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The Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis, YFP) is an endangered species endemic to the Yangtze River in China, and it is the only freshwater whale in the genus Neophocaena. In terms of protection, three effective conservation strategies exist: in situ conservation, ex situ conservation, and artificial breeding, all of which have been implemented by the Chinese government. Of these, ex situ conservation involves the relocation of Yangtze finless porpoises to semi-natural waters with less human interference, and artificial breeding involves the relocation of Yangtze finless porpoises to a controlled environment that is more strictly managed. To compare and analyze the responsive changes in gene expression of the YFPs between the ex situ and controlled environments, we performed the RNA sequencing of blood tissues from these YFPs. A total of 1201 differentially expressed genes (DEGs) were identified, of which 423 were up-regulated in the ex situ population and 778 were up-regulated in the controlled-environment population. Gene enrichment analysis showed that 1201 DEGs between the ex situ and controlled-environment populations were generally enriched for vision-, digestion- and immune-system-related pathways. Further analysis revealed that several key immune system pathways, such as the chemokine signaling pathway and B cell receptor signal pathway, were activated in the ex situ population. In addition, the key pathways related to vision, including phototransduction and the inflammatory mediator regulation of TRP channels, as well as the pathways related to the digestive system, such as protein digestion and absorption and salivary secretion, were activated in the controlled-environment population. These results suggest that the ex situ populations may respond to complex environmental conditions in semi-natural waters by enhancing their immune function through the increased expression of immune-related genes and that the visual function and protein digestion of the YFPs were improved compared to those of the ex situ population based on the conditions of artificial feeding, such as the higher transparency of the water and regular feeding. This study provides clues for evaluating the adaptability of YFPs to different environments and is a useful reference for future ex situ conservation and artificial breeding. [ABSTRACT FROM AUTHOR]

Manipulating granular materials is a crucial function of robotic grippers. However, the existing approaches always suffer from low efficiency when dealing with large quantities of dispersed granules. To overcome this challenge, inspiration is drawn from an African elephant (Loxodonta Africana), which can employ both fingertip extensions on the trunk tip to efficiently grasp dispersed granular food all at once by mediating state transition of granules. Herein, this bio‐inspired intelligent strategy is integrated into a soft pneumatic gripper for transporting dispersed granules. To evaluate the critical actuation pressures while grasping granules, a library is constructed experimentally, and the effects of the initial relative height and relative lifting speed on the grasping success rate are examined. It is indicated in the experimental results that this trunk‐inspired robotic strategy leads to a success rate of over 90% and saves ≈50% duration of manipulation compared to the individual gripping fashion. Herein, new insights may be offered in this study into a novel manipulation strategy for efficiently transporting dispersed granular materials. [ABSTRACT FROM AUTHOR]

In recent times, there has been a swift advancement in the field of cryptocurrency. The advent of cryptocurrency has provided us with convenience and prosperity, but has also given rise to certain illicit and unlawful activities. Unlike classical currency, cryptocurrency conceals the activities of criminals and exposes their behavioral patterns, allowing us to determine whether present cryptocurrency transactions are legitimate by analyzing their behavioral patterns. There are two issues to consider when determining whether cryptocurrency transactions are legitimate. One is that most cryptocurrency transactions comply with laws and regulations, but only a small portion of them are used for illegal activities, which is related to the sample imbalance problem. The other issue concerns the excessive volume of data, and there are some unknown illegal transactions, so the data set contains an abundance of unlabeled data. As a result, it is critical to accurately distinguish between which transactions among the plethora of cryptocurrency transactions are legitimate and which are illegal. This presents quite a difficult challenge. Consequently, this paper combines mutual information and self-supervised learning to create a self-supervised model on the basis of mutual information that is used to improve the massive amount of untagged data that exist in the data set. Simultaneously, by merging the conventional cross-entropy loss function with mutual information, a novel loss function is created. It is employed to address the issue of sample imbalance in data sets. The F1-Score results obtained from our experimentation demonstrate that the novel loss function in the GCN method improves the performance of cryptocurrency illegal behavior detection by four points compared with the traditional loss function of cross-entropy; use of the self-supervised network that relies on mutual information improves the performance by three points compared with the original GCN method; using both together improves the performance by six points. [ABSTRACT FROM AUTHOR]