An artificial neural network-based rainfall runoff model for improved drainage network modelling

This Presentation is brought to you for free and open access by the City College of New York at CUNY Academic Works. It has been accepted for inclusion in International Conference on Hydroinformatics by an authorized administrator of CUNY Academic Works. For more information, please contact [email protected] th International Conference on Hydroinformatics HIC 2014, New York City, USAModelling rainfall-runoff processes enables hydrologists to plan their response to flooding events. Urban drainage catchment modelling requires rainfall-runoff models as a prerequisite. In the UK, one of the main software tools used for drainage modelling is InfoWorks CS, based on relatively simple methods which are relatively robust in predicting runoff. This paper presents an alternative approach to modelling runoff that will allow for the complex inter-relation of runoff that occurs from impermeable areas, permeable areas, local surface storage and variation in rainfall induced infiltration. Apart from the uncertainties associated with the measurement of connected surfaces to the drainage system, the physical processes involved in runoff are nonlinear, making artificial neural networks (ANNs) an ideal candidate for modelling them. ANNs have been used for runoff prediction in natural catchments, and recently on a study for predicting the performance of urban drainage systems. This study seeks to determine an input set that predicts sewerage flow in urban catchments where the runoff is dominated by infiltration, a major issue for the water industry. A framework is proposed in which an ANN is trained by an evolutionary algorithm, which optimises ANN weights; results are assessed using the Nash-Sutcliffe Efficiency Coefficient. The model is demonstrated on a real-world case study site for which rainfall, flow, air temperature and groundwater levels in three boreholes have been measured. Various combinations of these data are used as model inputs, examining a mixture of daily and sub-daily timesteps. The best predictions are generated from daily linearly combined antecedent rainfall and air temperature, although sub-daily information improves the worst-case performance of the model. Although infiltration is affected by groundwater levels, incorporating groundwater into the model does not improve predictions. The proposed ANN model is capable of producing acceptable predictions, thus avoiding many of the uncertainties involved in traditional infiltration modelling

Monogenic Epilepsies: Disease Mechanisms, Clinical Phenotypes, and Targeted Therapies

A monogenic aetiology can be identified in up to 40% of people with severe epilepsy. To address earlier and more appropriate treatment strategies, clinicians are required to know the implications that specific genetic causes might have on pathophysiology, natural history, comorbidities and treatment choices. In this narrative review, we summarise concepts on the genetic epilepsies based on the underlying pathophysiological mechanisms and present the current knowledge on treatment options based on evidence provided by controlled trials or studies with lower classification of evidence. Overall, evidence robust enough to guide antiseizure medication (ASM) choices in genetic epilepsies remains limited to the more frequent conditions for which controlled trials and observational studies have been possible. Most monogenic disorders are very rare and ASM choices for them are still based on inferences drawn from observational studies and early, often anecdotal, experiences with precision therapies. Precision medicine remains applicable to only a narrow number of patients with monogenic epilepsies and may target only part of the actual functional defects. Phenotypic heterogeneity is remarkable, and some genetic mutations activate epileptogenesis through their developmental effects, which may not be reversed postnatally. Other genes seem to have pure functional consequences on excitability, acting through either loss- or gain-of-function effects, and these may have opposite treatment implications. In addition, the functional consequences of missense mutations may be difficult to predict, making precision treatment approaches considerably more complex than estimated by deterministic interpretations. Knowledge of genetic aetiologies can influence the approach to surgical treatment of focal epilepsies. Identification of germline mutations in specific genes contraindicates surgery while mutations in other genes do not. Identification, quantification and functional characterization of specific somatic mutations before surgery using cerebrospinal fluid liquid biopsy or after surgery in brain specimens, will likely be integrated in planning surgical strategies and re-intervention after a first unsuccessful surgery as initial evidence suggests that mutational load may correlate with the epileptogenic zone. Promising future directions include gene manipulation by DNA or mRNA targeting; although most are still far from clinical use, some are in early phase clinical development

Spatial audio in small display screen devices

Our work addresses the problem of (visual) clutter in mobile device interfaces. The solution we propose involves the translation of technique-from the graphical to the audio domain-for expliting space in information representation. This article presents an illustrative example in the form of a spatialisedaudio progress bar. In usability tests, participants performed background monitoring tasks significantly more accurately using this spatialised audio (a compared with a conventional visual) progress bar. Moreover, their performance in a simultaneously running, visually demanding foreground task was significantly improved in the eye-free monitoring condition. These results have important implications for the design of multi-tasking interfaces for mobile devices

Augmented Evolutionary Intelligence: Combining Human and Evolutionary Design for Water Distribution Network Optimisation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordEvolutionary Algorithms (EAs) have been employed for the optimisation of both theoretical and real-world problems for decades. These methods although capable of producing near-optimal solutions, often fail to meet real-world application requirements due to considerations which are hard to define in an objective function. One solution is to employ an Interactive Evolutionary Algorithm (IEA), involving an expert human practitioner in the optimisation process to help guide the algorithm to a solution more suited to real-world implementation. This approach requires the practitioner to make thousands of decisions during an optimisation, potentially leading to user fatigue and diminishing the algorithm’s search ability. This work proposes a method for capturing engineering expertise through machine learning techniques and integrating the resultant heuristic into an EA through its mutation operator. The human-derived heuristic based mutation is assessed on a range of water distribution network design problems from the literature and shown to often outperform traditional EA approaches. These developments open up the potential for more effective interaction between human expert and evolutionary techniques and with potential application to a much larger and diverse set of problems beyond the field of water systems engineering.Engineering and Physical Sciences Research Council (EPSRC

All You Need Is Fats-for Seizure Control: Using Amoeba to Advance Epilepsy Research

Since the original report of seizure control through starvation in the 1920s, the ketogenic diet has been considered an energy-related therapy. The diet was assumed to be functioning through the effect of reduced carbohydrate intake regulating cellular energy state, thus giving rise to seizure control. From this assumption, the generation of ketones during starvation provided an attractive mechanism for this altered energy state; however, many years of research has sought and largely failed to correlate seizure control and ketone levels. Due to this focus on ketones, few studies have examined a role for free fatty acids, as metabolic intermediates between the triglycerides provided in the diet and ketones, in seizure control. Recent discoveries have now suggested that the medium-chain fats, delivered through the medium-chain triglyceride (MCT) ketogenic diet, may provide a key therapeutic mechanism of the diet in seizure control. Here we describe an unusual pathway leading to this discovery, beginning with the use of a tractable non-animal model—Dictyostelium, through to the demonstration that medium-chain fats play a direct role in seizure control, and finally the identification of a mechanism of action of these fats and related congeners leading to reduced neural excitability and seizure control

People with learning disabilities and ‘active ageing’

Background: People (with and without learning disabilities) are living longer. Demographic ageing creates challenges and the leading policy response to these challenges is ‘active ageing’. ‘Active’ does not just refer to the ability to be physically and economically active, but also includes ongoing social and civic engagement in the communities of which older people are a part. Active ageing should apply to all citizens, including the experiences of older people with learning disabilities. Materials and Methods: This literature based paper explores the focus of active ageing discussions in relation to the general population drawing comparisons with the experiences of older people with learning disabilities. Results: It points out that older people with learning disabilities and their experiences are largely missing from broader policy discussions of active ageing. Conclusion: The paper concludes by arguing for inclusive research in active ageing which takes account of the concerns and interests of older people with learning disabilities

The regulation of television sports broadcasting: a comparative analysis

Based on seven different sports broadcasting markets (Australia, Brazil, Italy, India, South Africa, United Kingdom and the United States), this article provides a comparative analysis of the regulation of television sports broadcasting. The article examines how contrasting perspectives on television and sport – economic and sociocultural – have been reflected in two main approaches to the regulation of sports broadcasting, namely competition law and major events legislation. The results of this analysis suggest that in many cases, the balance between commerce and culture in sports broadcasting has shifted too far in favour of the commercial interests of dominant pay-TV operators and sports organisations. Here, the case is made for the pursuit of an approach to sports broadcasting regulation that seeks to balance the commercial priorities of broadcasters and sports organisations with the wider sociocultural benefits citizens gain from freeto- air sports broadcasting

Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth's future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple "big-leaf" approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified the first- and second-order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks for a tropical site with an extent of 1×1°. While the photosynthetic capacity parameter (Vc;max25) is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity

Pharmacokinetics and pharmacodynamics of azithromycin in severe malaria bacterial co-infection in African children (TABS-PKPD): a protocol for a Phase II randomised controlled trial [version 1; peer review: awaiting peer review]

Background: African children with severe malaria are susceptible to Gram-negative bacterial co-infection, largely non-typhoidal Salmonellae, leading to a substantially higher rates of in-hospital and post-discharge mortality than those without bacteraemia. Current evidence for treating co-infection is lacking, and there is no consensus on the dosage or length of treatment required. We therefore aimed to establish the appropriate dose of oral dispersible azithromycin as an antimicrobial treatment for children with severe malaria and to investigate whether antibiotics can be targeted to those at greatest risk of bacterial co-infection using clinical criteria alone or in combination with rapid diagnostic biomarker tests. Methods: A Phase I/II open-label trial comparing three doses of azithromycin: 10, 15 and 20 mg/kg spanning the lowest to highest mg/kg doses previously demonstrated to be equally effective as parenteral treatment for other salmonellae infection. Children with the highest risk of bacterial infection will receive five days of azithromycin and followed for 90 days. We will generate relevant pharmacokinetic data by sparse sampling during dosing intervals. We will use population pharmacokinetic modelling to determine the optimal azithromycin dose in severe malaria and investigate azithromycin exposure to change in C-reactive protein, a putative marker of sepsis at 72 hours, and microbiological cure (seven-day), alone and as a composite with seven-day survival. We will also evaluate whether a combination of clinical, point-of-care diagnostic tests, and/or biomarkers can accurately identify the sub-group of severe malaria with culture-proven bacteraemia by comparison with a control cohort of children hospitalized with severe malaria at low risk of bacterial co-infection. Discussion: We plan to study azithromycin because of its favourable microbiological spectrum, its inherent antimalarial and immunomodulatory properties and dosing and safety profile. This study will generate new data to inform the design and sample size for definitive Phase III trial evaluation