For time and space constraints, 5G base stations will have more serious energy consumption problems in some time periods, so it needs corresponding sleep strategies to reduce energy consumption. Based on the analysis of 5G super dense base station network structure, through the analysis of current situation and user demand, a cluster sleep method based on genetic algorithm is constructed under the support of genetic algorithm, which can realize the dynamic matching of energy consumption in time domain and space, and the low load base station enters the sleep state. In order to verify the performance of the algorithm, the simulation network structure is built on the MATLAB platform, and the advantages of the algorithm in this study are obtained through comparative analysis, and the relevant test parameters are set for the technical performance analysis of this study. The research shows that the method proposed in this paper has a certain energy-saving effect, can meet the energy efficiency requirements of 5G ultra dense base station, and in the ultra dense base station group, the complexity can also meet the system operation requirements, which has a certain degree of practicality, and can provide reference for the follow-up related research.
In the current WSN operation process, there are two major problems of data collection difficulty and network energy consumption, which seriously affects the reliability of the WSN. In this paper, the improved ant colony algorithm proposed is compared with other algorithms. Wireless sensor network nodes based on improved ant colony algorithm have lower energy consumption, and sensor nodes have more residual energy. In addition, the energy model, data transmission balance model is established and verified in the WSN transmission target function. The experimental results show that the WSN node after the improved ant colony algorithm is used to help in the determination of the location information of the public node, and then use the location information of the node to make the protocol have effective routing performance and effective target node location discrimination ability. Thus, the improved ant colony algorithm studied in this paper has important practical significance for improving the life cycle/energy consumption of wireless sensor networks. In addition, aiming at the characteristics and routing performance of wireless sensor networks, a low-power routing method based on location and direction is designed to make the message reach the target node accurately and safely, which effectively increases the data packet transmission rate.
Oxidative stress induced by hypertension has been reported to cause alterations in neural cytoarchitecture and cognitive dysfunction. Green tea catechins, especially (-)-epigallocatechin-3-gallate (EGCG), have potent antioxidative properties and protect against various oxidative damages. In this study, we examined the impact of hypertension in rats on locomotor activity, learning, and memory, and EGCG was tested for its potential therapeutic effects in treating hypertension-induced impairment. Blood pressure was measured by the tail-cuff method to confirm high blood pressure in spontaneous hypertension rats (SHRs). Locomotor activity in the open field was increased in SHRs, along with learning and memory impairment in the Morris water maze. Daily EGCG treatment reduced the progressive increase in blood pressure in SHRs, and prevented most of the increased locomotor activity in addition to improving learning and memory. EGCG treatment also decreased the increased level of lipid peroxide production in SHRs and enhanced the antioxidation power in plasma that was observed to be decreased in SHRs. EGCG also decreased the concentration of reactive oxygen species in the hippocampi of SHRs. These indicate a therapeutic effect of EGCG in treating hypertension-induced learning and memory impairment, most probably through its powerful antioxidative properties.
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