Abstract
While place attachment has been a hot research topic in tourism, place meanings generally have received less attention from researchers. By bridging environmental psychology to the context of tourism, this research employs schema theory to explore how the home environment influences place meanings perceived in foreign destinations by tourists belonging to the same cultural group, i.e., Chinese and Macau outbound tourists in Europe. Semi-structured interviews were conducted, and the findings show that there is much overlap in both groups' place meanings regarding Europe as they are culturally Chinese. Nonetheless, the Portuguese symbolic settings of their home environment are profoundly integrated in the Macau interviewees' autobiographical memories and self-identity, which turns them into "vicarious insiders" of Portugal prior to their actual visits, thus rendering Portugal a specifically meaningful destination. This study makes theoretical contributions to the tourism place literature and provides practical implications regarding meaning marketing for destination management organizations.

Abstract
Vicarious experiences in tourism possess significant marketing implications. While numerous studies have explored how various forms of vicarious experiences can impact an individual, the role of different time spans as a key factor determining the extent of said impact has been neglected in prior research. To address this gap, the present study thus bridges environmental psychology with the context of tourism and applies the theory of mental representations. An experiment (n = 359) was designed to examine differences in select mental representation dimensions (cognitive, affective, conative, and sensorial) among male and female Chinese college students who have zero/medium/maximum durations of constant vicarious experiences related to European destinations in their home environment. The results indicate that the medium duration of constant vicarious experiences leads to the most positive changes in cognitive and conative dimensions, while the longest constant vicarious experiences produce desirable affective dimension outcomes. Moreover, male college students seem to be more susceptible to the influences of such constant vicarious experiences.

Abstract

Abstract
As we delve into how technology enhances supply chain management efficiency and tackles specific e-business challenges, we must recognize the critical synergy with recommendation systems. These systems align with digital transformation goals, enhancing customer experiences, enabling data-driven decisions, promoting innovation, and embracing a customer-centric approach. During the 2020 COVID-19 surge, e-commerce experienced increased activity, highlighting the significance of recommendation systems in forecasting new purchases. This chapter introduces a novel approach to understanding customer–product interactions through multilayer bipartite networks, employing a hybrid recommendation system with k-means and weighted slope one algorithms. This approach enhances clarity, explainability, and information gains, aiding tasks like inventory optimization. The study concludes that the model’s predicted results differ from the actual ratings and that the system is effective in improving decision-making processes and customer recommendations. © 2025 selection and editorial matter, Adelaide Martins and Carolina Machado.

Abstract
Medical image analysis is crucial for the efficient diagnosis of many diseases. Typically, hospitals maintain vast repositories of images, which can be leveraged for various purposes, including research. However, access to such image collections is largely restricted to safeguard the privacy of the individuals whose images are being stored, as data protection concerns come into play. Recently, the development of solutions for Automated Medical Image Analysis has gained significant attention, with Deep Learning being one solution that has achieved remarkable results in this area. One promising approach for medical image analysis is Federated Learning (FL), which enables the use of a set of physically distributed data repositories, usually known as nodes, satisfying the restriction that the data do not leave the repository. Under these conditions, FL can build high-quality, accurate deep-learning models using a lot of available data wherever it is. Therefore, FL can help researchers and clinicians diagnose diseases and support medical decisions more efficiently and robustly. This article provides a systematic survey of FL in medical image analysis, specifically based on Magnetic Resonance Imaging, Computed Tomography, X-radiography, and histology images. Hence, it discusses applications, contributions, limitations, and challenges and is, therefore, suitable for those who want to understand how FL can contribute to the medical imaging domain.

Abstract
This study focuses on the task of developing automated models for complex aerobatic aircraft maneuvers. The approach employed here utilizes Behavioral Cloning, a technique in which human pilots supply a series of sample maneuvers. These maneuvers serve as training data for a Machine Learning algorithm, enabling the system to generate control models for each maneuver. The optimal instances for each maneuver were chosen based on a set of objective evaluation criteria. By utilizing these selected sets of examples, resilient models were developed, capable of reproducing the maneuvers performed by the human pilots who supplied the examples. In certain instances, these models even exhibited superior performance compared to the pilots themselves, a phenomenon referred to as the clean-up effect. We also explore the application of transfer learning to adapt the developed controllers to various airplane models, revealing compelling evidence that transfer learning is effective for refining them for targeted aircraft. A comprehensive set of intricate maneuvers was executed through a meta -controller capable of orchestrating the fundamental maneuvers acquired through imitation. This undertaking yielded promising outcomes, demonstrating the proficiency of several Machine Learning models in successfully executing highly intricate aircraft maneuvers.