Noise equivalent count measurements in a neuro-PET scanner with retractable septa

The noise-equivalent count-rate (NEC) performance of a neuro-positron emission tomography (PET) scanner has been determined with and without interplane septa on uniform cylindrical phantoms of differing radii and in human studies to assess the optimum count rate conditions that realize the maximum gain. In the brain, the effective gain in NEC performance for three-dimensions (3-D) ranges from >5 at low count rates to approximately 3.3 at 200 kcps (equivalent to 37 kcps in 2-D). The gains of the 3-D method assessed by this analysis are significant, and are shown to be highly dependent on count rate and object dimensions.     

A Sequential Design Approach for Switching ℌ∞ LPV Control

This paper proposes a sequential design scheme for switching ℌ_∞ LPV (Linear Parameter-Varying) control, aiming to reduce the computational complexity of the associated optimization problem. Different from the traditional approach that simultaneously designs switching LPV controllers and solves a high-dimensional optimization problem, the proposed sequential design approach renders a bundle of low-dimensional optimization problems to be solved iteratively. Individual ℌ_∞ LPV controller for each subregion is synthesized by independent PLMIs (Parametric Linear Matrix Inequalities) to guarantee ℌ_∞ performance, and controller variables are interpolated on the overlapped subregions such that the ℌ_∞ performance is also guaranteed on the overlapped subregion. Numerical examples are used to demonstrate the effectiveness of this method to reduce the computational load in each design iteration and improved ℌ_∞ performance over the conventional simultaneous design method with well-tuned interpolation coefficient.

     

Influence of a high‐head dam as a dispersal barrier to fish community structure of the Upper Mississippi River

In river systems, high‐head dams may increase the distance‐decay of fish community similarity by creating nearly impermeable dispersal barriers to certain species from upstream reaches. Substantial evidence suggests that migratory species are impacted by dams, and most previous studies in stream/river networks have focused on small streams and headwaters. Here, we assess whether a high‐head dam (Lock and Dam 19; LD 19) on a large river, the Upper Mississippi River (UMR), substantially alters fish community structure relative to variability expected to occur independent of the dam's effect as a fish dispersal barrier. Using fish catch per unit effort data, we modelled the distance‐decay function for the UMR fish community and then estimated the similarity that would be expected to occur across LD19 and compared it with measured similarity. Measured similarity in the fish community above and below LD19 was close to the expected value based on the distance‐decay function, suggesting LD19 does not create an abrupt transition in the fish community. Although some migratory fish species no longer occur above LD19 (e.g., skipjack herring, Alosa chrysochloris), these species do not occur in high abundance below the dam and so do not drive variation in fish community structure. Instead, much of the variation in species structure is driven by the loss/gain of species across the latitudinal gradient. Lock and Dam 19 does not appear to be a clear transition point in the river's fish community, although it may function as a meaningful barrier for particular species (e.g., invasive species) and warrant future attention from a management perspective.     

Antioxidant Activity of Osage Orange Extract in Soybean Oil and Fish Oil during Storage

The food industry is seeking natural antioxidants for edible oils that have comparable activity to synthetic counterparts. In this study, Osage orange extract (OOE) rich in osajin (42.9%) and pomiferin (30.0%) was obtained after hexane extraction of the fruit, and its antioxidant activity was examined in stripped soybean oil (SBO) and fish oil (FO), in which antioxidants and polar compounds were removed. The antioxidant activity of OOE was compared with commercial natural antioxidants (i.e., rosemary extract and mixed tocopherols) and a synthetic antioxidant, butylated hydroxytoluene (BHT), during storage at 25 and 40 °C. The 0.1% OOE had stronger antioxidant activity than 0.1% rosemary extract and 0.1% mixed tocopherols in both oils at 25 and 40 °C. Its activity was similar to 0.02% BHT in SBO and was similar or slightly stronger than 0.02% BHT in FO. When OOE was studied at 0.05, 0.1, and 0.2%, there was a weak dose–response in SBO but a stronger dose–response in FO. Headspace volatile analysis using solid phase micro-extraction (SPME) combined with GC–MS indicated that 0.1% OOE was very effective in preventing the formation of volatile oxidation products in both oils. Although it should be further tested for safety before the actual use, this study shows that OOE can be developed as an antioxidant for edible oils.     

Evaluation of the coupled multielectrode array sensor as a real-time corrosion monitor

Coupled multielectrode array sensors made of carbon steel and stainless steels were evaluated and compared with electrochemical noise (EN) sensors. Good correlations between sensor signals and solution corrosivity were observed for all multielectrode array sensors. Some correlation between the average pit index and solution corrosivity was observed for the carbon steel EN sensors, but not for the stainless steel EN sensors. The time-average noise resistances from the stainless steel EN sensors correlate well with solution corrosivity. There were, however, large random fluctuations and drifting for all EN signals, which make the EN sensors unreliable as real-time monitors.     

SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors

Getting enough quality sleep is a key part of a healthy lifestyle. Many people are tracking their sleep through mobile and wearable technology, together with contextual information that may influence sleep quality, like exercise, diet, and stress. However, there is limited support to help people make sense of this wealth of data, i.e., to explore the relationship between sleep data and contextual data. We strive to bridge this gap between sleep-tracking and sense-making through the design of SleepExplorer, a web-based tool that helps individuals understand sleep quality through multi-dimensional sleep structure and explore correlations between sleep data and contextual information. Based on a two-week field study with 12 participants, this paper offers a rich understanding on how technology can support sense-making on personal sleep data: SleepExplorer organizes a flux of sleep data into sleep structure, guides sleep-tracking activities, highlights connections between sleep and contributing factors, and supports individuals in taking actions. We discuss challenges and opportunities to inform the work of researchers and designers creating data-driven health and well-being applications.     

Ontology-based feature mapping and verification between CAD systems

Data interoperability between computer-aided design (CAD) systems remains a major obstacle in the information integration and exchange in a collaborative engineering environment. The use of CAD data exchange standards causes the loss of design intent such as construction history, features, parameters, and constraints, whereas existing research on feature-based data exchange only focuses on class-level feature definitions and does not support instance-level verification, which causes ambiguity in data exchange. In this paper, a hybrid ontology approach is proposed to allow for the full exchange of both feature definition semantics and geometric construction data. A shared base ontology is used to convey the most fundamental elements of CAD systems for geometry and topology, which is to both maximize flexibility and minimize information loss. A three-branch hybrid CAD feature model that includes feature operation information at the boundary representation level is constructed. Instance-level feature information in the form of the base ontology is then translated to local ontologies of individual CAD systems during the rule-based mapping and verification process. A combination of the Ontology Web Language (OWL) and Semantic Web Rule Language (SWRL) is used to represent feature classes and properties and automatically classify them by a reasoner in the target system, which requires no knowledge about the source system.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

A dynamic workflow framework for mass customization using web service and autonomous agent techniques

Custom software development and maintenance is one of the key expenses associated with developing automated systems for mass customization. This paper presents a method for reducing the risk associated with this expense by developing a flexible environment for determining and executing dynamic workflow paths. Strategies for developing an autonomous agent-based framework and for identifying and creating web services for specific process tasks are presented. The proposed methods are outlined in two different case studies to illustrate the approach for both a generic process with complex workflow paths and a more specific sequential engineering process.     

ICCD: Interactive Continuous Collision Detection between Deformable Models Using Connectivity-Based Culling

We present an interactive algorithm for continuous collision detection between deformable models. We introduce multiple techniques to improve the culling efficiency and the overall performance of continuous collision detection. First, we present a novel formulation for continuous normal cones and use these normal cones to efficiently cull large regions of the mesh as part of self-collision tests. Second, we introduce the concept of “procedural representative triangles” to remove all redundant elementary tests between nonadjacent triangles. Finally, we exploit the mesh connectivity and introduce the concept of “orphan sets” to eliminate redundant elementary tests between adjacent triangle primitives. In practice, we can reduce the number of elementary tests by two orders of magnitude. These culling techniques have been combined with bounding volume hierarchies and can result in one order of magnitude performance improvement as compared to prior collision detection algorithms for deformable models. We highlight the performance of our algorithm on several benchmarks, including cloth simulations, N-body simulations, and breaking objects.