Abstract
The increased use of smartphones and the COVID-19 pandemic directly influenced the development of remote tools in several areas. In the context of oncology, it was no different, as several studies address health care or services related to mobile devices. Apps aimed at the medical field (m-health) focus directly on monitoring symptoms and improving interaction between health professionals and patients, combined with the convenience of smartphones. In this context, this work aims to address recent studies on the use of m-health in the clinical practice of oncological diseases and report the characteristics of the apps involved. For this, a review of m-health focused on oncology was conducted using the PubMed and Science Direct databases. The investigation was carried out using tools inherent in international databases and was limited to articles published between 2015 and 2022. In total, 34 articles were analyzed, with a higher frequency of publications between 2019 and 2022. The resources observed were patient follow-up, prevention of signs and symptoms, monitoring of treatment and aid in prognosis and diagnosis of patients. It is concluded that a close collaboration among patients, health professionals, and information technology professionals is necessary to optimize symptom recognition and improve patient-professional communication. Although the pandemic has intensified the increase in the use of m-health, its use is expected to increase in the post-pandemic scenario, bearing in mind the changes in social dynamics and the growing dissemination of technologies. © 2023 ITMA.

Abstract
When designing a custom-made product it is important to provide the customer with a budget that resembles the final price. In this work it will be developed a simple application in Python to perform automatic data extraction from computer aided design (CAD) files to estimate multiple linear regression models with the intent of obtaining a more accurate cost estimate. The application will provide an estimate of the amount of raw material needed and time taken to produce a simple inflatable and related products. © 2023 ITMA.

Abstract

Abstract
This work aims to assess the performance of European countries on the deployment of low-emission vehicles in road transportation. For this purpose, a model based on Data Envelopment Analysis (DEA) is used to calculate a composite indicator for several European countries, aggregating seven sub-indicators built from a data set for the 2019 year. Various virtual weight restrictions schemes of the sub-indicators are studied to explore the robustness of the performance results. By adopting the most robust scheme, the performance results obtained indicate that most European countries have the potential to improve their practices towards better road transport sustainability, by emulating the best practices observed in the four identified benchmarks. Thus, the inefficient countries should take measures to better support the market share of plug-in electric vehicles. In addition, the railway sector and the penetration of renewable energies should be enhanced to improve road transportation sustainability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Speech recognition aims to convert human speech into text and has applications in security, healthcare, commerce, automobiles, and technology, just to name a few. Inserting residual neural networks before recurrent neural network cells improves accuracy and cuts training time by a good margin. Furthermore, layer normalization instead of batch normalization is more effective in model training and performance enhancement. Also, the size of the datasets presents tremendous influences in achieving the best performance. Leveraging these tricks, this article proposes an automatic speech recognition model with a stacked five layers of customized Residual Convolution Neural Network and seven layers of Bi-Directional Gated Recurrent Units, including a logarithmic so f tmax for the model output. Each of them incorporates a learnable per-element affine parameter-based layer normalization technique. The training and testing of the new model were conducted on the LibriSpeech corpus and LJ Speech dataset. The experimental results demonstrate a character error rate (CER) of 4.7 and 3.61% on the two datasets, respectively, with only 33 million parameters without the requirement of any external language model.

Abstract
[No abstract available]