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Exploring the stainless-steel beam-to-column connections response: A hybrid explainable machine learning framework for characterization
No full textStainless-steel provides substantial advantages for structural uses, though its upfront cost is notably high. Consequently, it’s vital to establish safe and economically viable design practices that enhance material utilization. Such development relies on a thorough understanding of the mechanical properties of structural components, particularly connections. This research advances the field by investigating the behavior of stainless-steel connections through the use of a four-parameter fitting technique and explainable artificial intelligence methods. Training was conducted on eight different machine learning algorithms, namely, Decision Tree, Random Forest, K-nearest neighbors, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, Adaptive Boosting, and Categorical Boosting. SHapley Additive Explanations was applied to interpret model predictions, highlighting features like spacing between bolts in tension and end-plate height as highly impactful on the initial rotational stiffness and plastic moment resistance. Results showed that Extreme Gradient Boosting achieved a coefficient of determination score of 0.99 for initial stiffness and plastic moment resistance, while Gradient Boosting model had similar performance with maximum moment resistance and ultimate rotation. A user-friendly graphical user interface (GUI) was also developed, allowing engineers to input parameters and get rapid moment–rotation predictions. This framework offers a data-driven, interpretable alternative to conventional methods, supporting future design recommendations for stainless-steel beam-to-column connections
Disparity estimation and enhancement from holoscopic elemental images
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London3D technology has acquired an enormous interest in the last few decades compared to
standard 2D imaging. It has the advantage of projecting depth and motion parallax, pro
viding a more realistic and natural image. Holoscopic imaging is a promising technique
that captures full-colour spatial images using a single-aperture system. A micro-lens array
shows the scene fromslightly varying angles between neighbouring lenses, mimicking the
f
ly’s eye approach and capturing four-dimensional information on a two-dimensional sur
face. This technique has the potential to overcome key limitations of traditional 2D imag
ing issues like depth, scalability, and multi-perspective problems due to its simple data
collection and visualisation method, which provides robust and scalable spatial informa
tion, and the additional features of motion parallax, binocular disparity, and convergence.
Unlike stereoscopic technology, a holoscopic system is underexplored. Researchers con
front a lack of holoscopic cameras to choose from: Lytro (no longer available) and Raytrix
(industrial application, more expensive). The need for a low-cost holoscopic image simu
lator and dataset to experiment with configurations, build prototypes, and use ground-truth
data for exploration, benchmarking, and evaluation is evident. This research introduces
a 4D Plenoptic function-based synthetic holoscopic image simulator. The simulator was
developed to produce raw holoscopic images faster and simpler than prior versions. A
comprehensive dataset with several micro-lens array configurations was developed and
provided via the simulator. This dataset can be used to assess the resolution trade-off be
tween angular and spatial data to help researchers choose micro-lens array configurations.
Aninnovative disparity map generation technique is proposed, which produces a disparity
mapof ascene from a single holoscopic image based on angular information preserved in
its micro-images, also known as elemental images (EIs) The use of EIs for disparity es
timation instead of traditional viewpoint images (VPIs) has not been extensively studied.
This research aims to explore the feasibility of utilising angular perspective information
instead of spatial orthographic information. Computing the disparity starts with enhanc
ing the quality of EIs, which often suffer from low resolution and lack of texture, a pre
processing phase is carried out using noise reduction and contrast enhancement. The dis
parity between EIs pixels is calculated using the Semi-Global Block Matching (SGBM)
technique, which is improved by employing a multi-resolution approach to overcome the resolution constraints of EIs as well as performing a content-aware analysis to dynami
cally modify the SGBMwindowsize settings generating disparities across different levels
of texture and complexity within the EIs. Finally, a weighted least squares (WLS) filter is
used to improve the optimisation process. In addition, EIs with inaccurate backgrounds
are detected and corrected with the use of a background mask and neighbouring EIs that
contain accurate background information. The evaluation has revealed that the suggested
technique has successfully generated disparity maps that surpass the accuracy of VPIs in
real images and outperform two state-of-the-art deep learning algorithms. The method
was also evaluated across many EI resolutions to test out the ideal resolution.
Current disparity estimation approaches based on EIs suffer from significant performance
decreases, particularly in texture-less regions. A novel method is proposed for performing
EIs labelling and grouping directly from a holoscopic image by selecting the appropriate
EIs that include the same object to use this information to reduce the disparity error in the
texture-less regions. To begin, a subset of VPIs are extracted from the holoscopic image
and subjected to conventional image segmentation. Next, labels are applied to the EIs
that correspond to each object segmented from the VPIs using VPIs/EIs pixel mapping.
To further improve the segmentation, we employ content-based image retrieval (CBIR),
in which the query images are automatically chosen from the previously established seg
mentation. The texture-less elemental images of the disparity map computed from the
holoscopic image are updated using the labelling data. Positive findings from the evalua
tion indicate that the proposed technique has helped reduce the disparity map error.
The evaluation result outperformed state-of-the-art depth generation techniques, and the
proposed technique enlarges the industrial application of 3D imaging applications such as
AR/VR, inspection, robotics, security and entertainment
The external dimension of Italian migration policy in the wider Mediterranean
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The dataset underpinning this publication can be accessed at https://umap.openstreetmap.fr/en/map/depmi-dimensione-esterna-politica-migratoria-itali_711517#4/37.34/11.34This article provides the first systematic and comprehensive analysis of the external dimension of Italy’s migration policy (EXMIPO) in the broader Mediterranean, over the past three decades. Building on an original dataset spanning over 30 years and 125 instruments, it investigates how and to what extent Italy cooperated with countries of origin and transit in the management of migration flows. The article argues that the external dimension of Italy’s migration policy is far richer than initially expected. From the immediate neighbourhood, Italy’s EXMIPO has gradually extended well beyond its geographical borders. If initially, it relied on a strategy of issue-linkage between quotas and return agreements, this gradually faded away in favour of more informal tools. While governments’ political ideology did not play a key role in defining the direction of Italy’s EXMIPO tools, we find that the evolving dynamics of migratory flows, and the pursuit of flexible tools to promptly address rising numbers, were crucial aspects behind the country’s external migration policy.The work was supported by the Siracusa International Institute for Criminal Justice and Human Rights, Project ‘DEPMI: Dimensione Esterna Politica di Migrazione Italiana’ (2021-2022)
An Investigation into the systems used to select contract forms for construction projects in Kuwait
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe construction industry plays a vital role in the economic development of Kuwait, and the selection of appropriate contract forms for construction projects is a pivotal decision that directly impacts project outcomes. This research aims to investigate the existing system for the selection of contract forms in Kuwait's construction projects and develop strategies for improving existing practices. The objectives of the research included: a critical analysis of the existing system in place for the selection of contract forms on construction projects in Kuwait; the development of a conceptual framework on best practices for contract form selection; proposing and validating strategies for improving contract form selection process in Kuwait. Kuwait's "New Kuwait 2035" vision has positioned the construction sector as a strategic priority for economic growth and development. However, the existing system for selecting construction contract forms faced challenges related to project delays, cost overruns, and contractual disputes. Motivated by the need to enhance the efficiency and effectiveness of the construction industry, this research undertook a comprehensive analysis of the current state of construction contracts in Kuwait. To achieve the research objectives, a mixed-methods approach was adopted combining a comprehensive literature review, quantitative data collection through questionnaires from construction professionals, and qualitative data collected via in-depth interviews with experts and stakeholders in the Kuwaiti construction industry. The research revealed that various construction contract forms are prevalent in Kuwait, Standard domestic form (CAPT forms) with Design bid build and lump sum contracts being the most commonly used. Selection of contract forms often lacked a systematic approach and was influenced by factors such as project type, complexity, and client preferences.
The survey data reveals that the current contract selection process in Kuwait faces challenges related to inefficiencies, risk allocation and resource mismanagement. The qualitative findings echo these concerns, indicating the importance of a new approach. Furthermore, a critical analysis of the existing system identifies shortcomings in contract selection processes and risk allocation. The findings of this research have significant implications for Kuwait's construction industry. The development of a systematic framework for contract form selection is crucial for enhancing the sector's efficiency, reducing disputes and optimising project outcomes. Real Options Theory guides contract selection, treating projects as options to manage choices over time. In Kuwait's uncertain construction projects, ROT integration empowers stakeholders to evaluate contracts strategically for long-term success. The research contributes to bridging the existing knowledge gap in the current method used for selecting contract forms by introducing a strategy framework that integrates theoretical and legal frameworks to select the suitable type of contract form related to the nature of the project, size, type, sector, responsibility and funding sources by using a Multi-Criteria Decision Making (MCDM) approach
Model-independent search for pair production of new bosons decaying into muons in proton-proton collisions at √ = 13 TeV
Get PDFA version of the article is available at arXiv:1506.00424v2 [hep-ex] (https://arxiv.org/abs/1506.00424). Comments: Replaced with published version. Added journal reference. Report number: CMS-HIG-13-010, CERN-PH-EP-2015-116. Journal reference: Phys. Lett. B 752 (2016) 146. Submission history: From: The CMS Collaboration: [v1] Mon, 1 Jun 2015 10:24:15 UTC (3,003 KB); [v2] Wed, 9 Dec 2015 22:13:10 UTC (2,995 KB).The results of a model-independent search for the pair production of new bosons within a mass range of 0.21 < m < 60 GeV, are presented. This study utilizes events with a four-muon final state. We use two data sets, comprising 41.5 fb⁻¹ and 59.7 fb⁻¹ of proton-proton collisions at s = 13 TeV, recorded in 2017 and 2018 by the CMS experiment at the CERN LHC. The study of the 2018 data set includes a search for displaced signatures of a new boson within the proper decay length range of 0 < cτ < 100 mm. Our results are combined with a previous CMS result, based on 35.9 fb⁻¹ of proton-proton collisions at √ = 13 TeV collected in 2016. No significant deviation from the expected background is observed. Results are presented in terms of a model-independent upper limit on the product of cross section, branching fraction, and acceptance. The findings are interpreted across various benchmark models, such as an axion-like particle model, a vector portal model, the next-to-minimal supersymmetric standard model, and a dark supersymmetric scenario, including those predicting a non-negligible proper decay length of the new boson. In all considered scenarios, substantial portions of the parameter space are excluded, expanding upon prior results.SCOAP³
Posit and floating-point based Izhikevich neuron: A Comparison of arithmetic
Get PDFData availability: No data was used for the research described in the article.Reduced precision number formats have become increasingly popular in various fields of computational science, as they offer the potential to enhance energy efficiency, reduce silicon area, and improve processing speed. However, this is often at the expense of introducing arithmetic errors that can impact the accuracy of a system. The optimal balance must be struck, judiciously choosing a number format using as few bits as possible, while minimising accuracy loss.
In this study, we examine one such format, posit arithmetic as a replacement for floating-point when conducting spiking neuron simulations, specifically using the Izhikevich neuron model. This model is capable of simulating complex neural firing behaviours, 20 of which were originally identified by Izhikevich and are used in this study. We compare the accuracy, spike count, and spike timing of the two arithmetic systems at different bit-depths against a 64-bit floating-point gold-standard. Additionally, we test a rescaled set of Izhikevich equations to mitigate against arithmetic errors by taking advantage of posit arithmetic’s tapered accuracy.
Our findings indicate that there is no difference in performance between 32-bit posit, 32-bit floating-point, and our 64-bit reference for all but one of the tested firing types. However, at 16-bit, both arithmetic systems diverge from the 64-bit reference, albeit in different ways. For example, 16-bit posit demonstrates an 18× improvement in accumulated spike timing error over a 1000ms simulation compared to 16-bit floating-point when simulating regular (tonic) spiking. This finding holds particular importance given the prevalence of this particular firing type in specific regions of the brain. Furthermore, when we rescale the neuron equations, this error is eliminated altogether. Although current Posit Arithmetic Units are no smaller than Floating Point Units of the same bit-width, our results demonstrate that 64-bit floating-point can be replaced with 16-bit posit which could enable significant area savings in future systems.TFH was part funded by Sundance Multiprocesssor Ltd., UK. and an EPSRC Doctoral Training Partnerships (DTP) grant. JK was funded by the EPSRC (grant EP/V052241/1)
African returnees in international knowledge transfer: A social capital perspective
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The data that has been used is confidential.In response to the fast growing number of African returnees and the important roles that they play in transferring international knowledge back to the African continent, this qualitative, exploratory study unpacks the role of African returnees in delivering international knowledge obtained from another social context of the Global South through their work and/or study experience, and identifies social factors that facilitate or hinder international knowledge transfer from a social capital perspective. Drawing on qualitative interview data collected from 20 Ghanaian returnees plus an expert interview, observation notes and archival data, we develop an enhanced social capital model in the Global South context. Our model strengthens the understanding of the role of diaspora in international knowledge transfer in general, and that of African returnees in knowledge transfer in the Global South in particular. Specifically, this study offers insights on the interconnections among the three dimensions of social structure (i.e., market relations, social relations and hierarchical relations), African returnees' relations in their social structure, the sources of social capital derived from social relations through opportunity, motivations and ability, and the value created for successful knowledge transfer as a result of the integrative effects of returnees' social capital.University Of Leeds, Leeds Challenge Fund (International Knowledge Transfer and Co-creation through International Entrepreneurship)
Revealing the Supply Chain 4.0 Potential within the European Automotive Industry
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The datasets presented in this article are not readily available [the data are part of an ongoing study].With the rapid advancements in Information and Communication Technologies (ICT) and the widespread enthusiasm of both theoreticians and practitioners, the broader transition to Industry 4.0 (I4.0) in major industries appears imminent. This empirical study analyzes business data from 1140 automotive companies operating in Europe, utilizing various business intelligence platforms and employing decision tree analytics to establish connections between enablers, drivers, company size, and financial resources. The goal is to identify persistent barriers hindering the rational transition to Industry 4.0. The findings reveal an uneven transformation within the industry nexus. While larger companies possess the financial means to allocate collective intelligence, technical resources, and drive necessary for fulfilling I4.0 requirements, smaller members of the nexus lag behind despite their enthusiasm and intent. This imbalanced evolution poses a threat to the comprehensive transformation required for realizing all the benefits of Industry 4.0 within the sector. The primary discovery indicates that small to medium-sized enterprises do not exhibit the same rates of Industry 4.0 adoption, a lag highly correlated with their available financial and human resources for digital transition. The decision tree proposed in this study offers guidelines for achieving an Industry 4.0-compliant nexus. Given its diversity and substantial global impact, the case study from the automotive industry proves intriguing and may later be generalized to other sectors. The study’s outcome could empower engineering managers and researchers to implement, execute, and assess the impact of digital strategies based on the financial capabilities of industrial institutions.This research received no external funding
Search for Inelastic Dark Matter in Events with Two Displaced Muons and Missing Transverse Momentum in Proton-Proton Collisions at √s=13 TeV
No full textA search for dark matter in events with a displaced nonresonant muon pair and missing transverse momentum is presented. The analysis is performed using an integrated luminosity of 138 fb-1 of protonproton (pp) collision data at a center-of-mass energy of 13 TeV produced by the LHC in 2016.2018. No significant excess over the predicted backgrounds is observed. Upper limits are set on the product of the inelastic dark matter production cross section σ(pp → A′ → χ1χ2) and the decay branching fraction ℬ(χ2 → χ1μ+μ-), where A′ is a dark photon and +++χ1 and χ2 are states in the dark sector with near mass degeneracy. This is the first dedicated collider search for inelastic dark matter.SCOAP
In Silico and In Vitro Mapping of Receptor-Type Protein Tyrosine Phosphatase Receptor Type D in Health and Disease: Implications for Asprosin Signalling in Endometrial Cancer and Neuroblastoma
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Data will be available upon reasonable request.Background: Protein Tyrosine Phosphatase Receptor Type D (PTPRD) is involved in the regulation of cell growth, differentiation, and oncogenic transformation, as well as in brain development. PTPRD also mediates the effects of asprosin, which is a glucogenic hormone/adipokine derived following the cleavage of the C-terminal of fibrillin 1. Since the asprosin circulating levels are elevated in certain cancers, research is now focused on the potential role of this adipokine and its receptors in cancer. As such, in this study, we investigated the expression of PTPRD in endometrial cancer (EC) and the placenta, as well as in glioblastoma (GBM). Methods: An array of in silico tools, in vitro models, tissue microarrays (TMAs), and liquid biopsies were employed to determine the gene and protein expression of PTPRD in healthy tissues/organs and in patients with EC and GBM. Results: PTPRD exhibits high expression in the occipital lobe, parietal lobe, globus pallidus, ventral thalamus, and white matter, whereas in the human placenta, it is primarily localised around the tertiary villi. PTPRD is significantly upregulated at the mRNA and protein levels in patients with EC and GBM compared to healthy controls. In patients with EC, PTPRD is significantly downregulated with obesity, whilst it is also expressed in the peripheral leukocytes. The EC TMAs revealed abundant PTPRD expression in both low- and high-grade tumours. Asprosin treatment upregulated the expression of PTPRD only in syncytialised placental cells. Conclusions: Our data indicate that PTPRD may have potential as a biomarker for malignancies such as EC and GBM, further implicating asprosin as a potential metabolic regulator in these cancers. Future studies are needed to explore the potential molecular mechanisms/signalling pathways that link PTPRD and asprosin in cancer.This research was funded by GRACE, Registered Charity No. 1189729 and by the General Charities of the City of Coventry, Registered Charity No. 216235
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