Selective toxicity of hydroxyl-rich carbon nanodots for cancer research

Nano Research - The toxicity of nanoparticles in a biological system is an integration of effects arising from surface functionality, particle size, ionic dissolution, etc. This complexity suggests...     

Direct Visible Surface Interpolation

The computation of visible surfaces is usually formulated in a regularization framework based on thin-plate and membrane splines. When discretized, this formulation leads to large sparse linear systems. Most surface interpolation methods solve these sparse systems with iterative methods. Here we explore the use of direct methods. Through a careful analysis of the regularization operator, we derive direct methods that efficiently make use of all zeros in the sparse discretization of the operator. Experimental results show that, compared with iterative interpolation methods, the direct methods we present are competitive in general, and they provide significant speed-ups for problems involving discontinuities. In addition to their use in visible-surface interpolation, the presented methods also support very efficient time integration for deformable surfaces.     

Parallel algorithms for image enhancement and segmentation by region growing, with an experimental study

This paper presents efficient and portable implementations of a powerful image enhancement process, the Symmetric Neighborhood Filter (SNF), and an image segmentation technique that makes use of the SNF and a variant of the conventional connected components algorithm which we call -Connected Components. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient connected components algorithm based on a novel approach for parallel merging. The algorithms have been coded in Split-C and run on a variety of platforms, including the Thinking Machines CM-5, IBM SP-1 and SP-2, Cray Research T3D, Meiko Scientific CS-2, Intel Paragon, and workstation clusters. Our experimental results are consistent with the theoretical analysis (and provide the best known execution times for segmentation, even when compared with machine-specific implementations). Our test data include difficult images from the Landsat Thematic Mapper (TM) satellite data.Also affiliated with the Department of Electrical Engineering.Also affiliated with the Department of Computer Science and the Center for Automation Research.     

Integratable non-clogging microconcentrator based on counter-flow principle for continuous enrichment of CaSki cells sample

This study addresses the need to reduce the risk of clogging when preparing samples for cell concentration, i.e., the CaSki Cell-lines (epidermoid cervical carcinoma cells). Aiming to develop a non-clogging microconcentrator, we proposed a new counter-flow concentration unit characterized by the directions of penetrating flows being at an obtuse angle to the main flow, due to employment of streamlined turbine blade-like micropillars. Based on the optimization results of the counter-flow unit profile, a fractal arrangement for the counter-flow concentration unit was developed. A counter-flow microconcentrator chip was then designed and fabricated, with both the processing layer and collecting layer arranged in terms of the honeycomb structure. Visualized experiments using CaSki cell samples on the microconcentrator chip demonstrated that no cell-clogging phenomena occurred during the test and that no cells were found in the final filtrate. The test results show an excellent concentration performance for the microconcentrator chip, while a concentrating ratio of >4 with the flow rate being below 1.0 ml/min. As only geometrical structure is employed in the passive device, the counter-flow microconcentrator can be easily integrated into advanced microfluidic systems. Owing to the merit of non-clogging and continuous processing ability, the counter-flow microconcentrator is not only suitable for the sample preparation within biomedical field, but also applicable in water-particle separation.     

Surrogate modeling approximation using a mixture of experts based on EM joint estimation

An automatic method to combine several local surrogate models is presented. This method is intended to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems. It strongly relies on the Expectation−Maximization (EM) algorithm for Gaussian mixture models (GMM). To the end of regression, the inputs are clustered together with their output values by means of parameter estimation of the joint distribution. A local expert is then built (linear, quadratic, artificial neural network, moving least squares) on each cluster. Lastly, the local experts are combined using the Gaussian mixture model parameters found by the EM algorithm to obtain a global model. This method is tested over both mathematical test cases and an engineering optimization problem from aeronautics and is found to improve the accuracy of the approximation.     

Development of a framework for truss-braced wing conceptual MDO

The paper describes the development of a multidisciplinary design optimization framework for conceptual design of truss-braced wing configurations. This unconventional configuration requires specialized analysis tools supported by a modular and flexible framework to accommodate different configurations. While the previous framework developed at Virginia Tech was a monolithic Fortran-77 code, the need for more flexibility for complex truss-braced wing configurations was addressed by the development of this new framework, which is based on Phoenix Integration ModelCenter^TM environment. The framework uses updated structural and aerodynamic design modules that enable a more general geometry definition. The new framework, thus, provides a foundation for future design concepts, especially multi-member truss-braced wing configurations. The fuel saving potential of these truss-braced wing configurations is presented by comparing different truss designs with gradually increased level of complexity.     

Scheduling jobs with equal processing times subject to machine eligibility constraints

We consider the problem of nonpreemptively scheduling a set of n jobs with equal processing times on m parallel machines so as to minimize the makespan. Each job has a prespecified set of machines on which it can be processed, called its eligible set. We consider the most general case of machine eligibility constraints as well as special cases of nested and inclusive eligible sets. Both online and offline models are considered. For offline problems we develop optimal algorithms that run in polynomial time, while for online problems we focus on the development of optimal algorithms of a new and more elaborate structure as well as approximation algorithms with good competitive ratios.     

A knowledge-based analysis of global function computation

Consider a distributed system N in which each agent has an input value and each communication link has a weight. Given a global function, that is, a function f whose value depends on the whole network, the goal is for every agent to eventually compute the value f (N). We call this problem global function computation. Various solutions for instances of this problem, such as Boolean function computation, leader election, (minimum) spanning tree construction, and network determination, have been proposed, each under particular assumptions about what processors know about the system and how this knowledge can be acquired. We give a necessary and sufficient condition for the problem to be solvable that generalizes a number of well-known results (Attyia et al. in J ACM 35(4):845–875, 1988; Yamashita and Kameda in IEEE Trans Parallel Distrib Syst 7(1):69–89, 1996; Yamashita and Kameda in IEEE Trans Parallel Distrib Syst 10(9):878–887, 1999). We then provide a knowledge-based (kb) program (like those of Fagin et al. (Reasoning about knowledge, MIT Press, Cambridge, 1995, Distrib Comput 10(4):199–225, 1997)) that solves global function computation whenever possible. Finally, we improve the message overhead inherent in our initial kb program by giving a counterfactual belief-based program (Halpern and Moses in Distrib Comput 17(2):91–106, 2004) that also solves the global function computation whenever possible, but where agents send messages only when they believe it is necessary to do so. The latter program is shown to be implemented by a number of well-known algorithms for solving leader election.     

Extracting a largest redundancy-free XML storage structure from an acyclic hypergraph in polynomial time

Given a hypergraph and a set of embedded functional dependencies, we investigate the problem of determining the conditions under which we can efficiently generate redundancy-free XML storage structures with as few scheme trees as possible. Redundancy-free XML structures guarantee both economy in storage space and the absence of update anomalies, and having the least number of scheme trees requires the fewest number of joins to navigate among the data elements. We know that the general problem is intractable. The problem may still be intractable even when the hypergraph is acyclic and each hyperedge is in Boyce–Codd normal form (BCNF). As we show here, however, given an acyclic hypergraph with each hyperedge in BCNF, a polynomial-time algorithm exists that generates a largest possible redundancy-free XML storage structure. Successively generating largest possible scheme trees from among hyperedges not already included in generated scheme trees constitutes a reasonable heuristic for finding the fewest possible scheme trees. For many practical cases, this heuristic finds the set of redundancy-free XML storage structures with the fewest number of scheme trees. In addition to a correctness proof and a complexity analysis showing that the algorithm is polynomial, we also give experimental results over randomly generated but appropriately constrained hypergraphs showing empirically that the algorithm is indeed polynomial.