Abstract
The study of user logs plays a crucial role in understanding user behavior and preferences in various online environments. By analyzing user logs, researchers can gain valuable insights into how users interact with a system and make informed decisions on system improvements. They can also assess the effectiveness of different features and functionalities. In the field of game design, the exploration of user logs becomes even more important as it provides valuable information on player motivations, preferences, and gameplay patterns. This research explores the impact of Bartle Taxonomy on user behavior analysis through a Game with a Purpose (GWAP) named "BartleZ."By analyzing user logs and decisions within the game, BartleZ aims to determine the dominant player type according to the Bartle Taxonomy classification. This research also investigates how different player types engage with the game and the implications for user experience design. © 2025 Elsevier B.V., All rights reserved.

Abstract
The literature reveals that science parks offer numerous benefits and support services to the activity of a technological startup. However, the decision of choosing the best science park for the startup tends to be an informal process, technically not very rigorous and planning, arising essentially by affinities with the research centre and university. In this study, a decision support system is presented to support entrepreneurs in the process of selecting a science park for the implementation of their startup. The AHP method is used to compare the importance of the criteria for selecting a science park, which includes factors such as location, activity sector, infrastructure, cost, and size. The findings reveal that the use of this decision support system helps entrepreneurs to find a science park that is suitable for the needs of their startup and allows them to comparatively identify the most relevant criteria when choosing a science park. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

Abstract
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processing (NLP) tools can be applied to radiology reports to extract labels for medical images automatically. Compared to manual labeling, this approach requires smaller annotation efforts and can therefore facilitate the creation of labeled medical image data sets. In this article, we summarize the literature on this topic spanning from 2013 to 2023, starting with a meta-analysis of the included articles, followed by a qualitative and quantitative systematization of the results. Overall, we found four types of studies on the extraction of labels from radiology reports: those describing systems based on symbolic NLP, statistical NLP, neural NLP, and those describing systems combining or comparing two or more of the latter. Despite the large variety of existing approaches, there is still room for further improvement. This work can contribute to the development of new techniques or the improvement of existing ones.

Abstract
The gastrointestinal tract is part of the digestive system, fundamental to digestion. Digestive problems can be symptoms of chronic illnesses like cancer and should be treated seriously. Endoscopic exams in the tract make detecting these diseases in their initial stages possible, enabling an effective treatment. Modern endoscopy has evolved into the Wireless Capsule Endoscopy procedure, where patients ingest a capsule with a camera. This type of exam usually exports videos up to 8 hours in length. Support systems for specialists to detect and diagnose pathologies in this type of exam are desired. This work uses a rarely used dataset, the ERS dataset, containing 121.399 labelled images, to evaluate three models from the EfficientNet family of architectures for the binary classification of Endoscopic images. The models were evaluated in a 5-fold cross-validation process. In the experiments, the best results were achieved by EfficientNetB0, achieving average accuracy and F1-Score of, respectively, 77.29% and 84.67%. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

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.