Abstract
To achieve carbon neutrality by 2050, commercial buildings have installed photovoltaic systems to reduce carbon emissions and operational costs. Nevertheless, PV generation does not always match the building’s energy demand profile, therefore storage systems are needed to store excess energy and supply it when necessary. This paper presents a Mixed Integer Linear Programming optimisation algorithm designed to schedule the operation of the electric storage system, aiming to minimise the building’s energy-related costs. An annual hourly simulation of the optimised system was performed to assess the cost reduction. To prevent excessive operation of the electric storage system, an approach to penalise low energy charging was studied, with results showing a significant increase in the system’s lifespan. Key words. MILP, optimisation, renewable energy, energy storage system, commercial building

Abstract
The integration of AI into organizational settings leads to a growing need for hybrid human-AI collaborative approaches, necessary due to the increasing autonomy, impact and responsibility AI-based solutions have. Moreover, to ensure a sustainable integration into existing processes, such approaches must be context-aware, transparent, and human-centric. In line with the Industry 5.0 paradigm, this paper presents a novel Multi-Agent System architecture that enables meaningful collaboration between human and artificial agents through a socio-technical design approach. The proposed architecture is grounded in a structured, real-time context stream derived from organizational data sources, which semantically describe human actors, processes, and industrial resources. Central to this system is a set of four core LLM-based agents, each responsible for orchestrating hybrid human-AI tasks along distinct dimensions of timing, role selection, resource allocation, and execution sequencing. To assess the feasibility and effectiveness of the architecture, we report on an early-stage validation conducted within a representative industrial use case in the automotive sector, focused on information retrieval. In this use case, the architecture was tasked with answering a set of representative, domain-specific questions by dynamically interacting with distributed industrial databases. Results demonstrate the architecture's ability to coordinate relevant human and artificial agents, retrieve semantically-relevant data, and present explainable outputs, showcasing its potential for supporting decision-making processes in hybrid collaborative networks.

Abstract
The automotive sector is undergoing continuous technological evolution driven by the demand for sustainable and safe vehicles. Among the main factors influencing safety, driver behaviour has been identified as a critical contributor to road crashes. This systematic review explores recent innovations in detecting risky driver behaviours, addressing six research questions: the most relevant datasets used for algorithm development and evaluation; system architectures and methodologies for anomaly detection; the most studied driver behaviours and related environmental, human, and mechanical factors; advances in machine learning, deep learning, and statistical methods; performance metrics and validation approaches; and the role of embedded technologies and sensors in practical applications. The review included 93 peer-reviewed articles published between 2020 and 2024, sourced from ACM, IEEE, ScienceDirect, and Scopus. Exclusion criteria were duplicates, non-open access, retracted works, and studies unrelated to outlier detection or driver behaviour. The Parsifal tool was used to support systematic data processing. Results highlight the most frequently used datasets, proposed models, and their performance in detecting driver behaviours, as well as the influence of contextual factors such as traffic rules, road conditions, and sensor limitations. Despite advances, real-world integration remains challenging, requiring further research and development. This review aims to guide researchers in understanding the current state of anomaly detection in driving contexts and to emphasize the need for broader collaboration to create effective, deployable solutions that enhance road safety worldwide.

Abstract
Modern data-intensive systems rely on in-memory caching to achieve high throughput and low latency. CacheLib, Meta's general-purpose caching engine, provides high performance and flexibility for building specialized caches for a variety of applications. However, despite its wide adoption in large-scale infrastructures, CacheLib's data management mechanisms exhibit inefficiencies in shared environments. Particularly, its static and uncoordinated memory allocation leads to fragmented resource usage, unfair memory distribution, and degraded performance across tenants and instances. We present Holpaca, a general-purpose caching middleware that enables holistic and adaptable orchestration of shared caching environments. Holpaca introduces a shim data layer co-located with each cache instance and a centralized orchestrator with system-wide visibility, enabling global memory management and per-tenant QoS policies. Using production traces from Twitter, results show that, by continuously readjusting memory allocations based on workload dynamics, Holpaca achieves up to 3 higher throughput in multi-tenant and 2.2× improvement in multi-instance settings over CacheLib's rigid built-in mechanisms.

Abstract

Abstract
Abstract Materials science is at the crossroad between fundamental and applied sciences. Whether enabling clean energy, next-generation computing, or advanced manufacturing, it shapes the tools we use and the systems we build. As our societies undergo rapid digital, environmental, and technological transitions, materials science becomes even more central. It's a space where innovation can respond to practical challenges while aligning with broader social values. In the European context, that means supporting sustainability, openness, and solidarity -while also strengthening competitiveness.This roadmap explores how digital tools-especially simulation, data science, and AI-are transforming materials research, in connection with the twin digital and energy transition. The digital transition refers to the widespread adoption of digital technologies and data-driven methods across sectors, while the green (or energy) transition focuses on shifting toward sustainable, lowcarbon energy systems-together forming what is often called the twin transition, a joint effort to make economies both smarter and more sustainable.The Roadmap outlines both the technical directions and the cultural shifts needed to make this transformation inclusive and effective. Chapters span from atomic-scale simulations to advanced experimentation, from reproducibility to intelligent optimization, and from institutional reform to education for the next generation.Importantly, this isn't a single viewpoint. The document brings together a wide range of voices: researchers from different disciplines, working across length and time scales. It includes early-career scientists and senior experts. Enabled by activities supported by European Cooperation in Science and Technology (COST), it reflects a commitment to gender and geographic diversity. This plurality doesn't just enrich the content-it makes the vision more robust and relevant.We hope this collection serves not just as a guide, but as an invitation to collaborate-across fields, sectors, and borders-as we reimagine the future of materials science in a digital age.