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Rule-Based Generative Modeling and Synthetic Image-Driven Deep Learning Classification in Ceramic Industry Applications
No full textThe ceramic industry, with its deep historical roots and complex production methodologies, has undergone significant transformation through the adoption of modern technologies. Moving away from labor-intensive techniques, the industry has embraced advancements such as computer-assisted design and computer-assisted manufacturing, which have markedly improved production efficiency, quality control, and customization capabilities. Nevertheless, the growing demand for personalized and bespoke ceramic products introduces new challenges, particularly in automating design generation and production line workflows, which are increasingly reliant on deep learning classification techniques.
This dissertation introduces an approach for generating new three-dimensional ceramic models through a rule-based system by integrating procedural generation methods. This system ensures structural integrity and manufacturability in the creation of diverse ceramic pieces and supports the generation of unique ceramic collections and the replication of ceramic pieces. Given the handiness of the method a three-dimensional ceramic tableware dataset was created and made available to the use in several fields of research. The system was made available through a web-based application which enhances user interaction and integrates augmented reality offering a more immersive experience allowing users to visualize the generated models overlaid in real environments.
With the advancements in the design phase of ceramics and with the potential faster introduction of new models into production, such as those generated by the rule-based system, acquiring real-world images for production line classification systems has become increasingly challenging due to the inherently labor-intensive nature and time-consuming of the task, hindering efficient dataset creation and deep learning model training, leading to more inefficient production lines. To address this issue, this dissertation presents CeramicFlow - an innovative computer graphics rendering pipeline designed to create synthetic images by employing three-dimensional design models of ceramic pieces. By leveraging domain randomization techniques, CeramicFlow generates synthetic image datasets that are validated for real-world ceramic classification tasks. The findings demonstrate that synthetic images can effectively support dataset development and reduce dependency on real-world data for deep learning applications in ceramic classification. The resulting Synthetic CeramicNet dataset provides a valuable resource for future research. The proposed methods show significant promise to be adapted to other industrial fields, potentially transforming how these industries approach automated design and production.Project “STC 4.0 HP - New Generation of Stoneware Tableware in Ceramic 4.0 by High Pressure Casting Robot work cell”, (Refª: Nº 69654) I&DT Empresarial (Copromoção, Parcerias Internacionais) – in the typology of Research Fellowship.Project “I DECOR - I&D sistema avançado de aplicação de decorações em tableware” within the scope of the scholarship “Personalização Inteligente”, (Refª Candidatura: C679416409-00009762, Refª Submissão: T679994395-00003392) – in the typology of Research Fellowship
Family Experiences of Loss and Bereavement in Palliative Care Units during the COVID-19 Pandemic: An InterpretativePhenomenological Study
No full textThe COVID-19 pandemic significantly interrupted the grieving experiences of bereaved
families and drastically changed their ways of dealing with loss. Our study aims to gain an indepth
understanding of the lived experience of bereaved relatives of patients who died in
palliative care units during the COVID-19 pandemic. The phenomenological research design
included sixteen family members of hospitalized palliative patients who died from November
2021 to June 2022. The study involved conducting qualitative in-depth semi-structured
interviews with family members 12–24 months after the death of their loved ones. The
interviews aimed to gather information about the experiences of the families both before and
after the death. The COREQ guidelines were applied in the study. Participants were mainly
female (n = 13) with a mean age of 47.25 (SD = 12.58). Data were analysed using the
Interpretative Phenomenology Analysis (IPA). The following three categories were identified:
(1) navigating loved ones’ final weeks and days (troubled deaths); (2) the last farewell was
robbed; (3) looking for adjustment after loss. One overall main theme emerged, which was as
follows: “Struggling between stolen moments and painful losses to get back into the flow of
life”. This study provides novel insights into end-of-life care and bereavement from the
perspectives of family. Our findings suggest that developing and promoting family-centered
culture can lead to compassionate palliative care focused on a myriad ways of affirming that
their loved one matters
Definição de Modelo Dinâmico e Análise de Performance de um Veículo Terrestre não Tripulado
No full textEste trabalho focou-se no desenvolvimento de um veículo terrestre não tripulado (UGV – Unmanned Ground Vehicle) capaz de operar em ambientes industriais e agrícolas. O conceito inovador do chassis extensível, tanto em comprimento quanto em largura, foi desenvolvido para melhorar a manobrabilidade e a capacidade de transporte de carga em diferentes cenários operacionais, considerando superfícies e condições diversificadas.
Para alcançar os objetivos, foi realizada a modelação de todos os componentes e sistemas no software SolidWorks, permitindo uma integração eficiente entre a direção, suspensão, travagem e propulsão. Adicionalmente, análises estruturais foram feitas no software ANSYS Student, avaliando as tensões e a deformada da estrutura de suporte da roda e do chassis extensível. Complementarmente, um código em Python foi implementado para simular as trajetórias circulares, investigando a influência da distância entre eixos e da largura entre rodas no desempenho do UGV.
Os resultados obtidos demonstraram eficácia do design proposto, com o veículo operando de forma eficiente em superfícies com maior aderência, como o asfalto seco, mas apresentando limitações em pisos com baixa aderência, como terra molhada. Este estudo não só validou o conceito do UGV como também identificou áreas de melhoria, especialmente na modelação de componentes específicos. O trabalho estabelece uma base sólida para o desenvolvimento futuro, incluindo a integração de sistemas de automação e sensores para otimizar a navegação e performance do veículo
Alginate nanoparticles for p53 encoding pDNA delivery
No full textIn developed countries with high life expectancies, colorectal cancer (CRC) ranks as the third most common cancer and the second deadliest globally. In 2022 alone, over 1.9 million cases were diagnosed, with CRC causing more than 900.000 deaths annually. Projections indicate a potential increase to 2.5 million cases per year by 2035. Adenocarcinoma, primarily originating from mucus-producing cells, constitutes over 90% of CRC cases. The prognosis of CRC significantly hinges on disease stage, treatment regimen, and patient conditions.
The tumour suppressor protein p53 plays a pivotal role in regulating cell growth and preventing tumour formation and is often mutated, or inactivated in CRC cancer cells, making it a prime target for therapeutic intervention. Proper p53 function enables DNA repair or initiates programmed cell death (apoptosis) in cells with irreparable DNA damage, thwarting cancer cell proliferation. Low expression or loss of p53 function in CRC cells is common and can lead to increased genomic instability, impaired DNA repair, and resistance to apoptosis, thereby promoting tumour progression and aggressiveness. Additionally, low p53 expression correlates with chemotherapy resistance and poorer prognosis in CRC patients. Genetic therapy in CRC presents a tailored approach to fight against the disease at a genetic level. One promising strategy involves using plasmid DNA (pDNA) carrying the tp53 gene to restore or enhance p53 function within CRC cells. This targeted genetic intervention holds the potential to inhibit tumour growth, induce cancer cell death, and overcome resistance mechanisms encountered in conventional CRC therapies. However, pDNA's susceptibility to degradation leads to the need to use a delivery system to transport it to target cells. In this context, a growing interest in using natural polymers from renewable sources as biomaterials, such as alginate nanoparticles emerge as promising carriers due to their inert, biocompatible, and easily manipulable properties, with crosslinking achievable through divalent ions without adverse host effects. In this context, the present work focuses on the development of a simple and sustainable process for the extraction and purification of sodium alginate (SA) from brown Sargassum muticum and Saccorhiza polyschides aiming to develop DNA-loaded alginate nanoparticles. These nanoparticles were further characterized and tested in CRC cell lines (CaCo-2) and healthy cells (fibroblasts). Overall, the extraction and purification process parameters play a key role and significantly influence the quality of alginate and the extraction yield. Research efforts are focused on achieving simple and sustainable alginate extraction technologies to improve process efficiency, enhance extraction capacity, reduce cost and promote environmental friendliness. Alginate extraction from brown seaweed S. muticum and S. polyschides showed an optimized yield of SA extraction during 24 hours at 40 °C yielding 20% and 22%, respectively. Characterization wise, H-NMR, FT-IR, viscosity, molecular weight, and Thermal behaviour were studied. Throught this evaluation the efficiency and purity of the extraction were proved.
Regarding the development of alginate nanoparticles, the optimized nanoparticle formulation showed a medium size of 167.9 ± 2.49 nm, a PDI of 0.1608 ± 0.009, and a stable zeta potential around 0 mV. Encapsulation efficiency reached 97%, with negligible changes in size when loaded with pDNA. Release tests indicated faster release at lower pH levels and a lower swelling behaviour for lower pH. At 72 hours CaCo-2 transfected with loaded nanoparticles exhibited a 30% diminish in cell viability, in healthy cells, the alginate-base nanocarrier didn’t show any cytotoxic effect.Em países desenvolvidos com alta expectativa de vida, o cancro colorretal (CCR) é globalmente o terceiro tipo de cancro mais comum e o segundo mais mortal. Apenas em 2022, foram diagnosticados mais de 1,9 milhões de casos, sendo responsável por mais de 900,000 mortes anualmente. Projeções indicam um potencial aumento para 2,5 milhões de casos por ano até 2035. O adenocarcinoma, originado principalmente de células produtoras de muco, constitui mais de 90% dos casos de CCR. O prognóstico de CCR depende aignificativamente do estágio da doença, do regime de tratamento e das condições do paciente. A proteína supressora de tumores p53 desempenha um papel fundamental na regulação do crescimento celular e na prevenção da formação de tumores. A função adequada da p53 facilita a reparação do DNA ou inicia a morte celular programada (apoptose) em células com danos irreparáveis no DNA, impedindo a proliferação de células cancerígenas. A baixa expressão ou perda de função da p53 em células de CCR é comum e pode levar ao aumento da instabilidade genómica, prejudicando a reparação de DNA e levando à resistência à apoptose, promovendo assim a progressão e a agressividade do tumor. Além disso, a baixa expressão de p53 está correlacionada com a resistência à quimioterapia e um pior prognóstico em pacientes com CCR. A terapia genética no CCR apresenta-se como uma abordagem personalizada para combater a doença a nível genético. Uma estratégia promissora envolve o uso de DNA plasmidial (pDNA) portador do gene TP53 para restaurar ou aprimorar a função da p53 dentro das células de CCR. Essa intervenção genética direcionada tem o potencial de inibir o crescimento do tumor, induzir a morte das células cancerígenas e superar os mecanismos de resistência encontrados nas terapias convencionais para o CCR. No entanto, a suscetibilidade do pDNA à degradação exige um sistema de entrega para transportá-lo até as células-alvo. As nanopartículas de alginato surgem como portadores promissores devido às suas propriedades inertes, biocompatíveis e facilmente manipuláveis, com a reticulação alcançável por meio de iões divalentes sem efeitos adversos ao hospedeiro. Neste contexto, o presente trabalho centra-se no desenvolvimento de um processo simples e sustentável para a extração e purificação de alginato de sódio a partir das algas castanhas Sargassum muticum e Saccorhiza polyschides com o objetivo de desenvolver nanopartículas de alginato carregadas com ADN. Estas nanopartículas foram ainda caracterizadas e testadas em linhas celulares de CRC (CaCo-2) e células audáveis (fibroblastos). Em geral, os parâmetros do processo de extração e purificação desempenham um papel fundamental e influenciam significativamente a qualidade do alginato e o rendimento da extração. Os esforços de investigação centram-se na obtenção de tecnologias de extração de alginato simples e sustentáveis para melhorar a eficiência do processo, aumentar o rendimento de extração, reduzir os custos e promover o respeito pelo ambiente. A extração de alginato a partir de algas castanhas S. muticum e S. polyschides mostrou um rendimento de extração optimizado de alginato de sódio durante 24 horas a 40 °C, produzindo 20% e 22%, respetivamente. Foram efetuados estudos de caraterização, H-NMR, FT-IR, viscosidade, peso molecular e perfil térmico. No que diz respeito ao desenvolvimento de nanopartículas de alginato, a formulação optimizada das nanopartículas apresentou um tamanho médio de 167,9 ± 2,49 nm, um PDI de 0,1608 ± 0,009 e um potencial zeta estável em torno de 0 mV. A eficiência de encapsulação atingiu 97%, com alterações negligenciáveis no tamanho quando carregadas com pDNA. Os testes de libertação indicaram uma libertação mais rápida a níveis de pHs mais baixos e um comportamento de retanção de água mais baixo para pHs mais baixo. Após 72 horas, as células CaCo-2 transfectadas com nanopartículas carregadas apresentam uma diminuição de 30% da viabilidade cellular, em células saudáveis, as nanoparticulas à base de alginato não apresentaram qualquer efeito citotóxico
OPTIMIZING IMAGE-BASED TASKS IN MANUFACTURING WITH RGB-D FUSION
No full textThe manufacturing industry is undergoing a significant transformation with the
onset of the Fourth Industrial Revolution. A key aspect of this shift is the integration
of advanced technologies, such as smart sensors and automation, into production
processes. Within this context, 3D cameras have become invaluable, enabling
manufacturers to capture precise surface measurements of the products. In response,
the computer vision community has begun exploring new methods to combine
depth information with color data, enhancing existing solutions for classification and
design generation. By harnessing these advancements, manufacturers can streamline
production lines, reduce waste, and elevate product quality.
This dissertation presents two key innovations for classification and generation tasks:
(1) a novel branched Convolutional Neural Network (CNN), which achieves stateof-
the-art performance in RGB-Depth (RGB-D) image classification, and (2) a novel
branched Generative Adversarial Network (GAN), inspired by the same branched
architecture, that delivers state-of-the-art results on the Stanford Cars dataset. The
core idea of this branched approach is to specialize each branch to handle a specific
modality.
In the experiments, the classification performance improved by approximately 1%,
while achieving nearly three times the speed of the next best method. For image
generation, results varied depending on the dataset. On the Stanford Cars benchmark,
the model showed slight improvements in image quality and better diversity
ADVANCED UAV MONITORING: DEEP LEARNING FOR MULTI -TARGET DETECTION, TRACKING, AND WILDFIRE PREDICTION
No full textRising 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
PROMOÇÃO DA LITERACIA RELACIONADA COM A AVALIAÇÃO E INTERVENÇÃO PREVENTIVA, ANTECIPATÓRIA E CURATIVA EM CASOS DE DELIRIUM NUMA UNIDADE DE CUIDADOS INTENSIVOS: Relatório Final de Estágio
No full textDesenvolvimento de um equipamento para produção por extrusão de fitas em compósito de matriz termoplástica
No full textOs Compósitos de matriz termoplástica são um produto emergente na indústria e vêm criar novos desafios ao nível dos processos de fabrico quando comparados com os de matriz termoendurecível. A suas propriedades mecânicas, tempos de ciclo, características das matrizes termoplásticas, nomeadamente o comportamento à fadiga, a sua tenacidade, a tolerância ao dano, a reciclabilidade e consequentemente a durabilidade, conduzem, cada vez mais, a uma maior utilização deste tipo de materiais.
Por outro lado, a elevada viscosidade dos polímeros termoplásticos quando comparada com a das resinas termoendurecíveis, dificulta a sua impregnação nos reforços fibrosos e consequentemente os processos de produção, sendo o espalhamento prévio do feixe de fibras para posterior impregnação pela matriz termoplástica, a opção seguida neste projeto de modo a minimizar as questões associadas ao este processo.
Neste sentido, realizou-se neste trabalho o projeto e o dimensionamento de uma linha de produção de fitas pré-impregnadas em fibra de carbono com matriz termoplástica de poliamida pelo processo de impregnação por extrusão. Em seguida, procedeu-se ao estudo, fabrico e à otimização de um sistema de espalhamento mecânico de fibras.
No estudo do espalhamento das fibras foram comparados os resultados obtidos com um modelo analítico para validação do sistema projetado e foi aplicada a metodologia do Desenho de Experiências (DOE), realizando testes com as variáveis do processo mais significativas, nomeadamente a velocidade de puxo, a utilização de ar comprimido, a variação da distância dos rolos de espalhamento e a geometria dos rolos. Pelos resultados obtidos, verificou-se que o sistema funciona devidamente e com a aplicação do DOE foi possível alcançar uma solução ótima dos parâmetros do processo para obter o melhor espalhamento do feixe de fibras em estudo.
Foram também analisadas as fibras após o seu espalhamento por microscopia eletrónica de varrimento (SEM) para avaliar os efeitos do sistema sobre as mesmas e conclui-se que as fibras não sofrem qualquer dano com a passagem no sistema mecânico de espalhamento
PRESENTEEISM, JOB SATISFACTION, AND PSYCHOLOGICAL DISTRESSAMONG PORTUGUESE WORKERS IN A PRIVATE SOCIAL SOLIDARITY INSTITUTION DURING THE COVID-19 PANDEMIC: A CROSS-SECTIONAL STUDY
No full textUnder normal circumstances, the working population exhibits high levels of
psychological distress and presenteeism, a scenario which was exacerbated by the
COVID-19 pandemic. Moreover, few studies have analyzed presenteeism during the
COVID-19 pandemic, prompting the current research. We aimed: a) to evaluate the
levels of presenteeism, job satisfaction, and psychological distress in a sample of
Portuguese workers in a Private Social Solidarity Institution (the acronym in Portugal
is IPSS); b) to analyze the relationship between the variables under analysis; and c) to
determine the predictors of presenteeism.
In 2022, an observational, cross-sectional survey of workers from an IPSS in the
central region of Portugal was conducted. The study included a total of 71 employees
who granted written permission. The survey collected general and professional
information, as well as the Stanford Presenteeism Scale (SPS-6), the Job Satisfaction
Questionnaire (S20/23), and the Kessler Psychological Distress Scale (K10).
Participants were aged 41.55 ± 8.12 years old (ranging between 21 and 65) and had
seniority in the institution of 9.79 ± 8.9 years (ranging between 1 and 28). Most
participants were female (95.8%); had completed a higher education degree (29.6%);
lived with a partner (63.4%); and had children (62%). About 55% characterized their
sleep as restorative, with an average of about 7 hours of sleep a day (ranging between
5 and 9). Participants reported a moderate level of global health (3.27 ± 0.82) related
to the previous month. Presenteeism was reported by 32 (45.1%) workers and
sickness absence by 38 (54.3%). Most of the individual S20/23 evaluations indicated
a greater level of satisfaction (mean ≥ 4.5 pts.), except for the question related to
salary, which received a higher level of dissatisfaction (mean = 3.36 ± 1.9 pts.). Around
50.7% of participants had a high or very high risk of suffering or of suffering a mental
disorder (K10 ≥ 22). The correlation matrix indicated a significant moderate positive
correlation between presenteeism and job satisfaction and a significant moderate
negative correlation between presenteeism and psychological distress (p < 0.01). We
found five predictors for presenteeism: marital status, quality of sleep, sickness
absenteeism, health perception, and psychological distress (R2
= 0.358).
We anticipate that our results will spark more studies about the practical consequences
of presenteeism for fostering better health and well-being at work. The implementation of mental health and well-being programs, including encouraging practices such as
mindfulness, can create significant changes, with gains in health and for the
organizations, through the positive impact on the person and the organization. its
productivity. Currently, it is essential to promote coordination between the PsychiatricMental Health Nurse and the managers of organizations, by looking after the interests
of workers and improving mental health, the bond, performance, human capital, and
productivit
CIBERSEGURANÇA EM SOLUÇÕES DE ROBÓTICA DISTRIBUÍDA
No full textEmergent technologies have brought the possibility to solve complex problems and
improve available solutions in a way to increase their performance and provide brand
new advanced solutions. The industry sector is one that can greatly benefit from this
continuous evolution, by which many companies are rushing to migrate to modern
solutions. This leads us to address the latest industrial revolution witnessed, the
so-called Industry 4.0. Being very advanced, however complex, many concerns arise
regarding security and safety issues that cannot be ignored. Cybersecurity threats
are also evolving in a way that it becomes more difficult to detect and entirely
mitigate the risk. It is then crucial to have security considerations while developing
modern industrial solutions so that companies and institutions are prepared to face
incoming challenges without unexpected disruptions. Since much research for modern
technologies has been made in the past years, in this document it is proposed several
guidelines for the implementation of distributed robotic environments with security
in mind. From a robotic use case scenario, it is followed the functions Identify,
Protect, and Detect from NIST’s Cybersecurity Framework, while performing a
detailed analysis over each category. This results in a baseline document that helps
future researchers and companies interested in developing robotic solutions with
security in mind.Project STC 4.0 HP - New Generation of Stoneware
Tableware in Ceramic 4.0 by High Pressure Casting Robot work cell (ref: POCI-01-
0247-FEDER-069654) for providing funding in the form of a scholarshi