HABCSm: A Hamming Based t-way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

Search-based software engineering that involves the deployment of meta-heuristics in applicable software processes has been gaining wide attention. Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t-way testing strategies (where t points the interaction strength among parameters). Although helpful, no single meta-heuristic based t-way strategy can claim dominance over its counterparts. For this reason, the hybridization of meta-heuristic algorithms can help to ascertain the search capabilities of each by compensating for the limitations of one algorithm with the strength of others. Consequently, a new meta-heuristic based t-way strategy called Hybrid Artificial Bee Colony (HABCSm) strategy, based on merging the advantages of the Artificial Bee Colony (ABC) algorithm with the advantages of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. HABCSm is the first t-way strategy to adopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance as its core method for generating a final test set and the first to adopt the Hamming distance as the final selection criterion for enhancing the exploration of new solutions. The experimental results demonstrate that HABCSm provides superior competitive performance over its counterparts. Therefore, this finding contributes to the field of software testing by minimizing the number of test cases required for test execution

The ‘Big Four’ price promotions in predicting decision utility and efficacy

One way that retailers help the consumer make choices is via promotions – price framing methods that explicitly offer a price reduction of value for money off the regular retail price (RRP). However, there is a growing body of research that has indicated that merely the word ‘promotion’ or ‘deal’ can increase purchase intentions despite the deal offering no savings. Despite these findings, almost no research has quantifiably considered which, how and to what extent different promotional methods can bias decisions. Furthermore, very little is known about how consumers go about making promotional decisions or which psychological factors impact the decision-making process. Considering a broad range of decision-making frameworks and psychological theories, this thesis aims to explore the extent that promotional practices influence decision-making outcomes. Furthermore, it will consider how psychological traits like financial literacy, experience and brand relationships moderate any found effects. To achieve these objectives the effect of the four most common promotional practices on decision utility will be tested in light of: the previous literature on decision-making and promotions (Chapter 1); expert interviews describing the traits or behaviours important in developing promotional strategies (Chapter 2); the effect of information processing on promotional decision making (Chapter 3); how prices are internalised (Chapter 4); and consumer relationships (Chapter 5). Finally, the results of each chapter will be used to create and test a framework of promotional decision-making. Creating and testing this framework in an experimental and more ecologically valid setting, i.e. a virtual supermarket will be the sole purpose of Chapter 6. The aim of creating and validating the framework will be to significantly contribute to: academia, by adding some novel research to the growing promotional literature; and practice, by considering how the practices specific effects to decision making can impact fair pricing practices and consumer education

RNA structure analysis : algorithms and applications

In this doctoral thesis, efficient algorithms for aligning RNA secondary structures and mining unknown RNA motifs are presented. As the major contribution, a structure alignment algorithm, which combines both primary and secondary structure information, can find the optimal alignment between two given structures where one of them could be either a pattern structure of a known motif or a real query structure and the other be a subject structure. Motivated by widely used algorithms for RNA folding, the proposed algorithm decomposes an RNA secondary structure into a set of atomic structural components that can be further organized in a tree model to capture the structural particularities. The novel structure alignment algorithm is implemented using dynamic programming techniques coupled by position-independent scoring matrices. The algorithm can find the optimal global and local alignments between two RNA secondary structures at quadratic time complexity. When applied to searching a structure database, the algorithm can find similar RNA substructures and therefore can be used to identify functional RNA motifs. Extension of the algorithm has also been accomplished to deal with position-dependent scoring matrix in the purpose of aligning multiple structures. All algorithms have been implemented in a package under the name RSmatch and applied to searching mRNA UTR structure database and mining RNA motifs. The experimental results showed high efficiency and effectiveness of the proposed techniques

Neuronal signalling molecules as targets for green peach aphid (Myzus persicae) control via RNA interference

The Green peach aphid (GPA) (Myzus persicae) is an important insect pest which causes substantial economic losses to many glasshouse and field crops. Alarmingly, GPAs are becoming resistant to many conventional insecticides, and this trend indicates that there is a real need to develop alternative strategies to protect crops from this insect pest. The aim of this research project was to investigate the potential of RNA interference (RNAi) technology as a strategy to control GPAs. Genes involved in insect neuronal signalling pathways were selected as RNAi targets. Bioinformatic analysis tools were used to identify ESTs putatively encoding sixty-three Neuronal Signalling Molecules (NSMs) from publicly available sequences and from GPA transcriptome data generated in-house. The NSMs included 30 Neuropeptides (NPs), 24 Neuropeptide Receptors (NPRs), and 9 Biogenic Amine Receptors (BARs). From these, transcripts for 24 NSMs were selected for in vitro RNAi assays to determine their suitability as targets for host-induced gene silencing (HIGS). Successful ingestion of dsRNA of target genes by nymphs was confirmed using the presence of a neutral red dye in the body of aphids, incorporated in the dsRNA+30% sucrose diet. Silencing effects of nine genes, e.g. Ecdysis triggering Hormone (eth), Capability (capa), Juvenille hormone binding protein (jhbp), Leucokinin (lk), Crustacean Cardioactive Peptide (ccap), Octopamine beta 3R (octβ3r), Muscarinic acetylcholine receptor 3 (mAChrM3), Short NPF (snpf ) and Insulin-related peptide 2/3 (irp2/3) were obvious 24 hours after feeding on the dsRNA diet. RNAi phenotypes included incomplete moulting, uncoordinated movement, lethargy, paralysis and lethality, whereas the control GPAs exposed to no-dsRNA and dsRNA of the green fluorescent protein (GFP) gene of the jellyfish, Aequorea victoria moved normally, showing no obvious effects of the treatment. For GPAs treated with dsRNAs of six of these genes (ccap, capa, mAChrM3, lk, octβ3r and irp2/3), silencing also significantly affected survival and fecundity when the aphids were later transferred to tobacco plants for 12 days. Silencing of ccap, capa, irp2/3, lk and octβ3r resulted in 100% lethal phenotypes on the tobacco plants. Knockdown of dscapar1 and dsnplp1 also affected GPA reproduction although no visible effects were observed 24 hours after ingestion of dsRNA. The effectiveness of nine of the 24 genes (ccap, jhbp, nplp1, capar1, irp5, lk, octβ3r, snpf and opsin) as targets for RNAi control of GPAs were evaluated using HIGS, in which two model plants, tobacco and Arabidopsis thaliana were used. Transgenic tobacco plants carrying hairpins (hp) of all nine GPA genes were developed of which those for six genes (except for lk, octβ3r and snpf), were advanced to the T2 generation, and used for GPA bioassays. In T1 tobacco, the mean population was reduced by 97% for hpoctβ3r event 2 and event 5, while significantly lower GPA populations were recorded for all the lines expressing hpccap, hpnplp1 and hplk after 12 days (p<0.05). As for the T1 generation, most of the T2 transgenic events also supported significantly fewer GPA nymphs, with reductions in numbers ranging from 3% to 69%. GPAs feeding on events of hpccap, hpnplp1 and hplk produced fewest nymphs, as was observed for T1 generation. In addition, an 80% to 100% reduction in GPAs was evident for T2 transgenic Arabidopsis plants expressing dsccap, dsjhbp and dsnplp1, and complete mortality was recorded for the hpccap event 3. The results obtained from two transgenic generations and two model plants therefore indicate that the genes studied were vital for the GPA life cycle and knocking down of these genes affects their fecundity or survival. An in vitro study was also conducted to evaluate the effects of silencing five different lengths of dsRNA from different regions of the same EST putatively encoding the JHBP protein, as well as siRNAs of the gene generated in vitro from digestion with an RNAseIII enzyme. The longest dsRNA (284 bp) was the most effective in inducing RNAi effects on treated nymphs, since there were more restricted movements in aphids 24 hours after exposure, and the fewest offspring were produced in the longer-term. One of the shorter dsRNAs (86 bp long, not the shortest,70 bp), also significantly reduced GPA movement, survival and reproduction at levels similar to that of the longest dsRNA. These results show that RNAi effects can vary with the target region from which the hp dsRNA is derived and in this case silencing was more effective for one of the sequences derived from the 3´ region. This study indicates that both the length of the dsRNA and the specific sequence chosen can influence the effectiveness of RNAi. This project provides new information on GPA neuronal genes as novel candidates for its control via gene silencing. It also offers additional data to achieve better RNAi effects related to the target sequence selected. The in planta RNAi study also demonstrated that RNAi can be used as a new strategy to control this important crop pest, and its use, either alone or in combination with other gene targets, is discussed

Characterisation of zebrafish haem oxygenase function in mycobacterial infection

Tuberculosis is one of the top 10 leading causes of death in the world. Infection with pathogenic mycobacteria such as Mycobacterium tuberculosis leads to the formation of granulomas, the hallmark histological feature of tuberculosis. Failure to maintain granuloma integrity results in loss of infection control or reactivation of tuberculosis disease. Hmox1 is a stress-responsive protein that can be upregulated rapidly and confers protection to the host. Haem oxygenase 1 (Hmox1) is a critical regulator of iron homeostasis by degrading haem into carbon monoxide, biliverdin and ferrous iron. The protective role of Hmox1 has been demonstrated in a variety of diseases. However, the role of Hmox1 in mycobacterial infection is equivocal with different results in different host-pathogen pairings. Chemokine production plays a crucial role in the recruitment of leukocytes to the focus of infection, particularly, monocyte chemoattractant protein-1 (MCP1) and its receptor CCR2. Hmox1 has been shown to regulate MCP1 expression in mice in the context of mycobacterial infection but limited information is available in zebrafish. Meanwhile, as a key regulator of iron metabolism, Hmox1 may also control the availability of iron during infection, since iron is essential both the host and pathogen and the restriction of iron is an important host defence against bacteria. Mycobacteria require iron as a redox cofactor for vital enzymes and utilise multiple strategies to acquire iron within the host. In vivo imaging using zebrafish embryos and their natural pathogen Mycobacterium marinum enables us to collect unique insight into the functions of Hmox1 in infection. This thesis describes the first generation of hmox1a mutant zebrafish and the first use of the zebrafish model system to investigate Hmox function in M. marinum infection. It connects mycobacterial infection-induced Hmox to iron restriction and prevention of deleterious ferroptosis during mycobacterial infection. This work provides insight into the role of Hmox in tuberculosis pathogenesis

A methodological critique of the Interpretive Ranking Process for examining IS project failure

YesThis research critically analyzes the Interpretive Ranking Process (IRP) using an illustrative empirically derived IS project failure related case study to articulate a deeper understanding of the method. The findings emphasize the suitability of the method for a number of practical applications, but also highlight the limitations for larger matrix sized problems. The IRP process to derive the dominance between IS project failure factors is judged to be methodical and systematic, enabling the development of clear dominating interactions

Technology Agnostic Analysis and Design for Improved Performance, Variability, and Reliability in Thin Film Photovoltaics

Thin film photovoltaics (TFPV) offer low cost alternatives to conventional crystalline Silicon (c-Si) PV, and can enable novel applications of PV technology. Their large scale adoption however, requires significant improvements in process yield, and operational reliability. In order to address these challenges, comprehensive understanding of factors affecting panel yield, and predictive models of performance reliability are needed. This has proved to be especially challenging for TFPV for two reasons in particular. First, TFPV technologies encompass a wide variety of materials, processes, and structures, which fragments the research effort. Moreover, the monolithic manufacturing of TFPV modules differs significantly from that of c-Si technology, and requires new integrated approaches to analysis and design for these technologies. In this thesis, we identify a number of features affecting the variability and reliability of TFPV technologies in general, and propose technology agnostic design solutions for improved performance, yield, and lifetime of TFPV modules. We first discuss the universal features of current conduction in TFPV cells, for both intrinsic dark and light currents, and parasitic (shunt) leakage. We establish the universal physics of space-charge-limited shunt conduction in TFPV technologies, and develop physics based compact model for TFPV cells. We examine the statistics of parasitic shunting, and demonstrate its universal log-normal distribution across different technologies. We also evaluate the degradation behavior of cells under reverse bias stress, and identify different degradation mechanisms for intrinsic and parasitic components. We then embed the physics and statistics of cell operation and degradation, in a circuit simulation framework to analyze module performance and reliability. With this integrated circuit-device simulation, we establish log-normal shunt statistics as a major cause of module efficiency loss in TFPV, and develop a in-line technique for module efficiency and yield enhancement. Finally, we study the features of TFPV module reliability under partial shading using this circuit simulation, and propose a geometrical design solution for shade tolerant TFPV modules. The most important theme of this thesis is to establish that TFPV technologies share many universal performance, variability, and reliability challenges. And, by using a technology agnostic approach for studying these problems, we can achieve fruitful cross coupling of ideas and enable broadly applicable solutions for important technological challenges in TFPV

MicroRNA: Molecular Micromanagers of Iron Metabolism and Oxygen Sensing

Iron deficiency (ID) is estimated to affect one-third of the world's population. As an essential micronutrient, iron is required for DNA synthesis, cellular proliferation, and oxygen transport. Iron is potentially toxic through its ability to promote the generation of ROS, thus cellular iron is tightly controlled. Although a family of cytosolic RNA binding proteins plays a central role in maintaining cellular iron homeostasis, evidence suggests that iron levels may be coordinated by microRNA (miRNA). miRNA are noncoding RNA that recognize and bind to partially complementary sites of target mRNA and regulate gene expression via translational repression and mRNA degradation. With the previous identification of ~10 differentially expressed miRNA in ID rat livers, we chose to study two of the identified miRNA, miR-181d and miR-210. The central hypothesis was miRNA regulated by dietary iron deficiency play a role in the modulation of target mRNA, and function as key elements in regulating iron homeostasis. Using the bioinformatics programs miRWalk and TargetScan, we identified mitoferrin 1 and isocitrate dehydrogenase 1 were conserved predicted targets of miR-181d and cytoglobin was a conserved predicted target of miR-210. Next, reporter assays confirmed the direct interaction of the miRNA and their respective mRNA targets. Finally, in vitro experiments were conducted to demonstrate iron chelation and miRNA overexpression influenced mRNA abundance and translational repression of target mRNA. Our results confirm that miR-181d contributes to the regulation of isocitrate dehydrogenase 1. Additionally, although miR-210 was significantly upregulated in response to ID in rat livers and in vitro iron chelation, cytoglobin expression was upregulated in both conditions. Therefore, the results demonstrate dietary iron deficiency and chelation upregulate (1) miR-181d expression that influences isocitrate dehydrogenase 1 gene expression and translation and (2) cytoglobin gene expression and translation.Nutritional Science

Circuits and Systems Advances in Near Threshold Computing

Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing