Modeling and Optimization of Time-Of-Use Electricity Pricing Systems.

Time-of-Use (TOU) pricing is an important strategy for electricity providers to manage supply and hence making the grid more efficient and for consumers to manage their costs. In this paper, we discuss a general stochastic modeling framework for consumer's power demand based on which the TOU contract characteristics can be selected, so as to minimize the mean electricity price paid by the customer. We exploit the characteristics of power demand observed in real grids to propose to model it during homogeneous peak periods as a constant level with fluctuations described by a scaled fractional Brownian motion.

Tumor phase recognition using cone-beam computed tomography projections and external surrogate information.

Purpose: Directly extracting the respiratory phase pattern of the tumor using cone-beam computed tomography (CBCT) projections is challenging due to the poor tumor visibility caused by the obstruction of multiple anatomic structures on the beam's eye view. Predicting tumor phase information using external surrogate also has intrinsic difficulties as the phase patterns between surrogates and tumors are not necessary to be congruent. In this work, we developed an algorithm to accurately recover the primary oscillation components of tumor motion using the combined information from both CBCT projections and external surrogates.

Inferring Genetic Interactions via a Data-Driven Second Order Model.

Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al.