CO₂ injection has great potentials to improve the oil production for the fractured tight oil reservoirs. However, Current works mainly focus on its operation processes; full examination of CO₂ molecular diffusion and adsorption was still limited in the petroleum industry. To fill this gap, we proposed an efficient method to accurately and comprehensively evaluate the efficiency of CO₂-EOR process. We first calculated the confined fluid properties with the nanopore effects. Subsequently, a reservoir simulation model was built based on the experiment test of the Eagle Ford core sample. History matching was performed for the model validation. After that, we examined the effects of adsorption and molecular diffusion on the multi-well production with CO₂ injection. Results illustrate that in the CO₂-EOR process, the molecular diffusion has a positive impact on the oil production, while adsorption negatively impacts the well production, indicating that the mechanisms should be reasonably incorporated in the simulation analysis. Additionally, simulation results show that the mechanisms of molecular diffusion and adsorption make great contributions to the capacity of CO₂ storage in tight formations. This study provides a strong basis to reasonably forecast the long-term production during CO₂ Huff-n-Puff process.

We consider problems of traffic matrix (TM) estimation and anomaly detection utilizing link-load traffic measurements. Models including structure regularized traffic monitoring (SRTM) and dynamic SRTM (DSRTM) are presented to realize traffic monitoring under static and dynamic routing configurations, respectively. Considering that real traffic data are usually approximately low-rank but exhibit strong spatial and temporal dependencies, we define spatial and temporal regularization matrices based on Moore-Penrose pseudoinverse and Laplacian matrix to structurally regularize the TM variables. Besides, in view of the fact that anomalies in traffic usually happen rarely and last briefly, sparsity-regularization is further implemented on traffic volume anomalies. This enables our models to jointly deal with the traffic monitoring issues of TM estimation and anomaly detection. The SRTM model is designed for static routing configurations, and the online traffic monitoring model DSRTM is presented for dynamic settings. In DSRTM, a forgetting parameter is introduced to incorporate information from both the latest and previous estimations, getting rid of the problem that storing a large amount of traffic data is a huge burden for computers. Furthermore, real-time monitoring enables DSRTM to be applied in scenarios that usually experience nonstationary. Efficient algorithms that are based on accelerated proximal gradient, gradient descent, and block coordinate descent methods are proposed to solve the related SRTM and DSRTM optimization problems, with experiments in synthetic and real networks under both static and dynamic routing configurations, verifying their feasibility and effectiveness.

Enterprise digital transformation (EDT) faces challenges of organizational resistance and weak willingness. While previous studies have examined the driving effect of signals from senior management, crucial research gaps remain concerning in multi-source external signals, synergistic signaling mechanisms, and signal measurement techniques. To address these gaps, this empirical study employs textual analysis to examine the impact of narrative signals from government annual work reports and online public opinions (OPO) on EDT of Chinese A-share listed companies between 2012 and 2023. Our results indicate that the government's consistent signals on EDT significantly stimulate corporate transformation, and this stimulation exhibits delayed and non-linear effects. This relationship is mediated by the EDT discussion frequency signal on social media. However, an overly optimistic sentiment in OPO weakens its influence. Further analysis reveals variations in these effects across signal content, readability, stakeholders involved, and firm heterogeneity. These findings contribute to a better understanding of how macro-level narrative signals connect to micro-level corporate decision-making behaviors, offering insights into facilitating high-quality EDT. Our results emphasize the importance of aligning policy signals and public discourse to support effective EDT strategies.

We consider the problem of semi-supervised graph-based learning upon multimodal and mixmodal data. Since in semi-supervised settings, the labeled information is very limited, we first propose a non-convex sparse-coding based label propagation (α-SLP) method to estimate the soft labels, and thereby to enrich the supervised information. By considering the structural properties of multimodal and mixmodal data, we present a semi-supervised graph-based embedding (SGE) approach that incorporates the soft label information with the hierarchical local geometric information of within-class, between-class, and overall-class data. Based on this, subspaces characterizing the multimodal and mixmodal data structure can be derived by maximizing the weighted between-class separability and minimizing the locality-preserved within-class as well as overall-class distances of the training samples. We further extend SGE into semi-supervised sparse subspace learning scenarios and present an α-structural-regularization-induced SGE (α-SSGE) model, which can give better results in extracting discriminative groups of features by utilizing the non-convex structural regularization techniques. Experiments for multimodal and mixmodal digit as well as face recognition verify the validity and effectiveness of the proposed models.