High-Efficiency Organic Solar Cells Based on a Low-Cost Fully Non-Fused Electron Acceptor

A series of tetrathiophene-based fully non-fused ring acceptors (4T-1, 4T-2, 4T-3, and 4T-4), which can be paired with the star donor polymer PBDB-T to fabricate highly efficient organic solar cells are developed. Tailoring the size of lateral chains can tune the solubility and packing mode of acceptor molecules in neat and blend films. It is found that the incorporation of 2-ethylhexyl chains can effectively change the compatibility with the donor polymer PBDB-T, and an encouraging power conversion efficiency of 10.15% is accomplished by 4T-3-based organic solar cells. It also presents good compatibility with the other polymer donor and an even higher power conversion efficiency (PCE) of 12.04% is achieved based on D18:4T-3 blend, which is the champion PCE for the fully non-fused acceptors. Importantly, these inexpensive tetrathiophene fully non-fused ring acceptors provide cost-effective photovoltaic performance. The results demonstrate a high photovoltaic performance from synthetically inexpensive materials could be achieved by the rational design of non-fused ring acceptor molecules.     

Techno-economic analysis of a hybrid energy system for CCHP and hydrogen production based on solar energy

A typical problem in Northeast China is that a large amount of surplus electricity has arisen owing to the serious photovoltaic power curtailment phenomenon. To effectively utilize the excess photovoltaic power, a hybrid energy system is proposed that uses surplus electricity to produce hydrogen in this paper. It combines solar energy, hydrogen production system, and Combined Cooling Heating and Power (CCHP) system to realize cooling, heating, power, and hydrogen generation. The system supplies energy for three public buildings in Dalian City, Liaoning Province, China, and the system configuration with the lowest unit energy cost (0.0615$/kWh) was obtained via optimization. Two comparison strategies were used to evaluate the hybrid energy system in terms of unit energy cost, annual total cost, fossil energy consumption, and carbon dioxide emissions. Subsequently, the annual total energy supply, typical daily loads, and cost of the optimized system were analyzed. In conclusion, the system is feasible for small area public buildings, and provides a solution to solve the phenomenon of photovoltaic power curtailment.     

粉体含能材料生产与储运中的静电安全及防护

分析了静电产生的原因,阐述了粉体含能材料生产中的静电起电现象、静电的危害、静电安全性评估标准以及建立在此标准基础上的静电放电危险的评价办法,提出了粉体含能材料在生产、运输中所需要采取的静电防护措施。     

An economic investigation of the wind-hydrogen projects: A case study

Hydrogen as an energy carrier can play a significant role in reducing environmental emissions if it is produced from renewable energy resources. This research aims to assess hydrogen production from wind energy considering environmental, economic, and technical aspect for the East Azerbaijan province of Iran. The economic assessment is performed by calculation of payback period, levelized cost of hydrogen, and levelized cost of electricity. Since uncertainty in the power output of wind turbines may affect the payback period, all calculations are performed for four different turbine degradation rates. While it is common in the literature to choose the wind turbine based on a single criterion, this study implements Multi-Criteria Decision-Making (MCDM) techniques for this purpose. The results of Step-wise Weight Assessment Ratio Analysis illustrates that economic issue is the most important criterion for this research. The results of Weighted Aggregated Sum Product Assessment shows that Vestas V52 is the most suitable wind turbine for Ahar and Sarab cities, while Eovent EVA120 H-Darrieus is a better choice for other stations. The most suitable location for wind power generation is found to be Ahar, where it is estimated to annually generate 2914.8 kWh of electricity at the price of 0.045 $/kWh, and 47.2 tons of hydrogen at the price of 1.38 $/kg, which result in 583 tons of CO₂ emission reduction.     

Life cycle cost of conventional,battery electric,and fuel cell electric vehicles considering traffic and environmental policies in China

Electric vehicles (EVs) are considered a promising alternative to conventional vehicles (CVs) to alleviate the oil crisis and reduce urban air pollution and carbon emissions. Consumers usually focus on the tangible cost when choosing an EV or CV but overlook the time cost for restricting purchase or driving and the environmental cost from gas emissions, falling to have a comprehensive understanding of the economic competitiveness of CVs and EVs. In this study, a life cycle cost model for vehicles is conducted to express traffic and environmental policies in monetary terms, which are called intangible cost and external cost, respectively. Battery electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), and CVs are compared in four first-tier, four new first-tier, and 4 s-tier and below cities in China. The comparison shows that BEVs and FCEVs in most cities are incomparable with CVs in terms of tangible cost. However, the prominent traffic and environmental policies in first-tier cities, especially in Beijing and Shanghai, greatly increase the intangible and external costs of CVs, making consumers more inclined to purchase BEVs and FCEVs. The main policy benefits of BEVs and FCEVs come from three aspects: government subsidies, purchase and driving restrictions, and environmental taxes. With the predictable reduction in government subsidies, traffic and environmental policies present important factors influencing the competitiveness of BEVs and FCEVs. In first-tier cities, BEVs and FCEVs already have a competitive foundation for large-scale promotion. In new first-tier and second-tier and below cities, stricter traffic and environmental policies need to be formulated to offset the negative impact of the reduction in government subsidies on the competitiveness of BEVs and FCEVs. Additionally, a sensitivity analysis reveals that increasing the mileage and reducing fuel prices can significantly improve the competitiveness of BEVs and FCEVs, respectively.     

Comparative techno-economic assessment of a large-scale hydrogen transport via liquid transport media

A large-scale point to point hydrogen transport is one strategy for a prospective energy import scenario for certain countries. The case for a hydrogen transport from Australia to Japan has been addressed in several studies. However, most studies lack transparency and detailed insights into the made assumptions thus a fair evaluation of different transport pathways is challenging. To address this issue, we developed a model where a large-scale point to point hydrogen transport of liquid hydrogen is compared with the transport via liquid organic hydrogen carrier (LOHC), namely via methyl cyclohexane and hydrogenated dibenzyl toluene. We analyzed, where energy is required along the different pathways, where hydrogen losses do occur and how the costs are put together. Furthermore, the influence of hydrogen feed costs is also considered. For hydrogen production costs of 5 €₂₀₁₈/kg_H2 the total delivery costs are in the range of 6.40– 8.10 €₂₀₁₈/kg_H2.     

基于Prophet人工智能算法的网络潮汐效应预测研究

本文使用Prophet人工智能算法研究与预测移动通信网络“潮汐效应”现象，探索网络“潮汐效应”在优化网络资源配置实现网络降本增效的作用。Prophet人工智能算法是一种简单、有效，且易于实现的人工智能算法。通过facebook的人工智能开源框架fbprophet，研究4G网络PRB利用率等网络资源指标的“潮汐效应”，并预测这些网络资源指标在未来的变化趋势，用来指导当前4G网络减容、扩容和4/5G节电节能等，实现优化网络资源配置达到降本增效的目的。     

基于k-shell的复杂网络最短路径近似算法

复杂网络最短路径经典算法的处理效率较低，不适用于大规模复杂网络，而现有近似算法通用性有限，且计算准确率不理想，不能满足规模日益扩大的复杂网络中的最短路径计算需求。针对于此，提出基于[k]-shell的复杂网络最短路径近似算法。算法利用节点的[k]-shell值进行网络划分并引导搜索路径，利用超点聚合处理[k]-shell子网来降低路径搜索中节点和连边的规模，通过在路径搜索过程使用双向搜索树方法提高算法的计算效率和准确率。实验结果表明，算法通用性较好，在现实与仿真大规模复杂网络中均具有较高的计算效率和准确率。     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.