Smart cities incorporate fundamental aspects such as sustainability and citizens’ well-being. Therefore, the objective of this study is to analyze the feasibility and effectiveness of the implementation of an evaluation model of the transformation processes towards smart cities as a strategy to improve the state of the transformation processes in Lima, Peru. The research is descriptive and basic. A questionnaire was administered to 80 municipal officials in Lima, focusing on the variable “smart cities evaluation model”, covering three key dimensions: open data, smart public transport and energy efficiency, with a total of 15 questions and the variable “state of the transformation processes”, analysed through the dimensions of educational level of the population and municipal budget, with 10 questions. The results revealed that 48% expressed a gap in terms of the availability and quality of accessible information. 53% argued that stronger energy conservation and sustainability strategies need to be implemented. In addition, 53% felt that the education level needs to focus on improving local education systems. In conclusion, transformation processes drive economic, social and environmental development, improving the quality of life and promoting equality among citizens. This study contributes to a broader understanding of how to address these challenges in order to build more sustainable and liveable cities in the future.

Financial markets have adopted measures aiming at strengthening insurance industry and digital financial assets. Efforts have also been made to strengthen the financial sector and expand lending opportunities in times of economic turmoil. The role of the central banks as a mega-regulator have played a crucial role in implementing coordinated policies and improving the stability of the financial sector. This review paper analyses 100 papers and proposes recommendations for policy makers. The results confirm the financial sector has shown positive performance indicators, and the capital market has become increasingly important along with non-credit financial institutions. However, the growing number of first-time investors in the capital market requires a renewed focus on consumer protection and financial literacy. In addition, the development of digital technologies has changed the landscape of financial services, forcing financial institutions to fight for continued customer loyalty.

The complex interactions of industrial Policy, structural transformation, economic growth, and competitive strategy within regional industries are examined in this research. Using a dynamic capabilities framework, the study examines the mediating roles of organizational innovation and adaptability in the link between competitiveness and macroeconomic variables. A two-way fixed effects model is used in this study to examine the influence of structural transformation (ST) on Industrial Policy (IP). Using regional data covering the years 2010 to 2022, the research undertaken in this paper explores the dynamics of the Indonesian economy by empirically assessing the consequences of structural change on industrial Policy. In order to establish a comprehensive model that clarifies the mechanisms through which industrial policies and structural shifts impact the development of dynamic capabilities, ultimately influencing competitiveness strategies, this research draws on a large amount of empirical data and integrates insights from seminal works. Our research adds to our knowledge of strategic management in regional industries by providing detailed information on how economic development and policy interventions influence businesses’ ability to adapt and gain a competitive edge. In addition to advancing scholarly discourse, this study offers business executives and politicians valuable insights for managing the intricacies of global economic processes.

This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.