Abstract
Cities are complex and dynamic systems in which a network of actors interact, creating value through different activities. Cities can, therefore, be viewed as service ecosystems. Municipalities take advantage of digitalization to implement a service-dominant logic in urban and mobility planning and management, developing strategies with which citizens, local authorities, and other actors can create value together. While citizens are offered a better service experience, local authorities use citizens' input to improve decision-making processes. This research considers that designing an integrated service supported by an integrated information system can respond to current challenges in decision-making and information access for transport and mobility. Through a multidisciplinary methodological approach, this work proposes some guidelines to design an integrated information system to improve citizens' participation in urban planning and mobility services.

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
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
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
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.