26013 research outputs found
Sort by
Low carbon heating transitions and Actor Network Theory: Entanglements with the fireside.
Get PDFWe share findings from 30 oral histories of home heating (1945 to present) gathered in the former coal mining town of Rotherham in Northern England. By analysing these rich personal accounts using Actor Network Theory (ANT), we reveal the coal fire (or coal-fired range) as a powerful actant shaping domestic life in the decades following the end of the Second World War. This exposes important, previously unacknowledged, relational-material entanglements with the fireside, which endure despite many decades of gas central heating in the UK. The nature and strength of these entanglements have implications for the socially and culturally sensitive handling of efforts (across Europe) to transition households to more technological low carbon heating systems, such as heat pumps. This paper sets out early findings from the UK component of a Europe-wide project which innovatively seeks to establish a social and cultural history of home heating in order to distil lessons for a more socially and culturally conscious transition to low carbon heating systems
Personalised Interactive Reinforcement Learning with Multi-Task Pre-training
Get PDFPersonalised robots have immense potential to enhance daily life through tailored interactions, yet achieving efficient personalisation remains challenging. This paper introduces a Multi-task Interactive Reinforcement Learning (MIRL) framework aimed at improving the efficiency of interactive learning with evaluative feedback. We demonstrate that pre-training the robot across diverse tasks significantly reduces the learning steps required during fine-tuning, thereby enhancing sample efficiency. Our approach effectively aligns robot behaviours with user preferences, as evidenced by experimental results. These advancements promise to advance the usability and effectiveness of personalised robotics in diverse applications
Transpiration Cooling in Hypersonic Flow and Mutual Effect on Turbulent Transition and Cooling Performance
Get PDFThis work presents recent advancements in the study of film cooling in hypersonic flows, considering experimental and numerical investigations, with the aim to characterize the wall-cooling performance in different coolant injection and baseflow conditions in a Mach number range 2–7.7. The study explores the mutual interaction between the injected coolant film and the boundary-layer flow, with emphasis on the effects of wall blowing on the boundary-layer characteristics, stability, and transition to turbulence, as well as the effect of transition on wall-cooling performance. Considered flow configurations include cases of effusion cooling in both wall-normal or slightly inclined and wall-parallel blowing, different types of coolant, cases of favorable pressure gradient compared to zero pressure gradient, as well as transpiration cooling cases at different blowing ratios and surface geometries. For the transpiration cooling case, experiments in different hypersonic wind tunnel facilities are presented for flat plate and cone geometries, with coolant injected through C/C porous samples, whereas numerical simulations of modeled porous injection are presented for a flat plate and a blunt cone, showing results for the boundary-layer receptivity with coolant injection and the associated effects on transition and cooling performance. A summary of the main findings is provided along with a critical analysis based on a comparative study to evaluate the effect of each configuration, injection strategy, and key parameters on the boundary-layer flow and the feedback on wall-cooling performance. Conclusions are drawn about potential directions of study for the further development and optimization of the film cooling technique for future hypersonic vehicles
Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach
Get PDFInter-individual cognitive variability, influenced by genetic and environmental factors, is crucial for understanding typical cognition and identifying early cognitive disorders. This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. We included 199 subjects, aged 20 to 70, and measured cognitive performance with the Wisconsin Card Sorting Test. Differentially expressed proteins (DEPs) were identified using label-free proteomics and analyzed with the Linear Model for Microarray Data. We discovered 213 DEPs between lower and higher cognition groups, with 155 upregulated in the lower cognition group and enriched in the IL-17 signaling pathway. Subsequent bioinformatic analysis linked these DEPs to neuroinflammation-related cognitive impairment. A random forest model classified cognitive ability groups with an accuracy of 81.5%, sensitivity of 65%, specificity of 85.9%, and an AUC of 0.79. By targeting a specific Thai cohort, this research provides novel insights into the link between neuroinflammation and cognitive performance, advancing our understanding of cognitive variability, highlighting the role of biological markers in cognitive function, and contributing to developing more accurate machine learning models for diverse populations
An Experimental Method for Fatigue Testing Cast Iron Water Pipes Using Combined Internal Water Pressure and Bending Loads
Get PDFBackground
Investigations into the fatigue failures mechanism of Grey Cast Iron (GCI) water pipes are inhibited by the lack of a lab-based method to conduct extensive high-cycle biaxial fatigue test programmes.
Objective
The work presented in this paper developed and tested a novel experiment capable of causing controlled fatigue failures of GCI pipe specimens in the high-cycle fatigue regime using bending and internal water pressure fatigue loading.
Methods
A novel four-point bending and internal water pressure fatigue testing system was developed to apply constant amplitude out-of-phase biaxial loading to 58 mm diameter GCI pipes at 1.7 Hz. To verify the ability of this equipment to apply known stresses and repeatable loads to pipe specimens a series of tests were conducted. A finite element model of the pipe specimen was used to estimate the strains and displacements applied by the equipment.
Results
Experimental strains and displacements were mainly within ± 10% of the estimated values and the pressure amplitudes measured over 103 cycles were within ± 3% of the average. Dynamic load effects occurred at higher bending loads, but these were quantified and accounted for. Trial destructive tests revealed that the lifespan of leaking fatigue cracks in GCI pipes with uniform wall-loss subject to combined internal pressure and bending fatigue loads is less than 1% of the total cycles-to-burst.
Conclusions
The experimental method developed was able to apply combined, out-of-phase internal pressure and bending fatigue loads accurately and consistently to small-dimeter GCI pipes, and cause these pipes to develop high-cycle fatigue regime failures
Special Operations: Deploying artists’ methods in investigative practices
Get PDFThis paper discusses two projects that illustrate arts research methods:
1. Doctoral research that commences with an observation at the Houses of Parliament, London, of the passage of the Investigatory Powers Act (2016), legislation that significantly extended the UK’s digital surveillance capabilities. The observation is followed by an analysis of archival film, video and photography from hidden cameras at the Stasi Records Agency, Berlin, that has failed, is sabotaged or misses its subject. Methods employ props, writing, performance-lectures, and exhibitions. Retro spyware is used covertly whilst the Investigatory Powers Bill is debated, to question what might become visible when surveillance techniques are repurposed to look at surveillance.
2. UNLAND is an exhibition and ancillary events, of photographic, video, and print works at NeMe, Cyprus (April–May 2023). This collaborative project (Kypros Kyprianou, Newcastle University / Jeremy Lee, Sheffield Hallam University) presents documented and fictional material of contested spaces within Cyprus. Sites include the UN buffer zone, restricted areas of Varosha, and British military bases.
The artworks employ contemporary imaging techniques (photogrammetry, LiDar and machine learning) to query their application through the ways that artists ‘look’ or the methods employed. The geographical, military, and forensic antecedence of the technologies is unsettled and ‘visioning’ becomes warped and ‘messy’ whilst also being extended. Alternative textures, disturb the image, representation and reporting of sites of conflict. Rather than enhancing the ‘quality’ of the image, technologies expose the gaps, flaws, and missing data to present the overlooked, hidden, accidental or malfunctioning ‘visioning’.
Research findings emphasise iterative, nuanced, and minor processes founded in making art that extend technique through grounded, situated and relational critique. A search for definition and considerations concerning surveillance and ethics, within both projects, is examined through image making, and arts research methods. The projects emphasise the importance of arts research within wider contexts and the potential to question established research orthodoxies
Automatic Video Analysis of Countermovement Jump Performance Using a Single Uncalibrated Camera
Get PDFThe countermovement jump (CMJ) assessment is widely employed for monitoring sports performance, traditionally relying on heavy and expensive force plates to extract performance variables like jump height and peak force. Inertial measurement unit (IMU)-based approaches and mobile applications have been developed to analyse CMJ performance with cost-effective devices, but they still require technical expertise and manual annotations during operation. We developed a new camera-based pipeline that can measure CMJ performance automatically by utilising computer vision techniques and biomechanical approaches from video captured by a single uncalibrated camera. Human segmentation and pose estimation techniques are used to understand the movement of the centre of mass and take-off and landing times. Combined with the biomechanical principles of object parabolic motion and inverse dynamics, the force–time data can be estimated for extracting CMJ performance variables. We recruited 77 elite athletes (29 females; height: 170.0 ± 9.0 cm; mass: 72.2 ± 17.7 kg) to evaluate the developed method against a commercial force platform. The developed method enables fully automatic CMJ analysis for both force–time data and performance variables from video captured by a camera without calibration. The results showed superior correlations (R > 0.7) and high reliability (%CV < 10 %) for most CMJ variables compared to the IMU-based approach. This approach automates CMJ analysis, offering more variables than existing mobile apps while reducing the technical demands of IMU-based methods. It streamlines assessment, making it ideal for large-scale cohort studies. Grounded in biomechanics, it enhances sports and health monitoring, enabling data-driven optimisation of human performance
Written evidence submitted by Zoe Rodgers to the House of Commons Committee on the Crime and Policing Bill (CPB68)
Get PDFLong COVID clinical evaluation, research and impact on society: a global expert consensus
Get PDFBackground
Long COVID is a complex, heterogeneous syndrome affecting over four hundred million people globally. There are few recommendations, and no formal training exists for medical professionals to assist with clinical evaluation and management of patients with Long COVID. More research into the pathology, cellular, and molecular mechanisms of Long COVID, and treatments is needed. The goal of this work is to disseminate essential information about Long COVID and recommendations about definition, diagnosis, treatment, research and social issues to physicians, researchers, and policy makers to address this escalating global health crisis.
Methods
A 3-round modified Delphi consensus methodology was distributed internationally to 179 healthcare professionals, researchers, and persons with lived experience of Long COVID in 28 countries. Statements were combined into specific areas: definition, diagnosis, treatment, research, and society.
Results
The survey resulted in 187 comprehensive statements reaching consensus with the strongest areas being diagnosis and clinical assessment, and general research. We establish conditions for diagnosis of different subgroups within the Long COVID umbrella. Clear consensus was reached that the impacts of COVID-19 infection on children should be a research priority, and additionally on the need to determine the effects of Long COVID on societies and economies. The consensus on COVID and Long COVID is that it affects the nervous system and other organs and is not likely to be observed with initial symptoms. We note, biomarkers are critically needed to address these issues.\ud
Conclusions
This work forms initial guidance to address the spectrum of Long COVID as a disease and reinforces the need for translational research and large-scale treatment trials for treatment protocols