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The global increase in dementia cases highlights the importance of early detection and intervention, particularly for individuals at risk of mild cognitive impairment (MCI), a precursor to dementia. The aim of this study is to develop and validate machine learning (ML) models based on non-imaging features to predict the risk of MCI conversion in cognitively healthy older adults over a three-year period. Using data from 845 participants aged 65 to 87 years, we built five eXtreme Gradient Boosting (XGBoost) models of increasing complexity, incorporating demographic, self-reported, medical, and cognitive variables. The models were trained and evaluated using robust preprocessing techniques, including multiple imputation for missing data, Synthetic Minority Oversampling Technique (SMOTE) for class balancing, and SHapley Additive exPlanations (SHAP) for interpretability. Model performance improved with the inclusion of cognitive assessments, with the most comprehensive model (Model 5) achieving the highest accuracy (86%) and area under the curve (AUC = 0.8359). Feature importance analysis revealed that variables such as memory tests, depressive symptoms, and age were significant predictors of MCI conversion. In addition, an online risk calculator has been developed and made available free of charge to facilitate clinical use and provide a practical, cost-effective tool for early detection in diverse healthcare settings ( https://aimar-project.shinyapps.io/MCI-risk-calculator/ ). This study highlights the potential of non-imaging ML models for early detection of MCI and emphasizes their accessibility and clinical utility. Future research should focus on validating these models in different populations and examining their integration with personalized intervention strategies to reduce dementia risk.

Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario.

The kitchen environment is one of the scenarios in the home where users can benefit from Ambient Assisted Living (AAL) applications. Moreover, it is the place where old people suffer from most domestic injuries. This paper presents a novel design, implementation and assessment of a Smart Kitchen which provides Ambient Assisted Living services; a smart environment that increases elderly and disabled people’s autonomy in their kitchen-related activities through context and user awareness, appropriate user interaction and artificial intelligence. It is based on a modular architecture which integrates a wide variety of home technology (household appliances, sensors, user interfaces, etc.) and associated communication standards and media (power line, radio frequency, infrared and cabled). Its software architecture is based on the Open Services Gateway initiative (OSGi), which allows building a complex system composed of small modules, each one providing the specific functionalities required, and can be easily scaled to meet our needs. The system has been evaluated by a large number of real users (63) and carers (31) in two living labs in Spain and UK. Results show a large potential of system functionalities combined with good usability and physical, sensory and cognitive accessibility.

Background In response to the rapid global transmission of COVID-19, governments worldwide enacted lockdowns and other non-pharmaceutical interventions (NPI) to control the disease. In this study, we aim to quantify the influence of NPIs on the transmission of COVID-19 within selected European regions, specifically Spain (including the Basque Country) and Italy (including Tuscany), during the period of February to December 2020, which predates the initiation of COVID-19 vaccinations. We investigate potential correlations and associations between the implementation of NPIs, changes in COVID-19 transmission rates, and alterations in life expectancy across different age and sex categories from the year 2019 to 2020. Methods We use a Susceptible-Hospitalized-Asymptomatic/Mild-Recovered-Deceased (SHARD) ordinary differential equations model to analyze COVID-19 dynamics in the studied regions. The model calibration process was performed with empirical data on hospitalization and death to estimate the weekly transmission and death rates. To quantify reductions in life expectancy, we used established survival analysis techniques. Results The SHARD model effectively captures multiple waves of COVID-19, accurately representing peaks and aligning with the instantaneous reproduction number. Our analysis reveals a 66-78% reduction in transmission rates during the initial set of NPIs in March 2020, followed by a 34-55% reduction during the subsequent NPIs in October 2020. Additionally, the elderly and individuals with comorbidities experienced the most pronounced reductions in life expectancy. Conclusions Our model calibration approach provides a valuable tool for evaluating the effectiveness of interventions across multiple waves of an epidemic. By applying this method to COVID-19 dynamics, we have demonstrated the capacity to quantify the impact of non-pharmaceutical interventions (NPIs) on transmission rates. These findings offer practical insights into the effectiveness of NPIs in mitigating COVID-19 spread and contribute to the broader understanding of epidemic control strategies.

Arboviral diseases represent a growing global health challenge. While dengue cases surge in endemic regions, non-endemic areas in southern Europe are seeing a rise in imported cases of dengue, Zika, and chikungunya, along with the first autochthonous dengue transmissions. The expanding Aedes mosquito populations, influenced by climate change, and increased international travel introducing viremic cases further elevate the risk of outbreaks. These trends emphasize the urgent need for effective risk assessment and timely intervention strategies. We present a data-driven methodology to assess the spatio-temporal risk of Aedes -borne arboviral diseases in non-endemic settings, addressing key limitations of models developed primarily for endemic regions and challenges related to limited data availability. Our approach builds on the SIRUVY human–vector compartmental model and incorporates stochastic formulations to capture variability in imported cases and mosquito density - two critical drivers of autochthonous transmission and outbreak emergence. This framework improves risk estimation and offers insights into transmission dynamics in regions where outbreaks are rare and unpredictable, shaped by sporadic case importations and a non-persistent vector presence. Using data from the Basque Country (2019–2023), including Aedes mosquito egg counts as a proxy for vector abundance and records of imported cases, we mapped the monthly risk of local transmission at the municipal level and conducted a scenario-based risk assessment aligned with Spain’s entomological classification. Our findings indicate a growing presence of Aedes mosquitoes and an increasing transmission risk in urban and peri-urban areas of the Basque Country, revealing shifting hotspots of possible arboviral disease transmission. These results highlight the importance of sustained surveillance to identify high-risk locations and prioritize targeted public health interventions to prevent potential outbreaks.

In a city there are numerous items, many of them unnoticed but essential; this is the case of the signals. Signals are considered objects with reduced technological interest, but in this paper we prove that making them smart and integrating in the IoT (Internet of Things) could be a relevant contribution to the Smart City. This paper presents the concept of Smart Signal, as a device conscious of its context, with communication skills, able to offer the best message to the user, and as a ubiquitous element that contributes with information to the city. We present the design considerations and a real implementation and validation of the system in one of the most challenging environments that may exist in a city: a tunnel. The main advantages of the Smart Signal are the improvement of the actual functionality of the signal providing new interaction capabilities with users and a new sensory mechanism of the Smart City.

Las falsas informaciones a través de los medios de comunicación no son un fenómeno novedoso. Sin embargo, el acceso a internet y la configuración y el uso de herramientas tan útiles como las redes sociales potencian la polarización y la confrontación, primando un análisis emocional e impreciso de los hechos. Se presenta un trabajo de revisión teórica que tiene como objetivo fundamental detallar los principales mecanismos psicológicos que operan en el procesamiento de las falsas informaciones centrándonos de un modo específico en la hipótesis del razonamiento motivado, los sesgos cognitivos y la teoría del proceso dual.

De un modo resumido, el modelo S-REF defiende que no son las creencias auto-referenciales, como por ejemplo “soy un inútil” o “no valgo para nada”, las que causan el cuadro psicopatológico sinas estrategias autorreguladoras comportamentales y cognitivas inadecuadas que el individuo pone en marcha y que terminan cronificandichos contenidos cognitivos e incrementando el malestar emocional asociado ( Fisher y Wells, 2009). Fisher y Wells, 2009 ; ; L. Fisher A. Wells Psychological Models of Worry and Generalized Anxiety Disorder Oxford Handbook of Anxiety and Relateisorders Oxford University Press New York 2009 225-37 Wells, 2000 ; ; A. Wells Emotionaisorders & Metacognition: Innovative Cognitive Therapy 2000 Wells, 2009 ; ; A. Wells Metacognitive Therapy for Anxiety anepression 2009 Wells y Matthews, 1994 ; ; A. Wells G. Matthews Attention and Emotion: A Clinical Perspective 1994 Wells y Matthews, 1996 ; ; A. Wells G. Matthews Modelling cognition in emotionaisorder: The S-REF model Behaviour Research and Therapy 32 1996 867-70 Medline Rubén Sanz Blasco1 1Univ.

The present article presents the data from an experimental research with the primary goal of exploring the presence of memory bias in participants with high levels of evaluation anxiety in comparison with a group with low evaluation anxiety, as measured by the F1 factor of the Anxiety Situations and Responses Inventory (Miguel-Tobal & Cano-Vindel, 2002). For this purpose, an experimental task based on the conception of explicit memory was carried out, specifically a free recall test, cuing the beginning of words, using words with evaluation anxiety content versus neutral words. The results and their clinical implications, as well as the limitations in the experimental study concerning the processing of threatening information, are discussed. En el presente artículo se presentan los datos de una investigación experimental cuyo objetivo fundamental fue explorar la presencia del sesgo de memoria en participantes con niveles elevados de ansiedad de evaluación en comparación con un grupo con baja ansiedad evaluativa medidos mediante el factor F1 del Inventario de Situaciones y Respuestas de Ansiedad (Miguel- Tobal y Cano-Vindel, 2002). Para ello se llevó a cabo una tarea experimental basada en la concepción de memoria explícita, en concreto una prueba de recuerdo libre con clave de inicio de palabras, mediante el uso de palabras con contenido de ansiedad de evaluación vs palabras neutras. Se discuten los resultados encontrados, sus implicaciones a nivel clínico y las limitaciones que se dan en el estudio experimental del procesamiento de la información amenazante.