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Faced with the severe eco-environmental problems and economic slowdown, China started the exploration of public-private partnership mode on eco-environmental protection projects, aiming to solve the disbalance between environmental improvement and investment shortage. This study systematically analyzed the characteristics, policy and market reactions of eco-environmental protection public-private partnership projects since 2014. Till the end of 2018 in China, the number of eco-environmental protection public-private partnership projects was 2,914 and investment was 1.84 trillion RMB, accounting for 33.67% and 13.94% of the total, respectively. The peak year was in 2017, then the number of eco-environmental protection public-private partnership projects dropped significantly in 2018. It is estimated that from 2014 to 2018, the investment from eco-environmental protection public-private partnership projects in the implementation phase contributes to 3.2% of China's GDP, 0.2 % of economic growth. To some extent, it has accelerated the process of ecological and environmental governance in China, especially in the field of municipal environmental infrastructure construction and comprehensive environmental treatment. However, the expected effect of eco-environment improvement from eco-environmental protection public-private partnership projects is difficult to guarantee, with the absence of key mechanisms and policies.

The original version of this Article contained an error in the affiliation of the 13th author, Wei Xiong from ‘Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 15261, USA’, which was incorrectly given as ‘Center for Materials Innovation, University of Maryland, College Park, Maryland, 20742, USA’. The error has been corrected in both the PDF and HTML versions of the Article.

Multi-principal element alloys (MPEA) demonstrate superior synergetic properties compared to single-element predominated traditional alloys. However, the rapid melting and uniform mixing of multi-elements for the fabrication of MPEA structural materials by metallic 3D printing is challenging as it is difficult to achieve both a high temperature and uniform temperature distribution in a sufficient heating source simultaneously. Herein, we report an ultrahigh-temperature melt printing method that can achieve rapid multi-elemental melting and uniform mixing for MPEA fabrication. In a typical fabrication process, multi-elemental metal powders are loaded into a high-temperature column zone that can be heated up to 3000 K via Joule heating, followed by melting on the order of milliseconds and mixing into homogenous alloys, which we attribute to the sufficiently uniform high-temperature heating zone. As proof-of-concept, we successfully fabricated single-phase bulk NiFeCrCo MPEA with uniform grain size. This ultrahigh-temperature rapid melt printing process provides excellent potential toward MPEA 3D printing.

The regularized lattice Boltzmann method (RLBM) has the advantage of improving the accuracy of numerical simulations without increasing the computational overhead. The RLBM usually uses the standard Cartesian grid in numerical simulation. This paper introduces the multi-layer grid technique, the multi-layer grid has different resolutions, which is also more consistent with the physical laws of the flow field. Based on this, a multi-layer grid regularized lattice Boltzmann method (ML-RLBM) for solving complex flow problems is proposed. The proposed method performs local refinement of the grid on the standard Cartesian grid to achieve a multi-layer grid, which can reduce the computational overhead. In order to realize the local grid refinement easily, this paper adopts the center point format to store the multi-layer grid. The ML-RLBM designs a buffer scheme between different layers of grids, which realizes that only spatial interpolation is required at the interface of different layers of grids without temporal interpolation. The numerical results show that the ML-RLBM has excellent numerical accuracy and numerical stability, and it can effectively reduce the computational cost. [ABSTRACT FROM AUTHOR]

The regularized lattice Boltzmann method (RLBM) is an improvement of the lattice Boltzmann method (LBM). The advantage of RLBM is improved accuracy without increasing computational overheads. The paper introduces the method of multi-layer grids, the multi-layer grids have different resolutions which can accurately solve problems in computational fluid dynamics (CFD) without destroying the parallelism of RLBM. Simulating fluid flow usually requires a large number of grid simulations. Therefore, it is necessary to design a parallel algorithm for RLBM based on multi-layer grids. In this paper, a load-balancing-based grid dividing algorithm and an MPI-based parallel algorithm for RLBM on multi-layer grids are proposed. The load balancing-based grid dividing algorithm ensures that the workload is evenly distributed across processes, minimizing the discrepancies in computational load. The MPI-based parallel algorithm for RLBM on multi-layer grids ensures accurate and efficient numerical simulation. Numerical simulations have verified that the proposed algorithms exhibit excellent performance in both 2D and 3D experiments, maintaining high stability and accuracy. The multi-layer grids method is significantly better than single-layer grids in terms of CPU runtime and number of grids required. Comparative analysis with the OpenMP multi-threading method on the multi-layer grid RLBM shows that the proposed algorithm in this paper achieves superior speedup and efficiency. [ABSTRACT FROM AUTHOR]

The low nutrient content of soil in desert ecosystems results in unique physiological and ecological characteristics of plants under long-term water and nutrient stress, which is the basis for the productivity and stability maintenance of the desert ecosystem. However, the relationship between the soil and the plant nutrient elements in the desert ecosystem and its mechanism for maintaining ecosystem stability is still unclear. In this study, 35 sampling sites were established in an area with typical desert vegetation in the Qaidam Basin, based on a drought gradient. A total of 90 soil samples and 100 plant samples were collected, and the soil's physico-chemical properties, as well as the nutrient elements in the plant leaves, were measured. Regression analysis, redundancy analysis (RDA), the Theil–Sen Median and Mann–Kendall methods, the structural equation model (SEM), and other methods were employed to analyze the distribution characteristics of the soil and plant nutrient elements along the drought gradient and the relationship between the soil and leaf nutrient elements and its impact on ecosystem stability. The results provided the following conclusions: Compared with the nutrient elements in plant leaves, the soil's nutrient elements had a more obvious regularity of distribution along the drought gradient. A strong correlation was observed between the soil and leaf nutrient elements, with soil organic carbon and alkali-hydrolyzed nitrogen identified as important factors influencing the leaf nutrient content. The SEM showed that the soil's organic carbon had a positive effect on ecosystem stability by influencing the leaf carbon, while the soil's available phosphorus and the mean annual temperature had a direct positive effect on stability, and the soil's total nitrogen had a negative effect on stability. In general, the soil nutrient content was high in areas with a low mean annual temperature and high precipitation, and the ecosystem stability in the area distribution of typical desert vegetation in the Qaidam Basin was low. These findings reveal that soil nutrients affect the stability of desert ecosystems directly or indirectly through plant nutrients in the Qaidam Basin, which is crucial for maintaining the stability of desert ecosystems with the background of climate change. [ABSTRACT FROM AUTHOR]

Electricity sales is one of the important assessment indexes of a power grid company's operation. Since electricity sales is closely related to many factors, how to consider the influence of multiple factors and improve the accuracy of the calculation of electricity sales is a difficult problem that needs to be solved urgently. In this paper, we first analyze the six dimensions affecting electricity sales and select the key influencing factors that can be quantified statistically. Secondly, the key influencing factors are screened according to Pearson's correlation coefficient and then the calculation model of electricity sales is established based on the random forest algorithm. Finally, we validate the feasibility and validity of the proposed calculation method for electricity sales through a case study. [ABSTRACT FROM AUTHOR]

The worsening of climate conditions is closely related to the large amount of carbon dioxide produced by human use of fossil fuels. Under the guidance of the goal of "carbon peaking and carbon neutrality goals", with the deepening of the structural reform of the energy supply side, the hybrid energy system coupled with renewable energy has become an important means to solve the energy problem. This paper focuses on the comprehensive evaluation of hybrid energy systems. A complete decision support system is constructed in this study. The system primarily consists of four components: 1) Twelve evaluation criteria from economic, environmental, technological, and socio-political perspectives; 2) A decision information collecting and processing method in uncertain environment combining triangular fuzzy numbers and hesitation fuzzy language term sets; 3) A comprehensive weighting method based on Lagrange optimization theory; 4) Solution ranking based on the fuzzy VIKOR method that considers the risk preferences of decision-makers. Through a case study, it was found that the four most important criteria are investment cost, comprehensive energy efficiency, dynamic payback period and energy supply reliability with weights of 7.21%, 7.17%, 7.17%, and 7.15% respectively. A1 is the scheme with the best comprehensive benefit. The selection of solutions may vary depending on the decision-maker's risk preference. Through the aforementioned research, the decision framework enables the evaluation of the overall performance of the system and provides decision-making references for decision-makers in selecting solutions. [ABSTRACT FROM AUTHOR]

The attainment of the desired strength of the Inconel 718 superalloy heavily relies on the isothermal aging process, which plays a critical role in achieving the anticipated hardening effect. Surprisingly, there remains a dearth of dedicated studies investigating the influence of homogenization on phase transformations during the isothermal aging process, leaving a gap in the knowledge required to guide the design of post-heat treatment strategies. Addressing this gap, our work investigates the impact of homogenization time on phase transformations during isothermal aging at 730 °C in Inconel 718 alloys produced via additive manufacturing (AM) and suction casting (SC) methods. Intriguingly, we observe contrasting behaviors in the particle size of γ″ and γ′ in aged samples, depending on the homogenization time and the alloy processing method. Specifically, in AM alloys, extended homogenization time leads to an increase in the particle size of γ″ and γ′, whereas the opposite trend is observed in SC alloys. Furthermore, despite undergoing the same heat treatment, the AM alloys exhibit smaller particle sizes but higher precipitate number densities compared to the SC alloys, resulting in superior hardness. Notably, pronounced grain refinement during aging is evident in 1 h homogenized SC samples under 1180 °C, warranting further investigations into the underlying mechanisms. This study elucidates the crucial role of homogenization in attaining the desired microstructure following subsequent aging processes. Moreover, it offers novel insights for developing post-heat treatment strategies for superalloys. [ABSTRACT FROM AUTHOR]