Abstract
Green ammonia production stands as a pivotal component in the transition towards sustainable energy and agriculture, poised to revolutionize numerous industries. This paper presents an optimization control framework for industrial green ammonia fuel hubs to engage in electricity, hydrogen, and oxygen markets, addressing both economic and technical considerations. By evaluating scenarios with and without battery storage, this study demonstrates the potential for increased profitability and energy independence through secondary reserve market participation, alongside insights into the economic viability of photovoltaic investments. These findings underscore the importance of considering market dynamics and technological integration in the sustainable operation of green ammonia production hubs.

Abstract
In today's fast-paced global marketplace, businesses grapple with the relentless demand for innovation in marketing strategies. The landscape is ever-evolving, driven by digital advancements and shifting consumer behaviors, leaving companies scrambling to stay relevant. Academic scholars, educators, and managers face a formidable challenge: deciphering the complex web of trends and technologies shaping modern marketing while equipping themselves with actionable strategies to thrive in this dynamic environment. Marketing Innovation Strategies and Consumer Behavior is a comprehensive resource that serves as a beacon of clarity amidst the chaos, offering a structured roadmap for understanding and harnessing the power of innovation in marketing. Through meticulous analysis of current trends and in-depth exploration of cutting-edge strategies, this book provides a holistic understanding of the challenges and opportunities facing businesses today. By delving into topics ranging from digital marketing and sustainability initiatives to data analytics and emerging technologies, Marketing Innovation Strategies and Consumer Behavior equips readers with the tools they need to survive and thrive in the modern marketplace. Its emphasis on real-world case studies ensures practical relevance, while its theoretical underpinnings offer a solid foundation for academic inquiry. This book is an indispensable resource for scholars seeking to deepen their understanding of marketing innovation, providing actionable insights and strategic frameworks to navigate the complexities of the digital age with confidence and agility. © 2024 by IGI Global. All rights reserved.

Abstract
Various sectors within the heritage domain have developed linked data models to describe their cultural artefacts comprehensively. Within the archival domain, ArchOnto, a data model rooted in CIDOC CRM, uses linked data to open archival information to new uses through the prism of linked data. This paper seeks to investigate the potential to use information in archival records in a larger context. It aims to leverage classes and properties sourced from repositories deemed informal due to their crowd-sourcing nature and the possibility of inconsistencies or lack of precision in the data but rich in content, such as the cases of Wikidata and DBpedia. The anticipated outcome is attaining a more comprehensive and expressive archival description, fostering enhanced understanding and assimilation of archival information among domain specialists and lay users. To achieve this, we first analyse existing archive records currently described under the ISAD(G) standard to discern the typologies of entities involved. Subsequently, we map these entities within the ArchOnto ontology and establish correspondences with alternative models. We observed that entities associated with people, places, and events benefited the most from integrating properties sourced from Wikidata and DBpedia. This integration enhanced their comprehensibility and enriched them at a semantic level.

Abstract

Abstract
The Text2Story Workshop series, dedicated to Narrative Extraction from Texts, has been running successfully since 2018. Over the past six years, significant progress, largely propelled by Transformers and Large Language Models, has advanced our understanding of natural language text. Nevertheless, the representation, analysis, generation, and comprehensive identification of the different elements that compose a narrative structure remains a challenging objective. In its seventh edition, the workshop strives to consolidate a common platform and a multidisciplinary community for discussing and addressing various issues related to narrative extraction tasks. In particular, we aim to bring to the forefront the challenges involved in understanding narrative structures and integrating their representation into established frameworks, as well as in modern architectures (e.g., transformers) and AI-powered language models (e.g., chatGPT) which are now common and form the backbone of almost every IR and NLP application. Text2Story encompasses sessions covering full research papers, work-in-progress, demos, resources, position and dissemination papers, along with keynote talks. Moreover, there is dedicated space for informal discussions on methods, challenges, and the future of research in this dynamic field.

Abstract