Modulation control and spectral shaping of optical fiber supercontinuum generation in the picosecond regime

Numerical simulations are used to study how fiber supercontinuum generation seeded by picosecond pulses can be actively controlled through the use of input pulse modulation. By carrying out multiple simulations in the presence of noise, we show how tailored supercontinuum Spectra with increased bandwidth and improved stability can be generated using an input envelope modulation of appropriate frequency and depth. The results are discussed in terms of the non-linear propagation dynamics and pump depletion.Comment: Aspects of this work were presented in Paper ThJ2 at OECC/ACOFT 2008, Sydney Australia 7-10 July (2008). Journal paper submitted for publication 30 July 200

MammoWave Breast Imaging Device: Prospective Clinical Trial Results and AI Enhancement

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data

Learning and Action Alliance framework to facilitate stakeholder collaboration and social learning in urban flood risk management

Flood and water management governance may be enhanced through partnership working, intra- and cross-organisational collaborations, and wide stakeholder participation. Nonetheless, barriers associated with ineffective communication, fragmented responsibilities and ‘siloed thinking’ restrict open dialogue and discussion. The Learning and Action Alliance (LAA) framework may help overcome these barriers by enabling effective engagement through social learning, and facilitating targeted actions needed to deliver innovative solutions to environmental problems. By increasing the adaptive capacity of decision-makers and participants, social learning through LAAs may lead to concerted action and sustained processes of behavioural change. In this paper, we evaluate the LAA framework as a catalyst for change that supports collaborative working and facilitates transition to more sustainable flood risk management. We use a case study in Newcastle-upon-Tyne, UK, to demonstrate how the LAA framework brought together disparate City stakeholders to co-produce new knowledge, negotiate innovative actions and, ultimately, work towards implementing a new vision for sustainable urban flood risk management. The shared vision of Newcastle as a ‘Blue-Green City’ that emerged is founded on a strong platform for social learning which increased organisations’ and individuals’ capacities to manage differences in perspectives and behaviours, reframe knowledge, and make collective decisions based on negotiation and conflict resolution. Broad recommendations based on lessons learned from the Newcastle LAA are presented to aid other cities and regions in establishing and running social learning platforms