Abstract

Abstract
The limited in-person availability of administrative services at higher education institutions can delay the resolution of student queries and reduce satisfaction levels. To address this issue, we developed a conversational agent capable of understanding and responding to student questions in Portuguese using natural language processing and machine learning techniques. To enable non-technical management of the agent's knowledge base, a web-based service was implemented, allowing staff to update content and trigger model retraining. The system was evaluated by comparing multiple learning models, with the best performance achieved using Google's BERT language model combined with the DIET classifier, yielding an F1-score of 0.965. In a real-world deployment involving 256 questions, the chatbot achieved approximately 70% accuracy and received an average user satisfaction rating of 4.20 on a 0-5 scale. These results demonstrate the effectiveness of the proposed solution for improving accessibility and efficiency in academic student services.

Abstract
Concrete structures require precise temperature and humidity monitoring during curing to ensure optimal strength and to prevent defects such as cracking. A compact optical sensing system was developed using a single fiber that can be embedded directly within the concrete. When paired with a spectral interrogation unit operating in the low-loss single-mode communications band of 1500-1600 nm, the system functions as both a temperature and relative humidity (RH) sensor. Temperature monitoring is achieved using a Fiber Bragg Grating, while humidity sensing is provided by a Fabry-Perot interferometer (FPI) at the fiber tip. The interferometer cavity is formed with a layer of polyvinylpyrrolidone (PVP), an RH-sensitive polymer. The system exhibited a response time of approximately 3 h and operated over a relative humidity range of 50-95% RH and a temperature range of 30-60 degrees C, with normalized free spectral range sensitivities of the FPI up to 35 m(-1)/%RH at high humidity levels. The system was validated using a small-scale 50-day in-concrete test.

Abstract
Deep learning approaches are increasingly used to tackle forecasting tasks but require substantial training data. When samples are limited, synthetic data generation techniques can effectively augment datasets to improve model performance. Data augmentation is typically applied offline before training a model. However, when training with mini-batches, some batches may contain a disproportionate number of synthetic samples that do not align well with the original data characteristics. This work introduces an online data augmentation framework that generates synthetic samples during the training of neural networks. By creating synthetic samples for each batch alongside their original counterparts, we maintain a balanced representation between real and synthetic data throughout the training process. This approach fits naturally with the iterative nature of neural network training and eliminates the need to store large augmented datasets. We validated the proposed framework using 3797 time series from 6 benchmark datasets, three neural architectures, and seven synthetic data generation techniques. The experiments suggest that online data augmentation leads to better forecasting performance compared to offline data augmentation or no augmentation approaches. The framework and experiments are publicly available.

Abstract
This study presents a qualitative investigation into the current marketing and communication practices of the amateur sports modalities at Futebol Clube do Porto. Through structured exploratory interviews with key stakeholders, including the club’s marketing director, the marketing manager for the modalities, a player, a coach, and a club member, the research identified existing strengths, challenges, and critical gaps within their marketing efforts. The findings underscored specific areas for improvement, such as the need for dedicated marketing personnel, optimized digital engagement strategies, and innovative content creation. This study provides a robust foundation, offering nuanced guidance and critical insights that directly support a comprehensive digital marketing plan for the club’s diverse sports modalities. Ultimately, these conclusions aim to enable more effective communication, enhanced visibility, and stronger fan loyalty, thereby contributing to the sustained success and recognition of FC Porto’s non-football sports nationally and internationally.

Abstract
In today's rapidly evolving and competitive business environment, organizations must continuously innovate, leading to the development of new optimization techniques, methods, and tools to support decision-making. In project scheduling management, efficiency and effectiveness are crucial for organizational success, and the tools developed are designed to improve these two critical factors. This paper focuses on applying optimization techniques to project scheduling, with a particular emphasis on metaheuristics, specifically Simulated Annealing. A mathematical model was developed, incorporating the specific requirements of resource constrained project scheduling. A prototype was then implemented based on this model and tested using academic data to assess its effectiveness. The results demonstrated that the prototype could generate effective schedules and exhibited remarkable flexibility, adapting to different types of projects and multi-project environments. This article concludes that using metaheuristics, such as Simulated Annealing, provides a powerful and effective approach to solving complex project scheduling problems, offering significant advantages for organizations operating in dynamic and highly constrained environments.