Coagulation is the most sensitive step in drinking water treatment. Underdosing may not yield the required water quality, whereas overdosing may result in higher costs and excess sludge. Traditionally, the coagulant dosage is set based on bath experiments performed manually, known as jar tests. Therefore, this test does not allow real-time dosing control, and its accuracy is subject to operator experience. Alternatively, solutions based on machine learning (ML) have been evaluated as computer-aided alternatives. Despite these advances, there is open debate on the most suitable ML method applied to the coagulation process, capable of the most highly accurate prediction. This study addresses this gap, where a comparative analysis between ML methods was performed. As a research hypothesis, a data-driven (D²) fuzzy inference system (FIS) should provide the best performance due to its ability to deal with uncertainties inherent to complex processes. Although ML methods have been widely investigated, only a few studies report hybrid neuro-fuzzy systems applied to coagulation. Thus, to the best of our knowledge, this is the first study thus far to address the accuracy of this non-hybrid data-driven FIS (D²FIS) for such an application. The D²FIS provided the smallest error (0.69 mg/L), overcoming the adaptive neuro-fuzzy inference system (1.09), cascade-correlation network (1.18), gene expression programming (1.15), polynomial neural network (1.20), probabilistic network (1.17), random forest (1.26), radial basis function network (1.28), stochastic gradient tree boost (1.25), and support vector machine (1.17). This finding points to the D²FIS as a promising alternative tool for accurate real-time coagulant dosage in drinking water treatment. In conclusion, the D²FIS can help WTPs to reduce operating costs, prevent errors associated with manual processes and operator experience, and standardize the efficacy with real-time and highly accurate predictions, and enhance safety for the water industry. Moreover, the evidence from this study can assist in filling the gap with the most suitable ML method and identifying a promising alternative for computer-aided coagulant dosing. For further advances, future studies should address the potential of the D²FIS for the control and optimization of other unit operations in drinking water treatment.

Urban green spaces (UGSs) play a vital role in enhancing the quality of life in cities, particularly in rapidly urbanizing regions such as the Metropolitan Region of São Paulo (MRSP). However, significant challenges related to equitable management and access persist, often exacerbated by socio-environmental inequalities. While much of the existing literature on UGS management focuses on developed economies, there is a gap in our understanding of how community engagement influences UGS outcomes in emerging economies, which face unique socio-economic and infrastructural constraints. This study addresses this gap by investigating the impact of community engagement on UGS management in the MRSP, specifically examining how increased participation correlates with improved UGS access and reduced socio-environmental inequality. Utilizing survey data from 33 municipal environmental departments across the MRSP, this research applied correlation tests, generalized linear models, and a non-parametric analysis of variance to evaluate the relationships between community engagement, UGS coverage, and inequality. The findings reveal a moderate positive correlation between community engagement and UGS coverage, alongside a negative correlation with socio-environmental inequality. Increased levels of community participation were associated with greater access to UGSs and a reduction in disparities across socio-economic groups. These results underscore the potential of fostering community involvement in UGS management to promote urban equity and environmental sustainability in emerging economies, particularly through institutional support and transparent information sharing. Future research should focus on longitudinal studies to better understand the long-term effects of sustained community engagement and incorporate qualitative data from community members to provide a more comprehensive analysis of participatory processes. Additionally, expanding the scope of analysis to include informal and private green spaces will offer a more holistic understanding of urban greening dynamics.

Understanding the key factors that play an important role in students’ performance can assist improvements in the teaching-learning process. As an alternative, artificial intelligence (AI) methods have enormous potential, facilitating a new trend in education. Despite the advances, there is an open debate on the most suitable model for machine learning applied to forecast student performance patterns. This paper addresses this gap, where a comparative analysis between AI methods was performed. As a research hypothesis, a fuzzy inference system (FIS) should provide the best accuracy in this forecast task, due to its ability to deal with uncertainties. To do so, this paper introduces a model proposal based on AI using a FIS. An online survey was carried to collect data. Filling out a self-report, respondents declare how often they use some learning strategies. In addition, we also used historical records of students’ grades and retention from the last 5 years before the COVID pandemic. Firstly, two experimental groups were composed of students with failing and passing grades, compared by the Mann-Whitney test. Secondly, an association between the ‘frequency of using learning strategies’ and ‘occurrence of failing grades’ was quantified using a logistic regression model. Then, a discriminant analysis was performed to build an Index of Student Performance Expectation (SPE). Considering the learning strategies with greater discriminating power, the fuzzy AI-based model was built using the database of historical records. The learning strategies with the most significant effect on students’ performance were lesson review (34.6%), bibliography reading (25.6%), class attendance (23.5%), and emotion control (16.3%). The fuzzy AI-based model proposal outperformed other AI methods, achieving 94.0% accuracy during training and a generalization capacity of 91.9% over the testing dataset. As a practical implication, the SPE index can be applied as a tool to support students’ planning in relation to the use of learning strategies. In turn, the AI model based on fuzzy can assist professors in identifying students at higher risk of retention, enabling preventive interventions.

Urban green spaces (UGS) are vital for the quality of life in cities. However, managing these spaces in metropolitan areas is increasingly challenging. There is a critical gap regarding the specific challenges and strategies employed by municipalities of varying sizes in emerging economies. Most existing research focuses on developed countries, leaving a void in understanding how these economies manage UGS under different constraints. This study delves into the challenges and strategies associated with managing UGS in the metropolitan region of São Paulo (MRSP), Brazil, comparing the experiences of the municipalities to provide empirical evidence that can inform policy making and urban planning practices in similar contexts. A structured questionnaire was distributed to environmental departments across 39 municipalities in MRSP. The survey included questions on population size, priority given to UGS, challenges faced and strategies adopted to overcome barriers. Chi-square tests were used to analyze the association between municipality size and the identified challenges and strategies. This study found that larger municipalities face significant barriers related to high population density and land use conflicts, which are less pronounced in smaller municipalities. However, strategies adopted to manage UGS were similar across all municipalities. Policy makers should focus on promoting vertical green spaces, expanding urban greening programs, and ensuring equitable resource allocation. Public–private partnerships and community involvement in planning processes are crucial for sustainable UGS management. Integrating UGS policies with broader urban development plans is essential for creating cohesive urban environments. Further research should conduct comparative studies across multiple metropolitan regions in different countries to provide a more comprehensive understanding of UGS management globally. This study integrates insights from urban ecology, landscape architecture, and public policy, emphasizing the interdisciplinary approach required for effective UGS management. The findings offer practical guidance for urban planners and landscape architects to design sustainable urban landscapes.

Urban Green Spaces (UGS) are integral to advancing urban sustainability and improving the quality of life in cities. However, in rapidly urbanizing regions like the São Paulo Metropolitan Region (MRSP), significant environmental injustices in UGS accessibility present a complex challenge that requires in-depth understanding. Notably, existing studies predominantly focus on developed countries, leaving a gap in research concerning emerging economies in the Global South. This study aims to analyze the associations between sociodemographic factors and environmental racism in UGS accessibility within the municipalities of MRSP. The research utilizes Spearman Rank Correlation and multiple linear regression analyses on data sourced from the Brazilian Institute of Geography and Statistics and the Urban Green Data Platform. Key variables include the number of inhabitants, territorial area, population density, urbanization rate, gross domestic product (GDP), human development index (HDI), urban vegetation coverage, UGS per capita, and the difference between the total population and the Black or Indigenous populations residing outside the vicinity of UGSs as an indicator of environmental racism. The findings reveal significant correlations between higher GDP and HDI with increased environmental racism in UGS accessibility, suggesting that, in the absence of equitable policies, economic and human development may exacerbate disparities in green space distribution. Moreover, the study demonstrates that increased urban vegetation coverage is significantly associated with reduced environmental disparities, underscoring the role of urban greenery in mitigating inequality. These results emphasize the need for comprehensive urban planning and targeted policies that prioritize the equitable development of UGS, particularly in underserved areas. Future research should explore longitudinal data to establish causality and consider additional variables such as political governance and cultural factors, which could provide a more comprehensive understanding of environmental racism in UGS accessibility.

The global surge in urbanization and population growth has led to a significant increase in municipal solid waste generation, posing a considerable challenge in identifying suitable landfill sites. This study proposes a novel framework that enhances landfill site monitoring and assessment by combining GIS-based hierarchical analytical processes with a fuzzy inference system (FIS). The study employs a systematic approach involving phases such as feature selection, spatial analysis, criteria weighting, FIS building, and a case study conducted in São Paulo State, Brazil. The proposed framework effectively assesses landfill suitability and offers practical recommendations for landfill management and future site selection. This framework provides actionable recommendations for landfill monitoring and assessment, supporting landfill management while minimizing environmental and social impacts. It offers a comprehensive approach to landfill assessment, enhancing the sustainability of waste management practices. Further research can improve the proposed framework by refining feature selection and incorporating real-time data for continuous monitoring. Additionally, exploring the integration of emerging technologies, such as remote sensing and artificial intelligence, can further enhance landfill site monitoring and assessment.