Abstract
This chapter explores the internationalization path of ALPHA, a family-owned, medium-sized Portuguese company. The analysis reveals a two-stage process. Initially, ALPHA's gradual market entry aligns with the Uppsala model, prioritizing geographically close markets and leveraging accumulated experience. However, later stages demonstrate network-based theory influences. While lacking formal networks, ALPHA prioritizes strong B2B relationships with large international clients, mirroring network bridges for market access. The case highlights the importance of trust-based B2B relationships for success. ALPHA leverages these partnerships to gain market knowledge and access new opportunities. Exporting plays a vital role, keeping ALPHA updated on technological trends and fostering innovation through diverse client projects. The company prioritizes a pragmatic approach focused on strong client relationships and win-win partnerships, emphasizing trust as a key resource. While the RBV perspective highlights investment in internal resources, reliance on intermediaries introduces limitations.

Abstract
Aging involves complex biological processes leading to the decline of living organisms. As population lifespan increases worldwide, the importance of identifying factors underlying healthy aging has become critical. Integration of multi-modal datasets is a powerful approach for the analysis of complex biological systems, with the potential to uncover novel aging biomarkers. In this study, we leveraged publicly available epigenomic, transcriptomic and telomere length data along with histological images from the Genotype-Tissue Expression project to build tissue-specific regression models for age prediction. Using data from two tissues, lung and ovary, we aimed to compare model performance across data modalities, as well as to assess the improvement resulting from integrating multiple data types. Our results demostrate that methylation outperformed the other data modalities, with a mean absolute error of 3.36 and 4.36 in the test sets for lung and ovary, respectively. These models achieved lower error rates when compared with established state-of-the-art tissue-agnostic methylation models, emphasizing the importance of a tissue-specific approach. Additionally, this work has shown how the application of Hierarchical Image Pyramid Transformers for feature extraction significantly enhances age modeling using histological images. Finally, we evaluated the benefits of integrating multiple data modalities into a single model. Combining methylation data with other data modalities only marginally improved performance likely due to the limited number of available samples. Combining gene expression with histological features yielded more accurate age predictions compared with the individual performance of these data types. Given these results, this study shows how machine learning applications can be extended to/in multi-modal aging research. Code used is available at https://github.com/zroger49/multi_modal_age_prediction.

Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly used as wireless communications nodes, serving as Wi-Fi Access Points and Cellular Base Stations. To enable energy-efficient access networks, we previously introduced the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of UAVs as Flying Access Points (FAPs) to serve Ground Users (GUs). However, SUPPLY did not address the backhaul link. This paper presents the Simple Gateway Positioning (SGWP) solution, which optimizes the position of a Gateway (GW) UAV to ensure backhaul connectivity in a two-tier network. We integrate SUPPLY for FAP positioning with SGWP for GW placement and evaluate their combined performance under various scenarios involving different GUs' Quality of Service (QoS) requirements and positions. Our results demonstrate that SUPPLY and SGWP can be used jointly in a two-tier network with minimal performance degradation.

Abstract
Nowadays, there is the paradox of technology: although smartphones have revolutionized our way of living, bringing convenience and connectivity, they have also introduced new challenges, notably distracted driving. This paper addresses the issue of visual distraction, one of the main contributors to traffic accidents, through the development of an innovative system that combines the application of convolutional neural networks and the functionality of mobile devices. The adopted methodology focused on the collection of a broad set of images to train an artificial intelligence model capable of classifying a qualitative variable with two distinct categories: attention and distraction of a driver. In particular, the study concentrated on creating a mobile application that uses a smartphone's camera to monitor the driver and issue auditory alerts if it detects prolonged distraction. The achieved results highlighted the efficacy of the model, especially after its optimization for the TensorFlow Lite format, suitable for implementation on mobile devices due to its efficiency in terms of speed and resource consumption.

Abstract
Data management solutions became highly expensive and ineffective mainly due to the lack of transparent processes and procedures to measure and provide clear guidance on the steps needed to implement them. The organizations and specialists agree that the only manner solve the data management issues requires the implementation of data governance. Many of those attempts had failed previously because they were based only on IT, with rigid processes and activities frequently split by systems or the areas supported by systems and their data. It shows that Data governance has been acquiring significant relevance. However, a consensus or even a holist approach was not achieved so far. This paper that is part of an ongoing thesis research that aims to identify the main gaps and opportunities by summarizing and study the literature consistently and as result at the end of the research it will propose a standard framework for data governance measuring its impact on the Data Governance maturity level before and after its implementation and thus as contribute to the community by trying to mitigate the problems found.

Abstract