Autonomous frequency domain identification: Theory and experiment

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability

Electric Field Tunable Ultrafast Interlayer Charge Transfer in Graphene/WS₂ Heterostructure

Van der Waals heterostructures composed of two-dimensional materials offer an unprecedented control over their properties and have attracted tremendous research interest in various optoelectronic applications. Here, we study the photoinduced charge transfer in graphene/WS2 heterostructure by time-dependent density functional theory molecular dynamics. Our results show that holes transfer from graphene to WS2 two times faster than electrons, and the occurrence of interlayer charge transfer is found correlated with vibrational modes of graphene and WS2. It is further demonstrated that the carrier dynamics can be efficiently modulated by external electric fields. Detailed analysis confirms that the carrier transfer rate at heterointerface is governed by the coupling between donor and acceptor states, which is the result of the competition between interlayer and intralayer relaxation processes. Our study provides insights into the understanding of ultrafast interlayer charge transfer processes in heterostructures and broadens their future applications in photovoltaic devices

Mass enhancement in narrow band systems

A perturbative study of the Holstein Molecular Crystal Model which accounts for lattice structure and dimensionality effects is presented. Antiadiabatic conditions peculiar of narrow band materials and an intermediate to strong electron-phonon coupling are assumed. The polaron effective mass depends crucially in all dimensions on the intermolecular coupling strengths which also affect the size of the lattice deformation associated with the small polaron formation.Comment: Istituto Nazionale di Fisica della Materia - Dipartimento di Matematica e Fisica, Istituto Nazionale di Fisica della Materia Universita' di Camerino, 62032 Camerino, Ital

Elasticity-driven Nanoscale Texturing in Complex Electronic Materials

Finescale probes of many complex electronic materials have revealed a non-uniform nanoworld of sign-varying textures in strain, charge and magnetization, forming meandering ribbons, stripe segments or droplets. We introduce and simulate a Ginzburg-Landau model for a structural transition, with strains coupling to charge and magnetization. Charge doping acts as a local stress that deforms surrounding unit cells without generating defects. This seemingly innocuous constraint of elastic `compatibility', in fact induces crucial anisotropic long-range forces of unit-cell discrete symmetry, that interweave opposite-sign competing strains to produce polaronic elasto-magnetic textures in the composite variables. Simulations with random local doping below the solid-solid transformation temperature reveal rich multiscale texturing from induced elastic fields: nanoscale phase separation, mesoscale intrinsic inhomogeneities, textural cross-coupling to external stress and magnetic field, and temperature-dependent percolation. We describe how this composite textured polaron concept can be valuable for doped manganites, cuprates and other complex electronic materials.Comment: Preprin

Statistical evaluation of on-road vehicle emissions measurement using a dual remote sensing technique.

On-road remote sensing (RS) is a rapid, non-intrusive and economical tool to monitor and control the emissions of in-use vehicles, and currently is gaining popularity globally. However, a majority of studies used a single RS technique, which may bias the measurements since RS only captures a snapshot of vehicle emissions. This study aimed to use a unique dual RS technique to assess the characteristics of on-road vehicle emissions. The results show that instantaneous vehicle emissions are highly dynamic under real-world driving conditions. The two emission factors measured by the dual RS technique show little correlation, even under the same driving condition. This indicates that using the single RS technique may be insufficient to accurately represent the emission level of a vehicle based on one measurement. To increase the accuracy of identifying high-emitting vehicles, using the dual RS technique is essential. Despite little correlation, the dual RS technique measures the same average emission factors as the single RS technique does when a large number of measurements are available. Statistical analysis shows that both RS systems demonstrate the same Gamma distribution with ≥200 measurements, leading to converged mean emission factors for a given vehicle group. These findings point to the need for a minimum sample size of 200 RS measurements in order to generate reliable emission factors for on-road vehicles. In summary, this study suggests that using the single or dual RS technique will depend on the purpose of applications. Both techniques have the same accuracy in calculating average emission factors when sufficient measurements are available, while the dual RS technique is more accurate in identifying high-emitters based on one measurement only

The Structure, Kinematics and Physical Properties of the Molecular Gas in the Starburst Nucleus of NGC 253

We present 5.2" x 2.6" resolution interferometry of CO J=1-0 emission from the starburst galaxy NGC 253. The high spatial resolution of these new data, in combination with recent high resolution maps of 13CO, HCN and near-infrared emission, allow us for the first time to link unambiguously the gas properties in the central starburst of NGC 253 with its bar dynamics. We confirm that the star formation results from bar-driven gas flows as seen in "twin peaks" galaxies. Two distinct kinematic features are evident from the CO map and position-velocity diagram: a group of clouds rotating as a solid body about the kinematic center of the galaxy, and a more extended gas component associated with the near-infrared bar. We model the line intensities of CO, HCN and 13CO to infer the physical conditions of the gas in the nucleus of NGC 253. The results indicate increased volume densities around the radio nucleus in a twin-peaks morphology. Compared with the CO kinematics, the gas densities appear highest near the radius of a likely inner Linblad resonance, and slightly lead the bar minor axis. This result is similar to observations of the face-on, twin-peaks galaxy NGC 6951, and is consistent with models of starburst generation due to gas inflow along a bar.Comment: To appear in the ApJ, 28 pages, 12 figure file

Rapid detection of high-emitting vehicles by on-road remote sensing technology improves urban air quality.

Vehicle emissions are the most important source of air pollution in the urban environment worldwide, and their detection and control are critical for protecting public health. Here, we report the use of on-road remote sensing (RS) technology for fast, accurate, and cost-effective identification of high-emitting vehicles as an enforcement program for improving urban air quality. Using large emission datasets from chassis dynamometer testing, RS, and air quality monitoring, we found that significant percentages of in-use petrol and LPG vehicles failed the emission standards, particularly the high-mileage fleets. The RS enforcement program greatly cleaned these fleets, in terms of high-emitter percentages, fleet average emissions, roadside and ambient pollutant concentrations, and emission inventory. The challenges of the current enforcement program are conservative setting of cut points, single-lane measurement sites, and lack of application experience in diesel vehicles. Developing more accurate and vertical RS systems will improve and extend their applications

The Role of the Gut Microbiota in the Effects of Early-Life Stress and Dietary Fatty Acids on Later-Life Central and Metabolic Outcomes in Mice

Early-life stress (ELS) leads to increased vulnerability for mental and metabolic disorders. We have previously shown that a low dietary ω-6/ω-3 polyunsaturated fatty acid (PUFA) ratio protects against ELS-induced cognitive impairments. Due to the importance of the gut microbiota as a determinant of long-term health, we here study the impact of ELS and dietary PUFAs on the gut microbiota and how this relates to the previously described cognitive, metabolic, and fatty acid profiles. Male mice were exposed to ELS via the limited bedding and nesting paradigm (postnatal day (P)2 to P9 and to an early diet (P2 to P42) with an either high (15) or low (1) ω-6 linoleic acid to ω-3 alpha-linolenic acid ratio. 16S rRNA was sequenced and analyzed from fecal samples at P21, P42, and P180. Age impacted α- and β-diversity. ELS and diet together predicted variance in microbiota composition and affected the relative abundance of bacterial groups at several taxonomic levels in the short and long term. For example, age increased the abundance of the phyla Bacteroidetes, while it decreased Actinobacteria and Verrucomicrobia; ELS reduced the genera RC9 gut group and Rikenella, and the low ω-6/ω-3 diet reduced the abundance of the Firmicutes Erysipelotrichia. At P42, species abundance correlated with body fat mass and circulating leptin (e.g., Bacteroidetes and Proteobacteria taxa) and fatty acid profiles (e.g., Firmicutes taxa). This study gives novel insights into the impact of age, ELS, and dietary PUFAs on microbiota composition, providing potential targets for noninvasive (nutritional) modulation of ELS-induced deficits. IMPORTANCE Early-life stress (ELS) leads to increased vulnerability to develop mental and metabolic disorders; however, the biological mechanisms leading to such programming are not fully clear. Increased attention has been given to the importance of the gut microbiota as a determinant of long-term health and as a potential target for noninvasive nutritional strategies to protect against the negative impact of ELS. Here, we give novel insights into the complex interaction between ELS, early dietary ω-3 availability, and the gut microbiota across ages and provide new potential targets for (nutritional) modulation of the long-term effects of the early-life environment via the microbiota

Towards a Robuster Interpretive Parsing

The input data to grammar learning algorithms often consist of overt forms that do not contain full structural descriptions. This lack of information may contribute to the failure of learning. Past work on Optimality Theory introduced Robust Interpretive Parsing (RIP) as a partial solution to this problem. We generalize RIP and suggest replacing the winner candidate with a weighted mean violation of the potential winner candidates. A Boltzmann distribution is introduced on the winner set, and the distribution’s parameter $T$ is gradually decreased. Finally, we show that GRIP, the Generalized Robust Interpretive Parsing Algorithm significantly improves the learning success rate in a model with standard constraints for metrical stress assignment