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Joint Interpretation of Multi-Frequency Ground Penetrating Radar and Ultrasound Data for Mapping Cracks and Cavities in Tree Trunks
No full textAs the Earth's lungs, trees are a natural resource that provide, amongst others, food, lumber, and oxygen. Therefore, monitoring these wooden structures with non-destructive testing (NDT) techniques such as ground penetrating radar (GPR) and ultrasound can provide valuable information about inner flaws and decays, which is an essential step for tree conservation.
In recent years, GPR and ultrasound have been used to delineate the interior architecture of tree trunks [1-3]. However, more research is required to improve results and consequently have a more reliable interpretation. Due to limitations in depth penetration and signal-to-noise ratio [4], these approaches have a limited capacity for resolving features. The use of gain functions and higher frequencies to compensate for wave attenuation may exaggerate events and reduce resolution, respectively.
In this context, an integration between GPR multi-frequency and ultrasound data can be used to address this issue. Data were collected on a tree trunk log at the Faringdon Centre for Non-Destructive Testing and Remote Sensing using two high-frequency GPR systems (2GHz and 4GHz central frequencies) and an ultrasound (supporting a wide range of transducers from 24 kHz up to 500 kHz) testing equipment. Internal features of interest in terms of extended perimetric air gaps at the bark-wood interface, natural cracks and small artificial cavities were investigated through electromagnetic and mechanical waves. After compilation of data, a joint interpretation strategy for data analysis is developed. The processed data were mapped against the cut sections of the tree for validity purposes.
Although study of stand tree trunks would be more challenging, the findings of this research may be applied for wood timbers and pave the way to future research for living tree trunks.
Acknowledgements
This research was funded by the Vice-Chancellor’s PhD Scholarship at the University of West London
Investigations of brain-wide functional and structural networks of dopaminergic and CamKIIα-positive neurons in VTA with DREADD-fMRI and neurotropic virus tracing technologies
Get PDFAbstract
Background: The ventral tegmental area (VTA) contains heterogeneous cell populations. The dopaminergic neurons in VTA play a central role in reward and cognition, while CamKIIα-positive neurons, composed mainly of glutamatergic and some dopaminergic neurons, participate in the reward learning and locomotor activity behaviors. The differences in brain-wide functional and structural networks between these two neuronal subtypes were comparatively elucidated.
Methods: In this study, we applied a method combining Designer Receptors Exclusively Activated by Designer Drugs (DREADD) and fMRI to assess the cell type-specific modulation of whole-brain neural networks. rAAV encoding the cre-dependent hM3D was injected into the right VTA of DAT-cre or CamKIIα-cre transgenic rats. The global brain activities elicited by DREADD stimulation were then detected using BOLD-fMRI. Furthermore, the cre-dependent antegrade transsynaptic viral tracer H129ΔTK-TT was applied to label the outputs of VTA neurons.
Results: We found that DREADD stimulation of dopaminergic neurons induced significant BOLD signal changes in the VTA and several VTA-related regions including mPFC, Cg and Septum. More regions responded to selective activation of VTA CamKIIα-positive neurons, resulting in increased BOLD signals in VTA, Insula, mPFC, MC_R (Right), Cg, Septum, Hipp, TH_R, PtA_R, and ViC_R. Along with DREADD-BOLD analysis, further neuronal tracing identified multiple cortical (MC, mPFC) and subcortical (Hipp, TH) brain regions that are structurally and functionally connected by VTA dopaminergic and CamKIIα-positive neurons.
Conclusions: Our study dissects brain-wide structural and functional networks of two neuronal subtypes in VTA and advances our understanding of VTA functions.
Keywords: DREADD; HSV; Neural networks; Ventral tegmental area (VTA); fMRI
In-the-moment or feed-forward: a review of online formative assessments used in ELT modules in British universities
No full textFeedback is central to student learning and achievement. There is, however, a wide-ranging student dissatisfaction with feedback in the British universities (Bloxham, 2014), and worryingly, as research suggest, students do not read or engage with teacher-written feedback. The ‘feedback gap’ (Evans, 2013) between teacher efforts and student reflection contradicts the effectiveness of feedback to help improve student performance. 'Help not hinder' - has been the basis of assessment strategies in the British universities as they face prolonged closure of face-to-face teaching with all traditional examinations suspended due to Covid-19 pandemic. In this chapter, I focus on 6 purposively-selected universities – 3 traditional and 3 modern (post-1992) – to look at their assessment strategies in ELT modules (Level 4-7) as all universities moved to various online modes of assessments. “We cannot do away with exams; it is integral to the system" - is a concern came strongly from all universities amidst worries about equity, validity, and transparency of online assessments and feedback. Findings show that universities have relied on a variety of methods – coursework, essay portfolio to one-to-one online formative assessment. In a distance learning environment, one-to-one formative feedback meetings are not about ‘grading’ but about students’ engagement, reflection and learning from the feedback. ‘In-the-moment’ or spontaneous observation of student engagement helps teachers act on the factors that affect student learning – their experiences, circumstances and preferences, and this is more so, as teachers argue, for ELT modules that typically have international and mixed-ability cohorts. Small modern universities have gone further to consider self-assessment and home-based assessments, and are in touch with students regularly to motivate and train them to take responsibilities of their own learning. In addition (not necessarily as an alternative of the above), universities have explored and employed ‘feed-forward’ strategies within ELT modules. In order to challenge the feedback gap and student non-engagement with feedback, feed-forward is teacher guidance or notes either (a) to be given post-assignment with specific direction linked to future assignments, or b) to have a direct impact upon an upcoming assignment. These findings suggest that online formative assessments in ELT modules and beyond have decisively moved towards a ‘future-orientation’ of the feedback process to help promote meaningful teacher and student dialogue about feedback and to maximise its impact on future student performance
Satellite remote sensing and non-destructive testing methods for transport infrastructure monitoring: advances, challenges and perspectives
Get PDFHigh-temporal-frequency monitoring of transport infrastructure is crucial to facilitate maintenance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long inspection times are required for data collection and their implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructure (roads, railways and airfields) with a much higher temporal frequency of investigation and the capability to cover wider areas. Nevertheless, the integration of information from (i) satellite remote sensing and (ii) ground-based NDT methods is a subject that is still to be fully explored in civil engineering. This paper aims to review significant stand-alone and combined applications in these two areas of endeavour for transport infrastructure monitoring. The recent advances, main challenges and future perspectives arising from their mutual integration are also discussed
Analysis of deep convolutional neural network models for the fine-grained classification of vehicles
Get PDFIntelligent transportation systems (ITS) is a broad area that encompasses vehicle identification, classification, monitoring, surveillance, prediction, management, reduction of traffic jams, license plate recognition, etc. Machine learning has practical and significant applications in ITS. Intelligent transportation systems rely heavily on vehicle classification for traffic management and monitoring.
This research uses convolutional neural networks to classify cars at fine-grained classifications (make and model). Numerous obstacles must be overcome in order to complete the task, the greatest of which are intra- and inter-class similarities between the manufacturer and model of vehicles, different lighting effects, the shape and size of the vehicle, shadows, camera view angle, background, vehicle
speed, colour occlusion and environmental conditions. This paper studies various machine learning algorithms used for the fine-grained classification of vehicles and presents a comparative analysis in terms of accuracy and the size of the implemented deep convolutional neural network (DCNN).
Specifically, four DCNN models, mobilenet-v2, inception-v3, vgg-19 and resnet-50, are evaluated with three datasets, BMW-10, Stanford Cars and PAKCars. The evaluation results show that mobileNet-v2 is the smallest model as it is not computationally intensive due to depthwise separable convolution.
However, resnet-50 and vgg-19 outperform inception-v3 and mobilenet-v2 in terms of accuracy due to their complex structure
2023 Challenges and opportunities: enabling technologies for connected smart cities
No full textIEEE Women in Engineering is an initiative with the goal to facilitate the recruitment and retention of women in technical disciplines globally. We envisage a vibrant community of IEEE women and men collectively using their diverse talents to innovate for the benefit of humanity.
The Industrial Internet of Things research group (IIoT) at the University of West London hosted this year’s IEEE WIE 2023 Challenges and Opportunities event, organised by the IEEE Women in Engineering UK&I Affinity Group – Early Profession programme. This 2023 workshop with the theme “Enabling Technologies for Connected Smart Cities” was organised by women and open to all.
It was be a full-day event including six technical talks and panel discussions by speakers from industry, academia, regulators and policymakers. The event introduced the participants to state-of-the-art research in the smart cities field, highlighting the challenges and opportunities and is aimed at anyone with an interest or experience in the subject. We particularly welcomed graduate students, early career researchers (ECR), and final-year students from STEM subjects
Associations of social networks with physical activity enjoyment among older adults: walkability as a modifier through a STROBE-compliant analysis
Get PDFThe available evidence suggests that social networks can contribute to physical activity (PA) enjoyment, which is necessary for the maintenance of PA over the life course. This study assessed the associations of active and sedentary social networks with PA enjoyment and ascertained whether walkability moderates or modifies these associations. A cross-sectional design compliant with STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) was employed. The participants were 996 community-dwelling older Ghanaians aged 50 years or older. A hierarchical linear regression analysis was used to analyse the data. After adjusting for age and income, the study found that the active social network size (β = 0.09; p < 0.05) and sedentary social network size (β = 0.17; p < 0.001) were positively associated with PA enjoyment. These associations were strengthened by walkability. It is concluded that active and sedentary social networks may better support PA enjoyment in more walkable neighbourhoods. Therefore, enabling older adults to retain social networks and live in more walkable neighbourhoods may be an effective way to improve their PA enjoyment
The influence of isokinetic trunk flexor and extensor strength on dynamic balance in children
Get PDFThis study assessed if trunk flexor and extensor strength were predictors of time to stability (TTS) and centre of pressure (CoP) during hop and hold tasks in children. Seventeen boys (age; 10.1 ± 1.6 years; height, 1.45 ± 0.11 m; mass, 26.7 ± 7.83 kg) undertook isokinetic strength assessments of concentric and eccentric trunk flexors/extensors at 60°∙s-1, and anterior/medial hop tasks. Hierarchical multiple regressions determined if concentric and eccentric trunk flexor/extensor peak torques predict TTS using a composite score (CompX Compy Compz) and CoPX and CoPY. Concentric trunk flexors were the strongest predictor for TTS CompXY, with concentric flexion and eccentric extension predicting TTS CompY. All muscle actions were also strong predictors for CoPY during hop tasks. These findings have implications for the assessment of trunk musculature strength and measures of postural control within a young healthy population. The development of trunk musculature strength may aid improvements in dynamic balance tasks in children, with implications for fall and injury risk. To improve trunk musculature strength and the potential to maintain postural control, a combination of concentric and eccentric exercises with other training modalities appears relevant due to the increased relevance to the demands of balance maintenance
Development of an artificial intelligence-based framework for biogas generation from a micro anaerobic digestion plant
Get PDFDespite the advantages of the Anaerobic Digestion (AD) technology for organic waste management, low system performance in biogas production negatively affects the wide spread of this technology. This paper develops a new artificial intelligence-based framework to predict and optimise the biogas generated from a micro-AD plant. The framework comprises some main steps including data collection and imputation, recurrent neural network/ Non-Linear Autoregressive Exogenous (NARX) model, shuffled frog leaping algorithm (SFLA) optimisation model and sensitivity analysis. The suggested framework was demonstrated by its application on a real micro-AD plant in London. The NARX model was developed for predicting yielded biogas based on the feeding data over preceding days in which their lag times were fine-tuned using the SFLA. The optimal daily feeding pattern to obtain maximum biogas generation was determined using the SFLA. The results show that the developed framework can improve the productivity of biogas in optimal operation strategy by 43 % compared to business as usual and the average biogas produced can raise from 3.26 to 4.34 m3/day. The optimal feeding pattern during a four-day cycle is to feed over the last two days and thereby reducing the operational costs related to the labour for feeding the plant in the first two days. The results of the sensitivity analysis show the optimised biogas generation is strongly influenced by the content of oats and catering waste as well as the optimal allocated day for adding feed to the main digester compared to other feed variables e.g., added water and soaked liner
Comparative analysis of the whole life carbon of three construction methods of a UK-based supermarket
Get PDFThe built environment has been a significant contributor to global carbon emissions. It, therefore, has a vital role to play in the reduction efforts of future climate change. While the design of buildings may determine future energy use for cooling, heating, and lighting during the operational stage of the building, this study aims to observe the effect of the building design on the operational as well as the whole-life carbon emissions. Past studies have focused on either the operational carbon or the embodied carbon of a building. Using a cradle-to-grave assessment of a typical UK supermarket, this study explores the relationship between embodied carbon and operational carbon. Additionally, it examines the effects of the variables between three approved construction methods of the same design on the whole life of carbon. These methods are a steel structural frame and cladding panel external wall, steel frame and poroton walls, precast concrete and glulam frame and precast concrete walls. The findings of this research will contribute to mitigation strategies for the environmental impacts of supermarket building construction whilst providing a framework for future assessment of the whole-life carbon of supermarket buildings.
Practical Application: Employing the life cycle assessment methodology, this paper examines the potential of minimising both embodied and operational carbon by observing the whole life carbon. Highlighting the influence of the GHG emission contributing factors in each stage on each other. Additionally, the recommended methodology for the supermarket building types of this case study, could be adapted for other types of buildings. The findings could also augment carbon emission research and guide the development of supermarket buildings to low carbon intensive. Furthermore, collaboration with the industry in carrying out this research aids in adopting the findings as practical and theoretical guides for engineers and designers in reducing the building sector’s harmful environmental impact