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
Wikipedia is the largest and most globally well-known online encyclopedia, but its collaborative nature leads to a significant disparity in article quality. In this work, we explore real-time and automatic quality assessment within Wikipedia through machine-learning. We first constructed a dataset of 36,000 English articles and 145 features, then compared the performance of multiple classification and regression algorithms and studied how the number of classes and features affects the model's performance. The six-class experiments achieved a classifier accuracy of 64% and a mean absolute error of 0.09 in regression methods, which matches or beats most state-of-the-art approaches. Our model produces similar results on some non-English Wikipedias, but the error is slightly higher on other versions. We have also determined that the features measuring the article's content and revision history bring the largest performance boost.

Abstract

Abstract
This paper solves the resource-constrained project scheduling problem (RCPSP) with a satisfiability problem (SAT) solver. This paper builds further on various existing SAT models for this well-known project scheduling problem and extends them with two methods to satisfy the resource constraints. Specifically, we use the wellknown minimal forbidden sets and compare them with the so-called covers that are traditionally used in SAT implementations. Moreover, we also implement an existing binary decision trees approach under various settings and extend the model with networks with adders, so far never used for solving the RCPSP, to guarantee that resource constraints are satisfied. The algorithms are tested under different settings on a set of 13,413 project instances with diverse network and resource structures, and the experiments demonstrate that a combination of these approaches help in finding better solutions within a reasonable time. Moreover, 393 new lower bounds, 62 new upper bounds, and 290 optimally solved instances (including 18 from the PSPLIB) have been discovered, which, to the best of our knowledge, had not been found before. The strong performance of the new algorithm motivated additional experiments, and the preliminary results suggest several promising directions for future research.

Abstract
With the growing popularity of DevOps culture among companies and the corresponding increase in Microservices architecture development-both known to boost productivity and efficiency in software development-an increasing number of organizations are aiming to integrate them. Implementing DevOps culture and best practices can be challenging, but it is increasingly important as software applications become more robust and complex, and performance is considered essential by end users. By following the Design Science Research methodology, this paper proposes an iterative framework that closely follows the recommended DevOps practices, validated with the assistance of expert interviews, for implementing DevOps practices into Microservices architecture software development, while also offering a series of tools that serve as a base guideline for anyone following this framework, in the form of a theoretical use case. Therefore, this paper provides organizations with a guideline for adapting DevOps and offers organizations already using this methodology a framework to potentially enhance their established practices.

Abstract
With the advent of Information and Communication Technologies in recent decades, organizations face several challenges today. Adopting Digital Transformation (DT) offers numerous opportunities for Small and Medium Enterprises (SMEs) to improve their efficiency and operations, reaching new markets, shareholders, and customers. However, there are potential risks associated with this process. With Digital Transformation (DT), the radius of connectivity and interconnection between devices and systems increases in Mozambique and worldwide, creating more significant space cyberattacks. As Small and Medium-sized Enterprises (SMEs) connect to the digital world and move forward with adopting innovative digital technologies, they become more vulnerable to digital security risks. Hence, managing digital security risks effectively is crucial to realizing the benefits of Digital Transformation (DT). This position paper proposes to present the research work that will culminate in the proposal to develop a framework that fits Mozambican Small and Medium Enterprises (SMEs) through a Design Science Research (DSR) methodology, which can help to assist Mozambican Small and Medium Enterprises (SMEs) in the Digital Transformation (DT) process.