Abstract
The present study examines the impact of family ownership and control on the internationalization strategies of Portuguese manufacturing firms. The study contributes to the existing literature by providing evidence that different forms of international market presence are asymmetrically influenced by family control and by underscoring the importance of innovative strategies. The analysis includes a sample of 25,533 firms observed from 2018 to 2021. Econometric models address the role of ownership in alternative internationalization endeavors, demonstrating that these firms differ from their non-family counterparts. By comparing the export propensity, intensity, and reach of family businesses to non-family businesses, the research sheds light on the challenges faced by family-owned firms and the significance of structural characteristics such as technological regimes and regional competitive advantages. The findings emphasize the negative impact of family presence on internationalization while highlighting the importance of innovation and ecosystem support. Additionally, the study contributes to the empirical refinement of firm classification by proposing a more reliable segmentation method. It also presents alternative econometric methods to appraise internationalization strategies better. Future research directions are suggested, particularly regarding the use of additional information related to innovation and human capital, offering insights for enhancing the global engagement of family businesses in global markets. This research provides valuable empirical evidence and practical implications for policymakers and practitioners seeking to support the required actions to promote the growth and internationalization of family businesses in the context of the Portuguese manufacturing industry.

Abstract
The decline in student attendance and engagement in Higher Education (HE) is a pressing concern for educational institutions worldwide. Traditional lecture-style teaching is no longer effective, and students often become disinterested and miss classes, impeding their academic progress. While Gamification has improved learning outcomes, the integration of Artificial Intelligence (AI) has the potential to revolutionise the educational experience. The combination of AI and Gamification offers numerous research opportunities and paves the way for updated academic approaches to increase student engagement and attendance. Extensive research has been conducted to uncover the correlation between student attendance and engagement in HE. Studies consistently reveal that regular attendance leads to better academic performance. On the other hand, absenteeism can lead to disengagement and poor academic performance, stunting a student's growth and success. This position paper proposes integrating Gamification and AI to improve attendance and engagement. The approach involves incorporating game-like elements into the learning process to make it more interactive and rewarding. AI-powered tools can track student progress and provide personalised feedback, motivating students to stay engaged. This approach fosters a more engaging and fruitful educational journey, leading to better learning outcomes. This position paper will inspire further research in AI-Gamification integration, leading to innovative teaching methods that enhance student engagement and attendance in HE.

Abstract
The aim of this work is to determine, based on a market research, the level of passenger satisfaction with public transport services, in order to support better marketing decisions. This survey involves dimensions such as the level of satisfaction with timetables and frequency, vehicle conditions, driver attitudes and behavior, fares and information made available to passengers. The study was applied to the case of public transport in the Porto Metropolitan Area, Portugal, and aims to help define recommendations to improve the quality of service and define more effective marketing strategies.

Abstract
As distributed and multi-organization Machine Learning emerges, new challenges must be solved, such as diverse and low-quality data or real-time delivery. In this paper, we use a distributed learning environment to analyze the relationship between block size, parallelism, and predictor quality. Specifically, the goal is to find the optimum block size and the best heuristic to create distributed Ensembles. We evaluated three different heuristics and five block sizes on four publicly available datasets. Results show that using fewer but better base models matches or outperforms a standard Random Forest, and that 32 MB is the best block size.

Abstract
In global commerce, marked by the relentless advance of digital technology, businesses find themselves constantly challenged to devise innovative and disruptive marketing strategies. Adapting to these changes is no longer a choice but a necessity. To thrive, companies must remain vigilant, updating their resources and adopting emerging trends with unwavering agility. Contemporary Trends in Innovative Marketing Strategies explores the demands and dynamics of modern marketing. This book is tailored to meet the needs of students, educators, and managers seeking a profound understanding of today's marketing trends. Firstly, the book delves deep into the current trends steering marketing innovation. It dissects the latest developments that are reshaping the marketing landscape, identifies pivotal trends, and elucidates their ramifications for businesses. Secondly, the book embarks on a journey to explore innovative marketing strategies engineered to confront contemporary business challenges and seize emerging opportunities. It unlocks novel approaches that adeptly cater to the market, providing insights into strategic frameworks, methodologies, and practices. Lastly, the book illustrates these concepts with real-world case studies, offering proof of innovative marketing's successful applications across diverse business sectors. These cases serve to inspire and demonstrate how innovative marketing strategies can be put into action, resulting in tangible outcomes. This book is designed for a diverse audience, including academics and students keen on exploring the latest trends in innovative marketing, educators searching for compelling case studies to enhance their teaching materials, and practitioners eager to bridge the gap between research and practical application in innovative marketing. Its contents span a wide array of topics at the forefront of marketing innovation. Key themes encompass digital marketing strategies, sustainability-driven marketing, the marriage of data analytics and marketing intelligence, and the impact of emerging technologies on marketing strategies. Additionally, it delves into the challenges and best practices of driving marketing innovation within organizations, touching on subjects such as entrepreneurship, supply chain management, and internal marketing. © 2024 by IGI Global. All rights reserved.

Abstract
Context. The GRAVITY beam-combiner at the Very Large Telescope Interferometer has recently made important contributions to many different fields of astronomy, from observations of the Galactic centre to the study of massive stars, young stellar objects, exoplanet atmospheres, and active galactic nuclei. These achievements were only made possible by the development of several key technologies, including the development of reliable and high-performance fringe trackers. These systems compensate for disturbances ranging from atmospheric turbulence to vibrations in the optical system, enabling long exposures and ensuring the stability of interferometric measurements. Aims. As part of the ongoing GRAVITY+ upgrade of the Very Large Telescope Interferometer infrastructure, we aim to improve the performance of the GRAVITY fringe tracker, and to enable its use by other instruments. Methods. We modified the group-delay controller to consistently maintain tracking in the white-light fringe, which is characterised by a minimum group delay. Additionally, we introduced a novel approach in which fringe-tracking is performed in the non-observable optical path length state-space using a covariance-weighted Kalman filter and an auto-regressive model of the disturbance. We outline this new state-space representation and the formalism we used to propagate the state vector and generate the control signal. While our approach is presented specifically in the context of GRAVITY/GRAVITY+, it can easily be adapted to other instruments or interfero-metric facilities. Results. We successfully demonstrate phase-delay tracking within a single fringe, with any spurious phase jumps detected and corrected in less than 100 ms. We also report a significant performance improvement, as shown by a reduction of similar to 30 to 40% in phase residuals, and a much better behaviour under sub-optimal atmospheric conditions. Compared to what was observed in 2019, the median residuals have decreased from 150 nm to 100 nm on the Auxiliary Telescopes and from 250 nm to 150 nm on the Unit Telescopes. Conclusions. The improved phase-delay tracking combined with white-light fringe tracking means that from now on, the GRAVITY fringe tracker can be used by other instruments operating in different wavebands. The only limitation remains the need for an adjustment of the optical path dispersion.