We argue that important measures include implementing flexible power pricing mechanisms instead of the current fixed pricing approach, formulating a more reasonable mechanism for distributing benefits and costs, and designing an appropriate market structure for large-scale wind power utilization to promote market liquidity and to send clear market equilibrium signals.
The approach formulates the coupling as a control problem where the states are the solutions of the nonlocal and local equations, the objective is to minimize their mismatch on the overlap of the nonlocal and local domains, and the controls are virtual volume constraints and boundary conditions.
The proposed approach formulates the problem in a large margin classification framework, where the combination function is embedded in a flexible reproducing kernel Hilbert space.
In contrast, the new approach formulates the coalescence term as a single entity and directly accounts for the effect of turbulence on the probability of coalescence.
This article also introduces a generalised background knowledge driven optimisation based temporal encoding framework for encoding time series data and as an illustration of this approach, further formulates a temporal encoding algorithm, namely GAGamma, designed to efficiently compress fMRI data using discrete spike-times.
We found a lack of adequately powered clinical trials in this field and evidence that "palliative care" is often defined as a withdrawal of treatment or of single interventions (i.e. "fever management policies") rather than a holistic approach that formulates a management plan according to individual needs of the patients and their families.
This paper presents a novel local active noise control (ANC) approach formulated using a 3D fast Boundary Element Method (BEM).
The behaviour of the stored solid is modelled using a continuum mechanics approach formulated in an Arbitrary Lagrangian Eulerian (ALE) frame of reference.
The static equilibrium analysis of the spring-loaded planar articulated manipulator is based on the energy approach, formulated by a constant stiffness block matrix and its associated configuration block matrices.
To increase the precision, we use a machine learning-based approach formulating the relation detection as a binary classification problem: each instance (consisting of a pair of entities) is classified either as not-containing a relation or belonging to one of the four-relation classes.
