Moment Estimation Research Articles

Société de Biomécanique young investigator award 2023: Estimation of intersegmental load at L5-S1 during lifting/lowering tasks using force plate free markerless motion capture

Accurate estimation of joint load during a lifting/lowering task could provide a better understanding of the pathogenesis and development of musculoskeletal disorders. In particular, the values of the net force and moment at the L5-S1 joint could be an important criterion to identify the unsafe lifting/lowering tasks. In this study, the joint load at L5-S1 was estimated from the motion kinematics acquired using a multi-view markerless motion capture system without force plate. The 3D human pose estimation was first obtained on each frame using deep learning. The kinematic analysis was then performed to calculate the velocity and acceleration information of each segment. Then, the net force and moment at the L5-S1 joint were calculated using inverse dynamics with a top-down approach. This estimate was compared to a reference with a bottom-up approach. It was computed using a marker-based motion capture system combined with force plates and using personalized body segment inertial parameters derived from a 3D model of the human body shape constructed for each subject using biplanar radiographs. The average differences of the estimates for force and moment among all subjects were 14.0 ± 6.9 N and 9.0 ± 2.3 Nm, respectively. Meanwhile, the mean peak value differences of the estimates were 10.8 ± 8.9 N and 11.9 ± 9.5 Nm, respectively. This study then proposed the most rigorous comparison of mechanical loading on the lumbar spine using computer vision. Further work is needed to perform such an estimation under realistic industrial conditions.

Librations and obliquity of the largest moons of Uranus

Following the discovery of several ocean worlds in the solar system, and the selection of Uranus as the highest priority objective by the Planetary Science and Astrobiology Decadal Survey 2023-2032, the five largest moons of Uranus (Miranda, Ariel, Umbriel, Titania and Oberon) have been receiving renewed attention as they may also harbor a subsurface ocean. We assess how rotation measurements could help confirm the internal differentiation of the bodies and detect internal oceans if any. Because of the time-varying gravitational torque of Uranus on the flattened shape of its synchronous satellites, the latter librate with respect to their mean rotation and precess with a non zero obliquity. For a range of interior models with a rocky core surrounded by a hydrosphere, either solid or divided into an outer ice shell with a liquid ocean underneath, we compute their diurnal libration amplitude and obliquity. We find that if the Uranian satellites were two-layer solid bodies, libration measurement accuracies from around 0.25 m for Oberon to around 6 m for Miranda would rule out the possibility of homogeneous interiors. In combination with independent estimates of the mean moment of inertia (MOI), libration measurements could also be used to detect the presence of an ocean, the measurement precision required for this depending on the actual value of the libration amplitude. To compute the obliquity, we first build series for the orbital precession of all five satellites with a secular perturbations model. With the exception of Miranda, we show that due to the mutual gravitational interactions between the satellites, the obliquity of the large Uranian moons exhibits relatively large periodic variations around the mean value. We find that an obliquity measurement accuracy from around 1 m for Ariel to around 400 m for Oberon can rule out the homogeneous case. The presence of an internal global ocean could allow a resonant amplification of the obliquity, facilitating its detection. If no such resonance occurs, the obliquity would be almost indistinguishable from that expected for a solid body. The effect of tidal deformations on the rotation of the small to medium-sized Uranian moons is showed to be limited. Librations would be reduced by up to 10% and obliquity increased by up to 15% for Titania and Oberon, the effects being negligible for Miranda.

Method of moments estimation for the superposition of square-root diffusions

Abstract We consider the problem of parameter estimation for the superposition of square-root diffusions. We first derive the explicit formulas for the moments and auto-covariances based on which we develop our moment estimators. We then establish a central limit theorem for the estimators with the explicit formulas for the asymptotic covariance matrix. Finally, we conduct numerical experiments to validate our method.

Reconstruction of Langevin dynamics using the second-order approximation of Kramers-Moyal conditional moments and its application to model price fluctuations

Reconstruction of Langevin dynamics using the second-order approximation of Kramers-Moyal conditional moments and its application to model price fluctuations

Does Institutional Quality Enhance the Effect of Health Outcomes on Economic Growth? Insights from Sub-Saharan African Countries

Institutional quality (InQ) plays an important role in shaping economic growth (ECG), influencing how economies develop and perform. The literature addresses the nexus between InQ and ECG and the link between health and ECG; findings are often contradictory, creating knowledge gaps. Importantly, research on the interplay between InQ, health, and ECG in Sub-Saharan African (SSA) countries is particularly limited. This study aims to address this gap by evaluating how health impacts ECG, with an emphasis on the mediating role of InQ in the health–growth nexus in SSA. This study examines these interplays across 35 SSA countries from 2012 to 2022. The life expectancy at birth (LEX) and real gross domestic product per capita (GDP) are used as proxies for health outcomes and ECG, respectively. The system generalised method of moments estimator is employed to analyse data. Results show that the LEX has a strong positive effect on economic growth in SSA countries. Furthermore, the InQ indicators (such as control of corruption, government effectiveness, rule of law and political stability, and absence of violence) are positively correlated with ECG. When the LEX interacts with InQ indicators, InQ is identified as a key channel through which LEX influences ECG. The findings confirm that InQ plays a crucial role in the health–growth nexus, with the positive impact of LEX on ECG being more pronounced in countries with higher levels of InQ, while the effect is weaker in countries with lower levels of InQ. The findings of this study have crucial policy implications, highlighting the intricate link among institutional quality, health outcomes, and economic growth. This study’s findings provide essential insights for policymakers to design focused strategies that improve InQ and health outcomes to achieve sustained ECG in SSA.

Task-agnostic exoskeleton control via biological joint moment estimation.

Lower-limb exoskeletons have the potential to transform the way we move1-14, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We introduce a task-agnostic controller that assists the user on the basis of instantaneous estimates of lower-limb biological joint moments from a deep neural network. By estimating both hip and knee moments in-the-loop, our approach provided multi-joint, coordinated assistance through our autonomous, clothing-integrated exoskeleton. When deployed during 28 activities, spanning cyclic locomotion to unstructured tasks (for example, passive meandering and high-speed lateral cutting), the network accurately estimated hip and knee moments with an average R2 of 0.83 relative to ground truth. Further, our approach significantly outperformed a best-case task classifier-basedmethod constructed from splines and impedance parameters. When tested on ten activities (including level walking, running, lifting a 25 lb (roughly 11 kg) weight and lunging), our controller significantly reduced user energetics (metabolic cost or lower-limb biological joint work depending on the task) relative to the zero torque condition, ranging from 5.3 to 19.7%, without any manual controller modifications among activities. Thus, this task-agnostic controller can enable exoskeletons to aid users across a broad spectrum of human activities, a necessity for real-world viability.

Uniform inference in linear error-in-variables models: Divide-and-conquer

. It is customary to estimate error-in-variables models using higher-order moments of observables. This moments-based estimator is consistent only when the coefficient of the latent regressor is assumed to be nonzero. We develop a new estimator based on the divide-and-conquer principle that is consistent for any value of the coefficient of the latent regressor. In an application on the relation between investment, (mismeasured) Tobin’s q and cash flow, we find time periods in which the effect of Tobin’s q is not statistically different from zero. The implausibly large higher-order moment estimates in these periods disappear when using the proposed estimator.

Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution systems at any desired load factor. The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. In order to save utility engineers time and effort, this study introduces a BiLSTM network-based machine learning model to directly estimate the optimal values of DGs and SHCs, rather than relying on load flow estimates. At first, a multi-objective grey wolf optimizer determines the most suitable locations and capacities of DGs and SHCs at the unity load factor and the same locations are used to obtain optimum sizing of DGs and SHCs at other load factors also. The base case data sets consisting of substation apparent power, real power load, reactive power load, real power loss, reactive power loss and minimum node voltage at various load factors in per unit values are taken as input training data for the machine learning model. The optimal sizes of the DGs and SHCs for the corresponding load factors obtained using GWO algorithm are taken as target data sets in per unit values for the machine learning model. An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. The efficacy of the proposed ML-based sizing algorithm is demonstrated via simulation studies.

Age of air from in situ trace gas measurements: insights from a new technique

Abstract. The age of air is an important transport diagnostic that can be derived from trace gas measurements and compared to global chemistry climate model output. We describe a new technique to calculate the age of air, measuring transport times from the Earth's surface to any location in the atmosphere based on simultaneous in situ measurements of multiple key long-lived trace gases. The primary benefits of this new technique include (1) optimized ages of air consistent with simultaneously measured SF6 and CO2; (2) age of air from the upper troposphere through the stratosphere; (3) estimates of the second moment of age spectra that have not been well constrained from measurements; and (4) flexibility to be used with measurements across multiple instruments, platforms, and decades. We demonstrate the technique on aircraft and balloon measurements from the 1990s, the last period of extensive stratospheric in situ sampling, and several recent missions from the 2020s, and compare the results with previously published and modeled values.

Novel closed-form point estimators for the beta distribution

In this paper, we propose and investigate novel closed-form point estimators for the beta distribution. The estimators of the first type are a modified version of Pearson's method of moments. The underlying idea is to involve the sufficient statistics, i.e., log-moments in the moment estimation equations and solve the mixed type of moment equations simultaneously. The estimators of the second type are based on an approximation to Fisher's likelihood principle. The idea is to solve two score equations derived from the log-likelihood function of generalized beta distributions. Both two resulted estimators are in closed forms, strongly consistent and asymptotically normal. In addition, through theoretical analyses and extensive simulations, the proposed estimators are shown to perform very close to the maximum likelihood estimators in both small and large samples, and they significantly outperform the method of moment estimators.

Prediction of fracture toughness of concrete using the machine learning approach

In the process of structural design, it is useful to estimate the fracture toughness of concrete samples. This research showcases the effectiveness of utilizing machine learning methods to determine the fracture toughness of concrete. Taking into account variables such as mix design, machine learning techniques can accurately predict the mode I fracture toughness of concrete. Dimensionless stress intensity factor of concrete prediction using twelve different machine learning techniques namely, Linear regression (LR), Extreme Gradient-Boosting (XGboost), K-Nearest Neighbors (KNN), Random Forest (RF), Category Boosting (CB), Decision Tree (DT), Extra Trees (ET), Light Gradient-Boosting (LightGB), Adaptive boosting (AdaBoost), Bagging (BA), Gaussian Process (GP), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The result of utilizing the training adaptive moment estimation algorithm has been developed to create an outstanding machine learning-based system. After carefully analyzing comparisons between the predictions produced by different models and experimental findings, it has been discovered that the models demonstrate an impressive accuracy rate of about 90 percent when it comes to forecasting concrete fracture toughness. The research findings emphasize that the ANN model exhibited superior accuracy in its predictions (R2 value of 0.90, RMSE of 0.1517, and MAE of 0.1238). Upon conducting a thorough examination of the ANN method’s sensitivity, the cement parameter holds utmost significance in accurately estimating concrete’s fracture toughness using the available dataset. So, the ANN model can be used as a valuable method to provide practical assistance in predicting the fracture toughness of concrete. When evaluating the effective parameters, the cement and metakaolin dosage and the notch height to specimen height ratio have the greatest effect on the fracture resistance, while the coarse aggregate content is minimal. This result is consistent with the experimental data.

Investigating the mediating role of national governance bundles and institutional ownership on the relationship between risk governance disclosure and market valuation: evidence from Sub-Saharan Africa

Purpose The paper aims to comparatively examine the impact of risk governance disclosure (RGD) on the market valuation of firms in Sub-Saharan Africa (SSA) and the mediating role of institutional investment and national governance bundles (NGB). Design/methodology/approach Using a dynamic system generalized method of moments estimation to control for endogeneity, the data for this research is manually collected from the annual reports of small and large firms in Nigeria (80 firms) and South Africa (100 firms) for the period 2012–2017 (900 firm years). Findings The authors find that firm RGD directly impacts firm valuation positively, but this association is significantly mediated by national governance practices (bundles) and institutional investment. The authors also develop a conceptual framework that shows the direct and indirect impact of RGD on firm market valuation. Originality/value The paper contributes to the comparative corporate governance literature in three ways. First, the authors show that differences in country-level RGD are explained by the maturation of governance regulations and institutions in each country. Second, despite the differences in the level of maturity of governance institutions across countries, stock markets value risk governance information. Finally, the study develops a conceptual framework that addresses prior inconsistent findings by showing that firm-level NGB and institutional investment significantly mediate the association between RGD and market valuation.

AdaLo: Adaptive learning rate optimizer with loss for classification

Gradient-based algorithms are frequently used to optimize neural networks, with various methods developed to enhance their performance. Among them, the adaptive moment estimation (Adam) optimizer is well-known for its effectiveness and ease of implementation. However, it suffers from poor generalization without a learning rate scheduler. Additionally, it has the disadvantage of a large computational burden because of individual learning rate term, as known as second-order moments of gradients. In this study, we propose a novel gradient descent algorithm called AdaLo, which stands for Adaptive Learning Rate Optimizer with Loss. AdaLo addresses two problems using its adaptive learning rate (ALR). Firstly, the proposed ALR adjusts the learning rate, based on the model's training progress, specifically the loss value. Therefore AdaLo's ALR effectively replaces traditional learning rate schedulers. Secondly, the ALR is a scalar global learning rate, reducing the computational burden. In addition, the stability of the proposed method is analyzed from the perspective of the learning rate. The superiority of AdaLo was proven by non-convex functions. Simulation results indicated that the proposed optimizer outperformed the Adam, AdaBelief, and diffGrad with regard to the training error and test accuracy.

Ankle Moment Estimation Based on A Novel Distributed Plantar Pressure Sensing System.

Ankle moment plays an important role in human gait analysis, patients' rehabilitation process monitoring, and the human-machine interaction control of exoskeleton robots. However, current ankle moment estimation methods mainly rely on inverse dynamics (ID) based on optical motion capture system (OMC) and force plate. These methods rely on fixed instruments in the laboratory, which are difficult to be applied to the control of exoskeleton robots. To solve this problem, this paper developed a new distributed plantar pressure system and proposed an ankle plantar flexion moment estimation method using the plantar pressure system. We integrated eight pressure sensors in each insole to collect the pressure data of the key area of the foot and then used the plantar pressure data to train four neural networks to obtain the ankle moment. The performance of the models was evaluated using normalized root mean square error (NRMSE) and cross-correlation coefficient (ρ). During experiments, eight subjects were recruited for the overground walking tests, and OMC and force plate were used as the gold standard. The results indicate that the Genetic algorithm - Gated recurrent unit estimation algorithm (GA-GRU) was the best estimation model which achieved the highest accuracy in generalized ankle moment estimation (NRMSE = 7.23%, ρ = 0.85) compared with the other models. The designed novel distributed plantar pressure system and the proposed method could serve as a joint moment estimation approach in wearable robot control and human motion state monitoring.

Reweighted Nadaraya–Watson estimation of stochastic volatility jump-diffusion models

In this paper, we construct the reweighted Nadaraya–Watson estimators of the infinitesimal moments for the volatility process of the stochastic volatility models, with the application of the threshold estimator of the unobserved volatility process. Our model includes jumps in both the underlying asset price and its volatility process. We derive the asymptotic properties of the estimators under the infill and long span assumptions. The results are useful for identification of the process. The finite-sample performance of the estimators is studied through Monte Carlo simulation.

On the estimation of the switching moment of utility functions in cooperative differential games

On the estimation of the switching moment of utility functions in cooperative differential games

Dynamic Stability of the Hybrid Airship Influenced by Added Mass Effect

This paper first derives the second-order approximation of force and moment based on the definition of added mass and stability derivatives. Further, it establishes a small perturbation model for high-altitude hybrid airships. Subsequently, a typical hybrid airship’s added mass parameters and stability derivatives are calculated using the computational fluid dynamics method. Based on the calculation results and the dynamic model, linear system analysis theory is applied to calculate and compare the eigenvalues of the state matrix and the step response of the system with the added mass effect. The results indicate that the stability of the hybrid airship remains unaffected by the added mass effect. In contrast, its control is significantly influenced, with a notable difference of approximately 23.7% in the steady-state value of the velocity step response between the two conditions. The method proposed in this paper can provide theoretical support for the design of hybrid airships.

Determinants of financial inclusion in sub-Saharan Africa and OECD countries

Using a dynamic panel data analysis, we explore the factors that influence financial inclusion in sub-Saharan Africa (SSA) and other regions, using member countries of the Organization for Economic Cooperation and Development (OECD) as a benchmark. We employ a system generalized methods of moments estimator and assess 31 SSA and 38 OECD countries from 2000 to 2021. We show that the literacy rate, trade openness, political stability, bank efficiency, income, and remittances are key factors with various impacts across regions. We further show that various dimensions of a financial system (access, usage, and quality) are impacted by different indicators and to varying extent. We account for events during the period, such as the global financial crisis and COVID-19 outbreak. We highlight the importance of quality literacy policies and a more efficient financial system in promoting financial inclusion. We recommend improving trade regulatory frameworks that promote trade openness through stronger institutions.

Bounded rationality consensus reaching process with regret theory and weighted Moment estimation for multi-attribute group decision making

Probabilistic linguistic term sets perform a particularly active role in the field of decision-making, particularly regarding decision-makers (DMs) who are inclined to convey evaluative information through natural linguistic variables. To effectively improve the current dilemma of multi-attribute group decision-making (MAGDM), this article put forward a new probabilistic linguistic MAGDM method with weighted Moment estimation. First, taking into account the psychological aspect of regret aversion among DMs, we use regret theory to transform the original decision-making matrix into the utility matrix, in which DMs usually exhibit limited rationality during the process of MAGDM. Then, a combined weighting method and a weighted Moment estimation model are investigated to determine the attribute weights, which are more scientifically and reasonably. Subsequently, in the process of consensus reaching process, a new trust propagation mechanism is designed to derive the weights of experts and the adjustment coefficients, in which we consider the shortest and longest propagation paths among DMs. Finally, an empirical validation of the MAGDM method's applicability is conducted utilizing raw coal quality assessment, accompanied by sensitivity and comparative analyses that underscore its advantages and robustness.

How Does Tax Avoidance Affect a Firm's Investment Inefficiency in Taiwan's Semiconductor Industry?

ABSTRACTThis study examines an important issue regarding how tax avoidance affects a firm's investment inefficiency in Taiwan's semiconductor industry. In addition, the dynamic effect of a firm's investment inefficiency and spillover effect of investment inefficiency from competitors are also explored. This study adopts 44 listed firms from 2013 to 2021 and constructs the “competitor‐dependence matrix” to replace the “spatial‐dependence matrix” in the dynamic spatial autoregressive model. According to the difference generalized method of moments estimation, the primary finding of this study is that the tax avoidance has a statistically and significantly positive effect on a firm's investment inefficiency while it is overinvestment, but a negative effect while it is underinvestment. Moreover, the dynamic effect is statistically insignificant, but the spillover effect is statistically and significantly negative.