Significantly Enhanced the Energy Density of Dielectric Composites by Sandwich Structure with Highly Insulating Mica Nanosheets.

Dielectric polymer composites exhibit great application prospects in advanced pulse power systems and electric systems. However, the decline of breakdown strength by loading of single high dielectric constant nanofiller hinders the sustained increase in energy density of the composites. Here, a sandwich-structured nanocomposite prepared with mica nanosheets as the second filler exhibits decoupled modulation of dielectric constant and breakdown strength. The traditional layered clay mineral mica is exfoliated into nanosheets and filled into polyvinylidene difluoride (PVDF), which shows a special depolarization effect in the polymer matrix. In Kelvin probe microscopy characterization and thermally stimulated depolarization current indicates that the mica nanosheets provided space charge traps for the polymer matrix and effectively suppressed the carrier motion. A sandwich structure composite material with mica nanosheets as the central layer has achieved a high energy density of 11.48 J cm-3, 2.4 times higher than the pure PVDF film. This is due to the fact that randomly oriented distribution of nanosheets in a polymer matrix provide better current blocking. This work provides an effective method to improve the energy density of dielectric polymer composites by synergistically introducing insulating nanosheets and high dielectric constant nanofillers.

The impact of environmental regulation on green investment efficiency of thermal power enterprises in China-based on a three-stage exogenous variable model.

Due to the increased frequency of extreme weather events and the implementation of the China's dual-carbon target, thermal power companies have been under pressure to construct green infrastructure and to actively pursue low-carbon transformation in response to stricter environmental regulations. This research thus selects 30 listed thermal power enterprises in China as study objects and assesses their green investment efficiency in the low-carbon transition process using three-stage DEA evaluation model with environmental regulation as an exogenous variable. Based on this, a benchmark regression model is used to corroborate the relationship between environmental regulation and green investment. Simultaneously, we carry out analysis to compare the correlation between thermal power firms' green investment efficiency and their focus on green investments. The results show in terms of total efficiency that environmental regulation significantly improves the total efficiency of 80% of thermal power enterprises compared to the absence of this exogenous variable. With the addition of environmental regulation, firms' total efficiency declines gradually in general from 2018 to 2022, with the mean value of efficiency falling by 0.068. In terms of stage-specific efficiency, the efficiency of the green investment stage of the majority of firms is between 0.3 and 0.6, which is much lower than that of the operational stage and the market performance stage. In terms of sub-indicator efficiency, both green investment efficiency and social donation efficiency among thermal power enterprises show obvious polarization, with 30% of them having an efficiency of 1 and 30% less than 0.1. In terms of green investment focus, thermal power unit renovation has a more obvious role in boosting the green investment efficiency of thermal power enterprises than do wind power and photovoltaic projects. Therefore, both governmental departments and thermal power enterprises need to take active measures in order to achieve green transformation from the perspective of green investment efficiency. Through the segmentation of important projects of green investment, this paper provides a reasonable investment direction reference for the sustainable transformation of China's thermal power industry. It also provides a rich and novel theoretical basis for the Chinese government to further improve the relevant environmental protection laws and regulations of thermal power industry.

A multi-objective fuzzy programming model for port tugboat scheduling based on the Stackelberg game.

To solve the optimization problem of tugboat scheduling for assisting ships in entering and exiting ports in uncertain environments, this study investigates the impact of the decisions of tugboat operators and port dispatchers on tugboat scheduling under the scenario of dynamic task arrival and fuzzy tugboat operation time. Considering the features of the shortest distance tugboat principle, the first available tugboat principle, and the principle of fairness in the task volume of each tugboat, the tugboat company aims to minimize the total daily fuel consumption of tugboat operations, maximize the total buffer time of dynamic tasks, and minimize the total completion time as the objective functions. Due to the limitations of port vessel berthing and departure, as well as the allocation standards for piloting or relocating tugboats, the present study proposes a Stackelberg game-based fuzzy model for port tugboat scheduling with the tugboat operator and port dispatcher acting as decision makers at the upper and lower levels, respectively. A seagull optimization algorithm based on priority encoding and genetic operators is designed as a solution approach. CPLEX, genetic algorithm, standard seagull optimization algorithm, and simulated annealing algorithm are used to compare and analyze the solution results for the 45 problem cases generated from the actual data obtained from the Guangzhou Port. The results verify the efficiency of the proposed seagull optimization algorithm based on priority encoding and genetic operators. Furthermore, additional experiments are conducted to evaluate the changes in fairness coefficient, uncertain parameter correlation coefficients, and objective function correlation coefficients to demonstrate the practicality of the fuzzy programming model. This analysis involves adjusting the confidence level incrementally from 0 to 100% with respect to the model's uncertain parameters.

An Improved Migratory Birds Optimization Algorithm for Closed- Loop Supply Chain Network Planning in a Fuzzy Environment.

Recycling of used products can provide substantial economic and environmental benefits for supply chain players. However, many factors associated with the design of closed-loop supply chain networks are uncertain in their nature, including demand, opening cost of facilities, capacity of opened facilities, transportation cost, and procurement cost. Therefore, this study proposes a novel fuzzy programming model for closed-loop supply chain network design, which directly relies on the fuzzy ranking method based on a credibility measure. The objective of the presented optimization model aims at minimizing the total cost of the network when selecting the facility locations and transportation routes between the nodes of the network. Based on the problem characteristics, a Migratory Birds Optimization Algorithm with a new product source encoding scheme is developed as a solution approach. The inspiration for the product source coding method originates from the label information of raw material supplier and manufacturing factories on product packaging, as well as the information of each logistics node on the delivery order. This novel encoding method aims to address the limitations of four traditional encoding methods: Prüfer number based encoding, spanning tree based encoding, forest data structure based encoding, and priority based encoding, thereby increasing the likelihood of heuristic algorithms finding the optimal solution. Thirty-five illustrative examples are developed to evaluate the proposed algorithm against the exact optimization method (LINGO) and a Genetic Algorithm, Ant Colony Optimization, Simulated Annealing, which are recognized as well-known metaheuristic algorithms. The results from extensive experiments show that the proposed algorithm is able to provide optimal and good-quality solutions within acceptable computational time even for large-scale numerical examples. The suitability of the model is confirmed through a meticulous sensitivity analysis. This analysis involves adjusting the confidence level incrementally from 50% to 100%, in 5% intervals, with respect to the model's uncertain parameters. Consequently, it yields valuable managerial insights. The outcomes of this research are expected to provide scientific support for related supply chain enterprises and stakeholders.

Impact of the digital economy on urban green growth: Empirical evidence from China.

Using the digital economy to empower urban economic green growth provides essential opportunities for China to achieve high-quality growth. This paper assesses the level of digital economy and green growth in Chinese cities, seeking to explore the mechanisms and effects of the digital economy on urban green growth in a unified framework. The results indicate that the digital economy can drive cities' green growth. This conclusion still holds after a set of robustness tests. Meanwhile, the green value of the digital economy is fully released among the eastern cities, major urban agglomerations, and high-level cities. Further research shows that the digital economy can indirectly enhance urban green growth in the neighboring regions through spatial spillover effects. Moreover, labor resource mismatch, capital resource mismatch and green technology innovation are significant mediating mechanisms. The findings could guide policymakers on green growth in emerging economies from a digital economy perspective.