Sustainability of ride-hailing services in China’s mobility market: A simulation model of socio-technical system transition

As a symbol of sharing economy, ride-hailing services have spread to Asia, where various forms of services have been established according to the existing socio-technical regimes, including legal, policy, and environmental concerns. China, in particular, has accepted ride-hailing services by revising existing institutions. This study analyzes China’s acceptance of ride-hailing services in the socio-technical system context set up for legacy services. Simulation modeling, combined with transition theory and an agent-based model, is used to analyze the data. This study calculates consumer disutility based on mobility market share, reflecting consumer preference, and predicts the sustainability of ride-hailing services. The simulation results conclude that legalization and socio-technical context are of importance for sustainable mobility.     

Costs Impact of a Transition to Hydrogen-fueled Vehicles on the Spanish Power Sector

It is expected that demand response might provide soon ancillary services to the power system. This could be done, for example, by managing the use of Electric Vehicles (EV) batteries, or the production of flexible energy commodities such as hydrogen (H₂), that can be used for fuel cell vehicles (H2EV) or in industrial processes. This paper analyses the impact of a transition to H2EV as an alternative to EV for passengers’ cars on a Spanish-like power sector. A simple H₂ demand estimation is developed and provided to CEVESA, an operation and expansion model for the Iberian Power System Electricity Market (MIBEL). For this study, CEVESA was extended to include the investments and operation decisions of H₂ production. Simulations were performed to determine the optimal evolution of the H₂ production capacity and of the electricity generation mix, considering scenarios with different shares of EV and H2EV. The impact of H2EV vs EV mobility is assessed based on the recent Spanish National Plan for Energy and Climate (NECP) as the base case scenario. Results show that, even if H2EV mobility alternative is still more costly than EV, H₂ production could provide a significant flexibility to the system that should also be appraised. Indeed, H2EV mobility could become a feasible and complementary alternative to decarbonize mobility by powering H₂ production with the renewable generation surplus. This, together with the on-going learning process of this technology that will decrease its production costs and increase its efficiency in the coming years, could boost, even more, the development of the H₂ economy.     

The status of and opportunities for utility-scale battery storage in Australia: A regulatory and market perspective

Australia's electricity market is rapidly adding renewable energy generation. Utility-scale batteries could have a major role in facilitating these transitions; however, their deployment is still largely state-subsidized. We summarize the current and future roles for batteries from a legal-economic perspective in the context of Australia's electricity market framework. We find that the future of batteries in Australia is not only a function of the large-scale deployment of renewables, their cost development and the comparative future cost of competing gas turbines but also of national electricity market and state policy reforms focusing on reliability.     

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.     

计及Spark和属性权重的售电套餐推荐方法

针对电力市场用户群庞大，交易过程中售电套餐选择困难的问题，在Spark环境下设计了一种售电套餐推荐方法，同时也解决了售电套餐推荐过程中在大数据环境下的可扩展性及实时性问题。首先，计算出每个套餐属性的权重值，从而计算得到售电套餐综合相似度。然后，计及用户和套餐两方面提出一种售电套餐推荐方法，实现售电套餐的精准推荐。实验表明，提出的推荐方法能够明显提高推荐的准确度，并且在分布式环境下具有良好的推荐效率和可扩展性。     

低价的市场竞争心理必须改变

概述了中国PCB产品必须走出低价的市场竞争的困境,坚决走自主创新来发展中国PCB工业的道路。     

3G用户细分研究

对3G用户的细分方法及3G目标市场的定位进行了初步的研究，提出了3G用户细分的体系框架和3G目标市场定位的考虑要素及初步的定位建议。     

天然气产业链下游市场风险评价模型探讨

随着我国天然气主干管网的逐步形成，天然气产业链下游市场正在进入快速成长阶段，建立风险预警系统的重要性开始显现，而风险评价模型是其基础工作之一。文章在天然气产业链下游区域市场风险评价指标体系的基础上，综合运用层次分析法和灰色系统理论，建立了对天然气产业链下游区域市场风险评价的模型，并以各区域市场的用气量占全国总用气量的比重为权重，建立起完整的市场风险评价模型，可为评价天然气产业链下游市场整体市场风险时参考。     

聚丙烯腈基碳纤维市场分析与预测

本文介绍了国内外聚丙烯腈基碳纤维装置的生产能力和碳纤维的市场需求情况，提出了国内碳纤维所面临的挑战，并对如何发展国内碳纤维提了一些建议。     

Joint scheduling and power control supporting multicasting in wireless ad hoc networks

This paper addresses the problem of power control in a multihop wireless network supporting multicast traffic. We face the problem of forwarding packet traffic to multicast group members while meeting constraints on the signal-to-interference-plus-noise ratio (SINR) at the intended receivers. First, we present a distributed algorithm which, given the set of multicast senders and their corresponding receivers, provides an optimal solution when it exists, which minimizes the total transmit power. When no optimal solution can be found for the given set of multicast senders and receivers, we introduce a distributed, joint scheduling and power control algorithm which eliminates the weak connections and tries to maximize the number of successful multicast transmissions. The algorithm allows the other senders to solve the power control problem and minimize the total transmit power. We show that our distributed algorithm converges to the optimal solution when it exists, and performs close to centralized, heuristic algorithms that have been proposed to address the joint scheduling and power control problem.