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.

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
Identifying influential spreaders in temporal networks is crucial for understanding and controlling the dynamics of spreading. However, existing methods, such as temporal betweenness, closeness, pagerank, degree, and local path-based centrality, face several limitations, including high computational complexity, reliance on shortest paths, convergence issues, inability to capture influence dynamics with insufficient neighboring nodes, and a primary focus on local structural information. This paper presents PathSAGE, a novel method that addresses these problems. It integrates GraphSAGE, a deep learning model, to capture global node information while incorporating temporal local path counts as a key feature. Unlike other global feature-capturing methods, PathSAGE optimises computational complexity. Experimental results on thirteen real-world temporal networks demonstrate that PathSAGE outperforms the state-of-the-art methods in accurately identifying influential spreaders. PathSAGE exhibits a strong correlation with the Temporal Susceptible-Infected-Recovered (TSIR) model and achieves a relative improvement percentage (eta%) ranging from 0.12% to 70.70%. Additionally, PathSAGE attains the lowest average robustness value of 0.17, highlighting its effectiveness in identifying influential spreaders within temporal networks.

Abstract
Artificial Intelligence (AI) integration in supply chain systems is growing, and with it grows its potential impact on inter-organisational collaborative networks. We review existing literature on how different AI archetypes (Reflexive, Anticipatory, Supervisory, Prescriptive) could support Collaborative Supply Chain Management (CSCM) activities, and how they impact information sharing, collaborative decision-making, and trust among supply chain partners at different integration levels. Adopting a sociotechnical perspective, we synthesise existing literature and map the archetypes along four levels of AI integration, varying in scope and decision autonomy. The results are conceptual frameworks demonstrating how AI impacts collaboration dynamics as it evolves from a decision-support tool to an autonomous coordination agent. Findings show differentiated effects along archetypes and integration levels, with implications for CSCM governance, transparency, and resilience. We contribute to the discussion on human-AI collaboration in CSCM and offer a baseline for research on the human-centric values of Industry 5.0.

Abstract
This paper presents a case study to illustrate the application of the directed qualitative content analysis (DQCA) technique to focus group transcriptions for data-driven qualitative persona creation, with broader applicability in human-computer interaction and software development. Using a case study from a project focused on creating an e-grocery marketplace for facilitating short agrifood supply chain trade in the Portuguese context, we demonstrate and validate how DQCA can systematically generate personas that reflect real user needs. For the focus group session, we involved one of the project's stakeholders: family farmers. Furthermore, we propose how these personas can be integrated into the Rational Unified Process software development methodology, guiding decision-making, user-centered design, and prioritization throughout all its phases. Despite being rooted in the e-grocery domain, this paper's methodological approach and insights into generating and integrating user-centered personas in software development processes apply to a broader range of industries and projects, offering guidelines for practitioners and researchers in diverse contexts.

Abstract
Sales forecasting in the presence of Missing Data poses significant challenges, particularly for short time series where limited observations amplify the impact of incomplete records. This study analyzes a real-world transactional dataset (2021-2024) to predict quantities and prices for 2025. We classify missingness patterns and mechanisms (MCAR, MAR, MNAR) to inform the selection of imputation strategies. We evaluate techniques including MICE, Mean, KNN, and Linear Regression under simulated missingness rates, with KNN emerging as the most robust for the MAR mechanism. Regarding very short-term series predictions, the naive forecast Max2 (maximum of the last two observed values) outperformed moving averages. The results highlight the importance of mechanismaware imputation and domain-tailored forecasting in sparse datasets. This work presents a practical framework for businesses to effectively utilize incomplete sales data.