MRC Laboratory of Molecular Biology
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Mining Graph-Fourier Transform Time Series for Anomaly Detection of Internet Traffic at Core and Metro Networks
No full textThis article proposes a framework to analyse traffic-data processes on a long-haul backbone infrastructure network providing internet services at a national level. This type of network requires low latency and fast speed, which means there is a large demand for research focusing on near real-time decision-making and resilience assessment. To this aim, this article proposes two innovative, complementary procedures: a multi-view approach for the topology analysis of a backbone network at a static level and a time-series mining approach of the graph signal for modelling the traffic dynamics. The combined framework provides a deeper understanding of a backbone network than classical models, allowing for backbone network optimisation operations and management at near real-time. This methodology was applied to the backbone infrastructure of a major UK internet service provider. Doing so increased accuracy and computational efficiency for detecting where and when anomalies and pattern irregularities occur in the network signal
Using adjoint-based optimization to enhance ignition in non-premixed jets
No full textGradient-based optimization is used to reliably and optimally induce ignition in three examples of laminar non-premixed mixture configurations. Using time-integrated heat release as a cost functional, the non-convex optimization problem identified optimal energy source locations that coincide with the stoichiometric local mixture fraction surface for short optimization horizons, while for longer horizons, the hydrodynamics plays an increasingly important role and a balance between flow and chemistry features determines non-trivial optimal ignition locations. Rather than identifying a single optimal ignition location, the results of this study show that there may be several equally good ignition locations in a given flow configuration
Estimating the impacts of climate change on electricity supply infrastructure: A case study of China
No full textUnderstanding the impacts of climate change on electricity supply infrastructure (ESI) is important to maintain a reliable power supply. Nonetheless, most existing studies focus on the physical impacts rather than the economic impacts, failing to provide references for the cost-benefit analysis of different abatement policies and measures. With this motivation, this study firstly employs a downscaled climate system model to project temperature paths in the future. Then, an integrated model is established to quantify both physical and economic impacts of long-term future temperature rise on the existing ESI components. Finally, the maximum climate-attributable impacts on China's ESI are assessed for the period from 2018 to 2099. Our major findings are that: (1) 10.2% of the generator ratings, 17.8% of the transmission and distribution line ratings and 10.0% of the transformer ratings are at risk of outage from expected climate change effects. (2) Around $258 billion of the existing ESI assets are at risk of outage due to the future surface temperature rise, representing 14.2% of the ESI assets in 2017. (3) The impacts of climate change on ESI vary substantially among different provinces and among different infrastructure components. These obtained results can provide important guidance for the mitigation and adaption strategies for the climate change impacts on the electricity sector
Building Impulse Toolkit (BIT): A novel IoT system for capturing the influence of façades on occupant perception and occupant-façade interaction
No full textThe design and operation of responsive resource-efficient buildings requires high resolution data in space and time on building performance and the associated occupant response, but capturing this high quality data has traditionally been technologically challenging, costly and disruptive to building occupants. Recent developments in Internet of Things (IoT) technologies provide an opportunity to monitor holistic indoor environmental quality (IEQ) and related occupant perception and behaviour in a more cost-effective and less disruptive manner whilst providing higher granularity data in space and time. Façades have a significant and dynamic influence on IEQ and building performance, and occupants often interact with them, but there is a dearth of IoT solutions for monitoring the façade-induced effects. This paper describes the development, deployment and assessment of the Building Impulse Toolkit (BIT), a prototype IoT system for capturing the holistic and transient influence of façades on IEQ and occupants. The methodology adopted in the design and development of the BIT prototype is first explained. The results obtained from a 9-month deployment in a real-world office are then reported and discussed, in particular the capabilities and limitations of the BIT prototype in: 1) capturing the influence of the façade on IEQ in space and time; 2) monitoring occupant environmental discomfort and satisfaction and in a non-disruptive manner; 3) monitoring occupant interaction with the façade. It was found that BIT is largely successful at meeting these objectives, but occupant engagement could be improved in the next generation prototypes
Role of Pile Spacing on Dynamic Behavior of Pile Groups in Layered Soils
No full textThis research investigates the influence of pile spacing on the dynamic behavior of pile groups by performing a series of specifically designed dynamic centrifuge experiments on pile foundations embedded in a two-layered soil profile. A single pile and two 3×1 row pile groups with different pile spacing were used as model pile foundations, and the soil models consisted of a soft clay underlain by dense sand. The influence of earthquake frequency on the dynamic behavior of two-layered soils is discussed using the centrifuge data and 1D site response analysis from DEEPSOIL. Further, the results of these centrifuge tests agreed with the conviction that the group effects will be diminished with the increase in pile-to-pile spacing in a pile group due to reduced pile-soil-pile interaction. However, these reduced pile group effects can lead to larger kinematic pile bending moments in the widely spaced pile group compared with a closely spaced pile group. Moreover, the single pile always has larger bending moments than both the tested pile groups - an exception to this is when there is a significant phase difference between the kinematic and inertial loads for a single pile but not for the widely spaced pile group. The influence of pile spacing on the shadowing effects and location of peak bending moments in the piles of a group are also discussed in this paper. Lastly, an attempt is made to evaluate the individual contribution of inertial and kinematic loads for the seismic design of pile foundations considering soil-pile-structure interaction effects
Physics-Inspired Structural Representations for Molecules and Materials
No full textThe first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field
The technological and social timelines of climate mitigation: Lessons from 12 past transitions
No full textDelivering net zero by 2050 will require governments to utilise all available abatement opportunities, yet current policy largely ignores socially-driven mitigation in favour of technological innovation in the energy sector. Past energy transitions have typically taken several decades, but policymakers remain confident that low carbon technology can be deployed in time for a 2050 mitigation target. We consider the potential for social transitions to deliver emissions reductions within this time frame. Based on detailed analysis of 12 past transitions, we develop frameworks for technological and social transitions which draw out the relationships between progress, policy and diffusion. Using our generalised frameworks we compare the duration of transitions, finding that the technological transitions in our study take on average four decades longer than the social transitions to go from initial conception to market or behaviour saturation. Our evidence suggests that policymakers who continue to rely on technology transitions to reduce emissions will be unable to deliver climate targets by 2050. By engaging with the public to stimulate social transitions governments could have more impact, more swiftly. Only by utilising both technological and social transitions can policymakers hope to achieve net zero emissions by 2050
Acceleration of surface tracking in Monte Carlo transport via distance caching
No full textA modification to the surface tracking algorithm commonly employed in Monte Carlo neutral particle transport is suggested. As nuclear reactor simulations often have multiple nested geometry levels, surface tracking algorithms must perform distance checks on each level to determine the distance to the closest surface. A simple optimisation to this procedure (which does not appear to have been previously proposed) is to store and decrement the distance to the boundary on each level during a particle flight, removing extraneous and relatively expensive distance calculations. This modified algorithm is implemented in the Monte Carlo code SCONE and compared against the standard surface tracking implementation, showing a favourable speed-up in the range of 7–20% for realistic Monte Carlo assembly geometries
Agile roadmapping: An adaptive approach to technology foresight
No full textTechnology roadmapping has become an important foresight tool for science, technology, and innovation (STI) policy and technology strategy development. There are, however, challenges in translating evidence from foresight into the strategies of STI agencies and the planning of research & technology development (RTD) organizations. While the foresight evaluation literature identifies methodological issues related to evidence granularity, scope, and stakeholder confidence, there is limited guidance on how to ensure roadmapping outputs are strategically relevant, appropriately detailed, and credible. This paper highlights the potential of using structured visual roadmapping frameworks to anticipate potential strategic foresight evidence failures and using the adaptive and iterative nature of roadmapping processes to address them. In this paper, we distinguish between: the roadmapping framework ‘canvas’; the foresight evidence captured on the canvas; the process of generating the evidence; and any final strategic plan developed using that evidence (with goals, milestones, actions, etc). We investigate efforts to use the roadmapping canvas as a research tool and diagnostic to explore emerging technology trajectories and innovation ‘pathways’. We demonstrate that key patterns of evidence distribution on the roadmapping canvas have the potential to reveal where further evidence may need to be gathered, or where further triangulation of stakeholder perspectives may be required. We argue that by adaptively addressing these patterns at key stages within the roadmapping process (and appropriately re-scoping, re-prioritizing, and re-focusing foresight effort and resources), the granularity, coverage, and consensus of the roadmapping evidence can be greatly enhanced. We conclude the paper by summarizing a set of novel principles for adaptive agile roadmapping, reflecting on the implications for foresight more generally, and outlining a future research agenda to test and refine this approach to agile foresight
Fixed-Time Cooperative Behavioral Control for Networked Autonomous Agents With Second-Order Nonlinear Dynamics
No full textIn this article, we investigate the fixed-time behavioral control problem for a team of second-order nonlinear agents, aiming to achieve a desired formation with collision/obstacle avoidance. In the proposed approach, the two behaviors(tasks) for each agent are prioritized and integrated via the framework of the null-space-based behavioral projection, leading to a desired merged velocity that guarantees the fixed-time convergence of task errors. To track this desired velocity, we design a fixed-time sliding-mode controller for each agent with state-independent adaptive gains, which provides a fixed-time convergence of the tracking error. The control scheme is implemented in a distributed manner, where each agent only acquires information from its neighbors in the network. Moreover, we adopt an online learning algorithm to improve the robustness of the closed system with respect to uncertainties/disturbances. Finally, simulation results are provided to show the effectiveness of the proposed approach