Studies on the ignition behaviour of boron powder

The ignition behaviour of boron powder, prepared through electrowinning process, was studied by using thermogravimetry coupled with simultaneous differential thermal analysis (TG-SDTA). The dependence of the inception of the ignition reaction on the partial pressure of oxygen, particle size of the boron powder and heating rate was investigated. It was observed that all these factors affect the ignition temperature. Boron powder with a mean particle size of about 10 mu m was found to be susceptible to ignition in oxygen even at 783K. In general, the susceptibility to ignition was found to vary inversely with the degree of crystallinity. Presence of carbon was found to retard the oxidation of boron and raise the ignition temperature. These results are useful in safe handling and storage of finely divided boron powder and in the subsequent production of boron carbide from it. (C) 2009 Elsevier B.V. All rights reserved.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

The 5-Methyl Group in Thymine Dynamically Influences the Structure of A-Tracts in DNA at the Local and Global Level

DNA sequences containing a stretch of several A:T basepairs without a 5'-TA-3' step are known as A-tracts and have been the subject of extensive investigation because of their unique structural features such as a narrow minor groove and their crucial role in several biological processes. One of the aspects under investigation has been the influence of the 5-methyl group of thymine on the properties of A-tracts. Detailed molecular dynamics simulation studies of the sequences d(CGCAAAUUUGCG) and d(CGCAAATTTGCG) indicate that the presence of the 5-methyl group in thymine increases the frequency of a narrow minor groove conformation, which could facilitate its specific recognition by proteins, and reduce its susceptibility to cleavage by DNase I. The bias toward a wider minor groove in the absence of the thymine 5-methyl group is a static structural feature. Our results also indicate that the presence of the thymine 5-methyl group is necessary for calibrating the backbone conformation and the basepair and dinucleotide step geometry of the core A-tract as well as the flanking CA/TG and the neighboring GC/GC steps, as observed in free and protein-bound DNA. As a consequence, it also fine-tunes the curvature of the longer DNA fragment in which the A-tract is embedded.

Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.

Multi-criteria seismic hazard evaluation for Bangalore city, India

Different seismic hazard components pertaining to Bangalore city,namely soil overburden thickness, effective shear-wave velocity, factor of safety against liquefaction potential, peak ground acceleration at the seismic bedrock, site response in terms of amplification factor, and the predominant frequency, has been individually evaluated. The overburden thickness distribution, predominantly in the range of 5-10 m in the city, has been estimated through a sub-surface model from geotechnical bore-log data. The effective shear-wave velocity distribution, established through Multi-channel Analysis of Surface Wave (MASW) survey and subsequent data interpretation through dispersion analysis, exhibits site class D (180-360 m/s), site class C (360-760 m/s), and site class B (760-1500 m/s) in compliance to the National Earthquake Hazard Reduction Program (NEHRP) nomenclature. The peak ground acceleration has been estimated through deterministic approach, based on the maximum credible earthquake of M-W = 5.1 assumed to be nucleating from the closest active seismic source (Mandya-Channapatna-Bangalore Lineament). The 1-D site response factor, computed at each borehole through geotechnical analysis across the study region, is seen to be ranging from around amplification of one to as high as four times. Correspondingly, the predominant frequency estimated from the Fourier spectrum is found to be predominantly in range of 3.5-5.0 Hz. The soil liquefaction hazard assessment has been estimated in terms of factor of safety against liquefaction potential using standard penetration test data and the underlying soil properties that indicates 90% of the study region to be non-liquefiable. The spatial distributions of the different hazard entities are placed on a GIS platform and subsequently, integrated through analytical hierarchal process. The accomplished deterministic hazard map shows high hazard coverage in the western areas. The microzonation, thus, achieved is envisaged as a first-cut assessment of the site specific hazard in laying out a framework for higher order seismic microzonation as well as a useful decision support tool in overall land-use planning, and hazard management. (C) 2010 Elsevier Ltd. All rights reserved.

Generalization of Dynkin's Identity and Some Applications

Let X(t) be a right continuous temporally homogeneous Markov pro- cess, Tt the corresponding semigroup and A the weak infinitesimal genera- tor. Let g(t) be absolutely continuous and r a stopping time satisfying E.( S f I g(t) I dt) < oo and E.( f " I g'(t) I dt) < oo Then for f e 9iJ(A) with f(X(t)) right continuous the identity Exg(r)f(X(z)) - g(O)f(x) = E( 5 " g'(s)f(X(s)) ds) + E.( 5 " g(s)Af(X(s)) ds) is a simple generalization of Dynkin's identity (g(t) 1). With further restrictions on f and r the following identity is obtained as a corollary: Ex(f(X(z))) = f(x) + k! Ex~rkAkf(X(z))) + n-1E + (n ) )!.E,(so un-1Anf(X(u)) du). These identities are applied to processes with stationary independent increments to obtain a number of new and known results relating the moments of stopping times to the moments of the stopped processes.

Dielectric relaxation and electrical conductivity in Bi5NbO10 oxygen ion conductors prepared by a modified sol–gel process

Crystalline Bi5NbO10 nanoparticles have been achieved through a modified sol–gel process using a mixture of ethylenediamine and ethanolamine as a solvent. The Bi5NbO10 nanoparticles were characterized by X-ray diffraction (XRD), differential scanning calorimetry/thermogravimetry (DSC/TG), Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM) and Raman spectroscopy. The results showed that well-dispersed 5–60 nm Bi5NbO10 nanoparticles were prepared through heat-treating the precursor at 650 °C and the high density pellets were obtained at temperatures lower than those commonly employed. The frequency and temperature dependence of the dielectric constant and the electrical conductivity of the Bi5NbO10 solid solutions were investigated in the 0.1 Hz to 1 MHz frequency range. Two distinct relaxation mechanisms were observed in the plots of dielectric loss and the imaginary part of impedance (Z″) versus frequency in the temperature range of 200–350 °C. The dielectric constant and the loss in the low frequency regime were electrode dependent. The ionic conductivity of Bi5NbO10 solid solutions at 700 °C is 2.86 Ω−1 m−1 which is in same order of magnitude for Y2O3-stabilized ZrO2 ceramics at same temperature. These results suggest that Bi5NbO10 is a promising material for an oxygen ion conductor.

Probabilistic evaluation of seismic soil liquefaction potential based on SPT data

The performance-based liquefaction potential analysis was carried out in the present study to estimate the liquefaction return period for Bangalore, India, through a probabilistic approach. In this approach, the entire range of peak ground acceleration (PGA) and earthquake magnitudes was used in the evaluation of liquefaction return period. The seismic hazard analysis for the study area was done using probabilistic approach to evaluate the peak horizontal acceleration at bed rock level. Based on the results of the multichannel analysis of surface wave, it was found that the study area belonged to site class D. The PGA values for the study area were evaluated for site class D by considering the local site effects. The soil resistance for the study area was characterized using the standard penetration test (SPT) values obtained from 450 boreholes. These SPT data along with the PGA values obtained from the probabilistic seismic hazard analysis were used to evaluate the liquefaction return period for the study area. The contour plot showing the spatial variation of factor of safety against liquefaction and the corrected SPT values required for preventing liquefaction for a return period of 475 years at depths of 3 and 6 m are presented in this paper. The entire process of liquefaction potential evaluation, starting from collection of earthquake data, identifying the seismic sources, evaluation of seismic hazard and the assessment of liquefaction return period were carried out, and the entire analysis was done based on the probabilistic approach.

Relationship between Low Strain Shear Modulus and Standard Penetration Test N Values

A low strain shear modulus plays a fundamental role in the estimation of site response parameters In this study an attempt has been made to develop the relationships between standard penetration test (SPT) N values with the low strain shear modulus (G(max)) For this purpose, field experiments SPT and multichannel analysis of surface wave data from 38 locations in Bangalore, India, have been used, which were also used for seismic microzonation project The in situ density of soil layer was evaluated using undisturbed soil samples from the boreholes Shear wave velocity (V-s) profiles with depth were obtained for the same locations or close to the boreholes The values for low strain shear modulus have been calculated using measured V-s and soil density About 215 pairs of SPT N and G(max) values are used for regression analysis The differences between fitted regression relations using measured and corrected values were analyzed It is found that an uncorrected value of N and modulus gives the best fit with a high regression coefficient when compared to corrected N and corrected modulus values This study shows better correlation between measured values of N and G(max) when compared to overburden stress corrected values of N and G(max)

Essays on Platforms: Business Strategies, Regulation and Policy in Telecommunications, Media and Technology Industries

The growth of the information economy has been stellar in the last decade. General-purpose technologies such as the computer and the Internet have promoted productivity growth in a large number of industries. The effect on telecommunications, media and technology industries has been particularly strong. These industries include mobile telecommunications, printing and publishing, broadcasting, software, hardware and Internet services. There have been large structural changes, which have led to new questions on business strategies, regulation and policy. This thesis focuses on four such questions and answers them by extending the theoretical literature on platforms. The questions (with short answers) are: (i) Do we need to regulate how Internet service providers discriminate between content providers? (Yes.) (ii) What are the welfare effects of allowing consumers to pay to remove advertisements from advertisement-supported products?(Ambiguous, but those watching ads are worse off.) (iii) Why are some markets characterized by open platforms, extendable by third parties, and some by closed platforms, which are not extendable? (It is a trade-off between intensified competition for consumers and benefits from third parties) (iv) Do private platform providers allow third parties to access their platform when it is socially desirable? (No.)

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Modeling Nonlinearities and Asymmetries in Asset Pricing

Financial time series tend to behave in a manner that is not directly drawn from a normal distribution. Asymmetries and nonlinearities are usually seen and these characteristics need to be taken into account. To make forecasts and predictions of future return and risk is rather complicated. The existing models for predicting risk are of help to a certain degree, but the complexity in financial time series data makes it difficult. The introduction of nonlinearities and asymmetries for the purpose of better models and forecasts regarding both mean and variance is supported by the essays in this dissertation. Linear and nonlinear models are consequently introduced in this dissertation. The advantages of nonlinear models are that they can take into account asymmetries. Asymmetric patterns usually mean that large negative returns appear more often than positive returns of the same magnitude. This goes hand in hand with the fact that negative returns are associated with higher risk than in the case where positive returns of the same magnitude are observed. The reason why these models are of high importance lies in the ability to make the best possible estimations and predictions of future returns and for predicting risk.

A chemical approach to an understanding of the fast ion conduction in silver iodide-silver oxysalt glasses

A number of AgI based fast ion conducting glasses, with a general formula AgI---Ag2O---MxOy (MxOy=MoO3, SeO3, WO3, V2O5, P2O5, GeO2, B2O3, As2O3, CrO3) have been studied. A chemical approach is made to investigate the origin of fast ion conduction in these glasses. An index known as Image tructural Image npinning Image umber, SUN (S), has been defined for the purpose, based on the unscreened nuclear charge of silver ions and the equilibrium lectronegativities of the halide-oxyanion matrix in these glasses. The variation of the glass transition temperature, Tg, conductivity, σ, and the energy of activation, Ea, with the concentration of AgI are discussed in the light of the structural unpinning number. Conductivities increase uniformly in any given glass series as a smooth function of S and level off at very high values. The entire range of conductivity appears to vary as ln Image , where ln σ0 corresponds roughly to the conductivity of the hypothetical AgI glass and “a” is a constant which could be obtained as the slope in the graph of ln Ea versus S. It is suggested that the increase in the concentration of AgI beyond 75–80 mole% in the glass is not advantageous from the conductivity point of view.

An Empirical Comparison of Linear and Nonlinear Volatility Models for Nordic Stock Returns

This paper examines how volatility in financial markets can preferable be modeled. The examination investigates how good the models for the volatility, both linear and nonlinear, are in absorbing skewness and kurtosis. The examination is done on the Nordic stock markets, including Finland, Sweden, Norway and Denmark. Different linear and nonlinear models are applied, and the results indicates that a linear model can almost always be used for modeling the series under investigation, even though nonlinear models performs slightly better in some cases. These results indicate that the markets under study are exposed to asymmetric patterns only to a certain degree. Negative shocks generally have a more prominent effect on the markets, but these effects are not really strong. However, in terms of absorbing skewness and kurtosis, nonlinear models outperform linear ones.