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Nowadays, the message diffusion links among users or websites drive the development of countless innovative applications. However, in reality, it is easier for us to observe the timestamps when different nodes in the network react on a message, while the connections empowering the diffusion of the message remain hidden. This motivates recent extensive studies on the network inference problem: unveiling the edges from the records of messages disseminated through them. Existing solutions are computationally expensive, which motivates us to develop an efficient two-step general framework, Clustering Embedded Network Inference (CENI). CENI integrates clustering strategies to improve the efficiency of network inference. By clustering nodes directly on the timelines of messages, we propose two naive implementations of CENI: Infection-centric CENI and Cascade-centric CENI. Additionally, we point out the critical dimension problem of CENI: instead of one-dimensional timelines, we need to first project the nodes to an Euclidean space of certain dimension before clustering. A CENI adopting clustering method on the projected space can better preserve the structure hidden in the cascades, and generate more accurately inferred links. This insight sheds light on other related work attempting to discover or utilize the latent cluster structure in the disseminated messages. By addressing the critical dimension problem, we propose the third implementation of the CENI framework: Projection-based CENI. Through extensive experiments on two real datasets, we show that the three CENI models only need around 20% $\sim$ 50% of the running time of state-of-the-art methods. Moreover, the inferred edges of Projection-based CENI preserves or even outperforms the effectiveness of state-of-the-art methods., Full-length version of the paper with the same title published in ICWSM 2015

The dispatching optimization of power grid is to obtain the greatest benefits on the basis of safe and stable operation of the power grid, which is of great significance for the energy-saving, reliability of the power grid. Therefore, a multi-objective model of power grid dispatching which considers the heat-power coupling characteristics of CHP units is developed in this study. The economic benefit and minimum wind power abandonment are taken as optimizing targets in the model. Meanwhile, the detail operation characteristics of condensing power plants and CHP units are considered in this model based on off-design models of power plant. Besides, the regional heat load and power load are scheduled together. Finally, the AHP-entropy method is used to obtain the weights of the two optimizing targets of economic benefit and wind power abandonment, and the multi-objective is converted into a single-objective.

The research of hydro-mechanical coupling properties is of great significance for the design of high-level radioactive waste repository. In this paper, the permeability evolution test of Tamusu clay under full stress-strain condition is carried out. Based on the basic theoretical equation of hydro-mechanical coupling, the relationship between the permeability and stress of the Tamusu clay is analyzed. Combineed with the results of in-situ stress measurement in the Tamusu area, the excavation simulation of the roadway with a burial depth of 500 m in the area is carried out, and the change law of pore water pressure in the surrounding rock near the field is analyzed. The results show that when the osmotic pressure is constant, the peak strength increases and the permeability decreases with the increase of confining pressure. When the confining pressure is constant and osmotic pressure increases, the result is reversed; The permeability of Tamusu clay is affected by confining pressure more than the osmotic pressure, and the relationship between permeability and axial stress follows the negative exponential function.

Factors such as the pollutant formation, pollution emission punishment and pollutant control devices are considered to optimize the coal blending method for a 300 MW boiler unit. The support vector machine (SVM) is used to establish the pollutant formation prediction model for the coal–fired boiler. Moreover, the model built above is trained and verified based on the actual operation data. Then the genetic algorithm is applied to optimize the coal blending method with the coal price to achieve the lowest operation cost. It can be concluded from the results that the precision of the prediction model is relatively high and the ammonia consumption, NOx emission punishment, CaCO3 consumption and desulfurization water consumption have all decreased after optimization, which means both the desulfurization cost and denitration cost are reduced.

Thermo-electric decoupling is an effective way to improve the adoption capacity of renewable power of power grids. Therefore, theoretical analysis models are established in this study to evaluate the decoupling performance of CHP with absorption heat pump, compression heat pump and low-pressure turbine renovation. Results show that absorption heat pump, compression heat pump and low pressure cylinder can all expand the feasible operation area and reduce the minimum electricity load rate of the reference CHP plant. When the heat load is 250 MW, The minimal electricity load rate of unit decreases by 12.98%, 56.47% and 36.34% with absorption heat pump, compression heat pump and low-pressure turbine renovation, respectively. The maximal heat-to-electricity ratio of unit is also increased by integration with heat pump and low-pressure renovation. Besides, the compression heat pump has the best decoupling performance among these three decoupling technologies under the same max heating power.