https://hdl.handle.net/20.500.12412/2474 Fri, 28 Aug 2026 09:03:10 GMT 2026-08-28T09:03:10Z https://hdl.handle.net/20.500.12412/7377 Visual Thinking to Explore “Relational Pharmacology”: Systemic Maps for Managing Non-Selective Antidepressants in Cardiovascular Prevention García-Domínguez, Irene; Rodríguez-Luna, Azahara; Machuca González, Manuel Abstract Relational pharmacology introduces an innovative approach using visual thinking to understand how drugs interact with multiple body systems, addressing the limitations of the traditional “reductionist approach”. While conventional pharmacology focuses on individual drug effects, it struggles with the complexities of polypharmacy, where multiple medications interact via shared metabolic pathways. This article highlights integrating systemic maps into educational methodologies to empower students in identifying and assessing medication risks. By visualizing the body and drug therapy as interconnected systems, students can better understand complex pharmacological interactions beyond linear frameworks. This approach enables active learning and real-life case analysis, such as cardiovascular prevention with non-selective antidepressants, where multiple drug interactions must be considered. It also fosters global health education by promoting the exchange of effective teaching practices and addressing challenges in healthcare training. Systemic maps prepare students for clinical decision-making by enhancing their ability to manage risks and complex cases effectively. Fri, 27 Jun 2025 00:00:00 GMT https://hdl.handle.net/20.500.12412/7377 2025-06-27T00:00:00Z https://hdl.handle.net/20.500.12412/7376 A Regressive Shock? The Distributional Consequences of Middle East Conflict-Driven Inflation for German Households Christl, Michael; Peichl, Andreas; Wibault, Tiphaine The escalation of the conflict in the Middle East in late February 2026 led to a sharp upward revision in inflation forecasts for Germany, which we attribute primarily to the conflict itself. Using microsimulation methods adapted to simulate a commodity price change, we translate this forecast revision into upper bounds on household-specific purchasing-power losses via a compensating variation approach, distinguishing the loss recovered through consumption taxes from the full loss including the pre-tax transfer to producers. Both measures are sizeable and regressive: in 2026, the average household loses 0.25% of income through the tax channel and 1.15% under the full measure, with bottom-decile losses of 0.63% and 2.87% against 0.12% and 0.65% for the top decile. The Gini coefficient rises by up to 0.14 points, and elderly households are disproportionately affected. Our results show that a supply-side shock originating outside Europe can have a sizeable and unequal impact on household living standards. Tue, 21 Jul 2026 00:00:00 GMT https://hdl.handle.net/20.500.12412/7376 2026-07-21T00:00:00Z https://hdl.handle.net/20.500.12412/7375 Discurso do Medo na Propaganda: Estratégias dos Líderes Políticos Espanhóis no X Para Influenciar a Agenda Mediática (2015–2023) Rebollo-Bueno, Sara; Tarín Sanz, Adrián Numa sociedade moldada por uma cultura do medo e caracterizada pelas dinâmicas da sociedade de risco, os partidos políticos recorrem cada vez mais ao medo enquanto instrumen-to de comunicação para captar a atenção do público. Este artigo examina como os principais partidos políticos espanhóis utilizam as suas contas oficiais no X (anteriormente Twitter) para influenciar a agenda mediática através de mensagens que enquadram questões políticas como ameaças iminentes. Com base numa análise de conteúdo de publicações entre 2015 e 2023, o es-tudo identifica tanto as estratégias retóricas mobilizadas como as ameaças mais frequentemente invocadas — sobretudo a crise económica e os movimentos separatistas. A análise incide sobre publicações que contêm entrevistas ou excertos de entrevistas partilhadas pelos seis partidos políticos de âmbito estatal representados no Congresso dos Deputados de Espanha, resultando num corpus de 968 unidades de análise. Foi desenvolvido um esquema de codificação fundamen-tado em critérios metodológicos estabelecidos para identificar a presença de desencadeadores do medo, os tipos de ameaças mencionadas, o foco temático das mensagens e os objetivos de natureza propagandística. Os resultados revelam que a relevância atribuída a determinadas ameaças varia em função da orientação ideológica, sublinhando a utilização estratégica do medo na comunicação partidária. Conclui-se que este tipo de mensagens não só perpetua uma cultura do medo e molda as perceções de segurança, como também contribui para a fragmentação do discurso público. Ao adaptar a sua retórica de modo a mobilizar as ansiedades dominantes, os partidos políticos consolidam a sua visibilidade mediática, reforçam narrativas ideológicas e re-configuram os contornos da preocupação coletiva Tue, 28 Apr 2026 00:00:00 GMT https://hdl.handle.net/20.500.12412/7375 2026-04-28T00:00:00Z https://hdl.handle.net/20.500.12412/7374 Real-time emotion recognition pipeline in videogames using physiological signals from a wearable device and facial action labels Luna-Perejón, Francisco; Civit, Miguel; Muñoz-Saavedra, Luis; Civit-Masot, Javier; Domínguez-Morales, Manuel; Miró-Amarante, Lourdes Advancing real-time emotion recognition in dynamic, naturalistic environments is pivotal for applications in gaming, mental health, and personalized education. This study introduces and evaluates an intelligent emotion recognition pipeline using physiological data from a wearable device, designed for this purpose. This system integrates continuous physiological sensing via the Empatica EmbracePlus wristband. Data were collected from 25 university students engaged in naturalistic gameplay across three commercial video games, forming the basis of the PaGER-Sync ADICVIDEO dataset. Emotions were labeled at a frame-level using FaceReader, providing high-granularity. The proposed classification pipeline employs a Random Forest classifier trained directly on short, lagged windows of raw physiological signals (EDA and BVP), eliminating the need for handcrafted feature extraction and achieving sub-50 ms inference latency, confirming suitability for real-time deployment. Addressing class imbalance with SMOTE-based data augmentation, the system achieved a robust performance, yielding 86.01% accuracy and a macro-averaged AUC of 0.98 across six discrete emotions. Model interpretability, crucial for building trustworthy intelligent systems, is quantified through SHAP-based analyses, revealing the individual contributions of physiological features. These compelling results demonstrate the feasibility of non-invasive, privacy-preserving, real-time emotion recognition using minimal wearable sensors, thereby supporting the development of scalable, interpretable, and computationally efficient affective computing applications in diverse real-world contexts. Thu, 09 Jul 2026 00:00:00 GMT https://hdl.handle.net/20.500.12412/7374 2026-07-09T00:00:00Z