Node Redeployment Algorithm Based on Stratified Connected Tree for Underwater Sensor Networks.

During the underwater sensor networks (UWSNs) operation, node drift with water environment causes network topology changes. Periodic node location examination and adjustment are needed to maintain good network monitoring quality as long as possible. In this paper, a node redeployment algorithm based on stratified connected tree for UWSNs is proposed. At every network adjustment moment, self-examination and adjustment on node locations are performed firstly. If a node is outside the monitored space, it returns to the last location recorded in its memory along straight line. Later, the network topology is stratified into a connected tree that takes the sink node as the root node by broadcasting ready information level by level, which can improve the network connectivity rate. Finally, with synthetically considering network coverage and connectivity rates, and node movement distance, the sink node performs centralized optimization on locations of leaf nodes in the stratified connected tree. Simulation results show that the proposed redeployment algorithm can not only keep the number of nodes in the monitored space as much as possible and maintain good network coverage and connectivity rates during network operation, but also reduce node movement distance during node redeployment and prolong the network lifetime.

Deep Subdomain Learning Adaptation Network: A Sensor Fault-Tolerant Soft Sensor for Industrial Processes.

Sensor faults are non-negligible issues for soft sensor modeling. However, existing deep learning-based soft sensors are fragile and sensitive when considering sensor faults. To improve the robustness against sensor faults, this article proposes a deep subdomain learning adaptation network (DSLAN) to develop a sensor fault-tolerant soft sensor, which is capable of handling both sensor degradation and sensor failure simultaneously. Primarily, domain adaptation works for process data with sensor degradation in industrial processes. Being founded on the basic structure of deep domain adaptation, a novel subdomain learner is added to automatically learn the subdomain division, enabling DSLAN adaptable to multimode industrial processes. Notably, the subdomain structure of each sample follows a categorical distribution parameterized by output of the subdomain learner. Based on the designed subdomain learner, a new probabilistic local maximum mean discrepancy (PLMMD) is presented to measure the difference in distribution between source and target features. In addition, a generator for failure data imputation is integrated in the framework, making DSLAN handle sensor failure simultaneously. Finally, the Tennessee Eastman (TE) benchmark process and two real industrial processes are used to verify the effectiveness of the proposed method. With the fault tolerance ability, soft sensing technology will take a step toward practical applications.

Isolation and hypoglycemic effects of water extracts from mulberry leaves in Northeast China.

Diabetes is the main chronic disease that greatly affects human life. Up to now, many measures have been taken to cope with the disease, among which natural products with hypoglycemic effects have aroused great interest. The objective of this study was to evaluate the hypoglycemic effects of Morus abla L. cv. longsang 1 leaf-derived water extract in vitro and in vivo. These leaves were firstly subjected to water extraction, and the obtained products were further isolated for polysaccharides, flavonoids and alkaloids. The α-glucosidase activity and anti-protein glycosylation activity of the aqueous extracts were examined in vitro. Hyperglycemic mouse models were used to evaluate the hypoglycemic effects of the aqueous extract by blood biochemical parameters, intestinal microbiota, and pathological changes to the kidneys. The results showed that the main hypoglycemic components in the aqueous extracts were flavonoids and alkaloids and their inhibition rates against α-glucosidase activity were 86.12 ± 1.79% and 87.29 ± 1.32%, respectively. High-dose mulberry leaf water extracts can reduce the blood glucose of diabetic mice by 28.17% and improve glucose tolerance by 19.02%. Furthermore, mulberry leaf water extracts could reduce the serum free fatty acid (FFA), tumor necrosis factor-α (TNF-α), insulin and glycated serum protein content, while alleviating kidney damage and improving intestinal microbiota. These results indicated that the synergistic effects among the different components of mulberry leaves might explain their alleviating effects on diabetic syndrome and thus provide a simple, convenient way to obtain the hypoglycemic components from mulberry leaves.