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.

Abstract
Car rental provides an essential mobility solution. The operational complexity requires efficient fleet and demand management, with significant challenges and opportunities in Operations Management. This chapter provides an overview of these issues, discussing key definitions, critical issues, recent developments, and future research directions. We focus on the car rental capacity-pricing problem under uncertainty, which integrates fleet and demand management. We explore mathematical models for this problem and discuss a co-evolutionary algorithmic framework to solve it. Current developments emphasize the need for a sustainability outlook in this business and its integration with other transportation modes. Future research should focus on advanced demand modeling, sustainable fleet management, and innovative pricing strategies, addressing the dynamic and competitive nature of the business. © 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Abstract

Abstract
Food sustainability is a vital global challenge, as dietary choices affect both human health and the environment. This study evaluates Portuguese dietary patterns' environmental and nutritional sustainability dimensions using data from the National Food, Nutrition, and Physical Activity Survey (IAN-AF) 2015-2016. Environmental indicators (carbon footprint, water footprint, and land use) and a nutritional quality index (NRD9.3) were analysed. Sustainability scores were calculated based on deviations from population medians, with the environmental score estimated from a weighted mean of the three indicators. A quadrant analysis classified individuals into four sustainability segments: better environmental and better nutritional scores (reference group); worse environmental and worse nutritional scores; worse environmental and better nutritional scores; and better environmental and worse nutritional scores. The reference group, with higher plant-based food consumption, had the lowest environmental impacts, 33% lower carbon footprint, 36% lower water footprint, and 50% lower land use, while exhibiting 87% better nutritional quality. In contrast, the worse environmental and worse nutritional scores group, with a diet rich in red and processed meats, sweets, and alcohol, showed higher environmental impacts and poorer nutritional quality. The group with worse environmental and better nutritional scores favored dairy and seafood, whereas the group with better environmental and worse nutritional scores had higher intakes of white meat, sweets, and alcohol. Sociodemographic factors, including sex, age, and education, show to influence the sustainability dimensions. These findings highlight the need for tailored dietary strategies that consider differing environmental and nutritional profiles, supporting more effective and practical public health interventions.

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.