Abstract
Colorectal cancer is a leading health concern worldwide, with late detection being a primary challenge due to its often-asymptomatic nature. Routine examinations like colonoscopies play a pivotal role in early detection. This study harnesses the potential of Deep Learning, specifically convolutional neural networks, in enhancing the accuracy of polyp detection from medical images. Three distinct models, YOLOv5, YOLOv7, and YOLOv8, were trained on the PICCOLO dataset, a comprehensive collection of polyp images. The comparative analysis revealed YOLOv5’s submodel S as the most efficient, achieving an accuracy of 92.2%, a sensitivity of 69%, an F1 score of 74% and a mAP of 76.8%, emphasizing the effectiveness of these networks in polyp detection. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
Over the last few years, Man-Machine collaborative systems have been increasingly present in daily routines. In these systems, one operator usually controls the machine through explicit commands and assesses the information through a graphical user interface. Direct & implicit interaction between the machine and the user does not exist. This work presents a man-machine symbiotic concept & system where such implicit interaction is possible targeting search and rescue scenarios. Based on measuring physiological variables (e.g. body movement or electrocardiogram) through wearable devices, this system is capable of computing the psycho-physiological state of the human and autonomously identify abnormal situations (e.g. fall or stress). This information is injected into the control loop of the machine that can alter its behavior according to it, enabling an implicit man-machine communication mechanism. A proof of concept of this system was tested at the ARTEX (ARmy Technological EXperimentation) exercise organized by the Portuguese Army involving a military agent and a drone. During this event the soldier was equipped with a kit of wearables that could monitor several physiological variables and automatically detect a fall during a mission. This information was continuously sent to the drone that successfully identified this abnormal situation triggering the take-off and a situation awareness fly-by flight pattern, delivering a first-aid kit to the soldier in case he did not recover after a pre-determined time period. The results were very positive, proving the possibility and feasibility of a symbiotic system between humans and machines.

Abstract

Abstract
Although implementing technologies is a continuous practice observed in organisations, many need help to achieve successful implementations and recognise its impact on their operations and outcomes. Therefore, this review paper aims to present the critical success factors that organisations consider when implementing technology in the Talent Management field. A comprehensive understanding of the technological implementation phenomenon requires adopting a strategic perspective. Consequently, this literature review centres on three clusters: challenges organisations are addressing (Challenges), the technological capabilities and the implementation/adoption process (Technology) and the expected impact (Impact). Findings indicate that a central area of research is the integration of technology in recruitment and, particularly, in the context of Small and Medium Enterprises. Digital Transformation, the Industrial Revolution, and a more diverse workforce are challenges that organisations face. Organisations aim to streamline Human Resources Management (HRM) practices, prioritising data-driven decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Este estudo tem como objetivo discutir o potencial das abordagens de Educação OnLIFE e de Ambientes Virtuais Imersivos na transformação do ensino-aprendizagem em engenharia, focando especialmente em superar os principais desafios contemporâneos da área, tais como a falta de experiência prática, a obsolescência das infraestruturas de laboratório e a inadequação dos métodos de ensino convencionais. Com base na fundamentação teórica, o estudo explorou como a Educação OnLIFE e a Aprendizagem Imersiva podem contribuir nos processos de ensino-aprendizagem da engenharia, proporcionando uma experiência educacional mais adaptativa e experiencial. A análise empírica do estudo apresentou exemplos específicos onde estas metodologias podem ser aplicadas para criar ambientes de aprendizado que simulam experiências industriais reais e projetos de engenharia, destacando o potencial desta abordagem para melhoria da interatividade e na personalização da aprendizagem. As conclusões reiteram que, ao enfrentar os problemas identificados, a adoção de Educação OnLIFE combinada com estratégias de aprendizagem imersiva tem potencial para enriquecer o currículo de ensino de engenharia, para desenvolver tanto a competência técnica quanto atitudinal dos graduandos para os desafios contemporâneos do mercado de trabalho.

Abstract
Histopathological analysis is an essential exam for detecting various types of cancer. The process is traditionally time-consuming and laborious. Taking advantage of deep learning models, assisting the pathologist in the diagnosis process is possible. In this work, a study was carried out based on the DenseNet neural network. It consisted of changing its architecture through combinations of Transformer and MBConv blocks to investigate its impact on classifying histopathological images of penile cancer. Due to the limited number of samples in this dataset, pre-training is performed on another larger lung and colon cancer histopathological image dataset. Various combinations of these architectural components were systematically evaluated to compare their performance. The results indicate significant improvements in feature representation, demonstrating the effectiveness of these combined elements resulting in an F1-Score of up to 95.78%. Its diagnostic performance confirms the importance of deep learning techniques in men's health.