2026· Proceedings of the 21st International Conference on Software Technologies· pp. 278-284· 0 citations· 39 references
TL;DR
A pattern language for green computing is envisioned, covering the various phases of the software lifecycle in which green computing solutions can be applied, as well as important application areas, such as artificial intelligence or cloud computing.
Abstract
: Software is used in our modern world to increase automation in almost all areas: From simple calculations to complex tasks, such as self-driving vehicles or coding agents based on artificial intelligence. While this automation increases efficiency and convenience for the users, it has an often overlooked negative impact on the environment, primarily caused by the severe energy and water consumption. Various approaches to reduce this impact and to develop and operate software more sustainably were developed in recent years. However, the wide adoption of these approaches is currently missing due to a lack of knowledge about green software development, operations, and usage. Hence, a common knowledge base must be established that facilitates applying green computing solutions. Patterns are a well-known concept to document and share knowledge about proven solutions to commonly recurring problems in an abstract manner. Therefore, they are also promising for documenting green computing solutions and best practices and to use them for reference or educational purposes. However, there exists no pattern language documenting and connecting relevant knowledge about green software development, operation, and usage. In this paper, we envision a pattern language for green computing, covering the various phases of the software lifecycle in which green computing solutions can be applied, as well as important application areas, such as artificial intelligence or cloud computing.
An integrated human‑automation teaming framework is presented that facilitates TDP development and supports cross‑disciplinary dialogue between designers, engineers, command staff, and policy‑makers and provides a structured basis for designing flexible and context‑appropriate adaptive automation in VUCA environments.
Jelle A Van Dijk, Rosa van Tuijn, Renske Verwaal-Bootsma et al.· AHFE International· 0 citations
The evolution of automotive technologies brought into play the large amount of hardware and software devices, with their subsequent software frameworks and programming languages, as well as the adoption of Cloud environments for data storage, management and analysis. In this general context, specialists are faced with information overload and users have become more and more confused about the measures that should be taken in order to optimize the vehicle use, maintenance and repair. AI technologies could greatly help both users and specialists in their vehicular interactions. On the one hand, AI could give very good answers as long as the information on which the responses are based is correct and complete. On the other hand, the questions should be well formulated, clear and specific in order to maximize the accuracy and correctness of the answers. This paper aims at building an algorithm of combining ISO 15765-4 and SAE J1979 compliant professional scan tools technologies with Microsoft Copilot for evolving the automotive diagnosis process. The resulting algorithm consists of a set of procedures and steps that should be followed, for building an intelligent agent. This agent incorporates a knowledge base that includes maintenance and repair manuals, diagnosis tests results and previous experience. In this way, the vehicle maintenance and repair activities should gain a higher level of efficiency.
Cosmin Tomozei, I. Furdu, Bogdan Pătruț· INTERNATIONAL JOURNAL OF COM...· 0 citations
The rapid diffusion of data‑driven automation and agentic AI systems is reshaping the foundations of work, decision‑making, and human–technology interaction. As organizations move toward Society 5.0— Japan’s vision for a human-centered “super smart” society in which cyber-physical intelligence augments human capability across economic and social systems—there is an urgent need for operational architectures that are not only technologically capable but also fundamentally human‑centric. This paper presents an applied model using Intelligent Operations framework that integrates agentic AI, enterprise data fabric, human‑in‑the‑loop governance, and secure multi‑system orchestration, and enterprise digital twins that simulate processes and operational states for context-aware decision support. The result is an adaptive socio‑technical system that enhances human decision‑making rather than replacing it, while simultaneously enabling automation at operational scale.The research builds on fieldwork across finance, supply chain, HR, and complex asset‑intensive environments, where organizational processes are distributed across heterogeneous platforms such as ERP, HCM, workflow systems, enterprise data lakes, RPA tools, and emerging AI orchestration layers. Traditional human‑computer interaction models are insufficient in these environments because workers face fragmented data landscapes, inconsistent process execution, and increasing cognitive load. The proposed Intelligent Operations framework addresses these pain points by introducing an orchestration layer that harmonizes data, interprets context (including real-time insights from digital twin models), and deploys agentic AI workers capable of completing multi‑step tasks across systems.A key contribution of this work is the definition of agentic AI in enterprise socio‑technical ecosystems—AI agents equipped not only with language models and planning capability but also with secure access to enterprise systems through structured patterns such as passthrough APIs, workflow orchestration, Model Context Protocol (MCP), and agent‑to‑agent (A2A) collaboration. Rather than relying on brittle rule‑based workflows, the agents dynamically interpret goals, assess context, and plan actionable sequences while maintaining traceability, decision lineage, and auditability. This supports a new form of “digital labor” that works alongside human employees to augment cognitive, administrative, and analytical tasks. However, the framework insists on human‑in‑the‑loop governance, recognizing that human oversight remains essential for ethical, safe, and responsible AI deployment. The DMO acts as a security and compliance boundary—enforcing identity controls, audit trails, approval checkpoints, policy enforcement, and anomaly detection throughout the agentic automation lifecycle. This hybrid model ensures that automation amplifies human capability without bypassing institutional safeguards or creating new forms of risk.The paper also discusses the human‑centric business implications: reduced cognitive load for knowledge workers, increased transparency of decision processes, improvements in cross‑functional collaboration, and the redefinition of roles as humans transition from transactional executors to supervisors, interpreters, and strategic actors. Proposed framework becomes the backbone for Society 5.0 organizational design—linking people, processes, data, and intelligent systems through a unified operational fabric.This research demonstrates that when designed with ergonomics, human values, and socio‑technical principles at the center, agentic AI become powerful enablers of human‑centric, resilient, and adaptive enterprises.
Elizabeth Koumpan, Laurentiu Gabriel Ghergu, Łukasz Strack et al.· AHFE International· 0 citations
It is demonstrated that multiple architectural views, continuous monitoring, domain-driven decomposition, and AI-assisted design techniques contribute significantly to improving scalability, maintainability, adaptability, and long-term software sustainability.
The work that the UK Data Service has so far undertaken to move ODRL from a standard to an implementable set of metadata, workflows, and tools is outlined and how standards such as Data Use Ontology (DUO) and Data Privacy Vocabulary (DPV) complement ODRL within that context is described.
D. Bell, S. McEachern, D. Lungley· International Journal of Pop...· 0 citations
Robotic Process Automation (RPA) has been a transformative technology that allows companies to automate plastic-based tasks that are repetitive and basis rule-based in heterogeneous information systems without having to change the underlying infrastructure. Since organizations are under growing pressure to enhance both operational efficiency, accuracy, and scalability, RPA provides a cost-effective solution to the digital transformation process since it simulates human interactions with programs. The following paper is a detailed analysis of the concept of RPA within the current enterprise processes in terms of its architecture, deployment patterns, and quantifiable business outcomes. The paper is meticulously conducting a scientific review of literature to determine existing trends, advantages, challenges, and gaps in the present research on RPA. It suggests using the structured methodology that encompasses the process discovery, bot design, orchestration, and governance mechanisms according to the enterprise standards. Measures of performance like reduction in execution time, minimization in error rate, cost savings, and return on investment are measured to check effectiveness. The convergence of RPA and artificial intelligence and machine learning is also discussed and results in intelligent automation that has the potential to process semi-structured and unstructured information. At the end of the paper, the authors establish the main issues associated with scalability, security, and maintainability and show future research perspectives of sustainable adoption of RPA in large-scale enterprise settings.
Kenji Sato, Aiko Yamamoto· International Journal of Mod...· 0 citations