31,228 research outputs found

    Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets.

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    Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset

    Sponsorship image and value creation in E-sports

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    .E-sports games can drive the sports industry forward and sponsorship is the best way to engage consumers of this new sport. The purpose of this study is to examine the effect of sponsorship image and consumer participation in co-creation consumption activities on fans’ sponsorship response (represented by the variables interest, purchase intention and word of mouth) in e-sports. Four antecedent variables build sponsorship image (i.e., ubiquity of sport, sincerity of sponsor, attitude to sponsor and team identification). A quantitative approach is used for the purposes of this study. Some 445 questionnaires were filled in by fans who watch e-sports in Spain; these are analyzed using partial least squares structural equation modeling (PLS-SEM). The outcomes show that sponsor antecedents are crucial factors if a sponsor wants to change their sponsorship image and influence sponsorship response, and that it is also possible to use participation to improve responsesS

    Incentivising research data sharing : a scoping review

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    Background: Numerous mechanisms exist to incentivise researchers to share their data. This scoping review aims to identify and summarise evidence of the efficacy of different interventions to promote open data practices and provide an overview of current research. Methods: This scoping review is based on data identified from Web of Science and LISTA, limited from 2016 to 2021. A total of 1128 papers were screened, with 38 items being included. Items were selected if they focused on designing or evaluating an intervention or presenting an initiative to incentivise sharing. Items comprised a mixture of research papers, opinion pieces and descriptive articles. Results: Seven major themes in the literature were identified: publisher/journal data sharing policies, metrics, software solutions, research data sharing agreements in general, open science ‘badges’, funder mandates, and initiatives. Conclusions: A number of key messages for data sharing include: the need to build on existing cultures and practices, meeting people where they are and tailoring interventions to support them; the importance of publicising and explaining the policy/service widely; the need to have disciplinary data champions to model good practice and drive cultural change; the requirement to resource interventions properly; and the imperative to provide robust technical infrastructure and protocols, such as labelling of data sets, use of DOIs, data standards and use of data repositories

    Development and evaluation of a treatment package for men with an intellectual disability who sexually offend

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    Sex offending in the general population has been a focus of interest for some time due to the damaging nature of the behaviour, and the need to reduce recidivism. Theoretical and clinical advances (Finke1hor, 1986; HM Prison Service, 1996; Marshall, Anderson, & Fernandez, 1999; Serran & Marshall, 2010) in treatment for sex offenders in the general population have been extended to men with an intellectual disability at risk of sexual offending (Lindsay, 2009). The purpose of this project is to develop and evaluate the SOTSEC-ID version cftrus model. Participants are adult males from 15 different locations across England and Wales, with an intellectual disability or borderline cognitive functioning and who have committed sexual offences. A pilot study clarified assessments and procedures, and individual data over several years is presented. A qualitative study using Interpretive Phenomenological Analysis (JP A) illustrates the 'meaning making' of participants' treatment experience through six major themes. A reliability and validity study assesses the four main quantitative measures, QACSO, SAKA, SOSAS, and VESA, finding limited support for criterion validity for the SOSAS and SAKA, excellent inter-rater reli"ability for all four main measures, and good to excellent inter-rater reliability on all but the SAKA Finally, a quantitative study, in collaboration with the wider SOTSEC-ID group, uses a repeated measures design to compare the QACSO, SOSAS and SAKA across pre-group, post-group and follow. up. Significant main effects and post-hoc comparisons were in the predicted direction for all measures. A range of information on demographic, clinical and criminogenic factors including offending during treatment or follow-up are also presented. A recidivism rate of 12.3% over a year was calculated for the sample. The treatment model and collaborative framework is recommended for wider adoption

    Political Islam and grassroots activism in Turkey : a study of the pro-Islamist Virtue Party's grassroots activists and their affects on the electoral outcomes

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    This thesis presents an analysis of the spectacular rise of political Islam in Turkey. It has two aims: first to understand the underlying causes of the rise of the Welfare Party which -later became the Virtue Party- throughout the 1990s, and second to analyse how grassroots activism influenced this process. The thesis reviews the previous literature on the Islamic fundamentalist movements, political parties, political party systems and concentrates on the local party organisations and their effects on the party's electoral performance. It questions the categorisation of Islamic fundamentalism as an appropriate label for this movement. An exploration of such movements is particularly important in light of the event of 11`x' September. After exploring existing theoretical and case studies into political Islam and party activism, I present my qualitative case study. I have used ethnographic methodology and done participatory observations among grassroots activists in Ankara's two sub-districts covering 105 neighbourhoods. I examined the Turkish party system and the reasons for its collapse. It was observed that as a result of party fragmentation, electoral volatility and organisational decline and decline in the party identification among the citizens the Turkish party system has declined. However, the WP/VP profited from this trend enormously and emerged as the main beneficiary of this process. Empirical data is analysed in four chapters, dealing with the different aspects of the Virtue Party's local organisations and grassroots activists. They deal with change and continuity in the party, the patterns of participation, the routes and motives for becoming a party activist, the profile of party activists and the local party organisations. I explore what they do and how they do it. The analysis reveals that the categorisation of Islamic fundamentalism is misplaced and the rise of political Islam in Turkey cannot be explained as religious revivalism or the rise of Islamic fundamentalism. It is a political force that drives its strength from the urban poor which has been harshly affected by the IMF directed neoliberal economy policies. In conclusion, it is shown that the WP/VP's electoral chances were significantly improved by its very efficient and effective party organisations and highly committed grassroots activists

    Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process

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    In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov- ernment through the ELKARTEK program (OILTWIN project, ref. KK- 2020/00052)

    Transportation Planning, Policy, and Electric Micro-Mobilities: A Knowledge Synthesis of Recent Publications

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    This SSHRC-funded (Grant #972-2020-1009) scoping review synthesizes existing research (2010-2021) related to both private and shared electric micro-mobilities (i.e. e-bikes, e-scooters, e-unicycles, e-skateboards). It considers themes such as: rider demographics, usage, and motivations; mobility justice; benefits of and barriers to EMM use; safety and injuries; modal shift among forms of transportation; rider satisfaction with mode choice; environmental impact; conflict and controversy; EMM pilot programs; and EMM integration, legislation, and policy recommendations. Aside from scholarship, media reports are also included, in order to speak to the environment in which the research takes place
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