Abstract
This study provides a comprehensive review of how game theory can be applied to model and optimize dynamic coalitions in contemporary energy markets. With the increasing decentralization of energy systems driven by technologies such as solar photovoltaics, home energy storage, and electric vehicles, consumers have begun to play a more active and influential role in the market. In this new context, where cooperative and collective decision-making is gaining importance, game theory emerges as a valuable tool for analyzing and structuring these interactions. The primary objective of this work is to systematically review existing models, assess their methodological strengths and limitations, and identify open research gaps that hinder their applicability to real-world settings. By synthesizing the current state-of-the-art, this study aims to highlight pathways toward the development of more realistic and effective models that capture the dynamic and interdependent behaviors of energy consumers and the coalitions they form. Ultimately, this review seeks to provide an updated overview of this growing field, serving both as a basis for future research and as a foundation for the design of solutions that promote fairer, more efficient, and more participatory energy markets, especially for small-scale consumers, who now have greater voice and power of choice.

Abstract
Achieving sustainable urban mobility requires shifting travelers toward public transport. However, policies often assume uniform preferences, leaving a critical gap in understanding how different travelers prioritize mobility factors. To address this, the study examines behavioral heterogeneity among urban travelers using a data-driven clustering approach based on the relative importance assigned to cost, comfort, environmental sustainability, and flexibility. Using data from 698 respondents in the Asprela area of Porto, Portugal, a mixed-use district combining universities, hospitals, and commercial facilities, the study applies principal component analysis (PCA) and K-means clustering to derive distinct traveler profiles. Unlike segmentation based solely on socio-demographics or observed mode choice, this approach groups individuals according to their underlying value structures. Six clusters were identified, ranging from car-dependent, comfort-oriented users to environmentally conscious and low-engagement groups. The findings show that one-size-fits-all policies are unlikely to address behavioral diversity effectively. Building on these insights, the study proposes tailored and cross-cutting policies to enhance the attractiveness of public transport and promote sustainability. By uncovering latent preference structures, the study contributes to more inclusive and value-informed mobility planning.

Abstract
The concept of the Digital Twin of the Ocean (DTO) has transitioned from a research vision to an operational paradigm in the ILIAD project. Several of the mature Digital Twin components are available as reusable, findable (through the Iliad Registry: https://iliad-registry.inesctec.pt) and interoperable application packages, enabling automated environmental monitoring and decision support. This contribution presents the Cretan Sea oil spill DTO, focusing on near-real-time oil spill detection and forecast.The presented system implements an end-to-end workflow based on Sentinel-1 SAR imagery, orchestrated through Common Workflow Language (CWL). Incoming satellite data are automatically ingested, processed, and analysed using containerized application packages, enabling scalable and reproducible execution across cloud and HPC infrastructures. Oil spill detection is performed using a deep learning approach based on a combination of FCOS and U-Net convolutional neural networks, trained to discriminate oil slicks from look-alike phenomena in SAR imagery. The results are systematically compared against a statistical detection methodology implemented via the SNAPpy library, providing robustness and methodological benchmarking.Detected oil spill events trigger downstream Digital Twin services, including high-resolution marine forecasting and oil spill transport modelling. The forecasting framework integrates dynamically downscaled atmospheric forcing from WRF, hydrodynamic fields from NEMO, and sea state information from WAVEWATCH III, providing coastal-scale predictions at kilometer resolution. Oil spill transport and fate are simulated using the already established and validated MEDSLIK-II software [1], with results visualized through operational web platforms to support rapid situational awareness. Additionally, a 4D immersive visualization tool is introduced to present the oil spill evolution and fate in an intuitive spatio-temporal environment, enhancing operational readiness and enabling first responders and non-expert stakeholders to rapidly interpret complex model outputs without reliance on conventional map-based products.By packaging satellite analytics, numerical modelling, and orchestration logic into reusable application packages, the system demonstrates how post-project DTO assets can be operationalized beyond the ILIAD lifecycle. The Cretan Sea DTO illustrates a transferable Digital Twin workflow for automated oil spill detection and response, supporting environmental monitoring authorities with timely, data-driven decision support.References[1] M. De Dominicis, N. Pinardi, G. Zodiatis, and R. Archetti, “MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting – Part 2: Numerical simulations and validations,” Geosci. Model Dev., vol. 6, pp. 1871–1888, 2013. doi: 10.5194/gmd-6-1871-2013

Abstract
Quality performance in manufacturing has a direct influence on efficiency, generated waste, and costs. In collaboration with a textile manufacturer as a case study, this paper develops an automated defect detection system for a weaving process and evaluates its impact on operational performance. The system identifies defects immediately at their onset and prevents their propagation to subsequent fabric and production stages. A deep learning image classification model is developed, with six well-established network architectures being compared, leveraging a non-invasive image acquisition method that averts machinery disturbances for data collection. Based on the best-performing model, key indicators of operational performance are estimated using Markov Chain modelling, addressing a gap in linking model performance to operational impacts. Notable operational gains are demonstrated, namely a cost reduction of 1.3% and over 90% of waste reduction. A sensitivity analysis guides the definition of the image acquisition frame rate to minimise false alarms and shows that different operational indicators are impacted differently by different predictive performance metrics, affecting model selection. This research not only underscores the potential of integrating deep learning into textile production but also guarantees the effective communication of its impact to industry stakeholders, thus offering valuable practical insights to enhance operational performance.

Abstract
Reliable black start capability is a critical design requirement for offshore wind-hydrogen energy islands, directly influencing system availability, asset utilization, and the levelized cost of hydrogen production. This paper investi gates black start restoration strategies for autonomous offshore wind-to-hydrogen systems, focusing on the role of grid-forming converter technologies in enabling system recovery following total shutdown. A comparative analy sis of grid-forming battery storage and grid-forming wind turbine generation is conducted using electromagnetic transient simulations of a 300MW offshore wind farm coupled with a 240MW electrolyzer plant. Both technologies are evaluated within a combined soft and hard energization framework incorporating controlled voltage ramping, switchable reactive compensation, and sequential feeder energization. Battery-based grid-forming achieves faster voltage restoration and higher short-term overload capability, while wind turbine-based grid-forming provides superior frequency damping through higher virtual inertia. The combined energization strategy significantly re duces converter sizing requirements compared to pure soft energization, while switchable reactive compensation reduces reactive power burden by 94 percent during multi-feeder restoration. Strategic activation of electrolyzer auxiliary systems provides controllable load management that further attenuates frequency excursions during staged restoration. The findings provide practical design guidelines for black start technology selection in off shore wind-hydrogen systems, with direct implications for converter sizing, capital investment, and hydrogen production continuity.

Abstract
K-12 digital learning is increasingly shaped by emerging technologies layered onto existing digital infrastructures. In practice, the technologies that dominate attention, especially generative and assistive AI, arrive bundled with new assessment tensions, data flows, acquisition constraints, and inequities in access and support. This article proposes a practitioner-oriented framework for understanding emerging technologies as sociotechnical-immersive systems rather than standalone tools. The framework connects three lenses: (1) a macro sociotechnical circle that foregrounds policy, markets, equity, and governance; (2) a meso environment-design circle that analyzes how learning experiences are configured through system, narrative, and agency; and (3) a micro educational-approaches circle that focuses on the instructional activities educators enact within those environments, using the Immersive Learning Brain (ILB) as a map of practice and strategies. We developed this framework through practitioner sensemaking grounded in practitioner focus group data and aligned it with recent research syntheses on emerging technologies. We illustrate the framework through one worked example and two comparative mini-cases. We conclude with an agenda for researchers and practitioners focused on assessment, equitable infrastructure and support, data stewardship, and environment-design descriptions that move beyond technocentric labels.