The production of highly unidirectional lower-hybrid waves

The development of a highly unidirectional lower-hybrid wave source would improve the electron current drive efficiency in tokamaks. Lower-hybrid waves launched from a phased wave array are shown to be reflected from a grid placed in a cold, low-density plasma. The antenna-grid combination results in highly unidirectional lower-hybrid waves

Alarm-Based Prescriptive Process Monitoring

Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof. Existing techniques in this field are able to predict, at each step of a case, the likelihood that the case will end up in an undesired outcome. These techniques, however, do not take into account what process workers may do with the generated predictions in order to decrease the likelihood of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive process monitoring approaches with the concepts of alarms, interventions, compensations, and mitigation effects. The framework incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptive process monitoring in a given setting. The paper also outlines an approach to optimize the generation of alarms given a dataset and a set of cost model parameters. The proposed approach is empirically evaluated using a range of real-life event logs

Profiles and trajectories of mental health service utilisation during early intervention in psychosis

Background: Early intervention in psychosis services (EIS) support individuals experiencing a first episode of psychosis. Support required will vary in response to the remittance and reoccurrence of symptoms, including relapses. Characterising individuals who will need more intensive support can inform care planning. This study explores service utilisation profiles and their trajectories of service use in a sample of individuals referred to EIS. Method: We analysed service utilisation during the 3 years following referral to EIS (n = 2363) in West London between 2011 and 2020. Mental health service utilisation data were submitted to model-based clustering. Latent growth models were then estimated for identified profiles. Profiles were compared regarding clinical and demographic characteristics and onward pathways of care. Results: Analyses revealed 5 profiles of individuals attending EIS based on their service utilisation over 3 years. 55.5% of the sample were members of a low utilisation and less clinically severe profile. The distinct service use patterns of these profiles were associated with Health of the Nations Outcome Scale scores at treatment initiation (at total, subscale, and individual item level), along with age and gender. These patterns of use were also associated with onward care and ethnicity. Conclusions: Profiles and trajectories of service utilisation call for development of integrated care pathways and use of more personalised interventions. Services should consider patient symptoms and characteristics when making clinical decisions informing the provision of care. The profiles represent typical patterns of service use, and identifying factors associated with these subgroups might help optimise EIS support

Rapid and accurate analysis of stem cell-derived extracellular vesicles with super resolution microscopy and live imaging

Extracellular vesicles (EVs) have prevalent roles in cancer biology and regenerative medicine. Conventional techniques for characterising EVs including electron microscopy (EM), nanoparticle tracking analysis (NTA) and tuneable resistive pulse sensing (TRPS), have been reported to produce high variability in particle count (EM) and poor sensitivity in detecting EVs below 50 nm in size (NTA and TRPS), making accurate and unbiased EV analysis technically challenging. This study introduces direct stochastic optical reconstruction microscopy (d-STORM) as an efficient and reliable characterisation approach for stem cell-derived EVs. Using a photo-switchable lipid dye, d-STORM imaging enabled rapid detection of EVs down to 20–30 nm in size with higher sensitivity and lower variability compared to EM, NTA and TRPS techniques. Imaging of EV uptake by live stem cells in culture further confirmed the potential of this approach for downstream cell biology applications and for the analysis of vesicle-based cell-cell communication

On landmark selection and sampling in high-dimensional data analysis

In recent years, the spectral analysis of appropriately defined kernel matrices has emerged as a principled way to extract the low-dimensional structure often prevalent in high-dimensional data. Here we provide an introduction to spectral methods for linear and nonlinear dimension reduction, emphasizing ways to overcome the computational limitations currently faced by practitioners with massive datasets. In particular, a data subsampling or landmark selection process is often employed to construct a kernel based on partial information, followed by an approximate spectral analysis termed the Nystrom extension. We provide a quantitative framework to analyse this procedure, and use it to demonstrate algorithmic performance bounds on a range of practical approaches designed to optimize the landmark selection process. We compare the practical implications of these bounds by way of real-world examples drawn from the field of computer vision, whereby low-dimensional manifold structure is shown to emerge from high-dimensional video data streams.Comment: 18 pages, 6 figures, submitted for publicatio

Balancing employee needs, project requirements and organisational priorities in team deployment

The 'people and performance' model asserts that performance is a sum of employee ability, motivation and opportunity (AMO). Despite extensive evidence of this people-performance link within manufacturing and many service sectors, studies within the construction industry are limited. Thus, a recent research project set out to explore the team deployment strategies of a large construction company with the view of establishing how a balance could be achieved between organisational strategic priorities, operational project requirements and individual employee needs and preferences. The findings suggested that project priorities often took precedence over the delivery of the strategic intentions of the organisation in meeting employees' individual needs. This approach is not sustainable in the long term because of the negative implications that such a policy had in relation to employee stress and staff turnover. It is suggested that a resourcing structure that takes into account the multiple facets of AMO may provide a more effective approach for balancing organisational strategic priorities, operational project requirements and individual employee needs and preferences more appropriately in the future

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Multiple guide star tomography demonstration at Palomar observatory

We have built and field tested a multiple guide star tomograph with four Shack-Hartmann wavefront sensors. We predict the wavefront on the fourth sensor channel estimated using wavefront information from the other three channels using synchronously recorded data. This system helps in the design of wavefront sensors for future extremely large telescopes that will use multi conjugate adaptive optics and multi object adaptive optics. Different wavefront prediction algorithms are being tested with the data obtained. We describe the system, its current capabilities and some preliminary results

Validity of numerical trajectories in the synchronization transition of complex systems

We investigate the relationship between the loss of synchronization and the onset of shadowing breakdown {\it via} unstable dimension variability in complex systems. In the neighborhood of the critical transition to strongly non-hyperbolic behavior, the system undergoes on-off intermittency with respect to the synchronization state. There are potentially severe consequences of these facts on the validity of the computer-generated trajectories obtained from dynamical systems whose synchronization manifolds share the same non-hyperbolic properties.Comment: 4 pages, 4 figure