Abstract
This article studies the influence of digital marketing in consumers' making process regarding plant-based diet. The investigation was exploratory, and adopted a descriptive method, consisting of 11 face-to-face interviews and four focus groups with plant-based diet adopters and supporters. A non-probabilistic convenience sampling method was used to recruit participants. This study found that supporters differ from adopters of a plant-based diet regarding their concerns with certification and the difficulty in understanding labels.

Abstract

Abstract
Visualization can be defined as a technique that allows us to obtain the perception of an object/event in a clear and consistent way. The use of visualization in education is a key factor to explain complex information in a clear way. Therefore, it is essential to have tools capable of visualizing various types of data. An example of a data type is the weather forecast data, which includes various atmospheric data for a given place, and allows the simulation of the atmospheric evolution. It is used for decision making in many areas, such as, agriculture, fishing, tourism, etc. Thus, it is beneficial to demonstrate the usefulness of this type of visualization to better understand the meteorological phenomena, as well as to teach scientific visualization techniques in order to enable access to information that otherwise can only be interpreted by qualified people. In this article it will be discussed the scientific visualization and its benefits to the area of meteorology, and it will be presented a case study of data visualization using the ParaView tools for meteorological data visualization and analysis. ParaView is a multiplatform tool based on the Visualization Toolkit (VTK) that provides features to process, analyze, and visualize various types of data. This study aims to present a tool for scientific visualization and to demonstrate its applications and usefulness for education. © 2021, Springer Nature Switzerland AG.

Abstract

Abstract
Photovoltaic (PV) power is considered as one of the most promising sustainable energy resources in recent years. However, the existing intermittency in the nature of solar energy is a significant problem for the optimization of smart grids. In this paper, to overcome PV generation uncertainty and provide an accurate spatio-temporal (ST) PV forecast, we propose a novel deep generative convolutional graph rough variational autoencoder (CGRVAE) that captures each PV site's probability distribution functions (PDFs) of future PV generation in a modeled weighted graph. Having the learned PDFs enables CGRVAE to accurately generate the future values of PV power time series. To train and evaluate our model, we used the measurements of a set of PV sites in California, US. The sites are modeled as a weighted graph where each node represents PV measurements at each site while edges reflect their correlations. Using graph spectral convolutions the proposed model extracts the most relevant information of the graph to estimate the future PV given the historical time series for each node in the modeled graph. Experimental results show the superiority of CGRVAE over state-of-the-art forecasting approaches in terms of the root mean square error (RMSE) and mean absolute error (MAE) metric.

Abstract
Cylindrical structure analysis is important in several areas and can be performed through the evaluation of the diameter changes of these structures. Two important areas can be mentioned: pipelines for oil or gas distribution and the condition and growth of trees. In the tree diameter changes, monitoring is directly related to irrigation, since it depends on the water soil deficit and trees are important in the global circulation of heat and water. This diameter can change in the order of 5 mm for some species. In this paper, a strain gauge sensor based on a core diameter mismatch technique for diameter measurement is proposed and investigated. The sensor structure is formed by splicing an uncoated short section of MMF (Multimode Fiber) between two standard SMFs (Singlemode Fiber) called SMF–MMF–SMF (SMS); the MMF length is 15 mm. Two cylindrical structures were placed on a 3D printer, with different diameter sizes ((Formula presented.) : 80 mm and 110 mm), to assist in monitoring the diameter changes. The SMS sensor was placed on the printed structure and fixed at two points, such that, by reducing the diameter of the structure, the sensor presents dips or peaks shift of the transmittance spectrum due to the induced curvature and strain. Three values were used for the spacing between the fixation points ((Formula presented.)): (a) 5 mm, (b) 10 mm, and (c) 15 mm. For each choice of fixation points, (Formula presented.) = 80 mm: (a) a sensitivity of -0.876 nm/mm, (Formula presented.) of 0.9909 and a dynamic range of 5 mm; (b) a sensitivity of -0.3892 nm/mm, (Formula presented.) of 0.9954 and a dynamic range of 4 mm; and (c) a sensitivity of -0.768 nm/mm, (Formula presented.) of 0.9811 and a dynamic range of 2 mm. For (Formula presented.) = 110 mm, the sensor presents for each choice of fixation points: (a) a sensitivity of -0.22 nm/mm, (Formula presented.) of 0.9979 and a dynamic range of 8 mm; (b) a sensitivity of -0.2284 nm/mm, (Formula presented.) of 0.9888 and a dynamic range of 6 mm; and (c) a sensitivity of -0.691 nm/mm, (Formula presented.) of 0.9892 and a dynamic range of 3.5 mm. © 2021 by the authors.