Partitioning 3-homogeneous latin bitrades

A latin bitrade $(T^{\diamond}, T^{\otimes})$ is a pair of partial latin squares which defines the difference between two arbitrary latin squares $L^{\diamond} \supseteq T^{\diamond}$ and $L^{\diamond} \supseteq T^{\otimes}$ of the same order. A 3-homogeneous bitrade $(T^{\diamond}, T^{\otimes})$ has three entries in each row, three entries in each column, and each symbol appears three times in $T^{\diamond}$. Cavenagh (2006) showed that any 3-homogeneous bitrade may be partitioned into three transversals. In this paper we provide an independent proof of Cavenagh's result using geometric methods. In doing so we provide a framework for studying bitrades as tessellations of spherical, euclidean or hyperbolic space.Comment: 13 pages, 11 figures, fixed the figures. Geometriae Dedicata, Accepted: 13 February 2008, Published online: 5 March 200

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

An approach to assess swarm intelligence algorithms based on complex networks

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordGenetic and Evolutionary Computation Conference (GECCO ’20), 8-12 July 2020, Cancún, MexicoThe growing number of novel swarm-based meta-heuristics has been raising debates regarding their novelty. These algorithms often claim to be inspired by different concepts from nature but the proponents of these seldom demonstrate whether the novelty goes beyond the natural inspiration. In this work, we employed the concept of Interaction Networks to capture the interaction patterns that take place in the algorithms during the optimisation process. The analyses of these networks reveal aspects of the algorithm such as the tendency to achieve premature convergence, population diversity, and stability. Furthermore, we propose the usage of Portrait Divergence, a state-of-the-art metric to assess the structural similarities between networks. Using this approach to analyse the Cat Swarm Optimisation algorithm, we were able to identify some of the algorithm’s characteristics, assess the impact of one its parameters, and compare it to two other well-known methods (Particle Swarm Optimisation and Artificial Bee Colony). Lastly, we discuss the relationship between the interaction network and the performance of the algorithm and demonstrate the similarities between Cat Swarms and Particle Swarms.University of Exete

Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection.

Published onlineJournal ArticleResearch Support, Non-U.S. Gov'tBACKGROUND: Clostridium difficile infection poses a significant healthcare burden. However, the derivation of a simple, evidence based prediction rule to assist patient management has not yet been described. METHOD: Univariate, multivariate and decision tree procedures were used to deduce a prediction rule from over 186 variables; retrospectively collated from clinical data for 213 patients. The resulting prediction rule was validated on independent data from a cohort of 158 patients described by Bhangu et al. (Colorectal Disease, 12(3):241-246, 2010). RESULTS: Serum albumin levels (g/L) (P = 0.001), respiratory rate (resps /min) (P = 0.002), C-reactive protein (mg/L) (P = 0.034) and white cell count (mcL) (P = 0.049) were predictors of all-cause mortality. Threshold levels of serum albumin ≤ 24.5 g/L, C- reactive protein >228 mg/L, respiratory rate >17 resps/min and white cell count >12 × 10(3) mcL were associated with an increased risk of all-cause mortality. A simple four variable prediction rule was devised based on these threshold levels and when tested on the initial data, yield an area under the curve score of 0.754 (P < 0.001) using receiver operating characteristics. The prediction rule was then evaluated using independent data, and yield an area under the curve score of 0.653 (P = 0.001). CONCLUSIONS: Four easily measurable clinical variables can be used to assess the risk of mortality of patients with Clostridium difficile infection and remains robust with respect to independent data.This work was funded by University of Exeter, Systems Biology Initiative, a small grants fund from the RD&E NHS Trust and The National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for the South West Peninsula (PenCLAHRC). This article presents independent research funded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for the South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health in England

Multi-objective optimisation of sewer maintenance scheduling

This is the final version. Available on open access from IWA Publishing via the DOI in this recordData availability statement: Data cannot be made publicly available; readers should contact the corresponding author for details.Effective functioning of sewer systems is critical for the everyday life of people in the urban environment. This is achieved, among other things, by the means of regular, planned maintenance of these systems. A large water utility would normally have several maintenance teams (or crews) at their disposal who can perform related jobs at different locations in the company area and with different levels of priority. This paper presents a new methodology for the optimisation of related maintenance schedules resulting in clear prioritisation of the ordering of maintenance tasks for crews. The scheduling problem is formulated as a multi-objective optimisation problem with the following three objectives, namely the minimisation of the total maintenance cost, the minimisation of travel times of maintenance teams and the maximisation of the job's priority score, all over a pre-defined scheduling horizon. The optimisation problem is solved using the Nondominated Sorting Genetic Algorithm-II (NSGA-II) optimisation method. The results obtained from a real-life UK case study demonstrate that the new methodology can determine optimal, low-cost maintenance schedules in a computationally efficient manner when compared to the corresponding existing company schedules. Daily productivity of maintenance teams in terms of number of jobs completed improved by 26% when the methodology was applied to scheduling in the case study. Given this, the method has the potential to be applied within water utilities and the water utility Welsh Water (Dŵr Cymru Welsh Water (DCWW)) is currently implementing it into their systems.Engineering and Physical Sciences Research Council (EPSRC

EMOCS: evolutionary multi-objective optimisation for clinical scorecard generation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordClinical scorecards of risk factors associated with disease severity or mortality outcome are used by clinicians to make treatment decisions and optimize resources. This study develops an automated tool or framework based on evolutionary algorithms for the derivation of scorecards from clinical data. The techniques employed are based on the NSGA-II Multi-objective Optimization Genetic Algorithm (GA) which optimizes the Pareto-front of two clinically-relevant scorecard objectives, size and accuracy. Three automated methods are presented which improve on previous manually derived scorecards. The first is a hybrid algorithm which uses the GA for feature selection and a decision tree for scorecard generation. In the second, the GA generates the full scorecard. The third is an extended full scoring system in which the GA also generates the scorecard scores. In this system combinations of features and thresholds for each scorecard point are selected by the algorithm and the evolutionary process is used to discover near-optimal Pareto-fronts of scorecards for exploration by expert decision makers. This is shown to produce scorecards that improve upon a human derived example for C.Difficile, an important infection found globally in communities and hospitals, although the methods described are applicable to any disease where the required data is available.Engineering and Physical Sciences Research Council (EPSRC

Understanding district metered area level leakage using explainable machine learning

This is the final version. Available on open access from IOP Publishing via the DOI in this record14th International Conference on Hydroinformatics, 4 - 8 July 2022, Bucharest, RomaniaUnderstanding the various interrelated effects that result in leakage is vital to the effort to reduce it. This paper aims to understand, at the district metered area (DMA) level, the relationship between leakage and static characteristics of a DMA, i.e. without considering pressure or flow. The characteristics used include the number of pipes and connections, total DMA volume and network density, as well as pipe diameter, length, age, and material statistics. Leakage, especially background and unreported leakage, can be difficult to accurately quantify. Here, the Average Weekly Minimum Night Flow (AWM) over the last 5 years is used as a proxy for leakage. While this may include some legitimate demand, it is generally assumed that minimum night flow, strongly correlates with leakage. A data-driven case study on over 800 real DMAs from UK networks is conducted. Two regression models, a decision tree model and an elastic net linear regression model, are created to predict the AWM of unseen DMAs. Reasonable accuracy was achieved, considering pressure is not an included feature, and the models are investigated for the most prominent features related to leakage.South West Water (SWW

Literature review of data analytics for leak detection in water distribution networks: A focus on pressure and flow smart sensors

This is the author accepted manuscript. The final version is available from ASCE via the DOI in this recordDATA AVAILABILITY STATEMENT: No data, models, or code were generated or used during the study (e.g. opinion or dateless paper)Leakage detection is one of the important aspects of water distribution management. Water companies are exploring alternative approaches to detect leaks in a timely manner with high accuracy to reduce water losses and minimise environmental and economic consequences. In this article, a literature review is presented to develop a step-by-step analytic framework for the leakage detection process based on flow and pressure data collected from water distribution networks. The main steps of the data analytic for leakage detection are: setting up the goals, data collection, preparing the gathered data, analysing the prepared data, and method evaluation. The issues of concern for each step of the proposed leakage detection framework are analysed and discussed. The smart sensor-based leakage detection methods can be categorised as data-driven methods and model-based methods. Data-driven methods can be further categorised as statistical process control-based methods, prediction-classification methods, and clustering methods. Hydraulic model-based methods can be further categorised as calibration-based methods, sensitivity analysis, and classifier-based methods. The advantages and disadvantages of each method are discussed, and suggestions for future research are provided. This review represents a new perspective on the subject from five aspects: 1) most of the leakage detection methods are focused on burst detection, and different types of leakages should be considered in future research; 2) it is important to consider data uncertainties, and more robust real-time leakage detection methods should be developed; 3) it is important to consider hydraulic model uncertainties; 4) unrealistic assumptions should be addressed in future research; 5) spatial relations between sensors could provide more information and should be considered.China Scholarship CouncilRoyal Academy of Engineerin

Multiobjective optimization of water distribution systems accounting for economic cost, hydraulic reliability, and greenhouse gas emissions

In this paper, three objectives are considered for the optimization of water distribution systems (WDSs): the traditional objectives of minimizing economic cost and maximizing hydraulic reliability and the recently proposed objective of minimizing greenhouse gas (GHG) emissions. It is particularly important to include the GHG minimization objective for WDSs involving pumping into storages or water transmission systems (WTSs), as these systems are the main contributors of GHG emissions in the water industry. In order to better understand the nature of tradeoffs among these three objectives, the shape of the solution space and the location of the Pareto-optimal front in the solution space are investigated for WTSs and WDSs that include pumping into storages, and the implications of the interaction between the three objectives are explored from a practical design perspective. Through three case studies, it is found that the solution space is a U-shaped curve rather than a surface, as the tradeoffs among the three objectives are dominated by the hydraulic reliability objective. The Pareto-optimal front of real-world systems is often located at the "elbow" section and lower "arm" of the solution space (i.e., the U-shaped curve), indicating that it is more economic to increase the hydraulic reliability of these systems by increasing pipe capacity (i.e., pipe diameter) compared to increasing pumping power. Solutions having the same GHG emission level but different cost-reliability tradeoffs often exist. Therefore, the final decision needs to be made in conjunction with expert knowledge and the specific budget and reliability requirements of the system. © 2013. American Geophysical Union. All Rights Reserved.Wenyan Wu, Holger R. Maier, and Angus R. Simpso

Targeting the affective brain-a randomized controlled trial of real-time fMRI neurofeedback in patients with depression.

open access articleFunctional magnetic resonance imaging neurofeedback (fMRI-NF) training of areas involved in emotion processing can reduce depressive symptoms by over 40% on the Hamilton Depression Rating Scale (HDRS). However, it remains unclear if this efficacy is specific to feedback from emotion-regulating regions. We tested in a single-blind, randomized, controlled trial if upregulation of emotion areas (NFE) yields superior efficacy compared to upregulation of a control region activated by visual scenes (NFS). Forty-three moderately to severely depressed medicated patients were randomly assigned to five sessions augmentation treatment of either NFE or NFS training. At primary outcome (week 12) no significant group mean HDRS difference was found (B = −0.415 [95% CI −4.847 to 4.016], p = 0.848) for the 32 completers (16 per group). However, across groups depressive symptoms decreased by 43%, and 38% of patients remitted. These improvements lasted until follow-up (week 18). Both groups upregulated target regions to a similar extent. Further, clinical improvement was correlated with an increase in self-efficacy scores. However, the interpretation of clinical improvements remains limited due to lack of a sham-control group. We thus surveyed effects reported for accepted augmentation therapies in depression. Data indicated that our findings exceed expected regression to the mean and placebo effects that have been reported for drug trials and other sham-controlled high-technology interventions. Taken together, we suggest that the experience of successful self-regulation during fMRI-NF training may be therapeutic. We conclude that if fMRI-NF is effective for depression, self-regulation training of higher visual areas may provide an effective alternative