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As an important irritant trace gas in the atmosphere, ammonia (NH3) significantly impacts human health and environment. Bottom-up emission inventories are widely used to estimate ammonia emissions and their geographical distributions over China. However, high uncertainties are still associated with emission inventories due to inaccurate emission factors used. The Differential Evolution (DE) algorithm is a population-based stochastic optimization algorithm used to solve complicated optimization problems. We quantify NH3 emissions and lifetime from Infrared Atmospheric Sounding Interferometer (IASI) NH3 observations together with MERRA-2 wind fields based on the DE algorithm. Two inland cities, Urumchi and Golmud in China, are chosen to study of the NH3 emissions based on the distributions of NH3 total columns and wind fields. The NH3 emissions rate estimated is about 5.84 × 10−11 and 4.19 × 10−11 kg·m−2s−1 in Urumchi and in the Golmud area from May to September from 2008 to 2023, respectively. The lifetime of NH3 estimated in the two areas is 4.31 and 9.19 h, respectively. We compare the NH3 emissions and lifetime estimated in this study with the values in other studies, and the results show the reliability of the method used. This work is one of few quantitative studies of NH3 emissions from cities using satellite observations in China. [ABSTRACT FROM AUTHOR]

The substantial challenge posed by inherent hydrological forecasting uncertainty has critical implications for the optimization of real-time reservoir operations. In response, this study introduces a stochastic framework explicitly devised to comprehensively quantify the ramifications of hydrological forecasting uncertainty, notably its temporal correlations, on the outcomes of real-time reservoir optimization and risk assessment. Furthermore, this framework seeks to delineate the pivotal influence of incorporating or neglecting these temporal dynamics on the eventual results, while concurrently elucidating the underlying mechanisms governing these discernible influences. The framework adopts a comprehensive approach to simulating hydrological forecast uncertainty through ensemble forecasts and scenario trees, employing three methods (two Monte Carlo sampling-based methods and one Gaussian copula method) to generate inflow forecast ensembles. To improve the adaptability to uncertainties in inflow forecasts, the framework incorporates a transformation of the generated ensembles into scenario trees, serving as input for a stochastic optimization model that derives the final optimal decision based on optimizing the expected value of the objective function for all scenarios. Additionally, a parallel differential evolution algorithm is proposed to solve the stochastic optimization model efficiently. Risk assessment is performed to capture the uncertainty and corresponding risk associated with the reservoir optimal decision. The proposed framework is demonstrated in a flood control reservoir system in China, where several numerical experiments are conducted to explore the effect of forecast uncertainty level and temporal correlation on real-time reservoir optimal operation. Results show that the temporal correlation of inflows must be considered in both inflow stochastic simulation and reservoir stochastic optimization to avoid overestimating or underestimating operational risk, potentially leading to operation failures. By examining the risk simulation surface, reservoir operators can evaluate the robustness of operational decisions and make more reliable final decisions. [ABSTRACT FROM AUTHOR]

The formation and evolution mechanisms of complex types and scales of marine–continental transitional shale pores are still indefinite, restricting the accurate evaluation of shale reservoir and the effective evaluation of coal-bearing marine–continental transitional shale gas resource quantity. Considering the Shanxi shale in Ordos basin of China as the research object, combining the FE-SEM images and petrophysical analysis, high-pressure mercury intrusion porosimetry, and CO2 and N2 adsorption–desorption experiments, the structure characteristics and differential evolution mechanisms of multiscale and multitype of coal-bearing shale pores were discussed. The results show that coal-bearing marine–continental transitional shales are rich in clay minerals and organic matters (OMs). Pores developed within organic matters, clay, and brittle minerals of coal-bearing shale have decreasing porosity values. OM pores are directly related to micro- and mesopores, with high specific surface areas, while the porosity of inorganic pores increases with the increasing pore diameter. The porosity of all pores shows a positive relationship with permeability, which changes periodically with the increase in maturity. Coal-bearing shale pores are mainly plate- and ink bottle-shaped, with multimodal pore size distributions. Controlled by both diagenesis and hydrocarbon generation, the evolution of coal-bearing shale pores could be mainly divided into four stages. Furthermore, the pore evolution model of coal-bearing marine–continental transitional shale was preliminarily constructed. This study would enhance the understanding of reservoir evolution of the coal-bearing shale and provide useful information for the assessment and evaluation of reservoir capacity. [ABSTRACT FROM AUTHOR]

The negative public opinions and views on overseas direct investment (ODI) of a multinational enterprise (MNE) will damage the image of its brand and are likely to bring it serious economic and social losses. So, it is important for the MNE to understand the formation and spread mechanism of public opinion risk (POR) in order to effectively respond to and guide the public opinion. This research proposed a multifractal-based situation optimization method to explore the POR evolution based on the media-based negative sentiment on China's ODI. The sentiment measurement is obtained by a directed crawler for gathering the text of media reports corresponding to a certain ODI event using a URL knowledge base from the GDELT Event Database. Taking the public opinion crisis of the tax evasion incident of the local arm of China's MNE in India as an example, the experiments show that this method could dynamically monitor the POR event in real-time and help MNE guide the effective control and benign evolution of public opinion of the event. [ABSTRACT FROM AUTHOR]

Over the past 20 years, the Gravity Recovery and Climate Experiment (GRACE), and its successor mission, GRACE‐Follow On (GRACE‐FO) have made significant contributions to time‐variable gravity field modeling. A Chinese low‐low satellite‐to‐satellite tracking gravimetry mission (i.e., Chinese future gravimetry mission) has been confirmed to be selected as the polar‐orbiting satellite gravimetry mission for China, because of the capability to collect gravity data globally. However, the analysis of potential contributions to geosciences from GRACE‐FO coupling with the Chinese future gravimetry mission is still limited. This study combines GRACE‐FO and Chinese future gravimetry missions as the Dual GRACE‐like Polar satellite Constellation (DGPC). By carefully choosing the initial orbit parameters of the Chinese future gravimetry mission with the differential evolution algorithm, the DGPC is expected to mitigate the temporal aliasing effects by improving the temporal resolution of time‐variable gravity solutions (i.e., 1‐day and 3‐day solutions). Regarding the spectral‐domain evaluation, zonal, tesseral, and sectorial coefficients estimated by the DGPC show approximately 6.01%–13.42% noise reductions compared with GRACE‐FO. Regarding the spatial‐domain evaluation, the DGPC can suppress noises of about 39.44% and 31.12% in annual amplitude and long‐term trend, respectively. On this basis, this paper analyzes the effectiveness of the DGPC in potential contributions to geosciences (e.g., hydrology, glaciology, and seismology). Specifically, the DGPC can improve accuracy by about 36.96%, 25.85%, and 33.16% with respect to GRACE‐FO for signals in the subhumid basin, signals of ice‐sheet mass balance over Greenland, and coseismic displacement of the fault zone, respectively. In general, the potential capability for high‐frequency signals recovery of the DGPC would facilitate contributions of satellite gravimetry to geosciences. Plain Language Summary: The Gravity Recovery And Climate Experiment (GRACE) mission and its successor GRACE Follow On (GRACE‐FO) have made significant contributions to time‐variable gravity field modeling. A polar gravity satellite mission (i.e., Chinese future gravimetry mission) has been confirmed to be selected as the polar‐orbiting satellite gravimetry mission for China because of its capability to collect gravity data globally. Including the GRACE‐FO mission, it is expected that there will be two polar pairs of gravity satellites (i.e., dual GRACE‐like polar satellite constellation) simultaneously within the next few years. What can we expect from the dual GRACE‐like polar satellite constellation? In this study, we introduce an optimization algorithm (i.e., differential evolution algorithm) to carefully choose the initial orbit parameters of the Chinese future gravimetry mission. The dual GRACE‐like polar satellite constellation has been demonstrated to have the potential to mitigate the temporal aliasing effects of time‐variable gravity solutions. It is found that the dual GRACE‐like polar satellite constellation can improve accuracy by about 36.96%, 25.85%, and 33.16% with respect to GRACE‐FO for signals in the subhumid basin, signals of ice‐sheet mass balance over Greenland, and coseismic displacement of the fault zone, respectively. The temporal improvement of Earth's mass transport estimated by coupling GRACE‐FO with a Chinese polar gravity satellite mission would facilitate contributions of satellite gravimetry to geosciences. Key Points: This paper introduces the differential evolution algorithm into the design of the dual Gravity Recovery And Climate Experiment (GRACE)‐like polar satellite constellationImproving the temporal resolution of time‐variable gravity solutions can potentially contribute to mitigating the temporal aliasing effectsThe dual GRACE‐like polar satellite constellation has high‐frequency signal recovery capabilities and stimulates potential contributions to geosciences [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a stochastic optimization technique that imitates the evolution process in nature. This paper uses an improved adaptive differential evolution to solve gravity inversion with multiplicative regularization. Compared with additive regularization, the advantage of multiplicative regularization is that it does not require the regularization parameter in the search process. The contributions in this paper mainly focus on two aspects: accelerating the convergence speed of adaptive DE and balancing the effect of model and data misfits in the objective function. The effectiveness of the proposed inversion method is verified by synthetic and field cases. For the synthetic cases, it is concluded that, based on the obtained results and analysis, the presented DE method is superior and competitive with its original version. Additionally, the designed parameter adaptation for multiplicative regularization is useful for trading off the effect of data and model misfits. For the field cases, two successful applications from China were conducted, and the obtained density source distributions were in accordance with those obtained from drilling wells. The synthetic and practical examples demonstrate that high-quality inversion results can be obtained using improved adaptive differential evolution and multiplicative regularization. [ABSTRACT FROM AUTHOR]

With the application of machine learning rapidly gaining popularity in computer science and other fields, neural network techniques have successfully simulated the performance of in-service pavements as they are efficient in predicting and solving nonlinear relationships and in dealing with uncertain large-area pavement problems. In this paper, we address the problem of the optimal timing of preventive maintenance of asphalt pavements to accurately predict the condition index (pavement condition index, PCI) of highway asphalt pavements and develop a highly accurate, long-period, multifactor prediction model with the suitability of preventive maintenance at its core. The prediction model is called differential evolution particle swarm optimization back propagation (DEPSO-BP) neural network, and the input dimension of the prediction model is determined by gray correlation analysis (GCA), and DEPSO is used to improve the search efficiency of BP neural network and the asphalt pavement usage performance with parameter continuity prediction model. Finally, the Qinglan Highway (G22) PCI of Gansu Province, China, is selected for example validation, and the prediction results are compared with those of the four models. The results show that the multifactor prediction model based on DEPSO-BP neural network has good generalization ability. This model is important for improving the economic efficiency of road maintenance, and can be used in the long-cycle process to provide model reference and scientific basis for the subsequent road maintenance budget application and decision-making scheme. [ABSTRACT FROM AUTHOR]

Objectives: Landslide disaster has become the first geological disaster in China. In the process of landslide movement instability, its surface will show certain deformation information, and this deformation information is an important external manifestation of landslide internal change instability, which can be used as an important reference information for landslide prediction and early warning. Therefore, the high-precision prediction of landslide displacement has important reference value for landslide disaster prediction and early warning. Methods: Considering the advantages of the intelligent optimization and machine learning algorithms, a landslide displacement combined prediction model is established. First, the multilayer perceptron (MLP) method is used to preliminary prediction of landslide displacement. Then, the artificial fish swarm algorithm (AFSA) improved based on differential evolution (DE) algorithm is further constructed, and combined with the support vector regression (SVR) to established an optimal combination SVR (OPS⁃ VR) to modify the prediction results calculated by MLP. Results: The two typical landslides monitored by BeiDou show that: (1) For HF08, the single SVR prediction model had a root mean square error (RMSE) of 23.8 mm between the prediction and the true values on the test set, and RMSE for MLP, genetic algorithm-SVR (GA-SVR), particle swarm optimization-SVR (PSO-SVR) and OPSVR prediction models are 19.9 mm, 10.9 mm, 8.3 mm and 8.3 mm, respectively. Compared with the SVR, MLP, GA-SVR, PSO-SVR and OPSVR prediction models, the prediction accuracy of MLP-OPSVR prediction model is improved by 6.2 times, 5.0 times, 2.3 times, 1.5 times and 1.5 times, respectively. The RMSE of MLP-OPSVR prediction model is only 3.3 mm. (2) For HF05, the single SVR prediction model had a RMSE of 26.5 mm between the prediction and the true values on the test set, and RMSE for MLP, GA-SVR, PSO-SVR and OPSVR prediction models are 32.8 mm, 15.4 mm, 15.6 mm and 15.5 mm, respectively. However, the RMSE of MLP-OPSVR prediction model is only 7.0 mm. Compared with the SVR, MLP, GA-SVR, PSO-SVR and OPSVR prediction models, the prediction accuracy of MLP-OPSVR prediction model is improved by 2.8 times, 3.7 times, 1.2 times, 1.2 times and 1.2 times, respectively. Conclusions: The DE algorithm can effectively overcome the outstanding problem that most artificial fish individuals are in random motion in the later stage of AFSA operation and cannot search for the global optimal solution, and improves its optimization performance. Further combined with SVR, it can more reasonably determine the super parameters of SVR and improve its prediction accuracy. Compared with a single MLP and SVR prediction model, as well as combined prediction models of conventional intelligent optimization algorithm (genetic algorithm, particle swarm optimization algorithm) and improved AFSA with SVR, the MLP-OPSVR combined prediction model has higher precision prediction results, and has a good value for popularization and application in landslide warning research. [ABSTRACT FROM AUTHOR]

Precise fertilizer application in agriculture requires accurate and dependable measurements of the soil total nitrogen (TN) concentration. Henan Province is one of the most important grain-producing areas in China. In order to promote the development of precision agriculture in Henan Province, this study took the high-standard basic farmland construction area in central Henan Province as the research area. Using single-phase images acquired from the ZY1-02D satellite hyperspectral sensor on 28 January 2021 (with a spatial resolution of 30 m × 30 m, a spectral range that covered 400–2500 nm, and a revisit period of 3 days) for spectral reflectance transformation and feature spectral band extraction. Based on multiple representation models, such as multiple linear regression, partial least squares regression, and support vector machine (SVM), all bands, feature bands, feature band combinations, and differential evolution (DE) algorithms were used to extract the secondary characteristic variables of the combination of characteristic bands, which were used as model inputs to estimate the content of TN in the study area. It was found that (1) the spectral reflectance transformation could help to improve the accuracy of prediction by reducing the interference from noise in the model, but the optimal spectral transformation method differed between different models and even between the training and test sets of the same model; (2) the estimation accuracy of the TN content model based on the minimum shrinkage and feature selection operator of the feature band was usually better than that of the full band, the feature combination band contained more effective information related to the TN content, and the combination of the DE algorithm and the SVM model achieved a better estimation accuracy for secondary feature extraction and TN content estimation of the feature combination band; and (3) ZY1-02D hyperspectral satellite data have the potential for the dynamic and non-destructive monitoring of regional TN content. [ABSTRACT FROM AUTHOR]

Net primary productivity (NPP) can be one of the most important indicators to evaluate the ecosystem structure and function in grassland protection. Therefore, it is necessary to rapidly and accurately evaluate the NPP under the carbon cycle of the ecosystem. The land surface process model can be used to realize the time series simulation of NPP using the physical mechanism. However, the default parameters of the models cannot be suitable for the target area, leading to the simulation deviation. There is also limited cognition of the physiological and biochemical characteristics of vegetation. Therefore, it is a high demand to optimize the parameters, when applied to the target area. The CESM framework has recently released the land surface model CLM5.0, indicating the most promising land surface process model for the evaluation of grassland productivity. In this study, the spatiotemporal simulation of grassland productivity was carried out in the Hulunbuir grassland, Inner Mongolia, China. Firstly, the local sensitivity analysis was used to choose the ten sensitive parameters of the CLM5.0_BGC module. Taking the global daily NPP data products as the reference, the Differential Evolution Adaptive Metropolis (DREAM) was then utilized to optimize ten sensitive parameters after sensitivity analysis. The accuracy of optimization was verified using global daily NPP data and GLASS annual net primary productivity products. Finally, the parameter setting was established for the CLM5.0 model for the grassland productivity simulation. The results were as follows: 1) Local sensitivity analysis was conducted to determine 74 parameters of the CLM5.0_BGC module. The most sensitive parameters were obtained to simulate the grassland productivity, including respiration, followed by carbon transfer. 2) Similar to the leaf C/N ratio, the Gaussian distribution was found in the posterior probability distribution in the slope parameter of the relationship between the stomatal conductance and photosynthesis. The optimization was observed in the benign constraint. The effectiveness of the carbon/nitrogen correlation and respiration parameters were verified to simulate the optimized model. 3) Parameter optimization effectively improved the simulation performance of CLM5.0 on the productivity of the grassland. The parameter optimization of the model shared a great improvement for the targeted pixel, where the relative error decreased from 33.82% to 10.97%, a relative improvement of 67%, and root mean square error decreased by 0.12 g/m². The optimization effect of the model in summer was also better than that in autumn. 4) The grassland productivity in the next year was evaluated on the simulation with the optimized parameter, where the relative error decreased from 8.74% to 2.51%, and the absolute difference in annual mean index decreased from 15.64 g/m² to 13.63 g/m². Spatially, the accuracy of the optimized model was improved in the different types of grassland. Among them, the relative errors of meadow grassland, lowland meadow, and typical grassland decreased from 43.43% to 37.81%, from 39.23% to 31.17%, and from 42.03% to 33.00%, respectively. The root mean square error was reduced from 0.58 g/m² to 0.52 g/m² in the meadow grassland, from 0.62 g/m² to 0.57 g/m² in the lowland meadow, and from 0.51 g/m² to 0.44 g/m² in the typical grassland. The finding can provide a strong reference to simulate the grassland productivity in the Hulunbuir region using CLM5.0. There can be offered a positive role in the reasonable assessment of the grassland ecosystem under the carbon cycle. [ABSTRACT FROM AUTHOR]

This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index–variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

Comprehensive utilization of mine water is of immense significance to avoid water wastage and protect the environment. To maximize the benefit of mine water utilization by calculating the available water in the goaf of Wucun Mine and analyzing the characteristics of water quality, combined with the water requirements of residents, power plants, irrigation, aquaculture, and ecology around the mining area and their water quality requirements, a mathematical model for optimal allocation of water quality-classified utilization in a 718-m3/h mine was constructed and solved using differential evolution algorithm and genetic algorithm, respectively. While the results of seven iterations of genetic algorithm varied between 215.24 and 232.36 China Yuan/h, those of differential evolution algorithm were all 232.36 China Yuan/h. When compared with genetic algorithm, differential evolution algorithm has the advantages of better convergence and stable and reliable calculation results. In the irrigation period, differential evolution algorithm indicated supply of 6.00, 334.95, 353.89, 19.76, and 3.40 m3/h treated mine water for residential usage, power plant, irrigation, aquaculture, and ecology, respectively, whereas in the non-irrigation period, differential evolution algorithm suggested supply of 6.00, 688.84, 0, 19.76, and 3.40 m3/h treated mine water, respectively. The annual income from quality-classified utilization of the mine water was the highest. Thus, the present study provides a model that can be used for reference for rational utilization of mine water resources. [ABSTRACT FROM AUTHOR]

Since the twenty-first century, with the improvement of China's comprehensive national strength and cultural confidence, our country has paid more and more attention to the protection and development of Chinese traditional culture, and more and more attention to the protection and development of China's traditional culture, traditional culture has no longer become a simple symbol in history books but has gradually entered people's daily life with the characteristics of vitality and gradually evolved into various characteristics and forms and integrated into all walks of life. Under the background of the evolution of this culture, this paper discusses the application of Chinese decorative color for interior decoration design and puts forward an application model of Chinese decorative color for interior design based on the AlexNet network improved by Adam, BN, dropout, and data enhancement optimization algorithm. Through analysis, it is found that the AlexNet network improved based on the above optimization algorithm has a good application prospect for interior decoration design. For the traditional AlexNet network, the improved AlexNet network is obviously better than the traditional AlexNet network in color extraction and can greatly reduce the loss value in the process of color recognition, up to 70.77%. Finally, the application forms and decorative functions of Chinese decorative color elements based on the improved AlexNet network in interior decoration design are analyzed. Relevant research can not only promote the integration of new and new technologies into traditional art but also effectively carry forward Chinese traditional culture and promote the internationalization of Chinese color. [ABSTRACT FROM AUTHOR]

Ordos Basin is a Mesozoic sedimentary basin that underwent long-term evolution on the North China Craton. Many scholars have confirmed that in the Late Triassic, the basin was surrounded by ancient continents, and there were multiple provenance supply directions. Combined with the nature of the basement of the basin and the characteristics of the present structure, it is believed that the Jiyuan area is located in the central and western parts of the basin, spanning two first-level structural units, the Tianhuan Depression and the Yishan Ramp. This special geographical location makes Jiyuan area affected by bidirectional provenance. Controlled by the northwest and northeast depositional systems in the basin, Jiyuan area has accepted complex sedimentation and diagenesis, forming a low-porosity ultralow-permeability reservoir. However, the understanding of bidirectional provenance has been neglected in many previous studies on reservoir characteristics in the Jiyuan area. Therefore, the differential evolution of sedimentation and diagenesis caused by bidirectional provenance will cause serious deviations in the original understanding of reservoir characteristics in the Jiyuan area, which will inevitably affect subsequent exploration and development research work. In this paper, the mineral composition, physical properties, diagenesis, and diagenetic evolution of the Jiyuan area are studied by combining a large number of tests such as core physical properties, casting thin sections, scanning electron microscopy, cathodoluminescence, and X-ray diffraction. Then, the origins of reservoir development in two areas dominated by bidirectional provenance are analyzed and compared. Furthermore, the diagenetic facies are characterized by a cluster analysis of logging data, and finally, the reasons for the differences in reservoir distribution and the genetic mechanism between the Yinshan provenance area (YPA) and Alxa provenance area (APA) are obtained. The results show that, first, due to the different provenance, compared with the YPA, the reservoir pore space in the APA is better developed and the physical properties are better. Second, the clay mineral content and diagenesis are more important causes of reservoir differentiation, and the reservoir pores in the YPA are more affected by kaolinite and chlorite filling than those in the APA. Although more dissolution improvements have been obtained, the damage to the reservoir caused by cementation in the middle and late stages is extremely fatal, while the chlorite film in the APA reservoir has a better protection effect on the primary intergranular pores. Third, after the evolution of pores in the APA reservoir, more intergranular pores are preserved, and the distribution range of high-quality diagenetic facies is wider than that in the YPA. Finally, sedimentation is the basis for high-quality reservoir development, and good mineral content composition and favorable diagenetic transformation cause reservoir dissimilarity. [ABSTRACT FROM AUTHOR]

A topologically based neural network algorithm is used to conduct an in-depth study and analysis of domino accident risk data in chemical parks, and this is used to construct a prevention and control system applied to the safety prediction of chemical parks. Firstly, the operating model of the flue gas turbine is expanded and analyzed according to the basic theory of topology, and the object element model is constructed to determine the feature vector and potential risk level. Then, the idea of differential evolution is introduced into the topological neural network to solve the problem that the learning rate and weighting coefficients are difficult to determine, and then the complete DE-ENN algorithm is proposed and tested with the UCI standard data set to verify the effectiveness of the algorithm. Finally, the algorithm is applied to the potential risk identification of the smoke machine operation model, and the experimental results show that the method not only has a simple structure, short running time, and high prediction accuracy but also has excellent generalization ability. For the inherent risk and domino effect risk of chemical equipment in chemical fiber enterprises, the accident risk assessment method based on the protection layer analysis method is proposed; combined with the probability of domino accident and personnel vulnerability model based on the comprehensive analysis of the research results of the allowable risk standard, the allowable risk standard applicable to chemical fiber production enterprises in China is proposed. Given the potential accident risk characteristics of chemical fiber production enterprises, the calculation method of firefighting demand and firefighting capacity of firefighting system is given; the index system of firefighting system emergency response capacity assessment is constructed from three aspects of firefighting system integrity, reliability, and effectiveness, and the assessment model and grade classification standard of firefighting emergency response capacity of chemical fiber production enterprises are determined. [ABSTRACT FROM AUTHOR]

Fault section diagnosis (FSD) is significant for the power system dispatching. Artificial neural network (ANN)‐based FSD method has strong fault tolerance but it looks like a black box and lacks the interpretability to the diagnosis outputs. In addition, when the topology of power systems changes, the ANN structure needs to be reconstructed and retrained, and thus has low adaptive capability. In order to tackle these challenges, in this paper, an ANN‐based FSD method by constructing universal transparent diagnosis models is proposed. The diagnosis models are constructed for the transmission line, transformer, and bus types rather than for a specific power system section. They can express the logical relations among sections, protective relays (PRs) and circuit breakers (CBs) clearly and intuitively. In addition, fuzzy values are used to model the uncertainties of PRs and CBs, and to determine the inputs of diagnosis models. Furthermore, the differential evolution algorithm is employed to optimize the network parameters of diagnosis models. The proposed method is verified on the IEEE 30‐bus test system and an actual local power system in Jilin Province of China. [ABSTRACT FROM AUTHOR]

To solve the imbalance problem of resource allocation of each link in the ground support process of transit flights, a support process equilibrium optimization model was proposed based on the systematic analysis of link business characteristics and equilibrium influencing elements. The link start time and resource usage were considered to constrain the priority and operation mode for good applicability. Based on the optimization objectives of minimizing the effective deviation, total operation time and comprehensive resource consumption of the links, a model objective function was established to measure the resource balance of each link in the support process. By NSGA-II algorithm with improved chaos and differential evolution, the optimization scheme of the constructed model was solved and designed with the actual operation data of a hub airport in central China. To verify the effectiveness of the model, the optimization scheme was evaluated based on the equilibrium evaluation index. The results show that the equilibrium optimization model is effective in terms of time utilization and resource allocation, and the total operating time is reduced by 10.6% with saving 30.3% of the total amount of idle resources. The evaluation shows that the equilibrium rate is increased by 10.7%, and the time loss coefficient and the resource smoothing coefficient are reduced by 65.7% and 54.7%, respectively. [ABSTRACT FROM AUTHOR]

Heterogeneity in the physical properties of tight sandstones is mainly caused by complex diagenetic interactions in various combinations during the burial process. The tight sandstone oil in the Linnan Sag of Bohai Bay Basin has great development potential but it is unclear how effective reservoirs were formed, and this is a significant reason why exploration in the deep reservoir is so difficult. Here we studied the diagenetic evolution of the tight sandstone of Lower Member 3 of the Shahejie Formation, Linnan Sag, Bohai Bay Basin, by using cores, thin sections, XRD, SEM, grain-size analysis, high-pressure mercury intrusion porosimetry, fluid inclusions, and carbon and oxygen stable isotopes. Compaction, replacement, cementation and dissolution diagenesis are identified; all four processes mainly occur in the A phase of mesodiagenesis. The interaction of formation burial, thermal evolution of organic matter and dehydration of gypsum leads to the alternating presence of three stages of alkaline fluids and two stages of acidic fluids. The average dissolution porosity of the dissolution sandstone facies is greater than the lower limit of the porosity for the effective reservoir in the study area, which is key to the formation of effective reservoirs. By determining differential diagenetic evolution sequences of tight sandstone reservoirs under the constraints of different lithologies where sandstone is interbedded with mudstone, we are able to elucidate the formation of the effective reservoir as follows: (1) In the eodiagenetic stage, a large amount of early carbonate cement developed in the sandstone adjacent to the mudstone, filling the primary pores and forming a cementation sandstone facies. Smaller amounts of early carbonate cement developed in the interior of the sandstone, which "fixed" the primary pores, and has a positive effect on the formation of a large number of secondary pores in the dissolution stage. (2) In the early stage of mesodiagenesis (A1), due to the filling of primary pores, it is difficult for acidic fluids to enter the cementation sandstone facies adjacent to mudstone, and the dissolution is weak. But dissolution within the sand body was strong, producing a large number of dissolution pores and forming a dissolution sandstone facies that clearly enhanced the physical properties. (3) In a second early stage of mesodiagenesis (A2), dissolution still played a dominant role in the dissolution sandstone facies, due to the development of dissolution pores in the previous stage (it is easy for acidic fluids to enter the dissolution sandstone facies). The physical properties of the dissolution sandstone facies were once again enhanced and an effective reservoir gradually began to form. (4) At the end of mesodiagenesis stages A1 and A2 (26.2–24.6 Ma; 7.5–2.8 Ma), the reservoir experienced two phases of petroleum charging. The charging of the cementation sandstone facies developed at the margins of the sandstones was difficult due to the filling of primary pores. The dissolution sandstone facies are oil-bearing, with higher porosity, which indicates an effective reservoir. By coupling the thermal evolution of organic matter and the dehydration of gypsum with differential diagenetic evolution and the porosity evolution of reservoir, we are able to establish a reservoir genetic model that involves alternating seepage of organic acids and alkaline brines. • The study is based on a "mudstone-encapsulated sandstone" lithofacies combination. • The dissolution sandstone facies is key to the formation of effective reservoirs. • The mechanism of diagenesis reservoir in tight sandstones is analyzed. • Alternating seepage of organic acids and alkaline brines forms effective reservoirs. [ABSTRACT FROM AUTHOR]

Frequent outbreaks of food quality and safety problems have seriously damaged the interests of consumers and reduced their confidence in China's food safety. In this study based on market supply and demand, we design a differential game model between food supplier and food retailer by considering different decision‐making situations. We also analyze the optimal revenue of the food supplier and food retailer on food quality efforts, the overall return of the supply chain, the level of food quality and safety, and their evolutionary characteristics. Results of the analysis indicate the following. (a) From the situation of decentralized decision‐making to the situation of decision‐making under the incentive strategy, a Pareto improvement occurs in the food quality and safety strategy of food supplier, food retailer, and even the entire food supply chain. (b) The optimal revenues of the supplier and retailer, overall supply chain revenue, and efforts of the supplier and retailer are all affected by changes in market supply and demand, resulting in drastic fluctuations. On the whole, food quality tends to improve over time and will fluctuate slightly due to changes in market supply and demand. (c) If the market supply is stable when supply exceeds demand and the market demand turns from a downward trend to an upward trend, then food quality safety risk will be higher than in other periods. [ABSTRACT FROM AUTHOR]

At present, the teaching of architectural art in China is still relatively traditional, and there are still some problems in the actual teaching. Based on this, this study combines the Naive Bayesian classification algorithm with the fuzzy model to construct a new architectural art teaching model. In teaching, the Naive Bayesian classification algorithm generates only a small number of features for each item in the training set, and it only uses the probability calculated in the mathematical operation to train and classify the item. Moreover, by combining the fuzzy model, the materials needed for architectural art teaching can be quickly generated, and the teaching principles and implementation strategies of architectural art are summarized. In addition, this paper proposes an attribute weighted classification algorithm combining differential evolution algorithm with Naive Bayes. The algorithm assigns weights to each attribute based on the Naive Bayesian classification algorithm and uses differential evolution algorithm to optimize the weights. The research shows that the method proposed in this paper has certain effect on the optimization of architectural art teaching mode. [ABSTRACT FROM AUTHOR]

Delivering relief supplies to victims of natural disasters is a very complicated process faced with many challenges: damaged road network, high demand for various materials, scarcity of resources, multi-supply and multi-demand sites, limited transport capacity, etc. Traditionally, outreach maximization is the focus of emergency relief. In this research, we propose a bi-level programming model which takes the aforementioned challenges into full account. We consider two objectives: minimizing the distribution time, and maximizing the allocation fairness. Because the problem is NP-hard, we design an improved differential evolution (IDE) algorithm and numerically compare it with several conventional differential evolution algorithms, including CoDE, SaDE, JADE and JDE. We found that the proposed IDE outperforms the existing algorithms. The feasibility and validity of the proposed model and the IDE algorithm are verified by applying them to the 2008 Wenchuan earthquake in western China. [ABSTRACT FROM AUTHOR]

As the largest developing country in the world, China is currently facing the contradiction between power supply shortage and demand growth. Almost three quarters of the electricity supply of China is derived from thermal power (mainly coal combustion). A proper projection of electricity consumption is helpful to deduce China's dependence on coal as a source of energy and thus will be helpful for the health of the environment. Therefore, it is necessary for the government to accurately predict the electricity demand. This paper employs both rolling mechanism and differential evolution algorithm to improve the prediction accuracy of the original grey model. Then, data from the China Federation of Electric Power Industry Development and Environmental Resources Department was adopted as database to test both the efficiency and accuracy of the improved prediction model. Experimental results show that the proposed model clearly outperforms the original grey model with regard to prediction accuracy. In addition, the future electricity consumption and power generation of China have been forecasted until 2025. The results will be useful to guide the electricity supply planning of the power department to promote the balance of power supply and demand. [ABSTRACT FROM AUTHOR]

A simulation-optimization model (ADINA-MMRDE) has been developed using the commercial finite-element software ADINA coupled with an improved differential evolution algorithm (MMRDE) to estimate the hydraulic conductivity of the aquifer of the Lu Dila hydropower project in China. The ADINA-MMRDE model employed the sum of the weighted square difference between the simulated value and the observed value as the objective function. To evaluate the accuracy of the MMRDE, the ADINA software was also coupled with a classical differential evolution (DE) algorithm and particle swarm optimization (PSO). Before estimating the initial seepage field of the Lu Dila hydropower station, the performance of the ADINA-MMRDE model was first tested using a boundary-simplified model based on the initial seepage field model. The effects of different objective functions, measurement errors and population sizes on the performance of ADINA-MMRDE were investigated. Then the ADINA-MMRDE model was applied to the Lu Dila hydropower project by considering water levels in 20 boreholes. The results showed that the head calculated using the estimated parameters was very close to its observed value. The water levels of 13 monitoring boreholes were used for further confirmation of the parameters obtained by the inversion. The results revealed that the calculated values of the monitoring borehole water levels were generally well fitted to the measured values. This study demonstrated the effectiveness of the ADINA-MMRDE model for practical and complex parameter inversion tasks. [ABSTRACT FROM AUTHOR]

• Four algorithms are used for ambient signal based load model parameter identification. • Improvements are proposed to validate the optimization ability for the algorithms. • Comparisons involve the identification accuracy, calculation time, and the robustness. • Simulation and the field PMU data show the different performance of the algorithms. The time-varying characteristic of the power load can be tracked with the ambient signal based load model parameter identification method proposed recently. Meanwhile, the small fluctuations of the ambient signal bring higher requirement for optimization algorithm as well. The interior-point algorithm (IPA) and the active-set algorithm (ASA) are two typical traditional nonlinear constrained optimization algorithms, while the differential evolution algorithm (DEA) is an excellent heuristic search algorithm. Besides, the grid search algorithm (GSA) is a violent search algorithm, which is also an effective method considering the fact that the real load model parameters are unknown in practical power system. Based on the review of the basic idea of the four algorithms, the applicability of the four algorithms is comparatively assessed for ambient signal based load model parameter identification. Their performance is compared using ambient data from both simulation and field phasor measurement unit (PMU) in China Southern Power Grid considering the identification accuracy, calculation time, and the robustness to measurement error and algorithm parameters. Some general conclusions are drawn from the analysis for several cases. [ABSTRACT FROM AUTHOR]

Bottom on the ripple of the multiplication of sharing economy, hydrogen energy storage (HES) shared calls for novel solutions to ameliorate the cleanness and economy of micro-grids under the dual carbon target in China. The traditional investment method may become impracticable due to conflicting interests, insufficient utilization, and poor interoperability. The practical and prospective design of HES relies on innovation at the joint investment mechanism under incomplete synchronized and the interested equilibrium under limited rationality. This paper studies the capacity planning method for HES by multiple micro-grids from the sharing perspective. The evolution game model is utilized for conflict of interest, which exactly accounts for limited rationality. The dynamic competitive as well as time-varying demand for HES among multiple micro-grids are established by a small-word complex network theory. The dual dynamic evolutionary game of investment decision in multi-micro-grid and capacity planning for sharing HES is realized by a proposed method of differential evolutionary game algorithm based on experience-weighted attraction learning. The numerical simulation is carried out to verify the effectiveness of the proposed method and interpret the development path for sharing HES. The result shows that, the sizing method with limited rationality reduces the cost by 4.75% compared with the traditional game under complete rationality and 9.15% compared with the multi-objective optimization. The willingness of the park to participate in joint investment in shared HES is influenced by the long-term regulatory demand for energy storage and the decisions of other parks in the neighbors. [Display omitted] • The investment strategies for sharing HES under limited rationality. • The dual evolution model incorporates the decision-making and planning strategies. • A differential evolutionary game algorithm to solving the replication dynamic system. • Analyzed the evolution process and influencing factors of shared HES mode. [ABSTRACT FROM AUTHOR]

The generation forecast helps to develop the scheduling plan of power sources, build a power balance system and ensure the adequacy of power supply to the power system at the macro level. Therefore, a novel structurally adaptive discrete grey Bernoulli model, called DHGBM(1,1), is proposed in this paper for the prediction of power generation. Firstly, the Hausdorff fractional order cumulative generation operation makes it possible to achieve new information prioritisation. As for the structure of the model, we have extended the nonlinear grey Bernoulli model with the introduction of a nonlinear dynamic structure term, which allows the model to simulate variable generation data and improves the adaptability of the model. In a comparative test of the algorithms, the well-performing differential evolution algorithm was used for hyperparameter optimisation of the model to obtain better predictive performance. Monte Carlo simulations and probability density analysis verified the excellent robustness of the model. In addition, through the simulation tests, we obtained a reasonable range of parameters for the structural terms, thus limiting the complexity of the model, and the phenomenon of model overfitting was effectively prevented. Finally, five actual cases of China's hydro, China's thermal, China's nuclear, China's wind power generation and China's electricity exports are predicted. • A novel nonlinear dynamic structural term is proposed. • A novel structural adaptive discrete grey Bernoulli model is developed. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other six models. [ABSTRACT FROM AUTHOR]

• Propose an optimal HELM-based method to diagnose power system failures. • Optimize the structure of HELM using adaptive differential evolution AQILDE. • AQILDE uses three adaptive strategies to boost its ancestor QILDE. • Propose an individual coding method and an improved training target function. • The proposed method has strong fault tolerance and high diagnostic accuracy. Accurate diagnosis of failures has a pivotal role to play in the stable operation of power systems. Neural networks have shown promising fault tolerance in solving this problem. However, the widely used BP and RBF networks have a tedious training process and are difficult to provide approving generalization performance. In this work, an optimal hierarchical extreme learning machine (HELM) via adaptive quadratic interpolation learning differential evolution (AQILDE) is designed to address this issue. HELM has good generalization performance but its optimal structure is hard to achieve. Thus, we present AQILDE to automatically search the structure parameters of HELM, including the number of hidden layers, the number of neurons per hidden layer, and the regularization factor. In addition, individual coding method and improved training target function are proposed to ensure the generalization performance and structural compactness. The size of decision variables can be adjusted during the training process. Both regression loss and classification loss are integrated into the target function. The feasibility of AQILDE-based HELM is evaluated in a 14-bus power system and a practical fault in the Siping power grid, China. Simulation results show that it has better generalization performance and diagnoses varied fault scenarios correctly with higher fault credibility. [ABSTRACT FROM AUTHOR]

• An algorithm (IDE) was proposed to design a new bionic subsoiling mechanism (BSM). • It is proved that IDE has better solution accuracy and speed. • Proposed a new method for establishing the soil model. • It was proved that the BSM has improved tillage performance based on DEM. • The driving parameters combination for the BSM was optimized. Because of the poor subsoiling effects and high tillage resistances of small subsoiling machines when used on red soil in southern China, a new subsoiling mechanism based on functional bionics and an optimization method that employed an improved differential evolution (IDE) algorithm are proposed in this paper. The tillage performance was improved by causing the subsoiler to mimic the digging motion of a mole forefoot. The path synthesis of the subsoiling mechanism was solved by the IDE, and the tillage performance of the four-bar bionic subsoiling mechanism was evaluated by the discrete element method. First, the digging trajectory of the mole forefoot was mapped as the desired subsoiling trajectory. An optimization model for the path synthesis was established to minimize the position error between the desired subsoiling trajectory and the coupler curve of the mechanism; it was solved by the differential evolution algorithm with improvements in the mutation and crossover operations. It was found that the IDE could more rapidly search for a more accurate solution. The optimization results were used to design a bionic subsoiling mechanism that could mimic the digging trajectory of a mole forefoot. Second, the microscopic contact parameters among the soil particles were calibrated by soil accumulation and direct shear tests. In addition, a discrete element soil model was established that included dual coupling of variable contact models for the tillage layer and the plow pan. The tillage processes of an existing common subsoiling mechanism (CSM), the bionic subsoiling mechanism equipped with a common subsoiler (BSM&CS), and the bionic subsoiling mechanism equipped with a bionic subsoiler (BSM&BS) were analyzed by a joint ADAMS and EDEM simulation. The simulation results were analyzed according to established evaluation indexes for tillage performance. The comprehensive soil disturbance performance of the BSM&CS improved by 257.03% and its drag reduced by 10.87% from the CSM values, while the comprehensive soil disturbance performance of the BSM&BS improved by 312.78% and its drag reduced by 18.40% from the CSM values. Finally, the driving parameter combination for the BSM was optimized using the central composite design method. [ABSTRACT FROM AUTHOR]

To decipher the geomorphic and drainage evolution between Tibet and the South China Sea (SCS), the Cenozoic source-to-sink evolution of the central northern SCS has always been a research hotspot. However, previous provenance studies mainly focus on Cenozoic deposits in the central Pearl River Mouth Basin (PRMB) and their implications on the evolution of the Pearl River and the source areas in South China, leaving the provenance characteristics of other places, especially the western and eastern PRMB, and their relationship with surrounding tectono-geomorphic evolution unknown. We obtained some new data from the western and eastern PRMB, which allows for a complete story about the Cenozoic source-to-sink evolution of the central northern SCS. Based on comparative analyses of zircon U-Pb and geochemical data of Cenozoic sediments and pre-Cenozoic bedrocks in the PRMB as well as modern river sand in surrounding potential drainage systems, considering tectonic and geomorphic evolution in/around the SCS, we find that the Cenozoic provenance of the central northern SCS varies both in time and space. Generally, most of the Eocene sediments show almost the same age spectra as their adjacent pre-Cenozoic bedrock highs/uplifts, implying that intrabasinal source-to-sink processes dominated before the latest Eocene; the Oligocene and upward deposits display similar age spectra to modern river sand in adjacent drainage systems, suggesting that extrabasinal have taken over since Oligocene; while sediments between them show more complex age spectra that vary vertically, indicating that provenance shifts from intrabasin to extrabasin occurred during Late Eocene. Geomorphic changes resulting from regional tectonic activities between Tibet and the SCS control the source-to-sink and drainage system evolution in/around the central northern SCS. The ENE-tilting axial topography and its differential evolution of the northern SCS have controlled the supply and changes of detritus from Central Vietnam eastwards to the Zhu II Depression since the Eocene. By contrast, the gradual topographic inversion of South China from W-tilting to E -tilting has resulted in the headward erosion and growth of drainage systems in South China since the latest Eocene and subsequent provenance shift and evolution in the central northern SCS. With the geomorphic and drainage evolution, the Pearl River provided detritus mainly to the central PRMB, while other smaller drainage systems fed to their adjacent area, and the South China detritus supply has become dominant in the central northern SCS since the Oligocene, in which the components from Cathaysia Block remained as the main contributor, though detritus from Yangtze Block did increase gradually. • Intrabasinal source-to-sink processes dominated in the central northern SCS in the Eocene. • Most drainage systems began providing detritus to the central northern SCS in the Late Eocene. • Extrabasinal source-to-sink processes have superseded intrabasinal ones since the Oligocene. • Surrounding tectono-geomorphic evolution drove source-to-sink changes in the central northern SCS. [ABSTRACT FROM AUTHOR]

In the fractional grey model, the new information priority principle has always been pursued by researchers. However, it does not mean that new information is the most important, because in reality, there are always some special moments, and the information they represent will profoundly affect the future development. That is, the most important data may be in the middle of the sequence, not the latest data. Therefore, this paper proposes a periodic variable order accumulated generating operation, which can switch the priority of new and old information with time. In terms of model structure, this paper presents a new variable coefficient whitening equation with self-adaptive structure. That is, a new fractional order grey Euler model with dynamic accumulation order (DOFGM(1,1)) is proposed in this paper. The new model combines the adjacent sequence operator, therefore the newly generated sequence is less random. By adjusting the model parameters, the structure of the model can be changed, so that the model has the features of selecting the adaptive structure. By comparison, the differential evolution algorithm is selected to optimize the hyperparameters of the model. In particular, the robustness of the model is validated by introducing perturbations to the raw data to imitate errors generated during data collection. Finally, three actual cases of China's hydropower generation, China's natural gas production and China's total primary energy production are predicted; the prediction result shows that the new model has higher prediction accuracy than the other six models. The results indicate that the proposed model benefits from its adaptive structure and produces reliable predictions. According to these prediction results, relevant suggestions on the development of China's energy are provided to decision makers. • A novel structurally adaptable grey Euler model is suggested. • A periodic variable-order accumulated generating operation is proposed. • The robustness analysis of the novel model is established. • Comparative study shows that novel model is superior to other various grey models. [ABSTRACT FROM AUTHOR]

To promote renewable energy consumption and achieve China's low-carbon energy transition, in 2019, China proposed the weight of consumption responsibility (WCR) for renewable energy in each province. This study aims to explore the micro-market behavior of new energy manufacturers in the context of WCR policy. A two-market bidding model for new energy manufacturers is first proposed, and a two-stage compound differential evolutionary game algorithm is proposed for solving the multi-party game problem. By selecting typical electricity demand scenarios, the evolution of new energy manufacturers' strategies in the electricity market and green certificate market as well as under different WCR and different thermal power unit parameters are analyzed. The research results show that when the non-hydropower WCR is relatively low, new energy manufacturers tend to adopt a low-price strategy for generation quotation and a low volume high price strategy for green certificate sales. Other conditions being unchanged, with the increase in non-hydropower WCR, all new energy manufacturers will still adopt a low-price strategy in the electricity market. New energy manufacturers with high installed capacity will increase progressively their certificate quotation. The higher output lower limit for thermal units is not conducive to electricity market bidding by new energy manufacturers. • A two-market bidding model for new energy manufacturers is established. • A two-stage compound differential evolutionary game algorithm for new energy manufacturers is proposed. • The optimal strategy of two-market bidding for new energy manufacturers is empirically analyzed. • The bidding strategies under different consumption responsibility weights are discussed. [ABSTRACT FROM AUTHOR]

Reactor Operation Digital Twin (RODT) is now receiving increasing attention and investment in nuclear engineering domain. A prototype of a RODT was first brought out by Gong et al. at Nuclear Power Institute of China. The RODT contains a forward solver for online real-time simulation and an inverse problem solver for parameter identification and state estimation. To further improve the efficiency and accuracy of RODT and promote the practical deployment of RODT (i) we first propose an advanced differential evolution algorithm to upgrade the inverse solver; (ii) then we bring out a systematical uncertainty quantification of RODT considering assimilation noisy observations. The accuracy and validity of the proposed RODT are tested along one cycle operating stage of HPR1000, at various operating temperatures, control rod steps, general power level and the inlet temperature of a practical nuclear reactor core. Numerous numerical results confirm its potential for practical engineering applications for on-line parameter identification and state estimation. • An advanced differential evolution algorithm is implemented into the proposed RODT. • Observation denoising is realized with PCA followed by a normalization process. • Numerical tests on HPR1000 proof its efficiency and accuracy of the improved RODT. [ABSTRACT FROM AUTHOR]

Sustainable highway pavement maintenance is important for achieving sustainability in the transportation sector. Because the three aspects included in sustainability metrics (environment, economy, and society) often contradict each other, maximising the sustainability performance of highway pavements is difficult, especially at the network level. This study developed a novel multi-objective heuristic algorithm to formulate sustainable highway pavement network maintenance plans considering carbon emissions (CE), life cycle agency cost (LCAC), and pavement long-term performance (LTP). The proposed algorithm is a new variant of multi-objective differential evolution (MODE) that incorporates self-adaptive parameter control and hybrid mutation strategies embedded in its framework (MOSHDE). Three state-of-the-art multi-objective heuristics, namely, the non-dominated sorting genetic algorithm II(NSGA-II), classic MODE, and multi-objective particle swarm optimisation (MOPSO), as well as the proposed MOSHDE, were applied to an existing highway pavement network in China for performance evaluation. Compared with other heuristic algorithms, the proposed self-adaptive parameter control strategy enables the automatic adjustment of the control parameters, avoiding the time-consuming process of selecting them and enhancing the robustness and applicability of differential evolution. The hybrid mutation strategy uses both exploration and exploitation operators for the mutation operations, thus leveraging both global and local searches. The results of the numerical experiment demonstrate that MOSHDE outperforms the other tested heuristics in terms of efficiency and quality and diversity of the obtained approximate Pareto set. The optimal solutions obtained by the proposed method correspond to a proactive maintenance policy, as opposed to the reactive maintenance policy commonly adopted in current practice. In addition, these solutions are more cost-effective and environmentally friendly and can provide better pavement performance to highway users over the project life cycle. Therefore, the proposed MOSHDE may help practitioners in the transportation sector make their highway infrastructure more sustainable. [ABSTRACT FROM AUTHOR]

• Coal-to-ethanol technology that converts dimethyl ether to anhydrous ethanol. • Considering different no-sharp separation strategies. • Large-scale industrial process optimization. • Differential evolution combined with parallel computation optimizes parameters and heat integration. Given China's energy structure and the limitations of bioethanol, the coal-to-ethanol (CTE) pathway, from dimethyl ether to ethanol (DMTE) via carbonylation and hydrogenation, is highly anticipated. Ethanol, methanol, methyl acetate, and ethyl acetate are the crude hydrogenation products that need to be purified, requiring at least an eight-column scheme. However, the optimization of the existing separation strategy with ethanol as the priority is unfavorable in the following aspects: it is usually plagued by tedious rules of thumb and, due to the large scale of the process, is prone to falling into local minima; pre-designed heat integration inevitably neglects the interaction of parameter optimization and heat integration; reports on alternative feasible distillation sequences are scarce in publications, let alone comparisons amongst these counterparts. Therefore, four viable separation strategies are proposed in this paper to compare with this faulted separation strategy. A self-adapting dynamic differential evolution (SADDE) algorithm, which is accelerated by parallel computation, is used to search for optimal column parameters of all the configuration options and facilitates simultaneous heat integration structure synthesis. Two strategies stand out after 3000 generations of evolution. Splitting methanol outperforms in specific steam consumption (SSC) of ethanol (1.8177), much better than the benchmark (2.4840), and splitting ethyl acetate with ethyl acetate priority has the most competitive total annual cost (TAC), 23.98% lower than the benchmark. In summary, this paper provides a reference for optimizing complex distillation systems like CTE product separation, or more specifically, the DMTE route, before the appearance of the most suitable separation strategy in demand. Furthermore, it will also serve for the CTE superstructure to further explore the optimal distillation sequence. [ABSTRACT FROM AUTHOR]

Due to the fact that escape panics are unexpected and dangerous, which excludes real-life experiments because of technical difficulties and ethical reasons, the evacuation simulations in real-life disasters are still rare. In this paper, the escape panics of classroom evacuation in real-life 2013 Ya’an earthquake in China are simulated and reproduced using the social force model. Firstly, a robust differential evolution (DE) is employed to calibrate and optimize the parameters of social force model to achieve a desired nonlinear evacuation speed which is consistent with real-life video data. It is demonstrated the DE-calibrated social force model can reproduce the characteristics of pedestrian flow in the real-life earthquake evacuation including the nonlinear evacuation speed curve and the pedestrian position distributions at different time. Moreover, the trained evacuation leader has remarkable impact on the evacuation process which can be beneficial to maintain the calm and order of crowd, decrease the desired velocity of crowd, and thus avoid fatal accident to the most degree. Finally, different layouts of classroom have similar evacuation efficiency because different internal layouts can only change partial flow of pedestrians in the left and right corridors, and total flow aggregating in the exit of classroom is still unchanged. However, total evacuation time can be sharply reduced to 17 s from above 36 s, and evacuation efficiency has almost doubled when the back door is open, which means we should try to keep all the emergency exits open in order to provide the crowd with more chances to escape when disasters occur. [ABSTRACT FROM AUTHOR]

Harris hawks optimization (HHO) algorithm inspired by the cooperative behavior and chasing style of harris' hawks in nature called surprise pounce is a relatively novel swarm intelligent optimization algorithm. Due to its simplicity and efficiency, the canonical HHO has displayed promising performance on a large number of continuous optimization problems and real-world optimization problems. However, how to balance contradictions between the exploration and exploitation capabilities and alleviate the premature convergence are two critical concerns that need to be dealt with in the HHO study. To address these two drawbacks, improve the optimization performance, and broaden its application domain, a dynamic multi-swarm differential learning Harris hawks optimizer (DMSDL-HHO) is proposed in this paper. To efficiently maintain the population diversity, the whole population is divided into many small sub-swarms, which are regrouped periodically, and information is exchanged among the swarms. In each generation, the differential evolution operator (including mutation, crossover, and selection operators) based on the personal historical best position is merged into each sub-swarm to augment the exploration capability, while the Quasi-Newton method as a local searcher is used to enhance the exploitation capability. Besides, aiming to prevent the algorithm from falling into local optima to some extent, the differential mutation operator candidate pool strategy is introduced into the late stage of the search process. Thus, different individuals in the same population can conduct distinct search behaviors in each generation, and the same individual can perform various search behaviors in different generations. The proposed algorithm is tested on 23 classic test functions and 30 CEC2014 benchmark functions and is compared with quite a few state-of-the-art algorithms in terms of often-used performance metrics with the help of statistical analysis, diversity measurement, exploration–exploitation investigations, ranking statistics, and Wilcoxon Signed Rank Test (WSRT). The experimental results verify the superior performance of the embedded strategies on balancing the exploration capability and the exploitation capability. In addition, the DMSDL-HHO is applied to the optimal dispatch problem of cascade hydropower stations (ODPCHS) based on a novel constraints handling method designed in this paper to demonstrate its good practicability and performance. The experimental results of a case study on the optimal dispatch problem of China's Wujiang cascade hydropower stations show that DMSDL-HHO can obtain better and more reliable optimal results than the canonical HHO and other compared algorithms. Moreover, the convergence speed of DMSDL-HHO is also competitive in contrast to other algorithms. Therefore, it can be concluded that DMSDL-HHO is a promising alternative tool for solving complex continuous optimization problems and real-world optimization problems with complex constraints. [ABSTRACT FROM AUTHOR]

Shale oil and source rock samples of the Carboniferous Keluke Formation from well Chaiye 2 in the Delingha Depression were analyzed by gas chromatography–mass spectrometry. Source rocks were highly mature at the gas generation stage with vitrinite reflectance (Ro) of 1.45–1.88%. However, the oil produced from the shale reservoir was characterized by abundant biomarkers but low abundance of diamondoid hydrocarbons with estimated Ro of ca. 0.78%, indicating hydrocarbons were still at a relatively low thermal maturity level. As the crude oil was generated and accumulated autochthonously, preliminary results indicate that crude oil and source rocks witnessed differential thermal evolution and significant disparity of the current thermal maturity in the shale reservoir due to rapid tectonic subsidence and clay mineral catalysts that accelerated the thermal maturation process. Although tectonic uplifts occurred afterwards, the vitrinite recorded the highest maturity that source rocks have ever reached, whereas the oil has not reached the same maturity level due to less impact from thermal alteration or mineral catalysis than source rocks in the shale reservoir. Such a discovery enlarges the hydrocarbon perseveration of maturity ranges in reservoirs, particularly for the unconventional tight formation, and benefits potential hydrocarbon exploration from highly mature sediments. [ABSTRACT FROM AUTHOR]

Production Optimization is a significant method for oilfields to control water cut and stabilize oil production. When the oilfield enters the high or ultra-high water cut stage, it becomes particularly important to use production optimization methods for improving the water-flooding efficiency. Currently, the commonly used production optimization methods are based on reservoir simulators. Such methods require lots of forward simulations during optimizing, which results in low computational efficiency. It's not applicable to the reservoir without numerical simulation models. Thus, a new production optimization method based on the reservoir proxy model is proposed in this work. Firstly, the dynamic production data of the oilfield are collected and preprocessed for training the Extreme Gradient Boosting model, and constructing the proxy model for water cut prediction of producers and the reservoir. Then, an optimal control model for minimizing the water cut of the reservoir can be constructed based on the proxy model. Finally, an optimal injection-production scheme can be obtained by using the differential evolution algorithm. For the evaluation and verification purposes, the proposed method is applied to a well block from SL oilfield, China. Empirical results demonstrated that the proposed method can effectively improve the water-flooding efficiency. • A proxy model based production optimization framework was proposed in this work. • The XGBoost based proxy model was established to predict the water cut of wells. • DE is introduced to solve the optimal model constructed by proxy and constraints. • The proposed method effectively optimize and improve the water-flooding efficiency. [ABSTRACT FROM AUTHOR]

Abstract: Information hidden in the characteristics and relationship data of a cascade hydropower stations can be extracted by data-mining approaches to be operation rules and optimization support information. In this paper, with Three-gorge and Gezhouba cascade hydropower stations as an example, two operation rules are proposed due to different operation efficiency of water turbines and tight water volume and hydraulic relationship between two hydropower stations. The rules are applied to improve optimization model with more exact decision and state variables and constraints. They are also used in the population initiation step to develop better individuals with culture algorithm with differential evolution as an optimization method. In the case study, total feasible population and the best solution based on an initial population with an operation rule can be obtained with a shorter computation time than that of a pure random initiated population. Amount of electricity generation in a dispatch period with an operation rule also increases with an average increase rate of 0.025%. For a fixed water discharge process of Three-gorge hydropower station, there is a better rule to decide an operation plan of Gezhouba hydropower station in which total hydraulic head for electricity generation is optimized and distributed with inner-plant economic operation considered. [Copyright &y& Elsevier]

Abstract: The solar–thermal refrigeration systems, covering the solar absorption refrigeration system (SAbRS) and the solar adsorption refrigeration system (SAdRS), were designed for typical office in the subtropical Hong Kong. The approach of simulation–optimization was adopted in order to determine the optimal design parameters for SAbRS and SAdRS against the conventional design practice. For simulation, dynamic model of each system was refined on the TRNSYS platform. For optimization, the objective was to minimize the annual primary energy consumption of SAbRS or SAdRS in response to the changing loading and climatic conditions throughout a year. This is a constrained optimization problem since the upper limit of comfort temperature was stipulated, such that the minimization of system energy would not sacrifice the indoor thermal comfort. Due to the complex, multidimensional and constrained nature of the dynamic simulation models, the differential evolution (DE), which has been proven effective in evolutionary computation (EC), was used for optimization purpose. Through the simulation–optimization run, the optimized designs of SAbRS and SAdRS were determined, and their corresponding primary energy consumptions could be 12.2% and 7.1% less than those based on the general design practice. The results provide useful guidelines for the equipment design of SAbRS and SAdRS. [Copyright &y& Elsevier]

• 55 pumping tests were analyzed in the Jianghan Plain, Central China. • Aquifer parameters were obtained by different methods. • Spatial variability of hydraulic conductivity (K) was analyzed. • Main factors controlling the spatial variability of hydraulic conductivity (K) were discussed. In recent times, the groundwater in Jianghan Plain, Central China has come under severe stress from agricultural and industrial demand. The spatial variability of hydraulic conductivity (K) in the region is important for better and sustainable groundwater exploration and management. In this study, we determined the spatial distribution of K using a multi-method approach, i.e. analytic and numerical simulation as well as intelligent optimization methods (particle swarm optimization (PSO) and differential evolution (DE)) and geostatistics. Data from 55 multi-format pumping tests situated across the middle-confined aquifer (consisting of late Pleistocene (Qp 3 s) and middle Pleistocene (Qp 2 j)) were used. Results showed that for transient pumping test with constant rate by multi-wells, PSO and DE were faster and more precise, especially DE. For transient pumping test with variable rate as well as transient pumping test with constant rate by single well, the numerical method fitted the drawdown better, while the PSO and DE had large deviations at the early stage of pumping. Overall, the values of K vary from 0.032 to 10.64 m/d within Qp 3 s and 0.33 to 36.24 m/d within Qp 2 j. The spatial variability model demonstrated that the K values in Qp 3 s are greater than those in Qp 2 j. The heterogeneity elucidates the sedimentary development of the basin. These methods and results can provide new insights for estimating the spatial distribution of K for regional groundwater resources assessment in similar sedimentary environments around the world. [ABSTRACT FROM AUTHOR]

Reference crop evapotranspiration (ET0) is an important indicator for precise regulation of crop water content, irrigation forecast formulation, and regional water resources management. The Hargreaves model (HG) is currently recognized as the simplest and most effective ET0 estimation model. To further improve the prediction accuracy of the HG model, this study is based on the data of 98 meteorological stations in southwest China (1961–2019), using artificial bee colony (ABC), differential evolution (DE) and particle swarm optimization (PSO) algorithms to calibrate the HG model globally. The standard ET0 value was calculated by FAO-56 Penman–Monteith (PM) model. We compare the calculation accuracy of 3 calibrated HG models and 4 empirical models commonly used (Hargreaves, Priestley–Taylor, Imark–Allen and Jensen–Hais). The main outcomes demonstrated that on a daily scale, the calibrated HG models (R2 range 0.74–0.98) are more accurate than 4 empirical models (R2 range 0.55–0.84), and ET0-PSO-HG has the best accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with average R2 of 0.83, 0.82 and 0.80, average RRMSE of 0.23 mm/d, 0.25 mm/d and 0.26 mm/d, average MAE of 0.52 mm/d, 0.53 mm/d and 0.57 mm/d, and average GPI of 0.17, 0.05, and 0.04, respectively; on a monthly scale, ET0-PSO-HG also has the highest accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with median R2 of 0.96, 0.95 and 0.94, median RRMSE of 0.16 mm/d, 0.17 mm/d and 0.18 mm/d respectively, median MAE of 0.46 mm/d, 0.50 mm/d, and 0.55 mm/d, median GPI of 1.12, 0.44 and 0.34, respectively. The calibrated HG models (relative error of less than 10.31%) are also better than the four empirical models (relative error greater than 16.60%). Overall, the PSO-HG model has the most accurate ET0 estimation on daily and monthly scales, and it can be suggested as the preferred model to predict ET0 in humid regions in southwest China regions. [ABSTRACT FROM AUTHOR]

Sine cosine algorithm (SCA) is an emerging meta-heuristic method for the complicated global optimization problems, but still suffers from the premature convergence problem due to the loss of swarm diversity. To improve the SCA performance, this paper develops a modified sine cosine algorithm coupled with three improvement strategies, where the quasi-opposition learning strategy is used to balance global exploration and local exploitation; the random weighting agent produced by multiple leader solutions is integrated into the agent's evolution equation to improve the convergence rate; the adaptive mutation strategy is designed to increase the swarm diversity. The proposed method is compared with several famous evolutionary methods on 12 classical test functions, 24 CEC2005 composite functions and 30 CEC2017 benchmark functions. The results show that the proposed method outperforms several control methods in both solution quality and convergence rate. Then, the long-term operation optimization of multiple hydropower reservoirs in China is chosen to testify the engineering practicality of the developed method. The simulation results indicate that in different scenarios, the proposed method can produce satisfying scheduling schemes with better objective values compared with several existing evolutionary methods. Hence, a novel optimizer is provided to handle the complicated engineering optimization problem. [ABSTRACT FROM AUTHOR]

• An optimal strategy for vaccine administration in COVID-19 pandemic treatment is determined considering both mono and multi-objective optimization context. • The present work proposes a methodology to solve an inverse problem aiming to determine the SIR model parameters to simulate the COVID-19 pandemic. • The results indicate that optimal control approaches based on epidemiological models can provide information to assist in mitigating the spread of disease. For decades, mathematical models have been used to predict the behavior of physical and biological systems, as well as to define strategies aiming at the minimization of the effects regarding different types of diseases. In the present days, the development of mathematical models to simulate the dynamic behavior of the novel coronavirus disease (COVID-19) is considered an important theme due to the quantity of infected people worldwide. In this work, the objective is to determine an optimal control strategy for vaccine administration in COVID-19 pandemic treatment considering real data from China. Two optimal control problems (mono- and multi-objective) to determine a strategy for vaccine administration in COVID-19 pandemic treatment are proposed. The first consists of minimizing the quantity of infected individuals during the treatment. The second considers minimizing together the quantity of infected individuals and the prescribed vaccine concentration during the treatment. An inverse problem is formulated and solved in order to determine the parameters of the compartmental Susceptible-Infectious-Removed model. The solutions for both optimal control problems proposed are obtained by using Differential Evolution and Multi-objective Optimization Differential Evolution algorithms. A comparative analysis on the influence related to the inclusion of a control strategy in the population subject to the epidemic is carried out, in terms of the compartmental model and its control parameters. The results regarding the proposed optimal control problems provide information from which an optimal strategy for vaccine administration can be defined. The solution of the optimal control problem can provide information about the effect of vaccination of a population in the face of an epidemic, as well as essential elements for decision making in the economic and governmental spheres. [ABSTRACT FROM AUTHOR]

The Jurassic terrestrial shale on the northern margin of Qaidam Basin is characterized by high contents of organic matter but low maturity resulting in a lack of further research on shale gas in this area. To better understanding the shale gas potential of organic matter with different types, we conducted thermal maturity experiment of hydrocarbon generation from different kerogen types of low-mature shales under closed conditions. The results show that type II and type III kerogen have experienced similar hydrocarbon generation stages, but they differ in the hydrocarbon generation interval and evolution process between liquid hydrocarbon and gaseous hydrocarbon. Compared with Type III kerogen, type II kerogen is characterized by a low initial temperature at each evolutionary stage and wide temperature range, which caused the overall evolution of type III Kerogen lagged behind that of type II kerogen. The differential evolutions mainly result from the differences in hydrocarbon generation mechanism, hydrocarbon generation activation energy and kerogen macerals among different kerogen types. We summarized the evolution model of type II and type III Kerogen based on the characteristics of kerogen in hydrocarbon generation, carbon isotope change, pore development and other aspects. This work can provide theoretical and data support for reconstructing the hydrocarbon generation process of terrestrial shale gas, investigating the hydrocarbon generation mechanism, and predicting the hydrocarbon resources of terrestrial shale oil and gas on the northern margin of Qaidam basin and even China. • Described differential evolution characteristics of different types of kerogen in terrestrial shale. • Reconstructed the hydrocarbon generation process of low-maturity terrestrial shale. • Determined the favorable range of organic matter maturity in terrestrial shale. • Established the evolution patterns for different types of kerogen in terrestrial shale. [ABSTRACT FROM AUTHOR]

The problem of insufficient water for ecological purposes in the Luanhe River basin requires urgent resolution. The regional hydrological cycle is affected by the Panjiakou-Daheiting Reservoir system and by changes in land use in the upper watershed. Therefore, this system was selected as a study area to simulate ecological reservoir operation and analyze the effects of changes in land use on ecological operation. A physical habitat simulation model (PHABSIM) was built to determine the minimum and suitable ecological water demand. Then, an ecological reservoir operation model using an improved differential evolution (DE) algorithm was established. Simulated inflow data under the land use conditions in 1970 and 2015 were input into the model, and the effects of changes in land use on ecological operation and on the weighted usable area (WUA) for cyprinoids were calculated. The results show that ecological reservoir operation significantly improves the ecological water supply and increases cyprinoid habitat. The changes in land use led to a decrease in annual runoff, which further reduced the urban water supply by 0.67%–2.25% and the ecological water supply by 0.45%–2.04% at different flow frequencies. The land use changes also caused the WUA to decrease by 1746–2999 m2/km at different flow frequencies, with a decrease rate of 1.04%–6.28%. This study could provide valuable information for water resource managers operating reservoirs in river ecosystems and planning to cope with water shortages caused by land use change in water-deficient areas. • Determining the ecological water demand by synthesize hydrological and habitat methods. • The changes in land use result in a decrease in water supply and fish habitat. • Urban water supply is most affected by changes in land use at different flow frequencies. • Effects on the ecological water supply become stronger as the flow frequency increases. [ABSTRACT FROM AUTHOR]

In recent years, China's terminal clean power replacement construction has experienced rapid development, and China's installed photovoltaic and wind energy capacity has soared to become the highest in the world. Precise and effective prediction of the scale of terminal clean power replacement can not only help make reasonable adjustments to the proportion of clean power capacity, but also promote the reduction of carbon emissions and enhance environmental benefits. In order to predict the prospects of China's terminal clean energy consumption, first of all, the main factors affecting the clean power of the terminal are screened by using the grey revelance theory. Then, an evolutionary game theory (EGT) optimized least squares support vector machine (LSSVM) machine intelligence algorithm and an adaptive differential evolution (ADE) algorithm are applied in the example analysis, and empirical analysis shows that this model has a strong generalization ability, and that the prediction result is better than other models. Finally, we use the EGT–ADE–LSSVM combined model to predict China's terminal clean energy consumption from 2019 to 2030, which showed that the prospect of China's terminal clean power consumption is close to forty thousand billion KWh. [ABSTRACT FROM AUTHOR]

• Importance of metaheusristic approaches in spatial modelling and also accuracy. • Flood spatial modeling and its mapping using metaheuristic models. • Ensemble of ANFIS by GA, SA, ICA, and DE approaches. • Selection of the best combined ANFIS for flood susceptibility and hazard mapping. This study is performed to determine the importance of metaheuristic approaches and their accuracy with respect to the spatial modeling process emphasising on flood phenomena. In this study, four ensemble metaheuristic approaches, including the combinations of ANFIS (Adaptive Neuro-Fuzzy Inference System) with the genetic algorithm (GA), simulated annealing (SA), imperialist competitive algorithm (ICA), and differential evolution (DE), are used for flood zoning in Dingnan County, China. Based on historical records, aerial photo interpretation, field surveys, and Google Earth, the location of 115 floods are recorded in the study area. Different categorical and continuous factors affecting floods, including the plan curvature, altitude, distance to rivers, slope degree, rainfall, land use, lithology, stream power index, topographic wetness index, soil type, aspect, normalized difference vegetation index, and profile curvature, are identified in the study area and entered in GIS software. Flood susceptibility maps (FSMs) are validated using the ROC curve. The results confirm that the AUCs of the four combined metaheuristic models are larger than 79%. The highest AUC value is obtained for the ANFIS–GA ensemble (0.903), followed by the ANFIS–SA (0.843), ANFIS–DE (0.812), and ANFIS–ICA (0.798). The RMSEs obtained for the training data of the different models are 0.2562 (ANFIS-DE), 0.3121 (ANFIS-GA), 0.3345 (ANFIS-ICA), and 0.3398 (ANFIS-SA). The results of this study show that the proposed ensemble approaches are useful for flood hazard management and land use planning in Dingnan County, China, and other places. [ABSTRACT FROM AUTHOR]

Precise analysis and forecasting of energy consumption not only affects energy security and environment of a nation but also provides a useful decision basis for policy makers. This study proposes a new enhanced optimization model based on the bagged echo state network improved by differential evolution algorithm to estimate energy consumption. Bagging is applied to reduce forecasting error and improve generalization of network. Further, three parameters of echo state network are optimized using differential evolution algorithm. Thus, the proposed model combines the merits of three techniques which are echo state network, bagging, and differential evolution algorithm. The proposed model is applied to two comparative examples and an extended application to verify its accuracy and reliability. Results of the comparative examples show the proposed model achieves better forecasting performance compared with basic echo state network and other existing popular models. Mean absolute percentage error of the proposed model is 0.215% for total energy consumption forecasting of China. Therefore, the proposed model can be a satisfactory tool for forecasting energy consumption because of its high accuracy and stability. • Propose an enhanced optimization model using bagged echo state network improved by differential evolution. • The proposed model is firstly used for energy consumption forecasting. • The proposed model achieves better forecasting performance than the existing best models for two comparative examples. • Mean absolute percentage error of the proposed model is 0.215% for total energy consumption forecasting in China. [ABSTRACT FROM AUTHOR]