Asymmetric Function Research Articles

Mitigating the choice of the duration in DDMS models through a parametric link

One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is the duration of the hidden states. Because there is currently no procedure for estimating this duration or testing whether a given duration is appropriate for a given data set, an ad hoc duration choice must be heuristically justified. In this paper, we propose and examine a methodology that mitigates the choice of duration in DDMS models when forecasting is the goal. The novelty of this paper is the use of the asymmetric Aranda-Ordaz parametric link function to model transition probabilities in DDMS models, instead of the commonly applied logit link. The idea behind this approach is that any incorrect duration choice is compensated for by the parameter in the link, increasing model flexibility. Two Monte Carlo simulations, based on classical applications of DDMS models, are employed to evaluate the methodology. In addition, an empirical investigation is carried out to forecast the volatility of the S&P500, which showcases the capabilities of the proposed model.

Statistical models for classification by handedness of Olympic Trap shooters in digital training services and remote coaching

AbstractIn this paper, we address the problem of classification by handedness of Olympic Trap shooters applying statistical methods to newly available data gathered from the field. We assess the performance of binary classification models based on KNN and Binary Regression, with both symmetric and asymmetric link functions, in a context characterized by unbalanced data. Our results show promising classification performance, suitable for first non-critical applications in data driven training services and remote coaching, encouraging further future research.

Fixing imbalanced binary classification: An asymmetric Bayesian learning approach.

Most statistical and machine learning models used for binary data modeling and classification assume that the data are balanced. However, this assumption can lead to poor predictive performance and bias in parameter estimation when there is an imbalance in the data due to the threshold election for the binary classification. To address this challenge, several authors suggest using asymmetric link functions in binary regression, instead of the traditional symmetric functions such as logit or probit, aiming to highlight characteristics that would help the classification task. Therefore, this study aims to introduce new classification functions based on the Lomax distribution (and its variations; including power and reverse versions). The proposed Bayesian functions have proven asymmetry and were implemented in a Stan program into the R workflow. Additionally, these functions showed promising results in real-world data applications, outperforming classical link functions in terms of metrics. For instance, in the first example, comparing the reverse power double Lomax (RPDLomax) with the logit link showed that, regardless of the data imbalance, the RPDLomax model assigns effectively lower mean posterior predictive probabilities to failure and higher probabilities to success (21.4% and 63.7%, respectively), unlike Logistic regression, which does not clearly distinguish between the mean posterior predictive probabilities for these two classes (36.0% and 39.5% for failure and success, respectively). That is, the proposed asymmetric Lomax approach is a competitive model for differentiating binary data classification in imbalanced tasks against the Logistic approach.

Asymmetric Amination of Unstrained C(sp3)-C(sp3) Bonds.

The asymmetric functionalization of unstrained C(sp3)-C(sp3) bonds could be a powerful strategy to stereoselectively reconstruct the backbone of an organic compound, but such reactions are rare. Although allylic substitutions have been used frequently to construct C-C bonds by the cleavage of more reactive C-X bonds (X is usually an O atom of an ester) by transition metals, the reverse process that involves the replacement of a C-C bond with a C-heteroatom bond is rare and generally considered thermodynamically unfavorable. We show that an unstrained, inert allylic C-C σ bond can be converted to a C-N bond stereoselectively via a designed solubility-control strategy, which makes the thermodynamically unfavorable process possible. The C-C bond amination occurs with a range of amine nucleophiles and cleaves multiple classes of alkyl C-C bonds in good yields with high enantioselectivity. A novel resolution strategy is also reported that transforms racemic allylic amines to the corresponding optically active allylic amine by the sequential conversion of a C-N bond to a C-C bond and back to a C-N bond. Mechanistic studies show that formation of the C-N bond is the rate-limiting step and is driven by the low solubility of the salt formed from the cleaved alkyl group in a nonpolar solvent.

Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings

The estimation of lateral strength in reinforced concrete (RC) columns subjected to cyclic loads is crucial in structural design. The failure of RC columns under lateral forces can lead to catastrophic structural collapses. This fact emphasizes the need for accurate assessments of their dynamic behavior. This paper proposes a data-driven model for estimating the lateral strength of RC columns. A historical dataset comprising 12 predictor variables and 247 samples has been compiled to train and validate of the proposed approach. The extreme gradient boosting machine (XGBoost) is employed to establish a predictive relationship between the lateral strength of RC columns and their influencing factors. Since model selection is critical for constructing a reliable prediction mode, this study relies on utilizing metaheuristic approaches, including Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, and Ant Colony Optimization, to optimize the performance of the XGBoost model. Experimental results show that the integration of Ant Colony Optimization and XGBoost can help attain outstanding prediction accuracy with a correlation of determination (R2) of 0.95. Additionally, an asymmetric squared error loss function is utilized to reduce overestimations by 12 %. The newly developed method can be utilized in practical applications where reliable predictions of the lateral strength of RC columns under cyclic loads are required.

Bayesian and non‐Bayesian inference of the process capacity index Spk′$S^{\prime }_{pk}$ under progressive type‐II right censored scheme

AbstractProcess capability indices (PCIs), are widely used to gauge how well a process performs within its requirements. While greater PCIs are indicative of improved process “quality,” they do not necessarily translate into lower rejection rates. Therefore, using a loss‐based PCI to gauge process capability makes more sense. In this paper, the PCI has been considered for normal random variable. The article attempts to study the classical and the Bayesian estimation of for type‐II progressive right censored sample under symmetric as well as asymmetric loss functions, namely squared error loss function, and LINEX loss function, respectively. The Markov chain Monte Carlo simulation technique has been efficiently used here to have the approximate solution for . Through an extensive Monte Carlo simulation study along with two real life examples related to electronic industry, we compare the performances of the classical and the Bayes estimates based on symmetric and asymmetric loss functions and compared among the asymptotic confidence interval and highest posterior density credible intervals, in terms of average width and corresponding coverage probabilities of , respectively.

Active fault-tolerant control with prescribed performance and reachability judgement for the altitude ground test facility

This research proposes an active fault-tolerant control (AFTC) method based on reachability judgement with asymmetric appointed-time prescribed performance to ensure the safety of altitude ground test facility (AGTF) under potential valve actuator and sensor faults. Firstly, a long short-term memory (LSTM) neural network is employed for fault diagnosis. Given the unique characteristics of AGTF, the equivalent multiplicative fault is introduced to ensure the successful fitting of the fault diagnosis network. Then, a reachability judgement network based on the distance field on grids (DFOG) method is designed to assess the impact of faults on subsequent tasks of the system, thereby ensuring the safety of the AGTF. Subsequently, this study proposes an error transformation function of prescribed performance control (PPC) that ensures error convergence even in cases of asymmetric performance function. This enables the performance boundaries to be further reduced to improve the control performance of the system. Finally, experiments validate the effectiveness of the methods proposed in this manuscript.

An Analysis of Type-I Generalized Progressive Hybrid Censoring for the One Parameter Logistic-Geometry Lifetime Distribution with Applications

Based on Type-I generalized progressive hybrid censored samples (GPHCSs), the parameter estimate for the unit-half logistic-geometry (UHLG) distribution is investigated in this work. Using maximum likelihood estimation (MLE) and Bayesian estimation, the parameters, reliability, and hazard functions of the UHLG distribution under GPHCSs have been assessed. Likewise, the computation is carried out for the asymptotic confidence intervals (ACIs). Furthermore, two bootstrap CIs, bootstrap-p and bootstrap-t, are mentioned. For symmetric loss functions, like squared error loss (SEL), and asymmetric loss functions, such as linear exponential loss (LL) and general entropy loss (GEL), there are specific Bayesian approximations. The Metropolis–Hastings samplers methodology were used to construct the credible intervals (CRIs). In conclusion, a genuine data set measuring the mortality statistics of a group of male mice with reticulum cell sarcoma is regarded as an application of the methods given.

Improving PPP positioning and troposphere estimates using an azimuth-dependent weighting scheme

Asymmetric troposphere modeling is crucial in Precise Point Positioning (PPP). The functional model of the asymmetric troposphere has been thoroughly studied, while the stochastic model lacks discussion. Currently, there is no suitable stochastic model for asymmetric tropospheric conditions, potentially degrading the positioning accuracy and the reliability of Zenith Total/Wet Delay (ZTD/ZWD) estimates. This paper introduces an Azimuth-Dependent Weighting (ADW) scheme that utilizes information from asymmetric mapping functions to adaptively weight Global Navigation Satellite System (GNSS) observations affected by azimuth-dependent errors. The concept of ADW has been validated using Numerical Weather Prediction data and International GNSS Service data. The results indicate that ADW effectively improves the coordinate repeatability of the PPP solution by approximately 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10\\%$$\\end{document} in the horizontal and 20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20\\%$$\\end{document} in the vertical direction. Additionally, ADW appears to be capable to improve the ZWD estimates during the PPP convergence period and yields smoother ZWD estimates. Consequently, it is recommended to adopt this new weighting scheme in PPP applications when an asymmetric mapping functions is employed.

Wearable network for multilevel physical fatigue prediction in manufacturing workers.

Manufacturing workers face prolonged strenuous physical activities, impacting both financial aspects and their health due to work-related fatigue. Continuously monitoring physical fatigue and providing meaningful feedback is crucial to mitigating human and monetary losses in manufacturing workplaces. This study introduces a novel application of multimodal wearable sensors and machine learning techniques to quantify physical fatigue and tackle the challenges of real-time monitoring on the factory floor. Unlike past studies that view fatigue as a dichotomous variable, our central formulation revolves around the ability to predict multilevel fatigue, providing a more nuanced understanding of the subject's physical state. Our multimodal sensing framework is designed for continuous monitoring of vital signs, including heart rate, heart rate variability, skin temperature, and more, as well as locomotive signs by employing inertial motion units strategically placed at six locations on the upper body. This comprehensive sensor placement allows us to capture detailed data from both the torso and arms, surpassing the capabilities of single-point data collection methods. We developed an innovative asymmetric loss function for our machine learning model, which enhances prediction accuracy for numerical fatigue levels and supports real-time inference. We collected data on 43 subjects following an authentic manufacturing protocol and logged their self-reported fatigue. Based on the analysis, we provide insights into our multilevel fatigue monitoring system and discuss results from an in-the-wild evaluation of actual operators on the factory floor. This study demonstrates our system's practical applicability and contributes a valuable open-access database for future research.

Credit risk assessment method driven by asymmetric loss function

Credit risk assessment is significantly hindered by the problem of class imbalance, and cost-sensitive methods represent an effective strategy to address this issue. However, most algorithms tend to approach the imbalance from a class perspective, overlooking the finer details at the sample level. Moreover, such methods are susceptible to interference from noise and outliers. In response to these challenges, this paper proposes an asymmetric cost-sensitive support vector machine (QTSVM) that combines the quadratic type squared error loss function (QTSELF) with support vector machine (SVM). It not only leverages the asymmetry of the loss function by applying varying penalties based on classification errors but also employs different processing measures for samples from different classes. Additionally, it enhances model robustness by imposing a tiny penalty on noise or outliers. The adaptive moment estimation (Adam) algorithm is employed to optimize QTSVM. Extensive experiments and statistical tests profoundly demonstrate the superior performance of QTSVM.

Infant Modified Constraint-Induced Movement Therapy Paired With Neuromuscular Electrical Stimulation: A Feasibility Study.

To determine the feasibility of modified constraint-induced movement therapy (mCIMT) paired with neuromuscular electrical stimulation (NMES) for infants with asymmetrical hand function (AHF). Five infants received an experimental ABA design: (A1) 3weeks of our Standard AHF Care, (B) 3weeks mCIMT-NMES, and (A2) 3weeks of our Standard AHF Care. Parents tracked key data in a daily log, and infants were assessed 4 times using the Hand Assessment for Infants and Peabody Developmental Motor Scale-2. There was a high level of participant enrollment, visit frequency adherence, and compliance with the treatment protocol. No adverse events were reported. Mean Hand Assessment for Infants Both Hands measure scores changed more after mCIMT-NMES than after our Standard AHF Care. mCIMT-NMES is a feasible early intervention for infants with AHF at risk for unilateral cerebral palsy. A future study in a larger sample should examine the efficacy of mCIMT-NMES in this population.

Empirical performance of the optimal predictors under asymmetric loss GARCH vs. realized GARCH models

ABSTRACT We assess the forecast performance of the optimal predictor for returns under asymmetric loss using conditional volatility forecasts from conventional GARCH and realized GARCH models. We compare two classes of models for conditional volatility that is essential to the correction term for the optimal predictor, under asymmetric loss function. We find that when the conditional volatility forecasts from the realized GARCH models are used, the optimal predictor has better performance. The results strongly suggest using conditional volatility forecasts from the joint models of conditional volatility and realized volatility for short-to-moderate forecast horizons and low-to-moderate degrees of asymmetry in the optimal predictor expression to forecast daily returns.

Reliability inference of a multicomponent stress-strength model for exponentiated Pareto distribution based on progressive first failure censored samples

Multicomponent stress-strength (MC-SS) analysis is crucial for risk management and decision-making in various fields such as engineering, manufacturing, and quality control. It helps in identifying vulnerable components and areas where improvements can enhance overall system reliability. A primary contribution of this research is the implementation of the progressive first-Failure censored (PFIF-C) scheme. This scheme offers a novel and efficient approach to time and cost censoring, surpassing many existing censoring schemes found in the literature. The current study investigates the issue of MC-SS reliability inference under PFIF-C from the exponentiated Pareto distribution. The reliability of the MC-SS system is considered under the condition that both stress and strength follow an exponentiated Pareto distribution with a common second shape parameter. The parameter estimates and reliability estimate of the MC-SS system is produced using the maximum likelihood and Bayesian procedures. The asymptotic confidence intervals and highest posterior density credible intervals for the MC-SS system reliability are produced. Bayesian estimates are yielded using Markov Chain Monte Carlo under both informative and non-informative priors, considering symmetric and asymmetric loss functions. In order to assess the efficacy of the suggested methodology, simulation analyses are conducted. According to the simulation data Bayesian estimates of the MC-SS reliability, employing both symmetric and asymmetric loss functions, consistently outperform maximum likelihood estimates in terms of estimated risks. In general, Bayesian estimates based on asymmetric loss function perform better than the other competing loss function. The procedure is further shown with one real-world data example about failure times of a specific software model to show how the recommended approach may be applied to assess the strength and stress of a multicomponent model.

Censored panel quantile regression with fixed effects via an asymmetric link function

In this study, we consider 2- and 3-step estimators for censored quantile regression models with fixed effects. In the first step of these estimators, the informative subset is determined through the estimation of censoring probability. While previous studies often employ symmetric link functions for estimating the censoring probability, these may not accurately identify the best subset when the distribution of the censoring probability is skewed. Given the prevalence of asymmetrically censored data in empirical analyses, we propose using an asymmetric link function for more accurate subset determination. We conduct Monte Carlo simulations and empirical analysis to assess our proposed methods. Our Monte Carlo simulations and empirical analysis confirm that an asymmetric link function offers a better fit for asymmetrically censored data. For symmetrically censored data, the symmetric link function performs better in moderate and large samples, while the asymmetric link function performs slightly better in small samples. These findings underscore the importance of considering both the censoring probability distribution and sample size in panel quantile regression.

Direct Enantioselective α-C-H Conjugate Addition of Propargylamines to α,β-Unsaturated Ketones via Carbonyl Catalysis.

Direct asymmetric α-C-H conjugate addition of propargylamines to α,β-unsaturated ketones remains a great challenge due to the low α-amino C-H acidity of propargylamines and the nucleophilic interference of the NH2 group. Utilizing a new type of pyridoxals featuring a benzene-pyridine biaryl skeleton and a bulky amide side chain as carbonyl catalyst, we have accomplished direct asymmetric α-C-H conjugate addition of NH2-unprotected propargylamines to α,β-unsaturated ketones. The adducts undergo subsequent in situ intramolecular cyclization, delivering a wide range of chiral polysubstituted 1-pyrrolines in high yields (up to 92%) with excellent diastereo- and enatioelectivities (up to >20:1 dr and 99% ee). This work has demonstrated a straightforward approach to access pharmaceutically important chiral 1-pyrrolines, and it has also provided an impressive instance of direct asymmetric functionalization of inert C-H bonds enabled by biomimetic organocatalysts.

Switchable terahertz polarization converter with dual-functional reflective conversion and asymmetric transmission based on vanadium dioxide

A dual-functional polarization converter designed for both reflective polarization conversion and asymmetric transmission in the terahertz band is proposed, leveraging the phase transition of vanadium dioxide. In its metallic state, vanadium dioxide facilitates reflective mode operation. Simulation results demonstrate that the polarization conversion rate exceeds 90%, converting linearly polarized or circularly polarized waves into cross-polarized waves, with relative bandwidths of 100.6% and 100.8%, respectively. Experimental validation confirms the polarization conversion capabilities of the device. In the insulating state of vanadium dioxide, the converter demonstrates a notable asymmetric transmission effect for wide-angle incident circularly polarized waves. The proposal of this converter fills the gap of switchable polarization conversion and asymmetric transmission functions, and has broad application prospects in multifunctional polarization devices and multi-channel polarization detection.

Irreversibility and response functions in the turbulent energy cascade

Abstract The statistical properties of turbulent flows are fundamentally different from those of systems at equilibrium due to the presence of an energy flux from the scales of injection to those where energy is dissipated by the viscous forces: a scenario dubbed “direct energy cascade”. Here, we aim at characterizing the non-equilibrium properties of turbulent cascades in a shell model of turbulence by studying an asymmetric time-correlation function and the relaxation behavior of an energy perturbation, measured at scales smaller or larger than the perturbed one. We shall contrast the behavior of these two observables in both non-equilibrium (forced and dissipated) and equilibrium (inviscid and unforced) cases. Finally, we shall show that equilibrium and non-equilibrium physics coexist in the same system, namely at scales larger and smaller, respectively, of the forcing scale.

Construction of mathematical models of thermal conductivity for modern electronic devices with elements of a layered structure

This paper considers a heat conduction process for isotropic layered media with internal thermal heating. As a result of the heterogeneity of environments, significant temperature gradients arise as a result of the thermal load. In order to establish the temperature regimes for the effective operation of electronic devices, linear and non-linear mathematical models for determining the temperature field have been constructed, which would make it possible to further analyze the temperature regimes in these heat-active environments. The coefficient of thermal conductivity for the above structures was represented as a whole using asymmetric unit functions. As a result, the conditions of ideal thermal contact were automatically fulfilled on the surfaces of the conjugation of the layers. This leads to solving one heat conduction equation with discontinuous and singular coefficients and boundary conditions at the boundary surfaces of the medium. For linearization of nonlinear boundary value problems, linearizing functions were introduced. Analytical solutions to both linear and nonlinear boundary value problems were derived in a closed form. For heat-sensitive environments, as an example, the linear dependence of the coefficient of thermal conductivity of structural materials on temperature, which is often observed when solving many practical problems, was chosen. As a result, analytical relations for determining the temperature distribution in these environments were obtained. Based on this, a numerical experiment was performed, and it was geometrically represented depending on the spatial coordinates. This proves that the constructed linear and nonlinear mathematical models testify to their adequacy to the real physical process. They make it possible to analyze heat-active media regarding their thermal resistance. As a result, it becomes possible to increase it and protect it from overheating, which can cause the destruction of not only individual nodes and their elements but also the entire structure

Barrier Lyapunov Based Adaptive Control for Hydraulic Servo Systems with Parametrical Nonlinearities and Modeling Uncertainties

Load variations, friction, and external disturbance degrade the control performance of hydraulic servo systems. To attain the high precision trajectory tracking performance for the hydraulic servo systems, a barrier Lyapunov based adaptive controller (BLBAC) is proposed in this paper. For the controller, the adaptive law is developed in each step of backstepping design such that the tracking error can converge to a prescribed accuracy. The state-constrained control of hydraulic servo systems is another issue worthy of attention. Hence, a novel time-varying asymmetric barrier Lyapunov function (TABLF) are adopted to guarantee that the states are not to violate their constraints. The closed-loop signals stability is proved by Lyapunov theory. Comparative simulation results verify the effectiveness of the proposed controller in comparison with the other two controllers for hydraulic servo systems.