Experimental study on radial gravity currents flowing in a vegetated channel

We present an experimental study of gravity currents in a cylindrical geometry, in the presence of vegetation. Forty tests were performed with a brine advancing in a fresh water ambient fluid, in lock release, and with a constant and time-varying flow rate. The tank is a circular sector of angle $30^\circ$ with radius equal to 180 cm. Two different densities of the vegetation were simulated by vertical plastic rods with diameter $D=1.6\ \textrm{cm}$ . We marked the height of the current as a function of radius and time and the position of the front as a function of time. The results indicate a self-similar structure, with lateral profiles that after an initial adjustment collapse to a single curve in scaled variables. The propagation of the front is well described by a power law function of time. The existence of self-similarity on an experimental basis corroborates a simple theoretical model with the following assumptions: (i) the dominant balance is between buoyancy and drag, parameterized by a power law of the current velocity $\sim |u|^{\lambda-1}u$ ; (ii) the current advances in shallow-water conditions; and (iii) ambient-fluid dynamics is negligible. In order to evaluate the value of ${\lambda}$ (the only tuning parameter of the theoretical model), we performed two additional series of measurements. We found that $\lambda$ increased from 1 to 2 while the Reynolds number increased from 100 to approximately $6\times10^3$ , and the drag coefficient and the transition from $\lambda=1$ to $\lambda=2$ are quantitatively affected by D, but the structure of the model is not.

Stride segmentation of inertial sensor data using statistical methods for different walking activities

Human gait data can be collected using inertial measurement units (IMUs). An IMU is an electronic device that uses an accelerometer and gyroscope to capture three-axial linear acceleration and three-axial angular velocity. The data so collected are time series in nature. The major challenge associated with these data is the segmentation of signal samples into stride-specific information, that is, individual gait cycles. One empirical approach for stride segmentation is based on timestamps. However, timestamping is a manual technique, and it requires a timing device and a fixed laboratory set-up which usually restricts its applicability outside of the laboratory. In this study, we have proposed an automatic technique for stride segmentation of accelerometry data for three different walking activities. The autocorrelation function (ACF) is utilized for the identification of stride boundaries. Identification and extraction of stride-specific data are done by devising a concept of tuning parameter ( $t_{p}$ ) which is based on minimum standard deviation ( $\sigma$ ). Rigorous experimentation is done on human activities and postural transition and Osaka University – Institute of Scientific and Industrial Research gait inertial sensor datasets. Obtained mean stride duration for level walking, walking upstairs, and walking downstairs is 1.1, 1.19, and 1.02 s with 95% confidence interval [1.08, 1.12], [1.15, 1.22], and [0.97, 1.07], respectively, which is on par with standard findings reported in the literature. Limitations of accelerometry and ACF are also discussed. stride segmentation; human activity recognition; accelerometry; gait parameter estimation; gait cycle; inertial measurement unit; autocorrelation function; wearable sensors; IoT; edge computing; tinyML.

Controlling asymmetric transmission phase in planar chiral metasurfaces

Metasurfaces with ultrathin artificial structures have attracted much attention because of their unprecedented capability in light manipulations. The recent development of metasurfaces with controllable responses opens up new opportunities in various applications. Moreover, metasurfaces composed of twisted chiral structures can generate asymmetric responses for opposite incidence, leading to more degrees of freedom in wave detections and controls. However, most past studies had focused on the amplitude responses, not to mention using bi-directional phase responses, in the characterization and light manipulation of chiral metasurfaces. Here, we report a birefringent interference approach to achieve a controllable asymmetric bi-directional transmission phase from planar chiral metasurface by tuning the orientation of the metasurface with respect to the optical axis of an add-on birefringent substrate. To demonstrate our approach, we fabricate planar Au sawtooth nanoarray metasurface and measure the asymmetric transmission phase of the metasurface placed on a birefringent sapphire crystal slab. The Au sawtooth metasurface-sapphire system exhibits large oscillatory behavior for the asymmetric transmission phase with the tuning parameter. We confirm our experimental results by Jones matrix calculations using data obtained from full-wave simulations for the metasurface. Our approach in the characterization and light manipulation of metasurfaces with controllable responses is simple and nondestructive, enabling new functionalities and potential applications in optical communication, imaging, and remote sensing.

Refining and evaluating a Horvitz– Thompson-like stand density estimator in individual tree detection based on airborne laser scanning

Horvitz--Thompson-like stand density estimation is a method for estimating the stand density from tree crown objects extracted from airborne laser scanning data through individual tree detection. The estimator is based on stochastic geometry and mathematical morphology of the (planar) set formed by the detected tree crowns. This set is used to approximate the detection probabilities of trees. These probabilities are then used to calculate the estimate. The method includes a tuning parameter, which needs to be known to apply the method. We present a refinement of the method to allow more general detection conditions than the previous papers and present and discuss the methods for estimating the tuning parameter of the estimator using a functional $k$-nearest neighbors method. We test the model fitting and prediction in two spatially separate data sets and examine the plot-level accuracy of estimation. The estimator produced a $13$\% lower RMSE than the benchmark method in an external validation data set. We also analyze the effects of similarity and dissimilarity of training and validation data to the results.

Learning a compass spin model with neural network quantum states

Neural network quantum states provide a novel representation of the many-body states of interacting quantum systems and open up a promising route to solve frustrated quantum spin models that evade other numerical approaches. Yet its capacity to describe complex magnetic orders with large unit cells has not been demonstrated, and its performance in a rugged energy landscape has been questioned. Here we apply restricted Boltzmann machines and stochastic gradient descent to seek the ground states of a compass spin model on the honeycomb lattice, which unifies the Kitaev model, Ising model and the quantum 120-degree model with a single tuning parameter. We report calculation results on the variational energy, order parameters and correlation functions. The phase diagram obtained is in good agreement with the predictions of tensor network ansatz, demonstrating the capacity of restricted Boltzmann machines in learning the ground states of frustrated quantum spin Hamiltonians. The limitations of the calculation are discussed. A few strategies are outlined to address some of the challenges in machine learning frustrated quantum magnets.

Core Effective and Relative Permeability Measurements for Conventional and Unconventional Reservoirs by Saturation Monitoring in High Frequency 3d Gradient NMR

A big challenge in tight conventional and unconventional rock systems is the lack of representative reservoir deliverability models for movement of water, oil and gas through micro-pore and nano-pore networks. Relative permeability is a key input in modelling these rocks; but due to limitations in core analysis techniques, permeability has become a knob or tuning parameter in reservoir simulation. Current relative permeability measurements on conventional core samples rely on density contrast between oil/water or gas/water on CT (Computed Tomography) scans and recording of effluent volumes to determine relative fluid saturations during the core flooding process. However, tight rocks are characterized by low porosities (< 10 %) and ultra-low permeabilities (< 1 micro-Darcy), that make effective and relative permeability measurements very difficult, time-consuming, and prone to high errors associated with low pore volumes and flow rates. Nuclear Magnetic Resonance (NMR) measurements have been used extensively in the industry to measure fluid porosities, pore size characterization, wettability evaluation, etc. Core NMR scans can provide accurate quantification of pore fluids (oil, gas, water) even in very small quantities, using T2, T1T2 and D-T2 activation sequences. We have developed a novel process to perform experiments that measure effective and relative permeability values on both conventional and tight reservoirs at reservoir conditions while accurately monitoring fluid saturations and fluid fronts in a 12 MHz 3D gradient NMR spectrometer. The experimental process starts by acquiring Micro-CT scans of the cylindrical rock plugs to screen the samples for artifacts or microcracks that may affect permeability measurements. Once the samples are chosen, NMR T2 and T1T2 scans are performed to establish residual fluid saturations in the as-received state. If a liquid effective permeability test is required, the samples are then saturated with the given liquid through a combination of humidification, vacuum-assisted spontaneous imbibition, and saturation under pressure and temperature. After saturation, NMR scans are obtained to verify the volumes of the liquids and determine if the samples have achieved complete saturation. The sample is then loaded into a special core-flooding vessel that is invisible to the NMR spectrometer to minimize interference with the NMR signals from the fluids in the sample. The sample is brought up to reservoir stress and temperature, and the main flowing fluid is injected from one side of the sample while controlling the pressures on the other side of the sample with a back pressure regulator. The saturation front of the injected fluid is continuously monitored using 2D and 3D gradient NMR scans and the volumes of different fluids in the sample are measured using NMR T2 and T1T2 scans. The use of a 12 MHz NMR spectrometer provides very high SNR (signal-to-noise ratio); and clear distinction of water and hydrocarbon signals in the core plug during the entire process. The scanning times are also reduced by orders of magnitude, thereby allowing for more scans to properly capture the saturation front and changes in saturation. Simultaneously, the fluid flowrates and pressures are recorded in order to compute permeability values. The setup is rated to 10,000 psi confining pressures, 9000 psi of pore pressure and a working temperature of up to 100 C. Flowrates as low as 0.00001 cc/min can be recorded. These tests have been done with brine, dead and live crudes, and hydrocarbon gases. The measured relative permeability values have been used successfully in both simulation and production modelling studies in various reservoirs worldwide.

Simultaneous Dirac-like Cones at Two Energy States in Tunable Phononic Crystals: An Analytical and Numerical Study

Simultaneous occurrence of Dirac-like cones at the center of the Brillouin zone (Г) at two different energy states is termed Dual-Dirac-like cones (DDC) in this article. The occurrence of DDC is a rare phenomenon. Thus, the generation of multiple Dirac-like cones at the center of the Brillouin zone is usually non-manipulative and poses a challenge to achieve through traditional accidental degeneracy. However, if predictively created, DDC will have multiple engineering applications with acoustics and vibration. Thus, the possibilities of creating DDC have been identified herein using a simple square periodic array of tunable square phononic crystals (PnCs) in air media. It was found that antisymmetric deaf bands may play critical roles in tracking the DDC. Hence, pivoting on the deaf bands at two different energy states, an optimized tuning parameter was found to achieve Dirac-like cones at two distinct frequency states, simultaneously. Orthogonal wave transport identified as key Dirac phenomena was achieved at two frequencies, herein. It was identified that beyond the Dirac-like cone, the Dirac phenomena remain dominant when a doubly degenerated state created by a top band with positive curvature and a near-flat deaf band are lifted from a bottom band with negative curvature. Utilizing a mechanism of rotating the PnCs near a fixed deaf band, frequencies are tracked to form the DDC, and orthogonal wave transport is demonstrated. Exploiting the dispersion behavior, unique acoustic phenomena, such as ballistic wave transmission, pseudo diffusion and acoustic cloaking are also demonstrated at the Dirac frequencies using numerical simulation. The proposed tunable acoustic PnCs will have important applications in acoustic and ultrasonic imaging, waveguiding and even acoustic computing.

OPTIMIZATION OF THE HIGUCHI METHOD

Background and Objective: Higuchi’s method of determining fractal dimension (HFD) occupies a valuable place in the study of a wide variety of physical signals. In comparison to other methods, it provides more rapid, accurate estimations for the entire range of possible fractal dimensions. However, a major difficulty in using the method is the correct choice of tuning parameter (kmax) to compute the most accurate results. In the past researchers have used various ad hoc methods to determine the appropriate kmax choice for their particular data. We provide a more objective method of determining, a priori, the best value for the tuning parameter, given a particular length data set. Methods: We create numerous simulations of fractional Brownian motion to perform Monte Carlo simulations of the distribution of the calculated HFD. Results: Experimental results show that HFD depends not only on kmax but also on the length of the time series, which enable derivation of an expression to find the appropriate kmax for an input time series of unknown fractal dimension. Conclusion: The Higuchi method should not be used indiscriminately without reference to the type of data whose fractal dimension is examined. Monte Carlo simulations with different fractional Brownian motions increases the confidence of evaluation results.

Stochastic approximation cut algorithm for inference in modularized Bayesian models

Bayesian modelling enables us to accommodate complex forms of data and make a comprehensive inference, but the effect of partial misspecification of the model is a concern. One approach in this setting is to modularize the model and prevent feedback from suspect modules, using a cut model. After observing data, this leads to the cut distribution which normally does not have a closed form. Previous studies have proposed algorithms to sample from this distribution, but these algorithms have unclear theoretical convergence properties. To address this, we propose a new algorithm called the stochastic approximation cut (SACut) algorithm as an alternative. The algorithm is divided into two parallel chains. The main chain targets an approximation to the cut distribution; the auxiliary chain is used to form an adaptive proposal distribution for the main chain. We prove convergence of the samples drawn by the proposed algorithm and present the exact limit. Although SACut is biased, since the main chain does not target the exact cut distribution, we prove this bias can be reduced geometrically by increasing a user-chosen tuning parameter. In addition, parallel computing can be easily adopted for SACut, which greatly reduces computation time.

Raman Response of Quantum Critical Ferroelectric Pb-Doped SrTiO3

A quantum paraelectric SrTiO3 is a material situated in close proximity to a quantum critical point (QCP) of ferroelectric transition in which the critical temperature to the ferroelectric state is suppressed down to 0 K. However, the understanding of the behavior of the phase transition in the vicinity of this point remains challenging. Using the concentration x of Pb in solid solution Sr1−xPbxTiO3 (PSTx) as a tuning parameter and applying the combination of Raman and dielectric spectroscopy methods, we approach the QCP in PSTx and study the interplay of classical and quantum phenomena in the region of criticality. We obtain the critical temperature of PSTx and the evolution of the temperature-dependent dynamical properties of the system as a function of x to reveal the mechanism of the transition. We show that the ferroelectric transition occurs gradually through the emergence of the polar nanoregions inside the non-polar tetragonal phase with their further expansion on cooling. We also study the ferroelastic cubic-to-tetragonal structural transition, occurring at higher temperatures, and show that its properties are almost concentration-independent and not affected by the quantum criticality.