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When CO2 saline aquifer storage is carried out, the heterogeneity of reservoir rock is an important factor affecting CO2 transport, and the reservoir heterogeneity in numerical simulations is mainly manifested as the heterogeneity of the parameter field. Since the parameter distributions across the reservoir are not available with the existing probes, the stochastic finite element method is combined with a two‐phase flow model to establish an unconditional random field of permeability, and computations are performed using the Monte Carlo method. The permeability, CO2 maximum migration distance (Md) and CO2 sweep area (Sa) were analyzed for mutual correlation. The permeability correlation area affecting Md and Sa was obtained, and the changes in the correlation area under the coefficient of variation (Cv) and correlation length (λx) of the permeability field in the different reservoirs were analyzed. The kriging superposition approach (KSA) was subsequently used to estimate both the Md and Sa of the target reservoir by establishing conditional random fields based on the sampling parameters in regions with different correlations, resulting in errors of 0.66% for Md and 0.96% for Sa in the high correlation region and 4.86% and 3.12% for Md and Sa in the low correlation region, which suggested that the sampling results from the high correlation region were less biased in the estimation. Under limited sampling conditions, it is recommended that samples be collected in regions with high correlations to reduce the uncertainty of CO2 transport analysis due to unknown heterogeneity. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: Web service recommendation as an emerging topic attracts increasing attention due to its important practical significance. As the number of available Web services continues to grow, users face the challenge of searching the most suitable services that meet their specific needs. Quality of service (QoS)‐based service recommendation becomes a popular approach to address this issue. However, existing QoS‐based service recommendation methods are inability to effectively capture valuable content and structural information from services. These methods often rely solely on low‐order explicit feature intersections in QoS information, do not fully utilize the high‐order implicit feature intersections, and ignore the rich semantic information existing in service descriptions and user preferences. To address this problem, this paper proposes a Web service recommendation method via combining knowledge distillation representation and DCNMIX quality prediction. This method combines content‐based and structure‐based service classification and service prediction based on multi‐dimensional service quality information. First, it builds a service relationship network using semantic features extracted from service descriptions. Second, it designs a graph neural network knowledge distillation framework. The teacher model extracts the knowledge of the graph neural network model, and the student model learns the structure‐based and feature‐based prior knowledge of the service relationship network. Then the student model is used to learn the knowledge of the teacher model, classify Web services, and obtain service representations. Finally, based on service representations and multi‐dimensional QoS information, it exploits the DCNMIX model to learn the explicit and implicit features intersections of Web services and obtain the prediction score and ranking of Web services. The experimental results on the ProgrammableWeb dataset show that the proposed method outperforms the state‐of‐the‐art baselines in terms of Recall, F1, Logloss, and AUC_ROC. [ABSTRACT FROM AUTHOR]

Different levels of EspP2 expression are seen in strains of Glaesserella parasuis with high and low pathogenicity. As a potential virulence factor for G. parasuis, the pathogenic mechanism of EspP2 in infection of host cells is not clear. To begin to elucidate the effect of EspP2 on virulence, we used G. parasuis SC1401 in its wild-type form and SC1401, which was made EspP2-deficient. We demonstrated that EspP2 causes up-regulation of claudin-1 and occludin expression, thereby promoting the adhesion of G. parasuis to host cells; EspP2-deficiency resulted in significantly reduced adhesion of G. parasuis to cells. Transcriptome sequencing analysis of EspP2-treated PK15 cells revealed that the Rap1 signaling pathway is stimulated by EspP2. Blocking this pathway diminished occludin expression and adhesion. These results indicated that EspP2 regulates the adhesion of Glaesserella parasuis via Rap1 signaling pathway. [ABSTRACT FROM AUTHOR]

Saline aquifer rocks exhibit significant spatial randomness due to geological sedimentation processes. To address the issue of the heterogeneity of rock formations in numerical simulations, it is common practice to homogenize rock layers with similar lithologies. However, the acceptability of the errors generated during homogenized computations is a major concern and should be investigated. Therefore, to study the influence of heterogeneity at the storage site on the CO2 migration behavior, the Monte Carlo simulation–random finite element method (MCS‐RFEM) was combined with a CO2 two‐phase flow model to compare the effects of the coefficient of variation (Cv) and correlation length (λx) of random reservoir permeability fields on the migration distance and extent of CO2 storage under the same mean conditions. The results showed that higher Cv and λx values significantly reduced the CO2 migration distance while increasing the spread extent. Compared to the homogeneous model, at a λx value of 100 m, the CO2 migration distance decreased by 5.05%, while the profile sweep area increased by 6.20%. Concurrently, with increasing Cv, the area with a CO2 volume fraction higher than 0.75 decreased by 20.22%, while an increase in λx resulted in a 42.35% increase in the area with a CO2 volume fraction higher than 0.75. Therefore, reservoirs with high Cv and low λx values are more suitable for safely storing CO2. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]