Optimal siting of electric vehicle charging stations (EVCSs) is crucial to the sustainable development of electric vehicle systems. Considering the defects of previous heuristic optimization models ...in tackling subjective factors, this paper employs a multi-criteria decision-making (MCDM) framework to address the issue of EVCS siting. The initial criteria for optimal EVCS siting are selected from extended sustainability theory, and the vital sub-criteria are further determined by using a fuzzy Delphi method (FDM), which consists of four pillars: economy, society, environment and technology perspectives. To tolerate vagueness and ambiguity of subjective factors and human judgment, a fuzzy Grey relation analysis (GRA)-VIKOR method is employed to determine the optimal EVCS site, which also improves the conventional aggregating function of fuzzy Vlsekriterijumska Optimizacijia I Kompromisno Resenje (VIKOR). Moreover, to integrate the subjective opinions as well as objective information, experts' ratings and Shannon entropy method are employed to determine combination weights. Then, the applicability of proposed framework is demonstrated by an empirical study of five EVCS site alternatives in Tianjin. The results show that A3 is selected as the optimal site for EVCS, and sub-criteria affiliated with environment obtain much more attentions than that of other sub-criteria. Moreover, sensitivity analysis indicates the selection results remains stable no matter how sub-criteria weights are changed, which verifies the robustness and effectiveness of proposed model and evaluation results. This study provides a comprehensive and effective method for optimal siting of EVCS and also innovates the weights determination and distance calculation for conventional fuzzy VIKOR.
Effective and safe hemodialysis is essential for patients with acute kidney injury and chronic renal failures. However, the development of effective anticoagulant agents with safe antidotes for use ...during hemodialysis has proven challenging. Here, we describe DNA origami-based assemblies that enable the inhibition of thrombin activity and thrombus formation. Two different thrombin-binding aptamers decorated DNA origami initiates protein recognition and inhibition, exhibiting enhanced anticoagulation in human plasma, fresh whole blood and a murine model. In a dialyzer-containing extracorporeal circuit that mimicked clinical hemodialysis, the origami-based aptamer nanoarray effectively prevented thrombosis formation. Oligonucleotides containing sequences complementary to the thrombin-binding aptamers can efficiently neutralize the anticoagulant effects. The nanoarray is safe and immunologically inert in healthy mice, eliciting no detectable changes in liver and kidney functions or serum cytokine concentration. This DNA origami-based nanoagent represents a promising anticoagulant platform for the hemodialysis treatment of renal diseases.
By analysing the mechanical and geometrical relations between the main cable, tower, and splay saddles, and considering the coupling effect of the tower and splay saddles, an improved algorithm is ...proposed to determine the cable saddles pre-offsets of suspension bridges. The equilibrium relationship of the cable saddles, the compatible deformation condition, and the basic equation of the main cable shape are considered to establish several coupled non-linear equations up to 19, and the tower and splay saddle pre-offsets are obtained by solving the above equations with the Newton-Raphson method. This paper presents the initial value selection principle and the constraint conditions for solving the cable saddle pre-offsets of the plane cable suspension bridge and the calculation process ensures convergence. The calculation example demonstrates that the improved algorithm without an exact initial value can achieve excellent convergence.
The Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) has concluded that climate change will have significant impacts on global water resources. The risk of ...production loss due to water scarcity can be transmitted through international trade to distant economies downstream the supply chain. In this research, how climate change may affect the global economy via reducing available water resources in some regions is investigated based on a multi-regional input output (MRIO) model that provides information on the current global economic structure. Key nation-sectors with the greatest virtual water scarcity risk (VWSR) exports are identified under two climate change scenarios, including the Agriculture sectors in Syria, Pakistan, Kazakhstan, India, Uzbekistan, Iran, and China. Improving water efficiency in these sectors is essential for increasing the resilience of the global economy against climate change-induced water scarcity. Nation-sectors with the largest VWSR imports are also identified under the two climate change scenarios, including Food & Beverages sectors, Textiles and Wearing Apparel sectors, and Petroleum, Chemical and Non-Metallic Mineral Products sectors in Saudi Arabia, the United States of America, Russia, Germany, Italy, and China. These are the most vulnerable nation-sectors facing the reduction in foreign water resources due to climate change. Additionally, through comparing the change of rankings of VWSR imports, VWSR exports, and LWSRs at country and sector level, the rankings of VWSR exports are relatively close to LWSRs, while the rankings of VWSR imports are quite different from LWSRs. The evaluation demonstrates that nations should cooperatively manage water resources and be aware of the transmission of virtual water scarcity risk through international trade under climate change. Moreover, nation-sectors with high VWSR imports may reduce the reliance on water-intensive products and diversify importing sources, and national governments can encourage residents to change consumption patterns.
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•Impact of climate change on the global trade system through water scarcity is studied.•A method quantifying climate change-induced WSR to global trade system is presented.•Nations and sectors that exert and receive risks through international trade are identified.•Suggestions for mitigating climate change-induced WSR to global trade are provided.
Unbalanced and inadequate development in China has resulted in significant temporal and spatial differences in carbon intensity, impeding the achievement of carbon reduction targets. This paper ...explores the spatial distribution and convergence of China’s provincial carbon intensity during 2000–2017 and its influencing factors employing spatial panel techniques. The spatial distribution analysis supports the existence of significant spatial agglomeration and radiation effects in China’s provincial carbon intensity, and several provinces play key roles in the spatial distribution of carbon intensity, which are an important focus of carbon emission reduction policies. The results of spatial convergence estimation support that China’s provincial carbon intensity presents significant spatial absolute and conditional convergence, and after considering regional differences, the spatial convergence speed is significantly accelerated. Meanwhile, economic level, urbanization, energy consumption structure, and industrial structure have significant spatial radiation effects on carbon intensity, and carbon intensity itself also has a spatial diffusion effect, indicating that carbon emission reduction requires multi-regional coordinated actions.
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This paper examined the spatial distribution and convergence of China’s provincial carbon intensities over 2000–2017. The empirical findings verified the spatial agglomeration and radiation effects, as well as the absolute and conditional spatial convergence of China’s provincial carbon intensities, which supports the policy-making related to the carbon reduction in China.
With the increasing development of renewable resources-based electricity generation and the construction of wind-photovoltaic-energy storage combination exemplary projects, the intermittent and ...fluctuating nature of renewable resources exert great challenges for the power grid to supply electricity reliably and stably. An energy storage system (ESS) is deemed to be the most valid solution to deal with these challenges. Considering the various types of ESSs, it is necessary to develop a comprehensive assessment framework for selecting appropriate energy storage techniques in establishing exemplary projects combining renewable resources-based electricity generation and an ESS. This paper proposes a multi-criteria decision making (MCDM) model combining a fuzzy-Delphi approach to establish the comprehensive assessment indicator system, the entropy weight determination method, and the best-worst method (BWM) to calculate weights of all sub-criteria, and a Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) comprehensive evaluation model to choose the optimal battery ESS. In accordance with the comprehensive evaluation results, the Li-ion battery is the optimal battery ESS to apply to wind-photovoltaic-energy storage combination exemplary projects. Based on the discussion on the comprehensive evaluation results, policy implications are suggested to improve the applicability of battery ESSs and provide some references for decision makers in related fields.
Carbon dioxide (CO2) emissions forecasting is becoming more important due to increasing climatic problems, which contributes to developing scientific climate policies and making reasonable energy ...plans. Considering that the influential factors of CO2 emissions are multiplex and the relationships between factors and CO2 emissions are complex and non-linear, a novel CO2 forecasting model called SSA-LSSVM, which utilizes the Salp Swarm Algorithm (SSA) to optimize the two parameters of the least squares support sector machine (LSSVM) model, is proposed in this paper. The influential factors of CO2 emissions, including the gross domestic product (GDP), population, energy consumption, economic structure, energy structure, urbanization rate, and energy intensity, are regarded as the input variables of the SSA-LSSVM model. The proposed model is verified to show a better forecasting performance compared with the selected models, including the single LSSVM model, the LSSVM model optimized by the particle swarm optimization algorithm (PSO-LSSVM), and the back propagation (BP) neural network model, on CO2 emissions in China from 2014 to 2016. The comparative analysis indicates the SSA-LSSVM model is greatly superior and has the potential to improve the accuracy and reliability of CO2 emissions forecasting. CO2 emissions in China from 2017 to 2020 are forecast combined with the 13th Five-Year Plan for social, economic and energy development. The comparison of CO2 emissions of China in 2020 shows that structural factors significantly affect CO2 emission forecasting results. The average annual growth of CO2 emissions slows down significantly due to a series of policies and actions taken by the Chinese government, which means China can keep the promise that greenhouse gas emissions will start to drop after 2030.
Multi-energy virtual power plants (MEVPPs) effectively realize multi-energy coupling. Low-carbon transformation of coal-fired units at the source side and consideration of demand response resources ...at the load side are important ways to achieve carbon peak and carbon neutralization. Based on this, this paper proposes a low-carbon economic dispatch model for the MEVPP system considering source-load coordination with comprehensive demand response. Combined with the characteristics of organic Rankine cycle (ORC) waste heat power generation and comprehensive demand response energy to increase the flexibility on both sides of the source and load, the problem of insufficient carbon capture during the peak load period in the process of low-carbon transformation of thermal power units has been improved. First, the ORC waste heat recovery device is introduced into the MEVPP system to decouple the cogeneration unit’s “heat-based electricity” constraint, which improves the flexibility of the unit’s power output. Secondly, we consider the synergistic effect of the comprehensive demand response and ORC waste heat recovery device and analyze the source-load coordination low-carbon dispatch mechanism. Finally, an example simulation is carried out in a typical system. The simulation example shows that this method effectively improves the carbon capture level of carbon capture power plants, takes into account the economy and low carbon of the system, and can provide a reference for the low-carbon economic dispatch of the MEVPP system.
Tea consumption has been identified to have an anti-obesity effect. Whether it is associated with gut microbiota modulation is investigated in this study. Phenolic profiles of infusions of green tea, ...oolong tea and black tea were comprehensively compared first, by utilizing ultra-performance liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOFMS). Subsequently, high-fat-diet induced obese C57BL/6J mice were orally administered these three types of tea infusions for 13 weeks to evaluate their anti-obesity and gut microbiota modulatory effects. In general, 8 phenolic acids, 12 flavanols, 9 flavonols, 2 alkaloids and 1 amino acid were identified from the three types of tea infusions. Though they possess diverse phenolic compounds, no significant differences in the prevention of the development of obesity in high-fat-fed mice were discovered among the three types of tea. Based on high-throughput MiSeq sequencing and multivariate statistical analysis, it was revealed that tea infusion consumption substantially increased diversity and altered the structure of gut microbiota. The linear discriminant analysis effect size algorithm identified 30 key phylotypes in response to high-fat diet and tea, including Alistipes, Rikenella, Lachnospiraceae, Akkermansia, Bacteroides, Allobaculum, Parabacteroides, etc. Moreover, Spearman's correlation analysis indicated that these key phylotypes might have a close association with the obesity related indexes of the host. This study provides detailed information regarding the impact of tea consumption on gut microbiota, which may be helpful in understanding the anti-obesity mechanisms of tea.
Accurate prediction of the carbon trading price (CTP) is crucial to the decision-making of relevant stakeholders, and can also provide a reference for policy makers. However, the time interval for ...the CTP is one day, resulting in a relatively small sample size of data available for predictions. When dealing with small sample data, deep learning algorithms can trade only a small improvement in prediction accuracy at the expense of efficiency and computing time. In contrast, fine-grained configurations of traditional model inputs and parameters often perform no less well than deep learning algorithms. In this context, this paper proposes a novel hybrid CTP prediction model based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a windowed-based XGBoost approach. First, the initial CTP data is decomposed into multiple subsequences with relatively low volatility and randomness based on the CEEMDAN algorithm. Then, the decomposed carbon valence series and covariates are subject to windowed processing to become the inputs of the XGBoost model. Finally, the universality of the proposed model is verified through case studies of four carbon emission trading markets with different modal characteristics, and the superiority of the proposed model is verified by comparing with seven other models. The results show that the prediction error of the proposed XGBoost(W-b) algorithm is reduced by 4.72%~81.47% compared to other prediction algorithms. In addition, the introduction of CEEMDAN further reduces the prediction error by 25.24%~89.28% on the basis of XGBoost(W-b).
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