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Thermo-hydraulic analysis of time-dependent anisotropic characteristics of crushed coal subjected to varied axial displacements 认领 引用
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作者 Yanchi Liu Baiquan Lin +2 位作者 Ting Liu Zhiyong Hao Jiahao He 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5489-5504,共16页
This study is aimed at investigating the transient anisotropic characteristics of coupled seepage and heat transfer in compressed crushed coal with complex void structures within abandoned mine reservoirs.To achieve t... This study is aimed at investigating the transient anisotropic characteristics of coupled seepage and heat transfer in compressed crushed coal with complex void structures within abandoned mine reservoirs.To achieve this aim,multiple computed tomography(CT)scans were conducted firstduring the axial compression process to reconstruct and model the structural evolution of the crushed coal under varying compression displacements.Subsequently,the reconstructed models were incorporated into finiteelement simulations to compute transient dimensionless parameters related to fluidflowand heat transfer.The results show that as axial compression of the crushed coal intensifies,topological changes in the seepage channels lead to higher steady-state flowvelocities and a more concentrated vortex distribution within the coal matrix.Moreover,the anisotropic differences in both thermal breakthrough time and the heat extraction rate at the breakthrough moment gradually decrease.The heat transfer coefficientduring the compression process ranges from 667.51 W/(m2·K)to 1062.05 W/(m2·K).The anisotropy factor of Reynolds number(Re)reaches a maximum of 0.19,while Nusselt number(Nu)varies between 1.17 and 1.71.The Nu-Re-Prandtl number(Pr)evolutionary surface during the coupled flowand heat transfer process follows a nonlinear trajectory.Overall,the findingsprovide a theoretical foundation for optimizing geothermal energy extraction from abandoned coal mines. 展开更多
关键词 Finite element method Different compression displacements Conjugate heat transfer Crushed coal Abandoned mine
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Influence of Surface Types on the Seasonality and Inter-Model Spread of Arctic Amplification in CMIP6 认领 引用 被引量：2
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作者 Yanchi LIU Yunqi KONG +1 位作者 Qinghua YANG Xiaoming HU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2023年第12期2288-2301,共14页
A robust phenomenon termed the Arctic Amplification(AA)refers to the stronger warming taking place over the Arctic compared to the global mean.The AA can be confirmed through observations and reproduced in climate mod... A robust phenomenon termed the Arctic Amplification(AA)refers to the stronger warming taking place over the Arctic compared to the global mean.The AA can be confirmed through observations and reproduced in climate model simulations and shows significant seasonality and inter-model spread.This study focuses on the influence of surface type on the seasonality of AA and its inter-model spread by dividing the Arctic region into four surface types:ice-covered,ice-retreat,ice-free,and land.The magnitude and inter-model spread of Arctic surface warming are calculated from the difference between the abrupt-4×CO2and pre-industrial experiments of 17 CMIP6 models.The change of effective thermal inertia(ETI)in response to the quadrupling of CO2 forcing is the leading mechanism for the seasonal energy transfer mechanism,which acts to store heat temporarily in summer and then release it in winter.The ETI change is strongest over the ice-retreat region,which is also responsible for the strongest AA among the four surface types.The lack of ETI change explains the nearly uniform warming pattern across seasons over the ice-free(ocean)region.Compared to other regions,the ice-covered region shows the maximum inter-model spread in JFM,resulting from a stronger inter-model spread in the oceanic heat storage term.However,the weaker upward surface turbulent sensible and latent heat fluxes tend to suppress the inter-model spread.The relatively small inter-model spread during summer is caused by the cancellation of the inter-model spread in ice-albedo feedback with that in the oceanic heat storage term. 展开更多
关键词 Arctic amplification surface type dependence seasonal energy transfer effective thermal inertia
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两端融合表达几丁质结合结构域提高几丁质酶抗真菌活性 认领 引用 被引量：4
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作者 谷天燕 刘晓楠 +5 位作者 李玲聪 刘妍池 胡少锋 吕晨茵 刘华 赵国刚 《微生物学报》 CAS CSCD 北大核心 2019年第4期762-770,共9页
【目的】通过两端融合表达几丁质结合结构域来提高几丁质酶的活性和生物防治植物病原真菌能力。【方法】以苜蓿链霉菌(Streptomyces alfalae)ACCC40021中唯一的GH19家族几丁质酶为模板,构建两端融合表达几丁质结合结构域的几丁质酶,并... 【目的】通过两端融合表达几丁质结合结构域来提高几丁质酶的活性和生物防治植物病原真菌能力。【方法】以苜蓿链霉菌(Streptomyces alfalae)ACCC40021中唯一的GH19家族几丁质酶为模板,构建两端融合表达几丁质结合结构域的几丁质酶,并进行原核表达;利用3,5-二硝基水杨酸法(DNS)测定几丁质酶活。【结果】成功构建了CatDChiB (催化结构域)、rChiB (含N-端几丁质结合结构域)、DChBDChiB(含两端几丁质结合结构域)三种形式的酶,并在大肠杆菌中得到了高效表达;与CatDChiB和rChiB相比,DChBDChiB显著地提高了对α-几丁质、胶体几丁质和黑曲霉几丁质的结合能力和活性;同时增强了其对病原真菌长枝木霉的抑制作用。【结论】两端融合表达几丁质结合结构域是简单有效的提高几丁质酶活性及抗真菌活性的策略。 展开更多
关键词 碳水化合物结合模块(CBMs) 几丁质结合结构域(ChBD) 苜蓿链霉菌(Streptomyces alfalae) 几丁质酶 植物真菌病害
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苜蓿链霉菌内切β-N-乙酰氨基葡萄糖苷酶的克隆、表达及酶学性质 认领 引用 被引量：1
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作者 李玲聪 胡少锋 +5 位作者 谷天燕 吕晨茵 刘妍池 刘华 顾金刚 赵国刚 《生物工程学报》 CAS CSCD 北大核心 2020年第5期932-941,共10页
内切β-N-乙酰氨基葡萄糖苷酶广泛应用于糖生物学研究和工业生产。本研究从苜蓿链霉菌Streptomyces alfalfae ACCC 40021中克隆并原核表达了一个新的内切β-N-乙酰氨基葡萄糖苷酶,该酶最适反应温度为35℃,最适pH为6.0,具有良好的pH稳定... 内切β-N-乙酰氨基葡萄糖苷酶广泛应用于糖生物学研究和工业生产。本研究从苜蓿链霉菌Streptomyces alfalfae ACCC 40021中克隆并原核表达了一个新的内切β-N-乙酰氨基葡萄糖苷酶,该酶最适反应温度为35℃,最适pH为6.0,具有良好的pH稳定性、温度稳定性和高比活(1×10^6 U/mg)的特性,可催化不同蛋白底物去糖基化,具有作为工具酶和生物催化剂的潜力。 展开更多
关键词 内切β-N-乙酰氨基葡萄糖苷酶 去糖基化 苜蓿链霉菌
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Polymethoxyflavones-eugenol nanoemulsion:Dual physiological and metabolomic insights into its antibacterial action on Escherichia coli 认领 引用
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作者 Hongyang Chen Zimao Ye +1 位作者 Yanchi Liu Zhiqin Zhou 《Food Bioscience》 SCIE 2025年第10期1509-1522,共14页
An integrated physiological and non-targeted metabolomics approach was used to construct a eugenol-based polymethoxyflavones nanoemulsion delivery system and examine its increased antibacterial activity on E.coli.Comp... An integrated physiological and non-targeted metabolomics approach was used to construct a eugenol-based polymethoxyflavones nanoemulsion delivery system and examine its increased antibacterial activity on E.coli.Compared with pure eugenol nanoemulsion(EG,MIC:640μg/mL),the polymethoxyflavones-eugenol nanoemulsion(EGP)exhibited significantly enhanced antimicrobial activity(MIC:320μg/mL).EGP treatment disrupted the integrity of E.coli cell membranes and walls,increased cell permeability.Non-targeted metabolomics analysis(compared with CK)revealed 685 upregulated and 642 downregulated differentially accumulating metabolites,lysine and glutamine are reduced in E.coli,and glutaric acid,aminoadipic acid,and D-lactic acid are increased.Pathway enrichment analysis showed that amino acid metabolism and energy metabolism were significantly disturbed by TCA cycle disruption.EGP exhibits superior antimicrobial efficacy against E.coli through a multi-targeted mechanism involving cell membrane disruption and metabolic pathway interference.This study provides valuable insights into the development of natural antimicrobial nanoemulsion as potential alternatives to synthetic preservatives for food safety applications. 展开更多
关键词 Polymethoxyflavones Eugenol nanoemulsion Escherichia coli Antimicrobial activity Metabolomics
Bayesian dual neural networks for recommendation 认领 引用 被引量：3
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作者 Jia HE Fuzhen ZHUANG +2 位作者 Yanchi LIU Qing HE Fen LIN 《Frontiers of Computer Science》 SCIE EI CSCD 2019年第6期1255-1265,共11页
Most traditional collaborative filtering(CF)methods only use the user-item rating matrix to make recommendations,which usually suffer from cold-start and sparsity problems.To address these problems,on the one hand,som... Most traditional collaborative filtering(CF)methods only use the user-item rating matrix to make recommendations,which usually suffer from cold-start and sparsity problems.To address these problems,on the one hand,some CF methods are proposed to incorporate auxiliary information such as user/item profiles;on the other hand,deep neural networks,which have powerful ability in learning effective representations,have achieved great success in recommender systems.However,these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the weights.Therefore,they maybe lack of calibrated probabilistic predictions and make overly confident decisions.To this end,we propose a new Bayesian dual neural network framework,named BDNet,to incorporate auxiliary information for recommendation.Specifically,we design two neural networks,one is to learn a common low dimensional space for users and items from the rating matrix,and another one is to project the attributes of users and items into another shared latent space.After that,the outputs of these two neural networks are combined to produce the final prediction.Furthermore,we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic predictions.Extensive experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors. 展开更多
关键词 collaborative filtering Bayesian neural network hybrid recommendation algorithm
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A Generative Model Approach for Geo-Social Group Recommendation 认领 引用 被引量：2
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作者 Peng-Peng Zhao Hai-Feng Zhu +5 位作者 Yanchi Liu Zi-Ting Zhou Zhi-Xu Li Jia-Jie Xu Lei Zhao Victor S. Sheng 《Journal of Computer Science & Technology》 SCIE EI CSCD 2018年第4期727-738,共12页
With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group... With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem： 1） the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2） users have different influences when in diffe- rent groups. In this paper, we propose a generative geo-social group recommendation model （GSGR） to recommend points of interest （POIs） for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods. 展开更多
关键词 group recommendation topic model social network
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