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Rehabilitation using virtual gaming for Hospital and hOMe-Based training for the Upper limb in acute and subacute Stroke (RHOMBUS II): results of a feasibility randomised controlled trial
Get PDFData availability statement:
Data are available upon reasonable request. Data will be made available on the Figshare data repository.Objective: To investigate the safety, feasibility and acceptability of the Neurofenix platform for upper-limb rehabilitation in acute and subacute stroke.
Design: A feasibility randomised controlled trial with a parallel process evaluation.
Setting: Acute Stroke Unit and participants’ homes (London, UK).
Participants: 24 adults (>18 years), acute and subacute poststroke, new unilateral weakness, scoring 9–25 on the Motricity Index (elbow and shoulder), with sufficient cognitive and communicative abilities to participate.
Interventions: Participants randomised to the intervention or control group on a 2:1 ratio. The intervention group (n=16) received usual care plus the Neurofenix platform for 7 weeks. The control group (n=8) received usual care only.
Outcomes: Safety was assessed through adverse events (AEs), pain, spasticity and fatigue. Feasibility was assessed through training and support requirements and intervention fidelity. Acceptability was assessed through a satisfaction questionnaire. Impairment, activity and participation outcomes were also collected at baseline and 7 weeks to assess their suitability for use in a definitive trial.
Randomisation: Computer-generated, allocation sequence concealed by opaque, sealed envelopes.
Blinding: Participants and assessors were not blinded; statistician blinded for data processing and analysis.
Results: 192 stroke survivors were screened for eligibility, and 24 were recruited and randomised. Intervention group: n=16, mean age 66.5 years; median 9.5 days post stroke. Control group: n=8, mean age 64.6 years; median 17.5 days post stroke. Three participants withdrew before the 7-week assessment, n=21 included in the analysis (intervention group n=15; control group n=6). No significant group differences in fatigue, spasticity, pain scores or total number of AEs. The median (IQR) time to train participants was 98 (64) min over 1–3 sessions. Participants trained with the platform for a median (range) of 11 (1-58) hours, equating to 94 min extra per week. The mean satisfaction score was 34.9 out of 40.
Conclusion: The Neurofenix platform is safe, feasible and well accepted as an adjunct to usual care in acute and subacute stroke rehabilitation. There was a wide range of engagement with the platform in a cohort of stroke survivors which was varied in age and level of impairment. Recruitment, training and support were manageable and completion of data was good, indicating that a future randomised controlled trial would be feasible.
Trial registration number: ISRCTN11440079.This work was supported by The Stroke Association and MedCity grant number SA MC 21\10001
Introduction to the Research Handbook on the International Court of Justice
No full textThis Research Handbook presents an in-depth examination of the International Court of Justice (ICJ) and its jurisprudence. Contributing authors dissect the global governance functions of the ICJ and its impact on national legal orders worldwide.This project was funded by the CaPE Project, Marie Skłodowska-Curie Actions, grant agreement number 708228, Horizon 2020
A UNIFIED THEORY FOR ARMA MODELS WITH VARYING COEFFICIENTS: ONE SOLUTION FITS ALL
Get PDFA preprint version of the article is available at: arXiv:2110.06168v1 [math.ST], https://arxiv.org/abs/2110.06168 under a CC BY licence. It has not been certified by peer review.A new explicit solution representation is provided for ARMA recursions with drift and either deterministically or stochastically varying coefficients. It is expressed in terms of the determinants of banded Hessenberg matrices and, as such, is an explicit function of the coefficients. In addition to computational efficiency, the proposed solution provides a more explicit analysis of the fundamental properties of such processes, including their Wold–Cramér decomposition, their covariance structure, and their asymptotic stability and efficiency. Explicit formulae for optimal linear forecasts based either on finite or infinite sequences of past observations are provided. The practical significance of the theoretical results in this work is illustrated with an application to U.S. inflation data. The main finding is that inflation persistence increased after 1976, whereas from 1986 onward, the persistence declines and stabilizes to even lower levels than the pre-1976 period.Magdalinos gratefully acknowledges financial support by the British Academy: grant SRG2324\241667. Alessandra Canepa acknowledges financial support under the National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1, Call for tender No. 104 published on February 2, 2022 by the Italian Ministry of University and Research (MUR), funded by the European Union—NextGenerationEU—Project Title 20223725WE—Methodological and computational issues in large-scale time series models for economics and finance – CUP J53D23003960006—Grant Assignment Decree No. 967 adopted on June 30, 2023 by the Italian Ministry of Ministry of University and Research (MUR)
UK Live Comedy Sector Survey Report 2024
Get PDFThe UK Live Comedy Sector Survey 2024 was jointly conducted by the Centre for Comedy Studies Research at
Brunel University, the Live Comedy Association, and British Comedy Guide. The UK Live Comedy Sector Survey was administered by Brunel University of London and ethical approval to conduct the survey was received from the College of Business, Arts and Social Sciences Research Ethics Committee at Brunel University of London.This report outlines the main findings of the UK Live Comedy Sector Survey 2024 conducted by the Centre for Comedy Studies Research (CCSR), the Live Comedy Association (LCA) and British Comedy Guide (BCG). Until now very little was known about the size, scale and impact of the UK live comedy sector. The survey provides detailed insights about the economics of the live comedy sector including its size and its longevity, numbers of shows and ticket sales, and turnover. It also provides insights into regional variations, venues used and performance types supported, and reveals inequalities and inequities prevalent in the sector. The survey serves to support and advocate live comedy in the UK politically, economically and socially.
366 people working in UK live comedy completed the survey. 67% of respondents were comedians.
33% of respondents were people working as comedy promoters, producers, venue managers, festival
organisers or agentsLive Comedy Association; Brunel University of London. Centre for Comedy Studies Research (CCSR); British Comedy Guide
Implicit bias in referrals to relational psychological therapies: review and recommendations for mental health services
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The dataset supporting this study is publicly available on Brunel University's Figshare repository. It can be accessed at the following link: https://doi.org/10.17633/rd.brunel.27332307.v2.Introduction: Timely and appropriate psychological treatment is an essential element required to address the growing burden of mental health issues, which has significant implications for individuals, society, and healthcare systems. However, research indicates that implicit biases among mental health professionals may influence referral decisions, potentially leading to disparities in access to relational psychological therapies. This study investigates bias in referral practices within mental health services, identifying key themes in referral procedures and proposing recommendations to mitigate bias and promote equitable access.
Methods: A systematic review of literature published between 2002 and 2022 was conducted, focusing on biases, referral practices, and relational psychological therapies. The search strategy involved full-text screening of studies meeting inclusion criteria, specifically those examining professional and organizational implicit bias in mental health referrals. Thematic synthesis was employed to analyze and categorize bias within these domains, providing a structured framework for understanding its impact on referral decision making processes.
Results: The search yielded 2,964 relevant papers, of which 77 underwent full-text screening. Ultimately, eight studies met the inclusion criteria and were incorporated into the review. The analysis revealed that bias development mechanisms in referral decisions occurred across five key domains: resource allocation, organizational procedures, clinical roles, decision-making, and referral preferences. These domains highlight organizational and practitioner-level factors contributing to disparities in access to psychological therapies.
Discussion: Findings suggest that implicit biases within referral processes can limit equitable access to psychological therapies, particularly relational therapies that emphasize therapeutic alliance and patient-centered care. This study provides recommendations to address these biases, including standardized referral guidelines, enhanced professional training on implicit bias, and improved oversight mechanisms within mental health services.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. CNWL NHS Foundation Trust provided a grant to Brunel University of London
An Impulsive Approach to State Estimation for Multirate Singularly Perturbed Complex Networks Under Bit Rate Constraints
Get PDFIn this article, the problem of ultimately bounded state estimation is investigated for discrete-time multirate singularly perturbed complex networks under the bit rate constraints, where the sensor sampling period is allowed to differ from the updating period of the networks. The facilitation of communication between sensors and the remote estimator through wireless networks, which are subject to bit rate constraints, involves the use of a coding-decoding mechanism. For efficient estimation in the presence of periodic measurements, a specialized impulsive estimation method is developed, which aims to carry out impulsive corrections precisely at the instants when the measurement signal is received by the estimator. By employing the iteration analysis method under the impulsive mechanism, a sufficient condition is established that ensures the exponential boundedness of the estimation error dynamics. Furthermore, an optimization algorithm is introduced for addressing the challenges related to bit rate allocation and the design of desired estimator gains. Within the presented theoretical framework, the correlation between estimation performance and bit rate allocation is elucidated. Finally, a simulation example is provided to demonstrate the validity of the proposed estimation approach.10.13039/501100019033-Key Area Research and Development Program of Guangdong Province (Grant Number: 2021B0101410005);
10.13039/501100003453-Natural Science Foundation of Guangdong Province of China (Grant Number: 2021A1515011634 and 2021B1515420008);
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: U22A2044 and 62206063);
Local Innovative and Research Teams Project of Guangdong Special Support Program of China (Grant Number: 2019BT02X353);
10.13039/501100004543-China Scholarship Council (Grant Number: 202208440312)
SharkNet Networks Applications in Smart Manufacturing Using IoT and Machine Learning
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The necessary research data have been presented in the article.With the advancement of Industry 4.0, 3D printing has become a critical technology in smart manufacturing; however, challenges remain in the integrated management, quality control, and remote monitoring of multiple 3D printers. This study proposes an intelligent cloud monitoring system based on the SharkNet dynamic network, IoT, and artificial neural networks (ANNs). The system utilizes a SharkNet dynamic network to integrate low-cost sensors for environmental monitoring to enable low-latency data transmission and deploys ANN models on the cloud for print quality prediction and process parameter optimization. Next, we experimentally validated the system using the Taguchi design and ANN-based analysis, focusing on optimizing printing process parameters and improving surface quality. The main results show that the designed system has a communication delay of 40–50 ms and 99.8% transmission reliability under moderate load, and the system reduces the surface roughness prediction error to less than 17.2%. In addition, the ANN model outperforms conventional methods in capturing the nonlinear relationships of the variables, and the system can be based on the model to improve print quality and productivity by enabling real-time parameter adjustments. The system retains a high degree of scalability in terms of real-time monitoring and parallel or complex control of multiple devices, which demonstrates its potential for applications in smart manufacturing.This research was funded by the Graduate Student Innovation Program of Shanxi Province, Grant No. 2023SJ214. It was also partly funded by Brunel University London
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning
Get PDFTo alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy. Under the synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a regression method to fit the quantitative-relationship between the cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the cut-layer selection problem for the clients and the computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed SFL framework.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62132004);
Jiangsu Major Project on Basic Researches (Grant Number: BK20243059)
Neural Combinatorial Optimization for Multiobjective Task Offloading in Mobile Edge Computing
Get PDFTask offloading is crucial in supporting resource-intensive applications in mobile edge computing. This paper explores multiobjective task offloading, aiming to minimize energy consumption and latency simultaneously. Although learning-based algorithms have been used to address this problem, they train a model based on one a priori preference to make the offloading decision. When the preference changes, the trained model may not perform well and needs to be retrained. To address this issue, we propose a neural combinatorial optimization method that combines an encoder-decoder model with reinforcement learning. The encoder captures task relationships, while the decoder, equipped with a preference-based attention mechanism, determines offloading decisions for various preferences. Additionally, reinforcement learning is employed to train the encoder-decoder model. Since the proposed method can infer the offloading decision for each preference, it eliminates the need to retrain the model when the preference changes, thus improving real-time performance. Experimental studies demonstrate the effectiveness of the proposed method by comparison with three algorithms on instances of different scales.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: U23A20347);
Royal Society International Exchange (Grant Number: IEC-NSFC-211264)
Citizens’ Inclusion in Public Services: a Systematic Review of the Public Administration Literature and Reflection on Future Research Avenues
No full textData Availability Statement:
Data sharing not applicable—no new data generated.Despite a resurgence of interest in social equity, citizens' inclusion in public services has attracted limited attention in the public administration literature so far, having often remained in the background of studies focusing on citizens' participation and representative bureaucracy. To fully comprehend and enhance the role of public administration in promoting inclusive public services and building inclusive societies, it is necessary to prioritize citizens' inclusion in public services as a central phenomenon. A first step in this direction is assessing existing knowledge and identifying new research avenues. Drawing on the “name, blame, claim” framework, this systematic literature review of 119 studies extends public administration scholarship by mapping and analyzing knowledge of citizens' inclusion in public services and identifying ways forward to strengthen the research and practice in this area
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