ABSTRACT
Given the complexity of issuing, verifying, and trading green power certificates in China, along with the challenges posed by policy changes, ensuring that China's green certificate market trading system receives proper mechanisms and technical support is crucial. This study presents a green power certificate trading (GC-TS) architecture based on an equilibrium strategy, which enhances the quoting efficiency and multi-party collaboration capability of green certificate trading by introducing Q-learning, smart contracts, and effectively integrating a multi-agent trading Nash strategy. Firstly, we integrate green certificate trading with electricity and carbon asset trading, constructing pricing strategies for the green certificate, carbon, and electricity trading markets; secondly, we design a certificate-electricity-carbon efficiency model based on ensuring the consistency of green certificates, green electricity, and carbon markets; then, to achieve diversified green certificate trading, we establish a multi-agent reinforcement learning game equilibrium model. Additionally, we propose an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism. Experiments show that trading prices have increased by 20%, and the transaction success rate by 30 times, with an analysis of trading performance from groups of 3, 5, 7, and 9 trading agents exhibiting high consistency and redundancy. Compared with models integrating smart contracts, it possesses a higher convergence efficiency of trading quotes.
ABSTRACT
@#Abstract: Objective To investigate the changes in expression of Toll-like receptor 3 (TLR3) and interferon-α (IFN-α) in patients with different clinical outcomes of hepatitis C virus (HCV) infection treated with direct-acting antiviral agents (DAAs), and to explore the relationship between the expression of TLR3 and IFN-α with the clinical outcomes of HCV infection. Methods A total of 149 HCV infected patients who received initial treatment were selected from Hainan General Hospital between September 2020 and August 2022. The patients were divided into two groups: chronic hepatitis C (CHC) group (n=129) and liver cirrhosis (LC) group (n=20). Additionally, 28 volunteers were selected as the control group during the same period. All patients with HCV infection were first treated with Sofosbuvir/Vipatavir tablets for 12 weeks. Blood samples were collected at 0, 4, 12, 24 and 48 weeks after treatment. Liver function indicators were detected by enzyme-linked immunosorbent assay (ELISA), while TLR3 mRNA were detected by real-time fluorescence quantitative polymerase chain reaction (qRCR), IFN-α was detected by Luminex multiplex cytokine assays. Measurement data subject to normal distribution were represented by x±s, and t test was used between groups. Compare differences between groups. Results TLR3 mRNA in CHC group was higher than that in LC group and control group at baseline (P<0.05). After 4 weeks of DAAs treatment, TLR3 mRNA in CHC and LC groups was significantly up-regulated (P<0.05). TLR3 mRNA in the CHC group was gradually down-regulated to the level of the control group at 12, 24, and 48 weeks. In addition, IFN-α expression gradually increased with prolonged treatment, while it decreased in the LC group. The liver inflammation indicators in both the CHC and LC groups partially recovered after treatment with DAAs. Conclusions TLR3 is involved in viral clearance and chronic inflammatory response. The expression difference of TLR3 in patients with different clinical outcomes of HCV infection after DAAs treatment may be related to the severity of the disease.
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@#Abstract:Alcoholic liver disease (ALD) is one of the most common liver diseases in the world. Long-term alcoholism causes a series of pathological changes in the liver, which eventually leads to the occurrence of liver diseases with an increasing incidence. At present, significant progress has been made in the pathogenesis and pathological development of alcoholic liver disease, but the relevant mechanism of ALD has not been thoroughly studied. It is necessary to improve the existing animal model or establish a new, more comprehensive animal ALD model to simulate human ALD. Experimental animal models of ALD, especially rodents, are often used to simulate human ALD, and the ideal rodent ALD model can effectively simulate all aspects of alcohol in human liver. But so far, the commonly used animal models all have certain defects, and there is no complete animal model that can simulate human ALD. This paper reviewed the pathogenesis of ALD, related methods and influencing factors of ALD model, and provided a theoretical basis for relevant researchers to establish the ALD rodent model.
ABSTRACT
Software-defined networks (SDNs) are improving the controllability and flexibility of networks as an innovative network architecture paradigm. Segment routing (SR) exploits an end-to-end logical path and is composed of a sequence of segments as an effective routing strategy. Each segment is represented by a middle point. The combination of SR and SDN can meet the differentiated business needs of users and can quickly deploy applications. In this paper, we propose two routing algorithms based on SR in SDN. The algorithms aim to save the cost of the path, alleviate the congestion of networks, and formulate the selection strategy by comprehensively evaluating the value of paths. The simulation results show that compared with existing algorithms, the two proposed algorithms can effectively reduce the consumption of paths and better balance the load of the network. Furthermore, the proposed algorithms take into account the preferences of users, actualize differentiated business networks, and achieve a larger comprehensive evaluation value of the path compared with other algorithms.
ABSTRACT
RPL (routing protocol for low-power and lossy networks) is an important candidate routing algorithm for low-power and lossy network (LLN) scenarios. To solve the problems of using a single routing metric or no clearly weighting distribution theory of additive composition routing metric in existing RPL algorithms, this paper creates a novel RPL algorithm according to a chaotic genetic algorithm (RPL-CGA). First of all, we propose a composition metric which simultaneously evaluates packet queue length in a buffer, end-to-end delay, residual energy ratio of node, number of hops, and expected transmission count (ETX). Meanwhile, we propose using a chaotic genetic algorithm to determine the weighting distribution of every routing metric in the composition metric to fully evaluate candidate parents (neighbors). Then, according to the evaluation results of candidate parents, we put forward a new holistic objective function and a new method for calculating the rank values of nodes which are used to select the optimized node as the preferred parent (the next hop). Finally, theoretical analysis and a series of experimental consequences indicate that RPL-CGA is significantly superior to the typical existing relevant routing algorithms in the aspect of average end-to-end delay, average success rate, etc.
