A series of laboratory tests were conducted on intact specimens of two loess soils to characterize their collapsibility, shear strength, microstructure and mineralogy. In addition, microstructural ...observations were performed on the specimens after various mechanical tests (oedometer-collapse tests and triaxial tests). The micrographs were processed using MATLAB program and the morphology properties of soil pores (including area, major axis length, eccentricity and orientation) were obtained. The micrographs and variations in distributions of the pore morphology properties were used to interpret the microstructural evolution of loess soils due to loading and wetting. Results of the study highlight that collapsible loess soils have an open structure, where clay-coated silts and clay-silt aggregates functioning as fundamental units are connected to each other with a few cementations. Upon loading and wetting, disintegration of clay aggregates (cementations), breakdown of carbonate cementations and other bonding agents initiate the failure of soil structure. This is followed by particle movement and rearrangement, transforming the initial open structure into a closer one. The microstructural evolution is dependent on the stress level and stress path. With the increase in stress level, large-sized inter-aggregate pores transform into small-sized intra-aggregate pores; the pores are flatter and still randomly orientated. Fissures with the size and connectivity related to the stress level may develop in the specimen under a triaxial stress state, depending on the stress path.
•Collapsibility, shear strength, microstructure and mineralogy of two loess soils from China are characterized.•Microstructural evolution of loess soils in various mechanical tests is investigated.•Connection between the microstructural characteristics and mechanical responses of loess soils is interpreted.
Foresight of CO
2
emissions from fuel combustion is essential for policy-makers to identify ready targets for effective reduction plans and to further improve energy policies and plans. A new method ...for forecasting the future development of China’s CO
2
emissions from fuel combustion is proposed in this paper by using grey forecasting theory. Although the existing fractional nonlinear grey Bernoulli model (denoted as FNGBM(1,1)) has been theoretically proven to enhance the adaptability to diverse sequences, its fixed integer-order differential derivative still impairs the performance to some extent. To this end, a varying-order differential derivative is introduced into the existing differential equation to enable a more flexible structure, thus improving the prediction ability of FNGBM(1,1). Specifically, because of the advantages of conformable fractional accumulation, the traditional differential derivative is first replaced by the conformable fractional differential derivative. As a consequence, the continuous conformable fractional nonlinear grey Bernoulli model (hereinafter referred to as CCFNGBM(1,1)) is proposed. To further increase the validity of the model, a metaheuristic algorithm, namely Grey Wolf Optimizer (GWO), is then applied to search for the optimal emerging coefficients for the proposed model. Two real examples and China’s CO
2
emissions from fuel combustion are considered to verify the effectiveness of the newly proposed model, the experimental results show that the newly proposed model outperforms other benchmark models in terms of forecasting accuracy. The proposed model is finally employed to forecast the future China’s CO
2
emissions from fuel combustion by 2023, accounting for 10,039.80 million tons. Based on the forecasts, several policy suggestions are provided to curb CO
2
emissions.
Natural gas, an important low-carbon and clean energy, is increasingly replacing high-pollution sources such as coal and gasoline. Accurate natural gas consumption forecasts are important to policy ...makers in making plans, saving costs, and improving efficiency. This study developed a discrete fractional grey model with a time power term (denoted as DFGM(1,1,tα)) with reference to the discretization technique. The new model is simplified, generalized, and overcomes existing model drawbacks. Moreover, the quantum genetic algorithm (QGA) is used to determine the new coefficients, namely, the fractional order and time-power coefficient. Based on observations from 2001 to 2018, the novel model predicted the natural gas consumption in China from 2019 to 2025 better than other benchmarks. Specifically, natural gas consumption was predicted to maintain a steady upward trend, reaching 315.30 billion cubic metres (hereinafter referred to as Bcm) in 2020 and 439.14 Bcm in 2025. Reasonable suggestions are put forward for associated sectors based on the forecasts.
The fractional grey model is an effective tool for modeling small samples of data. Due to its essential characteristics of mathematical modeling, it has attracted considerable interest from scholars. ...A number of compelling methods have been proposed by many scholars in order to improve the accuracy and extend the scope of the application of the model. Examples include initial value optimization, order optimization, etc. The weighted least squares approach is used in this paper in order to enhance the model's accuracy. The first step in this study is to develop a novel fractional prediction model based on weighted least squares operators. Thereafter, the accumulative order of the proposed model is determined, and the stability of the optimization algorithm is assessed. Lastly, three actual cases are presented to verify the validity of the model, and the error variance of the model is further explored. Based on the results, the proposed model is more accurate than the comparison models, and it can be applied to real-world situations.
The discrete grey model is increasingly used in various real-world forecasting problems, however, in the modeling procedure, neglecting the effect of the time power and requiring the integer-order ...accumulation impair the prediction performance to some extent. Considering this fact, this paper implements the fractional accumulating generation operator and time power term in the discrete grey polynomial model, and as a consequence, a generalized discrete grey polynomial model, namely GDGMP(1,1,
N
,
α
), is proposed. To further improve the prediction accuracy, a metaheuristic algorithm, namely the quantum genetic algorithm (QGA), is applied to determine the emerging coefficients. In the presence of alternate emerging coefficients, the GDGMP(1,1,
N
,
α
) model is compatible with other existing grey models. To demonstrate the effectiveness of the newly proposed model, this model is employed to forecast three real cases (i.e., natural gas consumption, electricity consumption, and elderly population) by comparing it with other benchmark models. The experimental results show that among these competitive models, the proposed model achieves the best prediction performance, and its MAPE (often referred to as the core indicator) for natural gas consumption, electricity consumption, and the elderly population achieves values of 6.05%, 3.22%, and 0.66%, respectively, which are all lower than those of the other models, indicating that the proposed model outperforms other benchmarks.
The use of interval-valued hesitant fuzzy sets (IVHFS) can aid decision-makers in evaluating a variable using multiple interval numbers, making it a valuable tool for addressing decision-making ...problems. However, it fails to obtain information with greyness. The grey fuzzy set (GFS) can improve this problem but studies on it have lost the advantages of IVHFS. In order to improve the accuracy of decision-making and obtain more reasonable results, it is important to enhance the description of real-life information. We combined IVHFS and GFS and defined a novel fuzzy set named interval grey hesitant fuzzy set (IGHFS), in which possible degrees of grey numbers are designed to indicate the upper and lower limits of the interval number. Meanwhile, its basic operational laws, score function, entropy method, and distance measures are proposed. And then, a multicriteria decision-making (MCDM) model IGHFS-TOPSIS is developed based on them. Finally, an example of MOOC platform selection issues for teaching courses illustrates the effectiveness and feasibility of the decision model under the IGHFS.
Currently, the theory and methodology of digital terrain analysis (DTA) has been well developed. However, this technique has not been widely applied in the research of loess landslides in China. This ...study investigated the application of DTA on loess landslides with the high-resolution terrain data obtained from low-cost unmanned aerial vehicles (UAVs). Taking a high-speed and long-runout landslide occurring on the Bailu Loess Tableland, a typical landform type on the Loess Plateau, as an example, we illustrated the fundamental characteristics and spatial patterns of the landslide from various perspectives and performed hydrology analysis, geomorphic change detection, hypsometric integral (HI) and stability analysis, morphology analysis, and structure analysis. The results prove that the DTA methodology cannot only advance understanding of the geomorphology and structure of landslides and detect geomorphic change but also reveal the evolution principles of landforms and demonstrate unique advantages in the prediction of the internal stability of landslides. In conclusion, the DTA methods adopted in this paper are useful to better understand loess landslide and its relationship with geomorphologic evolution.
The fractional nonlinear grey Bernoulli model, abbreviated as FANGBM(1,1), is a successful extension of NGBM(1,1). Although FANGBM(1,1) has numerous excellent characteristics, it has a more complex ...form of fractional accumulation (FA) operator than raw NGBM(1,1). In this study, we propose a novel fractional nonlinear grey Bernoulli model, named CFNGBM(1,1), which uses conformable fractional accumulation (CFA), which has a simpler form than FANGBM. Using two practical cases, the effectiveness of the proposed CFNGBM(1,1) in practical applications was illustrated. Results show that the CFNGBM(1,1) exhibited higher accuracy than other grey models, thus facilitating its promotion in engineering practices.
In this paper, a AgI@TCNQ photocatalyst with a core-shell structure was reported. A two-dimensional TCNQ (7,7,8,8-Tetracyanoquinodimethane) nanosheet, with a π-π conjugate structure, was used as a ...shell layer to realize the flexible coating on the surface of AgI nanoparticles. These special core-shell structure composites solve the key problems of the small interface of the bulk composites and the lesser charge transfer paths, which could accelerate the migration of photogenerated carriers. Thus, the AgI@TCNQ photocatalysts showed the better photodegradation performance for the methylene blue (MB) solution, and the degradation rate of AgI@TCNQ (1 wt.%) composite was 1.8 times than AgI under irradiation. The reactive species trapping experiments demonstrated that ·O
, h
, and ·OH all participated in the MB degradation process. The photocatalytic mechanism of AgI@TCNQ composites could be rationally explained by considering the Z-scheme structure, resulting in a higher redox potential and more efficient separation of charge carriers. At the same time, the unique core-shell structure provides a larger contact area, expands the charge transport channel, and increases the surface active sites, which are beneficial for improving photocatalytic performance.
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