High throughput blood plasma separation using a passive PMMA microfluidic device

Since plasma is rich in many biomarkers used in clinical diagnostic experiments, microscale blood plasma separation is a primitive step in most of microfluidic analytical chips. In this paper, a passive microfluidic device for on-chip blood plasma separation based on Zweifach–Fung effect and plasma skimming was designed and fabricated by hot embossing of microchannels on a PMMA substrate and thermal bonding process. Human blood was diluted in various times and injected into the device. The main novelty of the proposed microfluidic device is the design of diffuser-shaped daughter channels. Our results demonstrated that this design exerted a considerable positive influence on the separation efficiency of the passive separator device, and the separation efficiency of 66.6 % was achieved. The optimum purity efficiency of 70 % was achieved for 1:100 dilution times.     

Multi-stage genetic programming: A new strategy to nonlinear system modeling

This paper presents a new multi-stage genetic programming (MSGP) strategy for modeling nonlinear systems. The proposed strategy is based on incorporating the individual effect of predictor variables and the interactions among them to provide more accurate simulations. According to the MSGP strategy, an efficient formulation for a problem comprises different terms. In the first stage of the MSGP-based analysis, the output variable is formulated in terms of an influencing variable. Thereafter, the error between the actual and the predicted value is formulated in terms of a new variable. Finally, the interaction term is derived by formulating the difference between the actual values and the values predicted by the individually developed terms. The capabilities of MSGP are illustrated by applying it to the formulation of different complex engineering problems. The problems analyzed herein include the following: (i) simulation of pH neutralization process, (ii) prediction of surface roughness in end milling, and (iii) classification of soil liquefaction conditions. The validity of the proposed strategy is confirmed by applying the derived models to the parts of the experimental results that were not included in the analyses. Further, the external validation of the models is verified using several statistical criteria recommended by other researchers. The MSGP-based solutions are capable of effectively simulating the nonlinear behavior of the investigated systems. The results of MSGP are found to be more accurate than those of standard GP and artificial neural network-based models.     

Policy-based Awareness Management (PAM): Case study of a wireless communication system at a hospital

The present paper evaluates the use of software agents to identify relevance of information, called awareness. This evaluation is based on existing policies and scenarios in the context of wireless communication of a hospital in Norway. The study is to address the lack of literature for experimental studies on a method to employ software agents for awareness identification. Research in computer supported cooperative work indicates the significant contributions of software agents to assist individuals. There are bodies of work that show awareness provides the means for software agents in which effective cooperation can take place. In addition, the role of the methods to identify awareness is emphasized in the literature of both computer supported cooperative work and software agents. This paper explains a step-wise process, called Policy-based Awareness Management, which allows agents to use policies as a source to identify awareness and thus change their behaviors accordingly. The contribution of this method is based on the concepts proposed by the logic of general awareness. The present study applies Directory Enabled Networks-next generation as the policy structure for the method. The paper evaluates the process via its application to identify the relevance of information in wireless communication scenarios in a hospital. The present study conducts observations, interviews and discussions on the wireless communication system of the hospital to identify the different scenarios happening in the system. The paper presents a set of simulations on these scenarios and concludes that the method is effective and cost-efficient.     

A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets

Almost all subspace clustering algorithms proposed so far are designed for numeric datasets. In this paper, we present a k-means type clustering algorithm that finds clusters in data subspaces in mixed numeric and categorical datasets. In this method, we compute attributes contribution to different clusters. We propose a new cost function for a k-means type algorithm. One of the advantages of this algorithm is its complexity which is linear with respect to the number of the data points. This algorithm is also useful in describing the cluster formation in terms of attributes contribution to different clusters. The algorithm is tested on various synthetic and real datasets to show its effectiveness. The clustering results are explained by using attributes weights in the clusters. The clustering results are also compared with published results.     

Genetic Threading

The biological function of proteins is dependent, to a large extent, on their native three dimensional conformation. Thus, it is important to know the structure of as many proteins as possible. Since experimental methods for structure determination are very tedious, there is a significant effort to calculate the structure of a protein from its linear sequence. Direct methods of calculating structure from sequence are not available yet. Thus, an indirect approach to predict the conformation of protein, called threading, is discussed. In this approach, known structures are used as constraints, to restrict the search for the native conformation. Threading requires finding good alignments between a sequence and a structure, which is a major computational challenge and a practical bottleneck in applying threading procedures. The Genetic Algorithm paradigm, an efficient search method that is based on evolutionary ideas, is used to perform sequence to structure alignments. A proper representation is discussed in which genetic operators can be effectively implemented. The algorithm performance is tested for a set of six sequence/structure pairs. The effects of changing operators and parameters are explored and analyzed.     

Resource-efficient routing and scheduling of time-constrained streaming communication on networks-on-chip

Network-on-chip-based multiprocessor systems-on-chip are considered as future embedded systems platforms. One of the steps in mapping an application onto such a parallel platform involves scheduling the communication on the network-on-chip. This paper presents different scheduling strategies that minimize resource usage by exploiting all scheduling freedom offered by networks-on-chip. It also introduces a technique to take the dynamism in applications into account when scheduling the communication of an application on the network-on-chip while minimizing the resource usage. Our experiments show that resource-utilization is improved when compared to existing techniques.     

Efficient Regular Data Structures and Algorithms for Dilation, Location, and Proximity Problems

In this paper we investigate data structures obtained by a recursive partitioning of the multi- dimensional input domain into regions of equal size. One of the best known examples of such a structure is the quadtree . It is used here as a basis for more complex data structures. We also provide multidimensional versions of the stratified tree by van Emde Boas [vEB]. We show that under the assumption that the input points have limited precision (i.e., are drawn from the integer grid of size u ) these data structures yield efficient solutions to many important problems. In particular, they allow us to achieve O(log log u) time per operation for dynamic approximate nearest neighbor (under insertions and deletions) and exact on-line closest pair (under insertions only) in any constant number of dimensions. They allow O(log log u) point location in a given planar shape or in its expansion (dilation by a ball of a given radius). Finally, we provide a linear time (optimal) algorithm for computing the expansion of a shape represented by a region quadtree. This result shows that the spatial order imposed by this regular data structure is sufficient to optimize the operation of dilation by a ball. Received January 19, 1999; revised November 4, 1999.     

Efficient matching of nonrectangular shapes

Most known two-dimensional matching algorithms have a rectangular text (image) and rectangular pattern (template) as their input. These matching algorithms perform a row by row analysis followed by some column processing. These techniques are only efficient if all the rows are of equal length, hence the necessity for rectangular patterns.We present a novel method for analysing patterns with rows of different lengths. This enables finding all occurrences of a nonrectangular figure of heightm in ann×n text in time . We make use of thesmaller matching problem.The smaller matching problem is that of finding all appearances of a numerical one dimensional pattern in a numerical one-dimensional text, where every element of the pattern is no greater than the corresponding text element.Partially supported by NSF grant CCR-88-03641 and a University of Maryland full year research award.Supported by a University of Maryland Graduate Fellowship, an ACM Samuel M. Alexander Fellowship and NSF grant CCR-88-03641.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Maintaining awareness using policies; Enabling agents to identify relevance of information

The field of computer supported cooperative work aims at providing information technology models, methods, and tools that assist individuals to cooperate. The presented paper is based on three main observations from literature. First, one of the problems in utilizing information technology for cooperation is to identify the relevance of information, called awareness. Second, research in computer supported cooperative work proposes the use of agent technologies to aid individuals to maintain their awareness. Third, literature lacks the formalized methods on how software agents can identify awareness. This paper addresses the problem of awareness identification. The main contribution of this paper is to propose and evaluate a formalized structure, called Policy-based Awareness Management (PAM). PAM extends the logic of general awareness in order to identify relevance of information. PAM formalizes existing policies into Directory Enabled Networks-next generation structure and uses them as a source for awareness identification. The formalism is demonstrated by applying PAM to the space shuttle Columbia disaster occurred in 2003. The paper also argues that efficacy and cost-efficiency of the logic of general awareness will be increased by PAM. This is evaluated by simulation of hypothetical scenarios as well as a case study.