Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Operational research:methods and applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Medical Image Registration Using Deep Neural Networks

Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications

Creating immersive, play-anywhere handheld augmented reality stories, through remote user testing

This thesis outlines new instances of Extended Reality (XR) stories as well as associated user studies with them, to create more immersive story experiences delivered at a user’s choice of location through a mobile phone. This extends prior work on Location Based Experiences (LBEs), which have typically been designed to offer a game or story at a pre-determined location. A play-anywhere experience offers potential to open up LBEs to a wider audience, as well as to those may prefer to take part individually or closer to home, such attitude shifts becoming increasingly more common. The current research adopted an in the wild approach combining practice, studies and theory, with most user data being collected remotely. Each story application developed is subsequently referred to as an app, with each app offering a bespoke story incorporating Augmented Reality (AR) features, to better bring users’ location inline with the narrative. Testing the apps across various locations matched their intended use, and resulted in new guidelines for both incorporating AR into such LBEs, as well as for conducting remote user studies. A final app offered a site-specific curated story, with all study participants taking part under similar conditions at the same location, the ability to observe them using the app providing additional insights. The story apps used available local map data alongside Handheld Augmented Reality (HAR), to overlay interactable virtual objects on top of the physical environment, and visible on the phone’s display. Guidelines from related methodologies were used to better allow for the variety of factors that might influence different users’ immersion and engagement. These included the implementation of the AR features, the story itself, real world activity, and personal preferences including onboarding requirements. The approach taken contributed a reverse methodology to a lot of related research, that would typically begin with laboratory testing before moving to public spaces. User studies with the five mobile apps contributed guidelines for such experiences, that could benefit both practitioners and researchers in related fields. In the later case, a need was identified to develop new research tools specifically suited to the subtleties of handheld play-anywhere LBEs, such issues explored within the apps tested. The guidelines identified for offering more effective XR LBEs were also implemented in the creation of a new open source Unity project, called Map Story Engine. This offers a tool to test new features, as well as providing a fully customisable template for practitioners to author their own play-anywhere HAR stories and games