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Offshore carbon dioxide (CO2) storage is an effective method for reducing greenhouse gas emissions. However, when using traditional seismic wave methods to monitor the migration of sequestration CO2 plumes, the characteristics of wave velocity changes tend to become insignificant beyond a certain limit. In contrast, the controllable source electromagnetic method (CSEM) remains highly sensitive to resistivity changes. By simulating different CO2 plume migration conditions, we established the relevant models and calculated the corresponding electric field response characteristic curves, allowing us to analyze the CSEM's ability to monitor CO2 plumes. We considered potential scenarios for the migration and diffusion of offshore CO2 storage, including various burial depths, vertical extension diffusion, lateral extension diffusion, multiple combinations of lateral intervals, and electric field components. We also obtained differences in resistivity inversion imaging obtained by CSEM to evaluate its feasibility in monitoring and to analyze all the electric field (Ex, Ey, and Ez) response characteristics. CSEM has great potential in monitoring CO2 plume migration in offshore saltwater reservoirs due to its high sensitivity and accuracy. Furthermore, changes in electromagnetic field response reflect the transport status of CO2 plumes, providing an important basis for monitoring and evaluating CO2 transport behavior during storage processes. [ABSTRACT FROM AUTHOR]

Pyriproxyfen (PPF) has been shown to affect the pupal stage and ecdysone levels in holometabolous insects, such as silkworms and mealworms. It remains unknown whether it affects hemimetabolous insects with their hormone levels in insects lacking a pupal stage. In this laboratory study, bioassays were conducted to investigate the effects of varying doses of PPF on Aphis craccivora Koch (Hemiptera: Aphididae). Ultraperformance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) was used to determine the types and titers of juvenile hormone (JH) and 20-hydroxyecdysone (20E). Additionally, the effects of PPF on A. craccivora reproduction and molting, as well as its influence on relevant gene expression, were examined. The results revealed LC50 and LC90 values of 3.84 and 7.49 mg/l for PPF, respectively, after 48 h of exposure. The results demonstrated a significant reduction in the titer of JH III and a significant increase in the titer of 20E following treatment with PPF. However, there was no significant decrease observed in the titer of JH III skipped bisepoxide (JH SB3). A sublethal concentration of PPF was found to inhibit Krüppel homolog 1 (kr-h 1) gene expression and reduce aphid reproduction, but it did not significantly impact ecdysone receptor expression and aphid molting. The results of this study demonstrate that PPF exhibits a lethal effect on aphids, thereby providing an effective means of control. Additionally, sublethal concentrations of PPF have been found to inhibit the JH in aphids, resulting in a decline in their reproductive ability and achieving the desired control objectives. [ABSTRACT FROM AUTHOR]

Data‐driven deep learning technology has a strong non‐linear mapping ability and has good development potential in geophysical inversion problems. Traditional inversion techniques offer broad generality, but they can remain trapped in local minima, particularly for three‐dimensional tunnelling resistivity inversion. In this work, we present an inversion methodology that combines traditional physics‐driven and deep learning data‐driven inversion approaches. To further support deep neural networks' dependability on unseen data, the interpretability of their working mechanism is explored. We execute migration learning based on small sample data after identifying the critical parameters that restrict the effectiveness of inversion by analysing the feature maps of various model data. We demonstrate, using both synthetic examples and field data, that the proposed method can improve the accuracy in detecting water‐bearing anomalies (caves and faults), which are typically encountered during tunnel excavation. [ABSTRACT FROM AUTHOR]

Network security situational awareness (NSSA) aims to capture, understand, and display security elements in large-scale network environments in order to predict security trends in the relevant network environment. With the internet's increasingly large scale, increasingly complex structure, and gradual diversification of components, the traditional single-layer network topology model can no longer meet the needs of network security analysis. Therefore, we conduct research based on a multi-layer network model for network security situational awareness, which is characterized by the three-layer network structure of a physical device network, a business application network, and a user role network. Its network characteristics require new assessment methods, so we propose a multi-layer network link importance assessment metric: the multi-layer-dependent link entropy (MDLE). On the one hand, the MDLE comprehensively evaluates the connectivity importance of links by fitting the link-local betweenness centrality and mapping entropy. On the other hand, it relies on the link-dependent mechanism to better aggregate the link importance contributions in each network layer. The experimental results show that the MDLE has better ordering monotonicity during critical link discovery and a higher destruction efficacy in destruction simulations compared to classical link importance metrics, thus better adapting to the critical link discovery requirements of a multi-layer network topology. [ABSTRACT FROM AUTHOR]