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This paper presents a comprehensive state-of-the-art analysis and mathematical modeling of the National Program for Smart Cities (PRONACES) initiatives in Mexico's journey towards smart city development. The focus is on examining how PRONACES integrates technological innovation with urban planning and sustainability, leveraging mathematical models to optimize and predict the outcomes of various smart city initiatives. The methodology includes a review of current literature to establish the state of the art, followed by the development and application of mathematical models to assess and forecast the effectiveness of PRONACES strategies. These models are based on a variety of parameters including urban growth, technology adoption, resource allocation, and environmental impact. Key findings demonstrate the significant role of PRONACES in transforming urban landscapes in Mexico. The mathematical models provide valuable predictions and insights into the potential outcomes of various smart city strategies, indicating pathways for efficient resource management, infrastructure development, and sustainable urban growth. The paper concludes with an emphasis on the importance of mathematical modeling in planning and implementing smart city initiatives. The challenges and opportunities presented by Mexico’s unique urban landscape are discussed, along with recommendations for future research and policy-making in the field of smart urban development. [ABSTRACT FROM AUTHOR]

Smart Education is the influence of diverse technologies (Combinatorial Optimization, Machine Learning, Big Data, data visualisation, Internet of Education Things, Learning analytics, and others) to enhance the quality of education. In other words, Smart Education is the process of optimally managing human, economic and technological resources from educational institutions and research centres. Smart Education is part of the Smart Society (a component of a Smart City). The Smart Education components are the Internet of Education Things (Wireless Sensor Networks, RFID Technology), management of the education physical, infrastructure, smart classroom, smart campus, smart learning, Learning analytics, Smart Analysis, data science, Education Impact, and Educational Policy. This paper is a guide for understanding Smart Education components by presenting a survey of the characteristics, taxonomy (Education-Hard problems and Education-Soft problems), smart education indicators, history and future trends. [ABSTRACT FROM AUTHOR]