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This paper explores the internationalisation of small and medium-sized firms (SMEs) in Africa and Latin America. A total of 97 papers covering the period between 1995 and 2017 were analysed, providing a unique comparative perspective of the internationalisation of SMEs. The analysis of the papers revealed the following six main topics: international networking; financing, export promotion; internationalisation strategies; resources and business environment/context; e-business, e-commerce; and barriers to internationalisation. The topic 'internationalisation strategies' is the most researched topic both regarding the internationalisation of both African and Latin American SMEs. However, while the studies on Latin American SMEs focus on rapid internationalisation, international entrepreneurship orientation and export performance, the studies on African SMEs focus on supply performance, international behaviour, internationalisation process, knowledge and key-selection of foreign markets. This provides a clear perspective on how SMEs of those two emerging continents deal with the intricacies of internationalisation.

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Abstract
This research explores the trainability of Parameterized Quantum Circuit-based policies in Reinforcement Learning, an area that has recently seen a surge in empirical exploration. While some studies suggest improved sample complexity using quantum gradient estimation, the efficient trainability of these policies remains an open question. Our findings reveal significant challenges, including standard Barren Plateaus with exponentially small gradients and gradient explosion. These phenomena depend on the type of basis-state partitioning and the mapping of these partitions onto actions. For a polynomial number of actions, a trainable window can be ensured with a polynomial number of measurements if a contiguous-like partitioning of basis-states is employed. These results are empirically validated in a multi-armed bandit environment.

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Nowadays the concept of digitalization and Industry 4.0 is more and more important, and organizations must improve and adapt their processes and systems in order to keep up to date with the latest paradigm. In this context, there are multiple developed Maturity Models (MMs) to help companies on the processes of evaluating their digital maturity and defining a roadmap to achieve their full potential. However, this is a subject in constant evolution and most of the available MMs don't fill all the needs that a company might have in its transformation process. Thus, European Digital Innovation Hubs (EDIH) arose to support companies on the process of responding to digital challenges and becoming more competitive. Supported by the European Commission and the Digital Transformation Accelerator, they use tools to measure the digital maturity progress of their customers. This paper analyzes several MMs publicly available and compares them to the guidelines provided to the EDIH.

Abstract
This article presents a comprehensive state of the art analysis of the challenging domain of synthetic data generation. Focusing on the problem of synthetic data generation, the paper explores various difficulties that are identified, especially in real-world problems such as those is the scope of power and, energy systems, including the amount of data, data privacy concerns, temporal considerations, dynamic generation, delays, and failures. The investigation delves into the multifaceted nature of the challenges presented by these factors in the synthesis process. The review thoroughly examines different models used in synthetic data generation, covering Generative Adversarial Networks (GANs), Variational Autoencoder (VAE), Synthetic Minority Oversampling Technique (SMOTE), Data Synthesizer (DS) and E. Non-Parametric SynthPop (SP-NP). Each model is dissected with respect to its advantages, disadvantages, and applicability in different data generation scenarios. Special attention is paid to the nuanced aspects of dynamic data generation and the mitigation of challenges such as delays and failures. The insights drawn from this review contribute to a deeper understanding of the landscape around synthetic data generation, providing a valuable resource for researchers, practitioners, and stakeholders who aim to harness the potential of synthetic data in addressing real-world data challenges. The paper concludes by outlining possible avenues for future research and development in this ever-evolving field.