Asymmetric Function Research Articles

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.

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.

Enhancement in photo-response of CuZnS nanocrystals-based photodetector using asymmetric work function electrodes

In this study, an efficient visible light photodetector has been fabricated by using heavy metal-free CuZnS Nanocrystals with average particle size of 13 ± 1 nm and has been synthesized by a microwave (MW) assisted synthesis technique. The optical band gap of this nanocrytstal is ∼1.7 eV and has an absorbance spectra ranging from 300 to700 nm. This low bandgap nanocrystals has been used to fabricate a photoconductor device with a CuZnS/ZnO bilayer structure. Besides, the device performance has been enhanced by using an asymmetric work-function lateral electrodes. The work-function difference between the electrodes gives a driven voltage between the electrodes, which allows faster collection of charge carriers from the device. As a consequence, a significant photocurrent is generated by the device even without any external bias. The self-biased (at 0 V) external quantum efficiency (EQE) of this device is ∼ 4 % (at 430 nm) whereas this value reaches ∼ 20 % under 2 V external bias. The device possesses the responsivity (Rλ) and detectivity (D) of 8.8 A/W and 3.8 × 1012 Jones at 320 nm respectively under 2 V external bias.

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.

Nmix: a hybrid deep learning model for precise prediction of 2'-O-methylation sites based on multi-feature fusion and ensemble learning.

RNA 2'-O-methylation (Nm) is a crucial post-transcriptional modification with significant biological implications. However, experimental identification of Nm sites is challenging and resource-intensive. While multiple computational tools have been developed to identify Nm sites, their predictive performance, particularly in terms of precision and generalization capability, remains deficient. We introduced Nmix, an advanced computational tool for precise prediction of Nm sites in human RNA. We constructed the largest, low-redundancy dataset of experimentally verified Nm sites and employed an innovative multi-feature fusion approach, combining one-hot, Z-curve and RNA secondary structure encoding. Nmix utilizes a meticulously designed hybrid deep learning architecture, integrating 1D/2D convolutional neural networks, self-attention mechanism and residual connection. We implemented asymmetric loss function and Bayesian optimization-based ensemble learning, substantially improving predictive performance on imbalanced datasets. Rigorous testing on two benchmark datasets revealed that Nmix significantly outperforms existing state-of-the-art methods across various metrics, particularly in precision, with average improvements of 33.1% and 60.0%, and Matthews correlation coefficient, with average improvements of 24.7% and 51.1%. Notably, Nmix demonstrated exceptional cross-species generalization capability, accurately predicting 93.8% of experimentally verified Nm sites in rat RNA. We also developed a user-friendly web server (https://tubic.org/Nm) and provided standalone prediction scripts to facilitate widespread adoption. We hope that by providing a more accurate and robust tool for Nm site prediction, we can contribute to advancing our understanding of Nm mechanisms and potentially benefit the prediction of other RNA modification sites.

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.

Advances in the Intermolecular Asymmetric Allylic Functionalization of Unreactive Acyclic Alkenes

AbstractAllylic C(sp 3)–H functionalized architectures are not only widely present in natural products, pharmaceuticals, and functional organic materials, but also serve as versatile building blocks to furnish important functionalized molecules in synthetic chemistry. Accordingly, various strategies to access allylic functionalized alkenes in a stereoselective manner have been developed. However, chemo-, regio- and stereoselective intermolecular asymmetric allylic functionalization (AAF) of unreactive acyclic alkene (UAA) from readily available materials, representing a highly atom- and step-economic approach toward the generation of structural complexity, remains elusive and challenging. Herein, we review all intermolecular asymmetric catalyzed methods, with emphasis on the construction of chiral allylic units by activation of allylic C–H bonds of UAAs. Our analysis serves to document the considerable and rapid progress within the field, while also highlighting the limitations of current methods.1 Introduction2 Asymmetric Allylic Oxygenation3 Asymmetric Allylic Amination4 Asymmetric Allylic Carbonization5 Asymmetric Allylic Sulfuration6 Conclusion and Outlook

Directionally Asymmetric in Orbital Angular Momentum Generation Using Single‐Layer Dielectric Janus Metasurfaces

AbstractJanus metasurfaces, offering versatile light manipulation contingent on incident direction, are explored across microwave to mid‐infrared spectra. Nonetheless, prior designs often resort to spatial multiplexing or vertical stacking, entailing complex fabrication. Metallic Janus metasurfaces, owing to material properties, suffer notable Ohmic losses in the visible spectrum. In this study, Single‐layer TiO2‐based Janus metasurfaces with arbitrary polarization control is experimentally demonstrated, exhibiting directionally asymmetric functionalities in spin and orbital angular momentum (OAM). A novel Jones matrix formulation tailored for Janus metasurfaces is presented, enabling efficient generation of two asymmetric, high‐purity OAM states of vortex beams at wavelength of 532 nm, contingent on incidence direction. This innovation facilitates compact and versatile phase manipulation, encompassing applications such as lasers and optical combiners, thereby expanding its utility across diverse domains.

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.

Estimation of the Reliability Function of the Generalized Rayleigh Distribution under Progressive First-Failure Censoring Model

This study investigates the statistical inference of the parameters, reliability function, and hazard function of the generalized Rayleigh distribution under progressive first-failure censoring samples, considering factors such as long product lifetime and challenging experimental conditions. Firstly, the progressive first-failure model is introduced, and the maximum likelihood estimation for the parameters, reliability function, and hazard function under this model are discussed. For interval estimation, confidence intervals have been constructed for the parameters, reliability function, and hazard function using the bootstrap method. Next, in Bayesian estimation, considering informative priors and non-information priors, the Bayesian estimation of the parameters, reliability function, and hazard function under symmetric and asymmetric loss functions is obtained using the MCMC method. Finally, Monte Carlo simulation is conducted to compare mean square errors, evaluating the superiority of the maximum likelihood estimation and Bayesian estimation under different loss functions. The performance of the estimation methods used in the study is illustrated through illustrative examples. The results indicate that Bayesian estimation outperforms maximum likelihood estimation.

Finite time prescribed performance control for stochastic systems with asymmetric error constraint and actuator faults

This paper investigates the problem of finite time prescribed performance control (PPC) for a number of nonlinear stochastic systems with asymmetric error constraint, unknown control directions, and actuator faults. Firstly, instead of introducing the performance constraint function in the Lyapunov function, a new asymmetric error conversion function (AECF) is presented, which can successfully constrain the tracking errors into the specified asymmetric boundaries and eliminate the feasibility condition of requiring tracking errors to be bounded. Then, in iterative process, the unknown intermediate virtual controller is approximated by the fuzzy logic system (FLS), which makes each actual virtual controller to be reduced to only one item. Furthermore, the investigated finite time PPC strategy can fully compensate the faults impact on the systems and have only one adaptive parameter. In the end, the efficacy of investigated strategy is verified by inverted pendulum systems with stochastic disturbances.

Leveraging a Hybrid Machine Learning Approach for Compressive Strength Estimation of Roller-Compacted Concrete with Recycled Aggregates

In recent years, the use of recycled aggregate (RA) in roller-compacted concrete (RCC) for pavement construction has been increasingly attractive due to various environmental and economic benefits. Early determination of the compressive strength (CS) is crucial for the construction and maintenance of pavement. This paper presents the idea of combining metaheuristics and an advanced gradient boosting regressor for estimating the compressive strength of roller-compacted concrete containing RA. A dataset, including 270 samples, has been collected from previous experimental works. Recycled aggregates of construction demolition waste, reclaimed asphalt pavement, and industrial slag waste are considered in this dataset. The extreme gradient boosting machine (XGBoost) is employed to generalize a functional mapping between the CS and its influencing factors. A recently proposed gradient-based optimizer (GBO) is used to fine-tune the training phase of XGBoost in a data-driven manner. Experimental results show that the hybrid GBO-XGBoost model achieves outstanding prediction accuracy with a root mean square error of 2.64 and a mean absolute percentage error less than 8%. The proposed method is capable of explaining up to 94% of the variation in the CS. Additionally, an asymmetric loss function is implemented with GBO-XGBoost to mitigate the overestimation of CS values. It was found that the proposed model trained with the asymmetric loss function helped reduce overestimated cases by 17%. Hence, the newly developed GBO-XGBoost can be a robust and reliable approach for predicting the CS of RCC using RA.

Neural networks-based composite learning control for robotic systems with predefined time error constraints

This paper investigates the neural networks-based control problem for robotic systems with error constraints. By embedding a new predefined time performance function into an asymmetric barrier function, a predefined time constraints method is proposed with the following features: (1) it is a general approach that can handle asymmetric error constraints and can be directly applied to the predefined performance control for other high-order nonlinear systems without any changing; (2) both settling time and convergent compact set can be preassigned by the user; (3) the results are global i.e., the robot can start arbitrarily within the physical domain. By extending the regression operators with online data memory, a prediction error is constructed, by using which, a new neural networks based composite learning law is constructed to handle unknown dynamics. Compared with the traditional method such as gradient descent algorithm, σ-modification and ϵ-modification which cannot guarantee the convergence of the neural networks weights or can only force the weights to stay around preselected values, the neural networks guarantees that the weights converge to the region around the true values, in particular under a weak interval excitation (IE) condition rather than the typically stringent persistent excitation (PE) condition. The benefits and effectiveness of the proposed control are theoretically authenticated and experimentally validated.

A novel asymmetric loss function for deep clustering-based health monitoring and anomaly detection for spacecraft telemetry

A novel asymmetric loss function for deep clustering-based health monitoring and anomaly detection for spacecraft telemetry