119 research outputs found
Crystalline phase, surface morphology and electrical properties of monovalent-doped Nd0.75Na0.25Mn1-yCoyO3 manganites
No full textPerovskite-type manganites Nd0.75Na0.25Mn1-yCoyO3 (y = 0 – 0.05) have been investigated to clarify the influence of Co-doped on crystal phase and morphological study as well as electrical transport properties. The Nd0.75Na0.25Mn1-yCoyO3 samples are prepared via solid state synthesis method. X-ray diffraction analysis revealed all the samples are essentially single phased and the peaks are indexed to an orthorhombic structure with Pnma space. The morphological study from scanning electron microscope shows the improvement of the grains boundaries and sizes as well as the compaction of particles can be seen as cobalt doping increased. On the other hand, the temperature dependence of electrical resistivity measurements using four-point-probe technique indicates all samples maintained an insulator like behaviour down to low temperature. Analysis of the resistivity change with respect to temperature, dlnρ/dT-1 versus T reveals a slope changes of resistivity has been observed and a boarder peak exist for y = 0 sample and the peaks become significantly obvious for y = 0.02 and 0.05 samples. The peaks are observed in the range of charge ordering (CO) transition indicate the existence of CO in the system
Efficient probabilistic inversion of geophysical data
No full textEstimation of uncertainties is critical for subsequent decision making in all applications
of geosciences such as geological hazard analysis and risk mitigation, management and
exploitation of subsurface resources, and environmental waste disposal. More efficient
probabilistic inversion methods in geosciences are vital to making rapid and improved
predictions of geological hazards and estimation of subsurface resources from geophysical
data, and estimation of associated uncertainties. While this thesis focuses on seismic data
inversion for the estimation of geological properties, the methods developed may find a wide
variety of applications in all fields of research that involve spatial data analysis.
New concepts, models and methods are developed to perform more efficient
probabilistic inversion by making use of the latest developments in machine learning and
Bayesian inverse theory to solve geophysical inverse problems. The major contribution of this
thesis is the development of efficient geostatistical inversion methods for approximate
inference for structured inverse problems where probabilistic dependence between unknown
model parameters may be expressed as a Markov random field (MRF). These methods are
many orders of magnitude faster than the corresponding sampling based methods in such
types of inverse problems. Further, some of the commonly used but avoidable assumptions in
conventional geostatistical inversion methods are progressively relaxed and finally removed in
this research. The faster inversion methods allow more complex models to be evaluated for
more accurate predictions and improved estimation of uncertainty for given compute power
and time.
Most existing geostatistical inversion methods are based on the localized likelihoods
assumption, whereby the seismic data at a location are assumed to depend on the geology
only at that location. Such an assumption is unrealistic because of imperfect seismic data
acquisition and processing, and fundamental limitations of seismic imaging methods. It is also
assumed in most such previous research that the data are completely free of any correlated
noise or errors. Although these requirements are almost never met in reality, existing methods
use these assumptions to make solutions computationally tractable. Both of these
assumptions are progressively removed in this thesis while still allowing computationally
tractable solutions to be found for suitably structured problems. The class of problems
considered here spans a broad range of spatial data analysis and geosciences, where geology at a location is assumed to depend directly only on the geology within some pre-specified
neighbourhood of that location – the so called Markovian assumption – which is the core
assumption across the entire literature of geostatistics and has been proven to be valid for all
practical purposes.
Exact Bayesian inference is intractable in most models of practical interest because it
requires normalization of the posterior distribution by integrating model parameters over a
very high dimensional space. Therefore, approximate inference is used in practice. Stochastic
sampling (e.g., by using Markov-chain Monte Carlo – McMC) is the most commonly used
approximate inference method but is computationally expensive and detection of its
convergence is often based on subjective criteria and hence is unreliable. New Bayesian
inversion methods are introduced that estimate the spatial distribution of geological
properties from attributes of seismic data, by showing how the usual probabilistic inverse
problem can be solved using an optimization framework while still providing full probabilistic
results – the so called variational inference approach. The intractable posterior distribution is
replaced by a tractable approximation in the variational approach. Inference can then be
performed using the approximate distribution in an optimization framework, thus
circumventing the need for sampling, while still providing probabilistic results.
The methods developed in this thesis infer the post-inversion (posterior) probability
density of the unknown model parameters from seismic data and geological prior information.
These methods are shown to be robust against weak prior information and correlated noise in
the data. The methods are computationally efficient, and are expected to be applicable to 3D
models of realistic size on modern computers without incurring any significant computational
limitations
Public investment, private investment, governance and tourism growth in five South Asian Association for Regional Cooperation countries
No full textThe present research investigates the effects of public and private investment in Travel and Tourism (T&T), and their interaction effect on tourism growth in five South Asian
Association for Regional Cooperation (SAARC) countries. It also examines the interaction effect of public and private investment with governance on tourism growth in the region. The panel data for the five SAARC countries, from 1996 to 2015, is analyzed using Fully Modified Ordinary Least Squares and Pooled Mean Group methods. The study findings reveal that public investment, private investments, and their interaction positively affect tourism growth. The interaction effects of governance with public and private investments produce mixed results for the three indicators of governance. The interaction of political stability and absence of violence with private investment shows positive effect, however, its interaction with public investment illustrates negative effect on tourism growth. In addition, the interaction effect of control of corruption and public investment on tourism growth is positive, while there is an evidence of negative effect of the interaction of control of corruption and private investment. Similarly, the interaction effect of rule of law and public investment on tourism growth is positive, whereas, it is negative in case of the interaction of rule of law and private investment. Therefore, it is recommended that public investment needs to be increased in T&T, in addition to ensure conducive environment for private sector participation in order to reap its full potential. The study also suggests improving the governance, as it enhances the efficiency and productivity of public and private investments in T&T
Variational Bayesian Inversion of Seismically Derived Quasi-Localized Rock Properties for the Spatial Distribution of Geological Facies
No full textPituitary stalk interruption syndrome presenting in a euthyroid adult with short stature
No full textPituitary stalk interruption syndrome (PSIS) is a distinct and rare clinical entity responsible for congenital hypopituitarism resulting in deficiency of pituitary hormones with deficiency of the growth hormone (100%) and gonadotropins (97.2%) being its most common presentation at the time of hospital encounter (Wang et al., 2015). Isolated sparing of thyroid-stimulating hormone (TSH) with deficiency of the remaining anterior pituitary hormones may be present in PSIS, as is true in our case. Therefore, it should be kept in mind at the time of examination in suspected cases of PSIS
No full textError free transmission is one of the main aims in wireless communications. With the increase in multimedia applications, large amount of data is being transmitted over wireless communications. This requires error free transmission more than ever and to achieve error free transmission multiple antennas can be implemented on both stations i.e. base station and user terminal with proper modulation scheme and coding technique. The 4th generation of wireless communications can be attained by Multiple-Input Multiple-Output (MIMO) in combination with Orthogonal Frequency Division Multiplexing (OFDM). MIMO multiplexing (spatial multiplexing) and diversity (space time coding) having OFDM modulation scheme are the main areas of focus in our thesis study. MIMO multiplexing increases a network capacity by splitting a high signal rate into multiple lower rate streams. MIMO allows higher throughput, diversity gain and interference reduction. It also fulfills the requirement by offering high data rate through spatial multiplexing gain and improved link reliability due to antenna diversity gain. Alamouti Space Time Block Code (STBC) scheme is used with orthogonal designs over multiple antennas which showed simulated results are identical to expected theoretical results. With this technique both Bit Error Rate (BER) and maximum diversity gain are achieved by increasing number of antennas on either side. This scheme is efficient in all the applications where system capacity is limited by multipath fading
Bayesian Inversion of Seismic Attributes for Geological Facies using a Hidden Markov Model
No full textMarkov chain Monte-Carlo (McMC) sampling generates correlated random samples such that their distribution would converge to the true distribution only as the number of samples tends to infinity. In practice, McMC is found to be slow to converge, convergence is not guaranteed to be achieved in finite time, and detection of convergence requires the use of subjective criteria. Although McMC has been used for decades as the algorithm of choice for inference in complex probability distributions, there is a need to seek alternative approaches, particularly in high dimensional problems. Walker & Curtis (2014) developed a method for Bayesian inversion of two-dimensional spatial data using an exact sampling alternative to McMC which always draws independent samples of the target distribution. Their method thus obviates the need for convergence and removes the concomitant bias exhibited by finite sample sets. Their algorithm is nevertheless computationally intensive and requires large memory. We propose a more efficient method for Bayesian inversion of categorical variables, such as geological facies that requires no sampling at all. The method is based on a 2D Hidden Markov Model (2D-HMM) over a grid of cells where observations represent localized data constraining each cell. The data in our example application are seismic attributes such as P- and S-wave impedances and rock density; our categorical variables are the hidden states and represent the geological rock types in each cell – facies of distinct subsets of lithology and fluid combinations such as shale, brine-sand and gas-sand. The observations at each location are assumed to be generated from a random function of the hidden state (facies) at that location, and to be distributed according to a certain probability distribution that is independent of hidden states at other locations – an assumption referred to as localized likelihoods. The hidden state (facies) at a location cannot be determined solely by the observation at that location as it also depends on prior information concerning the spatial distribution of other hidden states elsewhere. The prior information is included in the inversion in the form of a training image which represents a conceptual depiction of local geologies that might be expected, but other forms of prior information can be used in the method as desired. The method provides direct estimates of posterior marginal probability distributions over each variable, so these do not need to be estimated from samples such as in McMC. Nevertheless, in case samples are desired, these can be generated. On a 2-dimensional test example the method is shown to outperform previous methods significantly, at a fraction of the computational cost. In many foreseeable applications there are therefore no serious impediments to extending the method to 3-dimensional cases
Solar light driven degradation of textile dye contaminants for wastewater treatment – studies of novel polycationic selenide photocatalyst and process optimization by response surface methodology desirability factor
No full textThe unplanned anthropogenic activities and raced industrial revolution detrimentally causes serious threat to terrestrial and aquatic life. A high discharge of wastewater from industries using dyes affects living organisms and the environment. This paper presents studies on polycationic selenides (PCS) synthesized by hydrothermal methods for photocatalytic degradation of dyes. The synthesized PCS were confirmed by various characterization techniques such as FTIR, SEM, EDX, UV/Vis, and XRD. The FTIR spectra revealed characteristic band at 843, 548 cm−1, and 417 cm−1 due to the M − Se stretching and intrinsic stretching vibrations, respectively. The optical bandgap of polycationic selenide lies in the visible light region (2.36 eV). The SEM images showed that PCS has a spherical shape with an average crystallite size of 29.23 nm calculated from XRD data using Scherer's equation. The PCS has a point of zero charge (PZC) at pH 7. The efficiency of synthesized PCS photocatalyst was confirmed in terms of its activity towards Eosin (EY) and Crystal violet (CV) dyes mineralization. The photocatalytic degradation for EY and CV dyes at optimum conditions was 99.47% and 99.31% and followed second order reactions kinetics with 1.4314 and 0.551 rate constant, respectively. The polynomial quadratic model is the best-fitted response surface methodology (RSM) model having a maximum desirability factors value and significant terms, with R2 (0.9994) and adj R2 values (1.0).No Full Tex
No full textError free transmission is one of the main aims in wireless communications. With the increase in multimedia applications, large amount of data is being transmitted over wireless communications. This requires error free transmission more than ever and to achieve error free transmission multiple antennas can be implemented on both stations i.e. base station and user terminal with proper modulation scheme and coding technique. The 4th generation of wireless communications can be attained by Multiple-Input Multiple-Output (MIMO) in combination with Orthogonal Frequency Division Multiplexing (OFDM). MIMO multiplexing (spatial multiplexing) and diversity (space time coding) having OFDM modulation scheme are the main areas of focus in our thesis study. MIMO multiplexing increases a network capacity by splitting a high signal rate into multiple lower rate streams. MIMO allows higher throughput, diversity gain and interference reduction. It also fulfills the requirement by offering high data rate through spatial multiplexing gain and improved link reliability due to antenna diversity gain. Alamouti Space Time Block Code (STBC) scheme is used with orthogonal designs over multiple antennas which showed simulated results are identical to expected theoretical results. With this technique both Bit Error Rate (BER) and maximum diversity gain are achieved by increasing number of antennas on either side. This scheme is efficient in all the applications where system capacity is limited by multipath fading
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