The concept of smart cities has gained relevance over the past few years. Public managers have been planning investments to turn their cities into smart cities. Maturity models can help managers to monitor the performance of urban indicators; however, these maturity models are not always capable of meeting their proposed goals. In this sense, this research aims to develop a maturity model that ranks the “smartness” of a city based on social and technological indicators. The Smart Cities Maturity Model (MMSC) variables were extracted from ISO’s 37153:2017, 37120:2018, 37107:2019. The MMSC is structured on a hybrid TOPSIS multicriteria decision-making method. In this paper, we modified TOPSIS and used it to generate a synthetic indicator, called smart index, that designates the level of maturity of a real city. For this change to be possible, we fixed some alternatives and changed the positive ideal and negative ideal solution. The methodology is proven to be very efficient in measuring the smart city maturity level, and it can be easily adapted for the upcoming ISOs.

This paper explores the transformative potential of digital platforms in fostering resilient and intelligent urban environments, a critical need considering rapid urbanization and climate change. Through a comparative analysis of various digital platforms in global cities, this study identifies their role in enhancing operational efficiency, participatory governance, and urban innovation. Utilizing a structured maturity model based on the ISO 37123 standard for resilient communities, this research highlights the specific challenges faced by cities at different stages of digital transformation and provides practical recommendations for implementing digital solutions that integrate resilience, sustainability, and smart governance. The analysis underscores the importance of aligning digital platform development with the United Nations Sustainable Development Goals (SDGs), offering a pathway for cities to enhance resilience, optimize resource use, and promote citizen engagement.

The disordered urban growth that may favour the emergence of the Aedes aegypti mosquito in cities is a problem of increasing magnitude in middle- and high-income countries in the tropical part of the world. Currently, the World Health Organization (WHO) considers the control and elimination of Ae. aegypti a world-wide high priority as it is the main vector of many rapidly spreading viral diseases, dengue in particular. A major difficulty in controlling the proliferation of this vector is associated with identification of the breeding sites. The use of Unmanned Aerial Vehicles (UAVs) can be an efficient alternative to manual search because of high mobility and the ability to overcome physical obstacles, particularly in urban areas where it can offer close-up images of potential breeding sites that are difficult to reach. The objective of this study was to find a way to select the most suitable UAV for the identification of Ae. aegypti habitats by providing images of potential mosquito breeding sites. This can be accomplished by a Multiple-Criteria Decision Method (MCDM) based on an Analytical Hierarchy Process (AHP) for the evaluation of weights of the criteria used for characterizing UAVs. The alternatives were analyzed and ranked using the Fuzzy Set Theory (FST) merged with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The methodology is explained and discussed with respect to identification and selection of the most appropriate UAV for aerial mapping of Aedes breeding sites.

Textile chemistry and textile processing laboratories are essential environments for textile product research and development, but they also pose hazards that require rigorous precautions. Among the most common risks is handling chemicals used in the textile industry, such as dyes, solvents, and finishing chemicals, which can be contaminants, corrosive, and flammable, presenting risks of poisoning and fire. Textile processing laboratories also require proper ventilation, as a lack of appropriate ventilation in these environments can accumulate toxic vapors in the air. The most relevant risks and hazards of using textile chemistry laboratories include using equipment such as dyeing autoclaves under pressure and high temperature; drying ovens like furnaces/lab stenters; cylinders of squeezing, calenders, and others, capable of causing severe accidents. These laboratories also generate or handle solid waste and effluents containing, heavy metals to pathogens (e.g., from industrial sludge). It is essential to adopt rigorous safety measures in textile chemistry laboratories, including using personal protective equipment (PPE), proper training of workers, effective ventilation systems, and safe waste disposal protocols. Good laboratory work practices not only reduce risk but also promote better research; more accurate results; and better data. Therefore, this study aimed to map the risks and hazards of textile processing laboratories with a view to accident prevention and formalizing a protocol for good practices.

Project evaluation is a process that involves multiple decision criteria for different objectives and involves several specialists, becoming a complex decision-making process. In this sense, to reduce projects evaluation subjectivity in start-ups selection processes, this paper aims at structuring an evaluation model to select projects that present business models focused on strategies for smart cities. To test the proposed model, it was considered a smart cities project selection program in a medium-size city located on southside of Brazil. To do so, it was considered a multicriteria analysis method, in which relevant evaluation criteria for projects focused on intelligent cities were identified. Subsequently, Decision-Making And Trial Evaluation Laboratories (DEMATEL) method was used to weigh these identified evaluation criteria and, finally, TOPSIS method was instantiated to select the best alternative – the evaluated projects – among all alternatives. As a result, the multicriteria proposed model proved to be efficient in evaluating projects aimed at smart cities concepts, showing that the use of MCDA methods can minimise uncertainty and bias in evaluating projects in a quantitative approach.