The Impact of Consumer-Company Micro Blog Interaction on Consumer Brand Loyalty

Micro blog has become an important platform of consumer-company interaction to enhance brand loyalty. A relational model is proposed to study the influence of company micro blog interaction on customer micro blog stickiness and brand loyally, based on use and gratification theory, organization support theory and union participation theory. Results show that company interaction tactics include two forms: relational interaction and commercial interaction. Relational interaction positively influences consumers ’ perceived socioemotional support and perceived informationsupport. Commercial interaction positively influence perceived information support. Perceived socioemotional support and information support are positively related to consumer micro blog stickiness, which is positively related to brand loyalty. In sum, relational interaction is more important in attracting sticky consumers and brand loyalty. Finally, some recommendations are presented for micro blog branding

Analysis of Political Decision-Making and Its Influencing Factors

Political decision-making means a country’s political parties, leaders or leadership compare and select implementing principles and approaches and means to achieve the target in political practical activities for the purpose, principles and direction of activities. The process of political decision-making is a dynamic political process that is related to the formation and implementation of major and general decisions of the national, political and social interest groups. This process is to integrate major and general decisions regarding national and social interests. The subjects are state organs, political parties and individual decision makers or decision-making participants, and the finally formed decision is backed by the country’s coercive power with mandatory features. Meanwhile, political decision-making is influenced by system pressure. In the decision-making process, there will be a certain degree of bias between the final decision and the targeted decision

Universal quantum gates between nitrogen-vacancy centers in a levitated nanodiamond

We propose a scheme to realize universal quantum gates between nitrogen-vacancy (NV) centers in an optically trapped nanodiamond, through uniform magnetic field induced coupling between the NV centers and the torsional mode of the levitated nanodiamond. The gates are tolerant to the thermal noise of the torsional mode. By combining the scheme with dynamical decoupling technology, it is found that the high fidelity quantum gates are possible for the present experimental conditions. The proposed scheme is useful for NV-center-based quantum network and distributed quantum computationComment: 7 pages, 6 figure

Protecting entanglement from correlated amplitude damping channel using weak measurement and quantum measurement reversal

Based on the quantum technique of weak measurement, we propose a scheme to protect the entanglement from correlated amplitude damping decoherence. In contrast to the results of memoryless amplitude damping channel, we show that the memory effects play a significant role in the suppression of entanglement sudden death and protection of entanglement under severe decoherence. Moreover, we find that the initial entanglement could be drastically amplified by the combination of weak measurement and quantum measurement reversal even under the correlated amplitude damping channel. The underlying mechanism can be attributed to the probabilistic nature of weak measurements.Comment: 11 pages, 5 figures, accepted by Quantum Information Processin

Forecasting Mid-Term Electricity Market Clearing Price Using Support Vector Machines

In a deregulated electricity market, offering the appropriate amount of electricity at the right time with the right bidding price is of paramount importance. The forecasting of electricity market clearing price (MCP) is a prediction of future electricity price based on given forecast of electricity demand, temperature, sunshine, fuel cost, precipitation and other related factors. Currently, there are many techniques available for short-term electricity MCP forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. The mid-term electricity MCP forecasting focuses electricity MCP on a time frame from one month to six months. Developing mid-term electricity MCP forecasting is essential for mid-term planning and decision making, such as generation plant expansion and maintenance schedule, reallocation of resources, bilateral contracts and hedging strategies. Six mid-term electricity MCP forecasting models are proposed and compared in this thesis: 1) a single support vector machine (SVM) forecasting model, 2) a single least squares support vector machine (LSSVM) forecasting model, 3) a hybrid SVM and auto-regression moving average with external input (ARMAX) forecasting model, 4) a hybrid LSSVM and ARMAX forecasting model, 5) a multiple SVM forecasting model and 6) a multiple LSSVM forecasting model. PJM interconnection data are used to test the proposed models. Cross-validation technique was used to optimize the control parameters and the selection of training data of the six proposed mid-term electricity MCP forecasting models. Three evaluation techniques, mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square root error (MSRE), are used to analysis the system forecasting accuracy. According to the experimental results, the multiple SVM forecasting model worked the best among all six proposed forecasting models. The proposed multiple SVM based mid-term electricity MCP forecasting model contains a data classification module and a price forecasting module. The data classification module will first pre-process the input data into corresponding price zones and then the forecasting module will forecast the electricity price in four parallel designed SVMs. This proposed model can best improve the forecasting accuracy on both peak prices and overall system compared with other 5 forecasting models proposed in this thesis

Electricity market clearing price forecasting under a deregulated electricity market

Under deregulated electric market, electricity price is no longer set by the monopoly utility company rather it responds to the market and operating conditions. Offering the right amount of electricity at the right time with the right bidding price has become the key for utility companies pursuing maximum profits under deregulated electricity market. Therefore, electricity market clearing price (MCP) forecasting became essential for decision making, scheduling and bidding strategy planning purposes. However, forecasting electricity MCP is a very difficult problem due to uncertainties associated with input variables. Neural network based approach promises to be an effective forecasting tool in an environment with high degree of non-linearity and uncertainty. Although there are several techniques available for short-term MCP forecasting, very little has been done to do mid-term MCP forecasting. Two new artificial neural networks have been proposed and reported in this thesis that can be utilized to forecast mid-term daily peak and mid-term hourly electricity MCP. The proposed neural networks can simulate the electricity MCP with electricity hourly demand, electricity daily peak demand, natural gas price and precipitation as input variables. Two situations have been considered; electricity MCP forecasting under real deregulated electric market and electricity MCP forecasting under deregulated electric market with perfect competition. The PJM interconnect system has been utilized for numerical results. Techniques have been developed to overcome difficulties in training the neural network and improve the training results