Abstract
Quality of Experience (QoE) in multi-view streaming systems is known to be severely affected by the latency associated with view-switching procedures. Anticipating the navigation intentions of the viewer on the multi-view scene could provide the means to greatly reduce such latency. The research work presented in this article builds on this premise by proposing a new predictive view-selection mechanism. A VGG16-inspired Convolutional Neural Network (CNN) is used to identify the viewer's focus of attention and determine which views would be most suited to be presented in the brief term, i.e., the near-term viewing intentions. This way, those views can be locally buffered before they are actually needed. To this aim, two datasets were used to evaluate the prediction performance and impact on latency, in particular when compared to the solution implemented in the previous version of our multi-view streaming system. Results obtained with this work translate into a generalized improvement in perceived QoE. A significant reduction in latency during view-switching procedures was effectively achieved. Moreover, results also demonstrated that the prediction of the user's visual interest was achieved with a high level of accuracy. An experimental platform was also established on which future predictive models can be integrated and compared with previously implemented models.

Abstract
Currently, there is increasing implementation of renewable energy communities, where consumers and producers come together to form energy cooperatives. This growing trend has been accompanied by several studies aiming to optimize energy exchanges and sharing inside the community, always taking into account the most favorable tariff regimes for community members. This paper presents an analysis that, based on applying a linear programming model, optimizes energy transactions in a renewable energy community with the integration of storage systems. The results show the developed model's effectiveness, presenting substantial profits for the community.

Abstract
Sponsored advertising on search engines is one of the fastest growing online advertising marketplaces. The space available for paid ads, or positions, is sold using auctions and payment is calculated considering the number of clicks each position receives. Two mechanisms are generally used in position auctions: Generalized Second Price (GSP) (e.g. Google, Yahoo!) and Vickrey-Clarke-Groves (VCG) (e.g. Facebook). To understand which mechanism guarantees the highest payoff to market players (search engines and advertisers), a multi-agent simulation is developed in Netlogo. Using the generated data, a supervised learning-based analysis on search engines and bidders' payoffs is made using linear regression models and regression trees. Results suggest that the average payoff for auctioneers (the search engines) and bidders (the advertisers), the price for each position, and first bidder's payment, are significantly different in the GSP and VCG mechanisms. We also found the mechanism that generates the highest payoff for the search engine is the VCG, while for the bidders it is the GSP.

Abstract
Virtual influencers are rapidly gaining significance in the digital marketing realm. This study offers an overview of the fragmented landscape of virtual influencer research, addressing key contributions, themes, methodologies, and future directions. Employing a bibliometric analysis approach, the research examined 52 articles from the Scopus database, shedding light on the evolving field. Findings underscore a dispersed literature landscape that began in 2020, spanning 44 distinct outlets and involving 138 authors. The prevailing research primarily centers on general social media users' perceptions of virtual influencers, leaving substantial gaps in the study of actual followers. Many pieces of research exhibit a lack of methodological rigor and theoretical framework. As the discipline progresses, there is an emergent need for enhanced methodologies that focus on genuine followers and encompass broader managerial objectives. The prospects for subsequent research in this domain are vast and promising. © 2024, IGI Global. All rights reserved.

Abstract

Abstract
This article compares the effectiveness of the PET/CT scan and bone scintigraphy for the detection of bone metastases in patients with breast cancer. Background Positron emission tomography/computed tomography (PET/CT) has become in recent years a tool for breast cancer (BC) staging. However, its accuracy to detect bone metastases is classically considered inferior to bone scintigraphy (BS). The purpose of this work is to compare the effectiveness of bone metastases detection between PET/CT and BS. Materials and Methods Prospective study of 410 female patients treated in a Comprehensive Cancer Center between 2014 and 2020 that performed PET/CT and BS for staging purposes. The image analysis was performed by 2 senior nuclear medicine physicians. The comparison was performed based on accuracy, sensitivity, and specificity on a patient and anatomical region level and was assessed using McNemar's Test. An average ROC was calculated for the anatomical region analysis. Results PET/CT presented higher values of accuracy and sensitivity (98.0% and 93.83%), surpassing BS (95.61% and 81.48%) in detecting bone disease. There was a significant difference in favor of PET/CT (sensitivity 93.83% vs. 81.48%), however, there is no significant difference in eliminating false positives (specificity 99.09% vs. 99.09%). PET/CT presented the highest accuracy and sensitivity values for most of the bone segments, only surpassed by BS for the cranium. There was a significant difference in favor of PET/CT in the upper limb, spine, thorax (sternum) and lower limb (pelvis and sacrum), and in favor of BS in the cranium. The ROC showed that PET/CT has a higher sensitivity and consistency across the bone segments. Conclusion With the correct imaging protocol, PET/CT does not require BS for patients with BC staging.