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The long-term brain health consequences of intimate partner violence related traumatic brain injury
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Mangrove biomass estimation through remote sensing and machine learning based approaches
Get PDFMangroves play a crucial role in providing valuable ecosystem services, particularly as highly efficient carbon sinks that mitigate climate change impacts Understanding their contribution to the global carbon cycle requires accurate assessment of carbon stocks, which typically depends on the estimation of biomass, especially aboveground biomass (AGB). Existing studies on accurate estimation of mangrove AGB have been constrained by uncertainties in modelling efforts, limited field data and methodological challenges in integrating multisource remote sensing datasets. This research develops improved methodologies of mangrove AGB estimation by addressing these challenges. First, two local mangrove forests in Mexico were used to evaluate the feasibility and performance of open access global digital elevation models (NASADEM, ALOS DSM and Copernicus GLO-30 DEM) for AGB estimation. After calibration with spaceborne LiDAR (Light Detection and Ranging) datasets, the DEMs produced comparable and spatially consistent AGB estimates. For stands with a mean canopy height of 15 m, the standard error was ~30% of the estimated AGB. Second, an approach was developed to upscale localised field inventory to a continental level (the Americas), by incorporating spaceborne LiDAR data. Third, a novel data fusion framework was introduced using extensive spaceborne LiDAR derived AGB estimates to train high-resolution optical mosaics and rasterised environmental variables through a machine learning algorithm. This integration produced wall-to-wall mangrove AGB estimates across the Americas, achieving a validation accuracy of R 2 = 0.72 and root mean square error (RMSE) = 37.24 Mg/ha. Ultimately, applying the improved methodologies of mangrove AGB estimation to the Americas revealed not only high agreements in AGB estimates across country-level undisturbed mangrove forests but also 5.10 million Mg AGB gains in regrowing mangroves between 2000 and 2020. The findings underscore the resilience of mangroves and their capacity to recover as significant carbon sinks, which is particularly relevant to climate change adaptation and conservation efforts. Overall, this research provides improved methodologies in mangrove AGB estimation by integrating multisource datasets at a local and a continental scale, which is transferable and valuable to other tropical coastal ecosystems, offering researchers and practitioners an effective means to better integrate mangrove carbon dynamics into global climate mitigation frameworks. Additionally, spatially explicit mangrove AGB estimates derived from the improved methodologies can inform conservation priorities, restoration strategies and national carbon accounting efforts
Improving measurement by accounting for time-varying fluctuations: design-based and model-based methods
Get PDFDuring an experimental or a survey session, participants adapt and change due to learning, fatigue, fluctuations in attention, or other physiological or environmental changes. This temporal variation affects measurement, potentially reducing research power and validity. This thesis discussed how time-varying fluctuations bias measurements and how dealing with these fluctuations can improve measurements in two typical psychological research environments: cognitive experiments and psychological measurements. Two methodological parts are presented. The first one reviews typical cognitive experimental designs, and provided methods to account for time-varying fluctuations and improve power in experimental studies. These methods are based on better randomization algorithm and advanced statistics models. The second part introduced how to control time-varying fluctuations in psychometric datasets by finite mixture models to increase validity. It also provides an online platform for better randomizing and counterbalancing surveys. Both parts found that dealing with time-varying fluctuations benefits to power and validity gains, therefore increasing the reproducibility of psychological studies
Measuring and treating negative symptoms: developments and challenges
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Three essays on digital economy and corporate tax avoidance
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Copyright governance of algorithms: towards a more transparent regime
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Evaluating military intelligence in war: The Burma Theatre as a case study for objective focused analysis
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Policies and practices of and conditions for professional development for middle leaders in Chinese higher vocational colleges
Get PDFPrevious studies have confirmed that middle leadership can have a positive impact on institutional governance and change. However, related theoretical conceptions were developed in Western contexts, raising questions about its suitability for Asian contexts, including China, where education systems are highly centralised. Reform efforts in professional development for middle leaders (PD for MLs) lack an informed view through the absence of empirical research in China. There is a reliance on learning from the experience and achievements of research on PD and MLs conducted in other countries. Notably, differences in social background and organisational characteristics make it ineffective to draw on practical experience from Western countries directly. This runs counter to contemporary understandings of the importance of context for system improvement efforts. Conducting localised research in China is therefore a key to institutional reform and enhancing the quality and training of educational institutions.
This study, positioned in Chinese higher vocational colleges (CHVCs), attempts to explore analysing the PD and capacity-building issues for MLs in the Chinese hierarchical education system. Specifically, this study aims to answer two main research questions: 1. What is the status of current provision for PD for MLs in CHVCs? 2. In what ways and to what extent do institutional leadership, organisational structure, and organisational culture influence PD for MLs in CHVCs? This study adopted a qualitative phenomenology approach and research methods include document analysis of policies from central, provincial, and institutional level, as well as semi-structured interviews with MLs from sampling CHVCs in Shandong Province. Grounded theory coding method is adopted as a generic approach to organise and interpret the data. On the basis of drawing a basic blueprint for PD for MLs in CHVCs, this study further explores the specific ways in which organisational conditions promote or hinder PD for MLs.
By juxtaposing research findings from different dimensions, this study analyses three basic manifestations and characteristics of professional development in CHVCs. Moreover, the combination of documentary data and interview data with middle leaders, five potential implementation challenges are identified - unbalanced allocation of learning resource, conflict of structural arrangements, internal tension between autonomy and control, ignorance of Professional Learning Communities (PLCs) practice, and ambiguous assessment of learning outcomes. This study further demonstrated that the role and conditions of institutions are clearly relevant to the middle leadership construction and the realisation of the institutional governance vision. As such, the mitigation of such challenges can be explained by collective endeavours involving leadership, structure and culture at institutional levels. The findings emphasise the importance of effective institutional leadership in developing PD for MLs practices in CHVCs, and how organisational structure and culture fundamentally shape the form and implementation of PD. Understanding these Chinese-based factors in professional development can help further enrich the knowledge base that has traditionally been generated in non-Chinese contexts
Optimisation of optical neuromorphic computing systems
Get PDFAs the exponential scaling of computing systems predicted by Moore’s law begins to slow, in part due to reaching fundamental physical limitations in the continued miniaturisation of transistors, the development of novel unconventional computing technologies with improved scaling laws is becoming increasingly important. Unconventional computing systems use substrates other than silicon transistors to encode and process information. As an umbrella term, it encompasses fields such as analogue, physical and neuromorphic computing, which focus on computation through continuous variables, complex physical processes, and artificial neurons respectively.
While there exist a wide range of physical processes and dynamics which one could imagine exploiting to process information, the key challenge is in designing scalable systems which can be easily programmed to solve specific tasks. The success of existing computing technologies relies on the strong predictions which can be made about their behaviour. When using alternative physical processes for computing, one must work against noise, instabilities, and unmeasurable internal dynamics, which can limit the determinism and predictability of a system.
In the following work, we present a series of results on optimising physical computing systems with these factors in mind, where we focus primarily on optical substrates due to their natural potential for energy efficiency, speed and parallelism. We approach this in two ways, first considering the design of high-level system architectures suitable for computing, and secondly considering ways of programming and optimising these systems to solve specific tasks. In the case of the former, we introduce a new physical computing architecture which uses quantum resources to improve scaling over an equivalent classical counterpart, and which is realisable with currently available technologies. In the latter, we apply meta-learning and reinforcement learning techniques to develop new optimisation strategies for training physical neural networks in situ in a scalable way. Throughout, we analyse the criteria necessary for efficiency and scalability, while also considering ways in which we can ensure the resulting systems are accessible and sustainable.
Beyond these examples, we discuss the broader motivations and requirements for the practical adoption of unconventional and neuromorphic computing systems, and the potential impact they could have on the scaling of future computing technologies, including the efforts towards realising artificial general intelligence and artificial consciousness
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