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In modern real-time embedded systems, time predictability is vital. This extends to I/O operations which require predictability, timing-accuracy, enhanced performance, scalability, parallel access and isolation. Currently, existing approaches cannot achieve all these requirements at the same time. In this thesis, we propose a framework of hardware-implemented real-time I/O virtualization system to meet all these requirements simultaneously, termed BlueIO. BlueIO integrates the important functionalities of I/O virtualization and low layer I/O drivers (achieved via Virtualized Complicated Device Controller (VCDC)), as well as a clock cycle level timing-accurate I/O controller (i.e. GPIO Command Processor (GPIOCP)). BlueIO provides this functionality in the hardware layer, supporting abstract virtualized access to I/O devices from the software domain. The hardware implementation includes I/O virtualization and I/O drivers provide isolation and parallel (concurrent) access to I/O operations and improves I/O performance. Furthermore, the approach includes GPIOCP to guarantee that I/O operations will occur at a specific clock cycle (i.e. be timing-accurate and predictable). This thesis proposes the design and implementation of BlueIO, together with its components - GPIOCP and VCDC. It is demonstrated how a BlueIO-based system can be exploited to meet real-time requirements with significant improvements in I/O performance and low running cost on different OSs. The thesis presents a hardware consumption analysis of BlueIO, in order to show that it linearly scales with the number of CPUs and I/O devices, evidenced by the implementation which targets both FPGA and VLSI. Finally, the thesis proposes a scalable real-time hardware hypervisor termed BlueVisor, which is built upon GPIOCP, VCDC and BlueIO. BlueVisor enables predictable virtualization on CPU, memory, and I/O; together with fast interrupt handling and inter-virtual machine communication. BlueVisor shows that the approaches towards I/O proposed in this thesis can be applied and expanded to different architectures and platforms, whilst maintaining real-time properties, performance and protection.

Contemporary labour immigration into the UK has been underpinned by two structural positions: the uneven development within the capitalist system and an intensification of competition driving towards flexibility and precarity. Immigrant workers are overwhelmingly concentrated in secondary sectors of the labour market with low pay, long working hours and poor health and safety and closely associated with non-standard work and informal economy where unions are often not available. How these immigrant workers in highly exploitative industries respond to work-related exploitations poses a great challenge to traditional trade unionism. While community unionism has received increasing attention from researchers and practitioners, an institution-centric approach is dominated in the scholarship which tends to overemphasize the role of institutional entity, such as trade unions and NGOs, in shaping collective agency and consider it as the centrality to immigrant workers activism. In contrast to such union-centred research, this study adopts a social movement perspective to explore whether and how community organizing approach can empower immigrant workers and enhance union organizing when globalization compromises its validity. By conducting the multi-method (interviews, surveys, participant observations and videos) ethnographic studies in an immigrant domestic worker self-help group-Justice for Domestic Workers in London over a year and a post EU-enlargement Polish association and local Polish neighbourhood in South Somerset over five months, the research shows that gendered and cultural space rather than traditional industrial entities could offer a political context in which immigrant workers start recognising structural class exploitations and develop an agency and activism for changes. This suggests that the collective mobilizations of immigrant workers in informal and individualised sectors may require creative leaps of sociological imagination in nurturing such communities of coping, wherever they may be occurring - in social clubs, cafés or churches. Community, however, is not a naturally harmonious and unified group setting. The internal divisions and competitions within immigrant communities pose limits to how far ethnic cohesion can serve as a basis for collective mobilization of immigrant workers. The research points to the potential tensions between immigrant community organizations and trade unions to compete for membership and social influence in the coalition building. There is a risk that the institutional goals of immigrant community organizations, in terms of securing funding and expanding its organizational influence, may take precedence over substantive goals of support provision. The research also suggests that academics and practitioners need to rethink the criteria that define the success of worker organising. To win union recognition and achieve collective bargaining agreements in the workplace is a rare case in community organizing of immigrant workers. A distinction should be made between capacity-building from the perspective of workers and organizations involved in community organizing of immigrant workers. There might be a contradiction between organizational developments and grassroots empowerment. Instead of merely focusing on political outcomes as the existing research indicates, more attention should paid to outcomes in social and cultural arenas and how gains in one arena facilitate or hinder gains in another.

Semiconductor nanowire materials and devices provide unique opportunities in the frontier between nanoelectronics and biology. The bottom-up paradigm enables flexible synthesis and patterning of nanoscale building blocks with novel structures and properties, and nano-to-micro fabrication methods allow the advantages of functional nanowire elements to interface with biological systems in new ways. In this thesis, I will focus on the development of bottom-up nanoscience platforms, which includes rational synthesis and assembly of semiconductor nanowires with new capabilities, as well as design and fabrication of the first free-standing three-dimensional (3D) nanoprobes, with special focus on applications in intracellular recording and stimulation. I will first introduce kinked p-n junction nanowires as a new and powerful family of high spatial resolution biological and chemical sensors with proof-of-concept applications. Next, I will discuss a variety of functional kinked nanowires with synthetically controlled properties and the potential of achieving more detailed and less invasive cellular studies. Furthermore, I will present a general shape-controlled deterministic nanowire assembly method to produce large-scale arrays of devices with well-defined geometry and position. Then, I will present the design of a general method to fabricate these nanowire structures into free-standing 3D probes. I will show that free-standing nanowire bioprobes can be manipulated to target specific cells and record stable intracellular action potentials. I will demonstrate simultaneous measurements from the same cell using both kinked nanowire and patch-clamp probes. Moreover, I will discuss two strategies of multiplexed recording using free-standing probes. Finally, I will report localized stimulation on single cells enabled by the unique properties of p-n kinked nanowires. I will show with simulation and electrical characterization that in reverse bias, localized electric field generated around the nanoscale p-n junction should exceed the threshold for opening voltage-gated sodium channels. Moreover, I will present measurements of localized cell stimulation using p-n nanowire free-standing probes. Together with the capability of stable intracellular recording, these results complete the two-way communication between semiconductor nanowire electronics and biological systems at a natural nanoscale, which can open up new directions in the fields ranging from cellular electrophysiology, brain activity mapping to brain-machine interface., Chemistry and Chemical Biology

Spatial Big Data (SBD), e.g., earth observation imagery, GPS trajectories, temporally detailed road networks, etc., refers to geo-referenced data whose volume, velocity, and variety exceed the capability of current spatial computing platforms. SBD has the potential to transform our society. Vehicle GPS trajectories together with engine measurement data provide a new way to recommend environmentally friendly routes. Satellite and airborne earth observation imagery plays a crucial role in hurricane tracking, crop yield prediction, and global water management. The potential value of earth observation data is so significant that the White House recently declared that full utilization of this data is one of the nation's highest priorities. However, SBD poses significant challenges to current big data analytics. In addition to its huge dataset size (NASA collects petabytes of earth images every year), SBD exhibits four unique properties related to the nature of spatial data that must be accounted for in any data analysis. First, SBD exhibits spatial autocorrelation effects. In other words, we cannot assume that nearby samples are statistically independent. Current analytics techniques that ignore spatial autocorrelation often perform poorly such as low prediction accuracy and salt-and-pepper noise (i.e., pixels predicted as different from neighbors by mistake). Second, spatial interactions are not isotropic and vary across directions. Third, spatial dependency exists in multiple spatial scales. Finally, spatial big data exhibits heterogeneity, i.e., identical feature values may correspond to distinct class labels in different regions. Thus, learned predictive models may perform poorly in many local regions. My thesis investigates novel SBD analytics techniques to address some of these challenges. To date, I have been mostly focusing on the challenges of spatial autocorrelation and anisotropy via developing novel spatial classification models such as spatial decision trees for raster SBD (e.g., earth observation imagery). To scale up the proposed models, I developed efficient learning algorithms via computational pruning. The proposed techniques have been applied to real world remote sensing imagery for wetland mapping. I also had developed spatial ensemble learning framework to address the challenge of spatial heterogeneity, particularly the class ambiguity issues in geographical classification, i.e., samples with the same feature values belong to different classes in different spatial zones. Evaluations on three real world remote sensing datasets confirmed that proposed spatial ensemble learning outperforms current approaches such as bagging, boosting, and mixture of experts when class ambiguity exists.

There are few references to this unusual reaction in the literature and no mechanistic studies. Therefore, a mechanistic study has been carried in order to provide a better understanding of the reaction. These studies revealed that the reaction is catalyzed by acid. Hammett and Bronsted correlation studies on acids indicate alpha=0.74 and rho=0.93 while Hammett correlation studies on aldehydes indicate rho=0.71, suggesting an acid pre-equilibrium. Thermodynamic studies have shown DeltaH≠ = 3.5 kcal/mol and DeltaS ≠ = -70 cal/mol.K suggesting a concerted transition state including aldehydes, acid (benzoic acid or oxazolinium) and oxazoline. The rate determining step is believed to be an nucleophilic attack from the 2-oxazoline to the carbonyl group during the proton transfer process in the acid equilibrium. The results of all studies are summarized and a putative mechanism is presented which has some similarity to enzymatic reactions. Attempts to carry out metal-free Aldol reactions based on these reactions, along with other studies of the potential synthetic utility of the imine ester products, are also discussed in this dissertation.