Estimation of Cavities beneath Plate Structures Using a Microphone: Laboratory Model Tests

The objective of this study is to detect a cavity and estimate its size using sound waves in a laboratory model chamber filled with dry sand. One side of the chamber is covered with an acrylic plate, and a cavity is placed between the plate and sand. Sound waves are generated by impacting the plate with an instrumented hammer, and are measured using a microphone. The measured sound waves are analyzed with four comprehensive analyses including the measured area under the rectified signal envelope (MARSE) energy, flexibility, peak magnitude of wavelet transform, and frequency corresponding to the peak magnitude. The test results show that the accuracy of cavity detection using the MARSE energy is higher for thicker plates, whereas that using flexibility is higher for thinner plates. The accuracies of cavity detection using the peak magnitude of wavelet transform, and frequency corresponding to the peak magnitude are consistently high regardless of the plate thickness. Moreover, the cavity size may be under- or overestimated depending on the plate thickness and the selected analysis method. The average of the cavity sizes estimated by these methods, however, is slightly larger than the actual cavity size regardless of the plate thickness. This study demonstrates that microphones may be effectively used for the identification of a cavity and the estimation of its size.

Voltage-Based Hybrid Algorithm Using Parameter Variations and Stockwell Transform for Islanding Detection in Utility Grids

This paper has introduced an algorithm for the identification of islanding events in the remotely located distribution grid with renewable energy (RE) sources using the voltage signals. Voltage signal is processed using Stockwell transform (ST) to compute the median-based islanding recognition factor (MIRF). The rate of change in the root mean square (RMS) voltage is computed by differentiating the RMS voltage with respect to time to compute the voltage rate of change in islanding recognition factor (VRCIRF). The proposed voltage-based islanding recognition factor (IRFV) is computed by multiplying the MIRF and VRCIRF element to element. The islanding event is discriminated from the faulty and operational events using the simple decision rules using the peak magnitude of IRFV by comparing peak magnitude of IRFV with pre-set threshold values. The proposed islanding detection method (IDM) effectively identified the islanding events in the presence of solar energy, wind energy and simultaneous presence of both wind and solar energy at a fast rate in a time period of less than 0.05 cycles compared to the voltage change rate (ROCOV) and frequency change rate (ROCOF) IDM that detects the islanding event in a time period of 0.25 to 0.5 cycles. This IDM provides a minimum non-detection zone (NDZ). This IDM efficiently discriminated the islanding events from the faulty and switching events. The proposed study is performed on an IEEE-13 bus test system interfaced with renewable energy (RE) generators in a MATLAB/Simulink environment. The performance of the proposed IDM is better compared to methods based on the use of ROCOV, ROCOF and discrete wavelet transform (DWT).

Holocene spatio-temporal patterns of biomass burning in the Bohemian-Bavarian Forest Mountains (Central Europe)

<p>Long-term perspectives on disturbance dynamics are important for the conservation of protected areas, yet restoration and conservation strategies in the Bohemian-Bavarian Forest Mountains do not consider the long-term role and patterns of forest fire, which is still deemed a negligible ecosystem disturbance in Central Europe. The scarcity of macroscopic charcoal studies in this area has likely hampered a complete understanding of local fire regime dynamics and its legacies in the present forest structure and composition. Here we used macroscopic charcoal (number, area and morphology of charred particles) and pollen analysis to investigate high resolution spatial and temporal patterns in Holocene fire regimes in the Bavarian-Bohemian Forest. We explored the relationship between changing forest composition dynamics and the influence topography had on spatial patterns of biomass burning. For this, we selected three lacustrine sites (two new, one published), located along a 30 km longitudinal transect within the studied area, at similar elevations in the mixed forest belt, with opposite (north vs. south) aspects. Results showed similar changes in biomass burning, fire frequency and peak magnitude at all sites, with a maximum during the early Holocene when fire resistant taxa (<em>Pinus</em> and <em>Betula</em>) dominated. Fire frequency decreased by half with the expansion of more fire-sensitive taxa (e.g., <em>Picea</em> and <em>Fagus</em>) during the mid-Holocene and reached a second maximum in the late Holocene, parallel with sustained increases in anthropogenic pollen indicators. We found a close north-south correspondence in the succession of fire patterns, i.e., fine-scale changes in biomass burning in the Bavarian Forest site (south-facing catchment) occurred around the same time with those observed at the Bohemian Forest sites (predominantly north-facing catchments), and these changes mirrored the Holocene dynamics of the main forest taxa. For example, the lowest biomass burning and peak magnitude intervals marked the beginning of <em>Picea abies</em> expansion at ~ 9 ka BP, <em>Fagus sylvatica</em> expansion at ~6 ka and <em>Abies alba</em> expansion at ~5 ka BP. Furthermore, we found a direct relationship between the abundance of charred morphotypes of conifer needles and deciduous leaves and the dominance of pine and birch in our pollen records, and a close correspondence between the abundance of non-woody charcoal morphotypes and pollen-derived landscape openness. Non-woody charcoal morphotypes dominated the charcoal records in the Early Holocene at the peak of biomass burning, whereas the abundance of woody morphotypes peaked around 6-8 ka BP and over the last millennium and their proportion in total charcoal influx increased starting 4 ka BP. Our study enables a better understanding of past and present fire regimes in the Bavarian-Bohemian Forest Mountains and highlights the need to consider the effects of fire as part of climate-change forest conservation strategies.</p>

Characterizing gait pattern dynamics during symmetric and asymmetric walking using autoregressive modeling

Gait asymmetry is often observed in populations with varying degrees of neuromuscular control. While changes in vertical ground reaction force (vGRF) peak magnitude are associated with altered limb loading that can be observed during asymmetric gait, the challenge is identifying techniques with the sensitivity to detect these altered movement patterns. Autoregressive (AR) modeling has successfully delineated between healthy and pathological gait during running; but has been little explored in walking. Thus, AR modeling was implemented to assess differences in vGRF pattern dynamics during symmetric and asymmetric walking. We hypothesized that the AR model coefficients would better detect differences amongst the symmetric and asymmetric walking conditions than the vGRF peak magnitude mean. Seventeen healthy individuals performed a protocol that involved walking on a split-belt instrumented treadmill at different symmetric (0.75m/s, 1.0 m/s, 1.5 m/s) and asymmetric (Side 1: 0.75m/s-Side 2:1.0 m/s; Side 1:1.0m/s-Side 2:1.5 m/s) gait conditions. Vertical ground reaction force peaks extracted during the weight-acceptance and propulsive phase of each step were used to construct a vGRF peak time series. Then, a second order AR model was fit to the vGRF peak waveform data to determine the AR model coefficients. The resulting AR coefficients were plotted on a stationarity triangle and their distance from the triangle centroid was computed. Significant differences in vGRF patterns were detected amongst the symmetric and asymmetric conditions using the AR modeling coefficients (p = 0.01); however, no differences were found when comparing vGRF peak magnitude means. These findings suggest that AR modeling has the sensitivity to identify differences in gait asymmetry that could aid in monitoring rehabilitation progression.

A luminosity distribution for kilonovae based on short gamma-ray burst afterglows

The combined detection of a gravitational-wave signal, kilonova, and short gamma-ray burst (sGRB) from GW170817 marked a scientific breakthrough in the field of multimessenger astronomy. But even before GW170817, there have been a number of sGRBs with possible associated kilonova detections. In this work, we re-examine these ‘historical’ sGRB afterglows with a combination of state-of-the-art afterglow and kilonova models. This allows us to include optical/near-infrared synchrotron emission produced by the sGRB as well as ultraviolet/optical/near-infrared emission powered by the radioactive decay of r-process elements (i.e. the kilonova). Fitting the light curves, we derive the velocity and the mass distribution as well as the composition of the ejected material. The posteriors on kilonova parameters obtained from the fit were turned into distributions for the peak magnitude of the kilonova emission in different bands and the time at which this peak occurs. From the sGRB with an associated kilonova, we found that the peak magnitude in H bands falls in the range [−16.2, −13.1] ($95{{\ \rm per\ cent}}$ of confidence) and occurs within $0.8\!-\!3.6\, \rm d$ after the sGRB prompt emission. In g band instead we obtain a peak magnitude in range [−16.8, −12.3] occurring within the first 18 h after the sGRB prompt. From the luminosity distributions of GW170817/AT2017gfo, kilonova candidates GRB130603B, GRB050709, and GRB060614 (with the possible inclusion of GRB150101B, GRB050724A, GRB061201, GRB080905A, GRB150424A, and GRB160821B) and the upper limits from all the other sGRBs not associated with any kilonova detection we obtain for the first time a kilonova luminosity distribution in different bands.

Rapid-Update Radar Observations of Downbursts Occurring within an Intense Multicell Thunderstorm on 14 June 2011

On 14 June 2011, an intense multicell thunderstorm produced one nonsevere and three severe downbursts within 35 km of the rapid-update, S-band phased array radar (PAR) at the National Weather Radar Testbed in Norman, Oklahoma, and the nearby polarimetric research Weather Surveillance Radar 1988-Doppler (KOUN). Data collected from these radars provided the opportunity to conduct a quantitative analysis of downburst precursor signature evolution depicted by 1-min PAR data and the associated evolution of differential reflectivity ZDR depicted by 5-min KOUN data. Precursors analyzed included descent of the reflectivity core, evolution of the magnitude and size of midlevel convergence (i.e., number of bins), and descending “troughs” of ZDR. The four downbursts exhibited midlevel convergence that rapidly increased to peak magnitude as the reflectivity core (65-dBZ isosurface) bottom and top descended. The ZDR troughs seen in the 5-min KOUN data appeared to descend along with the core bottom. Midlevel convergence size increased to a peak value and decreased as the reflectivity core descended in the three severe downbursts. In contrast, midlevel convergence size exhibited little change in the nonsevere downburst. The time scale of trends seen in the PAR data was 11 min or less and happened several minutes prior to each downburst’s maximum intensity. These results point to the importance of 1-min volumetric data in effectively resolving the evolution of downburst precursors, which could be beneficial to forecast operations.

Improved Clipping Technique for PAPR Reduction in OFDM

Multicarrier modulation technique also known as Orthogonal Frequency Division Multiplexing (OFDM) is considered to be the most rapidly growing technique for 4th Generation wireless communication system. Due to its high speed data rate and its ability of multipath fading channel robustness. OFDM becomes an attractive technique and it is widely adopted in many wireless communications system. OFDM signal carries its major drawback of high Peak to Average Power Ratio (PAPR) problem. Out of band radiation and in band distortion produced due to its high PAPR. Mobile phone communication suffer severe drawback during its amplification. When OFDM employed a non linear Power Amplifier (PA), it produces the fluctuation in amplitude of OFDM signal. This paper discusses the method to overcome PAPR in terms of its performance by using improved clipping technique for PAPR reduction. This method is easy to implement and reduces the amount of PAPR by clipping the peak of the maximum power signal. We present an improved method for PARR reduction for reducing peak magnitude of OFDM signal. This scheme simultaneously minimizes the peak magnitude of PAPR in OFDM signal.

EVOLUTION OF NON-GAUSSIANITY IN MULTI-SCALAR FIELD MODELS

We study the evolution of non-Gaussianity in multiple-field inflationary models, focusing on three fundamental questions: (a) How is the sign and peak magnitude of the non-linearity parameter f NL related to generic features in the inflationary potential? (b) How sensitive is f NL to the process by which an adiabatic limit is reached, where the curvature perturbation becomes conserved? (c) For a given model, what is the appropriate tool – analytic or numerical – to calculate f NL at the adiabatic limit? We summarise recent results obtained by the authors and further elucidate them by considering an inflection point model.