Abstract
In software architecture, architects, developers, and project managers frequently encounter uncertainties that impact project progress and outcomes. Drawing on foundational practices from a technique called ArchHypo, which uses hypothesis engineering to manage uncertainties in software architecture and improve decision-making processes, this paper aims to address these uncertainties. It recommends applying a structured approach that involves identifying the scenario and adopting technical patterns tailored to the project's specific needs. This paper introduces six distinct patterns designed from the integrated application of base technical patterns to help agile teams strategically manage and effectively address uncertainties. By implementing these patterns, technical challenges will be handled while ensuring successful project execution tailored to the dynamic landscape of agile development.

Abstract

First-order magnetostructural transitions underpin the functionality of many magnetocaloric materials and form the basis of emerging solid-state cooling technologies. However, their time-driven response remains underexplored, despite containing intrinsic kinetic information essential for understanding and further optimizing the transformation dynamics. Here, we developed a unique experimental setup to perform simultaneous measurements of magnetization, strain, and temperature change in the benchmark Heusler-alloy Ni–Mn–In under magnetic fields up to 30 T at sweep rates of 10 T/min. By implementing a kinetic measurement protocol, we access both the field-driven and time-driven evolutions of magnetic and structural order parameters along the forward and reverse transition directions. While magnetization rapidly stabilizes after field halting, the probed strain response continues to evolve over extended timescales, indicating distinct relaxation behavior of the measured properties. Quantitative analysis using an extended Avrami–Hay model reveals a secondary diffusive contribution that is required to describe this slow strain evolution. This long-term kinetic dominance of strain also coincides with the substantial structural entropy change characteristic of the Ni–Mn–X family, relating the primary entropy contributor to the strain’s extended response. These results provide a general framework for probing coupled order parameters in first-order multifunctional materials, offering insights for the development of efficient caloric devices.

Abstract
The adoption of Generative AI (GenAI) suggests major changes for software engineering, including technical aspects but also human aspects of the professionals involved. One of these aspects is how individuals perceive themselves regarding their work, i.e., their work identity, and the processes they perform to form, adapt and reject these identities, i.e., identity work. Existent studies provide evidence of such identity work of software professionals triggered by the adoption of GenAI, however they do not consider differences among diverse roles, such as developers and testers. In this paper, we argue the need for considering the role as a factor defining the identity work of software professionals. To support our claim, we review some studies regarding different roles and also recent studies on how to adopt GenAI in software engineering. Then, we propose a research agenda to better understand how the role influences identity work of software professionals triggered by the adoption of GenAI, and, based on that, to propose new artifacts to support this adoption. We also discuss the potential implications for practice of the results to be obtained. © 2026 Copyright held by the owner/author(s).

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.