Project Retrosight. Understanding the returns from cardiovascular and stroke research: Case Studies

Copyright @ 2011 RAND Europe. All rights reserved. The full text article is available via the link below.This project explores the impacts arising from cardiovascular and stroke research funded 15-20 years ago and attempts to draw out aspects of the research, researcher or environment that are associated with high or low impact. The project is a case study-based review of 29 cardiovascular and stroke research grants, funded in Australia, Canada and UK between 1989 and 1993. The case studies focused on the individual grants but considered the development of the investigators and ideas involved in the research projects from initiation to the present day. Grants were selected through a stratified random selection approach that aimed to include both high- and low-impact grants. The key messages are as follows: 1) The cases reveal that a large and diverse range of impacts arose from the 29 grants studied. 2) There are variations between the impacts derived from basic biomedical and clinical research. 3) There is no correlation between knowledge production and wider impacts 4) The majority of economic impacts identified come from a minority of projects. 5) We identified factors that appear to be associated with high and low impact. This report presents the key observations of the study and an overview of the methods involved. It has been written for funders of biomedical and health research and health services, health researchers, and policy makers in those fields. It will also be of interest to those involved in research and impact evaluation.This study was initiated with internal funding from RAND Europe and HERG, with continuing funding from the UK National Institute for Health Research, the Canadian Institutes of Health Research, the Heart and Stroke Foundation of Canada and the National Heart Foundation of Australia. The UK Stroke Association and the British Heart Foundation provided support in kind through access to their archives

High level optimizations in compiling process descriptions to asynchronous circuits

technical reportAsynchronous/'Self-Timed designs are beginning to attract attention as promising means of dealing with the complexity of modern VLSI technology. In this paper, we present our views on why asynchronous systems matter. We then present details of our high level synthesis tool SHILPA that can automatically synthesize asynchronous circuits from descriptions in our concurrent programming language, hopCP. We outline some of the high level communication abstractions available in hopCP. We illustrate how these abstractions are realized in the asynchronous circuits generated by SHILPA. We then present a series of examples that present many of the high level optimization strategies used by SHILPA. Some of these optimizations aim to speed up the generated circuits by avoiding un-necessary waiting. Others synthesize components that are much easier to realize in a variety of technologies. We also discuss some of the tradeoffs possible between optimizations and timing constraints

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Towards Hardware-Based Application Fingerprinting with Microarchitectural Signals for Zero Trust Environments

The interactions between software and hardware are increasingly important to computer system security. This research collects sequences of microprocessor control signals to develop machine learning models that identify software tasks. The proposed approach considers software task identification in hardware as a general problem with attacks treated as a subset of software tasks. Two lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during real (i.e., non-simulated) system operation. Second, experimental design is used to select hardware and software configuration to train and evaluate machine learning models. The machine learning models significantly outperform a Naive classifier based on Euclidean distances from class means. Various configurations produce balanced accuracy scores between 26.08% and 96.89%

A systematic review and meta-analysis of the effectiveness of hypertension interventions in faith-based organisation settings

From Crossref journal articles via Jisc Publications RouterHistory: epub 2023-10-13, issued 2023-10-13Publication status: PublishedDaniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-0420Abstract Background Hypertension is the global, leading cause of mortality and is the main risk factor for cardiovascular disease. Community-based partnerships can provide cost-saving ways of delivering effective blood pressure (BP) interventions to people in resource-poor settings. Faith-based organisations (FBOs) prove important potential health partners, given their reach and community standing. This potential is especially strong in hard-to-reach, socio-economically marginalised communities. This systematic review explores the state of the evidence of FBO-based interventions on BP management, with a focus on randomised controlled trials (RCTs) and cluster RCTs (C-RCTs). Methods Seven academic databases (English = 5, Chinese = 2) and grey literature were searched for C-/RCTs of community-based interventions in FBO settings. Only studies with pre- and post-intervention BP measures were kept for analysis. Random effects models were developed using restricted maximum likelihood estimation (REML) to estimate the population average mean change and 95% confidence interval (CI) of both systolic and diastolic blood pressure (SBP and DBP). The overall heterogeneity was assessed by successively adding studies and recording changes in heterogeneity. Prediction intervals were generated to capture the spread of the pooled effect across study settings. Results Of the 19 055 titles identified, only 11 studies of fair to good quality were kept for meta-analysis. Non-significant, average mean differences between baseline and follow-up for the intervention and control groups were found for both SBP (0.78 mm of mercury (mmHg) (95% CI = 2.11-0.55)) and DBP (-0.20 mm Hg (95% CI = -1.16 to 0.75)). Subgroup analysis revealed a significant reduction in SBP of -6.23 mm Hg (95% CI = -11.21 to -1.25) for populations with mean baseline SBP of ≥140 mm Hg. Conclusions The results support the potential of FBO-based interventions in lowering SBP in clinically hypertensive populations. However, the limited evidence was concentrated primarily in Christian communities in the US More research is needed to understand the implications of such interventions in producing clinically meaningful long-term effects in a variety of settings. Further research can illuminate factors that affect success and potential expansion to sites outside the US as well as non-Christian FBOs. Current evidence is inadequate to evaluate the potential of FBO-based interventions in preventing hypertension in non-hypertensive populations. Intervention effects in non-hypertensive population might be better reflected through intermediate outcomes.pubpu

Cognitive dimensions usability assessment of textual and visual VHDL environments

Visual programming languages promise to make programming easier with simpler graphical methods, broadening access to computing by lessening the need for would-be users to become proficient with textual programming languages, with their somewhat arcane grammars and methods removed from the problem space of the user. However, after more than forty years of research in the field, visual methods remain in the margins of use and programming remains the bailiwick of people devoted to the endeavor. VPL designers need to understand the mechanisms of usability that pertain to complex systems like programming language environments. Effective research tools for studying usability, and sufficiently constrained, mature subjects for investigation are scarce. This study applies a usability research tool, with its origins in applied psychology, to a programming language surrogate from the hardware description language class of notations. The substitution is reasonable because of the great similarity between hardware description languages and programming languages. Considering VHDL (the VHSIC Hardware Description Language) is especially worthwhile for several reasons, but primarily because significant numbers of digital designers regularly employ both textual and visual VHDL environments to meet the same real-world design challenges. A comparative analysis of Cognitive Dimensions assessments of textual and visual VHDL environments should further understanding of the usability issues specifically related to visual methods – in many cases, the same visual methods used in visual programming languages. Furthermore, with this real-world ‘field lab’ better understood, it should be possible to design experiments to pursue the formalization of the CDs framework as a theory

Towards Hardware-Based Application Fingerprinting with Microarchitectural Signals for Zero Trust Environments

The interactions between software and hardware are increasingly important to computer system security. This research collects sequences of microprocessor control signals to develop machine learning models that identify software tasks. The proposed approach considers software task identification in hardware as a general problem with attacks treated as a subset of software tasks. Two lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during real (i.e., non-simulated) system operation. Second, experimental design is used to select hardware and software configuration to train and evaluate machine learning models. The machine learning models significantly outperform a Naive classifier based on Euclidean distances from class means. Various configurations produce balanced accuracy scores between 26.08% and 96.89%