Business Methodology for the Application in University Environments of Predictive Machine Learning Models Based on an Ethical Taxonomy of the Student’s Digital Twin

Educational institutions are undergoing an internal process of strategic transformation to adapt to the challenges caused by the growing impact of digitization and the continuous development of student and labor market expectations. Consequently, it is essential to obtain more accurate knowledge of students to improve their learning experience and their relationship with the educational institution, and in this way also contribute to evolving those students’ skills that will be useful in their next professional future. For this to happen, the entire academic community faces obstacles related to data capture, analysis, and subsequent activation. This article establishes a methodology to design, from a business point of view, the application in educational environments of predictive machine learning models based on Artificial Intelligence (AI), focusing on the student and their experience when interacting physically and emotionally with the educational ecosystem. This methodology focuses on the educational offer, relying on a taxonomy based on learning objects to automate the construction of analytical models. This methodology serves as a motivating backdrop to several challenges facing educational institutions, such as the exciting crossroads of data fusion and the ethics of data use. Our ultimate goal is to encourage education experts and practitioners to take full advantage of applying this methodology to make data-driven decisions without any preconceived bias due to the lack of contrasting information

Central Banks Digital Currency: Detection of Optimal Countries for the Implementation of a CBDC and the Implication for Payment Industry Open Innovation

This article analyzes the current situation of Central Bank Digital Currencies (CBDCs), which are digital currencies backed by a central bank. It introduces their current status, and how several countries and currency areas are considering their implementation, following in the footsteps of the Bahamas (which has already implemented them in its territory), China (which has already completed two pilot tests) and Uruguay (which has completed a pilot test). First, the sample of potential candidate countries for establishing a CBDC was selected. Second, the motives for implementing a CBDC were collected, and variables were assigned to these motives. Once the two previous steps had been completed, bivariate correlation statistical methods were applied (Pearson, Spearman and Kendall correlation), obtaining a sample of the countries with the highest correlation with the Bahamas, China, and Uruguay. The results obtained show that the Baltic Sea area (Lithuania, Estonia, and Finland) is configured within Europe as an optimal area for implementing a CBDC. In South America, Uruguay (already included in the comparison) and Brazil show very positive results. In the case of Asia, together with China, Malaysia also shows a high correlation with the three pioneer countries, and finally, on the African continent, South Africa is the country that stands out as the most optimal area for implementing a CBDC

Detection of Financial Inclusion Vulnerable Rural Areas through an Access to Cash Index: Solutions Based on the Pharmacy Network and a CBDC. Evidence Based on Ávila (Spain)

The ability to access quality financial services and cash has been indicated by various organizations, such as the World Bank or UN, as a fundamental aspect to guarantee regional sustainable development. However, access to cash is not always guaranteed, especially in rural regions. The present study is based in the Ávila region of Spain. A parameter called the “access to cash index” is constructed here. It is used to detect rural areas where the ability to access cash and banking services is more difficult. Based on the “access to cash index”, two sustainable solutions are proposed: The first (in the short term), based on extending access to cash, takes advantage of the existing pharmacy network. With this measure, a notable reduction of more than 55% of the average distance required to access this service is verified here. The second is based on the implementation of a central bank digital currency. Here, the results show an acceptance of 75%. However, it is known that elderly people and those without relevant education and/or low incomes would reject its widespread use. Such a circumstance would require the development of training and information policies on the safety and effectiveness of this type of currency

Solutions to Financial Exclusion in Rural and Depopulated Areas: Evidence Based in Castilla y León (Spain)

Access to banking and financial services is defined by various international organizations as essential to ensure the development of countries and regions. However, this access is not always guaranteed, even in developed countries. Our study focuses on analyzing the current situation of several rural and depopulated areas of Castilla y León (Spain) in terms of access to banking services and cash. For this purpose, an initial spatial analysis has been carried out to compute the access to these services measured in kilometers needed to travel to access them. Subsequently, we included, as a possible solution, the access to these financial services through their implementation (as a cash back point) in the extensive Spanish network of pharmacies. The results obtained in the spatial analysis show that the introduction of the network of pharmacies as a point of access to cash means a significant reduction in the distance to travel in municipalities in rural and unpopulated areas in order to access cash. In the case of the province of Avila the distance would be reduced by 55%, in the province of Segovia the distance would be reduced by 38.5%, in the province of Soria the distance would be reduced by 20%, in the province of Palencia the distance would be reduced by 22%; and finally in the province of Zamora the distance would be reduced by 33%

Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction

This study proposes a crime prediction model according to communes (areas or districts in which the city of Buenos Aires is divided). For this, the Python programming language is used, due to its versatility and wide availability of libraries oriented to Machine Learning. The crimes reported (period 2016–2019) that occurred in the city of Buenos Aires selected to test the model are: homicides, theft, injuries, and robberies. With this, it is possible to generate a crime prediction model according to the city area based on the SEMMA (Sample, Explore, Modify, Model, and Assess) model and after data manipulation, standardization and cleaning; clustering is performed using K-means and subsequently the neural network is generated. For prediction, it is necessary to provide the model with the information corresponding to the predictive characteristics (predict); these characteristics being according to the developed neural network model: year, month, day, time zone, commune, and type of crime

Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste

The great advances produced in the field of artificial intelligence and, more specifically, in deep learning allow us to classify images automatically with a great margin of reliability. This research consists of the validation and development of a methodology that allows, through the use of convolutional neural networks and image identification, the automatic recycling of materials such as paper, plastic, glass, and organic material. The validity of the study is based on the development of a methodology capable of implementing a convolutional neural network to validate a reliability in the recycling process that is much higher than simple human interaction would have. The method used to obtain this better precision will be transfer learning through a dataset using the pre-trained networks Visual Geometric Group 16 (VGG16), Visual Geometric Group 19 (VGG19), and ResNet15V2. To implement the model, the Keras framework is used. The results conclude that by using a small set of images, and thanks to the later help of the transfer learning method, it is possible to classify each of the materials with a 90% reliability rate. As a conclusion, a model is obtained with a performance much higher than the performance that would be reached if this type of technique were not used, with the classification of a 100% reusable material such as organic material

Solutions to Financial Exclusion in Rural and Depopulated Areas: Evidence Based in Castilla y León (Spain)

Access to banking and financial services is defined by various international organizations as essential to ensure the development of countries and regions. However, this access is not always guaranteed, even in developed countries. Our study focuses on analyzing the current situation of several rural and depopulated areas of Castilla y León (Spain) in terms of access to banking services and cash. For this purpose, an initial spatial analysis has been carried out to compute the access to these services measured in kilometers needed to travel to access them. Subsequently, we included, as a possible solution, the access to these financial services through their implementation (as a cash back point) in the extensive Spanish network of pharmacies. The results obtained in the spatial analysis show that the introduction of the network of pharmacies as a point of access to cash means a significant reduction in the distance to travel in municipalities in rural and unpopulated areas in order to access cash. In the case of the province of Avila the distance would be reduced by 55%, in the province of Segovia the distance would be reduced by 38.5%, in the province of Soria the distance would be reduced by 20%, in the province of Palencia the distance would be reduced by 22%; and finally in the province of Zamora the distance would be reduced by 33%

Cryptocurrency Mining from an Economic and Environmental Perspective. Analysis of the Most and Least Sustainable Countries

There are different studies that point out that the price of electricity is a fundamental factor that will influence the mining decision, due to the cost it represents. There is also an ongoing debate about the pollution generated by cryptocurrency mining, and whether or not the use of renewable energies will solve the problem of its sustainability. In our study, starting from the Environmental Performance Index (EPI), we have considered several determinants of cryptocurrency mining: energy price, how that energy is generated, temperature, legal constraints, human capital, and R&D&I. From this, via linear regression, we recalculated this EPI by including the above factors that affect cryptocurrency mining in a sustainable way. The study determines, once the EPI has been readjusted, that the most sustainable countries to perform cryptocurrency mining are Denmark and Germany. In fact, of the top ten countries eight of them are European (Denmark, Germany, Sweden, Switzerland, Finland, Austria, and the United Kingdom); and the remaining two are Asian (South Korea and Japan)