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This chapter examines how GenAI and predictive modelling strategies affect hyperpersonalised marketing. Through a comprehensive literature review and case studies, it examines hyper-p ersonalisation's theoretical frameworks, technical infrastructures, and ethical and governance issues. Large language models, generative adversarial networks, and diffusion models combined with advanced predictive analytics allow firms to scale real- time, highly individualised customer experiences. Effective implementation requires sophisticated data architectures, algorithmic transparency, and strong privacy protections. Integration complexity and ethical accountability are major barriers to consumer engagement and conversion, according to the research. Based on these findings, the chapter proposes an integrated framework that combines technological innovation with ethics and customer focus. This research advances marketing theory and provides practical advice for companies using AI- driven hyper-personalisation while maintaining consumer trust and regulatory compliance. © 2026, IGI Global Scientific Publishing. All rights reserved.

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The Informatics Engineering degree at ISEP, aligned with international standards, was the first undergraduate degree in Portugal to be certified with EUR-ACE®. The programme emphasizes project-based learning, in which students, working in teams, develop interdisciplinary projects applying knowledge from all courses in each semester. A specific laboratory-project course coordinates an integrative project that aims to address complex problems. In the 2nd semester, two computer engineering courses (object-oriented programming and software engineering), and two mathematics courses (discrete mathematics and statistics) are involved, besides the laboratory/project course. This paper focuses on the integration of mathematics with informatics courses in this project, addressing real-world-like problems, bridging software engineering with mathematical topics. To assess the adopted PBL, enquiries were carried out among students. This approach fosters active learning and reinforces the relevance of mathematics within engineering, preparing students for job market demands. © 2026, IGI Global Scientific Publishing. All rights reserved.

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Abstract
Background: Aberrant or loss of cell adhesion drives invasion and metastasis, key hallmarks of cancer progression. In this work, we hypothesized that a gene signature related to cell adhesion could predict breast cancer prognosis. Methods: Highly variant genes were tested for association with overall survival using Cox regression. Adhesion-related genes were identified through gene ontology analysis and multivariate Cox regression, with AIC selection, defined the prognostic signature. The AdhesionScore was then calculated as a weighted sum of gene expression, with risk stratification assessed by Kaplan-Meier and log-rank tests. Results: We found that the AdhesionScore was a significant independent predictor of poor survival in three large independent datasets, as it provided a robust stratification of patient prognosis in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (HR: 2.65; 95% CI: 2.33-3.0, p = 2.34 x 10-51), The Cancer Genome Atlas (TCGA) (HR: 3.46; 95% CI: 2.35-5.09, p = 3.50 x 10-10), and the GSE96058 (HR: 2.83; 95% CI: 2.20-3.65, p = 6.29 x 10-16) datasets. The 5-year risk of death in the high-risk group was 32.41% for METABRIC, 27.8% for TCGA, and 17.54% for GSE96058 datasets. Consistently, HER2-enriched and triple-negative breast carcinomas (TNBC) cases showed higher AdhesionScores than luminal subtypes, indicating an association with aggressive tumor biology. Conclusions: We have developed, for the first time, a molecular signature based on cell adhesion, as well as an associated AdhesionScore that can predict patient prognosis in invasive breast cancer, with potential clinical application. We developed a novel adhesion-based molecular signature, the AdhesionScore, that robustly predicts prognosis in breast cancer across independent cohorts, highlighting its potential clinical utility for patient risk stratification.

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BackgroundTobacco smoke is the main cause of preventable mortality worldwide. Smoking increases the risk of developing many diseases and has been proposed as an aging accelerator. Yet, the molecular mechanisms driving smoking-related health decline and aging acceleration in most tissues remain unexplored.MethodsHere, we use data from the Genotype-Tissue Expression Project (GTEx) to perform a characterization of the effect of cigarette smoking across human tissues. We perform a multi-tissue analysis across 46 human tissues. Our multi-omics characterization includes analysis of gene expression, alternative splicing, DNA methylation, and histological alterations. We further analyze ex-smoker samples to assess the reversibility of these molecular alterations upon smoking cessation.ResultsWe show that smoking impacts tissue architecture and triggers systemic inflammation. We find that in many tissues, the effects of smoking significantly overlap those of aging. Specifically, both age and smoking upregulate inflammatory genes and drive hypomethylation at enhancers (odds ratio (OR) = 2). In addition, we observe widespread smoking-driven hypermethylation at target regions of the Polycomb repressive complex (OR = 2), which is a well-known aging effect. Smoking-induced epigenetic changes overlap causal aging CpGs, suggesting that these methylation changes may directly mediate the aging acceleration observed in smokers. Finally, we find that smoking effects that are shared with aging are more persistent over time.ConclusionOverall, our multi-tissue and multi-omic analysis of the effects of cigarette smoking provides an extensive characterization of the impact of tobacco smoke across tissues and unravels the molecular mechanisms driving smoking-induced tissue homeostasis decline and aging acceleration.