39,305 research outputs found
Fully-Autonomous, Vision-based Traffic Signal Control: from Simulation to Reality
Ineffective traffic signal control is one of the major causes of congestion in urban road networks. Dynamically changing traffic conditions and live traffic state estimation are fundamental challenges that limit the ability of the existing signal infrastructure in rendering individualized signal control in real-time. We use deep reinforcement learning (DRL) to address these challenges. Due to economic and safety constraints associated training such agents in the real world, a practical approach is to do so in simulation before deployment. Domain randomisation is an effective technique for bridging the reality gap and ensuring effective transfer of simulation-trained agents to the real world. In this paper, we develop a fully-autonomous, vision-based DRL agent that achieve adaptive signal control in the face of complex, imprecise, and dynamic traffic environments. Our agent uses live visual data (i.e. a stream of real-time RGB footage) from an intersection to extensively perceive and subsequently act upon the traffic environment. Employing domain randomisation, we examine our agent’s generalisation capabilities under varying traffic conditions in both the simulation and the real-world environments. In a diverse validation set independent of training data, our traffic control agent reliably adapted to novel traffic situations and demonstrated a positive transfer to previously unseen real intersections despite being trained entirely in simulation
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EEG microstates: Functional significance and short-term test-retest reliability
Appendix A: Supplementary data to this article can be found online at https://doi. org/10.1016/j.ynirp.2022.100089.Copyright /© 2022 The Authors. EEG signal power, may have clinical relevance; however, their functional significance and test-retest reliability remain unclear. To investigate the functional significance of the canonical EEG microstate classes and their pairwise transitions, and to establish their within-session test-retest reliability, we recorded 36-channel EEGs in 20 healthy volunteers during three eyes-closed conditions: mind-wandering, verbalization (silently repeating the word ‘square’ every 2 s), and visualization (visualizing a square every 2 s). Each condition lasted 3 min and the sequence of three conditions was repeated four times (two runs of two sequence repetitions). The participants' alertness and their sense of effort during the experiment were rated using visual-analogue scales. The EEG data were 2–20 Hz bandpass-filtered and analysed into the four canonical microstate classes: A, B, C, and D. EEG microstate classes C and D were persistently more dominant than classes A and B in all conditions. Of the first-order microstate parameters, average microstate duration was most reliable. The duration of class D microstate was longer during mind-wandering (106.8 ms) than verbalization (102.2 ms) or visualization (99.8 ms), with a concomitantly higher coverage (36.4% vs. 34.7% and 35.2%), but otherwise there was no clear association of the four microstate classes to particular mental states. The test-retest reliability was higher at the beginning of each run, together with higher average alpha power and subjective ratings of alertness. Only the transitions between classes C and D (from C to D in particular) were significantly higher than what would be expected from the respective microstates' occurrences. The transition probabilities, however, did not distinguish between conditions, and their test-retest reliability was overall lower than that of the first-order parameters such as duration and coverage. Further studies are needed to establish the functional significance of the canonical EEG microstate classes. This might be more fruitfully achieved by looking at their complex syntax beyond pairwise transitions. To ensure greater test-retest reliability of microstate parameters, study designs should allow for shorter experimental runs with regular breaks, particularly when using EEG microstates as clinical biomarkers.BIAL Foundation (grant number: 183/16)
Investigating the Drivers & Challenges of Implementing Immersive Sensory Technology within Construction Site Safety
The use of immersive sensory technology for safety management is generally shown positively in academic literature. Many researchers have demonstrated applications of this technology for improving safety training in a risk-free environment. Despite the reported benefits and a global pandemic forcing the digital agenda, the uptake of this technology for this purpose remains slow. This study aims to investigate current drivers and challenges of implementing this technology for safety from an industry-based perspective. To achieve this, qualitative data was collected through 4 online focus groups involving 21 industry professionals working within the field. The findings identified that even amongst these experts, the technology was rarely implemented on projects specifically for safety. Despite this lack of adoption, participants agreed that if implemented correctly this technology has the potential to enhance site safety processes such as inductions, toolbox talks and general safety training. The commitment to safety and legislative requirements were identified as key drivers, whilst deep rooted challenges surrounding client demand, costs and leadership dominated the discussion. The onsite practicalities, personal comfort and lack of digital skills were also identified as concerns if this technology was to be adopted more mainstream in safety training. Further recommendations are made to develop understanding of these specific challenges, including investigating the industry need and availability of specific skills in immersive safety applications. In addition, it is recommended that further empirical evidence including the impact of this technology when implemented for safety on projects is provided in literature
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Co-design As Healing: Exploring The Experiences Of Participants Facing Mental Health Problems
This thesis is an exploration of the healing role of co-design in mental health. Although co-design projects conducted within mental health settings are rising, existing literature tends to focus on the object of design and its outcomes while the experiences of participants per se remain largely unexplored. The guiding research question of this study is not how we design things that improve mental health, but how co-designing, as an act, might do so.
The thesis presents two projects that were organized in collaboration with the mental health charity Islington Mind and the Psychosis Therapy Project (PTP) in London.
The project at Islington Mind used a structured design process inviting participants to design for wellbeing. A case study analysis provides insights on how participants were impacted, summarizing key challenges and opportunities.
The design at PTP worked towards creating a collective brief in an emergent fashion, finally culminating in a board game. The experiences of participants were explored through Interpretative Phenomenological Analysis (IPA), using semi-structured interview data. The analysis served to identify key themes characterising the experience of co-design such as contributing, connecting, thinking and intentioning. In addition, a mixed-methods analysis of questionnaires and interview data exploring participants' wellbeing, showed that all participants who engaged fairly consistently in the project improved after the project ended, although some participants' scores returned to baseline six months later.
Reflecting on both projects, an approach to facilitation within mental health is outlined, detailing how the dimensions of weaving and layered participation, nurturing mattering and facilitating attitudes interlace. This contribution raises awareness of tacit dimensions in the practice of facilitation, articulating the nuances of how to encourage and sustain meaningful and ethical engagement and offering insights into a range of tools. It highlights the importance of remaining reflexive in relation to attitudes and emotions and discusses practical methodological and ethical challenges and ways to resolve them which can be of benefit to researchers embarking on a similar journey.
The thesis also offers detailed insights on how methodologies from different fields were integrated into a whole, arguing for transparency and reflexivity about epistemological assumptions, and how underlying paradigms shift in an interdisciplinary context.
Based on the overall findings, the thesis makes a case for considering design as healing (or a designerly way of healing), highlighting implications at a systems, social and individual level. It makes an original contribution to our understanding of design, highlighting its healing character, and proposes a new way to support mental health. The participants in this study not only had increased their own wellbeing through co-designing, but were also empowered and contributed towards healing the world. Hence, the thesis argues for a unique, holistic perspective of design and mental health, recognizing the interconnectedness of the individual, social and systemic dimensions of the healing processes that are ignited
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Vortex identification methods applied to wind turbine tip vortices
This study describes the impact of postprocessing methods on the calculated parameters of tip vortices of a wind turbine model when tested using particle image velocimetry (PIV). Several vortex identification methods and differentiation schemes are compared. The chosen methods are based on two components of the velocity field and their derivatives. They are applied to each instantaneous velocity field from the dataset and also to the calculated average velocity field. The methodologies are compared through the vortex center location, vortex core radius and jittering zone.
Results show that the tip vortex center locations and radius have good comparability and can vary only a few grid spacings between methods. Conversely, the convection velocity and the jittering surface, defined as the area where the instantaneous vortex centers are located, vary between identification methods.
Overall, the examined parameters depend significantly on the postprocessing method and selected vortex identification criteria. Therefore, this study proves that the selection of the most suitable postprocessing methods of PIV data is pivotal to ensure robust results
Fear of death and its relationship to resilience in nursing students: A longitudinal study
.Aim
Taking a corpus of nursing students enrolled in the 2017−2021 nursing degree, we aim to analyse how students' levels of resilience and fear of death evolve in the first three years of the degree and whether there are differences between students based on age and gender. In addition, we aim to describe the relationship between resilience and fear of deathS
Analysis of reliable deployment of TDOA local positioning architectures
.Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applicationS
A new index of resilience applicable to external pulse-disturbances that considers the recovery of communities in the short term
.Resilience is a key concept in the study of the recovery of ecosystems affected by disturbances. Currently, there are numerous indices to measure resilience, but many of them do not show the accuracy of the resilience value or the behaviour of ecological parameters in the face of disturbances. New approaches and technologies enable large amounts of information to be obtained, facilitating the proposal of new resilience indices that work consistently and intuitively for a wide variety of ecological response variables under different scenarios after pulse-disturbances. In this study, we propose and verify a new resilience index, comparing its performance with others previously published. We validated the performance of the new index using real data based on field measurements of changes in soil bacterial OTUs diversity and abundance after a wildfire. The new resilience index provided an automatic and robust functional classification of the behaviour of ecosystems after disturbances, supported by a bootstrap analysis. We identified 5 scenarios of ecosystem resilience performance according to their behaviour after a pulse-disturbance: resilient, non-resilient, recovering, rebound, and continuing.S
Measuring the impacts of maternal child marriage and maternal intimate partner violence and the moderating effects of proximity to conflict on stunting among children under 5 in post-conflict Sri Lanka
This study aimed to understand whether maternal child marriage and past year intimate partner violence (IPV) impact stunting among Sri Lankan children under 5 years old, and, secondarily, whether proximity to conflict is associated with stunting. Additionally, we assessed whether proximity to conflict moderates the relationships between maternal child marriage and past year IPV (sexual, physical, and emotional). We tested these questions using logistic regression analyses of the 2016 Sri Lankan Demographic and Health Survey (n = 4941 mother-child dyads). In country-wide adjusted analyses, we did not find associations between maternal child marriage or IPV and stunting (p \u3e 0.05). Children in districts proximal and central to conflict were significantly less likely to be stunted compared to children in districts distal to conflict (proximal adjusted odds ratio/aOR: 0.43, 95% confidence interval/CI: 0.22–0.82; central aOR: 0.53, CI: 0.29–0.98). We found significant interaction effects on stunting between proximity to conflict and both sexual and emotional IPV, which we further explored in stratified analyses. In districts distal to conflict, maternal sexual IPV was significantly associated with increased odds of stunting (aOR: 2.71, CI: 1.16–6.35), and in districts central to conflict, maternal emotional IPV was significantly associated with increased odds of stunting (aOR: 1.80, CI: 1.13–2.89). Maternal emotional IPV was significantly associated with decreased odds of stunting in districts proximal to conflict (aOR: 0.42, CI: 0.18–0.96). Maternal child marriage and physical IPV were not associated with stunting in Sri Lanka. Variations in associations between maternal IPV and stunting across Sri Lanka may reflect the lasting and differential impact of conflict, as well as differential humanitarian responses which may have improved child nutrition practices and resources in districts central and proximal to conflict. Policies and programs addressing stunting in Sri Lanka should consider the role of maternal IPV as well as community-level variations based on proximity to conflict
Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process
In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov-
ernment through the ELKARTEK program (OILTWIN project, ref. KK-
2020/00052)
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