58622 research outputs found
Sort by
“Shifting goalposts” and “fishing expeditions”:police case preparation and the application of the full code test in cases of RASSO
Get PDFPurposeDespite recent increases, the low level of convictions in cases of rape and serious sexual offences (RASSO), aligned with the subsequent lack of victim satisfaction with the process, has highlighted the various challenges and barriers throughout the criminal justice process. Whilst understanding the shortcomings of criminal justice processes in cases of RASSO requires a holistic approach, this paper focuses on case preparation and the application of the Full Code test. Its purpose is to explore the application of this test and preceding case preparation in police decision-making around submitting cases for charging decision, following the recommendations of the first year of Operation Soteria Bluestone.Design/methodology/approachThis study adopted a mixed-method approach, combining both interviews and case reviews of selected RASSO cases across five forces in England and Wales.FindingsThis research found a shift towards “threshold thinking”, whereby officers arguably no longer try to predict a prosecutor’s decision and instead focus on meeting the requirements for the application of the test. In terms of case preparation, the research demonstrates a move away from “fishing expeditions”, with a preference for more focused approaches to collecting evidence.Originality/valueTo the best of the authors’ knowledge, this is one of the first studies looking at the use of full code test principles in police decision-making in RASSO cases
Engineering interfacial charge transfer through modulation doping for 2D electronics
Get PDFTwo-dimensional (2D) semiconductors are likely to dominate next-generation electronics due to their advantages in compactness and low power consumption. However, challenges such as high contact resistance and inefficient doping hinder their applicability. Here, we investigate work-function-mediated charge transfer (modulation doping) as a pathway for achieving high-performance p-type 2D transistors. Focusing on type-III band alignment, we explore the doping capabilities of 27 candidate materials, including transition metal oxides, oxyhalides, and α-RuCl3, on channel materials such as transition metal dichalcogenides (TMDs) and group-III nitrides. Our extensive first-principles density functional theory (DFT) reveal p-type doping capabilities of high electron affinity materials, including α-RuCl3, MoO3, and V2O5. We predict significant reductions in contact resistance and enhanced channel mobility through efficient hole transfer without introducing detrimental defects. We analyze transistor geometries and identify promising material combinations beyond the current focus on WSe2 doping, suggesting new avenues for hBN, AlN, GaN, and MoS2. This comprehensive investigation provides a roadmap for developing high-performance p-type monolayer transistors toward the realization of 2D electronics.</p
OpenEarable 2.0:Open-Source Earphone Platform for Physiological Ear Sensing
Get PDFEarphones have evolved from pure audio devices to "earables" that are capable of advanced sensing. Bespoke research devices have shown the unique sensing capabilities of the earable platform; however, they are hard to replicate and require expertise to develop in the first place. In this paper, we present OpenEarable 2.0 - an open source, unified platform that integrates a larger number of sensors for conducting comprehensive earable research. OpenEarable 2.0 works as regular binaural Bluetooth earphones and features two ultrasound capable microphones (inward/outward), a 3-axis ear canal accelerometer/bone microphone, a 9-axis head inertial measurement unit, pulse oximeter, optical temperature sensor, ear canal pressure sensor, and microSD card. These capabilities allow for the detection and measurement of 30+ phenomena on the ear that can be used across a wide range of applications in health monitoring, activity tracking, human-computer-interaction and authentication. We describe the design and development of OpenEarable 2.0 which follows best open hardware practices and achieves commercial-level wearability. We provide justification for the selection and placement of integrated sensors and include in-depth descriptions of the extensible, open source firmware and hardware that are implemented using free to use tools and frameworks. For real-time sensor control and data recording we also contribute a web-based dashboard and mobile smartphone app. The wearability and ability to sense different phenomena are validated in four studies which showcases how OpenEarable 2.0 provides accurate measurements in comparison to established gold-standard measurements. We further demonstrate that OpenEarable 2.0 can be assembled by inexperienced users, and that undergraduate students can build applications using the OpenEarable platform
Beyond the Game:Exploring the Impact of Different Sports on Well-Being in Abu Dhabi
Get PDFThis study examines the associations between participation in different sports and key well-being indicators, including physical and mental healthsocial connections, and life satisfaction, among residents of Abu Dhabi. A large-scale cross-sectional survey was conducted in Abu Dhabi. Participants reported the type of sports they regularly engage in, and various well-being indicators were measured. A total of 25 sport types were analysed, focusing on determining the association of each sport with specific well-being indicators. The data was analysed using descriptive statistics. The findings reveal that walking was the most practiced sport, followed by jogging, running, and CrossFit. Team sports like football, volleyball, and cricket were strongly associated with well-being indicators such as mental health, satisfaction with family life and social relationships. In contrast, individual sports like running and cycling were more closely tied to physical health outcomes. Sports such as Jiu-Jitsu and fencing, though less commonly practiced, were found to contribute positively to mental resilience and emotional regulation. Gender differences were evident, with males participating more in high-intensity sports, while females favoured walking and dance, reflecting cultural preferences. The study highlights the diverse benefits of different types of sports on well-being. Team-based sports offer broader social and emotional benefits, while individual sports are linked more closely to personal health improvements. These findings emphasize the importance of promoting various sports to enhance different dimensions of well-being across the population. The results suggest that public health initiatives should tailor sports programs to address both social and individual health needs. Encouraging greater participation in a range of sports can help foster improved physical, mental, and social well-being across diverse demographic groups in Abu Dhabi.</p
Efficient and robust search of microbial genomes via phylogenetic compression
Get PDFComprehensive collections approaching millions of sequenced genomes have become central information sources in the life sciences. However, the rapid growth of these collections has made it effectively impossible to search these data using tools such as the Basic Local Alignment Search Tool (BLAST) and its successors. Here, we present a technique called phylogenetic compression, which uses evolutionary history to guide compression and efficiently search large collections of microbial genomes using existing algorithms and data structures. We show that, when applied to modern diverse collections approaching millions of genomes, lossless phylogenetic compression improves the compression ratios of assemblies, de Bruijn graphs and k-mer indexes by one to two orders of magnitude. Additionally, we develop a pipeline for a BLAST-like search over these phylogeny-compressed reference data, and demonstrate it can align genes, plasmids or entire sequencing experiments against all sequenced bacteria until 2019 on ordinary desktop computers within a few hours. Phylogenetic compression has broad applications in computational biology and may provide a fundamental design principle for future genomics infrastructure.</p
BHIS:A Bayesian-Heuristic Inference System for Recognition of Walking Gait Phases
Get PDFGait phase recognition is vital for advancing assistive robotics, enabling phase-based assistance throughout the gait cycle. This article presents a real-time method using wearable sensors and computational methods for classifying the seven gait subphases. Current methods often struggle with accuracy on unseen subjects. Furthermore, walking speed variability, hardware complexity, and response time hinder robustness, portability, and real-time performance, respectively. A Bayesian method constructs posterior belief by selecting likely phase transition candidates heuristically and combining biomechanical signal knowledge with pattern recognition techniques. The approach is validated and benchmarked against prevailing deep learning (DL) methods using two datasets, each containing data from two inertial measurement units (IMUs) attached to the midshanks of test subjects. The first dataset includes six participants, while the second dataset includes ten, all walking at their comfortable speeds. Additionally, the method is validated in real time for nine test subjects walking at varying speeds (2.2-3.5 mph). The proposed method demonstrates strong robustness, achieving average steady accuracies of 98% and 97.4% for seen and unseen subjects, respectively, with an average runtime of 1.8 ms.</p
Home and Epigenome:Exploring the Role of DNA Methylation in the Relationship Between Poor Housing Quality and Depressive Symptoms
Get PDFIntroduction Poor housing quality associates with risk for depression. However, previous research often lacks consideration of socioeconomic status (SES) baseline depressive symptoms and biological processes, leading to concerns of confounding and reverse causation.Methods In a sample of up to 9669 adults, we investigated cross-sectional and longitudinal associations between housing quality (assessed at age 28, 1-year and 2 year follow-ups) and depressive symptoms (at four intervals between enrolment and 18-year follow-up). In subsamples (n=871, n=731), we investigated indirect effects via DNA methylation.Results Poor housing quality associated with depressive symptoms cross-sectionally (beta range: 0.02–0.06) after controlling for SES and other factors. Longitudinally, this association persisted at the ~2 year, but not the ~18-year follow-up period. Indirect effects (β=0.002–0.012) linked to genes related to ageing, obesity and brain health.Conclusion These results highlight poor housing quality as a risk factor for depression and the potential role of DNA methylation in this association
Exploratory Proof-of-Concept:Predicting the Outcome of Tennis Serves Using Motion Capture and Deep Learning
Get PDFTennis serves heavily impact match outcomes, yet analysis by coaches is limited by human vision. The design of an automated tennis serve analysis system could facilitate enhanced performance analysis. As serve location and serve success are directly correlated, predicting the outcome of a serve could provide vital information for performance analysis. This article proposes a tennis serve analysis system powered by Machine Learning, which classifies the outcome of serves as “in”, “out” or “net”, and predicts the coordinate outcome of successful serves. Additionally, this work details the collection of three-dimensional spatio-temporal data on tennis serves, using marker-based optoelectronic motion capture. The classification uses a Stacked Bidirectional Long Short-Term Memory architecture, whilst a 3D Convolutional Neural Network architecture is harnessed for serve coordinate prediction. The proposed method achieves 89% accuracy for tennis serve classification, outperforming the current state-of-the-art whilst performing finer-grain classification. The results achieve an accuracy of 63% in predicting the serve coordinates, with a mean absolute error of 0.59 and a root mean squared error of 0.68, exceeding the current state-of-the-art with a new method. The system contributes towards the long-term goal of designing a non-invasive tennis serve analysis system that functions in training and match conditions
Over 200 times current enhancement via buckling triboelectric nanogenerator at ultra-low speed for monitoring the swelling of lithium ion batteries
Get PDFDataset for "Exploratory Proof-of-Concept: Predicting the Outcome of Tennis Serves Using Motion Capture and Deep Learning"
No full textDataset used for training a Machine Learning model to classify tennis serves into “In”, “Out” and “Net” as well as predict the outcome coordinates of the serve. A marker-based motion capture system provided and operated by The University of Bath’s Applied BioMechanics Suite was used to collect spatio-temporal data on participants completing tennis serves, whilst a high-speed video camera recorded the outcome. Dataset has two parts: 1) The spatio-temporal data used for training and validation 2) The serve outcome and coordinates for labelling of the data
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇