Smart cities (SC) promote economic development, improve the welfare of their citizens, and help in the ability of people to use technologies to build sustainable services. However, computational methods are necessary to assist in the process of creating smart cities because they are fundamental to the decision-making process, assist in policy making, and offer improved services to citizens. As such, the aim of this research is to present a systematic review regarding data mining (DM) and machine learning (ML) approaches adopted in the promotion of smart cities. The Methodi Ordinatio was used to find relevant articles and the VOSviewer software was performed for a network analysis. Thirty-nine significant articles were identified for analysis from the Web of Science and Scopus databases, in which we analyzed the DM and ML techniques used, as well as the areas that are most engaged in promoting smart cities. Predictive analytics was the most common technique and the studies focused primarily on the areas of smart mobility and smart environment. This study seeks to encourage approaches that can be used by governmental agencies and companies to develop smart cities, being essential to assist in the Sustainable Development Goals.

The aim of this paper is to determine the existence of gaps in the literature, by investigating studies that statistically analyzed the relationship between sustainable development and economic performance. A literature review was conducted in the Web of Science and Scopus databases. The study analyzed the authors, publication years, journals involved, methodologies used, and results obtained. The identified gaps and opportunities were (a) opportunity to create or employ different measurements for financial, social, and environmental performance and (b) to use different kinds of control or moderating variables, in order to further explore the relationship between sustainable development and financial performance.

The use of petroleum-based packaging and its disposal in the environment poses several environmental problems, driving research into the development of biopolymers as substitutes for conventional polymers. Therefore, this study used the by-product of potato industrialization as the main raw material, xanthan gum as a plasticizer, and natural oat fiber as reinforcement to develop a biodegradable foam through thermo-pressing. The morphology, mechanical properties, and biodegradability of the final product were investigated. The water absorption and solubility index were highest in the sample with 20% plasticizer and 20% fiber. The water activity was not affected by variations in additives. The samples with the highest concentration of additives had the highest mechanical tensile strength, but there was a limit to these levels for foam resistance. The most accentuated visual effect was the yellow color. It is believed that hydrolysis was the main foam degradation mechanism, which took between 14 and 20 days for total decomposition. The combination of a by-product from potato industrialization with xanthan gum and natural oat fiber made it possible to produce a promising substitute for synthetic polymers, providing an environmentally friendly solution for both the use of agro-industrial by-products and reducing the volume of petroleum-based packaging waste.

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Dimensionality reduction is used in microarray data analysis to enhance prediction quality, reduce computing time, and construct more robust models. In addition, the algorithm learning performance involves an expressive number of attributes (genes) relative to the classes (samples). Therefore, in this paper, we conducted a detailed comparison of two reduction methods, attribute selection and principal component analysis, to analyze gene expression data sets. Both reduction methods were employed in the pre-processing stage and then evaluated experimentally. Furthermore, we introduced a combination of consistency-based subset evaluation (CSE) and minimum redundancy maximum relevance (mRMR), which we referred to as CSE-mRMR, to improve classification efficiency. The results indicated a significant increase in classifier hit rates with both methods, compared to using all attributes. By employing cross-validation, attribute selection outperformed PCA consistently across classifiers and datasets, and CSE-mRMR demonstrated good classification performance in the data sets. Taken together, the literature and current results suggest that the attribute selection may be relevant in the analysis and future prediction of gene expression data sets.