The development of big data technologies, which have been applied extensively in various areas, has become one of the key factors affecting modern society, especially in the virtual reality environment. This paper provides a comprehensive survey of the recent developments in big data technologies, and their applications to virtual reality worlds, such as the Metaverse, virtual humans, and digital twins. The purpose of this survey was to explore several cutting-edge big data and virtual human modelling technologies, and to raise the issue of future trends in big data technologies and the Metaverse. This survey investigated the applications of big data technologies in several key areas—including e-health, transportation, and business and finance—and the main technologies adopted in the fast-growing virtual world sector, i.e., the Metaverse.

Hyperspectral imaging is a non-destructive method for the detection of chilling injury in fruit. However, the limitation of this technique is the lacking of an appropriate working parameters and a feasible discriminating model for chilling on-line sorting. This research was aimed to select the optimal spectral resolution, scanning speed, and classification model for green jujube chilling injury detection based on hyperspectral reflectance imaging. Criminisi algorithm was firstly carried out to reconstruct the specular reflection region in spectral images before deriving mean spectra, and thus the optimal wavelengths were selected by random frog. Results showed that the Criminisi algorithm presented a desirable ability of spectral image inpainting. The linear discriminant analysis (LDA) achieved overall accuracies of 98.3% and 93.3% for two-class and three-class classification, respectively, at the speed of 20 mm/s with the spectral resolution of 5.03 nm based on selected spectral features. The results demonstrated that 20 mm/s with the spectral resolution of 5.03 nm was more feasible for the detection of green jujube chilling injury in hyperspectral imaging system due to a higher scanning efficiency, but a less data size.

In heterogeneous environments, mobile species should occupy habitats in which their fitness is maximized. Mangrove rivulus fish inhabit mangrove ecosystems where salinities range from 0–65 ppt but are most often collected at ∼25 ppt. We examined rivulus’ salinity preference in a lateral salinity gradient, in the absence of predators and competitors. Fish could swim freely for 8 hours throughout the gradient with chambers containing salinities from 5–45 ppt (or 25 ppt throughout, control). We defined preference as the salinity in which the fish spent most of their time, and also measured preference strength, latency to begin exploring the arena, and number of transitions between chambers. To determine whether these traits were repeatable, each fish experienced three trials. Rivulus spent a greater proportion of time in salinities lower (5-15 ppt) than they occupy in the wild. Significant among-individual variation in the (multivariate) behavioral phenotype emerged when animals experienced the gradient, indicating strong potential for selection to drive behavioral evolution in areas with diverse salinity microhabitats. We also showed that rivulus had a significantly greater probability of laying eggs in low salinities compared to control or high salinities. Eggs laid in lower salinities also had higher hatching success compared to those laid in higher salinities. Thus, although rivulus can tolerate a wide range of salinities, they prefer low salinities. These results raise questions about factors that prevent rivulus from occupying lower salinities in the wild, whether higher salinities impose energetic costs, and whether fitness changes as a function of salinity.