Abstract
Prolog systems rely on an atom table for symbol management, which is usually implemented as a dynamically resizeable hash table. This is ideal for single threaded execution, but can become a bottleneck in a multi-threaded scenario. In this work, we replace the original atom table implementation in the YAP Prolog system with a lock-free hash-based data structure, named Lock-free Hash Tries (LFHT), in order to provide efficient and scalable symbol management. Being lock-free, the new implementation also provides better guarantees, namely, immunity to priority inversion, to deadlocks and to livelocks. Performance results show that the new lock-free LFHT implementation has better results in single threaded execution and much better scalability than the original lock based dynamically resizing hash table.

Abstract
Organizations find themselves at a pivotal crossroads in an era propelled by the sweeping tide of digital transformation, where the wake of the COVID-19 pandemic has reshaped the global landscape. Within these novel contexts, the imperative to cultivate Learning Organizations (LOs) has emerged as a beacon of adaptability and progress. Creating Learning Organizations Through Digital Transformation weaves the fabric of LOs within the digital tapestry, where minds perpetually expand, and learning begets learning. This journey hinges on the synergy of knowledge and digital prowess, as LOs harness data and digital content with finesse. From immersive learning to artificial intelligence, these technological frontiers reshape learning, spurring change. Unveiling the core concepts, implementations, and global impacts of LOs, this book is a compass for academics, researchers, and practitioners. It deciphers people capacities, digital contents, learning technologies, and evaluation, nurturing the symbiotic relationship between learning and transformation. Creating Learning Organizations Through Digital Transformation is the scholarly guidepost in a swiftly evolving landscape. It beckons to those attuned to academia and those shaping real-world organizations, resonating with the pursuit of knowledge in an era of unceasing change.

Abstract

Abstract
Virtual reality (VR) has emerged as a powerful promotional tool in tourism, providing consumers immersive and engaging experiences. However, its specific impact on the wine tourism sector remains underexamined. This study aims to both investigate and convincingly highlight the promotional influence of VR on the intention to visit wine tourism destinations. By providing an immersive VR experience to 405 participants, our research revealed that the quality of VR experiences is essential for generating consumer satisfaction. More crucially, we found that wine tourists' satisfaction with VR experiences plays a crucial role in motivating them to visit a destination. Our results not only fill a gap in understanding the impact of VR on wine tourist behaviour but also offer valuable insights for marketing professionals and companies in the sector. This study emphasises the critical need for enjoyable, high-quality and satisfying VR experiences to catalyse the intention to visit. In doing so, we contribute to academic knowledge and provide practical guidance for the industry, highlighting VR's effectiveness as a promotional strategy in wine tourism. This research is not merely an exploration but a compelling defense of VR's transformative influence on wine tourist behaviour.

Abstract
The rising adoption rates and integration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) into the energy grid introduces complex challenges, including the need to balance supply and demand and smooth peak consumptions. Addressing these challenges requires innovative solutions such as Demand Response (DR), Renewable Energy Communities (RECs), and more specifically for EVs, Vehicle-to-Grid (V2G). However, existing V2G approaches often fall short in real-world applicability, adaptability, and user engagement. To bridge this gap, this paper proposes EnergAIze, a Multi-Agent Reinforcement Learning (MARL) energy management algorithm leveraging the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. EnergAIze enables user-centric multi-objective energy management by allowing each prosumer to select from a range of personal management objectives. Additionally, it architects' data protection and ownership through decentralized deployment, where each prosumer can situate an energy management node directly at their own dwelling. The local node not only manages local EVs and other energy assets but also fosters REC wide optimization. EnergAIze is evaluated through a case study using the CityLearn framework. The results show reduction in peak loads, ramping, carbon emissions, and electricity costs at the REC level while optimizing for individual prosumers objectives.

Abstract
Keratitis is an inflammatory corneal condition responsible for 10% of visual impairment in low- and middleincome countries (LMICs), with bacteria, fungi, or amoeba as the most common infection etiologies. While an accurate and timely diagnosis is crucial for the selected treatment and the patients' sight outcomes, due to the high cost and limited availability of laboratory diagnostics in LMICs, diagnosis is often made by clinical observation alone, despite its lower accuracy. In this study, we investigate and compare different deep learning approaches to diagnose the source of infection: 1) three separate binary models for infection type predictions; 2) a multitask model with a shared backbone and three parallel classification layers (Multitask V1); and, 3) a multitask model with a shared backbone and a multi-head classification layer (Multitask V2). We used a private Brazilian cornea dataset to conduct the empirical evaluation. We achieved the best results with Multitask V2, with an area under the receiver operating characteristic curve (AUROC) confidence intervals of 0.7413-0.7740 (bacteria), 0.83950.8725 (fungi), and 0.9448-0.9616 (amoeba). A statistical analysis of the impact of patient features on models' performance revealed that sex significantly affects amoeba infection prediction, and age seems to affect fungi and bacteria predictions.