Abstract
This paper considers the single machine total weighted tardiness problem. A thorough computational evaluation of new and existing dispatching rules is performed. We considered several existing heuristics and proposed new backward rules. These procedures are analyzed together for the first time and coded in the same programming language. We also created a new and much larger dataset, which allows a more detailed comparison and provides a useful benchmark for future work.We first conducted preliminary tests to determine appropriate parameter values and to choose between three versions of the new rules. These tests showed a need to use instance characteristics to make better choices. We then analyzed the heuristics and identified the non-dominated procedures, considering solution quality and computational time. One of the new backward rules is non-dominated, achieving the best solution quality. The non-dominated set allows decision-makers to choose a procedure depending on problem size and available time.

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
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.