Abstract
Quasi-identifiers (QIDs) are attributes in a dataset that are not directly unique identifiers of the users/entities themselves but can be used, often in conjunction with other datasets or information, to identify individuals and thus present a privacy risk in data sharing and analysis. Identifying QIDs is important in developing proper strategies for anonymization and data sanitization. This paper proposes QIDLEARNINGLIB, a Python library that offers a set of metrics and tools to measure the qualities of QIDs and identify them in data sets. It incorporates metrics from different domains-causality, privacy, data utility, and performance-to offer a holistic assessment of the properties of attributes in a given tabular dataset. Furthermore, QIDLEARNINGLIB offers visual analysis tools to present how these metrics shift over a dataset and implements an extensible framework that employs multiple optimization algorithms such as an evolutionary algorithm, simulated annealing, and greedy search using these metrics to identify a meaningful set of QIDs.

Abstract
Introduction: Neurodegenerative disorders, such as Alzheimer's disease (AD) and frontotemporal dementia (bvFTD), reflect a spectrum of cognitive impairments unified by cognitive decline. Traditional diagnostic approaches often overlook shared landscapes of these disorders. A transdiagnostic approach, cutting across conventional boundaries, may improve understanding of shared mechanisms. This study uses lesion-symptom mapping (LSM) to identify critical brain structures responsible for cognitive impairments. Methods: Patients diagnosed with Mild Cognitive Impairment (MCI), probable AD, and probable bvFTD were recruited from our memory clinic. Diagnoses were made by a multidisciplinary team using established criteria. Participants underwent detailed medical and neurological examinations, neuroimaging, cerebrospinal fluid analysis, and neuropsychological assessment. MRI scans were processed using FreeSurfer. LSM was used to assess correlations between brain structures and cognitive performance. Results: Significant correlations were found between neuropsychological test scores and reduced volume in specific brain regions. The Free and Cued Selective Reminding Test was linked to the right hippocampus and left nucleus accumbens. The Brief Visuospatial Memory Test-Revised correlated with the right hippocampus, left nucleus accumbens, and right middle temporal gyrus. Verbal fluency was linked to the left superior temporal sulcus and left middle temporal gyrus. Digit Span forward correlated with left superior frontal gyrus and left inferior parietal region, while Digit Span backward was linked to the right precuneus. Digit-Symbol Coding was associated with the left inferior parietal region. Conclusions: This study highlights common neural targets in MCI, AD, and bvFTD and their link with cognitive impairment, emphasizing the value of LSM within a transdiagnostic approach to neurodegenerative diseases.

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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
Anomaly Detection has been a significant field in Machine Learning since it began gaining traction. In the context of Computer Vision, the increased interest is notorious as it enables the development of video processing models for different tasks without the need for a cumbersome effort with the annotation of possible events, that may be under represented. From the predominant strategies, weakly and semi-supervised, the former has demonstrated potential to achieve a higher score in its analysis, adding to its flexibility. This work shows that using temporal ranking constraints for Multiple Instance Learning can increase the performance of these models, allowing the focus on the most informative instances. Moreover, the results suggest that altering the ranking process to include information about adjacent instances generates best-performing models.