The role of user requirements research in medical device development

Aims and Objectives: This research aims to suggest a concise framework to help in the better conceptualisation and integration of users in the medical device development (MDD) process. The current economic, political and social climate concerning the matter of healthcare delivery has resulted in the emergence of numerous users and user groups for whom the healthcare system has not previously catered for. These users have created ambiguity for the designers and manufacturers of medical devices as the boundaries between their needs and requirements have blurred, outdating current methods of MDD to meet consumer needs. Research Design and Methodology: The research methodology begins primarily with conducting a literature search on the theories relating to user requirements and medical device development. The paper outlines these findings through initially describing users and user involvement and relating them to medical devices. The cross-disciplinary nature of healthcare influenced the investigation into multiple disciplines including; IT, Ergonomics – particularly participatory research, Psychology and Design. These disciplines expose various methods and processes, which are useful to user requirements research. These methods were analysed for their compatibility, and then used to construct a conceptual framework for user involvement in MDD. Results: The research insinuates the true significance of user involvement and hence resulted in the formation of a conceptual framework to aid user involvement in the MDD process. The framework is produced by the amalgamation of relevant methods examined across the disciplines, in a complimentary fashion. Conclusion: The originality of this research lies in its use of a multidisciplinary approach. Previous research claiming multi-methods has dealt with combining two disciplines or methods at a time i.e. Computer supported cooperative work (CSCW) with participatory research (Scandurra et al, 2008) for the needs analysis of healthcare professionals only. Collaboration across disciplines has also been investigated (Johnson et al, 2005), but this was for the purpose of redesign rather than initial designs. This framework can help medical device designers to fully access all user requirements through more extensive collaboration right at the start. It reduces the risk of high costs involved in device rejection, usually associated with belated recognition of user needs in the design cycle

Stop Co-Annihilation in the Minimal Supersymmetric Standard Model Revisited

We re-examine the stop co-annihilation scenario of the Minimal Supersymmetric Standard Model, wherein a bino-like lightest supersymmetric particle has a thermal relic density set by co-annihilations with a scalar partner of the top quark in the early universe. We concentrate on the case where only the top partner sector is relevant for the cosmology, and other particles are heavy. We discuss the cosmology with focus on low energy parameters and an emphasis on the implications of the measured Higgs boson mass and its properties. We find that the irreducible direct detection signal correlated with this cosmology is generically well below projected experimental sensitivity, and in most cases lies below the neutrino background. A larger, detectable, direct detection rate is possible, but is unrelated to the co-annihilation cosmology. LHC searches for compressed spectra are crucial for probing this scenario

Plane-stress, elastic-plastic states in the vicinity of crack tips

Plane stress analysis of elastic-plastic states in vicinity of straight crack tip in thin plat

Multimodal 3D Object Detection from Simulated Pretraining

The need for simulated data in autonomous driving applications has become increasingly important, both for validation of pretrained models and for training new models. In order for these models to generalize to real-world applications, it is critical that the underlying dataset contains a variety of driving scenarios and that simulated sensor readings closely mimics real-world sensors. We present the Carla Automated Dataset Extraction Tool (CADET), a novel tool for generating training data from the CARLA simulator to be used in autonomous driving research. The tool is able to export high-quality, synchronized LIDAR and camera data with object annotations, and offers configuration to accurately reflect a real-life sensor array. Furthermore, we use this tool to generate a dataset consisting of 10 000 samples and use this dataset in order to train the 3D object detection network AVOD-FPN, with finetuning on the KITTI dataset in order to evaluate the potential for effective pretraining. We also present two novel LIDAR feature map configurations in Bird's Eye View for use with AVOD-FPN that can be easily modified. These configurations are tested on the KITTI and CADET datasets in order to evaluate their performance as well as the usability of the simulated dataset for pretraining. Although insufficient to fully replace the use of real world data, and generally not able to exceed the performance of systems fully trained on real data, our results indicate that simulated data can considerably reduce the amount of training on real data required to achieve satisfactory levels of accuracy.Comment: 12 pages, part of proceedings for the NAIS 2019 symposiu

Hardware synthesis from DDL description

The details of digital systems can be conveniently input into the design automation system by means of hardware description language (HDL). The computer aided design and test (CADAT) system at NASA MSFC is used for the LSI design. The digital design language (DDL) was selected as HDL for the CADAT System. DDL translator output can be used for the hardware implementation of the digital design. Problems of selecting the standard cells from the CADAT standard cell library to realize the logic implied by the DDL description of the system are addressed

A transient PEMFC model with CO poisoning and mitigation by O2 bleeding and Ru-containing catalyst

In this paper we present a transient, fully two-phase, non-isothermal model of carbon monoxide poisoning and oxygen bleeding in the membraneelectrode assembly of a polymer electrolyte fuel cell. The model includes a detailed description of mass, heat and charge transport, chemisorption,electrochemical oxidation and heterogeneous catalysis (when oxygen is introduced). Example simulation results demonstrate the ability of themodel to qualitatively capture the fundamental features of the poisoning process and the extent of poisoning with respect to channel temperatureand concentration. Further examples show how the multi-step kinetics can interact with other physical phenomena such as liquid-water flooding,particularly in the anode. Carbon monoxide pulsing is simulated to demonstrate that the complicated reaction kinetics of oxygen bleeding canbe captured and even predicted. It is shown that variations in the channel temperature have a convoluted effect on bleeding, and that trends inperformance on relatively short time scales can be the precise opposite of the trends observed at steady state. We incorporate a bi-functionalmechanism for carbon monoxide oxidation on platinum–ruthenium catalysts, demonstrating the marked reduction in the extent of poisoning, theeffect of variations in the platinum–ruthenium ratio and the influence of temperature. Finally, we discuss the implications of the results, extensionsto the model and possible avenues for experimental work

Simultaneous Multiple Surface Segmentation Using Deep Learning

The task of automatically segmenting 3-D surfaces representing boundaries of objects is important for quantitative analysis of volumetric images, and plays a vital role in biomedical image analysis. Recently, graph-based methods with a global optimization property have been developed and optimized for various medical imaging applications. Despite their widespread use, these require human experts to design transformations, image features, surface smoothness priors, and re-design for a different tissue, organ or imaging modality. Here, we propose a Deep Learning based approach for segmentation of the surfaces in volumetric medical images, by learning the essential features and transformations from training data, without any human expert intervention. We employ a regional approach to learn the local surface profiles. The proposed approach was evaluated on simultaneous intraretinal layer segmentation of optical coherence tomography (OCT) images of normal retinas and retinas affected by age related macular degeneration (AMD). The proposed approach was validated on 40 retina OCT volumes including 20 normal and 20 AMD subjects. The experiments showed statistically significant improvement in accuracy for our approach compared to state-of-the-art graph based optimal surface segmentation with convex priors (G-OSC). A single Convolution Neural Network (CNN) was used to learn the surfaces for both normal and diseased images. The mean unsigned surface positioning errors obtained by G-OSC method 2.31 voxels (95% CI 2.02-2.60 voxels) was improved to $1.27$ voxels (95% CI 1.14-1.40 voxels) using our new approach. On average, our approach takes 94.34 s, requiring 95.35 MB memory, which is much faster than the 2837.46 s and 6.87 GB memory required by the G-OSC method on the same computer system.Comment: 8 page

Thermodynamics of Vortices in the Plane

The thermodynamics of vortices in the critically coupled abelian Higgs model, defined on the plane, are investigated by placing $N$ vortices in a region of the plane with periodic boundary conditions: a torus. It is noted that the moduli space for $N$ vortices, which is the same as that of $N$ indistinguishable points on a torus, fibrates into a $CP_{N-1}$ bundle over the Jacobi manifold of the torus. The volume of the moduli space is a product of the area of the base of this bundle and the volume of the fibre. These two values are determined by considering two 2-surfaces in the bundle corresponding to a rigid motion of a vortex configuration, and a motion around a fixed centre of mass. The partition function for the vortices is proportional to the volume of the moduli space, and the equation of state for the vortices is $P(A-4\pi N)=NT$ in the thermodynamic limit, where $P$ is the pressure, $A$ the area of the region of the plane occupied by the vortices, and $T$ the temperature. There is no phase transition.Comment: 17 pages, DAMTP 93-3