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.

Abstract
The dataset reports an up-to-date overview of the selective biowaste collection with a focus on food waste and organic kitchen waste across 308 municipalities in Portugal, to assess the compliance with the EU Waste Framework Directive that made biowaste collection mandatory from 1st January 2024. Data were collected through a structured survey sent to the totality of the municipalities, complemented by systematic research in secondary official sources such as municipal web-sites, reports and statistical data. The questionnaire covered aspects such as coverage, collection models (nearby bring points, door-to-door, co-collection), sector-specific deployment (household collection, non-domestic collection), operational characteristics, and performance indicators (capture rates, cost per tonne). The dataset was structured and validated through cross-checking the multiple sources assessed, prioritising direct municipal questionnaire responses. It includes disaggregated data at a municipality level, including detailed information on the characteristics and efficiency of the initiatives, when available. The database allows the cross-comparison across Portuguese regions and potentially with other international systems, in terms of biowaste collection strategies with focus on food waste and organic kitchen waste. Municipalities in Portugal have been carrying out pilot experiences within their territories, but there is no systematic assessment of what has been carried out nor the results obtained. Given the limited available data, this dataset provides a valuable resource for policy design and further research on biowaste management initiatives to further assess their efficiency and adaptability to different municipal realities at a national and even European level. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Abstract