Abstract
The growth in cloud computing, Big Data, AI and high-performance computing (HPC) necessitate the deployment of additional data centers (DC's) with high energy demands. The unprecedented increase in the Thermal Design Power (TDP) of the computing chips will require innovative cooling techniques. Furthermore, DC's are increasingly limited in their ability to add powerful GPU servers by power capacity constraints. As cooling energy use accounts for up to 40% of DC energy consumption, creative cooling solutions are urgently needed to allow deployment of additional servers, enhance sustainability and increase energy efficiency of DC's. The information in this study is provided from Start Campus' Sines facility supported by Alfa Laval for the heat exchanger and CO2 emission calculations. The study evaluates the performance and sustainability impact of various data center cooling strategies including an air-only deployment and a subsequent hybrid air/water cooling solution all utilizing sea water as the cooling source. We evaluate scenarios from 3 MW to 15+1 MW of IT load in 3 MW increments which correspond to the size of heat exchangers used in the Start Campus' modular system design. This study also evaluates the CO2 emissions compared to a conventional chiller system for all the presented scenarios. Results indicate that the effective use of the sea water cooled system combined with liquid cooled systems improve the efficiency of the DC, plays a role in decreasing the CO2 emissions and supports in achieving sustainability goals.

Abstract
Traditionally, there are two main market designs for user connected smart objects and smart appliances: cloud dependent and/or local centralized servers but both approaches bring concerns to the end-user side. The cloud-based approach raises concerns related with (apart from technical configuration and setup) security and privacy as user data may be exchanged with the cloud. Even in solutions that keep user data in the user side raises doubts and uncertainty to the final-user. On the other hand, the solutions based on local server may mitigate the security and privacy concerns but usually require end-user technical configuration and setup besides the fact that the local server becomes a single point of failure. Our aim is to address these concerns by the adoption of a peer-to-peer, self-contained and interoperable approach to ensure truly plug-and-play, to keep user data in the user side and to allow seamlessly interoperability among end-users' devices hence towards real Smart Environments. In this first paper we evaluate, for the first time, the oneM2M world wide IoT standard over peer-to-peer networking and the preliminary results are very promising, allowing us to move forward addressing other requirements such as IP provisioning, security and privacy, efficient peer discovery, etc. © 2025 Elsevier B.V., All rights reserved.

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Abstract
Background Intrusion Tolerant Systems (ITS) aim to maintain system security despite adversarial presence by limiting the impact of successful attacks. Current ITS risk managers rely heavily on public databases like NVD and Exploit DB, which suffer from long delays in vulnerability evaluation, reducing system responsiveness.Objective This work extends the HAL 9000 Risk Manager to integrate additional real-time threat intelligence sources and employ machine learning techniques to automatically predict and reassess vulnerability risk scores, addressing limitations of existing solutions.Methods A custom-built scraper collects diverse cybersecurity data from multiple Open Source Intelligence (OSINT) platforms, such as NVD, CVE, AlienVault OTX, and OSV. HAL 9000 uses machine learning models for CVE score prediction, vulnerability clustering through scalable algorithms, and reassessment incorporating exploit likelihood and patch availability to dynamically evaluate system configurations.Results Integration of newly scraped data significantly enhances the risk management capabilities, enabling faster detection and mitigation of emerging vulnerabilities with improved resilience and security. Experiments show HAL 9000 provides lower risk and more resilient configurations compared to prior methods while maintaining scalability and automation.Conclusions The proposed enhancements position HAL 9000 as a next-generation autonomous Risk Manager capable of effectively incorporating diverse intelligence sources and machine learning to improve ITS security posture in dynamic threat environments. Future work includes expanding data sources, addressing misinformation risks, and real-world deployments.

Abstract
This chapter examines how GenAI and predictive modelling strategies affect hyper-personalised marketing. Through a comprehensive literature review and case studies, it examines hyper-personalisation's theoretical frameworks, technical infrastructures, and ethical and governance issues. Large language models, generative adversarial networks, and diffusion models combined with advanced predictive analytics allow firms to scale real-time, highly individualised customer experiences. Effective implementation requires sophisticated data architectures, algorithmic transparency, and strong privacy protections. Integration complexity and ethical accountability are major barriers to consumer engagement and conversion, according to the research. Based on these findings, the chapter proposes an integrated framework that combines technological innovation with ethics and customer focus. This research advances marketing theory and provides practical advice for companies using AI-driven hyper-personalisation while maintaining consumer trust and regulatory compliance.