Improved Inner Approximation for Aggregating Power Flexibility in Active Distribution Networks and its Applications

Concise and reliable modeling for aggregating power flexibility of distributed energy resources in active distribution networks (ADNs) is a crucial technique for coordinating transmission and distribution networks. Our recent research has successfully derived an explicit expression for the exact aggregation model (EAM) of power flexibility at the substation level under linearized distribution network constraints. The EAM, however, is impractical for decision-making purposes due to its exponential complexity. In this paper, we propose an inner approximation method for aggregating flexibility in ADNs that utilizes the properties of the EAM to improve performance. Specifically, the geometric prototype of the inner approximation model is defined according to a subset of the coefficient vector set of the EAM, which enhances the accuracy. On the other hand, the computation efficiency of the inner approximation is also significantly improved by exploiting the regularity of coefficient vectors in the EAM in the parameter calculation process. The inner approximated flexibility model of ADNs is further incorporated into the security-constrained unit commitment problem as an application. Numerical simulations verify the effectiveness of the proposed method.Comment: 10 page

Parameter inference for coalescing massive black hole binaries using deep learning

In the 2030s, a new era of gravitational-wave (GW) observations will dawn as multiple space-based GW detectors, such as the Laser Interferometer Space Antenna, Taiji and TianQin, open the millihertz window for GW astronomy. These detectors are poised to detect a multitude of GW signals emitted by different sources. It is a challenging task for GW data analysis to recover the parameters of these sources at a low computational cost. Generally, the matched filtering approach entails exploring an extensive parameter space for all resolvable sources, incurring a substantial cost owing to the generation of GW waveform templates. To alleviate the challenge, we make an attempt to perform parameter inference for coalescing massive black hole binaries (MBHBs) using deep learning. The model trained in this work has the capability to produce 50,000 posterior samples for redshifted total mass, mass ratio, coalescence time and luminosity distance of a MBHB in about twenty seconds. Our model can serve as a potent data pre-processing tool, reducing the volume of parameter space by more than four orders of magnitude for MBHB signals with a signal-to-noise ratio larger than 100. Moreover, the model exhibits robustness when handling input data that contains multiple MBHB signals.Comment: 8 pages, 4 figure

Targeted antimicrobial therapy against Streptococcus mutans establishes protective non-cariogenic oral biofilms and reduces subsequent infection.

AimDental biofilms are complex communities composed largely of harmless bacteria. Certain pathogenic species including Streptococcus mutans (S. mutans) can become predominant when host factors such as dietary sucrose intake imbalance the biofilm ecology. Current approaches to control S. mutans infection are not pathogen-specific and eliminate the entire oral community along with any protective benefits provided. Here, we tested the hypothesis that removal of S. mutans from the oral community through targeted antimicrobial therapy achieves protection against subsequent S. mutans colonization.MethodologyControlled amounts of S. mutans were mixed with S. mutans-free saliva, grown into biofilms and visualized by antibody staining and cfu quantization. Two specifically-targeted antimicrobial peptides (STAMPs) against S. mutans were tested for their ability to reduce S. mutans biofilm incorporation upon treatment of the inocula. The resulting biofilms were also evaluated for their ability to resist subsequent exogenous S. mutans colonization.ResultsS. mutans colonization was considerably reduced ( +/- 0.4 fold reduction, P=0.01) when the surface was preoccupied with saliva-derived biofilms. Furthermore, treatment with S. mutans-specific STAMPs yielded S. mutans-deficient biofilms with significant protection against further S. mutans colonization (5 minutes treatment: 38 +/- 13 fold reduction P=0.01; 16 hours treatment: 96 +/- 28 fold reduction P=0.07).ConclusionS. mutans infection is reduced by the presence of existing biofilms. Thus maintaining a healthy or "normal" biofilm through targeted antimicrobial therapy (such as the STAMPs) could represent an effective strategy for the treatment and prevention of S. mutans colonization in the oral cavity and caries progression