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The COVID-19 Data Portal: Accelerating SARS-CoV-2 and COVID-19 research through rapid open access data sharing
No full textThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic will be remembered as one of the defining events of the 21st century. The rapid global outbreak has had significant impacts on human society and is already responsible for millions of deaths. Understanding and tackling the impact of the virus has required a worldwide mobilisation and coordination of scientific research. The COVID-19 Data Portal (https://www.covid19dataportal.org/) was first released as part of the European COVID-19 Data Platform, on April 20th 2020 to facilitate rapid and open data sharing and analysis, to accelerate global SARS-CoV-2 and COVID-19 research. The COVID-19 Data Portal has fortnightly feature releases to continue to add new data types, search options, visualisations and improvements based on user feedback and research. The open datasets and intuitive suite of search, identification and download services, represent a truly FAIR (Findable, Accessible, Interoperable and Reusable) resource that enables researchers to easily identify and quickly obtain the key datasets needed for their COVID-19 research
Sustainability transitions in manufacturing: the role of intellectual property
No full textIntellectual property rights (IPR) form an important component for unlocking sustainable innovation. Research highlights both the positive role of IPR in incentivizing sustainable innovation and the negatives, such as delaying diffusion. We review the state-of-art debates on incentivizing sustainable innovations and the role of IPR for sustainable manufacturing industries, summarizing three main debates: the role of incumbents versus new entrants, cross-industry collaboration, and IPR obstacles to the circular economy. The arguments bring forth the need for IPR systems to structurally support organizations in their move towards sustainability, removing institutional difficulties for cross industry diffusion, and in-depth research of IPR challenges for the circular economy. We conclude with directions for research that can enable better informed decisions on IPR for sustainability transitions
Good early stage design decisions can halve embodied CO<inf>2</inf> and lower structural frames’ cost
No full textMaterial efficiency is not currently a common driver of building design. Indeed, in previous studies, we estimated that 12% of the mass of steel used in structural frames would be saved by more accurate specification of steel members. However, this inefficiency is not the main reason structural frames are light or heavy. We show here for the case of steel structures that it is the layout of the grid and the choice of the decking which have the largest impact on the embodied carbon of frames. Using a database of real designs, associated to a generative design model, we quantify the impact of grid and decking selections. Using our model, we find that real designs are relatively efficient economically, but less so environmentally: the typical building frame could have 40–60% less embodied carbon, and be approximately 10–20% cheaper with the right selection. We show how more complex frames have higher embodied carbon than simpler grids. From our findings, we establish a list of design considerations that architects and structural engineers should account for when creating an initial design to lower the embodied carbon: the complexity of the layout, the optimisation of the design and the choice of the decking technology
Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks.
No full textAgainst the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SEIR model, informed by a socio-political classification of different interventions. First, we inquire the conceptual effect of mitigation parameters on the infection curve. Then, we illustrate the potential of our model to reproduce and explain empirical data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lockdown is an effective pandemic mitigation measure, a combination of social distancing and early contact tracing can achieve similar mitigation synergistically, while keeping lower isolation rates. This quantitative understanding can support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model
Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models
No full textThe parameters of a level-set flame model are inferred using an ensemble of heteroscedastic Bayesian neural networks (BayNNEs). The neural networks are trained on a library of 1.7 million observations of 8500 simulations of the flame edge, obtained using the model with known parameters. The ensemble produces samples from the posterior probability distribution of the parameters, conditioned on the observations, as well as estimates of the uncertainties in the parameters. The predicted parameters and uncertainties are compared to those inferred using an ensemble Kalman filter. The expected parameter values inferred with the BayNNE method, once trained, match those inferred with the Kalman filter but require less than one millionth of the time and computational cost of the Kalman filter. This method enables a physics-based model to be tuned from experimental images in real time
Hybrid Frequency Pacing for High-Order Transformed Wireless Power Transfer
No full textThis article proposes and implements a hybrid frequency pacing (HFP) technique for resonating a high-order transformed wireless power transfer (WPT) system with robust zero-voltage switching (ZVS). As a hybrid frequency modulation, the proposed HFP can efficiently tune the innate constant-frequency resonances of WPT. It can facilitate the pulsewidth modulated inverters to totally get rid of the high-frequency hard-switching while reducing the switching frequency and improving the system efficiency. For typical low-order boost WPT-based scenarios, the rectification effect may cause waveform distortions and involve a very low virtual capacitance, thus leading to great degradations on the ZVS and zero-phase-angle operation. In addition to achieve a load-independent constant voltage/current output, a high-order LCC network is deeply investigated to act as two-stage impedance transformations. By flexibly utilizing the rectification-caused virtual derivatives with the high-order transformations, it reliably contributes to a robust ZVS-HFP. The experimental system efficiency can be more than 91.5% with the full-range ZVS operation. Theoretical analysis and experimental results are both provided to verify the feasibility of the proposed ZVS-HFP for tuning the high-order LCC-transformed WPT system
A Converter-Level on-State Voltage Measurement Method for Power Semiconductor Devices
No full textThis letter proposes a converter-level method for measuring the on-state voltages of all power semiconductors in a single-phase inverter by using a single circuit only. The proposed circuit distinguishes itself by connecting to the middle point of each phase leg, instead of the two power terminals of individual devices as conventional methods do. It has the advantages of reduced circuit complexity, size, cost, and ease of connection. The principle and theoretical analysis of the proposed converter-level method are discussed. A case study on a single-phase full-bridge inverter is demonstrated to prove the concept
Joule heating-enabled electrothermal enrichment of nanoparticles in insulator-based dielectrophoretic microdevices
No full textInsulator-based dielectrophoresis (iDEP) exploits the electric field gradients formed around insulating structures to manipulate particles for diverse microfluidic applications. Compared to the traditional electrode-based dielectrophoresis, iDEP microdevices have the advantages of easy fabrication, free of water electrolysis, and robust structure, etc. However, the presence of in-channel insulators may cause thermal effects because of the locally amplified Joule heating of the fluid. The resulting electrothermal flow circulations are exploited in this work to trap and concentrate nanoscale particles (of 100 nm diameter and less) in a ratchet-based iDEP microdevice. Such Joule heating-enabled electrothermal enrichment of nanoparticles are found to grow with the increase of alternating current or direct current electric field. It also becomes more effective for larger particles and in a microchannel with symmetric ratchets. Moreover, a depth-averaged numerical model is developed to understand and simulate the various parametric effects, which is found to predict the experimental observations with a good agreement
Atomic permutationally invariant polynomials for fitting molecular force fields
No full textWe introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent machine learned potentials. We bring these two key ideas together to bridge the gap between established empirical force fields that have a high degree of transferability on the one hand, and the machine learned potentials that are systematically improvable and can converge to very high accuracy, on the other. Our framework extends the atomic permutationally invariant polynomials (aPIP) developed for elemental materials in (2019 Mach. Learn.: Sci. Technol. 1 015004) to molecular systems. The body order decomposition allows us to keep the dimensionality of each term low, while the use of an iterative fitting scheme as well as regularisation procedures improve the extrapolation outside the training set. We investigate aPIP force fields with up to generalised 4-body terms, and examine the performance on a set of small organic molecules. We achieve a high level of accuracy when fitting individual molecules, comparable to those of the many-body machine learned force fields. Fitted to a combined training set of short linear alkanes, the accuracy of the aPIP force field still significantly exceeds what can be expected from classical empirical force fields, while retaining reasonable transferability to both configurations far from the training set and to new molecules
A Distributed Maximum-Likelihood-Based State Estimation Approach for Power Systems
No full textThe distribution of measurement noise is commonly considered as an assumed Gaussian model in power systems, but this assumption is not always true in reality. This article introduces a distributed maximum-likelihood-based state estimation approach for multiarea power systems using the student's -distribution measurement noise model. The -distribution has the property of 'thick tail' to better model the occurrence of outliers and is fairly flexible to model different noise statistics. The finite-time average consensus algorithm is utilized in conjunction with an influence function to realize the proposed distributed approach within a totally distributed framework. Based on the local measurement residuals and the limited information exchanged with neighboring areas, each local area can obtain the global optimum system-wide robust state estimates, while the existing distributed state estimation methods can only get local estimates. Moreover, the communication scheme is more flexible and can be totally different from the transmission lines between local areas. Simulations tested on the IEEE 14-bus and 118-bus systems verify the effectiveness of the proposed distributed approach