The evaluations of incremental clustering results by the three methods with different numbers of new points.

<p>(a)Precision of incremental clustering results. (b) Recall of incremental clustering results. (c) F1-measure of incremental clustering results.</p

The relationship between a point and a boundary vector.

<p><i>b</i> is a boundary point and its boundary vector is marked with a red arrow. <i>b</i>_<i>end</i> represents the end of <i>b</i>’s boundary vector. <i>P</i>_<i>0</i> is a point in the neighbourhood of <i>b</i> and it becomes a new boundary point. <i>P</i>_<i>1</i> is a point outside of the boundary profile since it is closer to <i>b</i> than to <i>b</i>_<i>end</i>. <i>P</i>_<i>2</i> is a point inside of the boundary profile since it is closer to <i>b</i>_<i>end</i> than <i>b</i>.</p

The performance of BPIC method against different size of bucket.

<p>The performance of BPIC method against different size of bucket.</p

The execution time of the three methods against different numbers of new data points.

<p>The execution time of the three methods against different numbers of new data points.</p

The density distribution of a core point <i>c</i> and a boundary point <i>b</i>.

<p>(a) Point b and c are boundary and core point respectively. (b) The boundary vector of core point c within its neighbourhood. (c) The boundary vector of boundary point b within its neighbourhood.</p

The evolution process of the BPIC results, in which the bucket size is 3500.

<p>(a) The initial boundary clustering results on 3000 data points from the Chameleon DS3 dataset. (b) The first updated clustering results after 3500 data points are added. (c) The second updated clustering results after 7000 data points are added.</p

The number of data points maintained in memory by each method with different numbers of new data points.

<p>The number of data points maintained in memory by each method with different numbers of new data points.</p

Evaluation of the two methods’ boundary detection results.

<p>Evaluation of the two methods’ boundary detection results.</p

The discriminant tree of a data point.

<p>The discriminant tree of a data point.</p

Enhanced Precision of Nanoparticle Phototargeting in Vivo at a Safe Irradiance

A large proportion of the payload delivered by nanoparticulate therapies is deposited not in the desired target destination but in off-target locations such as the liver and spleen. Here, we demonstrate that phototargeting can improve the specific targeting of nanoparticles to tumors. The combination of efficient triplet–triplet annihilation upconversion (TTA-UC) and Förster resonance energy transfer (FRET) processes allowed in vivo phototargeting at a safe irradiance (200 mW/cm²) over a short period (5 min) using green light