Abstract
Stress is associated with many negative effects, including inadequate sleep, reduced learning and memory, and a higher risk of mental health conditions. Given these effects, it is important to explore effective strategies for stress management and intervention. One promising approach is the use of ecological momentary assessments (EMAs), which allow us to measure an individuals' experiences in their natural environments, offering valuable data to inform just-in-time adaptive interventions (JITAIs). Machine learning can further enhance JITAIs by forecasting stress-related emotional states, enabling proactive intervention delivery to prevent heightened stress. In this study, we focus on forecasting stress utilizing data from a large mental health project. During this project, EMA data was collected from different vulnerable target groups across Europe, including youth, older adults, migrants, and individuals with low socioeconomic status. We formulated the forecasting task as a binary classification problem: predicting either transitions from normal to elevated stress or the stability of normal stress, based on a person's stress distribution. This approach simplifies the task, supports personalized predictions, and enables actionable insights, as predicting elevated stress can directly trigger support. Our results demonstrate that machine learning models are capable of forecasting stress transitions (ROC-AUC = 0.70 vs. 0.50 for a random classifier), although predicting transitions to elevated stress proved more challenging than identifying stable normal stress. Models trained on combined data from all populations performed comparable to those trained on individual populations. Furthermore, cross-country evaluations indicated that population-specific models generalized well across most populations.

Abstract
This paper presents the ENPOWER Flexibility Marketplace Toolkit, an open-source platform for peer-to-peer trading of energy flexibility within energy communities. Using energy consumption and production time-series data, participants can publish flexibility needs, submit offers, and verify delivery against measured baselines. Transactions are settled automatically through blockchain smart contracts with collateral enforcement, while an Energy Data Space backbone governs data exchange, ensuring sovereignty and policy-controlled sharing. Non-fungible tokens provide immutable, auditable certificates of each fulfilled flexibility commitment. The toolkit covers the complete trading lifecycle from market creation and participant onboarding through offer matching, delivery verification, and financial settlement.

Abstract

Abstract
Software ecosystems have emerged as a paradigm to structure software products, communities and business models, in a form inspired by the natural ecosystems. Mobility solutions are also evolving from individual vehicles to soft mobility services based on electric vehicles. This paper aims to address the creation of a software platform to support an ecosystem of mobility solutions-the Intelligent Mobility Ecosystem, based on connected electric vehicles. It follows the paradigm of software ecosystems, in which a technological platform provides the functionalities needed to create solutions within the ecosystem. The work being carried out is part of the A-Mover project, which aims to develop a connected electric motorcycle and electronic services to support driving and use of the vehicle in individual and business contexts. The aim is to develop a set of functionalities around the vehicle to create specific mobility solutions. The concept of a software ecosystem is reviewed below and the proposed architecture for the software platform that will support the ecosystem is described.

Abstract
This work introduces Gen-JEMA, a generative approach based on joint embedding with multimodal alignment (JEMA), to enhance feature extraction in the embedding space and improve the explainability of its predictions. Gen-JEMA addresses these challenges by leveraging multimodal data, including multi-view images and metadata such as process parameters, to learn transferable semantic representations. Gen-JEMA enables more explainable and enriched predictions by learning a decoder from the embedding. This novel co-learning framework, tailored for directed energy deposition (DED), integrates multiple data sources to learn a unified data representation and predict melt pool images from the primary sensor. The proposed approach enables real-time process monitoring using only the primary modality, simplifying hardware requirements and reducing computational overhead. The effectiveness of Gen-JEMA for DED process monitoring was evaluated, focusing on its generalization to downstream tasks such as melt pool geometry prediction and the generation of external melt pool representations using off-axis sensor data. To generate these external representations, autoencoder (AE) and variational autoencoder (VAE) architectures were optimized using Bayesian optimization. The AE outperformed other approaches achieving a 38% improvement in melt pool geometry prediction compared to the baseline and 88% in data generation compared with the VAE. The proposed framework establishes the foundation for integrating multisensor data with metadata through a generative approach, enabling various downstream tasks within the DED domain and achieving a small embedding, allowing efficient process control based on model predictions and embeddings.

Abstract
Data augmentation is a crucial tool in time series forecasting, especially for deep learning architectures that require a large training sample size to generalize effectively. However, extensive datasets are not always available in real-world scenarios. Although many data augmentation methods exist, their limitations include the use of transformations that do not adequately preserve data properties. This paper introduces Grasynda, a novel graph-based approach for synthetic time series generation that: (1) converts univariate time series into a network structure using a graph representation, where each state is a node and each transition is represented as a directed edge; and (2) encodes their temporal dynamics in a transition probability matrix. We performed an extensive evaluation of Grasynda as a data augmentation method for time series forecasting. We use three neural network variations on six benchmark datasets. The results indicate that Grasynda consistently outperforms other time series data augmentation methods, including ones used in state-of-the-art time series foundation models. The method and all experiments are publicly available. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.