University of Porto

Objective: To identify and characterize the population of Pediatric patients referred to our hyperbaric oxygen therapy center. Methods: Retrospective and observational study, including pediatric patients treated with hyperbaric oxygen therapy, from 2006 to 2021, at the hyperbaric medicine reference center in the north of Portugal. Variables of interest were extracted from electronic medical records. Results: Our study included 134 patients. The most frequent reasons for referral were carbon monoxide poisoning (n=59) and sudden sensorineural hearing loss (n=41). In 75 cases (56%), treatment was initiated in an urgent context. Symptom presentation at Emergency Department varied among patients, the most frequent being headache and nausea/vomiting. Concerning carbon monoxide poisoning, the most common sources were water heater, fireplace/brazier, and boiler. Regarding adverse effects, it was identified one case of intoxication by oxygen and four cases of middle ear barotrauma. Conclusions: The most frequent cause for referral was carbon monoxide poisoning. All patients evolved favorably, with few side effects being reported, emphasizing the safety of this therapy. While most pediatricians may not be aware of the potential benefits arising with hyperbaric oxygen therapy, it is of upmost importance to promote them, so that this technique is increasingly implemented.

Catarina Freitas; Luis Salazar; Silvia Duarte-Costa; Catarina Fraga; Sara Monteiro; Oscar Camacho. Hyperbaric Medicine in Pediatrics - reality of a Portuguese reference center. 2025, 43, e2023230 .

Catarina Freitas, Luis Salazar, Silvia Duarte-Costa, Catarina Fraga, Sara Monteiro, Oscar Camacho. Hyperbaric Medicine in Pediatrics - reality of a Portuguese reference center. . 2025; 43 ():e2023230.

Catarina Freitas; Luis Salazar; Silvia Duarte-Costa; Catarina Fraga; Sara Monteiro; Oscar Camacho. 2025. "Hyperbaric Medicine in Pediatrics - reality of a Portuguese reference center." 43, no. : e2023230.

Metal foams are lightweight materials inspired by structures in nature. They are defined as three-dimensional frameworks with interconnected porous structures – nano-, meso- and macropores – that combine essential physical and mechanical features of metals with a highly porous nanoarchitectures. These types of porous materials can be used in a variety of different scientific fields and, as a result, have gained increasing attention in recent years. Given the heightened academic and industrial interest in efficient materials, sparked by the high demand for new clean technologies, this book is more relevant and timelier than ever.

Maria de Fátima Montemor; Diana M. Fernandes; Alberto Adan-Mas; Ana Catarina Alves; Gabriel Garcia Carvalho; Inês S. Marques. Transition Metal-based Nanofoams for Electrochemical Systems. 2024 .

Maria de Fátima Montemor, Diana M. Fernandes, Alberto Adan-Mas, Ana Catarina Alves, Gabriel Garcia Carvalho, Inês S. Marques. Transition Metal-based Nanofoams for Electrochemical Systems. . 2024; ():.

Maria de Fátima Montemor; Diana M. Fernandes; Alberto Adan-Mas; Ana Catarina Alves; Gabriel Garcia Carvalho; Inês S. Marques. 2024. "Transition Metal-based Nanofoams for Electrochemical Systems." , no. : .

Traffic forecasting problems involve jointly modeling the non-linear spatio-temporal dependencies at different scales. While graph neural network models have been effectively used to capture the non-linear spatial dependencies, capturing the dynamic spatial dependencies between the locations remains a major challenge. The errors in capturing such dependencies propagate in modeling the temporal dependencies between the locations, thereby severely affecting the performance of long-term predictions. While transformer-based mechanisms have been recently proposed for capturing the dynamic spatial dependencies, these methods are susceptible to fluctuations in data brought on by unforeseen events like traffic congestion and accidents. To mitigate these issues we propose an improvised spatio-temporal parallel transformer (STPT) based model for traffic prediction that uses multiple adjacency graphs passed through a pair of coupled graph transformer-convolution network units, operating in parallel, to generate more noise-resilient embeddings. We conduct extensive experiments on 4 real-world traffic datasets and compare the performance of STPT with several state-of-the-art baselines, in terms of measures like RMSE, MAE, and MAPE. We find that using STPT improves the performance by around \(10-34\%\) as compared to the baselines. We also investigate the applicability of the model on other spatio-temporal data in other domains. We use a Covid-19 dataset to predict the number of future occurrences in different regions from a given set of historical occurrences. The results demonstrate the superiority of our model for such datasets.

Rahul Kumar; João Mendes-Moreira; Joydeep Chandra. Spatio-Temporal Parallel Transformer Based Model for Traffic Prediction. ACM Transactions on Knowledge Discovery From Data 2024, 18, 1 -25.

Rahul Kumar, João Mendes-Moreira, Joydeep Chandra. Spatio-Temporal Parallel Transformer Based Model for Traffic Prediction. ACM Transactions on Knowledge Discovery From Data. 2024; 18 (9):1-25.

Rahul Kumar; João Mendes-Moreira; Joydeep Chandra. 2024. "Spatio-Temporal Parallel Transformer Based Model for Traffic Prediction." ACM Transactions on Knowledge Discovery From Data 18, no. 9: 1-25.