Introduçãouitos países estão enfrentando dificuldades para suprir a demanda crescente de energia de suas populações e, ao mesmo tempo, fornecer recursos energéticos para suprir seu crescimento econômico. Cabe, cada vez mais, ao poder público conhecer o comportamento dos consumidores para criar mecanismos que promovam o uso racional de energia nos diferentes setores e, assim, otimizar o uso de energia pela sociedade (Jannuzzi, 2005).Mecanismos legais de incentivo à conservação de energia têm sido empregados por vários países para reduzir o consumo de energia e as emissões de gases de efeito estufa. Os Estados Unidos, por exemplo, formularam suas primeiras normas de eficiência energética na década de 1970, quando ocorreu a crise de suprimento de petróleo (Dixon et al., 2010). A União Europeia também começou a formular seus instrumentos legais na mesma época, fixando metas de redução da demanda de energia e de emissão de poluentes, a serem atingidas pelos estados-membros (Fouquet, 2013).O Brasil começou a formular suas legislações de incentivo à eficiência energética na década de 1980. Segundo Geller et al. (2004, políticas nacionais para aumentar a geração de energia por fontes renováveis e a oferta interna de petróleo têm se mostrado bem-sucedidas. Enquanto políticas nacionais para promover o uso de medidas de eficiência energética, por sua vez, foram moderadamente bem-sucedidas.Há muito espaço para ampliar a gestão governamental na área de conservação de energia no Brasil, principalmente quanto à criação de instrumentos legais de incentivo à geração descentralizada de energia por fontes renováveis e de incentivo à eficiência energética. Objetivou-se com este trabalho discutir as atuais políticas brasileiras de eficiência energética, bem como desafios e oportunidades associados.

Spatial interpolation methods are frequently used to characterize soil attributes' spatial variability. However, inconclusive results, about the comparative performance of these methods, have been reported in the literature. Therefore, the present study aimed to analyze the efficiency of ordinary kriging (OK) and inverse distance weighting (IDW) methods in estimating the soil penetration resistance (SPR), soil bulk density (SBD), and soil moisture content (SM) using two distinct sampling grids. The soil sampling was performed on a 5.7 ha area in Southeast Brazil. For data collection, a regular grid with 145 points (20 x 20 m) was created. Soil samples were taken at a 0.20 m layer depth. In order to compare the accuracy of OK and IDW, another grid was created from the initial grid (A), by eliminating one interspersed line, which resulted in a grid with 41 sampled points (40 x 40 m). Results showed that sampling grid A presented less errors than B, proving that the more sampling points, the lower the errors that are associated with both methods will be. Overall, the OK was less biased than IDW only for SBD (A) and SM (B) maps, whereas IDW outperformed OK for the other attributes for both sampling grids.

The increasing market interest in coffee beverage, lead coffee growers around the world to adopt more efficient methods to select the best-quality coffee beans. Currently, coffee beans selection is carried out either manually, which is a costly and unreliable process, or using electronic sorting machines, which are often inefficient because some coffee beans defects, such as sour and immature beans, have similar spectral response patterns. In this sense, the present work aimed to analyze the importance of shape and color features for different machine learning techniques, such as Support Vector Machine (SVM), Deep Neural Network (DNN) and Random Forest (RF), to assess coffee beans' defects. For this purpose, an algorithm written in Python language was used to extract shape and color features from coffee beans images. The dataset obtained was then used as input to the machine learning algorithms, developed using Python and R programing languages. The data reported in this study pointed to the importance of color descriptors for classifying coffee beans defects. Among the variables used, the components G mean from RGB (Red, Green and Blue) color space and V mean from HSV (Hue, Saturation and Value) color space were some of the most relevant features for the classification models. The results reported in this study indicate that all the classifier models presented similar performance. In addition, computer vision along with machine learning algorithms can be used to classify coffee beans with a very high accuracy (> 88%).

The largest energy losses and voltage variations from electricity suppliers occur at times of peak demand. Daytime peaks are mainly influenced by large industrial and commercial consumers. The installation of photovoltaic systems without energy storage to supply part of the demand can contribute to stabilize the voltage and reduce losses. These and other benefits constitute the so-called externality of the decrease in off-peak demand due to photovoltaic generation. The objective of this study was to investigate the role of this externality in agroindustry, as a consumer of electrical energy, in order to better understand its effects on the consumer and the utility. The implementation of photovoltaic systems was simulated with the objective to reduce the off-peak contracted demand by agroindustries. Both the energy balance and the economic viability of the photovoltaic system implantation were analyzed. It was concluded that the photovoltaic system contributed to the reduction in energy costs, improved the load factor by about 47%, and reduced the off-peak contracted demand by about 20.2% and 54.2% for the small and medium-sized agroindustries considered, respectively.

The growing human population added to the rural exodus has aggravated the pressure in the agricultural sector for greater production. Faced with this problem, research has developed optical sensors for more productive agriculture with the purpose of minimizing the effects of rural exodus, obtaining rapid information and promoting the rational use of natural resources. Optical sensors have a differential consisting of the ability to use the spectral signature of an attribute or part of it to gain information, often not obvious. This review provides recent advances in optical sensors as well as future challenges. The studies have shown the wide range of applicability of optical sensors in agriculture, from detection of weeds to identification of soil fertility, which favors management in different areas of agriculture. The main limitation to the use of optical sensors in agriculture in most parts of the world has been the cost of purchasing the devices, especially in poor countries. So one of the future challenges is the reduction of final prices paid by consumers.