An octree-based,cartesian navier–stokes solver for modern cluster architectures

The Journal of Supercomputing - Adaptive Cartesian mesh approaches have proven useful for multi-scale applications where particular features can be finely resolved within a large solution domain....     

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Journal of Intelligent Manufacturing - In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size,...     

Photoacoustic‐Guided Surgery with Indocyanine Green‐Coated Superparamagnetic Iron Oxide Nanoparticle Clusters

A common cause of local tumor recurrence in brain tumor surgery results from incomplete surgical resection. Adjunctive technologies meant to facilitate gross total resection have had limited efficacy to date. Contrast agents used to delineate tumors preoperatively cannot be easily or accurately used in the real‐time operative setting. Although multimodal imaging contrast agents are developed to help the surgeon discern tumor from normal tissue in the operating room, these contrast agents are not readily translatable. This study has developed a novel contrast agent comprised solely of two Food and Drug Administration approved components, indocyanine green (ICG) and superparamagnetic iron oxide (SPIO) nanoparticles—with no additional amphiphiles or carrier materials, to enable preoperative detection by magnetic resonance (MR) imaging and intraoperative photoacoustic (PA) imaging. The encapsulation efficiency of both ICG and SPIO within the formulated clusters is ≈100%, and the total ICG payload is 20–30% of the total weight (ICG + SPIO). The ICG–SPIO clusters are stable in physiologic conditions; can be taken up within tumors by enhanced permeability and retention; and are detectable by MR. In a preclinical surgical resection model in mice, following injection of ICG–SPIO clusters, animals undergoing PA‐guided surgery demonstrate increased progression‐free survival compared to animals undergoing microscopic surgery.     

Linear Least-Squares Algorithms for Temporal Difference Learning

We introduce two new temporal difference (TD) algorithms based on the theory of linear least-squares function approximation. We define an algorithm we call Least-Squares TD (LS TD) for which we prove probability-one convergence when it is used with a function approximator linear in the adjustable parameters. We then define a recursive version of this algorithm, Recursive Least-Square TD (RLS TD). Although these new TD algorithms require more computation per time-step than do Suttons TD() algorithms, they are more efficient in a statistical sense because they extract more information from training experiences. We describe a simulation experiment showing the substantial improvement in learning rate achieved by RLS TD in an example Markov prediction problem. To quantify this improvement, we introduce the TD error variance of a Markov chain, TD, and experimentally conclude that the convergence rate of a TD algorithm depends linearly on TD. In addition to converging more rapidly, LS TD and RLS TD do not have control parameters, such as a learning rate parameter, thus eliminating the possibility of achieving poor performance by an unlucky choice of parameters.     

Analysis of sperm concentration and motility in a microfluidic device

A home-use device that allows rapid and quantitative sperm quality analysis is desirable but not yet fully realized. To aid this effort, this article presents a microfluidic device capable of quantifying sperm quality in terms of two critical fertility-related parameters—motile sperm concentration and motility. The microdevice produces flow field for sperms to swim against, and sperms that overcame the flow within a specified time are propelled along in a separate channel and counted via resistive pulse technique. Data are compared to two control methods clinically utilized for sperm quality exam—hemocytometer and the sperm quality analyzer. Results reveal the numbers of pulses generated by passage of sperms correlates strongly with the two control methods: pulse number from 0 to 335 corresponds to progressively motile sperm concentrations from 0 to 19 × 10⁶/ml (hemocytometer) and Sperm Motility Index from 0 to 204 (sperm quality analyzer). The microdevice should be applicable to facilitate self-assessment of sperm quality at home.     

Greedy algorithms for generalized k-rankings of paths

A k-ranking of a graph is a labeling of the vertices with positive integers 1,2,…,k so that every path connecting two vertices with the same label contains a vertex of larger label. An optimal ranking is one in which k is minimized. Let Pn be a path with n vertices. A greedy algorithm can be used to successively label each vertex with the smallest possible label that preserves the ranking property. We seek to show that when a greedy algorithm is used to label the vertices successively from left to right, we obtain an optimal ranking. A greedy algorithm of this type was given by Bodlaender et al. in 1998 [1] which generates an optimal k-ranking of Pn. In this paper we investigate two generalizations of rankings. We first consider bounded (k,s)-rankings in which the number of times a label can be used is bounded by a predetermined integer s. We then consider kt-rankings where any path connecting two vertices with the same label contains t vertices with larger labels. We show for both generalizations that when G is a path, the analogous greedy algorithms generate optimal k-rankings. We then proceed to quantify the minimum number of labels that can be used in these rankings. We define the bounded rank number to be the smallest number of labels that can be used in a (k,s)-ranking and show for n?2, where i=⌊log₂(s)⌋+1. We define the kt-rank number, to be the smallest number of labels that can be used in a kt-ranking. We present a recursive formula that gives the kt-rank numbers for paths, showing for all an−1<j?an where {an} is defined as follows: a₁=1 and an=⌊((t+1)/t)an−1⌋+1.