the vacuum (martingale). In this paper, we analyze the effective symmetry breaking patterns and the connected vacuum degeneracy for these special circumstances. Within the same scenario, we analyze the connection between the flow of information and the multiplicity of martingale states, providing in this way powerful tools for analyzing the dynamic of the stock markets.

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...

Crowdsensing exploits the sensing abilities offered by smart phones and users' mobility. Users can mutually help each other as a community with the aid of crowdsensing. The potential of crowdsensing has yet to be fully realized for improving public health. A protocol based on gamification to encoura...

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.

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.

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.

For a long time, Geography did not hold a specific mathematical approach for any interpretation of space and this was the key reason why Geography degrees covered a wide variety of subjects such as demography, geology or topography to fulfill its curriculum. Yet from the 90’s, Geography finally created its own research agenda to meet four vital questions of any true geographer: “Where is …?”, “Is there a general spatial pattern?”, “What are the anomalies?” and “Why do these phenomena pursue certain spatial distribution?” The present review article addresses ten different spatial (point, regression and event) issues for learning and teaching aim where statistics play a major background role on the outcomes of myGeoffice© free Web GIS platform. These include cluster analysis, geographically weighted regression (GWR), ordinary least squares (OLS) regression, path analysis, minimum spanning tree, linear regression, space-time clustering and point patterns, for instance. Although the technical viewpoint of the algorithms is not explained at fully, this review paper makes a rather strong emphasis on the result’s interpretation, their respective meaning and when these techniques should be applied in a learning/teaching context.

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.