Abstract
This study examines the role of online health communities in Brazil dedicated to cannabis treatments for chronic diseases as platforms for evidence-based activism. Using a mixed-methods approach, the research combines qualitative analysis with computational techniques, including Latent Dirichlet Allocation (LDA) topic modeling, to analyze six online groups from WhatsApp and Facebook. Key themes emerging from the analysis include treatment per pathology, treatment effects, access barriers, peer support, and advocacy efforts. The findings reveal how these communities act as epistemic networks, where patients and caregivers co-produce knowledge by sharing personal experiences and engaging in dialogue with healthcare professionals. This study highlights how online health communities transform experience sharing into structured evidence, enabling collective action to address barriers such as limited access to cannabis-based treatments. It underscores the potential of digital platforms to empower patients, foster collaboration with healthcare professionals, and influence health governance.

Abstract
Online health communities enable patients and caregivers to share experiences, seek advice, and collaboratively generate knowledge about treatments and condition. However, accessing relevant information often proves challenging due to platform limitations like insufficient search functionalities. A previous study identified key topics discussed in Brazilian online health groups centered on cannabis treatments for chronic diseases. Building on these findings, this study introduces a proof-of-concept chatbot designed to enhance access to the collective knowledge within these communities. The chatbot prototype, built using Google Dialogflow, was tailored to provide contextually relevant, accurate, and user-friendly responses. A user study involving 38 participants evaluated its performance, showing high user satisfaction, task completion rates, and trust in the information provided. The results highlight the chatbot’s potential enhance knowledge accessibility, promote patient engagement, and support evidence-based activism by organizing and disseminating community-generated content effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
O image stitching é uma técnica que permite combinar múltiplas imagens, formando uma imagem única com um campo de visão amplo. No contexto da retinografia, essa técnica é crucial para capturar uma visão detalhada da retina, permitindo que exames mais amplos sejam realizados. Neste trabalho, é apresentado um método de costura de imagens de baixa resolução, utilizando pontos de bifurcação como características da imagem. O método proposto apresenta um aumento das correspondências obtidas em relação aos detectores da literatura, obtendo o resultado de RMSE com uma redução de aproximadamente 14% em comparação ao SIFT (24,29) e ORB (24,32). Além disso, o método proposto obteve um PSNR médio superior, atingindo 26,27 para imagens de glaucoma, 26,72 para imagens normais e 26,71 para imagens de pacientes suspeitos, enquanto os métodos baseados em SIFT e ORB apresentaram valores inferiores, confirmando a eficácia da abordagem proposta.

Abstract
To maintain high levels of efficiency and compliance with delivery dates, automotive repair shops must have a good system for scheduling their activities. The scheduling of the activities of an automotive repair shop is a very complex task to be performed manually. Throughout this work, a Decision Support System (DSS) was developed and tested that considers two major constraints in an automotive workshop: human resources (technicians) and physical resources (work stalls). The proposed DSS has an embedded MIP model that assigns a technician and a work stall to each job, according to the input conditions. The DSS also generates schedules with the planning of technicians and jobs. The system was tested with real data from an automotive workshop and was able to create plans and schedules not only for the human and physical resources in but also to analyse the limiting resources of the workshop.

Abstract
Startups are vital to the economy and the digital future and are creators of around 50% of new jobs. Some studies indicate that around 90% of startups fail in their first months, mainly because they focus on launching products or services without adequate market validation. In addition, they have little or no experience in organisational management and lack the resources to apply quality models, which hinders their ability to face the challenges of a highly volatile and competitive environment. Therefore, this paper proposes the LightStartup framework, focused on startups in the service sector. LightStartup provides a lightweight, consistent and formalised process model, a process assessment model and a maturity model based on the ISO/IEC 33000 standard. LightStartup accompanies companies in transitioning from an informal management style to a formal and long-lasting management system, covering the management of services, people, customers and organisational governance.

Abstract