Intelligent Transport System (ITS) has become a hot research topic over the past decades. ITS is a system that applies the following technologies to the whole transportation management system efficiently, including information technique, wireless communication, sensor networks, control technique, and computer engineering. ITS provides an accurate, real time and synthetically efficient transportation management system. Obviously, Vehicular Ad Hoc NETworks (VANETs) attract growing attention from both the research community and industry all over the world. This is because a large amount of applications are enabled by VANETs, such as safety related applications, traffic management, commercial applications and general applications. When connecting to the internet or communicating with different networks in order to access a variety of services using VANETs, drivers and passengers in different cars need to be able to exchange messages with gateways from their vehicles. A secure gateway discovery process is therefore critical, because vehicles should not be subject to security attacks while they are communicating; however, currently there is no existing protocol focusing on secure gateway discovery. In this thesis, we first analyze and compare current existing secure service discovery protocols and then we propose a Secure Gateway Localization and Communication System for Vehicular Ad Hoc Networks (SEGAL), which concentrates on the security issue in gateway discovery. We focus on the authentication aspect by proposing secure cluster based VANETs, that can ensure the gateway discovery messages exchanged through secure clusters. We present the principle and specific process of our SEGAL protocol and analyze its performance to guarantee its outstanding practical applicability.

Electronic Product Code (EPC) refers to a numbering standard developed to uniquely identify physical objects, loads, locations, assets and other entities which are to be tracked or otherwise identified. The tracking technology consists of assigning Radio Frequency Identification (RFID) tags, holding universally unique EPC codes, to the entities to be identified. While the EPC-RFID technology is used to identify and capture data about the physical objects to be tracked in a supply chain, the EPCglobal Network ensures the exchange of the captured data between supply chain stakeholders. Such a real-time data exchange increases visibility and efficiency throughout the supply chain, and thus it increases both company profitability and customer satisfaction. The EPCglobal Network can be regarded as the backbone for the future Internet of Things (IoT). We focus our work in this thesis on Discovery Services (DS); a suite of network lookup services enabling users to retrieve all relevant information sources with regards to a given EPC. They can be viewed as search engines for the future business infrastructure deployed in the IoT. Motivated by the unprecedented and incessantly growing amount of EPC data, the expected epidemic growth in the solicitation frequency of the lookup service, and also the foreseen exceptionally large flow of highly sensitive EPC information, we focus on proposing solutions to problems pertaining to two main challenges; architecture design of Discovery Services and their security. On the architecture design level, we propose novel DS architectures with focus directed towards meeting four major requirements; network scalability, query responsiveness, service extensibility and acceptance. On the security level, we propose probabilistic security schemes aiming at securing even further Discovery Services in the IoT in general, and in the EPCglobal network in particular.

In 2005 and 2010, high sea surface temperatures caused widespread coral bleaching on Jamaica’s north coast reefs. Three shallow (9m) reef sites were surveyed during each event to quantify the prevalence and intensity of coral bleaching. In October 2005, 29-57% of the colonies surveyed were bleached. By April 2006, 10% of the corals remained pale/partially bleached. Similarly, in October 2010, 23-51% of corals surveyed at the same sites were bleached. By April 2011, 12% of the colonies remained pale/partially bleached. Follow-up surveys revealed low coral mortality following both events, with an overall mean of 4% partial colony mortality across all species and sites observed in April 2006, and 2% in April 2011. Mixed effects models were used to quantify the relationship between colony size and (a) bleaching intensity, and (b) bleaching related mortality among coral species. The bleaching intensity model explained 51% of the variance in the bleaching response observed during the two events. Of this 51%, fixed effects accounted for ~26% of the variance, 17% of which was attributed to species-specific susceptibility to bleaching , 5% to colony size, <1% colony morphology and 4% to the difference in bleaching intensity between the two events. The random factor (site) accounted for the remaining ~25% of the variance. The mortality model explained 16% of the variance in post bleaching mortality with fixed effects, including colony size, morphology and species explaining ~11% of the variance, and the random effect (site) explaining 5%. On average, there was a twofold difference in bleaching intensity between the smallest and the largest size classes. Modelling the relationship between colony level characteristics and site-specific environmental factors on coral species’ susceptibility to thermal stress can shed light on community level responses to future disturbances.

Handheld devices are becoming increasingly common, and they have varied range of resources. Mobile Cloud Computing (MCC) allows resource constrained devices to offload computation and use storage capacities of more resourceful surrogate machines. This enables creation of new and interesting applications for all devices. We propose a scheme that constructs a high-performance de-centralized system by a group of volunteer mobile devices which come together to form a resourceful unit (cloudlet). The idea is to design a model to operate as a public-resource between mobile devices in close geographical proximity. This cloudlet can provide larger storage capability and can be used as a computational resource by other devices in the network. The system needs to watch the movement of the participating nodes and restructure the topology if some nodes that are providing support to the cloudlet fail or move out of the network. In this work, we discuss the need of the system, our goals and design issues in building a scalable and reconfigurable system. We achieve this by leveraging the concept of virtual dominating set to create an overlay in the broads of the network and distribute the responsibilities in hosting a cloudlet server. We propose an architecture for such a system and develop algorithms that are requited for its operation. We map the resources available in the network by first scoring each device individually, and then gathering these scores to determine suitable candidate cloudlet nodes. We have simulated cloudlet functionalities for several scenarios and show that our approach is viable alternative for many applications such as sharing GPS, crowd sourcing, natural language processing, etc.

Encephalitis is a matter for serious public health concern because of the high morbidity and mortality associated with many cases. Epidemiological studies have shown that viral encephalitis (VE) is more common than the sum of encephalitis caused by all other pathogens. However, more than 95% of cases have no known cause. Thus, there is a significant need to develop a sensitive method for the diagnosis of these unknown cases. Previous sequence independent amplification (SIA) assays have proved successful in detecting new viruses in many biological samples but not in CSF samples. This may be due to the relatively low sensitivity of most available methods as CSF usually contains lower concentrations of pathogen than most other samples. A known problem with these types of assays is the annealing of the random primers to human DNA which facilitates preferential amplification of background human DNA. Thus, large scale sequencing is usually required to detect a virus, which in turn reduces the detection sensitivity to more than 1000 viral copies/µl, a CSF concentration that is rarely seen in cases of VE.This project was designed to develop a highly sensitive SIA assay for novel nucleic acid identification that could be used in testing CSF samples obtained from patients with neurological diseases of unknown cause. The study started with evaluation of two existing SIA assays commonly used for virus discovery; whole genome amplification (WGA) and random PCR (r-PCR). Sequential modification and adaptation of these methods was carried out to increase their sensitivity. Ultimately, a novel primer (Sa primer) that showed no binding to most human DNA sequences in GenBank was designed and synthesised. Its 3' end was tagged with 6 and 7 random nucleotides generating 2 r-primers; Sa-6 and Sa-7. The sensitivity of the r-primers was checked in a novel assay developed during this project and named Sa-SIA using known concentrations of HCMV and HSV-1. CSF samples from Malawian children were then tested using the developed assay. Results showed that adaptation of the existing WGA and r-PCR assays allowed detection of up to 1300 viral copies/µl. When the novel primers developed in this project were used in a random PCR assay (Sa-r-PCR), it was found that using Sa-6 primer 130, 13, and 1.3 HCMV copies/µl could be detected with 100, 60, and 50% efficiency respectively. When using Sa-7 primer, the same concentrations of virus were detected with 100, 42, and 28.6% efficiency. DNase-1 treatment of the samples pre-extraction resulted in an improvement in viral detection sensitivity in samples with a high background of host DNA. Starting with template concentrations of 11000, 110, 11, and 1.1 HSV-1 copies/µl, viral detection efficiency was increased from 33.3, 10, 0, and 0% to 92, 55.6, 16.7, and 0% respectively when pre-extraction DNase-1 treatment preceded Sa-r-PCR using Sa-6 primer. The final developed assay (Sa-SIA) consisted of centrifugation, DNase-1 treatment, DNA extraction, Sa-r-PCR using Sa-6 and Sa primers, gel electrophoresis, band excision, cloning, small scale sequencing (sequencing of ≤ 20 positive clones from one constructed DNA library), and bioinformatics. It had a detection sensitivity of 1.3-11 viral copies/µl. When this assay was applied to stored CSF samples, one 448bp sequence was identified which gave 96% coverage with 81% identity to Torque teno midi virus-1 and 93% coverage with 81% identity to small anellovirus-2. A 236bp sequence from another CSF sample showed 66% coverage with 97% homology to an unclassified sequence previously identified in a viral genomic survey of stool sample in an earlier published study. In conclusion, the standardised method had been shown to detect 1.3 to 11 viral copies/µl of two viruses; HCMV and HSV-1. The detection of these viruses was achieved with only small scale sequencing. Application of this method to CSF samples has shown promising results. However, this method could be followed by more advanced post amplification analyses such as next generation sequencing.

L’étude des interactions moléculaires au niveau des membranes biologiques est un enjeu capital pour le développement et le screening de nouvelles molécules médicamenteuses. La MA est la forme de démence sénile la plus répandue dans le monde et représente le principal problème socioéconomique en matière de soins de santé. L'apparition et la progression de cette maladie neurodégénérative sont associées à l'agrégation du peptide Aβ.Une stratégie thérapeutique contre la MA consiste à développer des molécules capables d'interférer à des étapes spécifiques de l’agrégation du peptide. Pour les identifier, des méthodes expérimentales sont nécessaires pour suivre et caractériser le peptide Aβ au cours de son processus de fibrillation. Ces méthodes doivent être suffisamment simples pour rester compatibles avec une démarche de drug discovery. Dans le présent travail de thèse, nous avons proposé de combiner des méthodes expérimentales pour permettre une caractérisation multiparamétrique de modulateurs potentiels de la fibrillation du peptide Aβ1-42, en y intégrant des liposomes de composition définie, comme membranes neuronales biomimétiques. Il est en effet établi que les lipides neuronaux sont un facteur important dans la formation des fibres amyloïdes et leur toxicité. Les liposomes ont été formulés par la méthode de réhydratation de film lipidique, et leurs propriétés physico-chimiques caractérisées par RMN, DLS, potentiel ζ.La détermination expérimentale du coefficient de partage de composés d’intérêt a pu être réalisée par spectrophotométrie, y compris de façon originale, par fluorescence, en utilisant ces liposomes, dans des tests miniaturisés. Des études cinétiques de l’agrégation du peptide Aβ1-42 ont été effectuées en présence de liposomes. La fluorescence de la ThT a été mesurée pour suivre la voie de la fibrillation du peptide Aβ, utilisé dans sa forme sauvage ou celle d’un mutant oligomérique, l’oG37C. Une analyse de fuite d’un fluorophore à partir des liposomes, appuyée par des mesures en DLS, a été réalisée afin d'évaluer l'impact des interactions entre le peptide et les membranes pour prévoir tout effet de déstabilisation. Les fibres toxiques formées par Aβ étant principalement organisées en feuillets β, les données ont été corrélées à l'analyse de la structure secondaire du peptide par spectroscopie ATR-FTIR. Après avoir mis en œuvre cette approche sur différentes molécules modèles et un hit d’intérêt potentiel dans le traitement de la MA, l’ensemble de ce travail a abouti à un test multiparamétrique permettant la caractérisation de l’interactome molécules/Aβ/membranes et la discrimination de modulateurs de l'agrégation du peptide Aβ1-42. Cette approche pourra être avantageusement transposée à d'autres maladies amyloïdes. / The study of molecular interactions at the level of biological membranes is a key issue for the screening and the development of new drugs. Alzheimer's disease (AD) is the most common form of senile dementia in the world and is the leading socio-economic problem in health care. The appearance and progression of this neurodegenerative disease are associated with the aggregation of the amyloid-β peptide (Aβ). A therapeutic strategy against AD consists in the development of molecules able to interfere with specific steps of Aβ aggregation. To identify such compounds, experimental methods are required to monitor and characterize the Aβ peptide during its fibrillation process. These methods must be simple enough to remain compatible with drug discovery. In this PhD project, we have proposed to combine experimental methods to allow a multiparametric characterization of potential Aβ1-42 fibrillation modulators, by integrating liposomes of defined composition as biomimetic neuronal membranes. It is indeed established that neuronal lipids are an important factor in the formation of amyloid fibers and their toxicity. The liposomes were formulated by the lipid film rehydration method, and their physicochemical properties characterized by NMR, DLS, ζ potential. The experimental determination of the compounds partition coefficient could be carried out by spectrophotometry, including in an original way, by fluorescence, these liposomes, in miniaturized tests. Kinetic studies of Aβ1-42 peptide aggregation were performed in the presence of liposomes.The ThT fluorescence was monitored to follow the Aβ peptide fibrillation pathway, used in its wild form or with an oligomeric mutant, oG37C. A fluorophore leakage analysis from liposomes, supported by DLS measurements, was performed to evaluate the impact of peptide/membranes interactions to predict any destabilization effects. The toxic fibers formed by Aβ being mainly organized in β-sheets, the data were correlated with the analysis of the peptide secondary structure by ATR-FTIR spectroscopy. After the implementation of this approach on different model molecules and a hit of potential interest in the AD treatment, all of this work has resulted in a multiparametric test allowing the molecules/Aβ/membranes interactome characterization and the discrimination of Aβ1-42 peptide aggregation modulators. This approach may be advantageously transposed to other amyloid diseases.

Vehicular crashes are the leading cause of death for young adult drivers, however, very little life course research focuses on drivers in their 20s. Moreover, most data analyses of crash data are limited to simple correlation and regression analysis. This thesis proposes a data-driven approach and usage of machine-learning techniques to further enhance the quality of analysis. We examine over 10 years of data from the Iowa Department of Transportation by transforming all the data into a format suitable for data analysis. From there, the ages of drivers present in the crash are discretized depending on the ages of drivers present for better analysis. In doing this, we hope to better discover the relationship between driver age and factors present in a given crash. We use machine learning algorithms to determine important attributes for each age group with the goal of improving predictivity of individual methods. The general format of this thesis follows a Knowledge Discovery workflow, preprocessing and transforming the data into a usable state, from which we perform data mining to discover results and produce knowledge. We hope to use this knowledge to improve the predictivity of different age groups of drivers with around 60 variables for most sets as well as 10 variables for some. We also explore future directions this data could be analyzed in.

Theoretical models suggest that ownership structure may be an important determinant of securities' market characteristics. For example, the presence of informed traders leads to greater bid-ask spreads (Copeland and Galai (1983), and Glosten and Milgrom (1985)), and strategic trading of informed and discretionary liquidity traders leads to intertemporal variation in both trading volume and trading costs (Admati and Pfleiderer (1988), and Foster and Viswanathan (1990)). However, the empirical studies on the effect of ownership structure on market characteristics are limited. Prior studies focus on either one type of market characteristics or one type of owners, and usually do not address the potential endogeneity problem between market characteristics and ownership structure. This dissertation extends existing literature with two essays on ownership and market characteristics. The first essay broadly examines the effect of ownership structure (inside ownership, institutional ownership, and individual ownership) on market characteristics such as order flow, price impact of trade, quoted spread and quoted depth. For each market characteristic examined, I establish an empirical model based on existing theories and empirical evidence. My results indicate that stocks with greater inside ownership have lower order flow, greater price impact of trade, greater quoted spread and lower quoted depth, while stocks with greater active institutional ownership and greater individual shareholders have greater order flow, smaller price impact of trade, lower spread and greater depth. These results may have implications for corporate governance. For example, while agency theory suggests managerial ownership may align interests of managers and shareholders, this essay finds that this comes with a liquidity cost. Further, my results suggest there are liquidity benefits of individual and institutional ownership. If as suggested by Amihud and Mendelson (1989), investors require a higher rate of return for illiquid stocks, firms can target their shares to specific types of investors (for example, active institutions and individuals) to improve liquidity, and reduce their cost of capital. The second essay is a specific application of the first essay and examines the effect of institutional ownership on price discovery around earnings announcements. I select earnings announcements as the event for my analysis because there are three well-documented regularities about earnings announcements. First, market participants anticipate the forthcoming earnings announcements. Second, the announcements of earnings news are usually accompanied by abnormal price changes and abnormal volume. Third, there is evidence that stock price continues to move in the direction of earnings surprise after the announcements of earnings news. Since results from the first essay suggest that institutional investors affect market characteristics such as price impact of trade and quoted spread, I expect that institutional participation would also affect the price discovery process around earnings announcements. My results indicate that institutional ownership is associated with greater anticipation of earnings news. Further, stocks with greater institutional ownership have a greater price response to announcements of earnings news. Finally, institutional investors have no significant effect on post-announcement drift. The results of the second essay suggest that institutional investors contribute to the price discovery process. / Ph. D.

<div>During the past decades, deep learning has been proven to be an important tool for statistical data analysis. Motivated by the promise of deep learning in tackling the curse of dimensionality, we propose three innovative methods which apply deep learning techniques to high-dimensional data analysis in this dissertation.</div><div><br></div><div>Firstly, we propose a nonlinear sufficient dimension reduction method, the so-called split-and-merge deep neural networks (SM-DNN), which employs the split-and-merge technique on deep neural networks to obtain nonlinear sufficient dimension reduction of the input data and then learn a deep neural network on the dimension reduced data. We show that the DNN-based dimension reduction is sufficient for data drawn from exponential family, which retains all information on response contained in the explanatory data. Our numerical experiments indicate that the SM-DNN method can lead to significant improvement in phenotype prediction for a variety of real data examples. In particular, with only rare variants, we achieved a remarkable prediction accuracy of over 74\% for the Early-Onset Myocardial Infarction (EOMI) exome sequence data. </div><div><br></div><div>Secondly, we propose another nonlinear SDR method based on a new type of stochastic neural network under a rigorous probabilistic framework and show that it can be used for sufficient dimension reduction for high-dimensional data. The proposed stochastic neural network can be trained using an adaptive stochastic gradient Markov chain Monte Carlo algorithm. Through extensive experiments on real-world classification and regression problems, we show that the proposed method compares favorably with the existing state-of-the-art sufficient dimension reduction methods and is computationally more efficient for large-scale data.</div><div><br></div><div>Finally, we propose a structure learning method for learning the causal structure hidden in the high-dimensional data, which consists of two stages:</div><div>we first conduct Bayesian sparse learning for variable screening to build a primary graph, and then we perform conditional independence tests to refine the primary graph. </div><div>Extensive numerical experiments and quantitative tests confirm the generality, effectiveness and power of the proposed methods.</div>

The multistage life cycle of malaria-causing P. falciparum is complex, making prevention and treatment difficult. As a result of resistance to many antimalarial drugs, novel compounds with unexplored targets are constantly sought after for the purpose of treating the symptoms of malaria. Here, novel compounds were screened for antiplasmodial activity against the symptom-causing asexual intraerythrocytic malaria-causing parasites. Unfortunately, many novel compounds in the drug discovery pipeline and drugs in clinical use possess underlying pharmacological issues that makes administration challenging. These include low aqueous solubility and short half-life which negatively impact bioavailability resulting in toxicity. This, in turn, increases patient non-compliance and the emergence of drug-resistant strains. Nanoparticles (NP) have the ability to mask drugs from the external environment while increasing circulation time and often alleviate many issues at once. Furthermore, the selected drugs do not need to be modified. Drug conjugation NPs with a targeting ligand and stimuli-responsive linkers have been extensively researched in many diseases, however, none have been reported for malaria clinically. Here, the first acid-labile targeted NP (tNP) that exploits the biology of infected erythrocytes and the specialised food vacuole (FV) of P. falciparum is interrogated for ability to decrease toxicity while retaining antimalarial activity. This dissertation describes the effect of tNPs on the efficacy and toxicity of selected compounds. In vitro haemolysis and cytotoxicity assays revealed that the tNPs are biocompatible to erythrocytes and HepG2 cells. The data also shows that tNPs decrease the toxicity of drugs and the chosen novel compound against human cells. A decrease in antiplasmodial activity was observed in vitro for the tNPs when compared to the novel compound and drugs on their own. However, this was due to the biogenesis of the FV and a shortened window of release. Nonetheless, the NP backbone was not active against P. falciparum intraerythrocytic parasites whereas tNPs were, showing activity due to released drug. The targeting ligand was also not specific for antiplasmodial activity. Although a significant loss in activity is observed, the results presented here suggests that these novel acid-labile tNPs serve as an attractive starting point for targeted treatment of malaria with an improved patient tolerance. Furthermore, novel compounds with issues can be selected without having to be modified or completely discarded. Therefore, increasing the chances of finding a variety of compounds that can be used to treat malaria while keeping patients safe. / Dissertation (MSc (Biochemistry))--University of Pretoria, 2020. / NRF / Biochemistry / MSc (Biochemistry) / Unrestricted