Asymmetric Function Research Articles

Modeling neural contrast sensitivity functions in human visual cortex

Abstract The contrast sensitivity function (CSF) characterizes visual function, and is widely used in research on visual perception and ophthalmological disorders. The CSF describes the lowest contrast level that participants can perceive as a function of spatial frequency. Here, we present a new method to estimate the neural equivalent of the CSF that describes how a population of neurons responds to contrast as a function of spatial frequency. Using functional magnetic resonance imaging (fMRI) at 7 Tesla, we measured neural responses while participants viewed gratings that varied systematically in contrast and spatial frequency. We modeled the neural CSF (nCSF) using an asymmetric parabolic function, and we model the transition from no response to full response using a contrast response function (CRF). We estimated the nCSF parameters for every cortical location by minimizing the residual variance between the model predictions and the fMRI data. We validate the method using simulations and parameter recovery. We show that our nCSF model explains a significant amount of the variance in the fMRI time series. Moreover, the properties of the nCSF vary according to known systematic differences across the visual cortex. Specifically, the peak spatial frequency that a cortical location responds to decreases with eccentricity and across the visual hierarchy. This new method will provide valuable insights into the properties of the visual cortex and how they are altered in both healthy and clinical conditions.

Asymmetric Functionalization Harnessing Radical-Mediated Functional-Group Migration.

Along with the renaissance of radical chemistry, the past decade has witnessed rapid development in radical-mediated rearrangement reactions. A wide diversity of radical approaches harnessing functional-group migration (FGM) have been devised to enhance both synthetic efficiency and molecular complexity. However, the application of FGM reactions to construct stereogenic centers remains underexplored owing to the inherent challenges of asymmetric radical reactions. In this Minireview, we summarize the emerging area of asymmetric functionalization involving radical-mediated FGM reactions. Enantioselectivity can be regulated by chiral substrates, auxiliaries or reagents, or achieved through asymmetric transition-metal catalysis. This synthetic strategy demonstrates its superiority in the production of valuable complex molecules that are otherwise difficult to obtain by conventional methods.

Asymmetric Functionalization Harnessing Radical‐Mediated Functional‐Group Migration

Along with the renaissance of radical chemistry, the past decade has witnessed rapid development in radical‐mediated rearrangement reactions. A wide diversity of radical approaches harnessing functional‐group migration (FGM) have been devised to enhance both synthetic efficiency and molecular complexity. However, the application of FGM reactions to construct stereogenic centers remains underexplored owing to the inherent challenges of asymmetric radical reactions. In this Minireview, we summarize the emerging area of asymmetric functionalization involving radical‐mediated FGM reactions. Enantioselectivity can be regulated by chiral substrates, auxiliaries or reagents, or achieved through asymmetric transition‐metal catalysis. This synthetic strategy demonstrates its superiority in the production of valuable complex molecules that are otherwise difficult to obtain by conventional methods.

Adaptive-constrained admittance control for physical human–robot interaction

In this paper, an adaptive constrained admittance control scheme is proposed, which can effectively solve physical human–robot interaction (pHRI) tasks with output constraints. To ensure the safety of robot behavior, the constraint controller is designed in the trajectory planning layer and the control layer. The asymmetric soft saturation function (ASSF) is designed to obtain variable compliant motion trajectories generated from the desired admittance model. In addition, the controller based on the asymmetric integral barrier Lyapunov function (AIBLF) is designed to deal directly with asymmetric Cartesian space constraints. Finally, radial basis function neural network (RBFNN) is utilized to approximate the dynamics uncertainty of the robot manipulator and to improve the tracking accuracy. According to the Lyapunov stability principles, it can be proved that all states of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed control scheme is demonstrated by several simulations and experiments.

Chiral Aldehyde/Palladium Catalysis Enables Asymmetric Branched-Selective Ring-Opening Functionalization of Methylenecyclopropanes with Amino Acid Esters.

Achieving catalytic asymmetric functionalization of methylenecyclopropanes (MCPs) by selective C-C bond cleavage is a notable challenge due to the intricate reaction partners involved. In this work, we report that chiral aldehyde/palladium combined catalysis enables the asymmetric functionalization of MCPs with NH2-unprotected amino acid esters. This reaction proceeds through a regiospecific branched ring-opening mechanism, resulting in optically active α,α-disubstituted α-amino acid esters bearing nonconjugated terminal alkene units. Mechanism studies indicate that the ring-opening pathways are irreversible and the ultimate regioselectivity is governed by palladium catalysis. The products can be utilized in the construction of chiral dihydropyrazoles, α-methyl aspartic acid derivatives, and analogues of VPC01091 and BMS-986104.

On the estimation of the stress-strength parameter for the inverse Lindley distribution based on lower record values

In this article, we consider the estimation of the stress-strength reliability parameter for the inverse Lindley distribution based on lower record values. The maximum likelihood estimator and its asymptotic distribution are obtained. An approximate classical confidence interval, as well as two bootstrap-type confidence intervals for the reliability parameter are derived. The Bayesian inference for the parameter has been considered using Tierney and Kadane’s approximation method, as well as two Monte Carlo methods, namely the Metropolis-Hastings and importance sampling techniques under both symmetric and asymmetric loss functions. Besides, the Chen and Shao shortest width credible intervals are constructed for the stress-strength parameter. A simulation study and a real data example are conducted to explore and compare the performances of the presented results.

Resolving severely overlapping ion mobility peaks using enhanced Fourier self-deconvolution.

Fourier self-deconvolution is an effective method for resolving overlapping spectra. However, the selection of the half-width for the deconvolving function is often subjective, which can lead to either excessive convolution or insufficient resolution enhancement. Additionally, ion mobility peaks exhibit tailing effects, which may be misinterpreted as new peaks when the deconvolving function is modelled with a Gaussian function. This paper proposes an improved Fourier self-deconvolution method based on continuous wavelet transform. The proposed method determines the signal half-width by calculating the horizontal distance between the peaks and troughs of the wavelet coefficients, offering a more accurate estimation. Furthermore, an asymmetric function is employed to optimize the peak shape of the deconvolving function, significantly reducing the likelihood of peak misidentification. The effectiveness of the proposed method is validated using both simulated and experimental ion mobility spectrometry data. Experimental results demonstrate that the proposed method effectively enhances peak resolution and resolves overlapping peaks. Moreover, compared with other peak segmentation algorithms based on Fourier self-deconvolution, the proposed method demonstrates lower parameter estimation error and higher computational efficiency, particularly for severely overlapping peaks.

A symmetric and asymmetric yield function based on normalized stress invariant suitable for sheet and bulk metals under various stress states

A symmetric and asymmetric yield function based on normalized stress invariant suitable for sheet and bulk metals under various stress states

Effects of Altered Haptic Feedback Gain Upon Balance Are Explained by Sensory Conflict Estimation.

Lightly touching a solid object reduces postural sway. Here, we determine the effect of artificially modifying haptic feedback for balance. Participants stood with their eyes closed, lightly gripping a manipulandum that moved synchronously with body sway to systematically enhance or attenuate feedback gain between +2 and -2, corresponding to motion in the same or opposite direction to the body, respectively. This intervention had a systematic effect on postural sway, which exhibited an asymmetric u-shape function with respect to haptic feedback gain. Sway was minimal around zero gain, corresponding to a static object. Sway increased slightly at gains below -0.25 but increased greatly at gains above +0.25. At +2, it was approximately double that of a no-touch condition. Mean interaction force between the hand and manipulandum remained < 0.9 N throughout, although it increased slightly at extreme gains. Cross-correlations between hand force and trunk position were highest during conditions of least sway, suggesting that higher quality haptic feedback is associated with greater sway reduction. We successfully replicated the sway behaviour using a feedback control model that attenuated haptic feedback signals when the discrepancy between haptic and proprioceptive signals reached a threshold. Our findings suggests the CNS can utilise augmented haptic feedback for balance, but only with relatively small changes to natural feedback gain. In healthy volunteers, it offers minimal benefit over a static object. Haptic feedback is therefore optimal when motion is physiologically realistic and subtle enough to be misinterpreted as self-motion.

Uniform-performance-constrained fixed-time neuro-control for stochastic nonlinear systems under dynamic event triggering.

This article studies the implementation of practical fixed-time control in stochastic nonlinear systems, implementing event-triggered communication between the controller and the actuator. Firstly, to accomplish the problem of uniform tracking error performance constraints, the improved performance function is investigated, which is combined with the asymmetric barrier Lyapunov function to achieve fast convergence speed and steady state accuracy. Secondly, the practical fixed-time stability is applied in the stochastic nonlinear closed-loop system, which fuses fixed-time command filtering and improved filtering error compensation mechanisms to avoid computational explosion issue. Furthermore, in order to relieve the communication load on the controller and actuator, adjustable trigger thresholds are designed, based on which dynamic event triggering mechanisms are presented for stochastic nonlinear systems. Additionally, the uncertain system behavior is estimated using RBF neuro-networks and the designed controller avoids the singularity problem. Finally, the proposed controller verifies that the system error converges to zero in a fixed time under the Lyapunov stability theory, and that the system output is within the preset boundaries, realizing boundedness of all signals. The superiority of the control method is further demonstrated by three simulation studies including two practical examples.

Bayesian Analysis of Two Parameter Weibull Distribution Using Different Loss Functions

This paper focuses on the Bayesian technique to estimate the parameters of the Weibull distribution. At this location, we use both informative and non-informative priors. We calculate the estimators and their posterior risks using different asymmetric and symmetric loss functions. Bayes estimators do not have a closed form under these loss functions. Therefore, we use an approximation approach established by Lindley to get the Bayes estimates. A comparative analysis is conducted to compare the suggested estimators using Monte Carlo simulation based on the related posterior risk. We also analyze the impact of distinct loss functions when using various priors.

Mathematical Models and Algorithms for Failed Equipment Planning Replacement

For the operability of deep well pump units under the conditions of oil and gas field equipment rental, it is necessary to plan the required resources. Failed equipment must be replaced with new one. If it is possible to repair the items, they are sent to repair service points. A review of literary sources on the subject of the study and the problem statement are provided. A large number of equipment application points and real operating conditions introduce great uncertainty into planning the failed equipment replacement. The value of the failure rate is a random variable. A mathematical model to manage the process of failed equipment replacing has been developed. Fuzzy logic methods have been used to take into account the uncertainty. Characteristics of fuzzy failure flow have been determined,based on the available information on failures. To perform computational operations, algorithms for working with fuzzy variables have been developed based on, in general case, asymmetric, and parabolic membership functions. The operations of addition, subtraction, multiplication, division, and exponentiation of fuzzy numbers are determined using interval arithmetic andα-cut method. The amount of equipment stock is determined by a fuzzy number. The use of a hybrid genetic algorithm provided an optimal solution to the optimization problem equipment replacement management. The calculation results are given. When solving the fuzzy optimization problem in comparative operations, the rank of the fuzzy number was calculated. The concept of centroid defuzzification of the fuzzy number is used to determine the rank value. The diagram of the fuzzy number membership function and the diagram of the optimization problem objective function: total costs were constructed. Compared with the optimization problem clear formulation, the total costs in the fuzzy formulation turned out to be higher, also the value of the safety stock was determined .

Analysis of the Pyrolysis Kinetics, Reaction Mechanisms, and By-Products of Rice Husk and Rice Straw via TG-FTIR and Py-GC/MS.

This study employed thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), and pyrolysis gas chromatography/mass spectrometry (Py-GC/MS) to characterize and provide insights into the pyrolysis behaviors and by-products of rice husk (RH) and rice straw (RS). The primary pyrolysis range is partitioned into three stages, designated as pseudo-hemicellulose, pseudo-cellulose, and pseudo-lignin pyrolysis, by an asymmetric bi-Gaussian function. The average activation energies of the three pseudo-components of RH were estimated by the Flynn-Wall-Ozawa and Starink methods to be 179.1 kJ/mol, 187.4 kJ/mol, and 239.3 kJ/mol, respectively. The corresponding values for RS were 171.8 kJ/mol, 185.8 kJ/mol, and 203.2 kJ/mol. The results of the model-fitting method indicated that the diffusion model is the most appropriate for describing the pseudo-hemicellulose reaction. The reaction of pseudo-cellulose and pseudo-lignin is most accurately described by a nucleation mechanism. An accelerated heating rate resulted in enhanced pyrolysis performance, with RS exhibiting superior performance to that of RH. RH produces 107 condensable pyrolysis by-products, with ketones, acids, and phenols representing the largest proportion; RS produces 135 species, with ketones, phenols, and alcohols as the main condensable by-products. These high-value added by-products have the potential to be utilized in a variety of applications within the agricultural, bioenergy, and chemical industries.

Breaking Left-Right Symmetry by the Interplay of Planar Cell Polarity, Calcium Signaling and Cilia.

The formation of the embryonic left-right axis is a fundamental process in animals, which subsequently conditions both the shape and the correct positioning of internal organs. During vertebrate early development, a transient structure, known as the left-right organizer, breaks the bilateral symmetry in a manner that is critically dependent on the activity of motile and immotile cilia or asymmetric cell migration. Extensive studies have partially elucidated the molecular pathways that initiate left-right asymmetric patterning and morphogenesis. Wnt/planar cell polarity signaling plays an important role in the biased orientation and rotational motion of motile cilia. The leftward fluid flow generated in the cavity of the left-right organizer is sensed by immotile cilia through complex mechanisms to trigger left-sided calcium signaling and lateralized gene expression pattern. Disrupted asymmetric positioning or impaired structure and function of cilia leads to randomized left-right axis determination, which is closely linked to laterality defects, particularly congenital heart disease. Despite of the formidable progress made in deciphering the critical contribution of cilia to establishing the left-right asymmetry, a strong challenge remains to understand how cilia generate and sense fluid flow to differentially activate gene expression across the left-right axis. This review analyzes mechanisms underlying the asymmetric morphogenesis and function of the left-right organizer in left-right axis formation. It also aims to identify important questions that are open for future investigations.

Bayes estimation of defective proportion for single shot device testing data with information on masking and manufacturing defects

The present work addresses the problem of estimating the defective proportion of Single-Shot-Devices under the Bayesian paradigm while taking failure cause information into account. In addition, the maximum likelihood estimation is also presented. The logit transformation is suggested to use for defective proportion parameter for better stability of the numerical maximum likelihood estimate. The same transformation is used for the posterior distribution. Since the posterior distribution becomes complex, we propose the use of Metropolis-Hastings method to draw the posterior samples and present posterior sample based inferences. Bayes estimation under several symmetric and asymmetric loss functions is presented in this work. Predictive posterior density of future failures is also derived. The prior distributions of the model parameters are assumed to follow specific functional forms. A comprehensive simulation study is conducted to examine the performance of the estimates in relation to sample size and model parameters. Our study demonstrates that integrating combined information on masking and defective proportions is crucial for parameter estimation. To demonstrate the practical application of our proposed methodology, we apply it to skin cancer data using the linear failure rate model as survival distribution.

Fuzzy Logic Approach for Evaluating Electromobility Alternatives in Last-Mile Delivery: Belgrade as a Case Study

This paper proposes a methodology based on the fuzzy approach, which provides decision-making support to the organizer of last-mile delivery (LMD) in selecting sustainable delivery models for a specific territory. Solving this task is essential to ensure that the delivery process is efficient and aligned with all three dimensions of sustainable development. The goal is to select the most suitable electromobility alternative for delivery implementation based on the characteristics of the requirements and the current circumstances. The proposed methodology involves the creation of a mechanism consisting of a series of fuzzy logic systems that will model expert opinions and produce a preference value as the output, defining the suitability of applying a particular LMD model. A specific methodological contribution is the creation of harmonized membership functions for fuzzy variables as a result of comparing symmetric and asymmetric membership functions aimed at achieving the most valid results. The results guide the delivery organizer in making the best decision when choosing from the analyzed models. The applicability and adequacy of the methodology are demonstrated through the results and analysis of a case study focused on the evaluation of electromobility alternatives in last-mile delivery in a part of the city of Belgrade. The obtained preference values, which range from 0 to 1 for all tested variants, are as follows within the interval: [0.481, 0.776] for e-motorcycles, [0.376, 0.564] for e-cargo bikes, and [0.5, 0.624] for e-scooters. The specific values of these indicators aim to support decision-makers in selecting a delivery model for a defined task based on the given constraints.

BAYESIAN ANALYSIS OF TOPP-LEONE EXPONENTIAL DISTRIBUTION WITH IDENTICAL PRIORS

This study focuses on estimating the shape and scale parameters of the Topp-Leone Exponential distribution. Bayes estimators are obtained using Exponential, Gamma, LogNormal, and Weibull distributions as the identical priors under asymmetric loss functions such as LINEX and Entropy and integrated with the Lindley approximation method. The simulation study was employed to determine identical prior and loss functions for the shape and scale parameters. It is observed that LINEX loss function with Exponential-Exponential prior for the shape parameter and the scale parameter Gamma-Gamma prior is most preferred for this distribution.

New model to predict thermomagnetic properties of nanostructured magnetic compounds

The development of new materials showing the magneto-caloric effect (MCE) requires fast and reliable characterization methods. For this purpose, a phenomenological model developed by M. A. Hamad has proven to be a useful tool to predict the magnetocaloric properties (the isothermal magnetic entropy change, ΔSM, the magnetization-related change of the specific heat, ΔCP,H, and the relative cooling power, RCP) via calculation from magnetization measurements as a function of temperature, M(T). However, fitting the M(T) data is difficult for broad, smoothed-out transition curves which are often observed for material systems such as core-shell nanoparticles, nanowires, nanowire fabrics or nanoparticle hybrid materials. Thus, in this contribution we present a different approach enabling proper fitting of such magnetization data via the use of an asymmetric Boltzmann sigmoid function, which provides a clear physical background and enables to properly describe the broad and smoothed out transitions of nanomaterials. As examples for our procedure, we present fits to M(T) curves of polycrystalline, bulk La0.67Ba0.33MnO3 as well as La1-xSrxMnO3 (x= 0.2, 0.3, 0.4) and La0.7Ca0.3MnO3 nanostructured materials from various authors.

Robust low-complexity vibration suppression control of rolling mill with time-varying state constraints

In this paper, a novel robust low-complexity vibration suppression control scheme is proposed for the vertical-torsional-horizontal coupling vibration of the rolling mill. To facilitate the control design, the overall coupling vibration system is decoupled into vertical-horizontal coupling vibration subsystem and torsional vibration subsystem. For the vertical-horizontal coupling vibration subsystem, a novel robust low-complexity controller is designed via constructed error transformations to provide strong robustness against uncertainties and disturbances. For the torsional vibration subsystem, a new robust state feedback controller is constructed via a backstepping technique to ensure the vibration angle of the motor rotor within given constraints via a proper asymmetric barrier function. The overall coupling vibration system is proved to be stable in the sense of uniform ultimate boundedness, and the vibration amplitude of the rolling mill can be reached arbitrarily small by selecting appropriate controller parameters. The numerical simulation is performed to verify the effectiveness and the superiority of the proposed control strategy.

Neuro-Adaptive-Based Fixed-Time Composite Learning Control for Manipulators With Given Transient Performance.

This article investigates an adaptive neural network (NN) control technique with fixed-time tracking capabilities, employing composite learning, for manipulators under constrained position error. The first step involves integrating the composite learning method into the NN to address the dynamic uncertainties that inevitably arise in manipulators. A composite adaptive updating law of NN weights is formulated, requiring adherence solely to the relaxed interval excitation (IE) conditions. In addition, for the output error, instead of knowing the initial conditions, this article integrates the error transfer function and asymmetric barrier function to achieve the specific performance for position error in both steady and transient states. Furthermore, the fixed-time control methodology and Lyapunov stability criterion are synergistically employed in order to guarantee the convergence of all signals in the manipulators to a compact neighborhood around the origin within a fixed-time. Finally, numerical simulation and experiments with the Baxter robot results both determine the capability of the NN composite learning technique and fixed-time control strategy.