Background: In North America and three European countries Translational Medicine (TM) funding has taken center stage as the National Institutes of Health (NIH), for example, has come to recognize that delays are common place in completing clinical trials based upon benchside advancements. Recently, there are several illustrative examples whereby the translation of research had untoward outcomes requiring immediate action. Methods: Focus more on three-dimensional (3D) simulation, biomarkers, and Artificial Intelligence may allow orthopaedic surgeons to predict the ideal practices before orthopaedic surgery. Using the best medical imaging techniques may improve the accuracy and precision of tumor resections. Results: This article is directed at the young surgeon scientist and in particular orthopaedic residents and all other junior physicians in training to help them better understand TM and position themselves in career paths and hospital systems that strive for optimal TM. It serves to hasten the movement of knowledge garnered from the benchside and move it quickly to the bedside. Conclusions: Communication is ongoing in a bidirectional format. It is anticipated that more and more medical Centers and institutions will adopt TM models of healthcare delivery.

Surgical smoke removal algorithms can improve the quality of intra-operative imaging and reduce hazards in image-guided surgery, a highly desirable post-process for many clinical applications. These algorithms also enable effective computer vision tasks for future robotic surgery. In this paper, we present a new unsupervised learning framework for high-quality pixel-wise smoke detection and removal. One of the well recognized grand challenges in using convolutional neural networks (CNNs) for medical image processing is to obtain intra-operative medical imaging datasets for network training and validation, but availability and quality of these datasets are scarce. Our novel training framework does not require ground-truth image pairs. Instead, it learns purely from computer-generated simulation images. This approach opens up new avenues and bridges a substantial gap between conventional non-learning based methods and which requiring prior knowledge gained from extensive training datasets. Inspired by the Generative Adversarial Network (GAN), we have developed a novel generative-collaborative learning scheme that decomposes the de-smoke process into two separate tasks: smoke detection and smoke removal. The detection network is used as prior knowledge, and also as a loss function to maximize its support for training of the smoke removal network. Quantitative and qualitative studies show that the proposed training framework outperforms the state-of-the-art de-smoking approaches including the latest GAN framework (such as PIX2PIX). Although trained on synthetic images, experimental results on clinical images have proved the effectiveness of the proposed network for detecting and removing surgical smoke on both simulated and real-world laparoscopic images.

With the rise in popularity of serious games there is an increasing demand for virtual environments based on real-world locations. Emergency evacuation or fire safety training are prime examples of serious games that would benefit from accurate location depiction together with any application involving personal space. However, creating digital indoor models of real-world spaces is a difficult task and the results obtained by applying current techniques are often not suitable for use in real-time virtual environments. To address this problem, we have developed an application called LevelEd AR that makes indoor modelling accessible by utilizing consumer grade technology in the form of Apple’s ARKit and a smartphone. We compared our system to that of a tape measure and a system based on an infra-red depth sensor and application. We evaluated the accuracy and efficiency of each system over four different measuring tasks of increasing complexity. Our results suggest that our application is more accurate than the depth sensor system and as accurate and more time efficient as the tape measure over several tasks. Participants also showed a preference to our LevelEd AR application over the depth sensor system regarding usability. Finally, we carried out a preliminary case study that demonstrates how LevelEd AR can be successfully used as part of current industry workflows for serious games level design.

We developed an application that makes indoor modelling accessible by utilizing consumer grade technology in the form of Apple’s ARKit and a smartphone to assist with serious games level design. We compared our system to that of a tape measure and a system based on an infra-red depth sensor and application. We evaluated the accuracy and efficiency of each system over four different measuring tasks of increasing complexity. Our results suggest that our application is more accurate than the depth sensor system and as accurate and more time efficient as the tape measure over several tasks. Participants also showed a preference to our LevelEd AR application over the depth sensor system regarding usability.

We describe a mixed reality environment that has been designed as an aid for training driving skills for a powered wheelchair. Our motivation is to provide an improvement on a previous virtual reality wheelchair driving simulator, with a particular aim to remove any cybersickness effects. The results of a validation test are presented that involved 35 able bodied volunteers divided into three groups: mixed reality trained, virtual reality trained, and a control group. No significant differences in improvement was found between the groups but there is a notable trend that both the mixed reality and virtual reality groups improved more than the control group. Whereas the virtual reality group experienced discomfort (as measured using a simulator sickness questionnaire), the mixed reality group experienced no side effects.

Modern digital devices offer huge potential for the delivery of engaging heritage experiences to visitors, offering a better visitor experience, higher visitor numbers, and opportunities for increased tourism income. However, all software development entails risk, including the risk of developing a product which few will want, or be able, to use. Identifying user experience priorities and problems at an early stage is therefore extremely important. This chapter describes work in progress on a shortened version of Jake Knapp’s Design Sprint approach, and its application to designing VR/AR solutions for a specific heritage case study.

We report on the design, implementation and evaluation of <VRIA>, a framework for building immersive analytics (IA) solutions inWeb-based Virtual Reality (VR), built upon WebVR, A-Frame, React and D3. The recent emergence of affordable VR interfaces have reignited the interest of researchers and developers in exploring new, immersive ways to visualize data. In particular, the use of open-standards web-based technologies for implementing VR in a browser facilitates the ubiquitous and platform-independent adoption of IA systems. Moreover, such technologies work in synergy with established visualization libraries, through the HTML document object model (DOM). We discuss high-level features of <VRIA> and present a preliminary user experience evaluation of one of our use-cases.

Purpose – Issues surrounding knowledge management, knowledge transfer and learning within organisations challenge continuity and resilience in the face of changing environments. While initiatives are principally applied within large organisations, there is scope to assess how the processes are handled within small and medium enterprises (SMEs) and to consider how they might be enhanced. This paper presents an evaluation of practice within an evolving software development unit to determine what has been learned and how the knowledge acquired has been utilised to further organisational development. These results provide the basis for the design and implementation of a proposed support tool to enhance professional practice. Design/methodology/approach – A small software development unit, which has successfully delivered bespoke systems since its establishment a number of years ago, was selected for analysis. The unit operates as a team whose actions and behaviours were identified and validated by the following means: in-depth interviews were carried out with each member of the team to elicit an understanding of individual and collective development. Interview data were recorded and transcribed and subjected to qualitative analysis to identify key themes underpinning knowledge acquisition and utilisation. Samples of project documentation were scrutinised to corroborate interview data. After analysing the data, a focus-group meeting was held to validate the results and to generate further insights into learning within the team. Findings - Qualitative analysis of the data revealed key changes in thinking and practice within the team as well as insight into the development of individual and collective contextual knowledge, tacit understanding and learning. This analysis informed the proposal of a bespoke, lightweight, web-based system to support knowledge capture and organisational learning (OL). This approach has the potential to promote resilience and to enhance practice in similar small or start-up enterprises. Research limitations/implications – Purposeful sampling was used in selecting a small software development team. This enabled in-depth interviewing of all members of the team. This offered a rich environment from which to derive awareness and understanding of individual and collective knowledge acquisition and learning. Focusing on a single small enterprise limits the extent to which the findings can be generalised. However, the research provides evidence of effective practice and learning and has identified themes for the development of a support tool. This approach can be extended to similar domains to advance research into learning and development. Practical implications – Results of the work undertaken so far have generated promising foundations for the proposed support tool. This offers software developers a system within which they can reflect upon, and record, key learning events affecting technical, managerial and professional practice. Originality/value – Small enterprises have limited resources to support OL. The qualitative research undertaken so far has yielded valuable insight into the successful development of a single software development team. The construction of a support tool to enhance knowledge acquisition and learning has the capacity to consolidate valuable, and potentially scarce, expertise. It also has the potential to facilitate further research to determine how the prototype might be extended or revised to improve its contribution to the team’s development.

Knowledge management and knowledge transfer within organisations challenge continuity and resilience in the face of changing environments. While issues are principally addressed within large organisations, there is scope to evaluate how knowledge assets are managed within small and medium enterprises and to consider how the process might be enhanced. The research reported here aimed to evaluate practice within an evolving software development unit to understand how knowledge has been acquired and utilised to further organisational development. In-depth interviews were carried out with members of the unit to elicit an understanding of individual and collective learning. Qualitative analysis of the data revealed key changes in thinking and practice as well as insight into the development of individuals' contextual knowledge and tacit understanding. This analysis led to the proposal of a bespoke, lightweight web-based system to support knowledge capture and organisational learning. This work is still in progress but it is anticipated that the results will provide a potentially novel and beneficial method for enhancing knowledge assets in small enterprises and consolidating valuable, and potentially scarce, expertise.

This research has applied evolutionary algorithms to evolve swarm communication. Controllers were evolved for colonies of artificial simulated ants during a food foriaging task which communicate using pheromone. Neuroevolution enables both weights and the topology of the artificial neural networks to be optimized for food foriaging. The developed model results in evolution of ants which communicate using pheromone trails. The ants successfully collect and return food to the nest. The controller has evolved to adjust the strength of pheromone which provides a signal to guide the direction of other ants in the colony by hill climbing strategy. A single ANN controller for ant direction successfully evolved which exhibits many separate skills including food search, pheromone following, food collection and retrieval to the nest.