Smoothing Approach Research Articles

Re-examination of therapeutic management of muscular dystrophies using a vascular smooth muscle-centered approach.

In contrast to the long-standing focus on the pathophysiology of skeletal muscles in the hunt for a cure for Duchenne muscular dystrophy (DMD), we opine that the malfunctioning of dystrophin produced by vascular smooth muscle is a major contributor to the pathology of the illness. We believe that a biological response modifier glucan (BRMG), which has been shown in clinical studies of DMD to boost the expression of vascular smooth muscle dystrophin and provide anti-fibrotic and anti-inflammatory effects, may play a key role in reducing the pathogenesis of DMD. According to the evaluation of biomarkers, this BRMG, which is safe and side-effect-free, reduces the pathogenesis of DMD. We describe the possible mechanisms of action by which this BRMG helps in alleviating the symptoms of DMD by targeting smooth muscle dystrophin, in addition to its advantages over other therapeutic modalities, as well as how it can serve as a valuable adjunct to existing therapies. We suggest that using BRMG adjuncts that target smooth muscle dystrophin would be a potential therapeutic approach that prolongs the lifespan and extends the duration of ambulation from the onset of DMD. Further studies are needed to validate this hypothesis.

Trajectory planning for complex shaped spacecraft proximity based on critical safety curve and disturbed fluid

Recent research on on-orbit service rarely considers the complex shapes of servicing spacecraft (chaser) and the target simultaneously, which could cause collision problems in ultra-close-range proximity. This paper presents a novel trajectory planning method via critical safety curve (TPCSC). The core of the framework is the construction of collision avoidance constraints called critical safety curve (CSC) using geometrized mathematical methods. CSC has good mathematical properties and innovatively combines the complex shape and relative attitude constraints of two spacecraft. Subsequently, TPCSC proposes an improved disturbed fluid method based on CSC that can handle non-convex constraints to generate smooth approach trajectories. Finally, the framework can achieve ultra-close-range proximity between spacecraft with complex shapes under complex non-convex constraints. The simulation results indicate that the planning of TPCSC is rapid, and the formed trajectory is safe, smooth, and easy to track.

Glottal Airflow Estimation using Neck Surface Acceleration and Low-Order Kalman Smoothing.

The use of non-invasive skin accelerometers placed over the extrathoracic trachea has been proposed in the literature for measuring vocal function. Glottal airflow is estimated using inverse filtering or Bayesian techniques based on a subglottal impedance-based model when utilizing these sensors. However, deviations in glottal airflow estimates can arise due to sensor positioning and model mismatch, and addressing them requires a significant computational load. In this paper, we utilize system identification techniques to obtain a low order state-space representation of the subglottal impedance-based model. We then employ the resulting low order model in a Kalman smoother to estimate the glottal airflow. Our proposed approach reduces the model order by 94% and requires only 1.5% of the computing time compared to previous Bayesian methods in the literature, while achieving slightly better accuracy when correcting for glottal airflow deviations. Additionally, our Kalman smoother approach provides a measure of uncertainty in the airflow estimate, which is valuable when measurements are taken under different conditions. With its comparable accuracy in signal estimation and reduced computational load, the proposed approach has the potential for real-time estimation of glottal airflow and its associated uncertainty in wearable voice ambulatory monitors using neck-surface acceleration.

Corruption, imported innovation, and growth: Evidence using the panel smooth transition regression approach for developing countries

AbstractThis article investigates the nonlinear relationship between corruption, imported innovation (imports of high technology), and economic growth in developing countries from 2009 to 2018. We used a sample of 38 countries divided into two subsamples. The first covers 21 African countries, and the second covers 17 non‐African countries. The empirical results from the panel smooth transition regression model indicate that there is a threshold effect in the two relationships: corruption–imported innovation and corruption–growth. Specifically, we found that corruption exerts a significant effect only for non‐African countries. However, no significant effect was found for the whole sample and African countries. Furthermore, we found that below certain thresholds, corruption significantly decreases the level of growth for both the whole sample and the two subsamples. Above these thresholds, the effect of corruption becomes positive and significant.

Model structure and ensemble size: Implications for predictions of groundwater age

This paper examines the influence of simplified vertical discretization using 50- to four- layer models and ensemble size on history matching and predictions of groundwater age for a national scale model of New Zealand (approximately 265,000 km2). A reproducible workflow using a combination of opensource tools and custom python scripts is used to generate three models that use the same model domain and underlying data with only the vertical discretization changing between the models. The iterative ensemble smoother approach is used for history matching each model to the same synthetic dataset. The results show that: 1) the ensemble based mean objective function is not a good indicator of model predictive ability, 2) predictive failure from model structural errors in the simplified models are compounded by history matching, especially when small (<100 member) ensembles are used, 3) predictive failure rates increase with iteration, 4) predictive failure rates for the simplified model reach 30–65% using 50-member ensembles, but stabilize at relatively low values (<10%) using the 300 member ensemble, 5) small (50 member) ensembles contribute to predictive failure of 22–30% after six iterations even in structurally “perfect” models, 6) correlation-based localization methods can help reduce prediction failure associated with small ensembles by up to 45%, 7) the deleterious effects of model simplification and ensemble size are problem specific. Systematic investigation of these issues is an important part of the model design, and this investigation process benefits greatly from a scripted, reproducible workflow using flexible, opensource tools.

A density-based WCSPH numerical scheme for investigation of multiphase flows with intricate interfaces at high density ratios

Numerical investigation of multiphase problems having complex interface at high density ratios is one of the numerical challenges associated with particle scattering and divergence. Fewer problems have been performed using density-based smooth particle hydrodynamics (WCSPH) in order to solve complex joint surface currents, and most of the simulations in this field have been performed using incompressible smooth particle hydrodynamics (ISPH). Solving high density flows by smooth particle hydrodynamics is associated with particle dispersion and divergence. Various methods have been used to eliminate the scattering of particles, such as a repulsive force at the interface or the corrected density re-value, but there is a problem of particle disintegration at interface at higher times. Numerical simulations of SPH multiphase problems with high density ratio present divergence and dispersion of soluble particles over time simulation. However, this problem is solved in the current model so that continuity is preserved and provides a more accurate solution over time simulation. In the present work, in order to simulate multiphase flows with complex surfaces and high density ratios fluids investigation, a new density-based smooth particle hydrodynamic approach has been utilized. To prevent the particles scattering at the interface, an incompatible particle removal method is used. In the present study, a particle displacement optimization scheme for regularization at phase interface is created by implementing a two-stage change algorithm to maintain the regular particle distribution continuously and conservatively. In order to investigate the accuracy of the current numerical scheme, it is firstly compared with two-phase Poiseuille flow with three fluids having diverse values of viscosity, Reynolds-Taylor instability, Single Bubble rising in a fully filled container, merging of two bubbles, and Bubble rising in a partially filled container; then it is compared with analytical and numerical solutions.

CSmoothing: a web-tool for controlled smoothing by segments of mortality data

CSmoothing allows an analyst to use the so-called Controlled Smoothing technique to estimate trends in a time series framework. In this Web-tool (Shiny), the analyst may apply the methodology to at most 3 mortality time series simultaneously, as well as to other kind of time series individually. Likewise, this smoothing approach allows the analyst to establish one, two or three segments in order to take into account possible changes in variance regimes. For estimating trends it uses different amounts of smoothness, both globally for the total data set and through some partial indices for each selected segment. It is also possible to endogenously fix the points where the segments start and end (the cutoff points) with continuous joints. Additionally, intervals of different standard deviations for their respective trends are given. Particular emphasis is placed on a big data set of log mortality rates, log(qx), taken from period life tables of the Human Mortality Database (HMD) (University of California Berkeley (USA) and and Max Planck Institute for Demographic Research (Germany)), 2021). In all cases, dynamic graphs and several statistics related to the Controlled Smoothing technique are illustrated.

Fault Ride-Through Techniques for Permanent Magnet Synchronous Generator Wind Turbines (PMSG-WTGs): A Systematic Literature Review

Global warming and rising energy demands have increased renewable energy (RE) usage globally. Wind energy has become the most technologically advanced renewable energy source. Wind turbines (WTs) must ride through faults to ensure power system stability. On the flip side, permanent magnet synchronous generators (PMSG)-based wind turbine power plants (WTPPs) are susceptible to grid voltage fluctuations and require extra regulations to maintain regular operations. Due to recent changes in grid code standards, it has become vital to explore alternate fault ride-through (FRT) methods to ensure their capabilities. This research will ensure that FRT solutions available via the Web of Science (WoS) database are vetted and compared in hardware retrofitting, internal software control changes, and hybrid techniques. In addition, a bibliometric analysis is provided, which reveals an ever-increasing volume of works dedicated to the topic. After that, a literature study of FRT techniques for PMSG WTs is carried out, demonstrating the evolution of these techniques over time. This paper concludes that additional research is required to enhance FRT capabilities in PMSG wind turbines and that further attention to topics, such as machine learning tools and the combination of FRT and wind power smoothing approaches, should arise in the following years.

Air–water flow in a plain stilling basin below smooth and stepped chutes

In recent decades, stepped chutes followed by a stilling basin became a standard for spillways of dams. The comprehensive knowledge of stepped chute approach flows on internal air–water flow properties of the hydraulic jump is still too limited for an appropriate design of stilling basins. Therefore, an experimental campaign was performed on a large-scale physical model of a plain stilling basin preceded by a 30° sloping smooth or stepped chute. Stepped chute approach flows induce lower deaeration rates along the jump roller as compared to smooth chute approach flows, indicating longer dimensionless jump lengths, normalized by the tailwater depth. Pronounced bottom air concentrations were observed within the first 40% of the jump length in the stilling basin downstream of the stepped chute. Thus, despite the significantly higher pressure fluctuations, this zone seems better protected against cavitation damage than in stilling basins following a smooth chute.

Robust fixed-lag smoothing under model perturbations

A robust fixed-lag smoothing approach is proposed in the case there is a mismatch between the nominal model and the actual model. The resulting robust smoother is characterized by a dynamic game between two players: one player selects the least favorable model in a prescribed ambiguity set, while the other player selects the fixed-lag smoother minimizing the smoothing error with respect to least favorable model. We propose an efficient implementation of the proposed smoother. Moreover, we characterize the corresponding least favorable model over a finite time horizon. Finally, we test the robust fixed-lag smoother in two examples. The first one regards a target tracking problem, while the second one regards a parameter estimation problem.

Quantile regression for competing risks data from stratified case-cohort studies: an induced-smoothing approach

ABSTRACT A case-cohort design offers an economical way of investigating an association between exposure variables and risks of disease outcomes compared to a large-scale full cohort study. A stratified sampling in such designs based on the information available for the entire cohort is often considered for improving efficiencies of estimators. In this paper, we consider fitting censored quantile regression models for competing risks data arising from stratified case-cohort designs. We model quantiles for cumulative incidence functions that provide desirable interpretation for competing risks data and flexible ways of assessing covariates effects. For estimation of regression parameters, we consider weighted estimating equations with two-types of weights: the inverses of censoring probabilities and sampling probabilities to account for censoring in competing risks data and biased features in stratified case-cohort samplings, respectively. An induced smoothing approach is applied to obtain computationally more reliable estimates. The resulting estimating functions are smooth in regression parameters, so standard numerical algorithms for point estimation can be readily applied and variances can be estimated via a closed-form expression or computationally efficient resampling method. An iterative algorithm is proposed to simultaneously estimate regression parameters and their variances. Asymptotic properties of the proposed estimators are established. The finite sample properties of the proposed estimators are investigated through extensive simulation studies. They perform reasonably well under practical settings considered. The proposed methods are illustrated with Hodgkin's disease data.

Numerical investigations of edge-based smoothed radial point interpolation method for transient wave propagations

It is known that the solutions of the standard finite element method for transient wave propagations are suffering from the numerical dispersion effect and the numerical anisotropy, resulting in the dispersive numerical waves along different propagation angles. In this work, the edge-based smoothed radial point interpolation method is combined with the Bathe time integration method to study transient wave propagations. By means of the generalized gradient smoothing approach, the smoothed radial point interpolation method can lead to the proper smoothed stiffness of the discrete model, thus greatly reducing the numerical dispersion effect and the numerical anisotropy. Besides, due to the appropriate high-frequency dissipative property, the Bathe time integration method can effectively depress the spurious high-frequency responses in transient wave solutions. Moreover, the wave velocity errors of several numerical methods for transient wave propagations are theoretically and numerically analyzed, revealing the superiority of the smoothed radial point interpolation method in comparison with the linear and quadratic finite element methods. Several numerical tests show that the smoothed radial point interpolation method combined with the Bathe time integration method can present the more accurate simulations of transient wave propagations than the standard finite element method.

An Adaptive Long-Term Bus Arrival Time Prediction Model with Cyclic Variations

Real-time bus arrival information systems at transit stops can be useful to passengers for efficient trip planning and reducing waiting times. The accuracy of such systems depends upon the ability of the model to account for variations in the data series and to adjust according to changing traffic conditions. Many of the existing studies on passenger information systems have modeled the system based on stationary relations, not taking into account the cyclic variations in data, which is often suitable for demonstration purposes but not for long-term implementation. The present study models the changing relationships using Double-Seasonal Holt-Winter’s Exponential Smoothing approach, which allows for self-updating parameters at four levels. It accounts for both long-term and short-term seasonal fluctuations in data while maintaining the dynamic treatment of real-time bus information. The model also takes into account delays using the real-time running information of the bus and incorporates it into subsequent forecasts for better accuracy. Real-time data from GPS transmitters in buses were used for validation of the proposed model. The results show that the proposed model performs better than the currently-used elementary field methods and is able to forecast bus travel times with a reasonable accuracy.

Non-linear Threshold Effect of Governance Quality on Economic Growth in African Countries: Evidence from Panel Smooth Transition Regression Approach

Non-linear Threshold Effect of Governance Quality on Economic Growth in African Countries: Evidence from Panel Smooth Transition Regression Approach

Nonlinear Impact of Inflation on Economic Growth in Nepal: A Smooth Transition Regression Approach

Macroeconomic stability is one of the most important macroeconomic goals for all countries. In recent years, the Nepalese economy has been experiencing inflation twice the economic growth rate, posing a threat to macroeconomic stability. Thus, careful investigation of the relationship between inflation and economic growth is required. This study assesses the existence of a nonlinear relationship between inflation and economic growth in the Nepalese context. The assessment is performed by considering the possibility of smoothly changing slope parameters to maximise economic growth or minimise growth losses and to determine the optimal inflation level along with the speed of inflation response to economic growth. The study applies a Logistic Smooth Transition Regression model that Terasvirta (1998, 2004) developed to estimate an optimal inflation rate using annual time-series data from 1976 to 2019. The study finds a nonlinear relationship between inflation and economic growth and estimates an optimal inflation threshold of 6.38±1.36 per cent for the Nepalese economy. It also shows that inflation positively correlates with economic growth up to the threshold level and that the association becomes negative beyond that level. This study suggests that inflation beyond the threshold level harms economic growth, while inflation below has a favourable effect. The findings assist policymakers in making an informed decision, providing leverage in inflation control and creating an atmosphere encouraging economic growth.

Enhancement of formant regions in magnitude spectra to develop children’s KWS system in zero resource scenario

The mismatches due to pitch, speaking rate, formant dispersion and ambient noise deteriorate the performance of an automatic keyword spotting (KWS) system. The work presented in this paper aims at reducing the aforementioned mismatches through front-end signal processing. In the proposed approach, the short-term magnitude spectra (ST-MS) are firstly computed with a smaller frameshift and then averaged over the adjacent frames to enhance the formant regions. The formant regions in the ST-MS have a higher magnitude than the nearing frequency regions. Consequently, temporal averaging of ST-MS over adjacent frames suppresses the high-frequency variation due to pitch, and formant dispersion. Furthermore, the formant peaks can be more accurately detected from the temporal averaged ST-MS when compared to detection from the original ST-MS. The Mel frequency cepstral coefficients (MFCC) computed from the temporally averaged magnitude spectra (TAS-MFCC) are pitch robust compared to the MFCC, and MFCC extracted from the reported spectral smoothing approaches employing variational mode decomposition (VMD-MFCC), pitch adaptive cepstral truncation (PACT-MFCC) and single-pole filter (SPS-MFCC). Performance of TA-MFCC feature in mismatched test condition is further improved by appending five logarithmically compressed resonant peaks at least separated by 400 Hz, here this feature is termed as TAS-MFCC-ARP. The spectral peaks mostly represent the formants in ST-MS. The performances of the deep neural network-hidden Markov model-based children’s KWS system reported in this work show that the TAS-MFCC-ARP provides a relative performance improvement of 103.83% compared to MFCC. The performance of the KWS system is further improved by data-augmented training through duration modification.

Acoustic float tracking with the Kalman smoother

Acoustically tracked subsurface floats provide insights into ocean complexity and were first deployed over 60 years ago. A standard tracking method uses a Least-Squares algorithm to estimate float trajectories based on acoustic ranging from moored sound sources. However, infrequent or imperfect data challenge such estimates. Acoustic tracking is currently the only feasible strategy for recovering float positions in the sea ice region, a focus of this study. Acoustic records recovered from under-ice floats frequently lack continuous sound source coverage. This is because environmental factors such as surface sound channels and sea ice attenuate acoustic signals, while operational considerations make polar sound sources difficult to deploy. Here we present a Kalman Smoother approach that, by including some estimates of float behavior, extends tracking to situations with more challenging data sets. The Kalman Smoother constructs dynamically constrained, error-minimized float tracks using all possible position data. The Kalman Smoother is applied to previously-tracked floats from the southeast Pacific (DIMES experiment), and the results are compared with existing trajectories constructed using the Least-Squares algorithm. The Kalman Smoother is also used to reconstruct the trajectories of a set of previously untracked, acoustically-enabled Argo floats in the Weddell Sea.

Assessing the Threshold Level of Ownership Structure and its Relationship with Tax Avoidance in Security Market: A Nonlinear Smooth Transition Regression Approach

Assessing the Threshold Level of Ownership Structure and its Relationship with Tax Avoidance in Security Market: A Nonlinear Smooth Transition Regression Approach

A primal-dual interior-point relaxation method with global and rapidly local convergence for nonlinear programs

Based on solving an equivalent parametric equality constrained mini-max problem of the classic logarithmic-barrier subproblem, we present a novel primal-dual interior-point relaxation method for nonlinear programs with general equality and nonnegative constraints. In each iteration, our method approximately solves the KKT system of a parametric equality constrained mini-max subproblem, which avoids the requirement that any primal or dual iterate is an interior-point. The method has some similarities to the warmstarting interior-point methods in relaxing the interior-point requirement and is easily extended for solving problems with general inequality constraints. In particular, it has the potential to circumvent the jamming difficulty that appears with many interior-point methods for nonlinear programs and improve the ill conditioning of existing primal-dual interior-point methods as the barrier parameter is small. A new smoothing approach is introduced to develop our relaxation method and promote convergence of the method. Under suitable conditions, it is proved that our method can be globally convergent and locally quadratically convergent to the KKT point of the original problem. The preliminary numerical results on a well-posed problem for which many interior-point methods fail to find the minimizer and a set of test problems from the CUTEr collection show that our method is efficient.

Global and explicit approximation of piecewise-smooth two-dimensional functions from cell-average data

Abstract Given cell-average data values of a piecewise-smooth bivariate function $f$ within a domain $\varOmega $, we look for a piecewise adaptive approximation to $f$. We are interested in an explicit and global (smooth) approach. Bivariate approximation techniques, as trigonometric or splines approximations, achieve reduced approximation orders near the boundary of the domain and near curves of jump singularities of the function or its derivatives. Whereas the boundary of $\varOmega $ is assumed to be known, the subdivision of $\varOmega $ to subdomains on which $f$ is smooth is unknown. The first challenge of the proposed approximation algorithm would be to find a good approximation to the curves separating the smooth subdomains of $f$. In the second stage, we simultaneously look for approximations to the different smooth segments of $f$, where on each segment we approximate the function by a linear combination of basis functions $\{p_i\}_{i=1}^M$, considering the corresponding cell averages. A discrete Laplacian operator applied to the given cell-average data intensifies the structure of the singularity of the data across the curves separating the smooth subdomains of $f$. We refer to these derived values as the signature of the data, and we use it for both approximating the singularity curves separating the different smooth regions of $f$. The main contributions here are improved convergence rates to the approximation of the singularity curves and the approximation of $f$, an explicit and global formula, and, in particular, the derivation of a piecewise-smooth high-order approximation to the function.