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This article presents a novel approach to classifying archaeological artefacts using machine learning, specifically deep learning, rather than relying on traditional, time-consuming human-based methods. By employing Convolutional Neural Networks (CNNs), this approach aims to expedite and enhance the identification process, making it more accessible to a wider audience. The study focuses on two types of artefacts- Roman Lusitanian amphorae (2nd-5th centuries) and Portuguese faience (16th-18th centuries)- chosen for their diversity. While Lusitanian amphorae lack decoration, Portuguese faience poses challenges with subtle colour variations. The study demonstrates the potential of this approach to overcome these hurdles. The paper outlines the methodology, dataset creation, and model training, emphasizing the importance of extensive data and computational resources. The ultimate objective of this research is to develop a mobile application that utilizes image classification techniques to accurately classify ceramic sherds and bring about a significant transformation in archaeological classification.

The main purpose of this work is to develop a tool to aid the design of a next generation multifunctional material system commonly referred to as a structural battery or a composite battery. The main goal is to develop analysis models to study and predict their material, structural and system behavior involving coupled fields phenomena occurring at several length scales. A numerical finite element framework to study the multi-physics evolution at a component/macro scale is proposed and focused on the fracture and damage characterization at the engineering fracture mechanics length scale. A modeling technique of loosely coupling temperature, chemical, or other relevant fields with the displacement, (elastic) strain and stress fields is proposed. On the other hand, a strong coupling between the elasticity fields and macroscopic crack evolution is considered. To capture relevant cracking phenomena such as crack initiation, propagation, and deflection, a phase field model for brittle fracture was implemented.The numerical analyses presented were validated by studying state-of-the-art benchmark problems usually encountered in other works concerned with phase field models for brittle fracture. A good agreement with benchmark results taken from the literature is obtained. A comparison between different modeling techniques is presented focusing on their effect on maximum strength, structural stiffness, crack propagation and load drop during collapse. It was concluded that the model is able to capture crack phenomena such as initiation, propagation, and crack irreversibility and stiffness recovery due to tension-compression load reversal.A phase-field fracture model loosely coupled with simple diffusion problems obeying Laplace’s equation was also considered. A numerical analysis for a coupled fracture and transient heat conduction single-edge notched tension specimen with prescribed temperature and insulation showed that physically accurate results can be obtained by considering loose coupling of fracture problems with thermal fields. Moreover, it was also highlighted that the presence of temperature gradients significantly influences the structural response of a material.An experimental mechanical characterization of a new composite battery is also addressed in this thesis. A three point bending test was performed until the point of structural collapse. The material system tested has been recently patented by the MatER - Materials for Energy Research-Group - and has resulted from a joint effort by the Physics and Mechanical departments at FEUP, and researchers at INEGI. The design is based on a all-solid-state glass electrolyte-based sodium battery with auto and thermal charge capability as the electrochemical harvesting material, and a carbon fibre thin-ply quasi-isotropic laminate as the structural element. The multi-functional (structural and energy storage) beam battery showed very promis-ing results for system-level mass reduction. Furthermore, by introducing multifunctionality, the failure mode obtained for the beam changed from a progressive load and stiffness degradation to a pseudo-ductile structural behavior improving the post-peak bending response of traditional tubular beams.

O presente trabalho tem como objectivo principal desenvolver o conhecimento sobre os contentores anfóricos exumados nas intervenções arqueológicas no sítio das Almoínhas, a cargo do MML (1995-2002) e da empresa ERA-Arqueologia (2005/2006), o qual está sito no concelho de Loures, com uma ocupação comprovada entre o inicio do século II e o século VI d.C..Trata-se de um sítio com uma arquitectura complexa, com diferentes espaços cujo carácter alterna entre funções habitacionais e produtivas, contando com três fornos de produção cerâmica. Importa esclarecer o papel que este sítio teve na sua zona de implantação, actualmente parcamente conhecida, com excepção da villade Frielas, situação que se procura clarificar através da sequenciação cronológica da evolução dos fenómenos comerciais de importação obtidos através das ânforas.Analisar-se-á assim a estratigrafia em paralelo com os materiais, de forma a estabelecer uma crono-estratigrafia que possa ser utilizada para compreender cronologicamente a evolução do espaço, bem como a sua relação com a sua região de implantação, adoptando-se uma perspectiva económica.

Esta dissertação, elaborada no âmbito do Mestrado em Ciências Juridico-Forenses. começa por abordar o regime das operações urbanisticas, em especial da utilização de edifícios e o procedimento que 4 controla, a autorização.Analisa as leis e decretos-lei que regulam a matéria das residências universitárias no nosso ordenamento, e a sua aplicabilidade em concreto à figura das residências universitárias privadas.Usando como objeto de estudo duas residências privadas. situadas no Porto c em Coimbra, explora ainda a possibilidade da criação de um enquadramento legal especial para as residências universitárias privadas, com o intuito de uniformizar O seu regime.

Octopus vulgaris, rearing/production presents several problems, mainly during the first stages, from the incubation of the eggs to the rearing of paralarvae. In the last years, several research groups have been doing an effort to produce O. vulgaris in aquaculture (large scale), focused on larval rearing phase, in order to solve these bottlenecks.In this sense, this study aims to analyse the effect of different temperatures on common octopus embryonic development from one single female brood; as well as, larval viability and biomarkers of growth, physiological stress, antioxidant defences and neuronal activity (RNA/DNA, HSP70, GST and AChE) respectively, in the first stages of Octopus vulgaris life.O. vulgaris eggs from the same female brood were incubated from stage XV to hatchlings at two different temperatures (19ºC and 22ºC), and then they were maintained at room temperature (22ºC ± 1) for 14 days. Survival, specific growth rate, biomass and dry weight were measured over time at the two temperatures. Samples from 0 and 14 days were taken in order to analyse biomarkers individually. Only HSP70 was analysed using a pool of 7-8 paralarvae.The results showed differences between temperatures in terms of survival and specific growth rate. RNA/DNA ratios and GST (0 and 14 days), and HSP70 results also showed differences between temperatures and ages (0, 7 and 14 days). No significant differences were found in AChE activity between groups (age and temperatures). The results point out at the RNA/DNA ratio, GST and HSP70 as sensitive biomarkers for growth, thermal stress and antioxidant defences in paralarvae. However, growth and temperature did not alter the neurotransmission system of the paralarvae.

Deep learning approaches have been successfully applied to perform automatic classification of phase-resolved partial discharge (PRPD) diagrams. Under the supervised learning paradigm, however, the performance of classifiers strongly depends on the availability of large and previously labeled data sets. Labeling is an intensive and time-consuming labor, typically involving the manual annotation of a large number of data examples by an expert. In this work, we propose a label propagation algorithm applied to PRPD data sets, aiming to reduce the time necessary to manually label PRPDs. Our basic pipeline is composed of three phases: pre-processing, dimensionality reduction procedures, and clustering. Different configurations of the basic pipeline are tested by using PRPDs obtained from online measurements in hydrogenerators. The performance of each configuration is assessed by using the Silhouette, Caliński–Harabasz, and Davies–Bouldin scores. The clustering of the best three configurations is compared with annotated PRPDs by using the Fowlkes-Mallows index. Results suggest our strategy can substantially decrease the time for manual labeling.

To test and evaluate the second installment of DENTIFY, a virtual reality haptic simulator for Operative Dentistry (OD), on preclinical dental students, by focusing on user performance and self-assessment. Twenty voluntary unpaid preclinical dental students, with different background experience, were enrolled for this study. After the completion of an informed consent, a demographic questionnaire, and being introduced to the prototype (on the first testing session), three testing sessions followed (S1, S2, S3). Each session involved the following steps: (I) free experimentation; (II) task execution; S3 also included (III) completion of questionnaires associated with the experiment (total of 8 Self-Assessment Questions (SAQ)); and (IV) guided interview. As expected, drill time decreased steadily for all tasks when increasing prototype use, verified by RM ANOVA. Regarding performance metrics (Comparisons by Student’s t-test and ANOVA) recorded at S3, in overall, a higher performance was verified for participants with the following characteristics: female, non-gamer, no previous VR experience and with over 2 semesters of previous experience of working on phantom models. The correlation between the participants’ performance (drill time), for the four tasks, and user self-assessment evaluation, verified by Spearman’s rho analysis, allowed to conclude that a higher performance was observed in students who responded that DENTIFY improved their self perception of manual force applied. Regarding the questionnaires, Spearman’s rho analysis showed a positive correlation between the improvement DENTIFY inputs on conventional teaching sensed by students, also enhancing their interest in learning OD, their desire to have more simulator hours and the improvement sensed on manual dexterity. All participating students adhered well to the DENTIFY experimentation. DENTIFY allows for student self-assessment and contributes to improving student performance. Simulators with VR and haptic pens for teaching in OD should be designed as a consistent and gradual teaching strategy, allowing multiplicity of simulated scenarios, bimanual manipulation, and the possibility of real-time feedback to allow for the student’s immediate self-assessment. Additionally, they should create performance reports per student to ensure self-perception/criticism of their evolution over longer periods of learning time.

University students are at increased risk of developing burnout and psychological distress from high academic workloads and performance expectations. The purpose of this study is to analyze the relationship between psychological and lifestyle variables and academic burnout, as well as to identify burnout risk profiles in psychology students. This study used a cross-sectional design and included 274 Portuguese psychology students, the majority being undergraduates (72.6%). Participants were assessed on psychological well-being, psychological distress, difficulties in emotional regulation, type of diet, physical activity, sleep quality, and burnout. The results showed that psychological distress, difficulties in emotional regulation, and sleep quality were positively associated with burnout, while psychological well-being was negatively associated. Using machine learning algorithms, two distinct profiles were found: “Burnout Risk” and “No Risk”. A total of 62 participants were identified as belonging to the burnout risk profile, showing higher levels of distress, emotional regulation difficulties, poor psychological well-being and sleep quality, pro-inflammatory diet, and less physical activity. The accuracy of the three machine learning models—Random Forest, XGBoost, and Support Vector Machine—was 95.06%, 93.82%, and 97.53%, respectively. These results suggest the importance of health promotion within university settings, together with mental health strategies focused on adaptive psychological functioning, to prevent the risk of burnout.

High-entropy alloys (HEAs) are increasingly preferred as structural materials in nuclear engineering and aerospace applications. These fields often require the design of dissimilar joints. Here, gas tungsten arc welding (GTAW) was used for the first time to join CoCrFeMnNi HEAs with 316 stainless steel. Microstructural characterization, including electron microscopy, high-energy synchrotron X-ray diffraction, and thermodynamic calculations, along with micro- and macroscale mechanical assessments, was utilized. These methods were instrumental in evaluating and clarifying the effects of the non-equilibrium solidification and weld thermal cycle on the microstructure evolution of the joint. In the fusion zone (FZ), distinctive peninsula-shaped macroscopic segregation area is observed, with its formation being related to the liquidus temperature differences between the base materials (BMs) and the welded metal, compounded by the Marangoni effect. The weld thermal cycle was found to promote multiple solid-state phase transformations in the heat-affected zone (HAZ) adjacent to the CoCrFeMnNi BM, leading to varying degrees of softening. The HAZ near the 316 stainless steel BM maintained its original microstructural and mechanical properties. Fracture predominantly occurred in the FZ, mainly due to the interplay of large columnar grains, macrosegregation effects, and emergence of BCC and σ brittle phases due to the complex chemistry within this region. Thermodynamic modeling validated the formation of these phases. The ultimate tensile strength and elongation at room temperature were approximately ≈493 MPa and ≈10.70%, respectively.

Summary Autophagy plays an important role in the defence against intracellular pathogens. However, some microorganisms can manipulate this host cell pathway to their advantage. In this study, we addressed the role of host cell autophagy during Plasmodium berghei liver infection. We show that vesicles containing the autophagic marker LC3 surround parasites from early time‐points after invasion and throughout infection and colocalize with the parasitophorous vacuole membrane. Moreover, we show that the LC3‐positive vesicles that surround Plasmodium parasites are amphisomes that converge from the endocytic and autophagic pathways, because they contain markers of both pathways. When the host autophagic pathway was inhibited by silencing several of its key regulators such as LC3, Beclin1, Vps34 or Atg5, we observed a reduction in parasite size. We also found that LC3 surrounds parasites in vivo and that parasite load is diminished in a mouse model deficient for autophagy. Together, these results show the importance of the host autophagic pathway for parasite development during the liver stage of Plasmodium infection.