8,912 research outputs found

    Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.

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    The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design

    Pragmatic randomised controlled trial of guided self-help versus individual cognitive behavioural therapy with a trauma focus for post-traumatic stress disorder (RAPID)

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    This is the final version. Available on open access from the NIHR Journals Library via the DOI in this recordData availability: All available data can be obtained from the corresponding author.BACKGROUND: Guided self-help has been shown to be effective for other mental conditions and, if effective for post-traumatic stress disorder, would offer a time-efficient and accessible treatment option, with the potential to reduce waiting times and costs. OBJECTIVE: To determine if trauma-focused guided self-help is non-inferior to individual, face-to-face cognitive-behavioural therapy with a trauma focus for mild to moderate post-traumatic stress disorder to a single traumatic event. DESIGN: Multicentre pragmatic randomised controlled non-inferiority trial with economic evaluation to determine cost-effectiveness and nested process evaluation to assess fidelity and adherence, dose and factors that influence outcome (including context, acceptability, facilitators and barriers, measured qualitatively). Participants were randomised in a 1 : 1 ratio. The primary analysis was intention to treat using multilevel analysis of covariance. SETTING: Primary and secondary mental health settings across the United Kingdom's National Health Service. PARTICIPANTS: One hundred and ninety-six adults with a primary diagnosis of mild to moderate post-traumatic stress disorder were randomised with 82% retention at 16 weeks and 71% at 52 weeks. Nineteen participants and ten therapists were interviewed for the process evaluation. INTERVENTIONS: Up to 12 face-to-face, manualised, individual cognitive-behavioural therapy with a trauma focus sessions, each lasting 60-90 minutes, or to guided self-help using Spring, an eight-step online guided self-help programme based on cognitive-behavioural therapy with a trauma focus, with up to five face-to-face meetings of up to 3 hours in total and four brief telephone calls or e-mail contacts between sessions. MAIN OUTCOME MEASURES: Primary outcome: the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, at 16 weeks post-randomisation. Secondary outcomes: included severity of post-traumatic stress disorder symptoms at 52 weeks, and functioning, symptoms of depression, symptoms of anxiety, alcohol use and perceived social support at both 16 and 52 weeks post-randomisation. Those assessing outcomes were blinded to group assignment. RESULTS: Non-inferiority was demonstrated at the primary end point of 16 weeks on the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [mean difference 1.01 (one-sided 95% CI -∞ to 3.90, non-inferiority p = 0.012)]. Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, score improvements of over 60% in both groups were maintained at 52 weeks but the non-inferiority results were inconclusive in favour of cognitive-behavioural therapy with a trauma focus at this timepoint [mean difference 3.20 (one-sided 95% confidence interval -∞ to 6.00, non-inferiority p = 0.15)]. Guided self-help using Spring was not shown to be more cost-effective than face-to-face cognitive-behavioural therapy with a trauma focus although there was no significant difference in accruing quality-adjusted life-years, incremental quality-adjusted life-years -0.04 (95% confidence interval -0.10 to 0.01) and guided self-help using Spring was significantly cheaper to deliver [£277 (95% confidence interval £253 to £301) vs. £729 (95% CI £671 to £788)]. Guided self-help using Spring appeared to be acceptable and well tolerated by participants. No important adverse events or side effects were identified. LIMITATIONS: The results are not generalisable to people with post-traumatic stress disorder to more than one traumatic event. CONCLUSIONS: Guided self-help using Spring for mild to moderate post-traumatic stress disorder to a single traumatic event appears to be non-inferior to individual face-to-face cognitive-behavioural therapy with a trauma focus and the results suggest it should be considered a first-line treatment for people with this condition. FUTURE WORK: Work is now needed to determine how best to effectively disseminate and implement guided self-help using Spring at scale. TRIAL REGISTRATION: This trial is registered as ISRCTN13697710. FUNDING: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 14/192/97) and is published in full in Health Technology Assessment; Vol. 27, No. 26. See the NIHR Funding and Awards website for further award information.National Institute for Health and Care Research (NIHR

    Innovation in Energy Security and Long-Term Energy Efficiency Ⅱ

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    The sustainable development of our planet depends on the use of energy. The increasing world population inevitably causes an increase in the demand for energy, which, on the one hand, threatens us with the potential to encounter a shortage of energy supply, and, on the other hand, causes the deterioration of the environment. Therefore, our task is to reduce this demand through different innovative solutions (i.e., both technological and social). Social marketing and economic policies can also play their role by affecting the behavior of households and companies and by causing behavioral change oriented to energy stewardship, with an overall switch to renewable energy resources. This reprint provides a platform for the exchange of a wide range of ideas, which, ultimately, would facilitate driving societies toward long-term energy efficiency

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Autonomous Radar-based Gait Monitoring System

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    Features related to gait are fundamental metrics of human motion [1]. Human gait has been shown to be a valuable and feasible clinical marker to determine the risk of physical and mental functional decline [2], [3]. Technologies that detect changes in people’s gait patterns, especially older adults, could support the detection, evaluation, and monitoring of parameters related to changes in mobility, cognition, and frailty. Gait assessment has the potential to be leveraged as a clinical measurement as it is not limited to a specific health care discipline and is a consistent and sensitive test [4]. A wireless technology that uses electromagnetic waves (i.e., radar) to continually measure gait parameters at home or in a hospital without a clinician’s participation has been proposed as a suitable solution [3], [5]. This approach is based on the interaction between electromagnetic waves with humans and how their bodies impact the surrounding and scattered wireless signals. Since this approach uses wireless waves, people do not need to wear or carry a device on their bodies. Additionally, an electromagnetic wave wireless sensor has no privacy issues because there is no video-based camera. This thesis presents the design and testing of a radar-based contactless system that can monitor people’s gait patterns and recognize their activities in a range of indoor environments frequently and accurately. In this thesis, the use of commercially available radars for gait monitoring is investigated, which offers opportunities to implement unobtrusive and contactless gait monitoring and activity recognition. A novel fast and easy-to-implement gait extraction algorithm that enables an individual’s spatiotemporal gait parameter extraction at each gait cycle using a single FMCW (Frequency Modulated Continuous Wave) radar is proposed. The proposed system detects changes in gait that may be the signs of changes in mobility, cognition, and frailty, particularly for older adults in individual’s homes, retirement homes and long-term care facilities retirement homes. One of the straightforward applications for gait monitoring using radars is in corridors and hallways, which are commonly available in most residential homes, retirement, and long-term care homes. However, walls in the hallway have a strong “clutter” impact, creating multipath due to the wide beam of commercially available radar antennas. The multipath reflections could result in an inaccurate gait measurement because gait extraction algorithms employ the assumption that the maximum reflected signals come from the torso of the walking person (rather than indirect reflections or multipath) [6]. To address the challenges of hallway gait monitoring, two approaches were used: (1) a novel signal processing method and (2) modifying the radar antenna using a hyperbolic lens. For the first approach, a novel algorithm based on radar signal processing, unsupervised learning, and a subject detection, association and tracking method is proposed. This proposed algorithm could be paired with any type of multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) FMCW radar to capture human gait in a highly cluttered environment without needing radar antenna alteration. The algorithm functionality was validated by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a hallway. The preliminary results demonstrate the promising potential of the algorithm to accurately monitor gait in hallways, which increases opportunities for its applications in institutional and home environments. For the second approach, an in-package hyperbola-based lens antenna was designed that can be integrated with a radar module package empowered by the fast and easy-to-implement gait extraction method. The system functionality was successfully validated by capturing the spatiotemporal gait values of people walking in a hallway filled with metallic cabinets. The results achieved in this work pave the way to explore the use of stand-alone radar-based sensors in long hallways for day-to-day long-term monitoring of gait parameters of older adults or other populations. The possibility of the coexistence of multiple walking subjects is high, especially in long-term care facilities where other people, including older adults, might need assistance during walking. GaitRite and wearables are not able to assess multiple people’s gait at the same time using only one device [7], [8]. In this thesis, a novel radar-based algorithm is proposed that is capable of tracking multiple people or extracting walking speed of a participant with the coexistence of other people. To address the problem of tracking and monitoring multiple walking people in a cluttered environment, a novel iterative framework based on unsupervised learning and advanced signal processing was developed and tested to analyze the reflected radio signals and extract walking movements and trajectories in a hallway environment. Advanced algorithms were developed to remove multipath effects or ghosts created due to the interaction between walking subjects and stationary objects, to identify and separate reflected signals of two participants walking at a close distance, and to track multiple subjects over time. This method allows the extraction of walking speed in multiple closely-spaced subjects simultaneously, which is distinct from previous approaches where the speed of only one subject was obtained. The proposed multiple-people gait monitoring was assessed with 22 participants who participated in a bedrest (BR) study conducted at McGill University Health Centre (MUHC). The system functionality also was assessed for in-home applications. In this regard, a cloud-based system is proposed for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. Range-Doppler maps generated from a dataset of real-life in-home activities are used to train deep learning models. The performance of several deep learning models was evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices

    Metaverse: A Vision, Architectural Elements, and Future Directions for Scalable and Realtime Virtual Worlds

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    With the emergence of Cloud computing, Internet of Things-enabled Human-Computer Interfaces, Generative Artificial Intelligence, and high-accurate Machine and Deep-learning recognition and predictive models, along with the Post Covid-19 proliferation of social networking, and remote communications, the Metaverse gained a lot of popularity. Metaverse has the prospective to extend the physical world using virtual and augmented reality so the users can interact seamlessly with the real and virtual worlds using avatars and holograms. It has the potential to impact people in the way they interact on social media, collaborate in their work, perform marketing and business, teach, learn, and even access personalized healthcare. Several works in the literature examine Metaverse in terms of hardware wearable devices, and virtual reality gaming applications. However, the requirements of realizing the Metaverse in realtime and at a large-scale need yet to be examined for the technology to be usable. To address this limitation, this paper presents the temporal evolution of Metaverse definitions and captures its evolving requirements. Consequently, we provide insights into Metaverse requirements. In addition to enabling technologies, we lay out architectural elements for scalable, reliable, and efficient Metaverse systems, and a classification of existing Metaverse applications along with proposing required future research directions

    Restoring and valuing global kelp forest ecosystems

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    Kelp forests cover ~30% of the world’s coastline and are the largest biogenic marine habitat on earth. Across their distribution, kelp forests are essential for the healthy functioning of marine ecosystems and consequently underpin many of the benefits coastal societies receive from the ocean. Concurrently, rising sea temperatures, overgrazing by marine herbivores, sedimentation, and water pollution have caused kelp forests populations to decline in most regions across the world. Effectively managing the response to these declines will be pivotal to maintaining healthy marine ecosystems and ensuring the benefits they provide are equitably distributed to coastal societies. In Chapter 1, I review how the marine management paradigm has shifted from protection to restoration as well as the consequences of this shift. Chapter 2 introduces the field of kelp forest restoration and provides a quantitative and qualitative review of 300 years of kelp forest restoration, exploring the genesis of restoration efforts, the lessons we have learned about restoration, and how we can develop the field for the future. Chapter 3 is a direct answer to the question faced while completing Chapter 2. This chapter details the need for a standardized marine restoration reporting framework, the benefits that it would provide, the challenges presented by creating one, and the solutions to these problems. Similarly, Chapter 4 is a response to the gaps discovered in Chapter 2. Chapter 4 explores how we can use naturally occurring positive species interactions and synergies with human activities to not only increase the benefits from ecosystem restoration but increase the probability that restoration is successful. The decision to restore an ecosystem or not is informed by the values and priorities of the society living in or managing that ecosystem. Chapter 5 quantifies the fisheries production, nutrient cycling, and carbon sequestration potential of five key genera of globally distributed kelp forests. I conclude the thesis by reviewing the lessons learned and the steps required to advance the field kelp forest restoration and conservation

    Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

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    Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness

    Adaptive vehicular networking with Deep Learning

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    Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networks’ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities. In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects. The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup. The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios. The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the “ideal” optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark
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