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Speech emotion recognition is key to many fields, including human–computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required within successful emotion recognition models. This work aimed to investigate whether splitting the features into two subsets based on their distribution and then applying commonly used feature reduction methods would impact accuracy. Filter reduction was employed using the Kruskal–Wallis test, followed by principal component analysis (PCA) and independent component analysis (ICA). A set of features was investigated to determine whether the indiscriminate use of parametric feature reduction techniques affects the accuracy of emotion recognition. For this investigation, data from three databases—Berlin EmoDB, SAVEE, and RAVDES—were organized into subsets according to their distribution in applying both PCA and ICA. The results showed a reduction from 6373 features to 170 for the Berlin EmoDB database with an accuracy of 84.3%; a final size of 130 features for SAVEE, with a corresponding accuracy of 75.4%; and 150 features for RAVDESS, with an accuracy of 59.9%. [ABSTRACT FROM AUTHOR]

Urban waters (UW) are complex environments, and their definition is related to water systems in urban zones, whether in a natural system or an urban facility. The health of these environments is related to public health and the quality of life because public health is the focal point of environmental and anthropic impacts. Infrastructure is paramount for maintaining public health and social and economic development sanitation. Insufficient infrastructure favors disease vectors. The population and environment suffer from deficient urban water infrastructure in Brazil despite government efforts to manage the existing systems. In this work, machine learning (regression trees) demonstrates the deficiency of sanitation and UW management fragmentation on public health by using the Aedes aegypti infestation index (HI) and water supply, wastewater, stormwater and drainage indicators (SNIS data). The results show that each Brazilian region faces different problems. The more infested regions were Northeastern, Northern and Southeastern. Moreover, municipalities with better SNIS data have lower infestation rates. Minimizing problems related to sanitation through the integrated management of water and urban areas is extremely important in developing countries. UW governance is connected to public health. Water management fragmentation leads to more complex issues, and managers must confront them to improve the quality of life in urban zones. [ABSTRACT FROM AUTHOR]

Urban water consumption can be influenced by several factors and may have different patterns due to regional and local specificities. This research aims to determine the influence of climatic, geographic, demographic, economic, social and water-supply-service-related factors on the per capita water consumption in Brazil using a dataset with 1746 municipalities. For this purpose, a multiple linear regression model using log-transformed data was fitted. The results show that household income, water tariff, number of residents per household, temperature, percentage of households with washing machine, city population, gender, percentage of households with piped water and the municipality's GDP significantly influence urban water consumption. [ABSTRACT FROM AUTHOR]

Public health is at the core of all environmental and anthropic impacts. Urban and territorial planners should include public health concerns in their plans. Basic sanitation infrastructure is essential to maintaining public health and social and economic development. This infrastructure deficiency causes diseases, death and economic losses in developing countries. Framing interconnections among health, sanitation, urbanization and circular economy will assist sustainable development goal achievements. This study aims to identify the relationships between solid waste management indicators in Brazil and the Aedes aegypti mosquito infestation index. Regression trees were employed for modelling due to the complexity and characteristics of the data. The analyses were performed separately from data collected from 3501 municipalities and 42 indicators from the country's five regions. Results show that expenses and personnel indicators were the most critical indicators (in the mid-western, southeastern and southern regions), operational (northeastern (NE) region) and management (northern region). The mean absolute errors ranged from 0.803 (southern region) to 2.507 (NE region). Regional analyses indicate that the municipalities with better SWM results display lower infestation rates in buildings and residences. This research is innovative as it analyses infestation rates rather than dengue prevalence, using a machine learning method, in a multidisciplinary research field that needs further study. [ABSTRACT FROM AUTHOR]

Water is largely used in urban-environment human activities; thus, it is essential to properly understand consumption characteristics so that measures to ensure a more efficient use of water resources can be implemented. This study aims to evaluate how different factors impact water consumption in office buildings using longitudinal and cross-sectional approaches in a case study conducted in southern Brazil. Data from 53 offices were used to build statistical water consumption models using the multiple linear regression method. In addition, the impact of the COVID-19 pandemic on water consumption in this building typology was assessed. The significant variables for the per capita water consumption regression model are the age of the building and the total number of employees. For consumption per floor-plan area (m3/m2/year), the variables are whether the building is owned or rented, its age, its size, and the total number of employees. The COVID-19 pandemic impact analysis showed a 10% reduction in the average water consumption during the first wave, when considering all offices in the sample. The analysis by business activity showed a reduction of 28% and 27% in the average consumption of water in law and accounting firms, respectively. In contrast, it went up by 15% in engineering offices during the same period. The analysis of the impact of the pandemic and the investigation of the main drivers of water consumption can provide parameters to promote water conservation in office buildings and can be used in the management of urban water demand. [ABSTRACT FROM AUTHOR]

Practical and theoretical evidence suggests that Sustainable Lean Six Sigma contributes to an organization's performance as it highlights environmental aspects. However, the theoretical contribution of Sustainable Lean Six Sigma has not been discussed enough in the literature, and many companies do not know where to begin integrating Sustainability and Lean Six Sigma in operation management. This paper aims to identify and discuss recent developments in Sustainable Lean Six Sigma frameworks through a systematic literature review, considering the benefits, outcomes, tools and practices, sustainability key performance indicators and critical success factors. The results show that Lean Six Sigma tools have been adapted to incorporate the measurement of environmental waste. However, sustainability key performance indicators must improve and there are few empirical studies on critical success factors for Sustainable Lean Six Sigma. This study also identified gaps in Sustainable Lean Six Sigma frameworks and gives directions for future research. [ABSTRACT FROM AUTHOR]

Clandestine connections represent a significant portion of the apparent water losses. The objective of this research work is to propose a systematic approach to inspect sites with possible illegal water consumption more precisely. A case study was conducted in a District Metered Area (DMA) located in the city of Joinville, Southern Brazil, using technical information and consumption history from 1400 consumer units located in this area. The proposed methodology uses the bagging technique called Random Forest, through a machine learning algorithm. With success rates of 88.10% when classifying frauds in the model training phase and 88.20% in the test phase, the obtained model shows great ability to properly classify frauds in the water supply system in the studied DMA. The use of the random forest classification model, combined with the SMOTE technique for data balancing, proves to be a viable technical alternative for the detection of unauthorized water consumption. [ABSTRACT FROM AUTHOR]

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