Growth of sculptured polymer submicronwire assemblies by vapor deposition

We have grown helical nanowire assemblies of parylene C, thereby demonstrating that polymeric sculptured thin films (STFs) can be fabricated by a combination of physical and chemical vapor deposition processes. The deposition method is explained in detail and electron micrographs of 200-400 nm size sculptured thin film of parylene are given. The shapes of the submicron and nanowire assemblies can be engineered so that the polymeric STF acts as a template for preferential attachment of biomolecules.     

Hydrogen and Oxygen Bubble Attachment to a Bitumen Drop

Air bubble – bitumen attachment is a critical step in the flotation of bitumen from mined oil sand. In this study, single bubble – bitumen drop attachment was observed directly using a novel experimental technique. Induction time is determined and used as an indication of bubble‐bitumen attachment potency for both hydrogen and oxygen bubbles. The attachment tests were conducted in deaerated municipal water (City of Edmonton tap water) at temperatures ranging from 22–50°C. Induction times measured for hydrogen bubble attachment were shorter than those for oxygen bubbles. Coalescence tests were also conducted in the absence of bitumen, and showed that hydrogen bubbles coalesced more rapidly than oxygen bubbles in both deaerated municipal water and clear (solids‐free) process water.     

Implications of variable fluid resistance caused by start-up flow in microfluidic networks

Electrical circuit analogies are often used to design microfluidic systems because they simplify device design, providing simple relationships between fluid flow rate, driving forces, and channel dimensions. However, such approximations often significantly overestimate flow rates in situations where start-up energy losses from establishing kinetic head are similar in magnitude to the energy required to overcome viscous shear stresses, as is often the case within complex microfluidic networks. These reduced flows can be more accurately predicted within an electrical analogy framework that accounts for the nonlinear flow resistance generated on the transient regime of start-up flow. In this work, standard flow resistance expressions are modified to account for such effects, and the onset of nonlinear resistance is predicted by a dimensionless parameter, $\xi = Re\frac{D}{L},$ which is dependent on the Reynolds number and the channel length. As a demonstration, variable fluid resistance is shown to dramatically affect the flow performance of common microfluidic units such as T-junctions and serpentine channels, and the change in performance is accurately predicted. Experimental and theoretical analysis of T-junctions further shows that variable flow resistance causes the ratio of flows through the junction to converge toward unity with respect to an increasing total flow rate. In addition, serpentine channels are shown to exaggerate these start-up effects, owing to compounded energetic demand associated with changing a flow’s direction. As a result, serpentine channels cause the ratio of flow rates exiting a T-junction to diverge from unity with respect to an increasing flow rate.     

An optimality criteria based method for discrete design optimization taking into account buildability constraints

This paper presents a heuristic design optimization method specifically developed for practicing structural engineers. Practical design optimization problems are often governed by buildability constraints. The majority of optimization methods that have recently been proposed for design optimization under buildability constraints are based on evolutionary computing. While these methods are generally easy to implement, they require a large number of function evaluations (finite element analyses), and they involve algorithmic parameters that require careful tuning. As a consequence, both the computation time and the engineering time are high. The discrete design optimization algorithm presented in this paper is based on the optimality criteria method for continuous optimization. It is faster than an evolutionary algorithm and it is free of tuning parameters. The algorithm is successfully applied to two classical benchmark problems (the design of a ten-bar truss and an eight-story frame) and to a practical truss design optimization problem.     

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.     

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.