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Formação Contínua de Professores no Ensino Superior: Um Estudo de Caso numa Instituição de Ensino Superior privada no Brasil
No full textConsiderando que a carreira docente no Brasil ainda não recebe o reconhecimento e a
valorização condizentes com sua relevância social, torna-se necessário analisar de que
forma as políticas públicas têm direcionado atenção à formação continuada dos
professores. Em meio a um contexto de investimentos insuficientes e de fragilidade nas
políticas de incentivo, observa-se que muitas instituições privadas de ensino superior vêm
assumindo o protagonismo na implementação de programas próprios de desenvolvimento
docente, estabelecendo a formação continuada como diretriz institucional para garantir a
qualidade acadêmica e pedagógica. Essas iniciativas, embora diversas em suas propostas
e alcances, representam esforços significativos para suprir lacunas históricas e promover
a atualização permanente dos professores, contribuindo para o fortalecimento da
educação superior e para a valorização da docência como prática social transformadora.
A literatura aponta que, embora a cultura acadêmica brasileira ainda idealize que a
experiência e a pesquisa sejam suficientes para o exercício da docência, é imprescindível
reconhecer que o desempenho pedagógico exige preparo contínuo. Nesse cenário, a
formação docente deve ser concebida como um processo profissionalizante e
institucionalmente estruturado, que valorize a atualização permanente. O presente estudo,
de natureza qualitativa e caráter descritivo-analítico, adota o método de estudo de caso
com o objetivo de investigar como se aplica o processo de formação continuada dos
docentes em uma instituição de ensino privada superior brasileira. Busca-se compreender
de que maneira a instituição identifica e atende às necessidades formativas do corpo
docente, promovendo ações que contribuam para a qualidade do ensino e para o
fortalecimento da prática pedagógica no ambiente universitário. Os resultados esperados
incluem a identificação de estratégias e diretrizes institucionais em paralelo as práticas
aplicadas no cotidiano e ações dos docentes da instituição
Efficacy of neuromodulation techniques (TMS and tDCS) in cancer pain management: A systematic review
No full textThe global incidence of oncological disease is rising, driven by increased longevity and greater exposure to carcinogens. Pain remains one of the most prevalent and disabling symptoms among cancer patients, requiring urgent evaluation and treatment. However, selecting an effective therapeutic approach remains a challenge. Neuromodulation techniques such as Transcranial Magnetic Stimulation (rTMS) and Transcranial Direct Current Stimulation (tDCS) have shown promise in managing chronic pain across various conditions, but evidence in the oncological context is still limited. This study systematically reviewed the effects of rTMS and tDCS on cancer pain management. A literature search was conducted in PubMed, Web of Science, Scopus, ProQuest, and CENTRAL, following PRISMA guidelines, yielding 657 potentially relevant studies. Quality assessment was performed using the Joanna Briggs Institute (JBI) checklist. Eight randomized controlled trials from four countries were included, involving 349 patients: three employing rTMS and five using tDCS. The studies examined diverse patient groups, cancer types, and neuromodulation protocols, producing mixed but generally short‑term reductions in pain. Indirect evidence suggests that rTMS may induce faster but shorter‑lived analgesia, whereas tDCS may be associated with modest persistence when delivered in repeated sessions. Overall, the findings provide preliminary evidence that non‑invasive brain stimulation techniques may contribute to cancer pain management. Future research should optimize stimulation parameters, conduct direct comparisons, and explore integration with pharmacological and behavioral strategies to enhance long‑term effectiveness
Ensemble of Temporal Weighting, Causal Inference, and Hierarchical Attribution towards SHAP Optimization
No full textDuring the past few years, the need for transparency and interpretability has been intensified owing to significant advancements in data-driven models, leading to the emergence of Explainable Artificial Intelligence (XAI). Several traditional XAI approaches are prevalent; however, these have limited competence in interpreting dynamic relations. The current research aims to address this limitation by proposing a novel Ensemble SHapley Additive exPlanations (SHAP) framework that focuses on temporal weighting, causal inference, hierarchical attribution, and interpretability optimization referred to as TCHSHAP. TCHSHAP prioritizes current information over historical information by temporal weighting through exponential decay. Further, causal inference separates correlation from causality to gain practical insights. Additionally, hierarchical attribution allows insights at granular (region level) and aggregated levels (feature-group impacts). These approaches are integrated to achieve a more interpretable and explainable model. To validate the efficacy of the proposed model, we carry out an experiment on the crop yield dataset collected from Kaggle. Ahead of experimental evaluation, data preprocessing is performed using one-hot encoding. Data normalization is done by min-max scaling, and outliers are removed through the Interquartile range. For the sake of experimental evaluation, the authors used the SHAP XAI model for Random Forest. When assessing the efficacy of the proposed TCHSHAP model, it is observed that while the average prediction for traditional SHAP is 161.137, it escalates to 161.506 after incorporating temporal weighting and causal inference, advocating the effectiveness of employing temporal and causal significance. Additionally, during hierarchical attribution, it is observed that agricultural features have the strongest dominance over the target variable. This dominance is followed by geographical and environmental factors in order. Thus, the obtained results authorize the efficacy of the proposed approach towards enhancing the global and local interpretability, strengthening the user's trust in model predictions. The current work offers ways to improve transparency and interpretability without affecting model performance. The suggested model also enables interpretable and efficient regression modelling in complex, data-driven applications, enabling its widespread application in real-world settings
Discricionariedade da administração pública no exercício do poder disciplinar no vínculo de emprego público
No full textA presente dissertação analisa a discricionariedade da Administração Pública no
exercício do poder disciplinar no vínculo de emprego público.
Neste sentido, entende-se por discricionariedade administrativa o poder
atribuído por uma norma de competência à Administração Pública, permitindo-lhe, com
base nos seus próprios juízos de apreciação e valoração, determinar qual a solução
mais adequada a aplicar numa situação concreta. Todavia, essa escolha deverá sempre
obedecer aos critérios estabelecidos pelos princípios e normas gerais de direito,
nomeadamente aos princípios da igualdade, da imparcialidade e da proporcionalidade.
Deste modo, a discricionariedade administrativa está presente em todas as áreas do
Direito Administrativo, não sendo o domínio disciplinar no emprego público uma
exceção. De facto, seria inconcebível para o legislador antecipar todas as circunstâncias
que exigem intervenção administrativa neste domínio e, por conseguinte, estabelecer
previamente as soluções mais adequadas à prossecução do interesse público.
Com efeito, entendemos que a discricionariedade acompanha, e bem, o
exercício do poder disciplinar, não só no âmbito da infração disciplinar, como nas
diversas fases do respetivo procedimento, desde a sua instauração até à fase decisória,
bem como na escolha da sanção disciplinar e na sua concreta dosimetria.
A reflexão a que nos propomos, revela-se de imperiosa importância, pois os
contornos e os limites do poder discricionário, neste domínio, deverão encontrar-se
rigorosamente definidos, sob pena de permitir-se uma ameaça aos direitos, liberdades
e garantias dos trabalhadores, podendo mesmo conduzir à sua restrição ou até
eliminação.This dissertation analyzes the discretion of Public Administration in the exercise
of disciplinary power within the framework of public employment.
In this regard, administrative discretion is understood as the power granted by a
rule of competence to Public Administration, allowing it, based on its own judgments of
assessment and evaluation, to determine the most appropriate solution to apply in a
specific situation. However, such a choice must always comply with the criteria
established by the general principles and rules of law, namely the principles of equality,
impartiality, and proportionality. In this way, administrative discretion is present in all
areas of Administrative Law, and the disciplinary domain within public employment is no
exception. In fact, it would be inconceivable for the legislator to anticipate all the
circumstances requiring administrative intervention in this field and, consequently, to
establish in advance the most suitable solutions for the pursuit of the public interest.
Indeed, we consider that discretion rightly accompanies the exercise of
disciplinary power, not only with regard to the disciplinary offence itself, but also
throughout the various stages of the respective procedure, from its initiation to the
decision-making phase, as well as in the selection of the disciplinary sanction and its
specific calibration (dosimetry).
The reflection we propose is of imperative importance, as the contours and limits
of discretionary power in this field must be strictly defined, lest it pose a threat to the
rights, freedoms, and guarantees of workers, potentially leading to their restriction or
even elimination
A Personalidade Jurídica da Natureza na Legislação Portuguesa: Desafios e perspectivas para a Proteção Ambiental
No full textA atribuição de personalidade jurídica à natureza é um tema controverso, com muitos Estados relutantes em reconhecer a proteção legal da natureza nesse sentido. Tradicionalmente, essa personalidade é associada a pessoas individuais ou coletivas, mas a crescente necessidade de proteção ambiental tem gerado debates sobre conferir à natureza um status legal similar ao de sujeito de direito. Iniciativas em alguns países, como na Colômbia e na Nova Zelândia, começaram a reconhecer entidades naturais como sujeito de direito, promovendo a ideia de que a natureza, com status jurídico próprio, teria uma defesa mais robusta contra os danos sofridos. Contudo, muitos países, incluindo Portugal, ainda não adotaram esse novo paradigma, optando por modelos convencionais de proteção. Este estudo inclui uma análise qualitativa comparativa das legislações e práticas em países que já reconhecem a natureza como sujeito de direito, como Colômbia e Nova Zelândia, além de uma análise da situação atual em Portugal, por meio de revisão bibliográfica e análise documental. Os resultados sugerem que o reconhecimento da natureza como sujeito de direito pode fortalecer a proteção ambiental e mitigar danos ecológicos, proporcionando uma responsabilidade extracontratual mais eficaz e preventiva
Qualitative analysis of HAART effects on HIV and SARS-CoV-2 coinfection model
No full textHIV is known for causing the destruction of the immune system by affecting different types of cells, while SARS-CoV-2 is an extremely contagious virus that leads to the development of COVID-19. Understanding how these two viruses interact in coinfected individuals is essential, especially in populations under antiretroviral treatment. In this study, we develop and analyze a novel mathematical model capturing the coinfection dynamics of HIV and SARS-CoV-2 under the influence of highly active antiretroviral therapy (HAART). In contrast to previous models, our formulation includes the effect of HAART on both infections and derives the basic reproduction numbers for each virus. We prove that transcritical bifurcations occur when the basic reproduction numbers cross the threshold value of 1, and we establish the conditions for stability of the disease-free equilibria. Numerical simulations show that HAART, although designed to control HIV, also reduces SARS-CoV-2 proliferation in coinfected hosts, which, as far as we know, has not been fully addressed in previous models in the literature. These findings reveal a potentially beneficial indirect effect of antiretroviral therapy on SARS-CoV-2 dynamics, offering new theoretical insights into the control of viral coinfections
Unraveling Emotions with Pre-Trained Models
No full textTransformer models have significantly advanced the field of emotion recognition. However, there are still open challenges when exploring open-ended queries for Large Language Models (LLMs). Although current models offer good results, automatic emotion analysis in open texts presents significant challenges, such as contextual ambiguity, linguistic variability, and difficulty interpreting complex emotional expressions. These limitations make the direct application of generalist models difficult. Accordingly, this work compares the effectiveness of fine-tuning and prompt engineering in emotion detection in three distinct scenarios: (i) performance of fine-tuned pre-trained models and general-purpose LLMs using simple prompts; (ii) effectiveness of different emotion prompt designs with LLMs; and (iii) impact of emotion grouping techniques on these models. Experimental tests attain metrics above 70% with a fine-tuned pre-trained model for emotion recognition. Moreover, the findings highlight that LLMs require structured prompt engineering and emotion grouping to enhance their performance. These advancements improve sentiment analysis, human-computer interaction, and understanding of user behavior across various domains
A. I. and the rule of law: Opportunities and risks in the digital age
No full textArtigo submetido e aceite para publicação em atas de conferência
Pre-Smoothing methods for transition probabilities in Complex Non-Markovian Multi-State Models
No full textMulti-state models are essential tools in longitudinal data analysis, enabling the estimation of transition probabilities that provide predictive insights into clinical outcomes across stages of disease progression or recovery. Conventional approaches to inference in these models often rely on the Markov assumption, which simplifies computation but may not hold in complex real-world settings. To address this limitation, we extend the landmark Aalen-Johansen estimator by incorporating presmoothing techniques, offering a robust alternative for estimating transition probabilities in non-Markovian multi-state models, including those with multiple states and reversible transitions. The proposed method effectively reduces estimation variability and mitigates biases arising from the selection of arbitrary landmark times. Through empirical evaluation using three real-world datasets with distinct multi-state structures, we demonstrate that the presmoothed estimator achieves enhanced precision and stability, particularly in the presence of high noise or small sample sizes. To facilitate its application, we provide an R package, presmoothedTP, which implements all the proposed methods
