Asymmetric Model Research Articles

Level set method for structure topology optimization with asymmetrical microstructure model

Level set method for structure topology optimization with asymmetrical microstructure model

Unraveling asymmetrical spillover effects originating from China's green finance markets: Insights from asymmetric TVP-VAR and interpretable machine learning.

This study combines an asymmetric TVP-VAR model with interpretable machine learning algorithms to confirm the presence of asymmetries in spillover effects within China's green finance market and to identify the macroeconomic drivers behind these effects. The key findings are as follows: First, China's green finance market has become a prominent transmitter of energy risk spillovers, with a significant asymmetry in its external effects-negative return spillovers exceed positive ones. This asymmetry is especially evident during extreme events like the 2014 oil price crash and the COVID-19 pandemic, indicating that investors in this market are more responsive to negative news. Second, using interpretable machine learning models, we identify the macroeconomic factors that significantly impact spillover effects in the green finance market, with Economic Policy Uncertainty and the U.S. Energy Price Index standing out as particularly influential. Third, while the drivers of positive and negative spillovers differ, their directional impact is consistent across both. These insights are crucial for investors aiming to diversify portfolios and for policymakers managing risks in asymmetric market conditions.

Estimating and Forecasting Tax Revenues Using GARCH Family of Models: A Case of Pakistan

Forecasting plays a vital role in effective planning and decision-making for policy formulation across a variety of fields of life. The Nonlinear models such as the GARCH family, including symmetric and asymmetric generalized autoregressive conditional heteroscedastic (GARCH) models with both normal and non-normal innovations are applied in this study to capture the dynamic and asymmetric features of the two tax revenue series, sales Tax and Direct Tax in Pakistan. Additionally, Autoregressive Moving Average (ARMA) model is used as the mean model. The prime objective of this research is to examine the estimating and forecasting performance of ARMA models along GARCH family models, for the monthly tax revenue series in Pakistan, particularly focusing on symmetric GARCH and asymmetric GARCH models (EGARCH, TGARCH, and PARCH). Empirical evidence based on the application of these models to the selected series reveals that the GARCH family models effectively confine the heteroscedasticity, highlighting the strength of these models. In addition, three distributions, normal, Student-t and generalized error distribution are considered for the residuals. Under the normal distribution, ARMA(5,4)-EGARCH(1,1,2) model is selected as the best model based on minimum values of MAE, RMSE, MAPE, and TIC. In the same way, for the Student-t and Generalized Error Distribution, the ARMA(5,4)-EGARCH(1,1,1) and ARMA(5,4)-EGARCH(1,1,3) models are selected as the best forecasting models for the sales tax series. The ARMA(3,3)-EGARCH(1,1,1) model is selected as the best forecasting model based on the minimum values of MAE, RMSE, MAPE, and TIC for the direct tax series assuming a normal distribution. Similarly, the ARMA(3,3)-TGARCH(1,1,1) and ARMA(3,3)-TGARCH(1,1,1) models are selected as the best forecasting models for the direct tax.

Resistance Theory of Asymmetric 2 × n Circuit Network

ABSTRACTThis article proposes a class of asymmetric 2 × n resistor network model which contains multiple independent resistors; the study of asymmetric resistor network models is a challenge. We conducted in‐depth research on this issue using the RT‐V theory (Chinese Physics B, 2017, 26(9): 090503) and achieved new theoretical breakthroughs. This paper derived five original equivalent resistance formulae for this asymmetric 2 × n circuit network and also discusses the analytical expressions for equivalent resistance in different special cases, and several interesting results have been derived, indicating that the original resistance formula has powerful functionality. Finally, visual graphics of two types of equivalent resistances are provided using MATLAB drawing tools, which reveals the law of equivalent resistance changing with resistance variables. The research theory and technology in this article will provide a new theoretical basis for related scientific, engineering, and simulation research.

Information propagation dynamics on heterogeneous-homogeneous coupling bi-layer networks

The proliferation of multi-platform network information has expanded communication channels for users, enabling the integration and dissemination of information across both Social Networking Services (SNS)-type app and Instant Message (IM)-type app. With the intensification of convergent communication, some users in the two types of apps show active alternation in spreading information to each other’s platforms. The study of the evolution trend of information in different platforms is of great practical significance for the mastery of the communication law. This study synthesizes the following three points: (1) The information in SNS-type app diffuses from key nodes with more followers to ordinary nodes, showing the characteristics of heterogeneous network with radial and explosive propagation. (2) The information in IM-type app mainly depends on the “relationship chain” diffusion, showing the characteristics of homogeneous network with gradual and multi-cluster propagation. (3) SNS-type apps and IM-type apps with some users showing coupled propagation characteristics. Therefore, this study constructs the heterogeneous-homogeneous asymmetric coupling two-layer network information propagation dynamics model. The propagation threshold R0 and the stability of the model are derived theoretically. Real network data sets are used to simulate the platform fusion. Numerical simulations confirm the rationality of the propagation threshold and perform changes analyses of parameters, such as the degree of cross-circulation of platforms, users’ tendency of multi-platform expression, and changes in users’ behaviors towards information dissemination. Simulation results reveal that promoting platform integration can improve communication efficiency in the real world. Dual-platform communication by IM-platform spreaders substantially contributes to the growth in the number of SNS-platform spreaders. The higher the level of disinterest in dual-platform spreaders, the more likely it is to inhibit the growth of spreaders and removers, with IM-type app demonstrating more pronounced effects.

Channel-Hopping Sequence and Searching Algorithm for Rendezvous of Spectrum Sensing.

In this paper, we propose a method for applying the p-ary m-sequence as a channel-searching pattern for rendezvous in the asymmetric channel model of cognitive radio. We mathematically analyzed and calculated the ETTR when the m-sequence is applied to the conventional scheme, and our simulation results demonstrated that the ETTR performance is significantly better than that of the JS algorithm. Furthermore, we introduced a new channel-searching scheme that maximizes the benefits of the m-sequence and proposed a method to adapt the generation of the m-sequence for use in the newly proposed scheme. We also derived the ETTR mathematically for the new scheme with the m-sequence and showed through simulations that the performance of the new scheme with the m-sequence is superior to that of the conventional scheme with the m-sequence. Notably, when there is only one common channel, the new scheme with the m-sequence achieved approximately four times the improvement in the ETTR compared to the conventional scheme.

The Robust Asymmetric Minimum Cost Consensus Models with Interval-type Opinions

Existing consensus models primarily rely on precise opinions from decision-makers in a predefined context, neglecting the dynamics of expert opinion adjustments. To address this limitation, we introduce the interval-type opinions and explores the group decision consensus model under asymmetric adjustment cost from an uncertainty perspective. Then, the robust optimization theory is applied to adress the uncertainty in adjustment costs of decision-making individuals. The robust asymmetric cost consensus model of interval opinions under three uncertain scenarios is built. Finally, the validity of proposed model is verified by numerical calculations, and a sensitivity analysis and comparative study are performed. The results show that: (1) Utilizing interval opinions can significantly reduce consensus costs when compared to precise opinions; (2) Comprehensively comparing the three proposed robust models, the consensus model with budget asymmetric cost has the best performance.

Unraveling the Link: Examining the Influence of Dollar Strength on Dirty Tanker Freight Rates

This study explores the asymmetric relationship between the U.S. dollar index (DXY) and dirty tanker freight rates, a largely unexamined area within maritime economics despite the dollar’s profound influence on global oil prices and economic stability. Tanker shipping, the primary mode of transportation for global oil and a capital-intensive sector, plays a crucial role in oil supply chains. Given that oil prices are quoted in U.S. dollars, fluctuations in dollar strength directly impact oil costs, demand for tankers, and the operational costs of tanker shipping. This study employs an asymmetric model, recognizing that dollar value changes affect the tanker market differently depending on whether the dollar is strengthening or weakening. Findings reveal that decreases in the DXY drive up tanker freight rates, while increases do not correspondingly decrease rates, highlighting the unique non-linear dynamics between currency strength and freight costs. This asymmetric approach provides a more accurate framework for understanding these interactions than traditional linear models.

A Zero-Inflated Poisson Asymmetric Power GARCH Model for $$\mathbb {Z}$$-valued Time Series

A Zero-Inflated Poisson Asymmetric Power GARCH Model for $$\mathbb {Z}$$-valued Time Series

Asymmetric Gaussian Echo Model for LiDAR Intensity Correction

In light detection and ranging (LiDAR) applications, correct intensities from echo data intuitively contribute to the characterization of target reflectivity. However, the power in raw echo waveforms may be clipped owing to the limited dynamic range of LiDAR sensors, which directly results in false intensity values generated by existing LiDAR systems working in scenarios involving highly reflective objects or short distances. To tackle the problem, an asymmetric Gaussian echo model is proposed in this paper so as to recover echo power–time curves faithfully to its optical physics. Considering the imbalance in temporal length and steepness between rising and falling edges, the echo model features a shared mean and two distinct standard deviations on both sides. The accuracy and effectiveness of the proposed model are demonstrated by correcting the power–time curve from a real LiDAR loaded with avalanche photodiode (APD) sensors and estimating the reflectivities of real targets. As when tested by targets with reflectivities from low to high placed at distances from near to far, the model achieves a maximum of 41.8-fold improvement in relative error for the same target with known reflectivity and a maximum of 36.0-fold improvement in the coefficient of variation for the same target along the whole range of 100 m. Providing accurate and stable characterization of reflectivity in different ranges, the model greatly boosts applications consisting of semantic segmentation and object recognition, such as autonomous driving and environmental monitoring.

Tunneling Times in an Asymmetric Harmonic Double-Well with Application to Electron Transfers in Biological Macromolecules.

Tunneling times were calculated in electron transfer processes using an asymmetric harmonic double-well model. The simplicity of a direct variational calculation in the approximate solution of the Schrödinger equation, along with the interpretation of tunneling times within the probabilistic framework of a two-level system, allows for the efficient and accurate determination of tunneling times with minimal computational cost. These calculations were applied to electron transfer processes in the study of the photosynthetic reaction center and in the context of catalysis in UV-induced DNA lesion repair and are in agreement with the experimental, computational, and theoretical results with which they were compared. It was seen that the donor-acceptor distance needed to be adjusted for closer agreement between the calculated and experimentally observed times. However, the adjusted values for this distance remain close to those reported in the literature.

An asymmetric hysteresis model for metal-rubber isolators under dynamic loading and its application to nonlinear vibration simulation

An asymmetric hysteresis model for metal-rubber isolators under dynamic loading and its application to nonlinear vibration simulation

Study on vertical-torsional chatter under asymmetric conditions in tandem cold rolling

The vibrations generated by the rolling mills significantly impair the production quality and efficiency of cold tandem rolling. Considering that the main drive system constitutes one of the energy sources of vibration, this study integrates the vertical structure of the rolling mill with the torsional structure of the main drive system for a comprehensive analysis. This study establishes an asymmetric dynamic rolling force model that accounts for the structural asymmetry of the rolling mill, the asymmetric power transmission between the upper and lower connecting shafts of the main drive system, and the long-term wear and friction conditions of the mechanical equipment. Furthermore, this study accounts for the speed differential between the upper and lower work rolls induced by torsional vibrations. Based on these interdependent parameters, this study develops a rolling mill vibration coupling model to comprehensively assess stability. The validation of the model confirms its high accuracy. Variations in rolling torque under different roll speed ratios, friction coefficient ratios, and roll diameter ratios were analyzed, along with the impact of the cross-shear zone's proportion on system stability under key rolling parameters. Furthermore, changes in rolling torque, amplitude, and the cross-shear zone during unstable operating conditions were examined. Finally, the interaction between the vertical and torsional structures was studied, providing a theoretical foundation for optimizing process parameters and mitigating vibrations in the continuous cold rolling process.

An Asymmetric Model of the Tropical Cyclone Surface Wind Field and Probabilistic Climatology of Structural Parameters

Abstract A new parametric model of a tropical cyclone’s (TC) surface wind field is proposed, in which parameters are calibrated on over 20 years of direct surface wind measurements from aircraft reconnaissance. The model extends previous formulations by representing structural asymmetries in the inner core and far field. The surface wind representation eliminates the need for uncertain aircraft flight (gradient) level-to-surface extrapolation or pressure gradient formulation conversions to wind speed. Parameters are expanded to wavenumber-1 asymmetric Fourier components around symmetric mean values; symmetric values are found to depend primarily on maximum wind speed and geographic location, while some asymmetric parameters show statistically significant relationships with motion and environmental vertical shear, consistent with recent studies. Finally, a probabilistic representation of the climatology of basin-wide TC wind field structure is constructed by fitting a multivariate statistical distribution to the optimized model parameters. The low-dimensional formulation is suitable for computationally efficient reconstruction of historical TC wind fields, from depression to Saffir–Simpson category-5 intensities, as well as for stochastic simulation in the context of catastrophe risk modeling.

Forecasting Financial Volatility: A Hybrid Approach Integrating ICEEMDAN and Asymmetric Volatility Models

Forecasting Financial Volatility: A Hybrid Approach Integrating ICEEMDAN and Asymmetric Volatility Models

Day-to-day dynamic traffic evolution in the urban traffic system with ride-sharing

The introduction of ride-sharing services has significantly diversified commuting options for urban residents. In multi-mode cities, residents select travel modes based on their perception of the costs for various available options, while these perceptions are updated daily. As a result, residents’ travel mode choices and mode-split traffic flows vary day by day. To capture the nonlinear evolution phenomenon of their mode choice behaviors and mode-split traffic flows, we focus on a linear monocentric city with the introduction of ride-sharing services and develop a deterministic discrete-time day-to-day dynamic evolution model. Our model incorporates residents’ limited perceptions to better reflect real-world scenarios. Moreover, considering one ride-sharing driver is allowed to pick up multiple passengers, specific constraints on the number of ride-sharing drivers and passengers (i.e., side constraints) are added to the model formulation. Thus, the proposed model is a discrete-time day-to-day dynamic asymmetric stochastic user equilibrium model with side constraints, which have rarely been studied. The uniqueness of optimal Lagrange multipliers corresponding to side constraints in the day-to-day dynamic evolution model is demonstrated, which makes us successfully extend related literature on static ride-sharing equilibrium to the study of dynamic stochastic ride-sharing user equilibrium problems. Furthermore, we consider the stability issue of the equilibrium and provide sufficient and necessary conditions for its asymptotic stability. Finally, numerical examples are conducted to validate the properties and effectiveness of our dynamic model.

Phase diagram of non-Hermitian BCS superfluids in a dissipative asymmetric Hubbard model

Phase diagram of non-Hermitian BCS superfluids in a dissipative asymmetric Hubbard model

A rapid estimation method for the probability of wind-induced first plastic displacement of SDOF systems based on the Slepian model

The conventional method based on the Slepian model for estimating the probability distribution of the first plastic displacement of an ideal elastic-plastic oscillator (EPO) is only applicable for zero-mean white noise loads. This study proposes an improved method applicable to wind loads with non-zero mean and non-white noise characteristics for structures whose resonant response dominates the fluctuating wind-induced response. The original EPO's symmetric constitutive model under non-zero mean loads is modified to be equivalent to an asymmetric constitutive model under zero mean loads; thus, a SDOF system subjected to non-zero mean loads is transformed into a SDOF system under zero mean loads. When the resonant response dominates the fluctuating wind-induced response, the fluctuating wind-induced response can be approximated as a narrow-band stochastic process, the extreme values following a Rayleigh distribution, and the probability distribution of the local maximum displacement of the associated linear oscillator (ALO) can be directly obtained, making the Slepian model-based method applicable for non-white noise loads. A case study is performed to validate the accuracy of the proposed improved method, and the results demonstrate that the improved method proposed in this study can significantly enhance computational efficiency. The accuracy of the improved method is independent of the load characteristics but rather depends on the narrowband characteristics of the structural response. When the narrowband characteristics of the structural response are prominent, the improved method exhibits high accuracy.

An exploration of sustainable innovation investment in emerging markets: An archival study

This study aims to examine the relationship between asymmetric cost behavior and innovation of listed firms in emerging markets, i.e., listed firms in the Indonesian capital market. The Sustainable Development Goals (SDGs) topic has become a hot issue worldwide. Innovation, as a part of SDGs, is a prerequisite to reducing the carbon emissions in a country, including the listed firms on the Indonesian capital market. This study employs quantitative method. To investigate Indonesian-listed firms investment in innovation, we use innovation scores from Thomson Reuters to investigate the investment in innovation by the Indonesian-listed firms. This study utilizes multiple regression tests to examine the empirical model. Our study uses an asymmetric cost behavior model to examine the investment from the firms. We apply data panel to examine the model, i.e., the listed firms of Indonesian capital market during 2010-2019. The result is that innovation influences asymmetric cost behavior. This study also performs a robustness check regarding the empirical model. The result shows that the model is robust. We contribute to the literature on sustainability accounting and the literature on the capital markets. The implication of this study is to give investors information related to the development of sustainability in developing markets. The developing market is a promising investment for investors worldwide. This study also gives feedback to regulators related to the development of innovation in developing markets, particularly Indonesian capital market.

Mathematical Formalization and Applications to Data with Excess of Zeros and Ones of the Unit-Proportional Hazard Inflated Models

In this study, we model the rate or proportion of a specific phenomenon using a set of known covariates. To fit the regression model, which explains the phenomenon within the intervals (0,1), [0,1), (0,1], or [0,1], we employ a logit link function. This approach ensures that the model’s predictions remain within the appropriate range of zero to one. In cases of inflation at zero, one, or both, the logit link function is similarly applied to model the dichotomous Bernoulli-type variable with a multinomial response. The findings demonstrate that the model yields a non-singular information matrix, ensuring valid statistical inference. This ensures the invertibility of the information matrix, allowing for hypothesis testing based on likelihood statistics regarding the parameters in the model. This is not possible with other asymmetric models, such as those derived from the skew-normal distribution, which has a singular information matrix at the boundary of the skewness parameter. Finally, empirical results show the model’s effectiveness in analyzing proportion data with inflation at zero and one, proving its robustness and practicality for analyzing bounded data in various fields of research.