Low Voltage Electrowetting-on-Dielectric Platform using Multi-Layer Insulators

A low voltage, two-level-metal, and multi-layer insulator electrowetting-on-dielectric (EWD) platform is presented. Dispensing 300pl droplets from 140nl closed on-chip reservoirs was accomplished with as little as 11.4V solely through EWD forces, and the actuation threshold voltage was 7.2V with a 1Hz voltage switching rate between electrodes. EWD devices were fabricated with a multilayer insulator consisting of 135nm sputtered tantalum pentoxide (Ta(2)O(5)) and 180nm parylene C coated with 70nm of CYTOP. Furthermore, the minimum actuation threshold voltage followed a previously published scaling model for the threshold voltage, V(T), which is proportional to (t/ε(r))(1/2), where t and ε(r) are the insulator thickness and dielectric constant respectively. Device threshold voltages are compared for several insulator thicknesses (200nm, 500nm, and 1μm), different dielectric materials (parylene C and tantalum pentoxide), and homogeneous versus heterogeneous compositions. Additionally, we used a two-level-metal fabrication process, which enables the fabrication of smaller and denser electrodes with high interconnect routing flexibility. We also have achieved low dispensing and actuation voltages for scaled devices with 30pl droplets.     

When Deceitful Chats Look Truthful

The amount of deception taking place via electronic text-based communication is increasing. Research has sought to automatically detect deception by analyzing the text from the communicator. However, the deceptive intent of the communication partner is being ignored. We compare the text from subjects who are trying to deceive each other, subjects trying to deceive truth tellers, subjects telling the truth to truth tellers, and subjects telling the truth to deceivers. We hypothesize that despite the intent of the partner, deceitful text will cluster closest to deceitful text. We cluster each of the four conditions using the text content. The cluster algorithm placed subjects trying to deceive each other closest to subjects telling the truth to each other. In this analysis, the language that led subjects to choose the same outcomes had a stronger effect than the language tied to being deceitful or truthful.     

A comparative study of iterative and non-iterative feature selection techniques for software defect prediction

Two important problems which can affect the performance of classification models are high-dimensionality (an overabundance of independent features in the dataset) and imbalanced data (a skewed class distribution which creates at least one class with many fewer instances than other classes). To resolve these problems concurrently, we propose an iterative feature selection approach, which repeated applies data sampling (in order to address class imbalance) followed by feature selection (in order to address high-dimensionality), and finally we perform an aggregation step which combines the ranked feature lists from the separate iterations of sampling. This approach is designed to find a ranked feature list which is particularly effective on the more balanced dataset resulting from sampling while minimizing the risk of losing data through the sampling step and missing important features. To demonstrate this technique, we employ 18 different feature selection algorithms and Random Undersampling with two post-sampling class distributions. We also investigate the use of sampling and feature selection without the iterative step (e.g., using the ranked list from a single iteration, rather than combining the lists from multiple iterations), and compare these results from the version which uses iteration. Our study is carried out using three groups of datasets with different levels of class balance, all of which were collected from a real-world software system. All of our experiments use four different learners and one feature subset size. We find that our proposed iterative feature selection approach outperforms the non-iterative approach.     

Connection and coordination: the interplay between architecture and dynamics in evolved model pattern generators

We undertake a systematic study of the role of neural architecture in shaping the dynamics of evolved model pattern generators for a walking task. First, we consider the minimum number of connections necessary to achieve high performance on this task. Next, we identify architectural motifs associated with high fitness. We then examine how high-fitness architectures differ in their ability to evolve. Finally, we demonstrate the existence of distinct parameter subgroups in some architectures and show that these subgroups are characterized by differences in neuron excitabilities and connection signs.     

FABRICATION AND EVALUATION OF SUPERPLASTICALLY FORMED/WELD BRAZED CORRUGATED COMPRESSION PANELS WITH BEADED WEBS

A study was made to investigate the feasibility of superplastically forming corrugated panels with beaded webs and to demonstrate the structural integrity of these panels by testing. The test panels in the study consisted of superplastically formed titanium alloy Ti-6Al-4V half-hat elements that were joined by weld-brazing to titanium alloy Ti-6Al-4V caps to form either single-corrugation compression panels or multiple-corrugation compression panels. Stretching and subsequent thinning of the titanium sheet during superplastic forming was reduced by approximately 35 percent with a shallow half-hat die concept instead of a deep die concept and resulted in a more uniform thickness across the beaded webs. The completed panels were tested in end compression at room temperature and the results compared with analysis. The heavily loaded panels failed at loads approaching the yield strength of the titanium material. At maximum load, the caps wrinkled locally accompanied with separation of the weld-braze joint in the wrinkle. None of the panels tested, however, failed catastrophically in the weld-braze joint. Experimental test results were in good agreement with structural analysis of the panels.     

Channeling knowledge: Expert Systems as communications media

Expert Systems (ES) are as yet imperfectly defined. Their two consistently cited characteristics are domain knowledge and expert-level performance. We propose that current structural definitions are inadequate and suggest a view of ES as communication channels. We proceed to explore the factors influencing applicability of ES technology to an enterprise and the impacts that could be expected. A consequence of this view is the idea of incremental information loss on the path from the expert to the ES user. Strategies for minimizing this loss derive naturally from our perspective and are evident in successful ES.     

From ethnographic record to system design

This paper explores the issues involved in moving from ethnographic explorations of work in context to a practical contribution to system design. It does so using the example of an interdisciplinary research project involving sociologists and computer scientists in the domain of air traffic control systems. It forms a pair with another paper (Sommerville et al., 1992) exploring these questions from the perspective of our computer science partners. We characterise ethnography as a research method, and consider the differences between undertaking it for strictly sociological or anthropological purposes by contrast with interdisciplinary and design purposes. We summarise some of our results in ethnographic explications of the work of air traffic controllers, and the sociality which it manifests. We describe the dialogues involved in rendering these observations informative for systems design, and the mutual translations implied in attempting to reconcile sociological with software engineering questions about supporting the work. We conclude by specifying some features of cooperative work which an engineering approach is in danger of overlooking; the ways, and limits, in which ethnographers can form a bridge between users and designers; and some of the conflicts of interest entrained in generating technical change.The research is funded by the UK MRC/SERC/ESRC Cognitive Science/HCI Initiative with the title Social Analysis of Control Systems for HCI Design, grant number SPG 8931598. The computer science partners are Professor Ian Sommerville, Dr Tom Rodden, Dr Pete Sawyer and Mr Richard Bentley. Thanks are due to Val King, Kjeldt Schmidt, Ian Sommerville and Tom Rodden for very helpful comments on earlier drafts of this paper.     

Mass spectrometric immunoassay and MRM as targeted MS-based quantitative approaches in biomarker development: Potential applications to cardiovascular disease and diabetes

Type 2 diabetes mellitus (T2DM) is an important risk factor for cardiovascular disease (CVD)—the leading cause of death in the United States. Yet not all subjects with T2DM are at equal risk for CVD complications; the challenge lies in identifying those at greatest risk. Therapies directed toward treating conventional risk factors have failed to significantly reduce this residual risk in T2DM patients. Thus newer targets and markers are needed for the development and testing of novel therapies. Herein we review two complementary MS-based approaches—mass spectrometric immunoassay (MSIA) and MS/MS as MRM—for the analysis of plasma proteins and PTMs of relevance to T2DM and CVD. Together, these complementary approaches allow for high-throughput monitoring of many PTMs and the absolute quantification of proteins near the low picomolar range. In this review article, we discuss the clinical relevance of the high density lipoprotein (HDL) proteome and Apolipoprotein A-I PTMs to T2DM and CVD as well as provide illustrative MSIA and MRM data on HDL proteins from T2DM patients to provide examples of how these MS approaches can be applied to gain new insight regarding cardiovascular risk factors. Also discussed are the reproducibility, interpretation, and limitations of each technique with an emphasis on their capacities to facilitate the translation of new biomarkers into clinical practice.     

Sensory Imbalance as Mechanism of Orientation Disruption in the Leafminer <Emphasis Type="Italic">Phyllocnistis citrella</Emphasis>: Elucidation by Multivariate Geometric Designs and Response Surface Models

Experimental designs developed to address mixtures are suited ideally to many areas of experimental biology, including pheromone blend studies, because such designs address the confounding of proportionality and concentration intrinsic to factorial and one-factor-at-a-time designs. Geometric multivariate designs coupled with response surface modeling allowed us to identify optimal blends of a two-component pheromone for attraction and trap disruption of the leafminer moth, Phyllocnistis citrella, a major pest in citrus growing areas around the world. Field trials confirmed that the natural 3:1 blend of (Z,Z,E)-7,11,13-hexadecatrienal:(Z,Z)-7,11-hexadecadienal was most effective as an attractant for male moths. However, the response surface generated in mating orientation trials revealed that the triene component alone was more effective than the natural blend in disrupting trap catch. Each individual component was effective at disrupting orientation in field trials, but (Z,Z,E)-7,11,13-hexadecatrienal was approximately 13 times more effective, at the same concentration, compared with (Z,Z)-7,11-hexadecadienal alone. In addition, the application of geometric design and response surface modeling to field studies provided insight into a possible mechanism of mating disruption and supported sensory imbalance as the operating mechanism for this species.