Organic‐acid‐catalyzed sol–gel route for preparing poly(methyl methacrylate)–silica hybrid materials

In this study, a series of organic–inorganic hybrid sol–gel materials consisting of a poly(methyl methacrylate) (PMMA) matrix and dispersed silica (SiO₂) particles were successfully prepared through an organic‐acid‐catalyzed sol–gel route with N‐methyl‐2‐pyrrolidone as the mixing solvent. The as‐synthesized PMMA–SiO₂ nanocomposites were subsequently characterized with Fourier transform infrared spectroscopy and transmission electron microscopy. The solid phase of organic camphor sulfonic acid was employed to catalyze the hydrolysis and condensation (i.e., sol–gel reactions) of tetraethyl orthosilicate in the PMMA matrix. The formation of the hybrid membranes was beneficial for the physical properties at low SiO₂ loadings, especially for enhanced mechanical strength and gas barrier properties, in comparison with the neat PMMA. The effects of material composition on the thermal stability, thermal conductivity, mechanical strength, molecular permeability, optical clarity, and surface morphology of the as‐prepared hybrid PMMA–SiO₂ nanocomposites in the form of membranes were investigated with thermogravimetric analysis, differential scanning calorimetry, dynamic mechanical analysis, gas permeability analysis, ultraviolet–visible transmission spectroscopy, and atomic force microscopy, respectively. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Using dynamic time warping distances as features for improved time series classification

Dynamic time warping (DTW) has proven itself to be an exceptionally strong distance measure for time series. DTW in combination with one-nearest neighbor, one of the simplest machine learning methods, has been difficult to convincingly outperform on the time series classification task. In this paper, we present a simple technique for time series classification that exploits DTW’s strength on this task. But instead of directly using DTW as a distance measure to find nearest neighbors, the technique uses DTW to create new features which are then given to a standard machine learning method. We experimentally show that our technique improves over one-nearest neighbor DTW on 31 out of 47 UCR time series benchmark datasets. In addition, this method can be easily extended to be used in combination with other methods. In particular, we show that when combined with the symbolic aggregate approximation (SAX) method, it improves over it on 37 out of 47 UCR datasets. Thus the proposed method also provides a mechanism to combine distance-based methods like DTW with feature-based methods like SAX. We also show that combining the proposed classifiers through ensembles further improves the performance on time series classification.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

Randomized constraint solvers: a comparative study

The complexity of constraints is a major obstacle for constraint-based software verification. Automatic constraint solvers are fundamentally incomplete: input constraints often build on some undecidable theory or some theory the solver does not support. This paper proposes and evaluates several randomized solvers to address this issue. We compared the effectiveness of a symbolic solver (CVC3), a random solver, two heuristic search solvers, and seven hybrid solvers (i.e. mix of random, symbolic, and heuristic solvers). We evaluated the solvers on a benchmark generated with a concolic execution of 9 subjects. The performance of each solver was measured by its precision, which is the fraction of constraints that the solver can find solution out of the total number of constraints that some solver can find solution. As expected, symbolic solving subsumes the other approaches for the 4 subjects that only generate decidable constraints. For the remaining 5 subjects, which contain undecidable constraints, the hybrid solvers achieved the highest precision (fraction of constraints that a solver can find a solution out of the total number of satisfiable constraints). We also observed that the solvers were complementary, which suggests that one should alternate their use in iterations of a concolic execution driver.     

Influence of pH on particle size and sensing response of chemically synthesized chromium oxide nanoparticles to alcohols

Nanoparticles of chromium oxide have been synthesized by following a co-precipitation route at various pH values of the precursor solution. Structural and morphological analysis were carried out by using XRD and TEM techniques which revealed that the size of nanoparticles synthesized at pH 9 was smaller as compared to those synthesized at other pH values. The thick films of synthesized samples were deposited on alumina substrate and their sensing response to methanol, ethanol and isopropanol was investigated at different operating temperatures. It was observed that all the sensors gave optimum response at 250 °C. It has been observed that sample prepared at pH 9, being a collection of smallest particles as compared to other samples, exhibited high sensing response to alcohol vapour. Sensor response of all the samples tested was significantly higher towards isopropanol vapour than towards methanol or ethanol.In the present study the effect of particle size on intergranular activation energy has been studied as well. It was found that smaller particles possess high activation energy and exhibit higher sensing response as compared to that of larger particles. This type of study may help in the selection of particle suitable for gas sensing.     

Lévy flight artificial bee colony algorithm

Artificial bee colony (ABC) optimisation algorithm is a relatively simple and recent population-based probabilistic approach for global optimisation. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the ABC, there is a high chance to skip the true solution due to its large step sizes. In order to balance between diversity and convergence in the ABC, a Lévy flight inspired search strategy is proposed and integrated with ABC. The proposed strategy is named as Lévy Flight ABC (LFABC) has both the local and global search capability simultaneously and can be achieved by tuning the Lévy flight parameters and thus automatically tuning the step sizes. In the LFABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Furthermore, to improve the exploration capability, the numbers of scout bees are increased. The experiments on 20 test problems of different complexities and five real-world engineering optimisation problems show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest-guided ABC, best-so-far ABC and modified ABC in most of the experiments.     

Formal Analysis of Multiparty Contract Signing

We analyze the multiparty contract-signing protocols of Garay and MacKenzie (GM) and of Baum and Waidner (BW). We use a finite-state tool, Mocha, which allows specification of protocol properties in a branching-time temporal logic with game semantics. While our analysis does not reveal any errors in the BW protocol, in the GM protocol we discover serious problems with fairness for four signers and an oversight regarding abuse-freeness for three signers. We propose a complete revision of the GM subprotocols in order to restore fairness.     

Fast and efficient extraction algorithm for high-speed interconnects with arbitrary boundaries

In this paper, we present an accurate and general interconnect model for planar transmission line interconnects with arbitrary boundary conditions. Based on the unified approach, we develop a SPICE-compatible parameter extraction algorithm that can be used in high-performance computer-aided-design applications. A range of multilayered interconnect geometries with arbitrary boundaries are analyzed. Different typical configurations of ground placement are considered to verify the applicability of this method. For all such cases, results are compared for admittance, line parameters, and delay giving physical insight on the effect of boundary conditions on them. Compared with existing industry standard numerical field-solvers, like HFSS, the proposed model demonstrates more than 10× speedup within 2% accuracy.     

Virtual Organizations in Arigatoni

Arigatoni is a lightweight overlay network that deploys the Global Computing Paradigm over the Internet. Communication for over the behavioral units of the overlay is performed by a simple resource discovery protocol (RDP). Basic Global Computers Units (GC) can communicate by first registering to a brokering service and then by mutually asking and offering services.Colonies and communities are the main entities in the model. A colony is a simple virtual organization composed by exactly one leader and some set (possibly empty) of individuals. A community is a raw set of colonies and global computers (think it as a soup of colonies and global computer without a leader).We present an operational semantics via a labeled transition system, that describes the main operations necessary in the Arigatoni model to perform leader negotiation, joining/leaving a colony, linking two colonies and moving one GC from one colony to another. Our formalization results to be adequate w.r.t. the algorithm performing peer logging/delogging and colony aggregation.     

Use of the radiofrequency-intermodulation distortion technique to investigate intrinsic nonlinearity at the electrode-electrolyte interface

We report on investigations of nonlinear radiofrequency responses of electrolytes with Na(+) and Cl(-) ions placed within gold electrodes of a capacitor. The sample was part of a frequency-adjustable inductance-capacitance-resistance (LCR) parallel resonant circuit, and measurements were carried out using the two frequencies intermodulation distortion technique. We employed double layer model to analyze the observed nonlinearities and their dependence on ionic concentration. Electrode-electrolyte interface polarization was found to be a predominant cause of this intrinsic nonlinearity and to be dependent on electrolytic ion concentration. We also measured and calculated coefficients of resistive and capacitive components of the observed nonlinearity.