Abstract

Abstract
3D models are crucial in predicting aesthetic outcomes in breast reconstruction, supporting personalized surgical planning, and improving patient communication. In response to this necessity, this is the first application of Radiance Fields to 3D breast reconstruction. Building on this, the work compares six SoTA 3D reconstruction models. It introduces a novel variant tailored to medical contexts: Depth-Splatfacto, designed to improve denoising and geometric consistency through pseudo-depth supervision. Additionally, we extended model training to grayscale, which enhances robustness under grayscale-only input constraints. Experiments on a breast cancer patient dataset demonstrate that Splatfacto consistently outperforms others, delivering the highest reconstruction quality (PSNR 27.11, SSIM 0.942) and the fastest training times (x1.3 faster at 200k iterations). At the same time, the depth-enhanced variant offers an efficient and stable alternative with minimal fidelity loss. The grayscale train improves speed by x1.6 with a PSNR drop of 0.70. Depth-Splatfacto further improves robustness, reducing PSNR variance by 10% and making images less blurry across test cases. These results establish a foundation for future clinical applications, supporting personalized surgical planning and improved patient-doctor communication.

Abstract
Python has become one of the most widely used programming languages, yet the transition fromPython 2 to 3 introduced a tension between innovation and compatibility. While new featuressuch as formatted string literals, type annotations, and structural pattern matching expanded thelanguage’s expressiveness, they also required substantial adaptation of legacy code. Despite theincreasing relevance of these features, there is still limited empirical evidence on how modernPython features are being adopted in practice—when developers start using them, how adoptionunfolds over time, and what motivations drive these decisions. This paper addresses this gapthrough a large-scale empirical study of 424 open-source Python projects. Our analysis revealstwo distinct adoption patterns: rapid adoption of small syntactic improvements and slowerintegration of features that require extensive refactoring or ecosystem support. On average,projects begin using with new features within 16 months after their release but take roughly 4years to achieve broader and sustained adoption. This observation may be partially explainedby the transition from Python 2 to 3, which did not preserve full backward compatibility.Complementary qualitative evidence from pull-request discussions indicates that developers areprimarily motivated to rejuvenate Python code through improvements in comprehension, safety,and performance, yet often constrained by compatibility requirements and maintenance costs.Together, these findings offer practical insights for tool developers and maintainers seeking tobalance innovation and stability in the ongoing rejuvenation of Python source code.

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).