Abstract
Rising adoption of AI-driven solutions in medical imaging is associated with an emerging need to develop strategies to introduce explainability as an important aspect of trustworthiness of AI models. This chapter addresses the most commonly used explainability techniques in medical image analysis, namely methods generating visual, example-based, textual, and concept-based explanations. To obtain visual explanations, we explore backpropagation- and perturbation-based methods. To yield example-based explanations, we focus on prototype-, distance-, and retrieval-based techniques, as well as counterfactual explanations. Finally, to produce textual and concept-based explanations, we delve into image captioning and testing with concept activation vectors, respectively. This chapter aims at providing understanding of the conceptual underpinning, advantages and limitations of each method, as well as to interpret their generated explanations in the context of medical image analysis.

Abstract
This theoretical study presents a D-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) based sensor designed for humidity detection in transformer oil. Humidity refers to the presence of water dissolved or suspended in the oil, which can affect its dielectric properties and, consequently, the efficiency and safety of the transformer's operation, failures in the sealing system and the phenomenon of condensation can be the main sources of this humidity. This sensor leverages the unique properties of the coupling between surface plasmons and fiber guided mode at the Au-PCF interface to enhance the sensitivity to humidity changes in the external environment. The research demonstrated the sensor's efficacy in monitoring humidity levels ranging from 0% to 100% with an average sensitivity of measured at 1106.1 nm/RIU. This high sensitivity indicates a substantial shift in the resonance wavelength corresponding to minor changes in the refractive index caused by varying humidity levels, which is critically important in the context of transformer maintenance and safety. Transformer oil serves as both an insulator and a coolant, and its humidity level is a key parameter influencing the performance and longevity of transformers. Excessive humidity can lead to insulation failure and reduced efficiency and, therefore, the ability to accurately detect and monitor humidity levels in transformer oil can significantly enhance preventive maintenance strategies, reduce downtime, and prevent potential failures, ensuring the reliable operation of electrical power systems. © The Authors.

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
[No abstract available]

Abstract
The paper draws attention to the use of Symbolic Data Analysis (SDA) in the field of Official Statistics. It is composed of three sections presenting three pilot techniques in the field of SDA. The three contributions range from a technique based on the notion of exactly unified summaries for the creation of symbolic objects, a model-based approach for interval data as an innovative parametric strategy in this context, and measures of similarity defined between a class and a collection of classes based on the frequency of the categories which characterize them. The paper shows the effectiveness of the proposed approaches as prototypes of numerous techniques developed within the SDA framework and opens to possible further developments.

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.