Reputation modelling in Citizen Science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Data reliability issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. Participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data reliability has become an urgent demand. This study aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we propose to design a reputation framework to enhance data reliability and also investigate some critical elements that should be aware of during developing and designing new reputation systems.

Assessment of precision timing and real-time data networks for digital substation automation

This project researched the performance of emerging digital technology for high voltage electricity substations that significantly improves safety for staff and reduces the potential impact on the environment of equipment failure. The experimental evaluation used a scale model of a substation control system that incorporated real substation control and networking equipment with real-time simulation of the power system. The outcomes confirm that it is possible to implement Ethernet networks in high voltage substations that meet the needs of utilities; however component-level testing of devices is necessary to achieve this. The assessment results have been used to further develop international standards for substation communication and precision timing.

Socio-demographic vulnerability to heatwave impacts in Brisbane, Australia : a time series analysis

Objective: Examining the association between socioeconomic disadvantage and heat-related emergency department (ED) visits during heatwave periods in Brisbane, 2000–2008. Methods: Data from 10 public EDs were analysed using a generalised additive model for disease categories, age groups and gender. Results: Cumulative relative risks (RR) for non-external causes other than cardiovascular and respiratory diseases were 1.11 and 1.05 in most and least disadvantaged areas, respectively. The pattern persisted on lags 0–2. Elevated risks were observed for all age groups above 15 years in all areas. However, with RRs of 1.19–1.28, the 65–74 years age group in more disadvantaged areas stood out, compared with RR=1.08 in less disadvantaged areas. This pattern was observed on lag 0 but did not persist. The RRs for male presentations were 1.10 and 1.04 in most and less disadvantaged areas; for females, RR was 1.04 in less disadvantaged areas. This pattern persisted across lags 0–2. Conclusions: Heat-related ED visits increased during heatwaves. However, due to overlapping confidence intervals, variations across socioeconomic areas should be interpreted cautiously. Implications: ED data may be utilised for monitoring heat-related health impacts, particularly on the first day of heatwaves, to facilitate prompt interventions and targeted resource allocation.

A time series analysis of presentations to Queensland health facilities for alcohol-related conditions, following the increase in 'alcopops' tax

Objective: In response to concerns about the health consequences of high-risk drinking by young people, the Australian Government increased the tax on pre-mixed alcoholic beverages ('alcopops') favoured by this demographic. We measured changes in admissions for alcohol-related harm to health throughout Queensland, before and after the tax increase in April 2008. Methods: We used data from the Queensland Trauma Register, Hospitals Admitted Patients Data Collection, and the Emergency Department Information System to calculate alcohol-related admission rates per 100,000 people, for 15 - 29 year-olds. We analysed data over 3 years (April 2006 - April 2009), using interrupted time-series analyses. This covered 2 years before, and 1 year after, the tax increase. We investigated both mental and behavioural consequences (via F10 codes), and intentional/unintentional injuries (S and T codes). Results: We fitted an auto-regressive integrated moving average (ARIMA) model, to test for any changes following the increased tax. There was no decrease in alcohol-related admissions in 15 - 29 year-olds. We found similar results for males and females, as well as definitions of alcohol-related harms that were narrow (F10 codes only) and broad (F10, S and T codes). Conclusions: The increased tax on 'alcopops' was not associated with any reduction in hospital admissions for alcohol-related harms in Queensland 15 - 29 year-olds.

Efficiency of parallelisation of genetic algorithms in the data analysis context

Most real-life data analysis problems are difficult to solve using exact methods, due to the size of the datasets and the nature of the underlying mechanisms of the system under investigation. As datasets grow even larger, finding the balance between the quality of the approximation and the computing time of the heuristic becomes non-trivial. One solution is to consider parallel methods, and to use the increased computational power to perform a deeper exploration of the solution space in a similar time. It is, however, difficult to estimate a priori whether parallelisation will provide the expected improvement. In this paper we consider a well-known method, genetic algorithms, and evaluate on two distinct problem types the behaviour of the classic and parallel implementations.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Impact of a new mandatory reporting law on reporting and identification of child sexual abuse: A seven year time trend analysis

Child sexual abuse is widespread and difficult to detect. To enhance case identification, many societies have enacted mandatory reporting laws requiring designated professionals, most often police, teachers, doctors and nurses, to report suspected cases to government child welfare agencies. Little research has explored the effects of introducing a reporting law on the number of reports made, and the outcomes of those reports. This study explored the impact of a new legislative mandatory reporting duty for child sexual abuse in the State of Western Australia over seven years. We analysed data about numbers and outcomes of reports by mandated reporters, for periods before the law (2006-08) and after the law (2009-12). Results indicate that the number of reports by mandated reporters of suspected child sexual abuse increased by a factor of 3.7, from an annual mean of 662 in the three year pre-law period to 2448 in the four year post-law period. The increase in the first two post-law years was contextually and statistically significant. Report numbers stabilised in 2010-12, at one report per 210 children. The number of investigated reports increased threefold, from an annual mean of 451 in the pre-law period to 1363 in the post-law period. Significant decline in the proportion of mandated reports that were investigated in the first two post-law years suggested the new level of reporting and investigative need exceeded what was anticipated. However, a subsequent significant increase restored the pre-law proportion, suggesting systemic adaptive capacity. The number of substantiated investigations doubled, from an annual mean of 160 in the pre-law period to 327 in the post-law period, indicating twice as many sexually abused children were being identified.

Real-time kinetic analysis of hCG-monoclonal antibody interaction using radiolabeled hCG probe: presence of two forms of Ag-mAb complex as revealed by solid phase dissociation studies

Real-time kinetics of ligand-ligate interaction has predominantly been studied by either fluorescence or surface plasmon resonance based methods. Almost all such studies are based on association between the ligand and the ligate. This paper reports our analysis of dissociation data of monoclonal antibody-antigen (hCG) system using radio-iodinated hCG as a probe and nitrocellulose as a solid support to immobilize mAb. The data was analyzed quantitatively for a one-step and a two-step model. The data fits well into the two-step model. We also found that a fraction of what is bound is non-dissociable (tight-binding portion (TBP)). The TBP was neither an artifact of immobilization nor does it interfere with analysis. It was present when the reaction was carried out in homogeneous solution in liquid phase. The rate constants obtained from the two methods were comparable. The work reported here shows that real-time kinetics of other ligand-ligate interaction can be studied using nitrocellulose as a solid support. (C) 2002 Elsevier Science B.V. All rights reserved.

Time-Frequency Analysis of Nonlinear Systems: The Skeleton Linear Model and the Skeleton Curves

The joint time-frequency analysis method is adopted to study the nonlinear behavior varying with the instantaneous response for a class of S.D.O.F nonlinear system. A time-frequency masking operator, together with the conception of effective time-frequency region of the asymptotic signal are defined here. Based on these mathematical foundations, a so-called skeleton linear model (SLM) is constructed which has similar nonlinear characteristics with the nonlinear system. Two skeleton curves are deduced which can indicate the stiffness and damping in the nonlinear system. The relationship between the SLM and the nonlinear system, both parameters and solutions, is clarified. Based on this work a new identification technique of nonlinear systems using the nonstationary vibration data will be proposed through time-frequency filtering technique and wavelet transform in the following paper.

Pressure data analysis of formula one racing engines

The measured time-history of the cylinder pressure is the principal diagnostic in the analysis of processes within the combustion chamber. This paper defines, implements and tests a pressure analysis algorithm for a Formula One racing engine in MATLAB1. Evaluation of the software on real data is presented. The sensitivity of the model to the variability of burn parameter estimates is also discussed. Copyright © 1997 Society of Automotive Engineers, Inc.

A Radial Basis Function Approach to Financial Time Series Analysis

Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.

A resource limited artificial immune system for data analysis.

Timmis J and Neal M J. A resource limited artificial immune system for data analysis. In Proceedings of ES2000 - Research and Development of Intelligent Systems, pages 19-32, Cambrige, U.K., 2000. Springer.

Qualitative analysis of the interdisciplinary interaction between data analysis specialists and novice clinical researchers.

BACKGROUND: The inherent complexity of statistical methods and clinical phenomena compel researchers with diverse domains of expertise to work in interdisciplinary teams, where none of them have a complete knowledge in their counterpart's field. As a result, knowledge exchange may often be characterized by miscommunication leading to misinterpretation, ultimately resulting in errors in research and even clinical practice. Though communication has a central role in interdisciplinary collaboration and since miscommunication can have a negative impact on research processes, to the best of our knowledge, no study has yet explored how data analysis specialists and clinical researchers communicate over time. METHODS/PRINCIPAL FINDINGS: We conducted qualitative analysis of encounters between clinical researchers and data analysis specialists (epidemiologist, clinical epidemiologist, and data mining specialist). These encounters were recorded and systematically analyzed using a grounded theory methodology for extraction of emerging themes, followed by data triangulation and analysis of negative cases for validation. A policy analysis was then performed using a system dynamics methodology looking for potential interventions to improve this process. Four major emerging themes were found. Definitions using lay language were frequently employed as a way to bridge the language gap between the specialties. Thought experiments presented a series of "what if" situations that helped clarify how the method or information from the other field would behave, if exposed to alternative situations, ultimately aiding in explaining their main objective. Metaphors and analogies were used to translate concepts across fields, from the unfamiliar to the familiar. Prolepsis was used to anticipate study outcomes, thus helping specialists understand the current context based on an understanding of their final goal. CONCLUSION/SIGNIFICANCE: The communication between clinical researchers and data analysis specialists presents multiple challenges that can lead to errors.

Modelling the impact of antibiotic use and infection control practices on the incidence of hospital-acquired methicillin-resistant Staphylococcus aureus: a time-series analysis

Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) is a major nosocomial pathogen worldwide. A wide range of factors have been suggested to influence the spread of MRSA. The objective of this study was to evaluate the effect of antimicrobial drug use and infection control practices on nosocomial MRSA incidence in a 426-bed general teaching hospital in Northern Ireland.

Methods: The present research involved the retrospective collection of monthly data on the usage of antibiotics and on infection control practices within the hospital over a 5 year period (January 2000–December 2004). A multivariate ARIMA (time-series analysis) model was built to relate MRSA incidence with antibiotic use and infection control practices.

Results: Analysis of the 5 year data set showed that temporal variations in MRSA incidence followed temporal variations in the use of fluoroquinolones, third-generation cephalosporins, macrolides and amoxicillin/clavulanic acid (coefficients = 0.005, 0.03, 0.002 and 0.003, respectively, with various time lags). Temporal relationships were also observed between MRSA incidence and infection control practices, i.e. the number of patients actively screened for MRSA (coefficient = -0.007), the use of alcohol-impregnated wipes (coefficient = -0.0003) and the bulk orders of alcohol-based handrub (coefficients = -0.04 and -0.08), with increased infection control activity being associated with decreased MRSA incidence, and between MRSA incidence and the number of new patients admitted with MRSA (coefficient = 0.22). The model explained 78.4% of the variance in the monthly incidence of MRSA.

Conclusions: The results of this study confirm the value of infection control policies as well as suggest the usefulness of restricting the use of certain antimicrobial classes to control MRSA.

Helping the most vulnerable out of the poverty trap and reducing inequality: Policies, strategies, and services for individuals with Autism Spectrum Disorder, including intellectual and neurodevelopmental disabilities: Benchmarking Autism Services Efficacy: BASE Project (Volume 3) Secondary Data analysis

1) Executive Summary
Legislation (Autism Act NI, 2011), a cross-departmental strategy (Autism Strategy 2013-2020) and a first action plan (2013-2016) have been developed in Northern Ireland in order to support individuals and families affected by Autism Spectrum Disorder (ASD) without a prior thorough baseline assessment of need. At the same time, there are large existing data sets about the population in NI that had never been subjected to a secondary data analysis with regards to data on ASD. This report covers the first comprehensive secondary data analysis and thereby aims to inform future policy and practice.
Following a search of all existing, large-scale, regional or national data sets that were relevant to the lives of individuals and families affected by Autism Spectrum Disorder (ASD) in Northern Ireland, extensive secondary data analyses were carried out. The focus of these secondary data analyses was to distill any ASD related data from larger generic data sets. The findings are reported for each data set and follow a lifespan perspective, i.e., data related to children is reported first before data related to adults.
Key findings:
Autism Prevalence:
Of children born in 2000 in the UK,
• 0.9% (1:109) were reported to have ASD, when they were 5-year old in 2005;
• 1.8% (1:55) were reported to have ASD, when they were 7-years old in 2007;
• 3.5% (1:29) were reported to have ASD, when they were 11-year old in 2011.
In mainstream schools in Northern Ireland
• 1.2% of the children were reported to have ASD in 2006/07;
• 1.8% of the children were reported to have ASD in 2012/13.

Economic Deprivation:
• Families of children with autism (CWA) were 9%-18% worse off per week than families of children not on the autism spectrum (COA).
• Between 2006-2013 deprivation of CWA compared to COA nearly doubled as measured by eligibility for free school meals (from near 20 % to 37%)
• In 2006, CWA and COA experienced similar levels of deprivation (approx. 20%), by 2013, a considerable deprivation gap had developed, with CWA experienced 6% more deprivation than COA.
• Nearly 1/3 of primary school CWA lived in the most deprived areas in Northern Ireland.
• Nearly ½ of children with Asperger’s Syndrome who attended special school lived in the most deprived areas.

Unemployment:
• Mothers of CWA were 6% less likely to be employed than mothers of COA.
• Mothers of CWA earned 35%-56% less than mothers of COA.
• CWA were 9% less likely to live in two income families than COA.

Health:
• Pre-diagnosis, CWA were more likely than COA to have physical health problems, including walking on level ground, speech and language, hearing, eyesight, and asthma.
• Aged 3 years of age CWA experienced poorer emotional and social health than COA, this difference increased significantly by the time they were 7 years of age.
• Mothers of young CWA had lower levels of life satisfaction and poorer mental health than mothers of young COA.
Education:
• In mainstream education, children with ASD aged 11-16 years reported less satisfaction with their social relationships than COA.
• Younger children with ASD (aged 5 and 7 years) were less likely to enjoy school, were bullied more, and were more reluctant to attend school than COA.
• CWA attended school 2-3 weeks less than COA .
• Children with Asperger’s Syndrome in special schools missed the equivalent of 8-13 school days more than children with Asperger’s Syndrome in mainstream schools.
• Children with ASD attending mainstream schooling were less likely to gain 5+ GCSEs A*-C or subsequently attend university.



Further and Higher Education:
• Enrolment rates for students with ASD have risen in Further Education (FE), from 0% to 0.7%.
• Enrolment rates for students with ASD have risen in Higher Education (HE), from 0.28% to 0.45%.
• Students with ASD chose to study different subjects than students without ASD, although other factors, e.g., gender, age etc. may have played a part in subject selection.
• Students with ASD from NI were more likely than students without ASD to choose Northern Irish HE Institutions rather than study outside NI.

Participation in adult life and employment:
• A small number of adults with ASD (n=99) have benefitted from DES employment provision over the past 12 years.
• It is unknown how many adults with ASD have received employment support elsewhere (e.g. Steps to Work).
•
Awareness and Attitudes in the General Population:
• In both the 2003 and 2012 NI Life and Times Survey (NILTS), NI public reported positive attitudes towards the inclusion of children with ASD in mainstream education (see also BASE Project Vol. 2).

Gap Analysis Recommendations:
This was the first comprehensive secondary analysis with regards to ASD of existing large-scale data sets in Northern Ireland. Data gaps were identified and further replications would benefit from the following data inclusion:
• ASD should be recorded routinely in the following datasets:
o Census;
o Northern Ireland Survey of Activity Limitation (NISALD);
o Training for Success/Steps to work; Steps to Success;
o Travel survey;
o Hate crime; and
o Labour Force Survey.
• Data should be collected on the destinations/qualifications of special school leavers.
• NILT Survey autism module should be repeated in 5 years time (2017) (see full report of 1st NILT Survey autism module 2012 in BASE Project Report Volume 2).
• General public attitudes and awareness should be assessed for children and young people, using the Young Life and Times Survey (YLT) and the Kids Life and Times Survey (KLT); (this work is underway, Dillenburger, McKerr, Schubolz, & Lloyd, 2014-2015).