Abstract
Introduction/Objective: the objective of this article is to analyze the current academic literature on smart cities in Brazil with evidence of the application of Digital Twin or Digital Shadow technology. Method: Integrative Literature Review was used as the research instrument, analyzing in the articles: a) objective; b) research method; c) study subject (location); d) application of Digital Twin or Digital Shadow; e) Results and conclusions. Results: portfolio with 25 articles on the topic and qualitative analysis regarding objective, method, study location, Digital Twin technology, Digital Shadow, and results. Studies with elements of Digital Shadow are perceived timidly in two cases of smart cities in Brazil. Conclusions: smart city technologies should be centered on the interests of users to not lose their humanity. It is worth adding that people’s needs change and, therefore, smart technologies should have a forward-looking vision to anticipate the needs of future generations. Digital Twin technology is a model that can contribute in this sense, monitoring and providing readings of future scenarios for smart cities. © 2024, Programa de Pos-Graduacao em Gestao da Informacao, Universidade Federal do Parana. All rights reserved.

Abstract
Water is at the core of sustainable development, and its use for human activities, including vehicle washing, should be done in a sustainable way. There are several technical solutions for washing buses offering different performances, making it difficult to choose the one that best meets the requirements of each specific case. The literature on the topic hardly analyzes the choice of the best technical solution for washing buses and does not apply and compare the results of different multicriteria decision-making (MCDM) methods for the problem. The unique information available is from the different suppliers in the market. Whereby, this work intends to give a technical-scientific contribution to fulfill this gaps. Therefore, the main objectives of this work are (1) to select the best sustainable technical solutions for washing buses depending on the specific conditions for a case study and (2) to analyze how different multicriteria decision-making methods behave in the selection process. To achieve these objectives, the problem was approached as a case study in a public transport company in Portugal and the methodology followed the next steps: started with the identification of the different types of commercial technical solutions for washing buses; the company's experts selected four main criteria: water consumption, operating costs, quality of washing, and time spent; the criteria weights were determined using the fuzzy-AHP method; then four representative MCDM methods were selected, namely, AHP, ELECTRE, TOPSIS, and SMART; the ranks obtained for the four methods were compared; and a sensitivity analysis was performed. Considering the input data for the criteria and their weights, the results for all the methods showed that the best and the worst solution was the same, mobile portico with a brush and porticoes with three brushes, respectively. Furthermore, the results of the sensitivity analysis performed with disturbances for the weights of each criterion presented that the results are slightly affected and the similarity in rankings for the four MCDM methods was validated by Spearman's rank correlation coefficient (rs) and Kendall's coefficient of concordance (W). Considering these results, the SMART method, the less complex one, showed no difference from the others. For that reason, simple methods, such as SMART, in line with other works in the literature perform well in most cases. As a final remark of this work, it can be said that the methodology employed in this project can also be deemed applicable to other similar companies seeking technical solutions for bus or truck washing. Furthermore, the application of the SMART method, the less complex one and the most understandable for people, showed no difference from the others, being able to be applied in similar situations.

Abstract
Considering the great challenge of implementing digital tools to improve collaboration in the value chain and promote the adoption of circularity strategies, as is the case with digital traceability tools and digital product passports. This paper presents an innovative proposal for implementing an industrial data sharing ecosystem, namely an architecture and platform for digital traceability between entities based on Data Spaces. To validate our proposal, a use case scenario was implemented as part of the BioShoes4All project.

Abstract
This paper discusses the Digital Product Passport (DPP) as a key tool for achieving a circular economy. An architecture of the DPP is presented built upon the principles of data spaces and W3C Decentralized Identifiers (DIDs). By leveraging data spaces, the DPP enables secure and controlled data exchange among stakeholders, fostering transparency, traceability, and collaboration throughout the product's lifecycle. The use of decentralized identifiers ensures the uniqueness and verifiability of product-related information, facilitating seamless access and sharing of data. The DPP architecture offers a promising framework for realizing the circular economy by promoting resource efficiency, sustainable practices, and informed decision-making. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
The footwear industry is energy-intensive and, consequently, a source of large amounts of greenhouse gas emissions every year. Issues related to climate change and growing conflicts on a global scale that impact the prices of raw materials and energy prices have led companies in the footwear industry to take actions to mitigate these impacts. Among these actions is the growing focus on producing its energy from energy systems based on renewable sources and battery energy storage units. This paper addresses the energy-efficient manufacturing scheduling in footwear industries with onsite energy production from a photovoltaic system with batteries. The problem is formulated as a mixed integer linear programming problem. Different objectives are presented, depending on the priorities of the entity that owns the footwear factory, namely, minimizing operation costs, minimizing CO2 emissions, or both. The case study is footwear factory located in Portugal that uses a manufacturing process based on injection molding. The results show the effectiveness of the proposed approach, with active demand side management playing a fundamental role in shifting periods of higher energy consumption to periods of lower prices or lower CO2 emissions. Also, Pareto fronts are depicted to make the trade-off between CO2 emissions and operation costs. As expected, the reduction of CO2 emissions promotes an increase on operation costs. Furthermore, a sensitivity analysis is carried out on the increase in photovoltaic capacity and battery capacity. The results show that increasing photovoltaic and battery capacity promotes reductions in costs up to 30% and in the emissions up to 37%.

Abstract
Quality inspection is a critical step in ensuring the quality and efficiency of textile production processes. With the increasing complexity and scale of modern textile manufacturing systems, the need for accurate and efficient quality inspection and defect detection techniques has become paramount. This paper compares supervised and unsupervised Machine Learning techniques for defect detection in the context of industrial textile production, in terms of their respective advantages and disadvantages, and their implementation and computational costs. We explore the use of an autoencoder for the detection of defects in textiles. The goal of this preliminary work is to find out if unsupervised methods can successfully train models with good performance without the need for defect labelled data. (c) 2023 The Authors. Published by Elsevier B.V.