MRC Laboratory of Molecular Biology
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Effect of Stack Geometry on the Dynamic Resistance Threshold Fields for Vertical Stacks of Coated Conductor Tapes
No full textThe expanding capabilities of HTS flux pumps and rectifiers to provide kA+ currents necessitates the exploration of high-current switching phenomenon. One such phenomenon, the dynamic resistance, occurs in type-II devices carrying dc transport currents while exposed to ac magnetic fields. In the following, finite element analysis of the threshold field for dynamic resistance in superconducting cables comprised of N tapes connected in parallel and stacked vertically is presented. Cables are modelled using the commercial software COMSOL and the H-formulation. The models employ Ic(B, θ) and n(B, θ) data obtained on short samples at 77 K as inputs to more accurately reflect the variation in local properties within the superconductor. The finite element results are then compared with calculations made using analytical models assuming a critical state. The finite element data closely resembles that predicted for a strip for a single tape, rapidly tending towards the slab result as N increases
A new approach to assess the built environment risk under the conjunct effect of critical slow onset disasters: A case study in Milan, Italy
No full textCitizens in dense built environments are susceptible to the simultaneous occurrence of Slow Onset Disaster (SLOD) events, being particularly prone to increasing temperatures and air pollution. Previous research works have assessed these events’ arousal separately and have identified when their intensity is critical. However, few have integrated their analysis, possibly limited by the quality and granularity of available data, the accessibility and distribution of sensors, and measurements not emulating the surroundings of a pedestrian. Thus, this work performed an outdoor meso-scale multi-hazard-based risk analysis to study the aggregated effects of the SLODs mentioned above. The study was carried out to narrow down the time-frames within 2019 in which these two events could have affected citizens’ health the most. A weighted fuzzy logic was applied to superimpose climatic (temperature, humidity, wind speed, and solar irradiance) and air quality (particulate matter, ozone, and ammonium) distress (true risk) on an hourly basis, allocated using set healthy and comfortable ranges for a specific dense urban climate context within Milan (Italy), processing data from Milano via Juvara station. The findings show that sensitive groups were at risk of high temperature and pollution separately during 26% and 29% of summer and mid-season hours, respectively; while multi-hazard risk would arise during 10.93% of summer and mid-season hours, concentrated mainly between 14:00 and 20:00
Sequential Inference Methods for Non-Homogeneous Poisson Processes with State-Space Prior
No full textThe non-homogeneous Poisson process provides a generalised framework for the modelling of random point data by allowing the intensity of point generation to vary across its domain of interest (time or space). The use of non-homogeneous Poisson processes have arisen in many areas of signal processing and machine learning, but application is still largely limited by its intractable likelihood function and the lack of computationally efficient inference schemes, although some methods do exist for the batch data case. In this paper, we propose for the first time a sequential framework for intensity inference which combines the non-homogeneous model of Poisson data with continuous-Time state-space models for their time-varying intensity. This approach enables us to design efficient online inference schemes, for which we propose a novel sequential Markov chain Monte Carlo (SMCMC) algorithm, as is demanded by many applications where point data arrive sequentially and decisions need to be made with low latency. The proposed approach is compared with competing methods on synthetic datasets and tested with high-frequency financial order book data, showing in general improved performance and better computational efficiency than the main batch-based competitor algorithm, and better performance than a simple baseline kernel estimation scheme
Covalently interconnected transition metal dichalcogenide networks via defect engineering for high-performance electronic devices
No full textSolution-processed semiconducting transition metal dichalcogenides are at the centre of an ever-increasing research effort in printed (opto)electronics. However, device performance is limited by structural defects resulting from the exfoliation process and poor inter-flake electronic connectivity. Here, we report a new molecular strategy to boost the electrical performance of transition metal dichalcogenide-based devices via the use of dithiolated conjugated molecules, to simultaneously heal sulfur vacancies in solution-processed transition metal disulfides and covalently bridge adjacent flakes, thereby promoting percolation pathways for the charge transport. We achieve a reproducible increase by one order of magnitude in field-effect mobility (µFE), current ratio (ION/IOFF) and switching time (τS) for liquid-gated transistors, reaching 10−2 cm2 V−1 s−1, 104 and 18 ms, respectively. Our functionalization strategy is a universal route to simultaneously enhance the electronic connectivity in transition metal disulfide networks and tailor on demand their physicochemical properties according to the envisioned applications
Validating Operator Event Sequence Diagrams: The case of an automated vehicle to human driver handovers
No full textPredicting what drivers will do as vehicle control is handed over to them from automation is a relatively new challenge for the motor vehicle industry. Operator Event Sequence Diagrams (OESDs) offer a way of modeling the interactions between the driver and vehicle automation in the handover of control. In this paper, two studies are presented in which a range of handover strategies are tested. The anticipated driver strategies were modeled using OESDs to serve as predictions of driver behavior. Drivers were then observed in two separate studies: (1) using a Lower-Fidelity (vehicle seat and controls) simulator and (2) using a Higher-Fidelity (whole vehicle) simulator. Driver behavior during a takeover task was categorized according to the signal detection paradigm into hits, misses, false alarms, and correct rejections. The results showed that for all strategies in both sets of studies, the median criterion for validity was exceeded ((Formula presented.) > 0.8), suggesting that OESDs made good predictions of driver behavior during the handover of the vehicle from automation to manual control
Electrochemical detection of redox molecules secreted by Pseudomonas aeruginosa – Part 1: Electrochemical signatures of different strains
No full textDuring infections, fast identification of the microorganisms is critical to improve patient treatment and to better manage antibiotics use. Electrochemistry exhibits several advantages for rapid diagnostic: it enables easy, cheap and in situ analysis of redox molecules in most liquids. In this work, several culture supernatants of different Pseudomonas aeruginosa strains (including PAO1 and its isogenic mutants PAO1ΔpqsA, PA14, PAK and CHA) were analyzed by square wave voltammetry on glassy carbon electrode during the bacterial growth. The obtained voltamograms shown complex traces exhibiting numerous redox peaks with potential repartitions and current amplitudes depending on the studied bacterium and/or growth time. Among them, some peaks were clearly associated to the well-known redox toxin Pyocyanin (PYO) and the autoinducer Pseudomonas Quinolone Signal (PQS). Other peaks were observed that are not yet attributed to known secreted species. Each complex electrochemical response (number of peaks, peak potential and amplitude) can be interpreted as a fingerprint or “ID-card” of the studied strain that may be implemented for fast bacteria strain identification
Multiarmed-Bandit-Based Decentralized Computation Offloading in Fog-Enabled IoT
No full textThe Internet-of-Things (IoT) environments have hard real-time tasks that need execution within fixed deadlines. As IoT devices consist of a myriad of sensors, each task is composed of multiple interdependent subtasks. Toward this, the cloud and fog computing platforms have the potential of facilitating these IoT sensor nodes (SNs) in accommodating complex operations with minimum delay. To further reduce operational latencies, we breakdown the high-level tasks into smaller subtasks and form a directed acyclic task graph (DATG). Initially, the SNs offload their tasks to a nearby fog node (FN) based on a greedy choice. The greedy formulation helps in selecting the FN in linear time while avoiding combinatorial optimizations at the SN, which saves time as well as energy. IoT environments are highly dynamic, which mandates the need for adaptive solutions. At the chosen FN, depending on the dependencies on the DATGs, its corresponding deadlines, and the varying conditions of the other FNs, we propose an \epsilon -greedy nonstationary multiarmed bandit-based scheme (D2CIT) for online task allocation among them. The online learning D2CIT scheme allows the FN to autonomously select a set of FNs for distributing the subtasks among themselves and executes the subtasks in parallel with minimum latency, energy, and resource usage. Simulation results show that D2CIT offers a reduction in latency by 17% compared to traditional fog computing schemes. Additionally, upon comparison with existing online learning-based task offloading solutions in fog environments, D2CIT offers an improved speedup of 59% due to the induced parallelism
A Low-Noise High-Order Mode-Localized MEMS Accelerometer
No full textThis paper reports a precision mode-localized accelerometer operating in a higher-order flexural mode. The accelerometer consists of two symmetric resonators coupled by a central rigid coupler to generate an ultra-weak coupling factor. A reduced noise floor is observed when the resonators operate in the higher-order flexural mode compared to the basic lower-order mode. The mode-localized accelerometer working in the fifth-order mode demonstrates an input-referred bias instability of 130 ng and noise floor of 85 ng/surd Hz, which are the best results obtained for accelerometers employing the mode localization paradigm to date. These results indicate that the performance of the mode-localized sensors can be improved by operating at a higher working frequency if the coupling factor and quality factor do not drop significantly. [2020-0365]
Linking business ecosystem and natural ecosystem together-a sustainable pathway for future industrialization
No full textChina has emerged as the second largest economy in the world during the globalization in the last forty years. However, in the last decade, Chinese manufacturing has also demonstrated its dark side causing wide range of concerns globally and directly jeopardize people’s health because of serious pollutions. How could the world keep its industrialization yet without damages to the natural environment? The paper proposes a new framework entitled ‘IE3' by integrating three domains of knowledge-Industrial Entrepreneurship, Industrial Engineering and Industrial Ecology. The IE3 model provides a potential answer to the future development pathway for industrialization, changing from pursuit of quantity to quality via considering resources efficiency and ecology efficiency. The novelty of the research lies in incorporating three originally separated theories into a comprehensive system
Solution-Processed High-Performance ZnO Nano-FETs Fabricated with Direct-Write Electron-Beam-Lithography-Based Top-Down Route
No full textZinc oxide (ZnO) has been extensively investigated for use in large-area electronics; in particular, the solution-processing routes have shown increasing promise towards low-cost fabrication. However, top-down fabrication approaches with nanoscale resolution, towards aggressively scaled device platforms, are still underexplored. This study reports a novel approach of direct-write electron-beam lithography (DW-EBL) of solution precursors as negative tone resists, followed by optimal precursor processing to fabricate micron/nano-field-effect transistors (FETs). It is demonstrated that the mobility and current density of ZnO FETs can be increased by two orders of magnitude as the precursor pattern width is decreased from 50 µm to 100 nm. These nano-FET devices exhibit field-effect mobility exceeding ≈30 cm2 V−1 s−1 and on-state current densities reaching 10 A m−1, the highest reported so far for direct-write precursor-patterned nanoscale ZnO FETs. Using atomic force microscopy and parametric modeling, the origin of such device performance improvement is investigated. The findings emphasize the influence of pre-decomposition nanoscale precursor patterning on the grain morphology evolution in ZnO and, consequently, open up large-scale integration, and miniaturization opportunities for solution-processed, high-performance nanoscale oxide FETs