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The rise in performance and health concerns presents a complex challenge to workplace well-being. While existing research highlights the positive impact of exposure to nature on well-being, there is a gap in supporting biophilic building designs in workplaces. This study addresses this issue by introducing a Type-1 Interval Fuzzy Set (IFS) framework to assess workplace health, and recommends Nature-Based Solutions (NbS) for enhancement. The proposed framework comprises two fuzzy subsystems targeting environmental comfort and occupational health that are integrated into a third subsystem to provide tailored NbS recommendations. A case study conducted in a coworking space validated the framework's practical application, revealing significant improvements in environmental and occupational conditions. Key findings include a reduction in absenteeism, enhancement in employee satisfaction, and creation of more comfortable and restorative environments. Recommendations, such as green walls, indoor gardens, and natural light filters, have been shown to effectively mitigate discomfort and promote well-being. The framework demonstrated flexibility in managing uncertainties, optimizing resource allocation, and providing targeted interventions for workplace conditions. These results highlight the framework's potential as a robust decision-support tool for improving workplace health and well-being through systematic and evidence-based strategies. [ABSTRACT FROM AUTHOR] more...

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. [ABSTRACT FROM AUTHOR] more...

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. [ABSTRACT FROM AUTHOR] more...

Landslides represent a growing global geological hazard, further intensified by climate‐induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio‐temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel‐2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning‐based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single‐class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1‐Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high‐accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context. [ABSTRACT FROM AUTHOR] more...

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. [ABSTRACT FROM AUTHOR] more...

Statement of Problem: Environmental engineering confronts complex challenges characterized by significant uncertainties. Traditional modeling methods often fail to effectively address these uncertainties. As a promising direction, this study explores fuzzy machine learning (ML) as an underutilized alternative. Research Question: Although the potential of fuzzy logic is widely acknowledged, can its capabilities truly enhance environmental engineering applications? Purpose: This research aims to deepen the understanding of the role and significance of fuzzy logic in managing uncertainty within environmental engineering applications. The objective is to contribute to both theoretical insights and practical implementations in this domain. Method: This research performs a systematic review carried out in alignment with PRISMA guidelines, encompassing 27 earlier studies that compare fuzzy ML with other methods across a variety of applications within the field of environmental engineering. Results: The findings demonstrate how fuzzy-based models consistently outperform traditional methods in scenarios marked by uncertainty. The originality of this research lies in its systematic comparison and the identification of fuzzy logic's transparent, interpretable nature as particularly suited for environmental engineering challenges. This approach provides a new perspective on integrating fuzzy logic into environmental engineering, emphasizing its capability to offer more adaptable and resilient solutions. Conclusions: The analysis reveals that fuzzy-based models significantly excel in managing uncertainty compared to other methods. However, the study advocates for a case-by-case evaluation rather than a blanket replacement of traditional methods with fuzzy models. This approach encourages optimal selection based on specific project needs. Practical Implications: Our findings offer actionable insights for researchers and engineers, highlighting the transparent and interpretable nature of fuzzy models, along with their superior ability to handle uncertainties. Such attributes position fuzzy logic as a promising alternative in environmental engineering applications. Moreover, policymakers can leverage the reliability of fuzzy logic in developing ML-aided sustainable policies, thereby enhancing decision-making processes in environmental management. [ABSTRACT FROM AUTHOR] more...

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. [ABSTRACT FROM AUTHOR] more...

For the monitoring and assessment of Atlantic Forest ecosystems, key indicators of the regeneration stage are considered. However, the current classification of these stages by experts does not consider the possible differences among such ecosystems. In order to test the hypothesis of significant differences, Atlantic Forest ecosystems in southern Brazil were compared at the same stage (initial, medium, and advanced stages of regeneration). An extensive database of the floristic forest inventory of Santa Catarina state, southern Brazil, with 460 sampling units, was used, addressing the seasonal deciduous forest (SDF), dense ombrophilous forest (DOF), and mixed ombrophilous forest (MOF). The regeneration stage of each sample unit was previously classified by experts using five key indicators (basal area per hectare—BA/ha; number of individuals per hectare—N/ha; number of species—S; Shannon biodiversity index—H'; and diameter at breast height—DBH). The Kruskal–Wallis method and pairwise multiple comparisons based on Dunn's procedure were applied, considering two-way tests with 5% significance, and 95% power. The statistical tests confirmed the research hypothesis, namely, that the key indicators had significant differences in the later regeneration stages of Atlantic Forest ecosystems. For instance, S in DOF was statistically higher (p < 0.001) than in MOF and SDF (44 ± 9); N in SDF was significantly lower (394 ± 248 at mid-stage) compared to MOF (475 ± 233) and DOF (545 ± 173); and H' showed increasing differences towards more advanced stages. Considering that the stage of forest regeneration is the main criterion for decision-making between suppression and conservation of forest ecosystems, the results achieved should support the review of current procedures applied to the classification of successional stages. Given the demonstrated differences, in the next steps of research, we will evaluate and propose specific standards for each Atlantic Forest ecosystem, i.e., intervals of discriminating values for the key indicators applicable to this biome. [ABSTRACT FROM AUTHOR] more...

The synergistic use of remote sensing and unsupervised machine learning has emerged as a potential tool for addressing a variety of environmental monitoring applications, such as detecting disaster-affected areas and deforestation. This paper proposes a new machine-intelligent approach to detecting and characterizing spatio-temporal changes on the Earth's surface by using remote sensing data and unsupervised learning. Our framework was designed to be fully automatic by integrating unsupervised anomaly detection models, remote sensing image series, and open data extracted from the Google Earth Engine platform. The methodology was evaluated by taking both simulated and real-world environmental data acquired from several imaging sensors, including Landsat-8 OLI, Sentinel-2 MSI, and Terra MODIS. The experimental results were measured with the kappa and F1-score metrics, and they indicated an assertiveness level of 0.85 for the change detection task, demonstrating the accuracy and robustness of the proposed approach when addressing distinct environmental monitoring applications, including the detection of disaster-affected areas and deforestation mapping. [ABSTRACT FROM AUTHOR] more...

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. [ABSTRACT FROM AUTHOR] more...

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. [ABSTRACT FROM AUTHOR] more...

Mental health benefits have been linked to human interaction with nature. However, most studies have been carried out in developed countries, limiting the generalizability of empirical findings to other parts of the world. To address this gap, this study was conducted in Brazil. The objective of the study was to assess whether the frequency of contact with nature affects the occurrence of anxiety, stress, and depression. Data were collected between June and July 2022 through an online survey (n = 1186, 1 − α = 0.95, p = 0.05, 1 − β = 0.85, rho = 0.1). Thus, the public online survey made it possible to carry out voluntary response sampling suitable for an exploratory study, with the purpose of an initial understanding of an under-researched population. First, a logistic regression was performed for quantifying the association between contact with nature and mental symptoms. In addition, three groups of people having different frequencies (low, medium, and high) of contact with nature and a reference group, comprised of those who reported no contact, were compared using Kruskal–Wallis and Dwass–Steel–Chritchlow–Fligner tests. This study employs a cross-sectional design and relies on retrospective recall. As a result, the research hypothesis was confirmed. People who very rarely have contact with nature had a 97.95% probability of moderate occurrence of stress, which decreases to 20.98% for people who have contact with nature frequently. Furthermore, in the same comparison, the probability of occurrence was 3.6 times lower for anxiety and 4.8 times lower for depression. In conclusion, the evidence indicates that the greater the frequency of contact with nature, the lower the occurrence of stress, anxiety, and depression symptoms. Nevertheless, the benefits of this contact were significant only when its frequency was moderate (about once or twice a week) or higher. [ABSTRACT FROM AUTHOR] more...

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This study evaluated the effect of the combination of three different onlay preparation designs and two restorative materials on the stress distribution, using 3D-finite element analysis. Six models of first lower molars were created according to three preparation designs: non-retentive (nRET), traditional with occlusal isthmus reduction (IST), and traditional without occlusal isthmus reduction (wIST); and according to two restorative materials: lithium-disilicate (LD) and nanoceramic resin (NR). A 600 N axial load was applied at the central fossa. All solids were considered isotropic, homogeneous, and linearly elastic. A static linear analysis was performed, and the Maximum Principal Stress (MPS) criteria were used to evaluate the results and compare the stress in MPa on the restoration, cement layer, and tooth structure (enamel and dentin). A novel statistical approach was used for quantitative analysis of the finite element analysis results. On restoration and cement layer, nRET showed a more homogeneous stress distribution, while the highest stress peaks were calculated for LD onlays (restoration: 69–110; cement layer: 10.2–13.3). On the tooth structure, the material had more influence, with better results for LD (27–38). It can be concluded that nRET design showed the best mechanical behavior compared to IST and wIST, with LD being more advantageous for tooth structure and NR for the restoration and cement layer. [ABSTRACT FROM AUTHOR] more...