Abstract
BackgroundDialysis recovery time (DRT) refers to the period during which fatigue and weakness subside following hemodialysis treatment, allowing patients to resume their daily routines. This study aimed to identify the factors influencing DRT in hemodialysis patients in Turkey and Portugal, where the prevalence of chronic kidney disease is notably high.MethodsA cross-sectional observational study was conducted in a private dialysis center in Turkey and three dialysis centers in Portugal. The study included hemodialysis patients aged 18 years or older who had been undergoing four-hour hemodialysis sessions three times a week for at least six months. Participants had no communication barriers and voluntarily agreed to take part in the study. Data were collected using a semi-structured questionnaire to gather descriptive characteristics and the Hospital Anxiety and Depression Scale. Logistic regression analysis was employed to identify independent variables influencing DRT.ResultsA total of 294 patients participated in the study, including 187 from Turkey and 107 from Portugal. In Turkey, increased interdialytic weight gain (P = 0.043) was associated with prolonged recovery time, while the use of high-flux dialyzers (P = 0.026) was linked to shorter recovery times. In Portugal, older age (P = 0.020) was found to extend recovery time.ConclusionRecovery time after dialysis is influenced by varying factors across different countries. Further research with larger sample sizes is needed to deepen understanding of these factors and their implications.Clinical trial numberNCT04667741.

Abstract
Contemporary transformations in the world of work, together with the growing emotional and physical demands in nursing, have led to the emergence of new labor phenomena such as quiet quitting, which reflects changes in professional engagement and in the management of nurses’ well-being. Objective: To translate, culturally adapt, and validate the Quiet Quitting Scale for European Portuguese, evaluating its psychometric properties among the nursing population. Methods: A cross-sectional validation study was conducted following COSMIN guidelines. The process included forward and back translation, expert panel review, and pretesting with 30 nurses. The psychometric evaluation was carried out with 347 nurses from Northern Portugal. Data were analyzed using descriptive and inferential statistics, internal consistency measures (Cronbach’s a and McDonald’s ?), and confirmatory factor analysis (CFA) with maximum likelihood estimation to assess construct validity. Results: The Portuguese version (QQS-PT) maintained the original three-factor structure (Detachment/Disinterest, Lack of Initiative, and Lack of Motivation). The model showed satisfactory fit indices (CFI = 0.936; GFI = 0.901; AGFI = 0.814; TLI = 0.905; RMSEA = 0.133). The overall internal consistency was excellent (a = 0.918; ? = 0.922), with subscale a ranging from 0.788 to 0.924. Composite reliability (CR) ranged from 0.815 to 0.924, and average variance extracted (AVE) from 0.606 to 0.859, confirming convergent and discriminant validity. Conclusions: The QQS-PT demonstrated a stable factorial structure, strong reliability, and solid validity evidence. It is a brief and psychometrically sound instrument for assessing quiet quitting among nurses, providing valuable insights for research and management of professional engagement and well-being in healthcare contexts.

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Abstract
The increasing integration of Artificial Intelligence (AI) across various sectors of society raises complex ethical challenges requiring systematic and scalable oversight mechanisms. While tools such as AIF360 and Aequitas address specific dimensions, namely fairness, there remains a lack of comprehensive frameworks capable of auditing multiple ethical principles simultaneously. This paper introduces a multidimensional AI auditing tool designed to evaluate systems across key dimensions: fairness, explainability, robustness, transparency, bias, sustainability, and legal compliance. Unlike existing tools, our framework enables simultaneous assessment of these dimensions, supporting more holistic and accountable AI deployment. We demonstrate the tool’s applicability through use cases and discuss its implications for building trust and aligning AI development with fundamental ethical standards.

Abstract
Objectives: Social isolation and loneliness among older people are widespread, with an impact on physical and mental health. Cycling Without Age (CWA) is an international cycling programme developed to minimise social isolation and loneliness in older people. It involves trishaw (electric bicycle) rides in the open air, led by volunteer riders. This study aimed to analyse the effects of CWA intervention on loneliness and social isolation among older people living in Porto, Portugal. Methods: Older adults (aged 55 years or older) living in the community or a nursing home were included. The intervention comprised at least four bicycle rides, with a duration between 30 and 60 min. A research protocol was applied before and after the intervention, which included the UCLA Loneliness Scale and the Abbreviated Lubben Social Network Scale. Results: A total of 47 participants (median age = 85 years) completed the intervention. Participants were mostly female (81%), widowed (66%) and living in nursing homes (72%). A statistically significant decrease in loneliness was found after the intervention (Median [IQR]_after = 24.0 [16.0] vs. before = 17.0 [6.0]; p < 0.05). Discussion: This preliminary work highlights the positive effect the CWA intervention may have on loneliness among older adults, which is consistent with other CWA programme studies. However, future research is required to evaluate whether these effects persist over time.

Abstract
Riding a motorcycle involves risks that can be minimized through advanced sensing and response systems to assist the rider. The use of camera-collected images to monitor road conditions can aid in the development of tools designed to enhance rider safety and prevent accidents. This paper proposes a method for developing deep learning models designed to operate efficiently on embedded systems like the Raspberry Pi, facilitating real-time decisions that consider the road condition. Our research tests and compares several state-of-the-art convolutional neural network architectures, including EfficientNet and Inception, to determine which offers the best balance between inference time and accuracy. Specifically, we measured top-1 accuracy and inference time on a Raspberry Pi, identifying EfficientNetV2 as the most suitable model due to its optimal trade-off between performance and computational demand. The model's top-1 accuracy significantly outperformed other models while maintaining competitive inference speeds, making it ideal for real-time applications in traffic-dense urban settings.