Abstract
The Vision Transformer (ViT) architecture has emerged as a potential game-changer in computer vision, offering scalability and global attention that have generated considerable interest in recent years. Its adaptability has fueled enthusiasm for its application. This work investigates the boundaries of the architecture, focusing on developing new techniques targeting explicitly complex tasks, such as medical imaging datasets, which often exhibit high variability, class imbalance, and limited sample sizes. We propose a set of mixed regularisation and augmentation techniques to enhance the performance of models. These include a novel loss function and a smoothly differentiable activation function, leading to more stable training and model performance. The results show that incorporating these techniques improves model performance and training convergence.

Abstract
Stress in the workplace is a major problem that affects people of all ages, backgrounds, and occupations. It can contribute to various health problems, from anxiety to insomnia, among others. Workplace stress significantly impacts employee well-being and productivity. Current stress-management approaches, while valuable, primarily address stress after it has occurred. This highlights the critical need for proactive systems capable of anticipating individual stress and preventing negative health consequences. This research presents the design and initial implementation of a novel microservice-based recommendation system for proactively mitigating workplace stress among computer users. The system leverages predicted stress levels to deliver timely, personalized, and easily implemented interventions. This study focuses on evaluating the system's architecture, core functionalities, and initial performance using a content-based filtering approach. A pilot study demonstrated the system's feasibility, highlighting areas for future development.

Abstract
Distributed acoustic sensing (DAS) is an instrumental approach that allows fiber optic cables to be turned into dense arrays of acoustic sensors. This technology, based on an optical time-domain reflectometer (OTDR) technique, is attractive in marine environments where instrumentation is difficult to implement. Its main applications lie in seismology, oceanography, and bioacoustics.Current technology limits the range of DAS to ca. 150 km making it very useful in the Azores, where seismic stations only exist on the Islands with a strong E-W alignment, as shown by Matias et al. (2021). The Azores have been suffering an increase in extreme wave conditions that affect navigation and coastal infrastructures. DAS can provide proxies for significant wave height, period, and surface currents on the shallow sections of the cable, complementing existing monitoring networks.The Azores region is part of the migration routes for fin and blue whales, which are known to produce acoustic signals during certain parts of the year. These vocalizations provide crucial data for Passive Acoustic Monitoring that can be used to support the establishment and update of mitigation measures addressing their preservation. DAS has already demonstrated its usefulness in detecting and tracking baleen whales using acoustic records.One issue that needs to be addressed in using DAS data is calibration. It is well demonstrated that strain or strain rate as measured by DAS can be converted to ground motion along the direction of the submarine cable section, if the apparent phase velocity is known. Similarity between DAS converted signals and co-located seismograms is well demonstrated but the absolute value is likely to vary with the cable coupling to the seafloor.This work reports on the recent operation of a DAS interrogator deployed at the Faial landing site to monitor the first 50 km of the telecommunication cable between Faial and Flores islands operated by Fibroglobal. The instrument used, HDAS developed by the IO-CSIC, recorded at 50 Hz for 11 months starting on the 15th of December 2023 with 10 m gauge length. For calibration purposes two 4C OBS were deployed close to the cable at ~10 km and ~30 km distance from the landing point. The OBSs were deployed in July 2024 and recovered in November 2024, providing 5 months of simultaneous recordings with the DAS.As expected, both earthquakes and whale vocalizations were identified on the DAS and OBS. We show that DAS can contribute to an improved localization of local offshore earthquake parameters due to its high density of sensors, particularly for the events occurring NW of Faial Island, with locations North of the cable. Clear landward and seaward oceanic waves are identified on the cable's shallow section. In all the applications the main question to address is the variable coupling of the cable to the seafloor in the Azores plateau of volcanic origin.This work is supported by the Portuguese Fundação para a Ciência e Tecnologia, FCT, I.P./MCTES through national funds (PIDDAC): UID/50019/2025 and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020), by the MODAS project 2022.02359.PTDC, and by EC project SUBMERSE project HORIZON-INFRA-2022-TECH-01-101095055.

Abstract
Uncertainty is pervasive in modern manufacturing settings. In order to cope with unexpected events, scheduling decisions are commonly taken resorting to dispatching rules, which are reactive in nature. However, rule performance varies according to shop utilisation and due date allowance, which often change in dynamic real-world job shops. Therefore, this paper explores systems that select dispatching rules as conditions change over time, namely periodic and real-time dispatching rule selection systems, which are based on supervised learning and reinforcement learning algorithms, respectively. These types of systems have been proposed in the past but have been further improved in this work by carefully selecting the most relevant state features and dispatching rules. Moreover, by testing both approaches on the same instances, it was possible to compare them and determine the most advantageous one. After the tests, which included a wide array of job shop instances, both periodic and real-time systems outperformed state-of-the-art dispatching rules by over 10% tardiness-wise. Nonetheless, the periodic rule selection approach was more robust across all tests than the real-time approach. These results demonstrate that there is a real incentive for managers to adopt dispatching rule selection systems.

Abstract

Abstract
Knowledge sharing and team dynamics are essential elements of entrepreneurial success, especially in teams that operate in innovative environments. This chapter explores how participation in an incubation program influences the formation and development of entrepreneurial team identity. It aims to understand the dynamics involved in creating entrepreneurial teams, the practices of knowledge sharing, and the role digital technologies play in supporting and sustaining these processes. The study focuses on teams that completed the CEU iLab Incubation Program, with data gathered through in-depth semistructured interviews from twenty-five entrepreneurs across various startups. Five cases, involving entire entrepreneurial teams, were central to this research. The findings offer valuable insights for enhancing incubation programs, promoting entrepreneurial identity formation, and improving the success of new ventures. These insights are beneficial for both scholars and practitioners in the entrepreneurship field. © 2025 by IGI Global Scientific Publishing. All rights reserved.