Abstract
Time series forecasting is pivotal across industries, as it fosters data-driven decision-making, increasing the chances of successful outcomes. Yet, certain instances that feature adverse characteristics, may lead models to manifest stress through decreases in performance (e.g., large errors). Hence, the ability to preemptively identify such cases, while establishing their root causes, would be advantageous to elevate the understanding of forecasting processes, informing users about the trustworthiness of predictions. Hence, we propose MASTFM, a method based on meta-learning that leverages statistical characteristics of input time series, and estimations of forecasting performance from model outputs, to build a metamodel that learns conditions for stress. Given that such occurrences are naturally rare, data augmentation is employed to ensure balance during training. Moreover, SHapley Additive exPlanations (SHAP) are used to explain how features impact forecasting behaviour.

Abstract
This chapter explores the potential of thermal energy storage (TES) systems towards the decarbonization of industry and energy networks, considering its coordinated management with electrochemical energy storage and renewable energy sources (RES). It covers various TES technologies, including sensible heat storage (SHS), latent heat storage (LHS), and thermochemical energy storage (TCS), each offering unique benefits and facing specific challenges. The integration of TES into industrial parks is highlighted, showing how these systems can optimize energy manage-ment and reduce reliance on external sources. A district heating use case also demonstrates the economic and environmental advantages of a multi-energy management strategy over single-energy approaches. Overall, TES technologies are presented as a promising pathway to greater energy effi-ciency and sustainability in industrial processes.

Abstract
Growing urbanisation, the development of smart cities, and environmental concerns have driven the implementation of advanced technologies and the modernisation of transport systems. Electric motorcycles have emerged as an effective solution for mobility, but they also present specific challenges, particularly related to the mode of riding, which is more complex than that of other vehicles and requires greater attention, skill, and preparation. Therefore, the interaction between the rider and the support system must be carefully designed, with particular emphasis on the interface and the adaptation of route information. This interface should be intuitive, accessible, and capable of presenting relevant information in a clear and objective manner, minimising distractions while riding. In addition, it must be adaptable to user preferences, allowing for customisation such as colour themes, levels of detail in the information displayed, or specific notifications regarding adverse weather and road conditions. Adapting route information provides a more efficient, safe, and satisfactory user experience. It enables riders to access personalised information, continuously updated in real time and tailored to the situation and their specific needs, including traffic conditions, road surface state, and weather conditions. This optimisation leads to better time management, energy consumption, and overall ride quality, enhancing urgent and non-urgent services. Moreover, integrating clearly and objectively features such as voice commands and compatibility with mobile or wearable devices (e.g., smartwatches) can facilitate real-time interaction without compromising safety. The interface should also offer advanced functionalities in line with technological developments and user needs, adapting to each rider’s specific requirements. This not only improves the individual experience but also promotes efficiency and sustainability, contributing to the advancement of smart cities and innovative mobility solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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[No abstract available]

Abstract
This study highlights the critical role of Large Language Model (LLM) in simplifying technical content and integrating visual data for accessible communication. It compares GPT-4 and Llama-3.2-90b-Vision-Preview, focusing on readability, semantic similarity, and multimodal interpretation using robust metrics like Flesch Reading Ease, Gunning Fog Index, and CLIP Score. GPT-4 retains key information and achieves high semantic and textual integration scores, making it more suitable for complex technical scenarios. Furthermore, LLaMA prioritizes readability and simplicity, outperforming in generating accessible captions. Both models show optimal performance with a temperature setting of 0.5, balancing simplicity and meaning preservation. The research underscores LLM potential to democratize technical knowledge across disciplines but notes precision and multimodal integration limitations. Future directions include fine-tuning for domain-specific applications and expanding input modalities to enhance accessibility and efficiency in real-world technical tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract