Human factors quantification via boundary identification of flight performance margin

AbstractA systematic methodology including a computational pilot model and a pattern recognition method is presented to identify the boundary of the flight performance margin for quantifying the human factors. The pilot model is proposed to correlate a set of quantitative human factors which represent the attributes and characteristics of a group of pilots. Three information processing components which are influenced by human factors are modeled: information perception, decision making, and action execution. By treating the human factors as stochastic variables that follow appropriate probability density functions, the effects of human factors on flight performance can be investigated through Monte Carlo (MC) simulation. Kernel density estimation algorithm is selected to find and rank the influential human factors. Subsequently, human factors are quantified through identifying the boundary of the flight performance margin by the k-nearest neighbor (k-NN) classifier. Simulation-based analysis shows that flight performance can be dramatically improved with the quantitative human factors

Quantitation and Dietary Risk Assessment of Hexazinone Residue in Blueberry Fruit

In order to determine the residue of hexazinone in blueberry fruit, field experiments in Zhejiang, Jilin, Liaoning and Beijing, China were conducted using 75% hexazinone water dispersible granules. An analytical method was established for determining residual hexazinone in blueberry fruit utilizing ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) with an electrospray ionization source in the positive ion mode (ESI+). The samples were extracted with acetonitrile while vortexing, salted out, and then purified on a column packed with primary secondary amine (PSA) and C18 before measurement. The linearity, matrix effect, limit of quantification (LOQ), trueness (recovery rate) and precision (relative standard deviation (RSD)) of the proposed method were studied. Good linearity (r > 0.999 8) was found in the concentration range from 0.000 1 to 0.01 mg/L. The matrix effect was −7.7%. The LOQ was 0.01 mg/kg. The average recovery of hexazinone from blueberry fruit at spiked concentration levels of 0.01, 0.1 and 1.0 mg/kg ranged from 87% to 91%, with a RSD less than 3.7%. The field experiments showed that the residual level of hexazinone in blueberry fruit was below 0.01 mg/kg at 90 and 100 days after application, which was lower than the maximum residue limits (MRL) established in the US, Japan and South Korea (0.6, 0.2 and 0.05 mg/kg, respectively). The results of chronic dietary risk assessment showed that the estimated daily intake of hexazinone for general populations was 0.002 2 mg. The dietary risk quotient (RQ) was only 0.084%, indicating a low risk of dietary hexazinone intake. Therefore, it is recommended that 75% hexazinone water dispersible granules (WG) be applied in a single dose up to 1 800 g/hm2 to blueberry orchards; the pre-harvest interval (PHI) be 90 days

Highly polarized carbon nano-architecture as robust metal-free catalyst for oxygen reduction in polymer electrolyte membrane fuel cells

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.nanoen.2018.04.021 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Metal-free electrocatalysts have eluded widespread adoption in polymer electrolyte membrane fuel cells due to their far inferior catalytic activity than most non-precious metal-N-C counterparts (M-Nx-C) for oxygen reduction reaction (ORR), despite their distinct advantages over the M-Nx-C catalysts, including lower cost and higher durability. Herein, we develop a rational bottom-up engineering strategy to improve the ORR performance of a metal-free catalyst by constructing a three-dimensional ultrathin N, P dual-doped carbon nanosheet. The resultant catalyst represents unprecedented ORR performance with an onset potential of 0.91 V, half-wave potential of 0.79 V. Impressively, a maximum power output at 579 mW cm−2 is generated in the fuel cell test, the best among reported metal-free catalysts and outperforms most of the M-Nx-C catalysts. The outstanding catalytic performance results from the highly active polarized carbon sites which are induced by selective graphitic nitrogen and phosphorous dual doping. Our findings provide new directions for the exploration of alternatives to Pt and bring a renew interests in the metal-free catalysts.National Natural Science Foundation of China || (21633008, 21433003, U1601211, 21733004) National Science and Technology Major Project || (2016YFB0101202) Jilin Province Science and Technology Development Program || (20150101066JC, 20160622037JC, 20170203003SF, 20170520150JH) Hundred Talents Program of Chinese Academy of Sciences and the Recruitment Program of Foreign Experts || (WQ20122200077

Enhancing low-temperature electrochemical kinetics and high-temperature cycling stability by decreasing ionic packing factor

Present-day Li+ storage materials generally suffer from sluggish low-temperature electrochemical kinetics and poor high-temperature cycling stability. Herein, based on a Ca2+ substituted Mg2Nb34O87 anode material, we demonstrate that decreasing the ionic packing factor is a two-fold strategy to enhance the low-temperature electrochemical kinetics and high-temperature cyclic stability. The resulting Mg1.5Ca0.5Nb34O87 shows the smallest ionic packing factor among Wadsley–Roth niobate materials. Compared with Mg2Nb34O87, Mg1.5Ca0.5Nb34O87 delivers a 1.6 times faster Li​+ ​diffusivity at −20 ​°C, leading to 56% larger reversible capacity and 1.5 times higher rate capability. Furthermore, Mg1.5Ca0.5Nb34O87 exhibits an 11% smaller maximum unit-cell volume expansion upon lithiation at 60 ​°C, resulting in better cyclic stability; at 10C after 500 cycles, it has a 7.1% higher capacity retention, and its reversible capacity at 10C is 57% larger. Therefore, Mg1.5Ca0.5Nb34O87 is an all-climate anode material capable of working at harsh temperatures, even when its particle sizes are in the order of micrometers

Development of Inplantable Surface Acoustic Wave Sensor for High Voltage Cable Core Temperature Monitoring in Intermediate Joint

In this paper, a kind of implantble passive wireless surface acoustic wave (SAW) sensor and its reader are developed to measure the operation temperature of high voltage crossed-linked cable core. A small type SAW temperature sensor is embedded into cable intermediate joint. A radio frequecny transceiver of the reader based on discrete devices is optimally designed to achieve high signal-to-noise ratio and receiving sensitivity. The characteristic of resonant frequency and temperature of SAW sensor is determined by frequency scan method. Through AC withstand voltage and temperature rise test of 110kV cable, it proves that the prototype can accurately measure the operation temperature of 110kV cable core in intermediate joint under high operation temperature and strong electromagnetic environment

Detection of Weak Signals Under Low SNR Stochastic Resonance System

To solve the problem that weak signals are difficult to detect accurately in low signal-to-noise ratios, this paper presents a method to achieve effective detection of weak signals, applying the method of stochastic resonance to bistable systems. The principle of the method is that by transferring part of the noise energy to the signal energy, enabling the detection of weak signals at low signal-to-noise ratios. It makes it easier to extract the signal at the receiving end. This model designs a parametrized conditioning system based on the factors influencing the output power spectrum and output SNR of a stochastic resonant system. Based on the experimental results, the influence of parameters a and b on the model can be analysed, and the optimal noise intensity range of the system can be found. At the receiving end of the system, the constellation diagram and BER are used as a measure of system performance. Simulation experiments show that stochastic resonance can effectively enhance the energy of weak signals under low signal-to-noise conditions, and the demodulation performance of the system is significantly better than that of the system without the use of stochastic resonance

Inter-layer Learning towards Emergent Cooperative Behavior

As applications for artificially intelligent agents increase in complexity we can no longer rely on clever heuristics and hand-tuned behaviors to develop their programming. Even the interaction between various components cannot be reduced to simple rules, as the complexities of realistic dynamic environments become unwieldy to characterize manually. To cope with these challenges, we propose an architecture for inter-layer learning where each layer is constructed with a higher level of complexity and control