Comparison and analysis of the emissions of a small non-road spark-ignition engine operating under different alcohol–gasoline blended fuels

In this experimental study we focused our interest on comparing the effect of lower and higher molecular mass alcohol–gasoline-blended fuels on the regulated emissions emitted by a small non-road spark-ignition engine. Twenty-one test fuels were used in this experimental study that included gasoline as a reference as well as low and high molecular mass alcohol–gasoline blends containing 5%, 10%, 20% and 40% v/v. In exhaust gases that originated from alcohol gasoline test fuels, low CO/HC and high CO₂/NO_x emissions were observed as the total percentage of alcohol in the blend increased. Methanol–gasoline blends seemed to achieve good combustion efficiency, but the engine will require a catalytic converter against high NO_x emissions. Butanol–gasoline blends in several cases gave lower emissions in comparison with the ethanol and propanol–gasoline blends. Finally, the pentanol–gasoline blends showed exactly the same emission patterns as those of neat gasoline.     

Discrimination of shifts in a soil microbial community associated with TNT-contamination using a functional ANOVA of 16S rRNA hybridized to oligonucleotide microarrays

A functional ANOVA analysis of the thermal dissociation of RNA hybridized to DNA microarrays was used to improve discrimination between two soil microbial communities. Following hybridization of in vitro transcribed 16S rRNA derived from uncontaminated and 2,4,6-trinitrotoluene contaminated soils to an oligonucleotide microarray containing group- and species-specific perfect match (PM) probes and mismatch (MM) variants, thermal dissociation was used to analyze the nucleic acid bound to each PM-MM probe set. Functional ANOVA of the dissociation curves generally discriminated PM-MM probe sets when Td values (temperature at 50% probe-target dissociation) could not. Maximum discrimination for many PM and MM probes often occurred at temperatures greaterthan the Td. Comparison of signal intensities measured prior to dissociation analysis from hybridizations of the two soil samples revealed significant differences in domain-, group-, and species-specific probes. Functional ANOVA showed significantly different dissociation curves for 11 PM probes when hybridizations from the two soil samples were compared, even though initial signal intensities for 3 of the 11 did not vary.     

Computing and managing cardinal direction relations

Qualitative spatial reasoning forms an important part of the commonsense reasoning required for building intelligent geographical information systems (GIS). Previous research has come up with models to capture cardinal direction relations for typical GIS data. In this paper, we target the problem of efficiently computing the cardinal direction relations between regions that are composed of sets of polygons and present two algorithms for this task. The first of the proposed algorithms is purely qualitative and computes, in linear time, the cardinal direction relations between the input regions. The second has a quantitative aspect and computes, also in linear time, the cardinal direction relations with percentages between the input regions. Our experimental evaluation indicates that the proposed algorithms outperform existing methodologies. The algorithms have been implemented and embedded in an actual system, CARDIRECT, that allows the user to 1) specify and annotate regions of interest in an image or a map, 2) compute cardinal direction relations between them, and 3) pose queries in order to retrieve combinations of interesting regions.     

Test generation in combinational networks by decomposition

Hierarchical Systems Theory attempts to analyze complex systems by decomposition. The computational benefit of decomposition is a substantial reduction in both computer memory and processing time.

This paper investigates the application of the theory to the problem of generating tests for the detection of digital faults. The digital network is decomposed into two or more loosely coupled subnetworks. For each subnetwork, a parametric test is derived. An optimal test for the entire network is then obtained from these parametric tests by selecting appropriate values for the parameters.

A complete and near-minimal test set can be constructed by repeated use of the above procedure. In each iteration, an optimal test is selected and the faults detected by this test are excluded from further consideration. The process is then repeated on the remaining undetected faults. The algorithm terminates when all faults in the original fault set have been excluded.

It is shown that as a result of decomposition, a reduction in the required amount of memory space of as much as 50% can be realized.     

Angular dependence in infrared linear dichroism: a reevaluation of the theory

Parameters such as the viewing angle and the extinction ratio (the ratio of the transmitted light over the total light intensity that reaches the sample) are very important in the design of novel optical devices such as new types of polarizers, liquid crystal displays, etc. The initial aim of this work was the comparison of experimentally obtained results to the currently accepted theoretical model that is based on Zbinden's theory of intensity ellipsoid. Due to observed discrepancies between the above theory and experimental data, a new mathematical model was generated in order to adequately explain the experimental results. This new theory allows the calculation of light absorbance at every combination of azimuthal and tilt angles in complete agreement with the experimentally determined values. In addition, in this paper we settle, once and for all, the confusion that exists in the spectroscopic literature with regard to the dependence of absorption and transmission values vis-à-vis the angle of incidence of the incoming light. We conclusively show that it is absorptance (alpha) and not absorption or absorbance (A) which shows a cos(2) dependence on the angle formed by the electric vector and the dipole.     

Adaptive,unsupervised stream mining

Sensor devices and embedded processors are becoming widespread, especially in measurement/monitoring applications. Their limited resources (CPU, memory and/or communication bandwidth, and power) pose some interesting challenges. We need concise, expressive models to represent the important features of the data and that lend themselves to efficient estimation. In particular, under these severe constraints, we want models and estimation methods that (a) require little memory and a single pass over the data, (b) can adapt and handle arbitrary periodic components, and (c) can deal with various types of noise. We propose (Arbitrary Window Stream mOdeling Method), which allows sensors in remote or hostile environments to efficiently and effectively discover interesting patterns and trends. This can be done automatically, i.e., with no prior inspection of the data or any user intervention and expert tuning before or during data gathering. Our algorithms require limited resources and can thus be incorporated into sensors - possibly alongside a distributed query processing engine [10,6,27]. Updates are performed in constant time with respect to stream size using logarithmic space. Existing forecasting methods (SARIMA, GARCH, etc.) and traditional Fourier and wavelet analysis fall short on one or more of these requirements. To the best of our knowledge, is the first framework that combines all of the above characteristics. Experiments on real and synthetic datasets demonstrate that discovers meaningful patterns over long time periods. Thus, the patterns can also be used to make long-range forecasts, which are notoriously difficult to perform. In fact, outperforms manually set up autoregressive models, both in terms of long-term pattern detection and modeling and by at least 10 x in resource consumption.Received: 2 January 2004, Accepted: 23 March 2004, Published online: 12 August 2004Edited by: S. AbitebouAnthony Brockwell: This material is based upon work supported by the National Science Foundation under Grants Nos. DMS-9819950 and IIS-0083148.Christos Faloutsos: This material is based upon work supported by the National Science Foundation under Grants Nos. IIS-9817496, IIS-9988876, IIS-0083148, IIS-0113089, IIS-0209107, IIS-0205224, INT-0318547, SE NSOR-0329549, EF-0331657, and IIS-0326322, by the Pennsylvania Infrastructure Technology Alliance (PITA) Grant No. 22-901-0001, and by the Defense Advanced Research Projects Agency under Contract No. N66001-00-1-8936. Additional funding was provided by donations from Intel and by a gift from Northrop-Grumman Corporation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funding parties.     

Multi-model minimum variance control

A new method for simultaneously selecting the order and identifying the parameters of an ARX model and the control strategy design has been developed. The method is based on the reformulation of the problem in the standard state space form and the subsequent implementation of a bank of minimum variance controllers, each fitting a different order model. Thus, the problem is reduced to selecting the true model among a set of candidate models, using the well-known multi-model partitioning theory, for general (not necessarily Gaussian) data pdf's. Thus, the cumulative control is the average of the model-conditional minimum variance controls, weighted by the respective a posteriori probability that each particular model is the true model. Simulation experiments indicate that the proposed method is 100% successful in selecting the correct model order and that it accurately identifies the model parameters, in a sufficiently small number of iterations. Furthermore, the method is insensitive in variations of the used filters' variance, and it is adaptive, in the sense that it has the ability of successfully tracking changes in the model structure, in real time. The proposed algorithm lends itself to parallel and VLSI implementations. © 1998 John Wiley & Sons, Ltd.     

An authoring tool for democratizing the creation of high-quality VR experiences

Virtual Reality - The contribution of this paper is toward three directions, namely (a) in identifying the advantages and disadvantages of the current state-of-the-art methods that allow laymen in...     

Chemical characteristics,fatty acid composition and conjugated linoleic acid (CLA) content of traditional Greek yogurts

Many studies with conjugated linoleic acid (CLA) indicate that it has a protective effect against mammary cancer. Because dairy products are the most important dietary sources of CLA, we have investigated the CLA concentrations and additionally the fatty acid profiles and chemical composition of several commercial, traditional, Greek yogurts from different geographical origin. The fat content of yogurts was in the order of goat < cow < sheep. Cow, sheep and goat milk yogurts contain respectively 0.128–1.501, 0.405–1.250 and 0.433–0.976 g CLA/100 g fat. Low-fat milk yogurts showed lower values of c-9, t-11 CLA content on lipid basis compared to full-fat yogurts. Samples from mountain areas showed average c-9, t-11 CLA content higher than those from prairie districts. The highest amounts of saturated fatty acids (SFA) were found in low-fat yogurts, of monounsaturated fatty acids (MUFA) in sheep milk yogurts and of polyunsaturated fatty acid (PUFA) in low-fat cow milk yogurts.