GRAIL: a scalable index for reachability queries in very large graphs

Given a large directed graph, rapidly answering reachability queries between source and target nodes is an important problem. Existing methods for reachability tradeoff indexing time and space versus query time performance. However, the biggest limitation of existing methods is that they do not scale to very large real-world graphs. We present a simple yet scalable reachability index, called GRAIL, that is based on the idea of randomized interval labeling and that can effectively handle very large graphs. Based on an extensive set of experiments, we show that while more sophisticated methods work better on small graphs, GRAIL is the only index that can scale to millions of nodes and edges. GRAIL has linear indexing time and space, and the query time ranges from constant time to being linear in the graph order and size. Our reference C++ implementations are open source and available for download at http://www.code.google.com/p/grail/.     

Harmonic waves in a prestressed thin elastic tube filled with a viscous fluid

In this work a theoretical analysis is presented for wave propagation ina thin-walled prestressed elastic tube filled with a viscous fluid. Thefluid is assumed to be incompressible and Newtonian, whereas the tubematerial is considered to be incompressible, isotropic and elastic.Considering the physiological conditions that the arteries experience, sucha tube is initially subjected to a mean pressure P_i and anaxial stretch _z. If it is assumed that in the course ofblood flow small incremental disturbances are superimposed on this initialfield, then the governing equations of this incremental motion are obtainedfor the fluid and the elastic tube. A harmonic-wave type of solution issought for these field equations and the dispersion relation is obtained.Some special cases, as well as the general case, are discussed and thepresent formulation is compared with some previous works on the samesubject.     

Coreless Fiber‐Based Whispering‐Gallery‐Mode Assisted Lasing from Colloidal Quantum Well Solids

Whispering gallery mode (WGM) resonators are shown to hold great promise to achieve high‐performance lasing using colloidal semiconductor nanocrystals (NCs) in solution phase. However, the low packing density of such colloidal gain media in the solution phase results in increased lasing thresholds and poor lasing stability in these WGM lasers. To address these issues, here optical gain in colloidal quantum wells (CQWs) is proposed and shown in the form of high‐density close‐packed solid films constructed around a coreless fiber incorporating the resulting whispering gallery modes to induce gain and waveguiding modes of the fiber to funnel and collect light. In this work, a practical method is presented to produce the first CQW‐WGM laser using an optical fiber as the WGM cavity platform operating at low thresholds of ≈188 µJ cm^?2 and ≈1.39 mJ cm^?2 under one‐ and two‐photon absorption pumped, respectively, accompanied with a record low waveguide loss coefficient of ≈7 cm^?1 and a high net modal gain coefficient of ≈485 cm^?1. The spectral characteristics of the proposed CQW‐WGM resonator are supported with a numerical model of full electromagnetic solution. This unique CQW‐WGM cavity architecture offers new opportunities to achieve simple high‐performance optical resonators for colloidal lasers.     

Effect of DEM Resolution,Source, Resampling Technique and Area Threshold on SWAT Outputs

Application of an inappropriate Digital Elevation Model (DEM) might lead to uncertainty in modelling of the hydrological cycle. The novelty of this work is the development of a comprehensive framework to evaluate the effect of DEM resolution (12 to 500 m), source (TanDEM-X, SRTM, AW3D30 and ASTER GDEM2), resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority) and area threshold (1000 to 50,000 ha) on Soil and Water Assessment Tool (SWAT) outputs based on five criteria: (1) river network extraction, (2) streamflow simulation, (3) topography, slope and basin characteristics, (4) hydrological and (5) water quality simulations. Kelantan River Basin, a tropical basin in Peninsular Malaysia was selected as study area. The major findings are summarized as follows: (1) TanDEM-X had better river network extraction capability than ASTER GDEM2, (2) better monthly streamflow simulations were obtained between 20 m and 60 m DEM resolutions, with the smallest area threshold (1000 ha), (3) TanDEM-X and SRTM DEMs outperformed ASTER GDEM2 on monthly streamflow simulation, (4) DEM resolution, source and resampling technique were insensitive to most of the hydrological components, except the lateral flow, (5) area threshold was sensitive to SWAT-simulated surface runoff, soil water content and evapotranspiration, (6) DEM scenarios had a larger impact on sediment yield simulations compared to the total nitrogen and total phosphorus simulations. We recommend a preliminary assessment of DEM uncertainties on SWAT outputs to obtain more reliable modelling outputs.     

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.     

Identification of step response estimates utilizing continuous time subspace approach

Direct identification procedures using raw data seem to face difficulties especially when the data is corrupted with noise or the data acquisition leads to huge amount of data to be processed. This will lead to complexity in obtaining the accurate model of the system and the increase of computational load and time may also arise. In this paper, we present 2-stage identification, in which, the first stage involves a process to obtain step response estimates. A multi input multi output frequency sampling filter model is used to simulate the estimates. With the aid of finite impulse response model, maximum likelihood method and the predicted sum of square statistics, this procedure able to clean the noise that occurred at high frequency region, compressed the data into the reduced amount and obtained only meaningful parameter that describes the system. Next, at the second stage the continuous time subspace model identification is conducted using the step response estimates obtained from the first stage. Here, three continuous time subspace methods will be observed to develop a state space mathematical model; those are the MOESP, CCA and ORT methods. A Monte Carlo simulation is performed as to see the efficacy and robustness of those models in identifying the step response estimates of the observed system. Comparative analysis with respect to two-stage identification and direct identification procedure is also conducted. This is to show the significant contribution of having MIMO FSF in the overall identification procedure. From results, the developed MIMO FSF is able to compress raw MIMO data into fewer numbers, and produce cleaned and unbiased step response estimates. When it is implemented to MIMO continuous-time subspace identification, MOESP method has demonstrated good performance based on the accuracy and robustness of the developed model.     

Nonlinear waves in an elastic tube with variable prestretch filled with a fluid of variable viscosity

In the present work, by employing the reductive perturbation method to the nonlinear equations of an incompressible, prestressed, homogeneous and isotropic thin elastic tube and to the exact equations of an incompressible Newtonian fluid of variable viscosity, we have studied weakly nonlinear waves in such a medium and obtained the variable coefficient Korteweg-deVries-Burgers (KdV-B) equation as the evolution equation. For this purpose, we treated the artery as an incompressible, homogeneous and isotropic elastic material subjected to variable stretches both in the axial and circumferential directions initially, and the blood as an incompressible Newtonian fluid whose viscosity changes with the radial coordinate. By seeking a travelling wave solution to this evolution equation, we observed that the wave front is not a plane anymore, it is rather a curved surface. This is the result of the variable radius of the tube. The numerical calculations indicate that the wave speed is variable in the axial coordinate and it decreases for increasing circumferential stretch (or radius). Such a result seems to be plausible from physical considerations, like Bernoulli’s law. We further observed that, the amplitude of the Burgers shock gets smaller and smaller with increasing time parameter along the tube axis. This is again due to the variable radius, but the effect of it is quite small.     

A complementary electrochromic device with highly improved performance based on brick-like hydrated tungsten trioxide film

Uniform and well adhesive nanostructured hydrated tungsten trioxide (3WO3 x H2O) films were grown on fluorine doped tin oxide (FTO) substrate via a facile and template-free crystal-seed-assisted hydrothermal method by addition of ammonium sulfate ((NH4)2SO4) and hydrogen peroxide (H2O2). X-ray diffraction (XRD) studies indicated that the films are of orthorhombic structure. Scanning electron microscopy (SEM) and high resolution transmission electron microscopy (HRTEM) analysis showed that the film was composed of brick-like nanostructures with a preferred growing direction along (002). The influence of seed layer, (NH4)2SO4 and H2O2 on the products were also studied. The film showed good cyclic stability, comparable switching speed and coloration efficiency (30.1 cm2 C(-1)). A complementary electrochromic device based on the film and Prussian blue depicted highly improved color contrast, coloration/bleaching response (1.8 and 3.7 s respectively) and coloration efficiency (164.6 cm2 C(-1)).     

Ab initio energy calculations and macroscopic rate modeling of hydroformylation of higher alkenes by Rh‐based catalyst

Ab initio quantum chemical computations have been done to determine the energetics and reaction pathways of hydroformylation of higher alkenes using a rhodium complex homogeneous catalyst. Calculation of fragments of the potential energy surfaces of the HRh(CO)(PPh₃)₃‐catalyzed hydroformylation of 1‐decene, 1‐dodecene, and styrene were performed by the restricted Hartree‐Fock method at the second‐order MØller‐Plesset (MP2) level of perturbation theory and basis set of 6‐31++G(d,p). Geometrically optimized structures of the intermediates and transition states were identified. Three generalized rate models were developed on the basis of above reaction path analysis as well as experimental findings reported in the literature. The kinetic and equilibrium parameters of the models were estimated by nonlinear least square regression of available literature data. The model based on H₂‐oxidative addition fitted the data best; it predicts the conversion of all the alkenes quite satisfactorily with an average deviation of 7.6% and a maximum deviation of 13%. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate. The common approach to handle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling, random oversampling, or Synthetic Minority Oversampling Technique (SMOTE) algorithms. This paper compared the classification performance of three popular classifiers (Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine) in predicting machine failure in the Oil and Gas industry. The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945 (97%) ‘non-failure’ and 528 (3%) ‘failure data’. The three independent variables to predict machine failure were pressure indicator, flow indicator, and level indicator. The accuracy of the classifiers is very high and close to 100%, but the sensitivity of all classifiers using the original dataset was close to zero. The performance of the three classifiers was then evaluated for data with different imbalance rates (10% to 50%) generated from the original data using SMOTE, SMOTE-Support Vector Machine (SMOTE-SVM) and SMOTE-Edited Nearest Neighbour (SMOTE-ENN). The classifiers were evaluated based on improvement in sensitivity and F-measure. Results showed that the sensitivity of all classifiers increases as the imbalance rate increases. SVM with radial basis function (RBF) kernel has the highest sensitivity when data is balanced (50:50) using SMOTE (Sensitivity_test =0.5686, F_test= 0.6927) compared to Naïve Bayes (Sensitivity_test =0.4033, F_test= 0.6218) and Logistic Regression (Sensitivity_test =0.4194, F_test= 0.621). Overall, the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases, but the sensitivity is below 50%. The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.