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Resumo Dados espacializados de precipitação diária são úteis para diversas aplicações relacionadas à meteorologia aplicada e à hidrologia, tais como a detecção de mudanças climáticas, estudo de inundações, e compreensão da interação do clima com os processos hidrológicos e biogeoquímicos. Contudo, cada espacialização da chuva deve ser avaliada para uma determinada região específica antes de sua ampla utilização. Nesse sentido, o presente trabalho objetivou, para as bacias hidrográficas do sul do Espírito Santo (bioma Mata Atlântica), a avaliação de métodos de interpolação espacial, a criação de uma base de dados espaciais de precipitação diária em formato grid, e a comparação desta base com outras bases de dados de precipitação diária em formato grid. A avaliação dos interpoladores foi feita por meio de validação cruzada, na qual o método da Distância Angular Ponderada (ADW) apresentou melhor desempenho. A aplicação do ADW, para todos os dias do período entre 1980 e 2018, originou uma base de dados de precipitação diária em formato grid com resolução espacial de 0,01°. A base de dados criada teve desempenho semelhante à de XAVIER para a representação espacial das chuvas na região, sendo melhor que as demais avaliadas.

The importance of daily data on reference evapotranspiration (ET0) has increased in recent years due to its relevance in planning and decision making regarding irrigated agriculture, water production, and forest restoration. Facing the scarcity of this information measured in loco, the study of interpolation methods capable of representing ET0 becomes important. Therefore, this study aimed to evaluate the adequacy of the Random Forest (RF) method in the spatialization of ET0 in the watersheds of the Mid-South region of the Espírito Santo State, located within the Atlantic Forest biome, Brazil. From this study, it was found that the RF method is the most suitable one for ET0 spatialization when compared to the Angular distance weighting (ADW) and the inverse distance weighting (IDW) techniques. Also, the spatializations carried out by this method were transformed into databases in a grid format and made available online. Furthermore, the RF database was also compared to other ET0 grid databases, and it was concluded that the RF database also carried out a better performance than the other ones. [ABSTRACT FROM AUTHOR]

Abstract Background With the further development of anaerobic digestion, an increasing output of anaerobically digested wastewater (ADW), which typically contained high concentrations of ammonium, phosphate, and suspended solids, was inevitable. Microalgae cultivation offered a potential waste-to-value strategy to reduce the high nutrient content in ADW and obtain high value-added microalgae. However, ADW generally contained a mass of pollutants (suspended solids, competitors, etc.), which could inhibit microalgae growth and even result in microalgae death by limiting light utilization. Thus, it is highly imperative to solve the problem by a novel modified photobioreactor for further practical applications. Results Four microalgae species, Scenedesmus dimorphus, Scenedesmus quadricauda, Chlorella sorokiniana, and Chlorella vulgaris ESP-6, were cultivated in the membrane photobioreactor (MPBR) fed with ADW to investigate the efficiency of ammonia and phosphorus removal. The results showed that C. sorokiniana had the best performance for the removal of ammonia and phosphorus from ADW. The highest amount of C. sorokiniana biomass was 1.15 g/L, and the removal efficiency of phosphate (66.2%) peaked at an ammonia concentration of 128.5 mg/L after 9 days’ incubation. Moreover, the MPBR with 0.1 μm membrane pore size had the best ammonia and phosphate removal efficiencies (43.9 and 64.9%) at an ammonia concentration of 128.5 mg/L during 9 days’ incubation. Finally, the continuous multi-batch cultivation of C. sorokiniana was performed for 45 days in MPBR, and higher removal ammonia amount (18.1 mg/day) and proteins content (45.6%) were obtained than those (14.5 mg/day and 37.4%) in an normal photobioreactor. Conclusion In this study, a novel MPBR not only eliminated the inhibitory effects of suspended solid and microorganisms, but also maintained a high microalgae concentration to obtain a high amount of ammonia and phosphate removal. The research provided a theoretical foundation for the practical application of MPBRs in various wastewater treatment schemes without pretreatment by algae, which could be used as biofuels or protein feed.

(1) Background: Elevated atmospheric CO2 concentration affects the growth and development of the rice crop. In Southern Brazil, rice is traditionally produced with continuous irrigation, implying a significant amount of water used. Besides, continuous flooding favors the uptake of toxic elements such as arsenic (As) and cadmium (Cd). In this work, one Brazilian rice cultivar (IRGA 424) was tested for the effects of elevated CO2 concentration and different water regimes on rice yield, and As and Cd accumulation in grain. (2) Methods: Rice was grown in two CO2 concentrations (400 and 700 µmol mol−1) and two irrigation regimes (continuous and intermittent). It was evaluated the number of tillers, plant height, aboveground dry weight (ADW), water use efficiency (WUE), rice yield components, and As and Cd concentration in rice grain. (3) Results: Rice plants were taller and had a higher WUE when cultivated at e[CO2]. The ADW and the rice yield component were not affected by CO2 levels nor water regimes. Intermittent flood regimes had a lower average As concentration. The Cd concentration in the samples in both growing seasons and all treatments was below the limit of quantitation (8.76 μg kg−1). (4) Conclusions: Enhanced CO2 concentration did not affect rice yield, increased the WUE, and reduced As concentration in grains. Regarding water management, the intermittent regime enhanced WUE and promoted a reduction in As concentration in grains.

Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

(1) Background: Elevated atmospheric CO2 concentration affects the growth and development of the rice crop. In Southern Brazil, rice is traditionally produced with continuous irrigation, implying a significant amount of water used. Besides, continuous flooding favors the uptake of toxic elements such as arsenic (As) and cadmium (Cd). In this work, one Brazilian rice cultivar (IRGA 424) was tested for the effects of elevated CO2 concentration and different water regimes on rice yield, and As and Cd accumulation in grain. (2) Methods: Rice was grown in two CO2 concentrations (400 and 700 µmol mol−1) and two irrigation regimes (continuous and intermittent). It was evaluated the number of tillers, plant height, aboveground dry weight (ADW), water use efficiency (WUE), rice yield components, and As and Cd concentration in rice grain. (3) Results: Rice plants were taller and had a higher WUE when cultivated at e[CO2]. The ADW and the rice yield component were not affected by CO2 levels nor water regimes. Intermittent flood regimes had a lower average As concentration. The Cd concentration in the samples in both growing seasons and all treatments was below the limit of quantitation (8.76 μg kg−1). (4) Conclusions: Enhanced CO2 concentration did not affect rice yield, increased the WUE, and reduced As concentration in grains. Regarding water management, the intermittent regime enhanced WUE and promoted a reduction in As concentration in grains. [ABSTRACT FROM AUTHOR]

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Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts' knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques. [ABSTRACT FROM AUTHOR]