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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. [ABSTRACT FROM AUTHOR]

The study of fish parental care is important for understanding the social structure and improving the survival rate and welfare of fish populations in aquaculture. However, little is known about the parental care of fish at present, especially their attack patterns, behaviour patterns and spatiotemporal statistical characteristics. In this study, Aequidens rivulatus was taken as the research object and the parental care processes were recorded including the attacker, the attackee, and the start and end attack time. Then, the attack pattern, behaviour pattern and their changes over time were further analysed. The results were as follows: (1) no biting occurred most frequently (58%) and had the highest velocity and acceleration during the parental care period compared with lateral biting and frontal biting. (2) Male attacked intruder the most (52%) with the lowest velocity and acceleration, while female attacked male the least (7%) with the highest acceleration. (3) There was a shift in the role of protection of larvae from females to males. This shift occurred approximately 30 h after the first parental care action. These results suggested that A. rivulatus display a high degree of parental care and an obvious pattern of young protection when facing intruder, which is an important consideration when designing breeding and holding facilities. [ABSTRACT FROM AUTHOR]

In this study, visible and near-infrared hyperspectral imaging (HSI) technique combined with deep learning algorithm was investigated for discriminating the freshness of shrimp during cold storage. Shrimps were labeled into two freshness grades (fresh and stale) according to their total volatile basic nitrogen contents. Spectral features were extracted from the HSI data by stacked auto-encoders (SAEs)-based deep learning algorithm and then used to classify the freshness grade of shrimp by a logistic regression (LR)-based deep learning algorithm. The results demonstrated that the SAEs-LR achieved satisfactory total classification accuracy of 96.55 and 93.97% for freshness grade of shrimp in calibration (116 samples) and prediction (116 samples) sets, respectively. An image processing algorithm was also developed for visualizing the classification map of freshness grade. Results confirmed the possibility of rapid and nondestructive detecting freshness grade of shrimp by the combination of hyperspectral imaging technique and deep learning algorithm. The SAEs-LR method adds a new tool for the multivariate analysis of hyperspectral image for shrimp quality inspections. [ABSTRACT FROM AUTHOR]

The behavior of fishes is very sensitive to the changes of the parameters of the environment, such as temperature, dissolved oxygen, light, and so on. The anomaly detection of fish school behavior can not only discover the relationship between the fish behaviors and the environmental parameters, but also provide an important method and tool for fish health monitoring and early warning. Moreover, it is very meaningful for the study of the mechanism of fish behavior and promotion of the informatization level in aquaculture. By using computer vision technology and based on a statistical method of motion features, the anomaly detection of fish school behavior was studied. The zebra fish was selected as the study object in this paper. First, based on the foreground object detection method with a threshold value method, the backgrounds were removed from the original video images to reduce the influence of noises. Secondly, by the Lucas-Kanade optical flow method, which is based on the local deference method and has better performance, the vectors of motion behavior could be obtained in different temporal and spatial conditions. Thirdly, from these data, the joint histograms and joint probability distributions of turning angles and velocities were calculated. Since from the practical point of view, the anomalous behaviors of a fish school mainly include the change of the moving velocity and the chaos of the moving direction. This is the reason to select turning angle and velocity as the features to analyze. At last, the NMI method and the LDOF methods were applied to study the anomaly detection of fish school behavior. By choosing proper threshold values, the NMI method and the LDOF methods can implement the behavior detection of the zebra fish school. The experiments showed that the accuracy rates of the NMI method and the LDOF method for anomaly detection of fish school behavior can achieve 99.92% and 99.88%, respectively, which implies that both of the two methods have better effects. [ABSTRACT FROM AUTHOR]