MRC Laboratory of Molecular Biology
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Designing the business model of an energy Datahub
No full textAn energy Datahub is a central model that allows all data to be stored, separated, analysed, and sent to other peers for specified actions. Digitalisation, increasing small-scale renewable generation and prosumers, flexibility, and Demand Response are the key parameters that pave the way for the implementation of Datahubs. In this paper, we present the business model of an energy Datahub in Turkey. The Datahub will be useful in better engagement with residential customers, introducing smart tariffs and achieving Demand Response, supporting prosumers, increasing transparency and efficiency in energy markets, and enabling new business models via innovation and development. The Datahub will provide an environment where data analytical tools could be employed, which will foster the use of machine learning and deep learning algorithms
A data-driven kinematic model of a ducted premixed flame
No full textReduced-order models of flame dynamics can be used to predict and mitigate the emergence of thermoacoustic oscillations in the design of gas turbine and rocket engines. This process is hindered by the fact that these models, although often qualitatively correct, are not usually quantitatively accurate. As automated experiments and numerical simulations produce ever-increasing quantities of data, the question arises as to how this data can be assimilated into physics-informed reduced-order models in order to render these models quantitatively accurate. In this study, we develop and test a physics-based reduced-order model of a ducted premixed flame in which the model parameters are learned from high-speed videos of the flame. The experimental data is assimilated into a level-set solver using an ensemble Kalman filter. This leads to an optimally calibrated reduced-order model with quantified uncertainties, which accurately reproduces elaborate nonlinear features such as cusp formation and pinch-off. The reduced-order model continues to match the experiments after assimilation has been switched off. Further, the parameters of the model, which are extracted automatically, are shown to match the first-order behavior expected on physical grounds. This study shows how reduced-order models can be updated rapidly whenever new experimental or numerical data becomes available, without the data itself having to be stored
A Practice-Based Conceptual Model on Building Information Modelling (BIM) Benefits Realisation
No full textMost of the existing research on BIM implementation and benefits realisation has departed from a technocentric perspective and instead promoted the belief that the benefits of using BIM technologies are an inevitable outcome of adoption in and of itself. Recently, scholars have acknowledged that important links between implementation practices and realisation of benefits have received little attention in the literature. There have been calls for more research taking a more detailed account of how outcomes can be achieved. Thus, by adopting a practice-based perspective as a theoretical lens, in this paper we investigate the ‘what’, ‘who’ and ‘how’ of successful BIM processes implementation. We propose a conceptual framework on the underlying conditions of successful implementation and assert that the achievement of benefits is dependent on the interaction of those three aspects. Building on qualitative data from nine construction projects from three client organisations in the UK, we show that the relationship between BIM implementation as a set of technologies and processes and performance cannot be understood without taking into account not only ‘what’ new processes exist in a BIM project and its interdependencies but also ‘who’ implements and engages with them and ‘how’ existing structures are reconfigured when enacting those practices. In alignment with recent research challenging the perceptions of BIM enactment as a linear process, our findings provide new insights into why the proclaimed benefits of BIM have not always been realised as an outcome of a ‘symbolic’ implementation of information management processes and lack of reconfiguration of existing institutions
Inline vector compression for computational physics
No full textA novel inline data compression method is presented for single-precision vectors in three dimensions. The primary application of the method is for accelerating computational physics calculations where the throughput is bound by memory bandwidth. The scheme employs spherical polar coordinates, angle quantisation, and a bespoke floating-point representation of the magnitude to achieve a fixed compression ratio of 1.5. The anisotropy of this method is considered, along with companding and fractional splitting techniques to improve the efficiency of the representation. We evaluate the scheme numerically within the context of high-order computational fluid dynamics. For both the isentropic convecting vortex and the Taylor–Green vortex test cases, the results are found to be comparable to those without compression. Performance is evaluated for a vector addition kernel on an NVIDIA Titan V GPU; it is demonstrated that a speedup of 1.5 can be achieved
Integration In Reproducing Kernel Hilbert Spaces Of Gaussian Kernels
No full textThe Gaussian kernel plays a central role in machine learning, uncertainty quantification and scattered data approximation, but has received relatively little attention from a numerical analysis standpoint. The basic problem of finding an algorithm for efficient numerical integration of functions reproduced by Gaussian kernels has not been fully solved. In this article we construct two classes of algorithms that use N evaluations to integrate d-variate functions reproduced by Gaussian kernels and prove the exponential or super-algebraic decay of their worst-case errors. In contrast to earlier work, no constraints are placed on the length-scale parameter of the Gaussian kernel. The first class of algorithms is obtained via an appropriate scaling of the classical Gauss–Hermite rules. For these algorithms we derive lower and upper bounds on the worst-case error of the forms exp(−c1N1/d)N1/(4d) and exp(−c2N1/d)N−1/(4d), respectively, for positive constants c1c2. The second class of algorithms we construct is more flexible and uses worst-case optimal weights for points that may be taken as a nested sequence. For these algorithms we derive upper bounds of the form exp(−c3N1/(2d)) for a positive constant c3
3D Printing of Functionally Graded Films by Controlling Process Parameters
No full textScaffolds are often used in bioengineering to replace damaged tissues. They promote cell ingrowth and provide mechanical support until cells regenerate. Such scaffolds are often made using the additive manufacturing process, given its ability to create complex shapes, affordability, and the potential for patient-specific solutions. The success of the implant is closely related to the match of the scaffold mechanical properties to those of the host tissue. Many biological tissues show properties that vary in space. Therefore, the aim is to manufacture materials with variable properties, commonly referred to as functionally graded materials. Here we present a novel technique used to manufacture porous films with functionally graded properties using 3D printers. Such an approach exploits the control of a process parameter, without any hardware modification. The mechanical properties of the manufactured films have been experimentally tested and analytically characterized
Magnetic polydomain liquid crystal elastomers–synthesis and characterisation
No full textAlthough liquid crystal elastomers (LCE) are a fascinating class of materials with interesting thermomechanical properties of their own, the aim is to enhance their performance or add new features, e.g. response to external stimuli. The generally weak response of organic materials can be significantly intensified by mixing nano-sized magnetic particles into the host polymer matrix. An alternative approach is chemically coupling the nanoparticles to the elastomer. We achieved this by bonding functionalised magnetic nanoplatelets to the backbone of a main-chain LCE and obtained polydomain magnetic liquid crystal elastomers. We measured the magnetisation curves in samples, which were exposed to either small or large magnetic fields–their response being a consequence of partial particle reorientation or magnetic moment flipping. In contrast to the samples, which were exposed to small magnetic field and in which the remanent magnetisation can be reset to zero by heating the sample, the samples with flipped magnetisation within the platelets cannot be reversed into the original state. Coupling of magnetic and mechanical properties shows a slight magneto-elastic response at elevated temperatures and a significant inverse magneto-elastic effect: the magnetisation caused by mechanical stretching is almost equal to the magnetisation caused by an external magnetic field
The potential of microplastics as adsorbents of sodium dodecyl benzene sulfonate and chromium in an aqueous environment
No full textConsidering the omnipresence of microplastics (MPs) in aquatic environments, they are expected to exert significatn impacts as carriers for diverse waterborne pollutants. In this work, the adsorptive behavior of two ionic components (i.e., sodium dodecyl benzene sulfonate (SDBS) and Cr(VI)) has been explored against the two types of MPs as model adsorbents, namely poly (ethylene terephthalate) (PET) and polystyrene (PS). The influence of key variables (e.g., pH, particle size, and dose of the MPs) on their adsorption behavior is evaluated from various respects. The maximum adsorption capacity values of SDBS on PET and PS are estimated to be 4.80 and 4.65 mg⋅g−1, respectively, while those of Cr(VI) ions are significantly lower at 0.080 and 0.072 mg⋅g−1, respectively, The adsorptive equilibrium of SDBS is best described in relation to pH and MP size by a Freundlich isotherm. In contrast, the adsorption behavior of Cr(VI) is best accounted for by a Langmuir isotherm to indicate its adsorption across at least two active surface sites
Representations of uncertainty: where art thou?
No full textPerception is often described as probabilistic inference requiring an internal representation of uncertainty. However, it is unknown whether uncertainty is represented in a task-dependent manner, solely at the level of decisions, or in a fully Bayesian manner, across the entire perceptual pathway. To address this question, we first codify and evaluate the possible strategies the brain might use to represent uncertainty, and highlight the normative advantages of fully Bayesian representations. In such representations, uncertainty information is explicitly represented at all stages of processing, including early sensory areas, allowing for flexible and efficient computations in a wide variety of situations. Next, we critically review neural and behavioral evidence about the representation of uncertainty in the brain agreeing with fully Bayesian representations. We argue that sufficient behavioral evidence for fully Bayesian representations is lacking and suggest experimental approaches for demonstrating the existence of multivariate posterior distributions along the perceptual pathway
Investigating the accessibility of crowdwork tasks on mechanical turk
No full textCrowdwork can enable invaluable opportunities for people with disabilities, not least the work fexibility and the ability to work from home, especially during the current Covid-19 pandemic. This paper investigates how engagement in crowdwork tasks is afected by individual disabilities and the resulting implications for HCI. We frst surveyed 1,000 Amazon Mechanical Turk (AMT) workers to identify demographics of crowdworkers who identify as having various disabilities within the AMT ecosystem-including vision, hearing, cognition/mental, mobility, reading and motor impairments. Through a second focused survey and follow-up interviews, we provide insights into how respondents cope with crowdwork tasks. We found that standard task factors, such as task completion time and presentation, often do not account for the needs of users with disabilities, resulting in anxiety and a feeling of depression on occasion. We discuss how to alleviate barriers to enable efective interaction for crowdworkers with disabilities