Abstract
Abstract Inductively coupled plasma-mass spectrometry analysis was conducted to examine the geochemical composition of K-feldspars from various aplite-pegmatites in the Barroso-Alvão field, focusing on the differences between Li-rich and Li-barren aplite-pegmatites. The study revealed significant variations in the concentrations of minor and trace elements (Rb, Tl, Li, Ga, Pb, Cs, Ba, Be, Ta, and Sn) present in the K-feldspars of Li-barren, spodumene-rich, and petalite-rich aplite-pegmatites. The data also indicate a geographical trend in both mineralogy and geochemistry across the aplite-pegmatites of the Barroso-Alvão field. Li-barren aplite-pegmatites are more concentrated in the southeast, spodumene-rich dominate the center, and petalite-rich varieties are more common in the northwest. Additionally, portable X-ray fluorescence analysis was performed on the crystals of the same samples to evaluate the feasibility of in situ geochemical analysis of K-feldspars, aiming to determine whether an aplite-pegmatite can be quickly identified as Li-rich. This approach seeks to provide a rapid field assessment of whether an aplite-pegmatite justifies further exploration for Li mining. Notably, the trace amounts of Li, Sn, P, and Ta found in K-feldspars are likely due to mineral inclusions of spodumene, cassiterite, apatite, and columbite–tantalite minerals, as observed petrographically in one of these Li-rich aplite-pegmatites.

Abstract
Joint Communication and Sensing (JCAS) systems are emerging as a core technology for next-generation wireless systems due to the potential to achieve higher spectral efficiency, energy savings, and new services beyond communications. This paper provides a review of the state-of-the-art in JCAS systems by focusing on obtrusive passive sensing capabilities and inherent security and privacy challenges that arise from the integration of communication and sensing. From this point of view, we discuss existing techniques for mitigating security and privacy issues, as well as important aspects for the designing of secure and privacy-aware JCAS systems. Additionally, we discuss future research directions by emphasizing on new enabling technologies and their integration on JCAS systems along with their role in privacy and security aspects. We also discuss the required modifications to existing systems and the design of new systems with privacy and security awareness, where the challenging trade-offs between security, privacy and performance of the JCAS system must be considered.

Abstract
Epileptic seizure classification based on seizure semiology requires automated, quantitative approaches to support the diagnosis of epilepsy, which affects 1 % of the world's population. Current approaches address the problem on a seizure level, neglecting the detailed evaluation of the classification of the underlying action features, also known as Movements of Interest (MOIs), which are critical for epileptologists in determining their classifications. Moreover, it hinders objective comparison of these approaches and attribution of performance differences due to datasets, intra-dataset MOI distribution, or architecture variations. Objective evaluation of action recognition techniques is crucial, with MOIs serving as foundational elements of semiology for clinical in-bed applications to facilitate epileptic seizure classification. However, until now, there were no MOI datasets available nor benchmarks comparing different action recognition approaches for this clinical problem. Therefore, as a pilot, we introduced a novel, simulated seizure semiology dataset carried out by 8 experienced epileptologists in an EMU bed, consisting of 7 MOI classes. We compare several computer vision methods for MOI classification, two image-based (I3D and Uniformerv2), and two skeleton-based (ST-GCN++ and PoseC3D) action recognition approaches. This study emphasizes the advantages of a 2-stage skeleton-based action recognition approach in a transfer learning setting (4 classes) and the multi-scale challenge of MOI classification (7 classes), advocating for the integration of skeleton-based methods with hand gesture recognition technologies in the future. The study's controlled MOI simulation dataset provides us with the opportunity to advance the development of automated epileptic seizure classification systems, paving the way for enhancing their performance and having the potential to contribute to improved patient care.

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
[No abstract available]

Abstract
Gastric cancer, ranking as the sixth most prevalent cancer globally and a leading cause of cancer-related mortality, follows a sequential progression known as Correa's cascade, spanning from chronic gastritis to eventual malignancy. Although endoscopy exams using NarrowBand Imaging are recommended by internationally accepted guidelines for diagnostic Gastric Intestinal Metaplasia, the lack of endoscopists with the skill to assess the NBI image patterns and the disagreement between endoscopists when assessing the same image, have made the use of biopsies the gold standard still used today. This proposal doctoral thesis seeks to address the challenge of developing a Computer-Aided Diagnosis solution for GIM detection in NBI endoscopy exams, aligning with the established guidelines, the Management of Epithelial Precancerous Conditions and Lesions in the Stomach. Our approach will involve a dataset creation that follows the standardized approach for histopathological classification of gastrointestinal biopsies, the Sydney System recommended by MAPS II guidelines, and annotation by gastroenterology experts. Deep learning models, including Convolutional Neural Networks, will be trained and evaluated, aiming to establish an internationally accepted AI-driven alternative to biopsies for GIM detection, promising expedited diagnosis, and cost reduction.