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Objective: optimizing the extraction process of methylated catechin from Zijuan tea. Methods: Using acid ethanol as extraction solvent, based on three single factor experiments, the extraction time, extraction temperature and liquid-solid ratio were used to investigate the interaction of the above three factors on the extraction rate of methylated catechin from Zijuan tea. Results: The optimum extraction conditions were as follows: extraction time 100 min, extraction temperature 75 ℃, solid-liquid ratio（m Zijuan tea∶VSolvent） 1∶74 （g/mL）; under these conditions, the mass concentration of methylated catechin in the extract could reach 26.7 mg/g, and the relative error with the verification value was 1.48%. Conclusion: HPLC and response surface methods were used to optimize the extraction process of methylated catechins in Zijuan tea. The process is simple and the verification results are reasonable.

Methane (CH4) emissions exert large effects on the global climate. Tree stems are vital sources of emissions in ecosystem CH4 budgets. This paper reviewed the number of publications, journals, authors, keywords, research hotspots, and challenges. A total of 990 articles from 2006 to 2022 were collected based on the Web of Science database. The intellectual base was analyzed using CiteSpace 6.3.1 and VOSviewer 1.6.20 softwares. The results illustrated a growing trend in the study of tree stem methane emissions. The United States was the most research-active country; however, the most active institution was the Chinese Academy of Sciences in China. The research on stem methane emission by Vincent Gauci, Katerina Machacova, Zhi-Ping Wang, Kazuhiko Terazawa, Kristofer R. Covey, and Sunitha R. Pangala has had a significant impact. Current research indicates that stem CH4 emissions significantly vary among different tree species and are influenced by leaf type, forest type, tree height, whether the trees are alive or dead, and other environmental conditions (such as soil water content, air temperature, CO2 fluxes, and specific density). Soil CH4 fluxes and production by methanogens in heartwood were the primary sources of tree stem methane. Some pectin or cellulose from trees may also be converted into methane. Moreover, methane can be produced and released during the decomposition of deadwood by basidiomycetes. Furthermore, there are some trends and challenges for the future: (1) distinguishing and quantifying emissions from various sources; (2) accurately assessing the impact of floods on methane emissions is crucial, as the water level is the main factor affecting CH4 emissions; and (3) addressing the limited understanding of the microbial mechanisms of methane production in different tree species and investigating how microbial communities affect the production and emission of methane is vital. These advances will contribute to the accurate assessment of methane emissions from global ecosystems. [ABSTRACT FROM AUTHOR]

Wind power generation has increased in China to achieve the target of decreasing CO2 emissions by 2050, but there are high levels of wind curtailment due to the mismatch between electricity supply and demand. This paper proposes a single-stage air source heat pump coupled with thermal storage for building heating purposes. The main objective is to find the proper system designs and operating strategy, which can help to avoid peak demand periods while obtaining minimized running costs and reduced wind energy curtailment. Dynamic simulations were performed using TRNSYS to investigate its use in a typical office building based on an actual electricity tariff, wind power, and meteorological data. The proper system designs, including the tank size and thermal storage temperature, were determined to maximize the system's performance. It was found that a proper combination of the two parameters exists for a specific application. Further, results showed that the use of auxiliary electric heating is necessary for single-stage air source heat pumps to participate in a wind curtailment reduction. The operating strategy of the system was also studied. Results indicate that by implementing a proper operating strategy, non-renewable power consumption can be reduced by 11% for the studied building, with a total wind power utilization of 3348 kWh during the heating season while still satisfying the heating demands of users. These findings can contribute to the green and low-carbon development of the building industry and further enhance the grid's accommodation capacity for renewable energy sources. [ABSTRACT FROM AUTHOR]

Super-precision bearing lubrication condition is essential for equipment's overall performance. This paper investigates a monitoring method of bearing lubrication using multi-sensors based on graph data. An experiment was designed and carried out, establishing a dataset including vibration, temperature, and acoustic emission signals. Graph data were constructed based on a priori knowledge and a graph attention network was employed to conduct a study on monitoring bearing lubrication abnormalities and discuss the influence of a missing sensor on the monitoring. The results show that the designed experiments can effectively respond to the degradation process of bearing lubrication, and the graph data constructed based on a priori knowledge show a good effect in the anomaly monitoring process. In addition, the multi-sensor plays a significant role in monitoring bearing lubrication. This work will be highly beneficial for future monitoring methods of bearing lubrication status. [ABSTRACT FROM AUTHOR]

Drug-target interaction prediction plays an important role in drug discovery and repositioning.However,existing prediction methods have the problem of insufficient predictive performance while processing data with highly unbalance positive and negative samples.Therefore,a novel computational method based on graph convolutional neural network(GCN) for predicting drug-target interactions is proposed.In this method,a heterogeneous information network is constructed,which integrates diverse drug-related information and target-related information.From the heterogeneous information network,low-dimensional vector representation of features,which accurately explains the topological properties of individual and neighborhood feature information,is learned by using GCN and then prediction is made based on these representations via a vector space projection scheme.The AUPR(Area Under the Precision-Recall Curve) values of the proposed method outperforms other four existing methods in the prediction of drug-target interaction on both DrugBank_FDA and Yammanishi_08 datasets,and it preforms well on bigger datasets.The experimental results indicate that the proposed method improves the prediction performance of drug-target interaction on datasets with highly unbalanced samples.Furthermore,we validate novel(unknown) drug-target interactions which are predicted by GCN in biomedical databases.