Analysis of the Pyrolysis Products of Dimethyldichlorosilane in the Chemical Vapor Deposition of Silicon Carbide in Argon

A study of the products and reactions occurring during the chemical vapor deposition of silicon carbide from dimethyl-dichlorosilane in argon is presented. Reaction conditions were as follows: 700° to 1100°C, a contact time of ∽1 min, and a pressure of 1 atm (∽0.1 MPa). At these conditions, the gases that formed were mainly methane, hydrogen, silicon tetrachloride, trichlorosilane, and methyltrichlorosilane. Hydrogen chloride might also be present, but was not determined. The silicon carbide solid that formed showed the presence of hydrogen and chlorine as impurities, which might degrade the silicon carbide properties. These impurities were eliminated slowly, even at 1100°C, forming hydrogen, trichlorosilane, and silicon tetrachloride.     

Stochastic dominance-based rough set model for ordinal classification

In order to discover interesting patterns and dependencies in data, an approach based on rough set theory can be used. In particular, dominance-based rough set approach (DRSA) has been introduced to deal with the problem of ordinal classification with monotonicity constraints (also referred to as multicriteria classification in decision analysis). However, in real-life problems, in the presence of noise, the notions of rough approximations were found to be excessively restrictive. In this paper, we introduce a probabilistic model for ordinal classification problems with monotonicity constraints. Then, we generalize the notion of lower approximations to the stochastic case. We estimate the probabilities with the maximum likelihood method which leads to the isotonic regression problem for a two-class (binary) case. The approach is easily generalized to a multi-class case. Finally, we show the equivalence of the variable consistency rough sets to the specific empirical risk-minimizing decision rule in the statistical decision theory.     

Cerebrospinal fluid biomarkers in clinically isolated syndromes and multiple sclerosis

A panel of three cerebrospinal fluid (CSF) markers for clinically isolated syndromes (CIS) and multiple sclerosis (MS), based on SDS-PAGE, 2-D maps, and immunoblot results, is here proposed. No individual marker has any specificity, though, since they appear in a number of other neurological diseases. However the set of three, with the respective modulation sign (up-regulated or maintained at constant level), appears to be unique for MS. These proteins are: tau protein (levels remaining constant and undistinguishable from controls, contrary to up- and downregulation in other neurological disorders); 14-3-3 protein (strong upregulation of distinct isoforms) and cystatin C (changing in accordance to disease stage and progression). As an additional evidence, one can rely in the pattern of isoforms of 14-3-3, as obtained by 2-D maps and Western blot analysis: this pattern further distinguishes the variation of this protein from other neurological syndromes, notably sporadic Creutzfeldt-Jakob disease (sCJD), motor neuron diseases and other dementias. In contrast, a similar qualitative and quantitative upregulation of 14-3-3 is observed in Guillain-Barré syndrome (GBS), a demyelinating condition affecting the peripheral nervous system. To the best of our knowledge, this is the first time in which such a panel of biomarkers is reported in MS.     

Cost Complexity-Based Pruning of Ensemble Classifiers

In this paper we study methods that combine multiple classification models learned over separate data sets. Numerous studies posit that such approaches provide the means to efficiently scale learning to large data sets, while also boosting the accuracy of individual classifiers. These gains, however, come at the expense of an increased demand for run-time system resources. The final ensemble meta-classifier may consist of a large collection of base classifiers that require increased memory resources while also slowing down classification throughput. Here, we describe an algorithm for pruning (i.e., discarding a subset of the available base classifiers) the ensemble meta-classifier as a means to reduce its size while preserving its accuracy and we present a technique for measuring the trade-off between predictive performance and available run-time system resources. The algorithm is independent of the method used initially when computing the meta-classifier. It is based on decision tree pruning methods and relies on the mapping of an arbitrary ensemble meta-classifier to a decision tree model. Through an extensive empirical study on meta-classifiers computed over two real data sets, we illustrate our pruning algorithm to be a robust and competitive approach to discarding classification models without degrading the overall predictive performance of the smaller ensemble computed over those that remain after pruning. Received 30 August 2000 / Revised 7 March 2001 / Accepted in revised form 21 May 2001     

Looking deeper into academic citations through network analysis: popularity,influence and impact

Google Scholar (GS) has progressively emerged as a tool which “provides a simple way to broadly search for scholarly literature across many disciplines and sources.” As a free tool that provides citation metrics, GS has opened the academic word to a much larger audience, according to an open information philosophy. GS’ profiles are largely used not only to have a quick look at the authors and their works but, more and more often, as a “de facto” metric to quickly evaluate the research impact. This process looks unstoppable and discussing about its fairness, advantages and disadvantages, as well as about social implications is out of the scope of this paper. We rather prefer to (1) briefly discuss the changes and the innovation that GS has introduced and to (2) propose possible improvements for analysis on academic citations. Our methods are aimed at considering a GS profile in its proper context, providing a social perspective on academic citations: Although maintaining a fundamentally quantitative focus, novel approaches, based on complex network analysis, distinguish between a research impact on the authors’ research network and a more general impact on the scientific community.     

Impact of influent data frequency and model structure on the quality of WWTP model calibration and uncertainty

Application of activated sludge models (ASMs) to full-scale wastewater treatment plants (WWTPs) is still hampered by the problem of model calibration of these over-parameterised models. This either requires expert knowledge or global methods that explore a large parameter space. However, a better balance in structure between the submodels (ASM, hydraulic, aeration, etc.) and improved quality of influent data result in much smaller calibration efforts. In this contribution, a methodology is proposed that links data frequency and model structure to calibration quality and output uncertainty. It is composed of defining the model structure, the input data, an automated calibration, confidence interval computation and uncertainty propagation to the model output. Apart from the last step, the methodology is applied to an existing WWTP using three models differing only in the aeration submodel. A sensitivity analysis was performed on all models, allowing the ranking of the most important parameters to select in the subsequent calibration step. The aeration submodel proved very important to get good NH(4) predictions. Finally, the impact of data frequency was explored. Lowering the frequency resulted in larger deviations of parameter estimates from their default values and larger confidence intervals. Autocorrelation due to high frequency calibration data has an opposite effect on the confidence intervals. The proposed methodology opens doors to facilitate and improve calibration efforts and to design measurement campaigns.     

Integrating trust measures in multiagent systems

Several models have been proposed in the past for representing both reliability and reputation. However, we remark that a crucial point in the practical use of these two measures is represented by the possibility of suitably combining them to support the agent's decision. In the past, we proposed a reliability–reputation model, called RRAF, that allows the user to choose how much importance to give to the reliability with respect to the reputation. However, RRAF shows some limitations, namely: (i) The weight to assign to the reliability versus reputation is arbitrarily set by the user, without considering the system evolution; (ii) the trust measure that an agent a perceives about an agent b is completely independent of the trust measure perceived by each other agent c, while in the reality the trust measures are mutually dependent. In this paper, we propose an extension of RRAF, aiming at facing the limitations above. In particular, we introduce a new trust reputation model, called TRR, that considers, from a mathematical viewpoint, the interdependence among all the trust measures computed in the systems. Moreover, this model dynamically computes a parameter measuring the importance of the reliability with respect to the reputation. Some experiments performed on the well‐known ART(Agent Reputation and Trust) platform show the significant advantages in terms of effectiveness introduced by TRR with respect to RRAF. © 2011 Wiley Periodicals, Inc.     

Comparison of the Pyrolysis Products of Dichlorodimethylsilane in the Chemical Vapor Deposition of Silicon Carbide on Silica in Hydrogen or Argon

A chemical analysis of the pyrolysis gases and solids formed during the deposition of silicon carbide from the decomposition of dichlorodimethylsilane in argon and hydrogen is reported. Depositions were performed at 1 atm pressure, temperatures from 700° to 1100°C, and a mean residence time of approximately 1 min. The chemical analysis shows that, under reactor conditions, the gases formed were mainly methane, hydrogen, silicon tetrachloride, trichloro-silane, and trichloromethylsilane. The presence of hydrogen chloride was not examined. The use of hydrogen, as a carrier gas, decreased the trichloromethylsilane and solid aerosol (smoke) in the reaction products, compared to that present in the argon system, and increased methane, trichlorosilane, and silicon production. Primarily, silicon and silicon carbide were deposited when hydrogen was used as the carrier gas. When argon was used, a complex mixture of silicon carbide and organosilicon compounds was formed. It is hypothesized that, when hydrogen was used as the carrier gas, silicon carbide formed from chlorosilanes and methane, which were products from the decomposition of dichlorodimethylsilane. These products subsequently reacted to form silicon, which then reacted with methane to form silicon carbide. In argon, however, it is hypothesized that silicon carbide can be formed in two ways: (1) from the pyrolysis of solid organosilicon compounds which are products from the pyrolysis of dichlorodimethylsilane in argon and (2) as the reduction of dichlorodimethylsilane to chlorosilanes and methane, caused by the hydrogen produced from the pyrolysis of dichlorodimethylsilane in argon.     

Generic polar harmonic transforms for invariant image representation

This paper introduces four classes of rotation-invariant orthogonal moments by generalizing four existing moments that use harmonic functions in their radial kernels. Members of these classes share beneficial properties for image representation and pattern recognition like orthogonality and rotation-invariance. The kernel sets of these generic harmonic function-based moments are complete in the Hilbert space of square-integrable continuous complex-valued functions. Due to their resemble definition, the computation of these kernels maintains the simplicity and numerical stability of existing harmonic function-based moments. In addition, each member of one of these classes has distinctive properties that depend on the value of a parameter, making it more suitable for some particular applications. Comparison with existing orthogonal moments defined based on Jacobi polynomials and eigenfunctions has been carried out and experimental results show the effectiveness of these classes of moments in terms of representation capability and discrimination power.     

Performance analysis of visualmarkers for indoor navigation systems

The massive diffusion of smartphones, the growing interest in wearable devices and the Internet of Things, and the exponential rise of location based services (LBSs) have made the problem of localization and navigation inside buildings one of the most important technological challenges of recent years. Indoor positioning systems have a huge market in the retail sector and contextual advertising; in addition, they can be fundamental to increasing the quality of life for citizens if deployed inside public buildings such as hospitals, airports, and museums. Sometimes, in emergency situations, they can make the difference between life and death. Various approaches have been proposed in the literature. Recently, thanks to the high performance of smartphones’ cameras, marker-less and marker-based computer vision approaches have been investigated. In a previous paper, we proposed a technique for indoor localization and navigation using both Bluetooth low energy (BLE) and a 2D visual marker system deployed into the floor. In this paper, we presented a qualitative performance evaluation of three 2D visual markers, Vuforia, ArUco marker, and AprilTag, which are suitable for real-time applications. Our analysis focused on specific case study of visual markers placed onto the tiles, to improve the efficiency of our indoor localization and navigation approach by choosing the best visual marker system.