In Situ Growth of MOF from Wood Aerogel toward Bromide Ion Adsorption in Simulated Saline Water.

Utilizing metal-organic framework (MOF) materials for the extraction of bromide ions (Br-) from aqueous solutions, as an alternative to chlorine gas oxidation technology, holds promising potential for future applications. However, the limitations of powdered MOFs, such as low utilization efficiency, ease of aggregation in water, and challenging recovery processes, have hindered their practical application. Shaping MOF materials into application-oriented forms represents an effective but challenging approach to address these drawbacks. In this work, a novel Ag-UiO-66-(OH)2@delignified wood cellulose aerogel (CA) adsorbent is synthesized using an oil bath impregnation method, involving the deposition of UiO-66-(OH)2 nanoparticles onto CA and the uniform dispersion of Ag0 nanoparticles across its surface. CA, characterized by the intertwined cellulose nanofiber structure and a highly hydrophilic surface, serves as an ideal substrate for the uniform growth of UiO-66-(OH)2 nanoparticles, which, in turn, spontaneously reduce Ag+ to form distributed Ag0 nanoparticles due to the abundant hydroxyl groups provided. Leveraging the well-defined biological structure of CA, which offers excellent mass transfer channels, and the highly dispersed Ag adsorption sites, Ag-UiO-(OH)2/CA exhibits remarkable adsorption capacity (642 mg/gAg) under optimized conditions. Furthermore, an integrated device is constructed by interconnecting Ag-UiO-(OH)2/CA adsorbents in series, affirming its potential application in the continuous recovery of Br-. This study not only presents an efficient Ag-UiO-(OH)2/CA adsorbent for Br- recovery but also sheds light on the extraction of other valuable elements from various liquid ores.

Multiple stochastic and inverse stochastic resonances with transition phenomena in complex corporate financial systems.

This study examines the role of periodic information, the mechanism of influence, stochastic resonance, and its controllable analysis in complex corporate financial systems. A stochastic predator-prey complex corporate financial system model driven by periodic information is proposed. Additionally, we introduce signal power amplification to quantify the stochastic resonance phenomenon and develop a method for analyzing stochastic resonance in financial predator-prey dynamics within complex corporate financial systems. We optimize a simplified integral calculation method to enhance the proposed model's performance, which demonstrates superiority over benchmark models based on empirical evidence. Based on stochastic simulations and numerical calculations, we can observe multiple stochastic and multiple inverse stochastic resonances. Furthermore, variations in initial financial information, periodic information frequency, and corporate growth capacity induced stochastic resonance and inverse stochastic resonance. These variations also led to state transitions between the two resonance behaviors, indicating transition phenomena. These findings suggest the potential for regulating and controlling stochastic and inverse stochastic resonance in complex corporate finance, enabling controllable stochastic resonance behaviors.

Empirical study and model simulation of global stock market dynamics during COVID-19.

At the beginning of 2020, COVID-19 swept the world and changed various aspects of human society, such as economy and finance, life and health, migration and population. We first empirically study how the dynamic behaviors of stock markets are affected by COVID-19, and focus on the large volatility dynamics, variation-fluctuation correlation function and epidemic-fluctuation correlation function. Then we generalize the Heston model to simulate the global stock market dynamics, and an epidemic index computed from empirical data is directly taken as the external force in the modelling.

The roles of liquidity and delay in financial markets based on an optimal forecasting model.

We investigate the roles of liquidity and delay in financial markets through our proposed optimal forecasting model. The efficiency and liquidity of the financial market are examined using stochastic models that incorporate information delay. Based on machine learning, we estimate the in-sample and out-of-sample forecasting price performances of the six proposed methods using the likelihood function and Bayesian methods, and the out-of-sample prediction performance is compared with the benchmark model ARIMA-GARCH. We discover that the forecasting price performance of the proposed simplified delay stochastic model is superior to that of the benchmark methods by the test methods of a variety of loss function, superior predictive ability test (SPA), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Using data from the Chinese stock market, the best forecasting model assesses the efficiency and liquidity of the financial market while accounting for information delay and trade probability. The rise in trade probability and delay time affects the stability of the return distribution and raises the risk, according to stochastic simulation. The empirical findings show that empirical and best forecasting approaches are compatible, that company size and liquidity (delay time) have an inverse relationship, and that delay time and liquidity have a nonlinear relationship. The most efficient have optimal liquidity.

Reverse resonance in stock prices of financial system with periodic information.

We investigate the stochastic resonance of the stock prices in a finance system with the Heston model. The extrinsic and intrinsic periodic information are introduced into the stochastic differential equations of the Heston model for stock price by focusing on the signal power amplification (SPA). We find that for both cases of extrinsic and intrinsic periodic information a phenomenon of reverse resonance emerges in the behaviors of SPA as a function of the system and external driving parameters. Moreover, in both cases, a phenomenon of double reverse resonance is observed in the behavior of SPA versus the amplitude of volatility fluctuations, by increasing the cross correlation between the noise sources in the Heston model.