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In this study, a novel glutathione (GSH) surface molecular imprinting polymer (SMIP) was successfully prepared by using macroporous adsorption resins (MAR) as substrate, which could separate and purify GSH efficiently.

SMIP was synthesized by chloromethylated modified MAR (LX1180-Cl) as the substrate, N, N’-methylenebisacrylamide (NMBA) as a crosslinker, GSH as a template, acrylamide (AM) and N-vinylpyrrolidone (NVP) as functional monomers. The morphology and structure of the polymer were characterized by scanning electron microscope and Fourier transforms infrared spectroscopy.

The maximum adsorption capacity toward GSH was 39.0 mg/g and the separation decree had relation to L-cysteine (L-cys) was 4.2. The optimal operation conditions were studied in detail and the got as follows: the molar ratios of NMBA, AM, GSH and NVP, were 7.0, 0.8 and 0.5. The optimal time and temperature were 14 h and 40°C, respectively. The Langmuir and pseudo-first-order model were fitting these adsorption characteristics well.

GSH has a diversity of medicinal and bioactive functions, so the purpose of this study representing a method in separate and purify technology of GSH, which provided a way for the development of medicine.

This contribution provided a novel way to separate GSH from L-cys. Under the optimal conditions, the maximum adsorption capacity toward GSH was 39.0 mg/g and the separation decree had relation to L-cys was 4.2.

This paper aims to evaluate the separation and purification characteristics of flavonoids from polygonum cuspidatum (PC) extracts by using macroporous adsorption resins (MAR) mixed bed to improve the utilization rate of flavonoids.

Taking the separation performance of flavonoids as an evaluation index, the best MAR were screened from 31 sorts of MAR and combined the best MAR to form a MAR mixed bed for adsorption and separation of flavonoids.

By studying the separation conditions that affect flavonoids, the results showed that resin LZ-72 has best separation and purification effect on flavonoids under the optimal adsorption and desorption conditions, the purity of the obtained flavonoid compound reaches 82.50%, 2.66 times of the initial extract, and the recovery rate reaches 89.70%. Theoretical research results have shown that the adsorption of flavonoids by MAR conforms to the pseudo-second-order kinetics and Freundlich models.

Because the flavonoids in PC have great medicinal value, the purpose of this work is to develop a method of separating and purifying flavonoids from PC, which will provide a certain foundation for the development of medicine.

This contribution provided a novel way to separate flavonoids from PC. Under the optimal conditions, the content of flavonoids in the product was increased 2.66-fold from 31.01% to 82.50%, and the recovery yield was 89.70%.

Using a reversible addition fragmentation chain transfer reaction, a series of resins were prepared by using N, N-diethyl acrylamide (DEA), poly (ß-hydroxyethyl methacrylate) (PHEMA) as hydrophilic blocks and poly (glycidyl methacrylate) (PGMA) as hydrophobic blocks (and as a target for immobilizing penicillin G acylase [PGA]) and the low critical solution temperature (LCST) of which could be adjusted by changing the segment length of blocks.

To make the catalytic conversion temperature of immobilized PGA fallen into the temperature range of the sol state of thermosensitive block resin, a type of thermosensitive block resin, i.e. PDEA-b-PHEMA-b-PGMA (DHGs) was synthesized to immobilize PGA, and the effect of segment order of block resin was investigated on the performance of PGA.

Carrier prepared with monomers molar ratio of n(DEA) : n(HEMA): n(GMA) = 100: 49: 36 presented loading capacity (L) and enzyme activity recovery ratio (Ar) of 110 mg/g and 90%, respectively, and a block resin with LCST value of 33 °C was essential for keeping higher Ar of PGA.

PGA has become an important biocatalyst in modern chemistry industry. However, disadvantages include difficulty in separation, poor repeatability and high cost, which limits the scope of PGA applications. The effective method is to immobilize the enzyme to the carrier, which could overcome the disadvantage of free enzyme.

This study aims to investigate the impact of board skill diversity (BSD) on corporate environmental responsibility (CER). In addition, this study explores the moderating effects of formal regulatory pressure and informal media pressure.

This study uses Chinese high polluting companies as the sample and uses regression analysis. Robustness checks, including instrumental variable regression, Heckman two-stage model and propensity score matching method, are performed to test the robustness of the results.

The findings suggest that BSD significantly improves CER performance. Both formal regulatory pressure and informal media pressure strengthen the positive impact of BSD on CER. Further channel analyses reveal that BSD improves CER performance by promoting corporate proenvironmental behaviors rather than by restricting environmental violations; skill diversity of executive directors has a more significant effect on CER than that of independent directors. Finally, the moderating effect of regulatory pressure is only significant after the implementation of the Environmental Protection Law, and the moderating effect of media pressure mainly concentrates on negative media coverage.

The involvement of directors with more diverse skills is essential to improve corporate proenvironmental behaviors. Companies should select qualified directors with different skills to further improve their performance on environmental protection and sustainable development.

Regulators and standard-setters should develop efficient guidelines on corporate board governance to enhance the positive role of companies in environmental and sustainable development.

This study broadens the research on the determinants of CER by examining the influence of BSD on CER and the moderating roles of various stakeholder pressures, thereby providing a deeper understanding of corporate environmental performance and sustainable development.

The authors develop a conceptual model to examine how three basic psychological needs (i.e. needs for competence, autonomy and relatedness) affect employee social media use (i.e. work- and social-related use). The authors propose that the need for autonomy positively moderates the relationship between need for competence and work-related use, whereas it negatively moderates that between need for relatedness and social-related use.

To test the proposed model, 332 internal and 271 external social media users in the workplace were recruited.

The results indicate that needs for competence and autonomy and needs for relatedness and autonomy positively affect the work- and social-related use, respectively, of internal and external social media. Need for autonomy positively moderates the relationship between need for competence and work-related use of internal social media, and it negatively moderates that between need for relatedness and the social-related use of internal social media. Need for autonomy has no moderating effect on the relationship between need for competence and work-related use, whereas it negatively moderates the relationship between need for relatedness and the social-related use of external social media.

First, the authors’ findings offer significant empirical support for the different social media uses, namely work and social related. Second, this study highlights the importance of psychological needs of employees in determining the form of social media use. Third, this study empirically demonstrates the differences in psychological needs and social media use between two different social media contexts.

A novel grafted temperature-responsive ReO₄⁻ Imprinted composite membranes (Re-ICMs) was successfully prepared by using polyvinylidene fluoride (PVDF) resin membranes as substrates, this study aimed to separate and purify ReO effectively.

Re-ICMs were synthesized by PVDF resin membranes as the substrate, acrylic acid (AA), acrylamide (AM), ethylene glycol dimethacrylate (EGDMA) were functional monomers. The morphology and structure of Re-ICMs were characterized by scanning electron microscope and Fourier transform infrared spectroscopy.

The maximum adsorption capacity toward ReO₄⁻ was 0.1,163 mmol/g and the separation decree had relation to MnO₄⁻ was 19.3. The optimal operation conditions were studied detailedly and the results as follows: the molar ratios of AA, AM, EGDMA, ascorbic acid, NH4ReO₄, were 0.8, 0.96, 0.02, 0.003 and 0.006. The optimal time and temperature were 20 h and 40°C, respectively. The Langmuir and pseudo-second-order models were fit these adsorption characteristics well.

Rhenium (Re) is mainly used to chemical petroleum and make superalloys for jet engine parts. This study was representing a technology in separate and purify of Re, which provided a method for the development of the petroleum and aviation industry.

This contribution provided a novel method to separate ReO₄⁻ from MnO₄⁻. The maximum adsorption capacity was 0.1163 mmol/g at 35°C and the adsorption equilibrium time was within 2 h. Meanwhile, the adsorption selectivity rate ReO₄⁻/MnO₄⁻ was 19.3 and the desorption rate was 78.3%. Controlling the adsorption experiment at 35°C and desorption experiment at 25°C in aqueous solution, it could remain 61.3% of the initial adsorption capacity with the adsorption selectivity rate of 13.3 by 10 adsorption/desorption cycles, a slight decrease, varied from 78.3% to 65.3%, in desorption rate was observed.

This paper aims to develop an effective framework which combines Bayesian optimized convolutional neural networks (BOCNN) with Monte Carlo simulation for slope reliability analysis.

The Bayesian optimization technique is firstly used to find the optimal structure of CNN based on the empirical CNN model established in a trial and error manner. The proposed methodology is illustrated through a two-layered soil slope and a cohesive slope with spatially variable soils at different scales of fluctuation.

The size of training data suite, T, has a significant influence on the performance of trained CNN. In general, a trained CNN with larger T tends to have higher coefficient of determination (R²) and smaller root mean square error (RMSE). The artificial neural networks (ANN) and response surface method (RSM) can provide comparable results to CNN models for the slope reliability where only two random variables are involved whereas a significant discrepancy between the slope failure probability (P_f) by RSM and that predicted by CNN has been observed for slope with spatially variable soils. The RSM cannot fully capture the complicated relationship between the factor of safety (FS) and spatially variable soils in an effective and efficient manner. The trained CNN at a smaller the scale of fluctuation (λ) exhibits a fairly good performance in predicting the P_f for spatially variable soils at higher λ with a maximum percentage error not more than 10%. The BOCNN has a larger R² and a smaller RMSE than empirical CNN and it can provide results fairly equivalent to a direct Monte Carlo Simulation and therefore serves a promising tool for slope reliability analysis within spatially variable soils.

A geotechnical engineer could use the proposed method to perform slope reliability analysis.

Slope reliability can be efficiently and accurately analyzed by the proposed framework.

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

This paper aims to develop an efficient algorithm combining straightforward response surface functions with Monte Carlo simulation to conduct seismic reliability analysis in a systematical way.

The representative slip surfaces are identified and based on to calibrate multiple response surface functions with acceptable accuracy. The calibrated response surfaces are used to determine the yield acceleration in Newmark sliding displacement analysis. Then, the displacement-based limit state function is adopted to conduct seismic reliability analysis.

The calibrated response surface functions have fairly good accuracy in predicting the yield acceleration in Newmark sliding displacement analysis. The seismic reliability is influenced by such factors as PGA, spatial variability and threshold value. The proposed methodology serves as an effective tool for geotechnical practitioners.

The multiple sources of a seismic slope response can be effectively determined using the multiple response surface functions, which are easily implemented within geotechnical engineering.

Effective safety supervision plays a crucial role in ensuring safe production within coal mines. Conventional coal mine safety supervision (CMSS) in China has suffered from the problems of power-seeking, excessive resource consumption and poor timeliness. This paper aims to explore the Internet+ CMSS mode being emerged in China.

The evolution of CMSS systems underwent comprehensive scrutiny through a blend of qualitative and quantitative approaches. First, evolutionary game theory was used to analyze the necessity of incorporating Internet+ technology. Second, a system dynamics model of Internet+ CMSS was crafted, encompassing a system flow diagram and equations for various variables. The model was subsequently simulated by taking the W coal mine in Shanxi Province as a representative case study.

It was revealed that the expected safety profit from the Internet+ mode is 296.03% more than that from the conventional mode. The precise dissemination of law enforcement information was identified as a pivotal approach through which the Internet+ platform served as a conduit to foster synergistic collaboration among diverse elements within the system.

The outcomes of this study not only raise awareness about the potential of Internet+ technology in safety supervision but also establish a vital theoretical foundation for enhancing the efficacy of the Internet+ CMSS mode. The significance of these findings extends to fostering the wholesome and sustainable progress of the coal mining industry.

This research stands out as one of the limited studies that delve into the influence of Internet+ technology on CMSS. Building upon the pivotal approach identified, to the best of authors’ knowledge, a novel “multi-blind” working mechanism for Internet+ CMSS is introduced for the first time.