Abstract
Reconhecendo as discussões em andamento no movimento da Economia Popular Solidáriano Brasil e tomando como base a pesquisa desenvolvida com a Rede de padariascomunitárias Fermento na Massa, este artigo conta sobre o retorno das atividades da Redepós Covid-19, a aproximação das pesquisadoras com a Rede e a troca que fizemos entreconhecimentos de design gráfico das pesquisadoras e as experiências como trabalhadorasda Economia Solidária. A partir disso buscamos discutir as relações entre a prática do designe a Economia Solidária, dialogando com os conceitos de design participativo e autogestãona prática da Economia Solidária buscamos refletir sobre as possibilidades de uma práticade um design mais solidário, co-produzido e buscando maior autonomia das trabalhadorasnos processos de comunicação e venda.

Abstract
[No abstract available]

Abstract
Textual health records of cancer patients are usually protracted and highly unstructured, making it very time-consuming for health professionals to get a complete overview of the patient's therapeutic course. As such limitations can lead to suboptimal and/or inefficient treatment procedures, healthcare providers would greatly benefit from a system that effectively summarizes the information of those records. With the advent of deep neural models, this objective has been partially attained for English clinical texts, however, the research community still lacks an effective solution for languages with limited resources. In this paper, we present the approach we developed to extract procedures, drugs, and diseases from oncology health records written in European Portuguese. This project was conducted in collaboration with the Portuguese Institute for Oncology which, besides holding over 10 years of duly protected medical records, also provided oncologist expertise throughout the development of the project. Since there is no annotated corpus for biomedical entity extraction in Portuguese, we also present the strategy we followed in annotating the corpus for the development of the models. The final models, which combined a neural architecture with entity linking, achieved..1 scores of 88.6, 95.0, and 55.8 per cent in the mention extraction of procedures, drugs, and diseases, respectively.

Abstract
Although different actions to prevent accidents at work have been implemented in companies, the number of accidents at work continues to be a problem for companies and society. In this way, companies have explored alternative solutions that have improved other business factors, such as predictive analysis, an approach that is relatively new when applied to occupational safety. Nevertheless, most reviewed studies focus on the accident dataset, i.e., the casualty’s characteristics, the accidents’ details, and the resulting consequences. This study aims to predict the occurrence of accidents in the following month through different classification algorithms of Machine Learning, namely, Decision Tree, Random Forest, Gradient Boost Model, K-nearest Neighbor, and Naive Bayes, using only organizational information, such as demographic data, absenteeism rates, action plans, and preventive safety actions. Several forecasting models were developed to achieve the best performance and accuracy of the models, based on algorithms with and without the original datasets, balanced for the minority class and balanced considering the majority class. It was concluded that only with some organizational information about the company can it predict the occurrence of accidents in the month ahead. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Given the relevance of the textile industry, over the years, for the portuguese economy, we intend to evaluate the economic performance of companies belonging to CAE 14131 through the indicators ROA, ROE, ROS and EVA/employees. Through the DEA technique, the BoD model is used to aggregate the various indicators in order to determine the composite indicator of 5.397 companies observed over the years 2011 to 2020, in order to deepen the knowledge about the Portuguese business economic textile sector. Through data analysis there is a progressive improvement of the indicators studied over the years which can be explained by the technological evolution occurred in this industry, although the sector under study uses mostly intensive labour. In each year, the efficient frontier is defined mostly by micro and small enterprises, which are predominantly located in the North of Portugal. © 2023 ITMA.

Abstract
This study proposes a framework for short-term production planning of a Portuguese company operating as a tier 2 supplier in the automotive sector. The framework is intended to support the decision-making process regarding a single progressive hydraulic press, which is used to manufacture cold-stamped parts for exhaust systems. The framework consists of two sequential levels: (1) a Mixed-Integer Linear Programming (MILP) model to determine the optimal production quantities per week while minimizing the total cost; (2) a dynamic production sequencing rule for scheduling operations on the hydraulic press. The two levels are combined and implemented in Excel, where the MILP model is solved using the Solver add-in, and the second level uses the optimal production quantities as inputs to determine the production sequence using a dynamic priority rule. To validate the framework, a proposed optimal plan was compared to a real plan executed by the company, and it was found that the framework could save up to 22.1% of the total cost observed in reality while still satisfying demand. To address uncertainties, the framework requires a rolling weekly planning horizon. © 2023, The Author(s).