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The coal-fired power plants have been confronted with new operation challenge since the unified carbon trading market was launched in China. To make the optimal decision for the carbon emissions and power production has already been an important subject for the plants. Most of the previous studies only considered the market prices of electricity and coal to optimize the generation plan. However, with the opening of the carbon trading market, carbon emission has become a restrictive factor for power generation. By introducing the carbon-reduction target in the production decision, this study aims to achieve both the environmental and economic benefits for the coal-fired power plants to positively deal with the operational pressure.

A dynamic optimization approach with both long- and short-term decisions was proposed in this study to control the carbon emissions and power production. First, the operation rules of carbon, electricity and coal markets are analyzed, and a two-step decision-making algorithm for annual and weekly production is presented. Second, a production profit model based on engineering constraints is established, and a greedy heuristics algorithm is applied in the Gurobi solver to obtain the amounts of weekly carbon emission, power generation and coal purchasing. Finally, an example analysis is carried out with five generators of a coal-fired power plant for illustration.

The results show that the joint information of the multiple markets of carbon, electricity and coal determines the real profitability of power production, which can assist the plants to optimize their production and increase the profits. The case analyses demonstrate that the carbon emission is reduced by 2.89% according to the authors’ method, while the annual profit is improved by 1.55%.

As an important power producer and high carbon emitter, coal-fired power plants should actively participate in the carbon market. Rather than trade blindly at the end of the agreement period, they should deeply associate the prices of carbon, electricity and coal together and realize optimal management of carbon emission and production decision efficiently.

This paper offers an effective method for the coal-fired power plant, which is struggling to survive, to manage its carbon emission and power production optimally.

The increase of turbulence sources and risk points under the complex social information network has brought severe challenges. This paper discusses risk perception and intelligent decision-making under the complex social information network to maintain social security and financial security.

Cross-modal semantic fusion and social risk perception, temporal knowledge graph and analysis, complex social network intelligent decision-making methods have been studied. A big data computing platform of software and hardware integration for security combat is constructed based on the technical support.

The software and hardware integration platform driven by big data can realize joint identification of significant risks, intelligent analysis and large-scale group decision-making.

The integrated platform can monitor the abnormal operation and potential associated risks of Listed Companies in real-time, reduce information asymmetry and accounting costs and improve the capital market's ability to serve the real economy. It can also provide critical technical support and decision support in necessary public opinion monitoring and control business.

In this paper, the theory of knowledge-enhanced multi-modal multi-granularity dynamic risk analysis and intelligent group decision-making and the idea of an inference think tank (I-aid-S) is proposed. New technologies and methods, such as association analysis, time series evolution and super large-scale group decision-making, have been established. It's also applied in behavior and situation deduction, public opinion and finance and provides real-time, dynamic, fast and high-quality think tank services.

Cobalt is an extremely useful element, but there are very few separate cobalt deposits in China and imported cobalt ores are usually toxic. In order to develop more low-toxicity cobalt resources, China has to encourage exploration and development of this element. Samples are taken from Hanxing iron ore tailings and analyzed by ICP-MS and barium chloride titration experiments. The results indicate that the cobalt content is relatively high in Hanxing iron ore tailings, and some exceed the industrial grade. Therefore, with the depletion of cobalt resources, iron ore tailings are bound to become an important resource in China, and as these tailings are abundant in the Hanxing area, this area is expected to be of high development value.

This research aims to discover the relationship between social media usage (SMU) and the critical thinking ability (CTA) of university students, and to answer the question that whether social media dependence (SMD) affects the development of CTA, and thus providing a reference for the social media access strategy of academic libraries from the perspective of media information literacy.

The research data were collected via 300 valid questionnaires whose respondents are students from three universities in China. Multistage stratified cluster sampling method was used to select the respondents, which guarantees statistical representativeness. A pre-test was conducted to ensure the validity of the questionnaire.

It is shown that the total score of CTA and the six sub-dimensions are significantly positively correlated with SMU, but strongly negatively correlated with SMD. Based on the mediating effect testing, it is discovered that the degree of SMD can affect the promoting relations between the usage intensity of social media (UISM) and CTA. Clearly, SMU is a double-edged sword. While it narrows the digital gap in terms of accessibility, it widens the digital gap in terms of usage.

The differences in SMU have a significant impact on the development of CTA of university students. This inspires us to consider the ability of “using social media in a balanced way” as an important evaluation and training direction when inquiring media literacy. As social media is becoming a critical channel in cultivating individual's thinking skills, it is highly suggested that the amount of time spent on reading fragmented information on the internet should be controlled.

Lightweight apps such as WeChat mini programs (WMPs) are an emerging mobile channel (m-channel) touchpoint that have gained remarkable popularity among consumers. Despite the focus of migration research on traditional m-channel touchpoints (e.g. native apps and mobile websites), but few researchers have examined why consumers switch from native to lightweight apps. Drawing on the push-pull-mooring framework, this study aims to identify the key factors influencing consumers' switching related to lightweight apps.

The data were collected using a questionnaire survey of 416 WMP consumers and the proposed model was analyzed through structural equation modeling.

The results show that the push effect, specifically, high privacy concern, nudges consumers away from native apps, whereas the pull effects, including relative ease of use, convenience of access and exit and socially-oriented interaction, entice consumers to lightweight apps. Further, consumer switching intention is influenced by habit and perceived technology control, both of which reflect the mooring effects. Switching intention also stands as an important precedent of actual behavior.

This study is among the first theoretical explorations of consumer switching across m-channel touchpoints in the context of mobile commerce. For information system practice, these findings provide new insights for both incumbent providers and newcomers on how to retain existing shoppers as well as attract potential shoppers effectively.

The tech giants Alibaba and Tencent compete on many fronts. This case focuses on three areas where they have competed very hard: new retailing, mobile payment, and ride-hailing. At the beginning of 2018, Alibaba and Tencent were gathering retail investments in bids to battle each other for shoppers' digital wallets. Key to the battle is China's mobile payment market, worth more than 200 trillion RMB, where Alibaba and Tencent are going head to head. The giants are not only directly competing in the payment platform area but also extensively fighting in other areas, such as ride-hailing, where they invested in and supported Didi and Kuaidi, respectively. To enhance understanding, this case also briefly goes through the history of the two giants. The purposes, methods, and consequences of their platform competition deserve an in-depth discussion