Abstract
This study addresses the critical need to enhance patient's experience with healthcare mobile applications. With the exponential growth of healthcare apps over the past decade, it is imperative to understand the patients' perceptions of what is lacking and how healthcare mobile applications can be improved in terms of design, features, and communication between patients and providers, such as doctors or hospitals. This research was conducted in four phases: gathering insights into User-Interface (UI) and User-Experience, constructing a patient-focused survey, experimenting with various UI designs, and statistically analyzing survey responses about hospital mobile applications. Key findings from 82 responses highlighted the necessity to redesign both hospital app transaction processes and the apps themselves in terms of functionality and UI. Technological innovations like chatbots were underutilized due to the lack of affective computing in developing these features and a reported lack of user awareness. Regarding UI preferences, respondents favored larger text, less bold text, and blue as the primary color. Future developments should include direct communication with doctors and self-check-in features. Addressing these areas can significantly enhance patient satisfaction and engagement with healthcare mobile applications, particularly hospital apps.

Abstract

Abstract
The aim of this work is to introduce a novel digital signal processing method for data acquired using low coherence interferometry (LCI) with a 1-kHz actuator oscillation frequency. Convolution and correlation operations are employed as efficient filters, reducing computational complexity for multilayer filtering. An envelope filtering technique is developed to address discrepancies in peak signal determination caused by nonlinear actuator motion. Additionally, a phase linearization method is presented to normalize the peak position relative to the actuator signal. Experimental results demonstrate a significant signal-to-noise ratio (SNR) improvement of 50 dB. Long-term measurements reveal an 11-dB noise reduction for frequencies below 1 mHz. This research enables LCI implementation at sampling rates of at least 1 kHz and expands its applicability to extreme measurement conditions.

Abstract
Event extraction is an NLP task that commonly involves identifying the central word (trigger) for an event and its associated arguments in text. ACE-2005 is widely recognised as the standard corpus in this field. While other corpora, like PropBank, primarily focus on annotating predicate-argument structure, ACE-2005 provides comprehensive information about the overall event structure and semantics. However, its limited language coverage restricts its usability. This paper introduces ACE-2005-PT, a corpus created by translating ACE-2005 into Portuguese, with European and Brazilian variants. To speed up the process of obtaining ACE-2005-PT, we rely on automatic translators. This, however, poses some challenges related to automatically identifying the correct alignments between multi-word annotations in the original text and in the corresponding translated sentence. To achieve this, we developed an alignment pipeline that incorporates several alignment techniques: lemmatization, fuzzy matching, synonym matching, multiple translations and a BERT-based word aligner. To measure the alignment effectiveness, a subset of annotations from the ACE-2005-PT corpus was manually aligned by a linguist expert. This subset was then compared against our pipeline results which achieved exact and relaxed match scores of 70.55% and 87.55% respectively. As a result, we successfully generated a Portuguese version of the ACE-2005 corpus, which has been accepted for publication by LDC.

Abstract
This paper presents a comprehensive assessment of the large-scale deployment of hydropower on the provision of frequency regulation services, when equipped with the extended flexibility solutions being developed and/or tested within the scope of the XFLEX HYDRO project. The current analysis is performed on the Iberian Peninsula (IP) power grid considering its interconnection to the Continental Europe (CE) system, since this power system zone is expected to have the most severe frequency transient behaviour in future scenarios with increased shares of variable renewable energies. For this purpose, prospective scenarios with increased shares of time variable renewable generation were identified and analysed. To assess the impacts of the hydropower flexibility solutions on frequency dynamics after a major active power loss, extensive time domain simulations were performed of the power system, including reliable reduced order dynamic models for the hydropower flexibility solutions under evaluation. This research assesses the effects of synchronous and synthetic inertia, and of the Frequency Containment Reserve (FCR) and Fast Frequency Response (FFR) services as specified in European grid codes. The main findings highlight the potential of hydropower inertia and of adopting a variable speed technology for enhancing frequency stability, while contribute to better understand the role of hydropower plants in future power systems.

Abstract
The human brain, with its vast network of billions of neurons and trillions of synapses (connections) between diverse cell types, remains one of the greatest mysteries in science and medicine. Despite extensive research, an understanding of the underlying mechanisms that drive normal behaviors and response to disease states is still limited. Advancement in the Neuroscience field and development of therapeutics for related pathologies requires innovative technologies that can provide a dynamic and systematic understanding of the interactions between neurons and neural circuits. In this work, we provide an up-to-date overview of the evolution of neurophotonic approaches in the last 10 years through a multi-source, literature analysis. From an initial corpus of 243 papers retrieved from Scopus, PubMed and WoS databases, we have followed the PRISMA approach to select 56 papers in the area. Following a full-text evaluation of these 56 scientific articles, six main areas of applied research were identified and discussed: (1) Advanced optogenetics, (2) Multimodal neural interfaces, (3) Innovative therapeutics, (4) Imaging devices and probes, (5) Remote operations, and (6) Microfluidic platforms. For each area, the main technologies selected are discussed according to the photonic principles applied, the neuroscience application evaluated and the more indicative results of efficiency and scientific potential. This detailed analysis is followed by an outlook of the main challenges tackled over the last 10 years in the Neurophotonics field, as well as the main technological advances regarding specificity, light delivery, multimodality, imaging, materials and system designs. We conclude with a discussion of considerable challenges for future innovation and translation in Neurophotonics, from light delivery within the brain to physical constraints and data management strategies.