Abstract
Technological developments are presenting new ways for companies to organize their businesses and offer new products, services, and experiences to their customers. The Metaverse allows the participation and interaction of individuals in immersive experiences that merge virtual and real worlds. The adoption of metaverse platforms by companies worldwide is growing steadily, with the potential to change business in various industries, including tourism. However, the literature on the Metaverse applied to tourism is very scarce. This chapter addresses this gap by exploring a case study of the implementation of a Metaverse strategy by a Portuguese wine brand, Sandeman, as part of their wine tourism experience offerings. The case study is built on secondary data, observation, and interviews with tourists. © 2025, IGI Global Scientific Publishing. All rights reserved.

Abstract
This study investigates the use of 9-ending pricing strategies in e-commerce by analyzing over 50,000 shoe prices. Using web scraping and a logit model from a German online retailer, the research assesses how product attributes influence the adoption of 9-ending prices. Key findings reveal that 9-ending prices are predominantly used for female and newly introduced products, as well as for items with lower and standard prices. The study also explores the effects of exclusivity and sustainability on pricing strategies, showing that their impact varies with different 9-ending price categories. Overall, this research demonstrates the complex nature of 9-ending pricing strategies, with the 9-zero removal model supporting all hypotheses, whereas the 99c and 95c models show differential effects. This extends our understanding of pricing tactics in online retail and highlights the significance of product attributes for marketing and sales strategies.

Abstract
The main aims of this chapter were to explore metaverse branding by identifying the main trends and contributions in extant literature. Through a bibliometry and the critical analysis of the main contributions in the literature, the chapter proposes a metaverse branding conceptualization, which shows how immersive metaverse experiences that provide multi- dimensional value enhance brand engagement, which leads to increased brand awareness, brand love, satisfaction, trust, and brand equity. These factors ultimately drive online and offline purchases and strengthen brand loyalty. Overall, this chapter and the proposed framework provide relevant insights for both managers defining metaverse branding strategies, and researchers interested in these topics. © 2025, IGI Global Scientific Publishing. All rights reserved.

Abstract
Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.

Abstract
The power transformer is one of the key components of any electrical grid, and, as such, modern day industrialization activities require constant usage of the asset. This increases the possibility of failures and can potentially diminish the lifespan of a power transformer. Dissolved gas analysis (DGA) is a technique developed to quantify the existence of hydrocarbon gases in the content of the power transformer oil, which in turn can indicate the presence of faults. Since this process requires different chemical analysis for each type of gas, the overall cost of the operation increases with number of gases. Thus said, a machine learning methodology was defined to meet two simultaneous objectives, identify gas subsets, and predict the remaining gases, thus restoring them. Two subsets of equal or smaller size to those used by traditional methods (Duval's triangle, Roger's ratio, IEC table) were identified, while showing potentially superior performance. The models restored the discarded gases, and the restored set was compared with the original set in a variety of validation tasks.

Abstract
As many automated algorithms find their way into the IT systems of the banking sector, having a way to validate and interpret the results from these algorithms can lead to a substantial reduction in the risks associated with automation. Usually, validating these pricing mechanisms requires human resources to manually analyze and validate large quantities of data. There is a lack of effective methods that analyze the time series and understand if what is currently happening is plausible based on previous data, without information about the variables used to calculate the price of the asset. This paper describes an implementation of a process that allows us to validate many data points automatically. We explore the K-Nearest Neighbors algorithm to find coincident patterns in financial time series, allowing us to detect anomalies, outliers, and data points that do not follow normal behavior. This system allows quicker detection of defective calculations that would otherwise result in the incorrect pricing of financial assets. Furthermore, our method does not require knowledge about the variables used to calculate the time series being analyzed. Our proposal uses pattern matching and can validate more than 58% of instances, substantially improving human risk analysts' efficiency. The proposal is completely transparent, allowing analysts to understand how the algorithm made its decision, increasing the trustworthiness of the method.