MRC Laboratory of Molecular Biology
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HBN-Encapsulated, Graphene-based, Room-temperature Terahertz Receivers, with High Speed and Low Noise.
No full textUncooled terahertz photodetectors (PDs) showing fast (ps) response and high sensitivity (noise equivalent power (NEP) 50 000 cm2 V-1 s-1) hexagonal boron nitride-encapsulated graphene, with asymmetric field enhancement produced by a bow-tie antenna, resonating at 3 THz. This produces a strong photo-thermoelectric conversion, which simultaneously leads to a combination of high sensitivity (NEP ≤ 160 pW Hz-1/2), fast response time (≤3.3 ns), and a 4 orders of magnitude dynamic range, making our devices the fastest, broad-band, low-noise, room-temperature terahertz PD, to date
Performance of continuous variable quantum key distribution system at different detector bandwidth
No full textWe evaluate the performance of Gaussian Modulated Coherent State (GMCS) Continuous-Variable Quantum Key Distribution (CV-QKD), in terms of secure key rate, at different detector bandwidths. We illustrate the advantages and limitations of high-speed CV-QKD systems using a general noise model in which we consider various noise sources and their dependence upon detection bandwidth. Experimentally, the feasibility of high speed CV-QKD is demonstrated by using a GHz bandwidth balanced homodyne detector (BHD) established with commercially available components. We provide secure key rates of transmitted and local local oscillator schemes under various clock rates
Analysing the evolution of aerospace ecosystem development
No full textAerospace manufacturing industry is predicted to continue growing. Rising demand is triggering the current global aerospace ecosystem to evolve and adapt to challenges never faced before. New players into the aerospace manufacturing industry and the development of new ecosystems are evidencing its evolution. Understanding how the aerospace ecosystem has evolved is thus essential to prepare optimal conditions to nurture its growth. Recent studies have successfully combined economics and network science methods to map, analyse and predict the evolution of industrial ecosystems. In comparison to previous studies which apply network science-based methodologies to macro-economic research, this paper uses these methods to analyse the evolution of a particular industrial ecosystem, namely the aerospace sector. In particular, we develop bipartite country-product networks based on trade data over 25 years, to identify patterns and similarities in the evolution of developed aerospace manufacturing countries ecosystems. The analysis is elaborated at a macroscopic (network) and microscopic (nodes) levels. Motivated by studies in ecological networks, we use nestedness analysis to find patterns depicting the distribution and evolution of exported products across ecosystems. Our analysis reveals that developed ecosystems tend to become more analogous, as countries lean towards having a revealed comparative advantage (RCA) in the same group of products. Countries also tend to become more nested in their aerospace product space as they start developing a higher RCA. It is revealed that although countries develop an advantage on unique products, they also tend to increase competition with each other. Further analysis shows that manufactured products have a stronger correlation to an aerospace ecosystem than primary products; and in particular, the automotive sector shows the highest correlation with positive aerospace sector evolution. Competition between countries with well-developed aerospace ecosystems tends to centre on automotive parts, general industrial machinery, power generating machinery and equipment, and chemical materials and products
Gatekeeping, fast and slow: an empirical study of referral errors in the emergency department
No full textUsing data from over 300,000 visits to an emergency department (ED), we study the accuracy of gatekeeping decisions - the choices that physicians make regarding patient discharge or admission to the hospital. In our study context, we focus specifically on the effectiveness of a second gatekeeping stage in the ED - a clinical decision unit (CDU). While only 9.9% of patients in our sample are routed through the CDU, we find that had the unit not been in place during the observation period, the rates of unnecessary hospitalization and wrongful patient discharge from the ED would have increased by 14.3% and 29.6%, respectively. We also find that the CDU is especially beneficial for patients with a high ex ante risk of experiencing unnecessary hospitalization, with the rate for the most high-risk patients reduced from 14.0% without the CDU to just 4.8% had all such patients been routed through the CDU. The appropriateness of referrals is therefore a key contributor to the CDU's effectiveness: We estimate that random allocation of patients in our study hospital to the CDU would have reduced the unit's effectiveness by more than half. Finally, we investigate a critical trade-off in designing a two-stage gatekeeping system: Resources must be split between the two stages, increasing congestion in the first stage when the second stage is enlarged. We demonstrate that in the study hospital, the combination of an ED and CDU performs better than a pooled system that combines the capacity of both stages to enlarge the ED but does not have a designated CDU. In fact, we estimate that in this specific case, reducing the size of the first-stage ED in order to expand CDU capacity from the current 9.9% of ED patients to 25% would further reduce unnecessary hospitalizations by up to 33%. We discuss the insights that these results provide as to the circumstances under which it may be advantageous to add a second stage to a gatekeeping system
High-contrast imaging of 180° ferroelectric domains by optical microscopy using ferroelectric liquid crystals
No full textFerroelectric liquid crystals (FLCs) couple the direction of their spontaneous electric polarization to the direction of tilt of their optic axis. Consequently, reversal of the electric polarization by an electric field gives rise to an immediate and lasting optical response when an appropriately aligned FLC is observed between crossed polarizers, with one field direction yielding a dark image and the opposite direction yielding a bright image. Here, this peculiar electro-optic response is used to image, with high optical contrast, 180° ferroelectric domains in a crystalline substrate of magnesium-doped lithium niobate. The lithium niobate substrate contains a few domains with upward electric polarization surrounded by regions with downward electric polarization. In contrast to a reference non-chiral liquid crystal that is unable to show ferroelectric behavior due to its high symmetry, the FLC, which is used as a thin film confined between the lithium niobate substrate and an inert aligning substrate, reveals ferroelectric domains as well as their boundaries, with strong black and white contrast. The results show that FLCs can be used for non-destructive readout of domains in underlying ferroelectrics, with potential applications in, e.g., photonic devices and non-volatile ferroelectric memories
Large Deviations Behavior of the Logarithmic Error Probability of Random Codes
No full textThis work studies the deviations of the error exponent of the constant composition code ensemble around its expectation, known as the error exponent of the typical random code (TRC). In particular, it is shown that the probability of randomly drawing a codebook whose error exponent is smaller than the TRC exponent is exponentially small; upper and lower bounds for this exponent are given, which coincide in some cases. In addition, the probability of randomly drawing a codebook whose error exponent is larger than the TRC exponent is shown to be double-exponentially small; upper and lower bounds to the double-exponential exponent are given. The results suggest that codebooks whose error exponent is larger than the error exponent of the TRC are extremely rare. The key ingredient in the proofs is a new large deviations result of type class enumerators with dependent variables
Condition-based maintenance for long-life assets with exposure to operational and environmental risks
No full textThis paper presents a new condition-based maintenance (CBM) model for long-life assets to address the potential risk caused by the decline of the operating environment. Two types of maintenance are formulated in the CBM model. Minor maintenance can mitigate the operational and environmental risk, and major maintenance can eliminate the accumulated damage within the asset. A continuous-time semi-Markov chain (CTSMC) is used for modeling the aging of the asset as well as the stochastic decline of the operating environment. To optimize the CBM policy in a mathematically tractable manner, we introduce a hypo-exponential approximation approach to match the first four moments of the sojourn time distribution of CTSMC. This approach guarantees a minimum representation of the CTSMC with non-fictitious surrogated Markov chain. The model provides both good mathematical tractability and sufficient generalizability. The practical impact of this research is demonstrated by applying it to a real industrial case of concrete bridge maintenance. It is observed that this approach results in a CBM plan with a lower asset lifecycle cost compared to current techniques
Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing
No full textAlthough predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features
Prediction and analysis of EMI spectrum based on the operating principle of EMC spectrum analyzers
No full textEMC spectrum analyzers are popularly used for electromagnetic interference (EMI) measurement in power electronics systems. Depending on the specifications of EMI standards, the EMI measurement could be very time consuming. Conventionally, the fast Fourier transform is used to derive the EMI spectrum from the measured time-domain waveforms. However, these results may not agree with the measurement results from spectrum analyzers, and sometimes the difference could be significant. In this paper, a technique to quickly and accurately predict and analyze the EMI spectrum from time-domain waveforms is proposed. The technique is developed based on the spectrum analyzer's operating principle and the requirements of EMI standards. The EMI spectra of three modulation schemes are also analyzed. Theoretical analysis, simulations, and experiments were all conducted. The predicted peak, quasi-peak, and average EMI matches the measured EMI in whole conductive frequency range. The developed technique can accurately predict EMI using much shorter time than conventional EMC spectrum analyzers and it saves cost of expensive spectrum analyzers
2D Metal Zn Nanostructure Electrodes for High-Performance Zn Ion Supercapacitors
No full textRecent supercapacitors show a high power density with long-term cycle life time in energy-powering applications. A supercapacitor based on a single metal electrode accompanying multivalent cations, multiple charging/discharging kinetics, and high electrical conductivity is a promising energy-storing system that replaces conventionally used oxide and sulfide materials. Here, a hierarchically nanostructured 2D-Zn metal electrode-ion supercapacitor (ZIC) is reported which significantly enhances the ion diffusion ability and overall energy storage performance. Those nanostructures can also be successfully plated on various flat-type and fiber-type current collectors by a controlled electroplating method. The ZIC exhibits excellent pseudocapacitive performance with a high energy density of 208 W h kg−1 and a power density from 500 W kg−1, which are significantly higher than those of previously reported supercapacitors with oxide and sulfide materials. Furthermore, the fiber-type ZIC also shows high energy-storing performance, outstanding mechanical flexibility, and waterproof characteristics, without any significant capacitance degradation during bending tests. These results highlight the promising possibility of nanostructured 2D Zn metal electrodes with the controlled electroplating method for future energy storage applications