Gallium-assisted diffusion bonding of stainless steel to titanium; microstructural evolution and bond strength

Strong joints between stainless steel 304L and pure titanium (grade-2) were made using the novel method of “gallium-assisted diffusion bonding”. The microstructural evolution and interfacial reactions were investigated in detail. The possible mechanisms of phase changes at the joint interface when bonding with and without a nickel interlayer were identified. Layers of FeTi and (Fe,Cr)2Ti intermetallic compounds were found at the reaction zone in the case of direct bonding, whereas (Fe,Ni)Ti and Fe2Ti phases were identified in the reaction zone of the samples bonded using nickel interlayers. A layer of αFe was observed on the steel side of the reaction zone in both the cases, probably due to the enrichment of Cr at the interface. The diffusion of gallium led to formation of a layer of αTi, while the diffusion of Fe and Ni assisted in the formation of a duplex (α+β)Ti phase in the inter-diffusion zone. The joints fractured along the intermetallic layers at the interface, during tensile testing, with limited ductility. The maximum tensile strengths of the bonded samples were 280 and 313 MPa with and without nickel interlayer, respectively. The latter equals 92% of the tensile strength of the pure grade-2 titanium used in this work (i.e. 340 MPa)

Prediction of neurodegenerative diseases from functional brain imaging data

Neurodegenerative diseases are a challenge, especially in thedeveloped society where life expectancy is high. Since these diseasesprogress slowly, they are not easy to diagnose at an earlystage. Moreover, they portray similar disease features, which makesthem hard to differentiate. In this thesis, the objective was todevise techniques to extract biomarkers from brain data for theprediction and classification of neurodegenerative diseases, inparticular parkinsonian syndromes. We used principal componentanalysis in combination with the scaled subprofile model to extractfeatures from the brain data to classify these disorders. Thereafter,the features were provided to several classifiers, i.e., decisiontrees, generalized matrix learning vector quantization, and supportvector machine to classify the parkinsonian syndromes. A validationof the classifiers was performed.The decision tree method was compared to the stepwise regressionmethod which aims at linearly combining a few good principalcomponents. The stepwise regression method performed better than thedecision tree method in the classification of the parkinsoniansyndromes. Combining the two methods is feasible. The decision treeshelped us to visualize the classification results, hence providing aninsight into the distribution of features. Both generalized matrixlearning vector quantization and support vector machine are betterthan the decision tree method in the classification of early-stageparkinsonian syndromes.All the classification methods used in this thesis performed well withlater disease stage data. We conclude that generalized matrix learningvector quantization and decision tree methods can be recommended forfurther research on neurodegenerative disease classification andprediction

Topology control for wireless mesh networks and its effect on network performance

A thesis submitted to the Faculty of Science and Agriculture in fulfilment of the Degree of Doctor of Philosophy in the Department of Computer Science at the University of Zululand, 2017InfrastructureWireless Mesh Networks (I-WMNs) are increasingly used to provide network connectivity and Internet access to previously under-served areas in the developing world. It is common for some of these deployments to be battery-powered due to a lack of electrical infrastructure in the targeted areas. Thus, the energy-efficiency of these networks gains additional importance. Topology Control (TC) has been previously reported to improve the energy-efficiency and network performance of wireless ad-hoc networks, including I-WMNs. However,simulation-based studies have been relied upon to reach these conclusions and the study of TC prototypes applicable to I-WMNs has largely been limited to design issues. Thus, the study of the efficacy of TC prototypes as a mechanism for improving energy-fficiency and network performance remains an open issue. The thesis addresses this knowledge gap by studying the dynamic, run-time behaviours and the network topologies created by two standards-compatible TC prototypes. This study provides unique insight into how the prototypes consume computational resources, maintain network connectivity, produce cumulative transceiver power savings and affect the workings of the routing protocol being employed. This study also documents the topology instability caused by transceiver power oscillations produced by the PlainTC prototype. A context-based solution to reduce transceiver power oscillations and the subsequent topology instability is proposed. This solution applies the Principal Component Analysis statistical method to historical network data in order to derive the weights associated with each of the identified context variables. A threshold value is defined that only permits a node to adjust its transceiver power output if the observed change in a node’s context exceeds the threshold. The threshold mechanism is incorporated into the PlainTC+ prototype and is shown to reduce topology instability whilst improving network performance when compared to PlainTC.The results obtained in this study suggest that I-WMN topologies formed by TC are able to closely match the performance of networks that do not employ TC. However, this study shows that TC negatively affects the energy efficiency of the network despite achieving cumulative transceiver power savings

Prediction of neurodegenerative diseases from functional brain imaging data

Neurodegenerative diseases are a challenge, especially in the developed society where life expectancy is high. Since these diseases progress slowly, they are not easy to diagnose at an early stage. Moreover, they portray similar disease features, which makes them hard to differentiate. In this thesis, the objective was to devise techniques to extract biomarkers from brain data for the prediction and classification of neurodegenerative diseases, in particular parkinsonian syndromes. We used principal component analysis in combination with the scaled subprofile model to extract features from the brain data to classify these disorders. Thereafter, the features were provided to several classifiers, i.e., decision trees, generalized matrix learning vector quantization, and support vector machine to classify the parkinsonian syndromes. A validation of the classifiers was performed. The decision tree method was compared to the stepwise regression method which aims at linearly combining a few good principal components. The stepwise regression method performed better than the decision tree method in the classification of the parkinsonian syndromes. Combining the two methods is feasible. The decision trees helped us to visualize the classification results, hence providing an insight into the distribution of features. Both generalized matrix learning vector quantization and support vector machine are better than the decision tree method in the classification of early-stage parkinsonian syndromes. All the classification methods used in this thesis performed well with later disease stage data. We conclude that generalized matrix learning vector quantization and decision tree methods can be recommended for further research on neurodegenerative disease classification and prediction

The effect of topology control for wireless multi-hop networks

Submitted to the Faculty of Science and Agriculture, in fulfillment ofthe requirements for the degree of Master of Science in Computer Science in the Department of Computer Science at the University of Zululand, 2007.Wireless multi-hop networks are not restricted to rural development efforts. They have found uses in the military, industry, as well as in urban areas. The focus of this study is on stationary wireless multi-hop networks whose primary purpose is the provisioning of Internet access using low cost, resource-constrained network nodes. Topology control algorithms have not yet catered for low cost, resource-constrained network nodes resulting in a need for algorithms that do cater for these types of wireless multi-hop network nodes. An algorithm entitled "Token-based Topology Control (TbTC)" was proposed. TbTC comprises three components, namely: transmit power and selection, network connectivity and next node selection. TbTC differs significantly in its treatment of the synchronisation required for a topology control algorithm to work effectively by employing a token to control the execution of the algorithm. The use of the token also ensures that all the network nodes eventually execute the topology control algorithm through a process called neighbour control embedded within the next node selection component. The proposed topology control algorithm, TbTC was simulated using ns-2 and the performances of a 30-node network before and after the algorithm was utilised, were compared. The Packet Delivery Ratio, Delay. Routing Protocol Overhead and Power Consumption were used as the simulation parameters. The neighbour control process was found to significantly reduce the number of hops taken by the token to visit each network node at least once. It was found that this process shortened the token traversal by 37.5%. Based on the results of its simulation, TbTC proves the positive benefits that can be accrued to the use of tokens in topology control as well as highlighting the negative benefits of the creation of uni-directional links in wireless multi-hop networks that utilise the IEEE 802.11 standard

Meeting the challenges related to material issues in chemical industries

Reliable performance and profitability are two important requirements for any chemical industry. In order to achieve high level of reliability and excellent performance, several issues related to design, materials selection, fabrication, quality assurance, transport, storage, inputs from condition monitoring, failure analysis etc. have to be adequately addressed and implemented. Technology related to nondestructive testing and monitoring of the plant is also essential for precise identification of defect sites and to take appropriate remedial decision regarding repair, replacement or modification of process conditions. The interdisciplinary holistic approach enhances the life of critical engineering components in chemical plants. Further, understanding the failure modes of the components through the analysis of failed components throws light on the choice of appropriate preventive measures to be taken well in advance, to have a control over the overall health of the plant. The failure analysis also leads to better design modification and condition monitoring methodologies, for the next generation components and plants. At the Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, a unique combination of the expertise in design, materials selection, fabrication, NDT development, condition monitoring, life prediction and failure analysis exists to obtain desired results for achieving high levels of reliability and performance assessment of critical engineering components in chemical industries. Case studies related to design, materials selection and fabrication aspects of critical components in nuclear fuel reprocessing plants, NDT development and condition monitoring of various components of nuclear power plants, and important failure investigations on critical engineering components in chemical and allied industries are discussed in this paper. Future directions are identified and planned approaches are briefly described

Trends in Legionnaires\u27 Disease-Associated Hospitalizations, United States, 2006-2010

Background: Legionella pneumophila is a waterborne cause of both healthcare-associated and community-acquired pneumonia. Legionella pneumophila serogroup 1 is responsible for 80% of infections. There is currently limited published disease burden data on Legionnaires\u27 disease-associated hospitalization in the United States. Methods: In this study, we estimated the annual incidence of Legionnaires\u27 disease-associated hospitalizations in United States and identified demographic, temporal, and regional characteristics of individuals hospitalized for Legionnaires\u27 disease. A retrospective study was conducted using the National Hospital Discharge Survey (NHDS) data from 2006 to 2010. The NHDS is a nationally representative US survey, which includes estimates of inpatient stays in short-stay hospitals in the United States, excluding federal, military, and Veterans Administration hospitals. All discharges assigned with the Legionnaires\u27 disease International Classification of Diseases 9th Clinical Modification discharge diagnostic code (482.84) were included in this study. Results: We observed the annual incidence and number of Legionnaires\u27 disease-associated hospitalizations (per 100 000 population) in the United States by year, age, sex, race, and region. Over a 5-year period, 14 574 individuals experienced Legionnaires\u27 disease-associated hospitalizations in the United States The annual population-adjusted incidence (per 100 000 population) of Legionnaires\u27 disease-associated hospitalizations was 5.37 (95% confidence interval [CI], 5.12-5.64) in 2006, 7.06 (95% CI, 6.80-7.40) in 2007, 8.77 (95% CI, 8.44-9.11) in 2008, 17.07 (95% CI, 16.62-17.54) in 2009, and 9.66 (95% CI, 9.32-10.01) in 2010. A summer peak of Legionnaires\u27 disease-associated hospitalizations occurred from June through September in 2006, 2007, 2008, and 2010. Conclusions: Legionnaires\u27 disease-associated hospitalizations significantly increased over the 5-year study period. The increasing disease burden of Legionnaires\u27 disease suggests that large segments of the US population are at risk for exposure to this waterborne pathogen

The Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) Scoring: the Diagnostic and Potential Prognostic Role

BACKGROUND: Necrotizing fasciitis (NF) is a devastating soft tissue infection associated with potentially poor outcomes. The Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score has been introduced as a diagnostic tool for NF. We aimed to evaluate the prognostic value of LRINEC scoring in NF patients. METHODS: A retrospective analysis was conducted for patients who were admitted with NF between 2000 and 2013. Based on LRINEC points, patients were classified into (Group 1: LRINEC /= 6). The 2 groups were analyzed and compared. Primary outcomes were hospital length of stay, septic shock and hospital death. RESULTS: A total of 294 NF cases were identified with a mean age 50.9 +/- 15 years. When compared to Group1, patients in Group 2 were 5 years older (p = 0.009), more likely to have diabetes mellitus (61 vs 41%, p \u3c 0.001), Pseudomonas aeruginosa infection (p = 0.004), greater Sequential Organ Failure Assessment (SOFA) score (11.5 +/- 3 vs 8 +/- 2, p = 0.001), and prolonged intensive care (median 7 vs 5 days) and hospital length of stay (22 vs 11 days, p = 0.001). Septic shock (37 vs. 15%, p = 0.001) and mortality (28.8 vs. 15.0%, p = 0.005) were also significantly higher in Group 2 patients. Using Receiver operating curve, cutoff LRINEC point for mortality was 8.5 with area under the curve of 0.64. Pearson correlation analysis showed a significant correlation between LRINEC and SOFA scorings (r = 0.51, p \u3c 0.002). DISCUSSION: Early diagnosis, simplified risk stratification and on-time management are vital to achieve better outcomes in patients with NF. CONCLUSIONS: Beside its diagnostic role, LRINEC scoring could predict worse hospital outcomes in patients with NF and simply identify the high-risk patients. However, further prospective studies are needed to support this finding