Interfacial tension (IFT) critically governs multiphase flow, mass transfer, and wettability within natural and engineered water systems. Understanding its behavior is essential for assessing fluid interactions and CO2 migration during geological carbon sequestration. However, accurate quantification of CO2-brine IFT under insitu reservoir conditions remains challenging due to the coupled effects of pressure, temperature, salinity, and ionic composition. In this study, a data-driven predictive framework integrating pendant-drop experiments with an extensive literature database was developed to characterize CO2-brine IFT under realistic subsurface conditions. Experiments were conducted at 313.15-363.15 K and 7.5-17 MPa using formation water from the South China Sea, complemented by 3,409 data points compiled from previous studies for model training and validation. A Bayesian-optimized XGBoost model achieved excellent agreement with measured data (R2 = 0.985), capturing nonlinear dependencies beyond conventional empirical correlations. SHAP analysis identified pressure as the primary factor influencing IFT, followed by temperature and ionic composition, and revealed distinct temperature-dependent variations even at constant pressure. These results provide advance insights into the water-phase interfacial processes governing CO2 transport and trapping, while the proposed framework offers a scalable, transferable approach for rapid IFT estimation across diverse subsurface and water-energy systems.
This study proposes a reverse logistics framework by introducing a value-preserving repurchase (VPR) policy for electric vehicle (EV) makers. Under VPR, users pay a service fee and retail price and can return EVs after a specified period. To motivate returns, EV makers guarantee refunds exceeding typical secondhand prices. EV makers can offer repurchase, exchange (crediting the refund toward a new EV), or mixed options. However, market uncertainty can inflate secondhand prices beyond contract refunds, driving users to sell directly in secondary markets instead of returning. This undermines EV makers’ value recovery and new-vehicle demand. While the existing literature focuses on post-purchase returns from valuation discrepancies, we integrate volatile secondhand market prices into the VPR design. Methodologically, we develop an analytical framework for the EV reverse logistics channel, characterize the participants’ utilities through three VPR modes, and derive how secondhand prices reshape optimal VPR terms under different market structures. Furthermore, the EV makers’ mode preferences across monopoly and competitive settings are identified, thereby providing a rigorous analytical foundation for when and why different VPR options are chosen. From a managerial perspective, the results offer actionable guidance for EV makers designing VPR contracts under uncertain resale values: in a monopoly, the exchange mode emerges as dominant, and the mixed mode exhibits subtle efficiency; whereas the mixed mode prevails under competition, even it does not dominate in a monopolistic market. Overall, the findings extend beyond EVs by offering a transferable framework for value-preserving trade-in policy design in other industries facing uncertain secondhand prices.
With the continuous growth in the number of parameters of transformer-based pretrained language models (PLMs), particularly the emergence of large language models (LLMs) with billions of parameters, many natural language processing (NLP) tasks have demonstrated remarkable success. However, the enormous size and computational demands of these models pose significant challenges for adapting them to specific downstream tasks, especially in environments with limited computational resources. Parameter Efficient Fine-Tuning (PEFT) offers an effective solution by reducing the number of fine-tuning parameters and memory usage while achieving comparable performance to full fine-tuning. The demands for fine-tuning PLMs, especially LLMs, have led to a surge in the development of PEFT methods, as depicted in Fig. 1. In this paper, we present a comprehensive and systematic review of PEFT methods for PLMs. We summarize these PEFT methods, discuss their applications, and outline future directions. Furthermore, we conduct experiments using several representative PEFT methods to better understand their effectiveness in parameter efficiency and memory efficiency. By offering insights into the latest advancements and practical applications, this survey serves as an invaluable resource for researchers and practitioners seeking to navigate the challenges and opportunities presented by PEFT in the context of PLMs.
This study investigated the interplay between peer support, writing self-concept, and the emotions of pride and anxiety in high school writing contexts. Recognizing the complexities of writing as a cognitive and social task, we explored how supportive peer interactions shape students’ emotional responses. Drawing on Social Cognitive Theory, we hypothesized that peer support is positively related to writing self-concept, which, in turn, relates to students’ feelings of pride and anxiety. Data collected from 1408 high school students across various regions in China were analyzed using structural equation modeling. Our findings revealed that peer support was positively associated with pride in writing but not with anxiety. Additionally, writing self-concept partially mediated the relationship between peer support and pride. However, it did not mediate the relationship between peer support and anxiety, highlighting distinct mechanisms for positive and negative emotional outcomes. This research underscores the importance of fostering supportive peer environments to enhance writing self-concept and pride, while suggesting targeted strategies to address writing anxiety. By integrating socio-cognitive dimensions, educators can create emotionally supportive writing instruction that promotes both academic success and emotional well-being.
In the context of escalating climate change, it is imperative to understand its multifaceted impacts on financial markets, as climate risks not only affect the low-order moments (mean and variance) but also the high-order moments (skew and kurtosis) of the energy market and the bond market. This study employs a quantile vector autoregressive framework, a combination of time-domain and frequency-domain analyses, and quantile-to-quantile regression to assess the dynamic spillover effects under varying market conditions. The results reveal that spillover effects are particularly pronounced during extreme events, both high positive shocks (above the 80th percentile) or high negative changes (below the 20th percentile). Furthermore, during periods of high climate risks, the dynamic interaction between the energy market and green bonds intensifies, strengthening their roles in the context of spillover effects and altering their respective positions. Our findings also exhibit that the coal markets and green bonds act as net recipients of spillovers, highlighting their potential as effective hedging instruments. Finally, climate risks contribute to an increasing spillover of risk in the new energy sector, with the long-term trend showing the most significant growth in spillover intensity.
