Virtual-bound, filamentary and layered states in a box-shaped quantum dot of square potential form the exact numerical solution of the effective mass Schrodinger equation

The effective mass Schrodinger equation of a QD of parallelepipedic shape with a square potential well is solved by diagonalizing the exact Hamiltonian matrix developed in a basis of separation-of-variables wavefunctions. The expected below bandgap bound states are found not to differ very much from the former approximate calculations. In addition, the presence of bound states within the conduction band is confirmed. Furthermore, filamentary states bounded in two dimensions and extended in one dimension and layered states with only one dimension bounded, all within the conduction band which are similar to those originated in quantum wires and quantum wells coexist with the ordinary continuum spectrum of plane waves. All these subtleties are absent in spherically shaped quantum dots, often used for modeling.

Discovery & effective use of quality association rules in multi-level datasets

In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the knowledge within is not immediately accessible, but rather has to be mined and extracted. This requires automated tools and they need to be effective and efficient. Association rule mining is one approach to obtaining knowledge stored with datasets / databases which includes frequent patterns and association rules between the items / attributes of a dataset with varying levels of strength. However, this is also association rule mining’s downside; the number of rules that can be found is usually very big. In order to effectively use the association rules (and the knowledge within) the number of rules needs to be kept manageable, thus it is necessary to have a method to reduce the number of association rules. However, we do not want to lose knowledge through this process. Thus the idea of non-redundant association rule mining was born. A second issue with association rule mining is determining which ones are interesting. The standard approach has been to use support and confidence. But they have their limitations. Approaches which use information about the dataset’s structure to measure association rules are limited, but could yield useful association rules if tapped. Finally, while it is important to be able to get interesting association rules from a dataset in a manageable size, it is equally as important to be able to apply them in a practical way, where the knowledge they contain can be taken advantage of. Association rules show items / attributes that appear together frequently. Recommendation systems also look at patterns and items / attributes that occur together frequently in order to make a recommendation to a person. It should therefore be possible to bring the two together. In this thesis we look at these three issues and propose approaches to help. For discovering non-redundant rules we propose enhanced approaches to rule mining in multi-level datasets that will allow hierarchically redundant association rules to be identified and removed, without information loss. When it comes to discovering interesting association rules based on the dataset’s structure we propose three measures for use in multi-level datasets. Lastly, we propose and demonstrate an approach that allows for association rules to be practically and effectively used in a recommender system, while at the same time improving the recommender system’s performance. This especially becomes evident when looking at the user cold-start problem for a recommender system. In fact our proposal helps to solve this serious problem facing recommender systems.

Asymmetrically decorated, doped porous graphene as an effective membrane for hydrogen isotope separation

We propose a new route to hydrogen isotope separation which exploits the quantum sieving effect in the context of transmission through asymmetrically decorated, doped porous graphenes. Selectivities of D2 over H2 as well as rate constants are calculated based on ab initio interaction potentials for passage through pure and nitrogen functionalized porous graphene. One-sided dressing of the membrane with metal provides the critical asymmetry needed for an energetically favorable pathway.

Quantum direct secret sharing with efficient eavesdropping-check and authentication based on distributed fountain codes

We propose to use a simple and effective way to achieve secure quantum direct secret sharing. The proposed scheme uses the properties of fountain codes to allow a realization of the physical conditions necessary for the implementation of no-cloning principle for eavesdropping-check and authentication. In our scheme, to achieve a variety of security purposes, nonorthogonal state particles are inserted in the transmitted sequence carrying the secret shares to disorder it. However, the positions of the inserted nonorthogonal state particles are not announced directly, but are obtained by sending degrees and positions of a sequence that are pre-shared between Alice and each Bob. Moreover, they can confirm that whether there exists an eavesdropper without exchanging classical messages. Most importantly, without knowing the positions of the inserted nonorthogonal state particles and the sequence constituted by the first particles from every EPR pair, the proposed scheme is shown to be secure.

Using the quantum probability ranking principle to rank interdependent documents

A known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP) has been proposed, which implicitly captures dependencies between documents through “quantum interference”. This paper explores whether this new ranking principle leads to improved performance for subtopic retrieval, where novelty and diversity is required. In a thorough empirical investigation, models based on the PRP, as well as other recently proposed ranking strategies for subtopic retrieval (i.e. Maximal Marginal Relevance (MMR) and Portfolio Theory(PT)), are compared against the QPRP. On the given task, it is shown that the QPRP outperforms these other ranking strategies. And unlike MMR and PT, one of the main advantages of the QPRP is that no parameter estimation/tuning is required; making the QPRP both simple and effective. This research demonstrates that the application of quantum theory to problems within information retrieval can lead to significant improvements.

On the use of complex numbers in quantum models for information retrieval

Quantum-inspired models have recently attracted increasing attention in Information Retrieval. An intriguing characteristic of the mathematical framework of quantum theory is the presence of complex numbers. However, it is unclear what such numbers could or would actually represent or mean in Information Retrieval. The goal of this paper is to discuss the role of complex numbers within the context of Information Retrieval. First, we introduce how complex numbers are used in quantum probability theory. Then, we examine van Rijsbergen’s proposal of evoking complex valued representations of informations objects. We empirically show that such a representation is unlikely to be effective in practice (confuting its usefulness in Information Retrieval). We then explore alternative proposals which may be more successful at realising the power of complex numbers.

Wheat breeding nurseries, target environments, and indirect selection for grain yield

Use of appropriate nursery environments will maximize gain from selection for yield of wheat (Triticum aestivum L.) in the target population of environments of a breeding program. The objective of this study was to investigate how well-irrigated (low-stress) nursery environments predict yield of lines in target environments that varied in degree of water limitation. Fifteen lines were sampled from the preliminary yield evaluation stage of the Queensland wheat breeding program and tested in 26 trials under on-farm conditions (Target Environments) across nine years (1985 to 1993) and also in 27 trials conducted at three research stations (Nursery Environments) in three years (1987 to 1989). The nursery environments were structured to impose different levels of water and nitrogen (N) limitation, whereas the target environments represented a random sample of on-farm conditions from the target population of environments. Indirect selection and pattern analysis methods were used to investigate selection for yield in the nursery environments and gain from selection in the target environments. Yield under low-stress nursery conditions was an effective predictor of yield under similar low-stress target environments (r = 0.89, P < 0.01). However, the value of the low-stress nursery as a predictor of yield in the water-limited target environments decreased with increasing water stress (moderate stress r = 0.53, P < 0.05, to r = 0.38, P > 0.05; severe stress r = -0.08, P > 0.05). Yield in the stress nurseries was a poor predictor of yield in the target environments. Until there is a clear understanding of the physiological-genetic basis of variation for adaptation of wheat to the water-limited environments in Queensland, yield improvement can best be achieved by selection for a combination of yield potential in an irrigated low-stress nursery and yield in on-farm trials that sample the range of water-limited environments of the target population of environments.

Si quantum dots embedded in an amorphous SiC matrix : nanophase control by non-equilibrium plasma hydrogenation

Nanophase nc-Si/a-SiC films that contain Si quantum dots (QDs) embedded in an amorphous SiC matrix were deposited on single-crystal silicon substrates using inductively coupled plasma-assisted chemical vapor deposition from the reactive silane and methane precursor gases diluted with hydrogen at a substrate temperature of 200 °C. The effect of the hydrogen dilution ratio X (X is defined as the flow rate ratio of hydrogen-to-silane plus methane gases), ranging from 0 to 10.0, on the morphological, structural, and compositional properties of the deposited films, is extensively and systematically studied by scanning electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, Raman spectroscopy, Fourier-transform infrared absorption spectroscopy, and X-ray photoelectron spectroscopy. Effective nanophase segregation at a low hydrogen dilution ratio of 4.0 leads to the formation of highly uniform Si QDs embedded in the amorphous SiC matrix. It is also shown that with the increase of X, the crystallinity degree and the crystallite size increase while the carbon content and the growth rate decrease. The obtained experimental results are explained in terms of the effect of hydrogen dilution on the nucleation and growth processes of the Si QDs in the high-density plasmas. These results are highly relevant to the development of next-generation photovoltaic solar cells, light-emitting diodes, thin-film transistors, and other applications.

Single-step, rapid low-temperature synthesis of Si quantum dots embedded in an amorphous SiC matrix in high-density reactive plasmas

A simple, effective and innovative approach based on low-pressure, thermally nonequilibrium, high-density inductively coupled plasmas is proposed to rapidly synthesize Si quantum dots (QDs) embedded in an amorphous SiC (a-SiC) matrix at a low substrate temperature and without any commonly used hydrogen dilution. The experimental results clearly demonstrate that uniform crystalline Si QDs with a size of 3-4 nm embedded in the silicon-rich (carbon content up to 10.7at.%) a-SiC matrix can be formed from the reactive mixture of silane and methane gases, with high growth rates of ∼1.27-2.34 nm s-1 and at a low substrate temperature of 200 °C. The achievement of the high-rate growth of Si QDs embedded in the a-SiC without any commonly used hydrogen dilution is discussed based on the unique properties of the inductively coupled plasma-based process. This work is particularly important for the development of the all-Si tandem cell-based third generation photovoltaic solar cells.

Increased size selectivity of Si quantum dots on SiC at low substrate temperatures : an ion-assisted self-organization approach

A simple, effective, and innovative approach based on ion-assisted self-organization is proposed to synthesize size-selected Si quantum dots (QDs) on SiC substrates at low substrate temperatures. Using hybrid numerical simulations, the formation of Si QDs through a self-organization approach is investigated by taking into account two distinct cases of Si QD formation using the ionization energy approximation theory, which considers ionized in-fluxes containing Si3+ and Si1+ ions in the presence of a microscopic nonuniform electric field induced by a variable surface bias. The results show that the highest percentage of the surface coverage by 1 and 2 nm size-selected QDs was achieved using a bias of -20 V and ions in the lowest charge state, namely, Si1+ ions in a low substrate temperature range (227-327 °C). As low substrate temperatures (≤500 °C) are desirable from a technological point of view, because (i) low-temperature deposition techniques are compatible with current thin-film Si-based solar cell fabrication and (ii) high processing temperatures can frequently cause damage to other components in electronic devices and destroy the tandem structure of Si QD-based third-generation solar cells, our results are highly relevant to the development of the third-generation all-Si tandem photovoltaic solar cells.

Surface fluxes of Si and C adatoms at initial growth stages of SiC quantum dots

Self-assembly of highly stoichiometric SiC quantum dots still remains a major challenge for the gas/plasma-based nanodot synthesis. By means of a multiscale hybrid numerical simulation of the initial stage (0.1-2.5 s into the process) of deposition of SiCSi (100) quantum dot nuclei, it is shown that equal Si and kst atom deposition fluxes result in strong nonstoichiometric nanodot composition due to very different surface fluxes of Si and C adatoms to the quantum dots. At this stage, the surface fluxes of Si and C adatoms to SiC nanodots can be effectively controlled by manipulating the SiC atom influx ratio and the Si (100) surface temperature. It is demonstrated that at a surface temperature of 800 K the surface fluxes can be equalized after only 0.05 s into the process; however, it takes more then 1 s at a surface temperature of 600 K. Based on the results of this study, effective strategies to maintain a stoichiometric ([Si] [C] =1:1) elemental ratio during the initial stages of deposition of SiCSi (100) quantum dot nuclei in a neutral/ionized gas-based process are proposed.

Effective control of nanostructured phases in rapid, room-temperature synthesis of nanocrystalline Si in high-density plasmas

An innovative and effective approach based on low-pressure, low-frequency, thermally nonequilibrium, high-density inductively coupled plasmas is proposed to synthesize device-quality nanocrystalline silicon (nc-Si) thin films at room temperature and with very competitive growth rates. The crystallinity and microstructure properties (including crystal structure, crystal volume fraction, surface morphology, etc.) of this nanostructured phase of Si can be effectively tailored in broad ranges for different device applications by simply varying the inductive rf power density from 25.0 to 41.7 mW/cm3. In particular, at a moderate rf power density of 41.7 mW/cm3, the nc-Si films feature a very high growth rate of 2.37 nm/s, a high crystalline fraction of 86%, a vertically aligned columnar structure with the preferential (111) growth orientation and embedded Si quantum dots, as well as a clean, smooth and defect-free interface. We also propose the formation mechanism of nc-Si thin films which relates the high electron density and other unique properties of the inductively coupled plasmas and the formation of the nanocrystalline phase on the Si surface.

A rapid and cost-effective method of producing recombinant proBNP and NT-proBNP variants in Escherichia coli for immunoassay of heart failure

The measurements of plasma natriuretic peptides (NT-proBNP, proBNP and BNP) are used to diagnose heart failure but these are expensive to produce. We describe a rapid, cheap and facile production of proteins for immunoassays of heart failure. DNA encoding N-terminally His-tagged NT-proBNP and proBNP were cloned into the pJexpress404 vector. ProBNP and NT-proBNP peptides were expressed in Escherichia coli, purified and refolded in vitro. The analytical performance of these peptides were comparable with commercial analytes (NT-proBNP EC50 for the recombinant is 2.6 ng/ml and for the commercial material is 5.3 ng/ml) and the EC50 for recombinant and commercial proBNP, are 3.6 and 5.7 ng/ml respectively). Total yield of purified refolded NT-proBNP peptide was 1.75 mg/l and proBNP was 0.088 mg/l. This approach may also be useful in expressing other protein analytes for immunoassay applications. To develop a cost effective protein expression method in E. coli to obtain high yields of NT-proBNP (1.75 mg/l) and proBNP (0.088 mg/l) peptides for immunoassay use.