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Artists are increasingly using blockchain as a tool for trading digital artwork as non-fungible tokens (NFTs); however, some are also beginning to experiment with the blockchain as a medium for generative art, using it as a seed for a generative process or to continuously modify an evolving piece. This paper surveys, reviews, and classifies the state-of-the-art in blockchain-interactive NFTs and presents a liberal-arts critique of the opportunities and threats posed by this technology, whilst addressing existing criticism on the broader topic of art-related NFTs. The paper examines some of the most experimental pieces minted on the Hic et Nunc (HEN) and Teia NFT marketplaces, for which a purpose-built research tool was developed. The survey reveals some reliance on centralised infrastructure, namely blockchain indexers, placing undesired trust on third parties which undermines the potential longevity of the artwork. The paper concludes with recommendations for artists and NFT platform designers for developing more resilient and economically sustainable architectures.
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"Macau's rapid economic development in recent decades and relatively low usage of public transportation have put considerable pressure on the city's carrying capacity. Improving the transportation system has been a major policy goal of Macau's urban planners. To deepen the understanding of the perspectives of local road users, this dissertation's main research question is: what factors determine the satisfaction of local road users of Macau? After collecting responses using an online questionnaire, quantitative research methods were adopted to analyze travel patterns, satisfaction toward different road usage dimensions, and sociodemographic characteristics of local residents. 145 responses were collected and quota samples were generated to match the distribution of each sociodemographic feature of the population. Most respondents used private vehicles to travel during peak hours on weekdays for work or for school and to travel during the entire afternoon and evening on weekends for shopping necessities and for leisure. The most traveled districts were Baixa de Taipa, Costa & Ouvidor Arriaga, and Baixa de Macau. It was found that the mean overall satisfaction score inclined to the dissatisfaction side (below 3). Only clarity of traffic lights and number of road signs (measuring infrastructure) and temperature and price of fares (measuring public transportation) had mean satisfaction scores that were significantly higher than 3, indicating higher satisfactions. Meaningful hypotheses regarding the differences of different road user groups were set out, then Kruskal-Wallis ANOVA tests and Mann-Whitney U tests were run. The significant findings were such that the elderly aged 65 or above were less satisfied and the unemployed were more satisfied with road usage. The better educated were more satisfied with the environment, and the unemployed were more satisfied with the public transportation. Drivers were less satisfied with transportation costs, and peakhour road users were less satisfied with the infrastructure. The Spearman correlation analyses found that infrastructure had moderately positive correlation with facilities and with travel safety. Based on the findings and their policy implications, policy suggestions could be made. The policies suggested in this study should have favorable short-term and long-term effects on more than one road usage aspects."
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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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