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Patient and parent perspectives on being invited to join a trial of night-time only versus full-time bracing for adolescent idiopathic scoliosis : a qualitative study
Get PDFAims: The Bracing Adolescent Idiopathic Scoliosis (BASIS) study is a randomized controlled non-inferiority pragmatic trial of 'full-time bracing' (FTB) compared to 'night-time bracing' (NTB) for the treatment of adolescent idiopathic scoliosis (AIS). We anticipated that recruiting patients to BASIS would be challenging, as it is a paediatric trial comparing two markedly different bracing pathways. No previous studies have compared the experiences of AIS patients treated with FTB to those treated with NTB. This qualitative study was embedded in BASIS to explore families' perspectives of BASIS, to inform trial communication, and to identify strategies to support patients treated in a brace. Methods: Semi-structured interviews were conducted with parents (n = 26) and young people (n = 21) who had been invited to participate in BASIS at ten of the 22 UK paediatric spine services in hospitals recruiting to BASIS. Audio-recorded interviews were transcribed and analyzed thematically. Results: Families viewed their interactions with BASIS recruiters positively, but were often confused about core aspects of BASIS, such as the aims, expectations of bracing, and the process of randomization. Participants typically expressed a preference for NTB, but recruiters may have framed NTB more favourably. Patients and parents reported challenges wearing a brace, such as physical discomfort, feelings of self-consciousness, difficulty participating in physical activities, and strain on financial resources to support brace use. Patients in FTB reported more pronounced challenges. While families valued health professional support, they felt there was a lack of social, emotional, and school support, and relied on online resources, as well private counselling services to address this need. Conclusion: The findings informed the development of resources and strategies, including guidance for schools and the recommendations in this paper, to support patients to wear NTB and FTB as prescribed. The results indicated opportunities for recruiters to enhance trial communication in ways that could improve informed consent and recruitment to BASIS, and inform future trials of bracing
Optimization-based state-of-charge management strategies for supercritical CO2 Brayton cycle pumped thermal energy storage systems
Get PDFWe present a study concerning the state-of-charge (SoC) management strategies for pumped thermal electrical energy storage (PTES) systems. The particular system under study is a recuperative Brayton Cycle PTES with supercritical CO2 as the working fluid and uses molten salt and water as hot and cold side thermal storage reservoirs. The charging and discharging cycles, including the turbomachinery, heat exchangers, and two-tank thermal storage units are modelled using Aspen HYSYS, considering variable speed operating characteristics of the turbomachines. An in-cycle SoC management strategy is proposed to maintain equal charging and discharging capacities between the hot and cold side thermal storage reservoirs, whereas a cycle-to-cycle SoC management strategy is used to constrain the PTES operating envelope for charge/discharge power and duration given operational objectives. The model is used in several case-studies to demonstrate the SoC management strategies. The case study results showed that, given an electricity price profile, the algorithm can determine feasible charge/discharge profiles while maximizing the operational profit. Additionally, if the PTES system is integrated with a wind farm, it enables the wind farm to provide dispatchable power. The round-trip efficiencies of the system is within the range of 35–60 % and in certain scenarios with increased part-load operation, such as the wind farm integration scenario, the average efficiency is observed to be 46.5 %. The SoC of both tanks displayed a negligible deviation of 0.24 % after five days of operation, including operation under part load conditions. The findings provide a new avenue for revenue stacking via flexible operations and can help accelerate the adoption of PTES systems
Understanding the Gender Gap in the Acceptance of Automated Vehicles: International Mobility Study Across 17 Countries
No full textThe common assumption is that men are more likely to accept automated vehicles (AVs) than women. However, studies have produced mixed results regarding this gender gap. Additionally, there is limited understanding of how the gender gap in the intention to use AVs might vary between countries. This study aims to enhance the understanding of the gender gap in willingness to use AVs and how this gap might differ across various countries. To accomplish this, survey data from 18,631 respondents across 17 countries: Brazil, China, Finland, France, Germany, Hungary, India, Indonesia, Italy, Japan, Russia, Spain, South Africa, Sweden, Turkey, the UK, and the US, was analyzed. In this research, the gender gap in willingness to use AVs is defined as the difference in willingness to use AVs between men and women. The results indicate that gender differences in willingness to use AVs are not universal; some countries show opposing trends between men and women, while in others, the gender difference is not statistically significant. This study contributes to existing literature by examining the influence of gender and country on the willingness to use AVs. The findings have the potential to significantly impact policy development and transport planning by promoting gender inclusivity in future transport solutions, ensuring that all potential users can benefit from adopting AVs
Exploring Digital Twins for Urban Mobility:Applications and the Case Study of Helsinki
Get PDFDigital twin technology is increasingly explored as a dynamic, data-driven tool for simulation, monitoring, and optimizing urban transport systems. This paper reviews the current landscape of digital twin applications in urban context, with a focus on their capabilities for predictive analytics, scenario evaluation ass a support for decision making for urban mobility interventions. Based on a review of the latest literature, it examines the conceptual frameworks and applied digital twins' models across different cities. Additionally, this study presents a real-world case study from Helsinki, offering insights into the development and integration of digital twins within a complex urban mobility system. The study contributes to the state of the art by identifying key implementation challenges and proposing future research directions to advance the practical application of digital twin technologies in urban mobility
Integrating multidimensional data analytics for precision diagnosis of chronic low back pain
Get PDFLow back pain (LBP) is a leading cause of disability worldwide, with up to 25% of cases become chronic (cLBP). Whilst multi-factorial, the relative importance of contributors to cLBP remains unclear. We leveraged a comprehensive multi-dimensional data-set and machine learning-based variable importance selection to identify the most effective modalities for differentiating whether a person has cLBP. The dataset included questionnaire data, clinical and functional assessments, and spino-pelvic magnetic resonance imaging (MRI), encompassing a total of 144 parameters from 1,161 adults with (n = 512) and without cLBP (n = 649). Boruta and random forest were utilised for variable importance selection and cLBP classification respectively. A multimodal model including questionnaire, clinical, and MRI data was the most effective in differentiating people with and without cLBP. From this, the most robust variables (n = 9) were psychosocial factors, neck and hip mobility, as well as lower lumbar disc herniation and degeneration. This finding persisted in an unseen holdout dataset. Beyond demonstrating the importance of a multi-dimensional approach to cLBP, our findings will guide the development of targeted diagnostics and personalized treatment strategies for cLBP patients
Evaluation of temporarily flowable self-compacting backfill materials in large-scale sewer applications in Germany
Get PDFGround subsidence due to inadequate compaction of backfill materials and damaged sewer pipelines poses significant risks to urban infrastructure. This study evaluates the performance of Temporarily Flowable Self-Compacting Backfill (TFSB) materials in large-scale sewer applications, addressing key properties such as flowability, volume stability, re-excavation capability, and recyclability. Five TFSB formulations were experimentally assessed using a large-scale test rig replicating real-world sewer construction conditions. The study employed a multi-criteria evaluation framework, integrating innovative testing methodologies such as the Mini-MAC system for pipe-soil stiffness measurement and a walkability test to determine early load-bearing capacity. Results demonstrated substantial variability in TFSB performance. While certain formulations exhibited superior flowability and bedding continuity, others faced challenges related to post-hardening and re-excavation difficulty. Compressive strength measurements revealed that materials exceeding 0.3 N/mm2 at 28 days hindered future removability, necessitating formulation adjustments for optimal structural integrity and maintainability. Additionally, environmental assessments identified gaps in existing standards, emphasizing the need for regulatory updates tailored to TFSB-specific properties. This research provides actionable insights for network operators and industry stakeholders, offering a framework for optimizing TFSB formulations to enhance urban infrastructure resilience. The findings contribute to the development of standardized guidelines for TFSB applications, promoting cost-effective, sustainable, and structurally reliable backfill solutions for sewer construction
Exploring Barriers to Unmanned Aerial Vehicle (UAV) Technology for Construction Safety Management Using Mixed-Methods Approach
Get PDFConstruction safety is critical, and unmanned aerial vehicles (UAVs) have emerged as a transformative tool to enhance safety management in the sector. While UAVs are widely recognized for their efficacy, limited research has specifically addressed the barriers to their integration into construction safety management systems. This study aims to identify, prioritize, and analyze the interrelationships among these barriers to aid in their effective resolution. Using a mixed-methods approach, this research combines a systematic literature review (SLR) to identify barriers and a questionnaire survey to prioritize and examine their interconnections. The findings reveal significant barriers, including restricted airspace, inadequate safety regulations, limited flight durations, collision risks, insufficient piloting skills, lack of UAV awareness, resistance to new technologies, human errors, training needs, and legal constraints. Restricted airspace emerged as the most critical barrier, strongly linked to flight duration limitations and piloting proficiency. This study also highlights regional disparities: respondents from developed nations emphasized collision risks, legal restrictions, and resistance to new technologies, while those from developing countries focused on restricted areas, limited flight time, and piloting expertise. These findings emphasize the importance of addressing region-specific challenges and tailoring strategies to facilitate UAV integration, paving the way for safer and more efficient construction practices
On the potential of ocean energy technologies to contribute to future sustainability
Get PDFTo achieve the United Nations Framework Convention Climate Change targets and the UN Sustainable Development Goals a major switch to clean sustainable energy is required. Both solar and wind energy are already making major contributions. Great interest is being shown in other processes including a suite of renewable technology that can has been labelled “Blue Energy” otherwise known as ocean energy or marine energy technologies. This suite of technologies comprises three principal categories relating to tides, to waves and to salinity gradients that can be created between seawater and fresh water. In each of these three categories there are various technologies for the extraction of the energy, and the various processes will be briefly described. Then the three are examined in order to assess which if any, from mainly a European perspective, have good growth prospects. Especially with respect to near-horizon implementation, tidal energy, especially tidal stream devises, are considered the most promising. Notwithstanding this observation, the brief review also concludes that no Marine Energy technology has the capacity to become as ubiquitous as solar and wind and in many cases the journeys from innovation to implementation have been or could be permanently stalled
Planning methods using data envelopment analysis and markov systems
No full textThis paper explores the extension of a modelling framework that integrates data envelopment analysis (DEA) and markov systems, into a two-stage setting. In a recent paper in EJOR, a single-stage DEA-markov hybrid model was introduced, establishing a research direction blending these seemingly distinct approaches to address the attainability problem in workforce planning. Markov systems are widely used in scenarios where a population system (e.g., staff profiles, patients with chronic conditions) begins the planning horizon in a specific state and aims to transition to a new state by the end of the horizon. Although it is common for this horizon to encompass multiple steps, this hybrid model considered attainability within a single-step horizon. In the current study, we investigate problems in two phases and integrate a network DEA approach with markovian population systems under various assumptions, resulting into new variations of the relevant models. The decision maker (DM) can specify potential future outcomes (e.g., personnel flows) in consecutive steps in time, and use DEA to identify feasible courses of action through convexity (or even use the second stage in a normative manner to identify optimal flows). The two-stage DEA model captures the DM’s relative preferences for future states and provides measures of efficacy of potential flows relative to the ultimate desired state. Consequently, the organization can plan interventions to enhance the probability of achieving some anticipated goal. The paper includes illustrations using data from workforce planning and concludes with a discussion on relevant issues in healthcare, circular economy and social radicalization
Pothole prediction based on machine learning and pavement condition indicators
Get PDFPotholes are dangerous defects on road surfaces, contributing to numerous crashes involving vehicles, motorcycles and bicycles. They also impose a significant economic burden on highway authorities. Currently, no effective tool predicts th enumber and location of potholes in a road network. In this work, an attempt to address this gap was conducted by developing novel pothole prediction tools built using two machine learning methods – random forest and K-nearest neighbour. The final prediction model requires nine pavement condition indicators, quantifiable through Surface Condition Assessment for the National Network of Roads surveys, commonly conducted in the UK. This unique approach allows for direct implementation by highway authorities. The model was trained on a large dataset of pavement condition and pothole data covering the Transport for London network. Validation results suggest that the model successfully predicted 55.5%of sections with potholes and 99.6% of sections without potholes. Although the model demonstrates limited accuracy in predicting potholes, recommendations are provided to enhance its performance. This work holds the potential to significantly aid strategic financial planning for pavement management in the UK and elsewhere
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