While large language models (LLMs) show transformative potential in healthcare, their development remains focused on high-resource languages. This creates a critical barrier for other languages, as simple translation fails to capture unique clinical and cultural nuances, such as endemic diseases. To address this, we introduce MedPT, the first large-scale, real-world corpus of patient-doctor interactions for the Brazilian Portuguese medical domain. Comprising 384,095 authentic question-answer pairs and covering over 3,200 distinct health-related conditions, the dataset was refined through a rigorous multi-stage curation protocol that employed a hybrid quantitative-qualitative analysis to filter noise and contextually enrich thousands of ambiguous queries, resulting in a corpus of approximately 57 million tokens. We further utilize of LLM-driven annotation to classify queries into seven semantic types to capture user intent. To validate MedPT's utility, we benchmark it in a medical specialty classification task: fine-tuning a 1.7B parameter model achieves an outstanding 94\% F1-score on a 20-class setup. Furthermore, our qualitative error analysis shows misclassifications are not random but reflect genuine clinical ambiguities (e.g., between comorbid conditions), proving the dataset's deep semantic richness. We publicly release MedPT on Hugging Face to support the development of more equitable, accurate, and culturally-aware medical technologies for the Portuguese-speaking world.

Anthropometric measurements are used for numerous biomedical applications, such as the management of dietary, medication, and orthopedic treatments. However, for some patients, these measures can be impaired due to the high complexity of their clinical condition with limitations and difficulties related to immobilization. Human Pose Estimation (HPE) models can be useful as an alternative anthropometric method that allows measuring individual body segments. This study aimed to compare the performance of HPE models for assessing limb length. A physiotherapist evaluated arms, forearm, trunk, and legs lengths in fifty-nine participants using both tape and five HPE models: PoseNet and four variations of MoveNet. Participants were positioned standing in front of a backdrop while photos were captured using the NLMeasurer mobile application. NLMeasurer is a smartphone application able to record postural, goniometric, and limb length assessments based on the identification of Key Joint Points (KJPs) performed by HPE models. The bias and error measures were employed to evaluate the difference and agreement between measurement methods. Results indicated that the performance of the models was similar, with an average error of 3.5 cm across most segments. However, the models' performance declined for hip-knee distance measurements, with errors ranging from 7.34 to 13.57 cm. HPEs showed promise for facilitating limb length assessments in an automated way, but they have not proven to be accurate enough to be incorporated into clinical practice and used without any human intervention.

This work presents FreeSVC, a promising multilingual singing voice conversion approach that leverages an enhanced VITS model with Speaker-invariant Clustering (SPIN) for better content representation and the State-of-the-Art (SOTA) speaker encoder ECAPA2. FreeSVC incorporates trainable language embeddings to handle multiple languages and employs an advanced speaker encoder to disentangle speaker characteristics from linguistic content. Designed for zero-shot learning, FreeSVC enables cross-lingual singing voice conversion without extensive language-specific training. We demonstrate that a multilingual content extractor is crucial for optimal cross-language conversion. Our source code and models are publicly available1.

Modeling and Simulation (M&S) is a fundamental approach for engineering disruptive solutions, widely applied in critical domains such as aerospace, military, healthcare, traffic, and energy.These domains often exhibit high complexity, uncertainty, and nonlinearity, making simulation and visualization particularly challenging.While M&S allows for the evaluation and analysis of innovative systems, enhancing the experience for analysts and engineers through complementary tools is crucial for improving realism, accuracy, and trustworthiness.In this paper, we present preliminary results on the development of a framework that integrates M&S with advanced visualization capabilities.Our approach bridges the Discrete-Event System Specification (DEVS) formalism with the Unity engine to provide a realistic graphical representation of simulations.By leveraging a Large Language Model (LLM) trained for this purpose, we automatically generate both simulation and animation codes, ensuring synchronized execution.The generated models exchange data, with the simulator serving as the computational engine and Unity as the visualization platform.Additionally, we provide a library of pre-defined simulation models for reuse in various domains.This integration enhances the interpretability of simulations, offering a more intuitive and immersive experience for system analysis.

Virtual Reality has significantly expanded possibilities for immersive museum experiences, overcoming traditional constraints such as space, preservation, and geographic limitations.However, existing virtual museum platforms typically lack dynamic, personalized, and contextually accurate interactions.To address this, we propose Spatially-Aware Retrieval-Augmented Generation (SA-RAG), an innovative framework integrating visual attention tracking with Retrieval-Augmented Generation systems and advanced Large Language Models.By capturing users' visual attention in real time, SA-RAG dynamically retrieves contextually relevant data, enhancing the accuracy, personalization, and depth of user interactions within immersive virtual environments.The system's effectiveness is initially demonstrated through our preliminary tests within a realistic VR museum implemented using Unreal Engine.Although promising, comprehensive human evaluations involving broader user groups are planned for future studies to rigorously validate SA-RAG's effectiveness, educational enrichment potential, and accessibility improvements in virtual museums.The framework also presents opportunities for broader applications in immersive educational and storytelling domains.

The use of forecasting models is becoming even more common in healthcare and administration applications because it can be a reliable decision support tool. Live birth rate is a health index that is directly linked with maternal and newborn health and its prediction can assist health managers to anticipate resources destined for obstetric and pediatric services. Thus, the objective of this work is to forecast the number of live births in the state of Golás (Brazil) for a 24-month horizon, providing useful information to support the planning and implementation of public policies. The model suggested is the Legendre Memory Unit (LMU) which is applied to data provided by the information system on live births of the information department of the single health system (SINASC-DATASUS). The dataset is composed of 252 monthly records of the number of live births for the 18 health regions of Golás. The results were measured in prediction ability by Mean Absolute Percentual Error (MAPE) and Mean Absolute Error (MAE). The average MAPE and MAE were 6.4614 and 19.9136, respectively.

This paper aims to do time series forecasting of live births in Brazil with modern tree-based machine learning models. These models are popular choices for time series forecasting due to their ability to model non-linear relationships, so they were applied to live birth forecasting with multiple covariates. The study uses data from the Brazilian Ministry of Health to train and evaluate forecasting models, following guidelines of the Ministry's expectations and needs for using forecasts for public policy planning. The study uses data from all 450 micro-regions in Brazil with records between the years 2000 and 2020. The objective is to train a tree-based model with all months between 2000 and 2018 years to assess the performance of forecasting the number of births over the years 2019 and 2020. LightGBM, XGBoost, and Catboost were evaluated and compared to AutoARIMA and simple linear regression. LightGBM performed slightly better than other models evaluated achieving a MAPE of 0.0797, with more consistent performance over the 24 months of the forecasting horizon. The results show that the tree-based models are reliable for dealing with multiple covariates and can be a useful tool for public policy planning.

This work extends PneumoCAD, a Computer-Aided Diagnosis system for detecting pneumonia in infants using radiographic images, with the aim of improving the system's accuracy and robustness. We implement and compare five con-temporary machine learning classifiers, namely: Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Decision Tree, combined with three dimensionality reduction algorithms: Sequential Forward Selection (SFS), Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA). Current results demonstrate that Naïve Bayes classifier combined with KPCA produces the best overall results.