Abstract
Objective: This study investigates the applicability of systematic methods in the identification and evaluation of studies on sustainable urban mobility, providing subsidies to guide managers and policymakers in the development of efficient and environmentally responsible public policies.   Method: The methodology adopted for this research comprises a Systematic Literature Review (SLR) associated with the Fuzzy-TOPSIS method, a multi-criteria model capable of evaluating and prioritizing studies considering the imprecision inherent in decision-making processes. The PICO technique was used to define the analysis criteria, and the PRISMA protocol ensured the transparency and replicability of the results. Six criteria were established in the qualitative analyses for treatment in the Fuzzy-TOPSIS method.   Results and Discussion: The proposed approach proved effective in selecting the most relevant studies. The discussion points to the need to integrate Fuzzy-TOPSIS with complementary methods, such as DEMATEL and Social Network Analysis (SNA), in order to improve the modeling of causal relationships and strengthen the reliability of prioritization.   Research Implications: The results offer important insights for urban planning and the formulation of public policies, contributing to energy efficiency, reducing GHG emissions and improving the quality of public transport.   Originality/Value: The innovation of this study lies in the combination of quantitative and qualitative approaches to the analysis of sustainable mobility, providing a robust benchmark that can positively influence practices and strategies in urban management.

Abstract
Crowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non-curated nature of the crowd-originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state-of-art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream-based scenario, and identifies future research opportunities.

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

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.