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Tracing the fate of fast irreversible chemical reactions remains a challenging and intriguing task in chemistry. While slow reactions can be monitored by routine NMR techniques at ease, real-time monitoring of fast reactions that are initiated by mixing, however, poses challenges to traditional NMR experiments and therefore requires specialised techniques. A state-of-theart stopped-flow piece of equipment was utilised in conjunction with a high-resolution NMR spectrometer, to enable the data collection of rapid reactions including identification of intermediates, side-products, and catalysts, and quantification of selectivity. This work consists of three main aspects regarding the stopped-flow technique: i) evaluating the physical parameters and functionalities of the stopped-flow instrument; ii) monitoring rapid irreversible reactions with half-lives shorter than seconds; and iii) exploiting the non-pre-magnetised input channel of the stopped-flow instrument for magnetisation transfer studies. Thus far, effective kinetic data collection for irreversible chemical reactions of half-lives in the order of milliseconds was achieved, with the aid of interleaved stopped-flow experiments. This was demonstrated by the base-catalysed protodeboronation of pentafluorophenyl boronic acid, of t1/2 = 45 milliseconds at 313 K. The functionality of the non-pre-magnetised stopped-flow input channel was experimentally validated by the chemical exchange studies without the necessity of isotopic labelling. In addition to the stopped-flow technique, a closely-related yet independent work on the measurements of longitudinal relaxation time constant (T1) was conducted. T1 is an important NMR parameter essential for quantitative NMR experiments and molecular dynamic studies. Therefore, prior to kinetic analysis, it is necessary to measure the T1 of spins in the system. Comparing to the most conventional approaches such as the 'Inversion Recovery' experiments, the methodologies developed in this work showed significant improvements in time efficiency and achieved sufficiently accurate T1 measurements. Furthermore, it was proved that continuous heteronuclear decoupling could be applied in T1 measurements, affording sensitivity enhancements from the removal of Jcouplings and the Nuclear Overhauser Effects. This application is particularly beneficial for insensitive nuclei, which intrinsically require longer experimental time. Throughout the developments of T1 methods with heteronuclear decoupling, a range of decoupling schemes were evaluated to highlight the appropriate choice for different spin systems.

Model Driven Engineering (MDE) is a state-of-the-art software engineering approach, which adopts models as first class artefacts. In MDE, modelling tools and task-specific model management languages are used to reason about the system under development and to (automatically) produce software artefacts such as working code and documentation. Existing tools which provide state-of-the-art model management languages exhibit the lack of support for automatic static analysis for error detection (especially when models defined in various modelling technologies are involved within a multi-step MDE development process) and for performance optimisation (especially when very large models are involved in model management operations). This thesis investigates the hypothesis that static analysis of model management programs in the context of MDE can help with the detection of potential runtime errors and can be also used to achieve automated performance optimisation of such programs. To assess the validity of this hypothesis, a static analysis framework for the Epsilon family of model management languages is designed and implemented. The static analysis framework is evaluated in terms of its support for analysis of task-specific model management programs involving models defined in different modelling technologies, and its ability to improve the performance of model management programs operating on large models.

Neuroendocrine tumours are rich sources of bioactive peptides and potentially-active fragments of proteins. As the cells that constitute such tumours have active regulated secretory pathways, they constantly deliver cocktails of these peptides into the circulation (tumours) or into the culture medium (cells). Many of these peptides may contribute to distinctive clinical tumour syndromes or may be involved in the growth and metastasis of the tumours themselves. This project, the first of its kind, was directed at the molecular inventory of the secretory peptidome of these tumours by application of high-throughput LC/MS/MS techniques. Individual peptides have been structurally-characterized and their parent proteins identified using bioinformatic techniques. They were then sorted into protein types, such as catalytic, structural, regulatory, etc., and comparisons made between individual tumour types to assess numbers of both common and unique components. Selected peptides were chemically-synthesized and subjected to appropriate bioassays. This project provides a body of basic information that may be of use to basic scientists and clinicians.

The global travel and tourism industry was on a rising trend prior to COVID-19. The World Travel and Tourism Council (2021) announced that the travel and tourism industry was fast-growing and contributed one-tenth of the global GDP before the COVID-19 pandemic. Studies into the traditional travel agency industry are sparse, as several services marketing researchers have expressed their concern over the uncertain future of traditional travel agencies in the travel and tourism industry. However, the travel agency industry has not been eliminated from the tourism distribution channel as predicted. On the contrary, the number of travel agencies in China has steadily expanded over the last decade, rising from 23,690 companies in 2011 to 38,943 in 2019. This study uses a comprehensive hierarchical modelling approach as a framework to: (1) identify the primary dimensions of service quality; (2) analyse the interrelationships between the five marketing constructs (service quality, customer perceived value, customer satisfaction, brand image, and customer loyalty); and (3) examine the effect of demographic factors in influencing customers’ perceptions of interrelationships between these five marketing constructs. The survey was conducted in four shopping mall complexes in Beijing and Shanghai. A total of 481 questionnaires were collected from the 1st to the 20th of December, 2017. The participants were customers who were over 18 years old and those who had booked travel using a travel agency in the past year. The data analysis techniques employed in this study were exploratory factor analysis, confirmatory factor analysis, and structural equation modelling. The research results confirm that four primary dimensions for conceptualising and measuring service quality in the Chinese travel agency industry are: interaction quality, physical environment quality, technical quality, and outcome quality. Interaction quality is the most important primary dimension compared to the other three primary dimensions for overall service quality performance. In addition, brand image is the only construct that has a direct and positive impact on customer loyalty. However, service quality and customer perceived value are also found to have an indirect impact on customer loyalty, and these impacts are mediated by the construct of customer satisfaction. The empirical results also confirm that three important indicators of travel agency customer satisfaction in China are: service quality, customer perceived value, and brand image. Finally, the research results reveal that customers from different demographic backgrounds (gender, age, marital status, and income level) do perceive the interrelationships between marketing constructs differently. This study adds to the body of knowledge on services marketing literature by conceptualising service quality and examining the interrelationship between five marketing constructs in China’s travel agency industry using a single conceptual research model. The findings from the statistical results provide valuable information for travel agency managers to establish and execute successful marketing strategies and tactics for them to compete in the fiercely competitive marketplace.

Preschool-aged children’s oral language skills are among the strongest predictors of their achievements in school. However, the ways in which the home environment supports preschoolers’ oral language development are not well understood in the context of Mainland China. This dissertation explores how diverse aspects of Chinese children’s home environment, including caregiver education, knowledge about child development, home learning activities, and caregiver communicative input, individually and jointly predict children’s oral language development. In particular, it focuses on one form of input found to facilitate children’s oral language skills in English-speaking, Western populations: decontextualized language (DL), language referring to abstract or displaced concepts, entities, individuals, or events (e.g., causal explanations; recalling past events). This dissertation includes three papers: a comprehensive literature review on the core features, variation, and developmental implications of caregiver DL input, a cross-cultural comparative study of DL use during Chinese and American caregiver-child interactions, and a longitudinal study investigating associations between the home environment (including DL input) and Chinese children’s oral language skills. Findings provide a theoretical foundation for culturally sensitive interventions aimed at helping Chinese caregivers understand the power of the home environment in supporting children’s language development. Empowering Chinese caregivers can be a critical step towards enhancing children’s oral language skills and mitigating the SES-based achievement gap in China.

The emerging internet of things (IoT) is all about connecting machines and systems together through sensors and actuators. The meaningful information collected from the sensors can be processed timely and possible actions can be taken either by the actuator or human to enhance productivity, efficiency, and reliability. We will see IoT devices and embedded systems become more and more prevalent in our daily lives. One such area is the power generation industry. Not surprisingly, the power generation industry has been one of the early adopters of IoT. The expensive electricity generator is embedded with massive internet-connected sensors for its operation condition monitor to prevent potential catastrophic failure.This dissertation presents the development of a low-cost, easy-to-deploy, self-powered wireless sensor nodes and integrates them with existing solutions into wireless sensor networks to monitor temperature and vibration information of the instruments in a power plant (e.g., motor, pump, etc.). Each sensor node includes: (a) energy harvesting components (which convert the vibrational motion of the instruments into electricity) made by a piezoelectric material; (b) a power management application-specific integrated circuit (ASIC) for AC to DC, DC to DC and voltage regulation; (c) the multi-physical sensor components, including temperature and vibration sensors; (d) an ultralow-power wireless communication unit using ZigBee protocol. We design a customer-specific printed circuit board (PCB) to integrate the sensor node components and a robust package to protect the sensor node for field deployment and harsh environment applications. The sensors communicate with a powered gateway unit intermittently while constantly harvesting and storing energy, maintaining always-on operation without external energy input. Attribute reporting allows the system to transmit data in a more intelligent and energy-efficient way. These sensor nodes can be installed by the end-users (requiring no professional training) in a non-intrusive way by retrofitting or mounting onto an equipment’s surface using strong magnetic tape. The proposed wireless sensors can be implemented in any industrial power plant and require minimal maintenance while providing continuous, long-term monitoring of equipment with the real-time readout of temperature and vibration, and possibly other parameters of interest.

Abstract: Background: The vascular endothelium regulates arterial diameter, and thus blood flow and pressure, through release of diffusible factors such as nitric oxide (NO), and spread of electrical charge to smooth muscle cells via myoendothelial gap junctions (MEGJs). Activation of these pathways is mediated by a rise in endothelial Ca2+ levels leading to recruitment of downstream effectors. Two of the most important effectors are NO synthase, and Ca2+ activated K+ (KCa) channels which mediate hyperpolarization of the endothelial cell membrane potential. This hyperpolarization spreads to surrounding smooth muscle cells to limit contraction by reducing the open probability of voltage-operated Ca2+ channels. The majority of research in this area has focused on activation of these mechanisms by stimuli acting directly on endothelial cells leading to the idea that KCa channels and NO mediate distinct pathways for vasodilation. However, our lab has shown that in resistance arteries stimulation of smooth muscle cells by 1-adrenoceptor agonists activates the converse signaling pathway, termed myoendothelial feedback. Briefly, flux of inositol trisphosphate (InsP3) from smooth muscle to endothelial cells elicits localized increases in Ca2+, activation of intermediate conductance (IKCa) KCa channels located at MEGJs and production of NO. Elucidation of the myoendothelial feedback pathway indicates that the role of endothelial KCa channels and NO in modulating vasoconstriction may depend on the contractile stimulus, and rather than being independent, these vasorelaxant pathways may interact. Further support for this proposal comes from studies showing that blockers of endothelial small (SKCa) and IKCa channels inhibit vasorelaxation mediated by endothelium-derived NO, and activators of endothelial KCa channels can elicit vasodilation that is blocked by inhibitors of NO signaling. My over-arching goal has been to further explore the functional role of SKCa and IKCa channels in regulating resistance artery diameter, and investigate potential interactions between KCa channels and NO in mediating vasodilation. To this end, I have addressed three hypotheses: 1.The functional contribution of InsP3/IKCa channel-mediated myoendothelial feedback to limiting arterial diameter is determined by the ability of the vasoconstrictor stimulus to engage the endothelium. 2. Small molecule activators of endothelial KCa channels modulate myogenic reactivity at least in part through endothelium-derived NO. 3.NO facilitates KCa channel mediated, endothelium-dependent smooth muscle hyperpolarization. Methods: To test these hypotheses I have used a combination of functional (wire and pressure myography), electrophysiological (intracellular recording of endothelial and smooth muscle membrane potential using sharp electrodes), biochemical (immunohistochemical localization of K+ channel proteins and qPCR to examine expression of mRNA for these channels) techniques to address 3 aims: i.To investigate the contribution of myoendothelial feedback to endothelial modulation of resistance artery responses to contractile agonists, increases in pressure and stimulation of perivascular sympathetic nerves. ii.To investigate the mechanisms underlying vasodilation to endothelial KCa channel activators in myogenically active vessels. iii. To determine if endothelium-dependent hyperpolarization of vascular smooth is modulated by NO. Results: My data show firstly, a differential ability of vasoconstrictor stimuli to engage IKCa channel-mediated myoendothelial feedback, and that in the presence of constant flow, shear stress-induced activation of SKCa channels plays a dominant role in modulating resistance artery vasoconstriction. Second, that pharmacological activators of endothelial KCa channels can modulate myogenic reactivity, an effect dependent on inwardly rectifying K+ channels, and possibly, NO. Finally, interactions between NO- and KCa channel-dependent vasorelaxant pathways occur at the level of both the endothelium (dilation to an activator of SKCa channels is dependent on NO) and smooth muscle (NO facilitates endothelium-dependent smooth muscle hyperpolarization) Conclusion: To conclude, rather than separate pathways, endothelial modulation of resistance artery diameter results from the integrated activity of SKCa and IKCa channels, and NO to fine-tune the contractile state of smooth muscle cells in a stimulus- and context-dependent manner. Such integration of voltage-dependent and -independent pathways will contribute to coordinated spatial and temporal control of blood flow in resistance artery beds.

With the popularity of online social networks such as Facebook, Twitter and LinkedIn, the scale of network data has become enormous. How to take samples that are representative of the full network has been a major research focus. For example, link-tracing sampling methods are effective for obtaining samples from hard-to-reach populations. However link-tracing methods often result in substantially biased samples.In this dissertation we study different link-tracing sampling methods and network models. We compare these methods with simple random sampling in terms of sampling mechanism, estimation bias and variances in estimating parameters and attributes of networks. We explore the root cause for simple average estimators to have large biases and variances. We investigate the interplay among network structure, sampling methods and the variable of interest for both simulated data and real-world social networks data. We propose new estimation methods to correct bias and improve estimation performances.Judgement Post-Stratification (JPS) is a data analysis method based on ideas similar to those in ranked set sampling. Besides serving a variance reduction role, as for traditional sampling schemes, post-stratification also helps reduce bias in a size-biased sampling scheme by down-weighting units that are more likely to be selected and up-weighting units that are less likely to be sampled. In this dissertation, we discuss the applications of JPS in improving estimation performance when using link-tracing sampling methods. We compare the JPS estimator with traditional size-bias compensation methods such as Horvitz-Thompson Estimators (HTE) and Volz-Hackethorn Estimators (VHE). We use machine learning methods to build and improve ranking functions and compare machine learning based JPS with traditional machine learning methods without post-stratification. Extending the ideas of JPS and VHE, we propose a new method, JVE, which is a combination of both. JPS and JVE have been demonstrated to provide a flexible framework that is able to incorporate various information to better rank observed units. We conduct analyses on both simulated data and real world social networks data.

All electronic devices need power sources to perform their functions including computing, communication, and sensing. Traditional high-capacity and long-lifetime batteries have a drawback of being bulky and face challenges for implementation and integration into today’s increasingly miniaturized devices such as many portable devices and microelectromechanical systems (MEMS). Directly harvesting energy from environments is a very attractive approach to supplying energy for low-power electronics and sensors. In recent years, many materials and techniques for harvesting ambient energy to power electronic devices have been explored. After a comprehensive survey of existing energy harvesting techniques, we choose to focus on investigating the conversion of mechanical vibrations to electricity by using piezoelectric devices. In this active research area, currently there are a number of important open challenges, including how to achieve highest possible energy conversion efficiency, and how to demonstrate novel practical applications. This thesis work first investigates and validates the fundamentals of resonant-mode piezoelectric energy harvesting to gain understandings of design principles toward achieving high energy conversion efficiency. We then design devices and circuits to demonstrate using spectra-selective energy harvesting to power up optoelectronic devices for solid-state lighting in ambient air.We first describe measurement and modeling of energy harvesters based on oscillating piezoelectric cantilevers, along with careful calibration of energy conversion properties of such devices in their dynamic response. We employ thin-film lead zirconate titanate (PZT)-based cantilevers fabricated by laser micromachining, with efficient proof masses enabled by a heavy alloy with a low melting temperature (65¿C) for tuning frequency and damping. By measuring devices with different circuit parameters, and analyzing the energy conversion in time-domain oscillations, we show a model that quantitatively reveals the effects of the loading circuit for energy harvesting. We also show the effects of device dimensions on their vibrations and converted voltage output waveforms. In harvesting vibrational energy through cycles of oscillations (in 80Hz-1 kHz devices), we obtained a 25%energy conversion efficiency.After carefully studying the principles of the PZT cantilevers, we demonstrate a miniature system on a circuit board for harvest vibrational energy from a small service pump. The miniature system features resonance-matched designs of the cantilevers, and the integration with a full-wave rectifying circuit and a smoothing capacitor in a less than 1cm2 area. The system is calibrated to have a power generation density of 173.6µW/mm3 is demonstrated to be capable of lighting up a blue LED using the harvested vibrational energy.