The global economy operates as a complex and interconnected system, necessitating the application of sophisticated network methods for analysis. This study examines economic data from all countries across the globe, representing each country as a node and its exports as links, covering the period from 2008 to 2019. Through the computation of relevant indices, we can discern shifts in countries’ positions within the world trade network. By interpreting these changes through geopolitical perspectives, we can gain a deeper understanding of their root causes. The analysis reveals a notable trend of slow growth in the world trade network. Additionally, an intriguing observation emerges: countries naturally form stable groups, shedding light on the underlying structure of global trade relations. Furthermore, this research highlights the trade balance as a reflection of geopolitical strength, making it a valuable contribution to the study of the evolution of global geopolitical stability.

In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective, and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of neural networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization was realized along with the study of special issues such as optical activation functions and cost functions. The main contribution of this research work is that it is the first time in the literature that the most well-known architectures, training methods, optimization and activations functions of the nanophotonic networks are presented in a single paper. This study also includes an extensive detailed meta-review analysis of the advantages and disadvantages of nanophotonic networks.

Open access to a wide range of economic databases enables researchers to explore the global economy using computational methods. This paper applies network analysis to global export data to investigate complex interdependencies. Given that the nature of exported products is a crucial element of foreign policy, the insights gained from such analysis are particularly valuable when viewed alongside concurrent geopolitical trends. This paper examines export data for light arms, ammunition, and related parts, as well as electronic integrated circuits and their components, across more than 220 countries and territories. By creating directed, weighted networks based on export volumes from 2003 to 2022, this analysis identifies key exporting countries in each category and traces shifts in geopolitical influence over time. In the light arms sector, traditional powers such as the USA, France, Germany, Great Britain, and Italy dominate, along with emerging players like Turkey. In contrast, in the field of electronic integrated circuits, East Asian countries-particularly Singapore, Malaysia, Japan, Hong Kong, Taiwan, and China- dominate global trade, surpassing traditionally strong Western economies, such as the USA, Canada, and the European Union.

: In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides, and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of Neural Networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization has been realized along with the study of special issues such as optical activation functions and cost functions.

Our analysis focuses on the stock cryptocurrency market, by studying a group of nineteen cryptocurrencies where their capitalisation is about 99% of the total market. Specifically, it is examined this group of cryptocurrencies for the period from 2017 up to 2024, taking into account the effect of COVID-19 pandemic. As far as we know, this is the first time that this kind of study has been published, where it takes place by creating various networks based on stock cryptocurrency correlation, in order to be possible to visualise and access these relationships using methods from Complex Networks. The evaluation results show that, there are three different communities within the crypto market and although COVID-19 pandemic and ongoing geopolitical changes, there is a notable trend towards an increase in the number of cryptocurrencies. Additionally, it is applied discriminant analysis to identify the differences among various cryptocurrencies based on their features.