Abstract
The advent of new technologies has led to significant changes in the field of marketing, demanding a rethinking of existing knowledge and skills. This research proposes a set of transversal curricula in digital marketing. The methodology employed included an exploratory analysis of digital marketing courses offered at universities and major online platforms, focus groups, and interviews, conducted in four countries. The countries included in the study were Finland, Poland, the Netherlands, and Portugal. The findings indicated that an introductory course and specialization blocks would be beneficial. Social media, analytics, digital advertising, search engine optimization (SEO), digital marketing strategies, web content, e-mail marketing, customer experience, landing pages, user experience, leads, conversion rate optimization, and E-commerce were identified as the key subjects of study for the introductory course in digital marketing.

Abstract
The present work is inspired by the aftermarket companies of the automotive industry. The goal is to investigate how companies react to market change, by understanding the effect of a perturbation (such as a business cessation) on the rest of the companies that are interconnected through peer-to-peer relationships. An agent-based model has been developed that simulates a multilayer network involving different types of companies: suppliers, aftermarket companies; retailers and consumers. The effect of the cessation is measured by the resilience of the multilayer network after suffering the perturbation. The multilayer network is inspired in a business model of the automobile industry’s aftermarket and each type of company has some defined characteristics. The agent-based model produces the network dynamics due to the changes in its configuration throughout time. No learning mechanism is introduced in this work. We demonstrate that the number of links, the volume of sales and the total profit of a node in the network has an impact on its survival throughout time. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Multimodal models, namely vision-language models, present unique possibilities through the seamless integration of different information mediums for data generation. These models mostly act as a black-box, making them lack transparency and explicability. Reliable results require accountable and trustworthy Artificial Intelligence (AI), namely when in use for critical tasks, such as the automatic generation of medical imaging reports for healthcare diagnosis. By exploring stresstesting techniques, multimodal generative models can become more transparent by disclosing their shortcomings, further supporting their responsible usage in the medical field.

Abstract

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.