Abstract
The integration of AI in supply chains offers opportunities to enhance efficiency, sustainability, and decision-making. However, effective implementation requires attention to both technical and socio-technical aspects. This study examines AI maturity in the pulp and paper sector using the SC-STAI profiling tool, assessing AI integration across technical, social, human, and organizational domains. Based on nine case studies from Brazil and Portugal, the research identifies key areas for improvement and highlights uneven AI adoption. Findings show that performance and resilience are most impacted, while job role adoption remains the lowest. The study emphasizes the importance of Socio-Technical AI Maturity Models in guiding responsible AI adoption and improving socio-technical alignment in supply chains, contributing to a better understanding of AI readiness in traditional industries and demonstrating the SC-STAI tool's applicability for strategic AI planning.

Abstract
Municipal meeting minutes are official documents of local governance that exhibit heterogeneous formats and writing styles. Effective information retrieval (IR) requires identifying metadata such as meeting number, date, location, participants, and start/end times, elements that are rarely standardized or easily extracted automatically. Existing named entity recognition (NER) models are ill-suited to this task, as they are not adapted to such domain-specific categories. In this paper, we propose a two-stage pipeline for metadata extraction from municipal minutes. First, a question-answering (QA) model identifies the opening and closing text segments containing metadata. Transformer-based models (BERTimbau and XLM-RoBERTa with and without a CRF layer) are then applied for fine-grained entity extraction, with deslexicalization explored as an additional modeling strategy. We benchmark the pipeline against open and closed-weight LLMs (Phi and Gemini), considering performance, inference cost, and carbon footprint. Our results demonstrate strong in-domain performance, outperforming the evaluated LLMs. Differences observed in cross-municipality evaluation highlight the linguistic diversity and structural variation across municipal records, underscoring the challenges of generalization in this domain and motivating future research in metadata extraction from municipal minutes.

Abstract
State-based Conflict-Free Replicated Data Types (CRDTs) are widely used in distributed systems to ensure high availability without coordination. However, their naive synchronization strategy, transmitting the full state, incurs high communication costs. In this paper, we: (1) propose ConflictSync, a digest-driven synchronization algorithm, which reduces total data transfer by up to 18× compared to full-state transmissions; (2) formulate state-based CRDT synchronization as set reconciliation over irredundant join decompositions; (3) generalize Rateless Set Reconciliation for variable-sized elements, at the cost of an additional communication step; (4) introduce a new generic set reconciliation solution, integrating Bloom Filters with rateless IBLTs; (5) experimentally evaluate the novel synchronization strategies. © 2026 Copyright held by the owner/author(s).

Abstract

Abstract
Conflict-free Replicated Data Types (CRDTs) enable available and eventually consistent data replication without coordination, making them well suited for open and partition-prone environments. Recent work has shown that CRDTs can be extended to tolerate Byzantine faults by ensuring that replicas eventually agree on the validity of operations, even in permis-sionless settings. However, validity alone does not prevent a Byzantine participant from inflicting unbounded damage by issuing large volumes of adversarial yet well-formed updates. For example, when editing text, an attacker can easily delete prior text. In this paper, we study how to bound the impact of Byzantine behavior in open CRDT systems. We introduce bounded Byzantine CRDTs, a rate-limiting framework for CRDTs in which each update carries an associated cost that limits the influence of adversarial operations relative to the resources they expend. Overall, this work bridges the gap between Byzantine-Tolerant CRDTs and resource-bounded adversarial models, providing a principled foundation for deploying CRDTs in fully open, adversarial environments. © 2026 Copyright held by the owner/author(s).

Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as key enablers in Non-Terrestrial Networks (NTNs) to provide flexible wireless coverage, particularly in infrastructure-limited scenarios. In our previous work, we proposed the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, a pioneering solution for the energy-efficient placement of multiple UAVs acting as Flying Access Points (FAPs). SUPPLY ensures continuous Ground User (GU) coverage while minimizing propulsion energy consumption. However, its quadratic time complexity in the GU grouping phase imposes scalability constraints, especially in larger time-sensitive scenarios. In this paper, we propose eSUPPLY, a computationally efficient enhancement to SUPPLY. By increasing the step size between candidate Flying Access Point (FAP) positions during the GU grouping phase, eSUPPLY significantly reduces the size of the optimization problem. Simulation results demonstrate up to a 97% reduction in execution time, with only a marginal increase in the number of FAPs and energy consumption, while maintaining network performance, enabling real-time operation in larger and more dynamic Flying Networks (FNs) compared to the SUPPLY algorithm.