Can Euseius alatus DeLeon (Acari: Phytoseiidae) prey on Aceria guerreronis Keifer (Acari: Eriophyidae) in coconut palm?

Ácaros do gênero Euseius são geralmente considerados especialistas na alimentação de pólen. Euseius alatus DeLeon é uma das seis espécies de ácaros fitoseídeos mais comumente encontrados em plantas de coqueiro no Nordeste do Brasil, associado com Aceria guerreronis Keifer. Apesar de a morfologia de E. alatus não favorecer a exploração da área meristemática do fruto habitada por A. guerreronis, o predador pode ter algum papel no controle do eriofídeo durante o processo de dispersão. O objetivo deste trabalho foi avaliar o desenvolvimento e a reprodução de E. alatus nas seguintes dietas: A. guerreronis, pólen de Ricinus communis (Euphorbiaceae); e Tetranychus urticae Koch (Tetranychidae) + pólen de R. communis + mel a 10 %. Euseius alatus desenvolveu-se mais rapidamente e ovipositou mais quando alimentada em dieta composta por T. urticae + pólen + mel. Contudo, os parâmetros da tabela de vida foram muito semelhantes em todas as dietas, sugerindo que E. alatus pode contribuir na redução da população de A. guerreronis no campo.Mites of the genus Euseius are generally considered specialist as pollen feeders. Euseius alatus DeLeon is one of the six species of phytoseiid mites most commonly found on coconut plants in northeast Brazil associated with Aceria guerreronis Keifer. Although the morphology of E. alatus does not favor the exploitation of the meristematic area of the fruit inhabited by A. guerreronis, the predator may have some role in the control of this eriophyid during the dispersion process. The objective of this work was to evaluate the development and reproduction of E. alatus on the following diets: A. guerreronis, Ricinus communis pollen (Euphorbiaceae), and Tetranychus urticae Koch (Tetranychidae) + R. communis pollen + honey solution 10%. Euseius alatus developed slightly faster and had slightly higher oviposition rate when feeding on the diet composed of T. urticae + pollen + honey. However, life table parameters were very similar on all diets, suggesting that E. alatus may contribute in reducing the population of A. guerreronis in the field.CNPqUniversidade Federal Rural de Pernambuc

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics

A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting

Wind speed is one of the primary renewable sources for clean power. However, it is intermittent, presents nonlinear patterns, and has nonstationary behavior. Thus, the development of accurate approaches for its forecasting is a challenge in wind power generation engineering. Hybrid systems that combine linear statistical and Artificial Intelligence (AI) forecasters have been highlighted in the literature due to their accuracy. Those systems aim to overcome the limitations of the single linear and AI models. In the literature about wind speed, these hybrid systems combine linear and nonlinear forecasts using a simple sum. However, the most suitable function for combining linear and nonlinear forecasts is unknown and the linear relationship assumption can degenerate or underestimate the performance of the whole system. Thus, properly combining the forecasts of linear and nonlinear models is an open question and its determination is a challenge. This article proposes a hybrid system for monthly wind speed forecasting that uses a nonlinear combination of the linear and nonlinear models. A data-driven intelligent model is used to search for the most suitable combination, aiming to maximize the performance of the system. An evaluation has been carried out using the monthly data from three wind speed stations in northeast Brazil, evaluated with two traditional metrics. The assessment is performed for two scenarios: with and without exogenous variables. The results show that the proposed hybrid system attains an accuracy superior to other hybrid systems and single linear and AI models

Diet-dependent life history, feeding preference and thermal requirements of the predatory mite Neoseiulus baraki (Acari: Phytoseiidae)

Neoseiulus baraki Athias-Henriot (Acari: Phytoseiidae) has been reported from the Americas, Africa and Asia, often in association with Aceria guerreronis Keifer (Acari: Eriophyidae), one of the most important pests of coconut (Cocos nucifera L.) in diVerent parts of the world. That phytoseiid has been considered one of the most common predators associated with A. guerreronis in Brazil. The objective of this study was to evaluate the feeding preference and the eVect of food items commonly present on coconut fruits and several temperature regimes on the life history of a Brazilian population of N. baraki. Completion of immature development was possible when N. baraki was fed A. guerreronis, Steneotarsonemus concavuscutum Lofego and Gondim Jr., and Tyrophagus putrescentiae (Schrank). Fecundity was highest on T. putrescentiae (39.4 eggs), followed by A. guerreronis (24.8 eggs). In choice tests, irrespective of the food on which N. baraki was reared, a larger number of adults of this predator chose leaf discs containing A. guerreronis than discs containing other food items, demonstrating a preference of the former for the latter as food. Egg to adult thermal developmental time was calculated as 84.2 degree-days, above a threshold of 15.8 degrees C. This lower developmental threshold is higher than previously published for phytoseiid species from higher latitudes. Neoseiulus baraki was shown to have higher biotic potential at 30 degrees C (r(m) 0.29). The results suggest N. baraki to be a promising biological control agent of A. guerreronis, well adapted to survive and develop in areas with relatively high temperatures, where that pest prevails.Brazilian Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)Austrian Governmen

Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review

The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS

Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods

The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector\u2019s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, and the autoregressive linear models, from the Box and Jenkins methodology. The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters