Browsing by Issue Date, starting with "2024-11-12"
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- A utilização da Inteligência Artificial para a criação de conhecimento: Impacto na tomada de decisão de marketing e performance do negócioPublication . Santos, Pedro Miguel Catarino dos; Vitorino, Liliana CoutinhoA indústria de retalho de eletrónica, de consumo e eletrodomésticos caracteriza-se por um ambiente de elevada inovação e competitividade. Neste setor, as Pequenas e Médias Empresas (PME's) enfrentam um desafio acrescido devido à disparidade de recursos em relação às grandes empresas. Com a limitação de recursos, as PME's, devem desenvolver estratégias de marketing eficazes para competir no mercado, podendo obter vantagens competitivas ao aproveitar as oportunidades que a tecnologia proporciona. Este estudo avalia o impacto que as ferramentas de inteligência artificial (IA) têm na criação de conhecimento interno e externo, nas PME's retalhistas de eletrónica de consumo e eletrodomésticos. Além disso, explora a relação entre a criação de conhecimento e a tomada de decisão de marketing baseada em dados, bem como o impacto dessas decisões de marketing na performance do negócio destas empresas. Para tal, foi desenvolvido um questionário que foi aplicado às PME’s do setor referido, totalizando uma amostra de 57 gestores de PME's. Os resultados revelaram que as ferramentas de IA são aliadas importantes na recolha e tratamento de dados, favorecendo a criação de conhecimento interno e externo. A relação entre o conhecimento e a tomada de decisão de marketing teve um resultado surpreendente, já que se verificou um impacto positivo apenas no caso do conhecimento interno. Não se constatou uma relação positiva entre a tomada de decisão de marketing e a performance do negócio. As conclusões deste trabalho destacam a importância de criar uma cultura "data-driven" nas PME's, desenvolvendo competências adequadas para implementar estratégias centradas em dados. O desenvolvimento empresarial passa pela conjugação de tecnologia, formação e estratégia, fomentando um ambiente orientado por dados que possa ser a chave para a competitividade e crescimento das PME's.
- INFLUENCE OF PARENTING STYLES AND HORMONAL LEVELS IN THE DEVELOPMENT OF NARCISSISM: A META-ANALYSISPublication . Reis, Ariana Isabel Nogueira dos; Santos, Rui Filipe Vargas de Sousa; Martins, João Paulo OliveiraThis meta-analysis explores the influence of parenting styles and hormone levels on the effect of narcissistic traits. The review focuses on the four parenting styles identified by Baumrind – authoritative, authoritarian, permissive, and neglectful – highlighting how each contributes to either fostering or mitigating narcissistic tendencies. Authoritarian parenting, marked by strict rules and limited emotional warmth, and permissive parenting, characterized by leniency and lack of boundaries, are linked to the development of inflated self-images and entitlement. Meanwhile, neglectful parenting, with its emotional detachment and lack of guidance, often leaves children struggling with controlling their emotions, coping effectively, and facing difficulties in maintaining and nurturing social relationships. Authoritative parenting characterized by developing a close, nurturing relationship with the children drives them confident, responsible, and able to self-regulate. The analysis also explores the role of testosterone and cortisol levels on narcissistic behaviors through traits like dominance, aggression, and stress responses. By integrating both environmental and biological perspectives, this meta-analysis provides a comprehensive understanding of how parenting and hormonal factors together influence the emergence of narcissism.
- CLOUD PLATFORM FOR MULTI - UAV MANAGEMENT WITH AI - DRIVEN MODULESPublication . Marques, Tomás Vieira da Graça; Pereira, António Manuel de Jesus; Ramos, João Pedro Ferreira; Costa, Nuno Alexandre Ribeiro daGiven the increasing severity of wildfires worldwide, there is a pressing need to automate forest fire prevention and surveillance systems, ensuring rapid and efficient responses to potential threats. To prevent and improve response to these global fire incidents, we have developed a solution that integrates visible and infrared spectrum images captured by UAVs, overcoming the limitations of single-sensor imagery in challenging conditions such as low light, fog, or smoke. This dissertation presents a comprehensive solution leveraging Internet-of-Drones (IoD) and advanced machine learning techniques for efficient multi-UAV area mapping and forest surveillance. The IoD concept shifts drone control towards cloud-based multi-UAV solutions, exemplified by this work. We propose IoD CloudStation a cloud multi-UAV management solution that supports high-level command abstraction, modular scalable platform integration, secure, and real-time data exchange. IoD CloudStation incorporates a hybrid communication protocol optimizing connectivity between UAVs, gateway servers, and web clients, facilitating numerous applications including data logging, AR/VR, area mapping, and drone vision projects to work with the groundstation making it AI-driven. To enable computer vision from the UAV perspective, a 4-channel deep-learning computer vision model based on an enhanced version of the YOLOv5 architecture was developed, demonstrating significant improvements in precision and Mean Average Precision (mAP) metrics, especially in low-visibility scenarios. This work also presents an approach using Reinforcement Learning (RL) techniques to enable cooperative multi-UAV area mapping, enhancing mapping efficiency and increasing the overall quality of video feed prompted to the computer vision module. The flexibility of RL allows UAVs to respond on demand to changing mapping parameters, such as the number of UAVs and the size of the area to be mapped. This combination of modules, when integrated into the IoD CloudStation, enables real-time UAV monitoring, forest fire prevention, and surveillance.
- ADVANCED UAV MONITORING: DEEP LEARNING FOR MULTI -TARGET DETECTION, TRACKING, AND WILDFIRE PREDICTIONPublication . Carreira, Samuel Vitorino de Sousa; Pereira, António Manuel de Jesus; Miragaia, Rolando Lúcio Germano; Ramos, João Pedro Ferreira; Ribeiro, José Carlos BregieiroRising global fire incidents necessitate effective solutions, making forest surveillance crucial. Current methods require substantial investment and labor but are often ineffective. This work proposes a comprehensive monitoring solution utilizing Unmanned Aerial Vehicles (UAVs) to integrate visible and infrared images for real-time detection of people, vehicles, and fires, addressing limitations in low-light conditions, fog, or smoke. We propose a new system architecture for real-time UAV footage transmission, processing, and analysis on a cloud server. For the detection of people and vehicles, we propose a new 4-channel object detection model that significantly improves precision metrics compared to traditional state-of-the-art models that utilize only RGB images. Additionally, our model performs better in conditions unfavorable to RGB images, successfully identifying objects in low light and reduced visibility. To train our model, we present a labeled dataset with aligned thermal and visible images from an aerial perspective. In order to enable object tracking in our solution, which refers to detecting and maintaining a unique identifier for each detection, we propose SAME, a new approach to Multiple Object Tracking (MOT) re-identification. The proposed model is designed to extend the capabilities of existing detectors by using the high-dimensional features they extract as inputs to a transformer-based architecture. This method applies attention and transformers to measure the similarity between tracks across multiple frames, significantly improving re-identification performance. SAME employs transformers to enable past context retrieval, standing out for its modularity while achieving competitive results in known datasets such as MOT17 and BDD100K. Finally, we introduce FireSeq, a novel approach leveraging state-of-the-art deep learning techniques such as VQ-VAEs and Transformers to model wildfire progression in real time. To support this research, we developed the FireSeq dataset, which includes both RGB and infrared (IR) aligned imagery capturing the behavior of wildfires from an aerial perspective. Additionally, the FireSeq dataset includes a labeled multi-class subset designed for early wildfire detection. FireSeq demonstrates a high degree of accuracy in predicting future frames of wildfire footage. The three developed components represent innovative research approaches that together form a comprehensive and robust wildfire monitoring solution. This marks a significant advancement in wildfire prevention and proactive management. By enabling continuous real-time monitoring, detection, and tracking, our solution supports critical applications such as risk analysis, crowd management, and searchand- rescue operations. Furthermore, it introduces a novel method for predicting and detecting wildfire progression, aimed at enhancing early detection capabilities and improving mission planning efficiency.