Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptibleinfected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at the Capital of Hungary. First, we validated the proposed methodology by comparing the Home and Work locations estimation and the commuting patterns derived from the cellular network dataset with reports of the national mini census. We investigated the statistical relationships between mobile phone indicators, such as Radius of Gyration, the distance between Home and Work locations or the Entropy of visited cells, and measures of economic status based on housing prices. Our findings show that the mobility correlates significantly with the socioeconomic status. We performed Principal Component Analysis (PCA) on combined vectors of mobility indicators in order to characterize the dependence of mobility habits on socioeconomic status. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs.

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

In this study, Call Detail Records (CDRs) covering Budapest for the month of June in 2016 were analyzed. During this observation period, the 2016 UEFA European Football Championship took place, which significantly affected the habit of the residents despite the fact that not a single match was played in the city. We evaluated the fans’ behavior in Budapest during and after the Hungarian matches and found that the mobile phone network activity reflected the football fans’ behavior, demonstrating the potential of the use of mobile phone network data in a social sensing system. The Call Detail Records were enriched with mobile phone properties and used to analyze the subscribers’ devices. Applying the device information (Type Allocation Code) obtained from the activity records, the Subscriber Identity Modules (SIM), which do not operate in cell phones, were omitted from mobility analyses, allowing us to focus on the behavior of people. Mobile phone price was proposed and evaluated as a socioeconomic indicator and the correlation between the phone price and the mobility customs was found. We also found that, besides the cell phone price, the subscriber age and subscription type also had effects on users’ mobility. On the other hand, these factors did not seem to affect their interest in football.

Sustainability has become one of the challenges of today's banks. Since sustainable business models are responsible for the environment and society along with generating economic benefits, they are an attractive approach to sustainability. Sustainable business models also offer banks competitive advantages such as increasing brand reputation and cost reduction. However, no framework is presented to evaluate the sustainability of banking business models. To bridge this theoretical gap, the current study using A Delphi-Analytic Hierarchy Process method, firstly, developed a sustainable business model to evaluate the sustainability of the business model of banks. In the second step, the sustainability performance of sixteen banks from eight European countries including Norway, , assessed. The proposed business model components of this study were ranked in terms of their impact on achieving sustainability goals. Consequently, the proposed model components of this study, based on their impact on sustainability, are respectively value proposition, core competencies, financial aspects, business processes, target customers, resources, technology, customer interface, and partner network. The results of the comparison of the banks studied by each country disclosed that the sustainability of the Norwegian and German banks' business models is higher than in other counties. The studied banks of Hungary and Spain came in second, the banks of the UK, Poland, and France ranked third, and finally, the Italian banks ranked fourth in the sustainability of their business models.

A holomorphic germ Φ : (C 2 , 0) → (C 3 , 0), singular only at the origin, induces at the links level an immersion of S 3 into S 5 . The regular homotopy type of immersions S 3 S 5 are determined by their Smale invariant, defined up to a sign ambiguity. In this paper we fix a sign of the Smale invariant and we show that for immersions induced by holomorphic gems the sign-refined Smale invariant Ω is the negative of the number of cross caps appearing in a generic perturbation of Φ. Using the algebraic method we calculate Ω for some families of singularities, among others the A-D-E quotient singularities. As a corollary, we obtain that the regular homotopy classes which admit holomorphic representatives are exactly those, which have non-positive sign-refined Smale invariant. This answers a question of Mumford regarding exactly this correspondence. We also determine the sign ambiguity in the topological formulae of Hughes-Melvin and Ekholm-Szűcs connecting the Smale invariant with (singular) Seifert surfaces. In the case of holomorphic realizations of Seifert surfaces, we also determine their involved invariants in terms of holomorhic geometry. A. Némethi and G. PintérAs we will see, Imm hol (S 3 , S 5 ) is not symmetric with respect to a sign change of Z, hence, in order to identify the subset Imm hol (S 3 , S 5 ) without any sign-ambiguity, we will fix a 'canonical' generator of π 3 (SO (5)). This will be done via the ismorphisms π 3 (U (3)) → π 3 (SO(6)) → π 3 (SO(5)) and by fixing a canonical generator in π 3 (U (3)) (see 4.2). Sometimes, to emphasize that we work with the Smale invariant with this fixed sign convention, we refer to it as the sign-refined Smale invariant. Our second goal is to determine the correct signs (compatibly with the above choice of generators) in the existing topological formulas, which were stated only up to a sign-ambiguity.1.2. The set Imm hol (S 3 , S 5 ). One expects that the analytic geometry of holomorphic realization imposes some rigidity restrictions, and also provides some further connections with the properties of complex analytic spaces. Mumford already in 1961 in his seminal article [16] asked for the characterization of the Smale invariant of a holomorphic (algebraic) immersion in terms of the analytic/algebraic geometry. This article provides a complete answer to his question. A more precise formulation of our guiding questions are: Question 1.2.1.(a) Which are the regular homotopy classes Imm hol (S 3 , S 5 ) and Emb hol (S 3 , S 5 ) represented by holomorphic germs?(b) How can a certain regular homotopy class be identified via complex singularity theory, that is, via algebraic or analytic invariants of the involved analytic spaces? Furthermore, if some Φ realizes some Smale invariant (e.g., if its Smale invariant is zero), then what kind of specific analytic properties Φ must have?The main results of this paper provide the following answer in the case of immersions. Theorem 1.2.2. (a) Imm hol (S 3 , S 5 ) is identified via the sign-refined Smale invariant Ω(f ) by the s...

Along with environmental pollution, urban planning has been connected to public health. The research indicates that the quality of built environments plays an important role in reducing mental disorders and overall health. The structure and shape of the city are considered as one of the factors influencing happiness and health in urban communities and the type of the daily activities of citizens. The aim of this study was to promote physical activity in the main structure of the city via urban design in a way that the main form and morphology of the city can encourage citizens to move around and have physical activity within the city. Functional, physical, cultural-social, and perceptual-visual features are regarded as the most important and effective criteria in increasing physical activities in urban spaces, based on literature review. The environmental quality of urban spaces and their role in the physical activities of citizens in urban spaces were assessed by using the questionnaire tool and analytical network process (ANP) of structural equation modeling. Further, the space syntax method was utilized to evaluate the role of the spatial integration of urban spaces on improving physical activities. Based on the results, consideration of functional diversity, spatial flexibility and integration, security, and the aesthetic and visual quality of urban spaces plays an important role in improving the physical health of citizens in urban spaces. Further, more physical activities, including motivation for walking and the sense of public health and happiness, were observed in the streets having higher linkage and space syntax indexes with their surrounding texture.