Asymmetric Model Research Articles

Implementation of WRF‐Urban Asymmetric Convective Model (UACM) for Simulating Urban Fog Over Delhi, India

AbstractAccurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF‐Urban Asymmetric Convective Model (WRF‐UACM) for Delhi, India, integrating explicit urban physics with Sentinel‐updated USGS land‐use and urban morphological parameters derived from the UT‐GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF‐BACM) using Winter Fog Experiment (WiFEX) data, WRF‐UACM significantly improved urban meteorological variables such as diurnal variations in 10‐m wind speed, 2‐m air temperature (T2), and 2‐m relative humidity (RH2) during a fog event. UACM also demonstrates improved accuracy in simulating temperature and significantly reducing biases for wind speed and daytime RH2 under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF‐BACM, WRF‐UACM reduces the cold bias soon after sunset, thus improving the fog onset error by ∼3 hr. This study highlights the UACM's potential to improve fog prediction and its application in operational settings. With further investigation into different fog types, the UACM can provide crucial insights for preventive measures and reducing disruptions in urban areas.

Combined effect of artificial cervical disc replacement and facet tropism on the index-level facet joints: a finite element study

BackgroundArtificial Cervical Disc Replacement (ACDR) is an effective treatment for cervical degenerative disc diseases. However, clinical information regarding the facet joint alterations after ACDR was limited. Facet tropism is common in the sub-axial cervical spine. Our previous research indicated that facet tropism could lead to increased pressure on the cervical facet joints. This study aimed to assess the impact of facet tropism on the facet contact force and facet capsule stress after ACDR.MethodsA C2–T1 cervical finite element model was constructed from computed tomography (CT) scans of a 28-year-old male volunteer. Symmetrical, moderate asymmetrical (7 degrees tropism), and severe asymmetrical (14 degrees tropism) models were created at the C5/C6 level by altering the facet orientation at the C5-C6 level. The C5/C6 ACDR was simulated in the intact, moderate asymmetrical and severe asymmetrical models. A 75-N follower load with 1.0-Nm moments was applied to the top of C2 vertebra in the models to simulate flexion, extension, lateral bending, and axial rotation with the T1 vertebra fixed. The range of motions (ROMs) under all moments, facet contact forces (FCFs) and facet capsule strains were tested.ResultsIn the asymmetrical model, the right FCFs considerably increased under flexion, extension, right bending, left rotation, especially under right bending the right sided FCF of the severe asymmetrical model was about 5.44 times of the neutral position, and 3.14 times of the symmetrical model. and concentrated on the cephalad part of the facets. The facet capsule stresses on both sides remarkably increased under extension, lateral bending and right rotation. In the moderate and severe asymmetrical models, the capsule strain was greater on both sides of each position than in the symmetric model.ConclusionsThe face tropism increased facet contact force and facet capsule strain after ACDR, especially under extension, lateral bending, and rotation, and also could result in abnormal stress distribution on the facet joint surface and facet joint capsule. The results suggest that face tropism might be a risk factor for post-operative facet joint degeneration progression after ACDR. Facet tropism may be noteworthy when ACDR is considered as a surgical option.

CellSAM: Advancing Pathologic Image Cell Segmentation via Asymmetric Large-Scale Vision Model Feature Distillation Aggregation Network.

Segment anything model (SAM) has attracted extensive interest as a potent large-scale image segmentation model, with prior efforts adapting it for use in medical imaging. However, the precise segmentation of cell nucleus instances remains a formidable challenge in computational pathology, given substantial morphological variations and the dense clustering of nuclei with unclear boundaries. This study presents an innovative cell segmentation algorithm named CellSAM. CellSAM has the potential to improve the effectiveness and precision of disease identification and therapy planning. As a variant of SAM, CellSAM integrates dual-image encoders and employs techniques such as knowledge distillation and mask fusion. This innovative model exhibits promising capabilities in capturing intricate cell structures and ensuring adaptability in resource-constrained scenarios. The experimental results indicate that this structure effectively enhances the quality and precision of cell segmentation. Remarkably, CellSAM demonstrates outstanding results even with minimal training data. In the evaluation of particular cell segmentation tasks, extensive comparative analyzes show that CellSAM outperforms both general fundamental models and state-of-the-art (SOTA) task-specific models. Comprehensive evaluation metrics yield scores of 0.884, 0.876, and 0.768 for mean accuracy, recall, and precision respectively. Extensive experiments show that CellSAM excels in capturing subtle details and complex structures and is capable of segmenting cells in images accurately. Additionally, CellSAM demonstrates excellent performance on clinical data, indicating its potential for robust applications in treatment planning and disease diagnosis, thereby further improving the efficiency of computer-aided medicine.

Forecast of electric transport property via two models

Temperature and synthesis condition-dependent resistivity are critical parameters for determining the electrical properties of materials. Modeling serves as a powerful tool for predicting the electrical transport characteristics of materials. The first objective of this work was to conduct a quantitative analysis of the reported metallic and insulating resistivity behaviors in double-phase materials. Using non-linear regression, both a phenomenological model and an Asym2sig asymmetric peak model were employed to quantitatively analyze the temperature-dependent resistivity. The fitting results align closely with the observed resistivity-temperature curves. The second objective of this work was to establish a method linking chemical composition (synthesis conditions) to the parameters derived from the two models, calculation on the principle of non-linear curve fitting, two numerical equations are highlighted for quantitatively analyzing chemical composition-dependent parameters. The observed effects of chemical composition on the shift in the temperature-resistive curve, which are fundamental to understanding resistivity behavior, are discussed quantitatively for the first time to our knowledge for resistive-temperature behavior. Hence, the resistivity of both crystalline and non-crystalline materials can be forecasted across various compositions and temperatures using the methods presented, even without experimental data.

J-Curve For Indonesia’s Trade Balance with Non-Linier ARDL

The performance of Indonesia’s Balance of Payments (BOP), especially for the trade balance/current account, which recorded a surplus and adequate foreign exchange reserves, controlled political conditions, and attractive yields, can impact the exchange rate. This improvement in the exchange rate further encouraged the entry of foreign investors, which caused the Indonesian economy to continue to grow when the world experienced a financial crisis. This study analyzes asymmetric models to prove the Marshall Lerner Condition of Indonesia with trading partners. Using NARDL methods, this study uses monthly data from 2005 to 2021 from the International Federal Reserve (IFS), a data source for foreign trade activities. The result of this study is a J curve in Indonesia’s trade pattern with its trading partners, formed in the United States, Singapore, Vietnam, and Japan. For the nonlinear ARDL method, the J curve is formed in trading partners Netherlands, Germany, Korea, Singapore, United Kingdom, Vietnam, and Japan. The implementation of Indonesia’s exchange rate policy should be followed by a policy that can suppress the exchange rate against inflation because if this policy is not followed, it will not significantly impact Indonesia’s trade performance in the long run.

PERBANDINGAN METODE EXPONENTIAL GARCH (EGARCH) DAN GLOSTEN-JAGANNATHAN-RUNKLE GARCH (GJR-GARCH) PADA MODEL VOLATILITAS SAHAM TUNGGAL

Financial data, such as stock prices, usually have a tendency to fluctuate rapidly and create a heteroscedastic effect on the variance of residuals. The Covid-19 pandemic that occurred from 2020 to 2022 is one factor that can affect economic movements, especially in Indonesia, and has an impact on the volatility of financial data. The problem of heteroscedasticity can be addressed using the ARCH/GARCH model. However, this model has a weakness in capturing the asymmetry of volatility resulting from good news and bad news. Several models that can overcome the problem of volatility asymmetry are EGARCH and GJR-GARCH. The purpose of this thesis research is to determine the best volatility model using the daily stock price data of PT Bank Rakyat Indonesia (Persero) Tbk. covering the period from February 2020 to February 2023. The result of the asymmetric GARCH model suggests that the ARIMA(2,0,2) EGARCH(1,1) model has an AIC value of -4.8850, and the ARIMA(2,0,2) GJR-GARCH(1,1) model has an AIC value of -4.8907. Therefore, the model with the minimum AIC value, which is the ARIMA(2,0,2) GJR-GARCH(1,1) model, is considered the best model. Furthermore, this model exhibits very good forecast accuracy, as evaluated by the sMAPE value of 5,17%.

Catch the Cyber Thief: A Multi-Dimensional Asymmetric Network Attack–Defense Game

This paper presents a novel multi-dimensional asymmetric game model for network attack–defense decision-making, called “Catch the Cyber Thief”. The model is built upon the concept of partially observable stochastic games (POSG) and is designed to systematically incorporate multi-dimensional asymmetry into network attack–defense problems. The attack agent is called a “thief” who wants to control a key host by exploring the unfamiliar network environment, and the defense agent is called a “police” who needs to catch the opponent before its goal is accomplished. The results indicate that the asymmetry of network attack and defense is not only influenced by attack and defense strategies but also by spatio-temporal factors such as the attacker’s initial position, network topology, and defense lag time. In addition, we have found that there may exist the “attack rhythm,” which makes “how to maintain a good attack rhythm” and “how to generate a robust defense strategy against different attackers” worth exploring. Compared with existing attack–defense game models, our game model can better generate a direct mapping relationship with real elements, enabling us to understand network attack and defense interactions better, recognize security risks, and design defense strategies that can directly serve real-world decision-making.

The Actualities of Public Relations Practice at an Eastern Ontario University: A Quasi-ethnographic Study

Although various studies have used ethnographic techniques to examine different concepts, contexts, and practices in public relations (Sriramesh, 2000; Daymon & Hodges, 2009), none of these studies ethnographically examined the culture within which public relations is practiced, and discourse is produced in a college or university context. This quasi-ethnographic study aims to: (1) produce an account of the core aspects of the culture within which public relations is practiced at a university in Eastern Ontario and (2) identify what PR model best represents the PR practice there. To collect data for this study, I conducted e-mail interviews with five public relations participants at the university under investigation to collect data for this study. The data collected was about the groups’ routines, practices, and behaviors that shape their experience and facilitate producing relevant discourse. I also collected some genres and documents. The findings showed that (1) collaboration characterized the culture within which PR performs its duties and activities and produces relevant discourse, and (2) the two-way asymmetrical model best represents the PR practice in the university examined. Besides filling the gap identified here, the study contributes to theory and practice in the field. It benefits those who teach public relations writing and the institution itself as it evaluates how excellent the PR practice in that institution is.

COVID-19 and time-frequency spillovers between oil and sectoral stocks and portfolio implications: Evidence from China and US economies

This paper examines the volatility spillovers and the time-frequency dependence between crude oil and stock sectors in the U.S. and China using both wavelet coherence and asymmetric BEKK GARCH models. This study also investigates the impact of the COVID-19 pandemic on spillover effects and portfolio management. The results reveal strong positive co-movements between WTI oil and US sector stock returns at medium and low frequencies particularly in 2020Q1. We find significant long-term co-movements between oil and Chinese sector stock returns (64–128 days). Furthermore, the findings reveal a significant and asymmetric volatility transmission between oil and sector stocks in both the US and Chinese economies. The evidence of spillovers is more pronounced during the COVID-19 period. It is recommended that equity investors should hold more stocks than oil assets to minimize risk without reducing the expected return. Finally, hedging with oil is expensive for U.S. sectors during the pandemic but inexpensive for Chinese sectors.

Electric Field Features and Charge Behavior in Oil-Pressboard Composite Insulation under Impulse Voltage

Oil-pressboard/paper insulation materials are essential in transformers for ensuring their safe and stable operation, primarily due to their roles in spatial electric field distribution and charge migration mechanisms. Current spatial distribution analyses rely on computational methods that lack empirical validation, particularly for oil-pressboard/paper composites. This study leverages the principles of the Kerr electro-optic effect to develop a rapid measurement platform for electric fields within oil-pressboard/paper insulation under impulse voltage conditions, which measures the spatial electric field characteristics using Cu-Cu and Al-Al electrodes under various scenarios: with asymmetric and symmetric pressboard coverage and different numbers of insulating paper layers. Findings indicated: (1) In asymmetric pressboard models, Cu-Cu electrodes exhibit a consistent peak electric field of approximately 16 kV/mm, while Al-Al electrodes show peak values of 18.13 kV/mm and −14.98 kV/mm. Charge density patterns are similar, with Cu-Cu at about 68 μC/m2 and Al-Al at 11.2 μC/m2 and −124.8 μC/m2. (2) Symmetric models present consistent peak electric fields and charge densities for both polarities. (3) Increasing insulating paper layers elevates electric field strengths. Both electrodes show the similar peak field of about 17 kV/mm with differing paper layers due to higher charge injection from the Al electrode. (4) Utilizing the Schottky effect and field emission principles, the study clarifies charge generation and migration mechanisms. These insights could provide a theoretical foundation for designing and verifying oil-pressboard/paper insulation structures in transformers.

Value-at-risk modeling of naira exchange rates

The naira exchange rate has experienced extreme volatility, and the use of derivatives to control interest rates and currency risks has grown quickly. These developments have made risk management research necessary. To this effect, this paper investigated the optimal volatility model that offers the best VaR forecasts and minimizes expected loss in the Naira exchange rate. The data was collected from FX time and consisted of the Euro to Naira exchange and the daily closing rate of the US dollar to Naira exchange rate from 24/10/2016 to 15/06/2018. These data were estimated using different symmetric and asymmetric GARCH models. The finding shows that there is less volatility in the USD to Naira exchange rate compared to the Euro to Naira exchange rate. The study shows that SPLINE- GJR under skewed student t distribution is the optimal model for the Euro to Naira exchange rate while GARCH is the optimal model for the US dollar to Naira exchange rate. This study suggests that model risk assessments be given careful consideration by regulators, banks, and other stakeholders and integrated into the process of operational and regulatory design.

Exploring asymmetries in cryptocurrency intraday returns and implied volatility: New evidence for high-frequency traders

This paper aims to analyze the return-volatility relationship of Bitcoin and Ethereum across different return frequencies and all conditional quantiles of implied volatility, based on a unique 6.5 million observations. We employ the newly constructed Model-Free Implied Volatility (MFIV) of Bitcoin (BitVol) and Ethereum (EthVol) and use an asymmetric Quantile Regression Model (QRM) to capture the intraday asymmetric return-volatility relationship at different quantiles of the distribution of the dependent variable. Our findings show that the estimated coefficient using daily data is significant only at medium- to high-volatility regimes, while the estimated coefficients using high-frequency data are highly significant across all volatility regimes. Moreover, our results indicate that the asymmetry varies across frequencies and quantiles, with weak asymmetric effects at low quantiles and high frequencies, and strong asymmetric effects at high quantiles and low frequencies. This study provides new insight, especially for high-frequency traders.

Impact of airway closure and lung collapse on inhaled nitric oxide effect in acute lung injury: an experimental study

BackgroundEfficacy of inhaled therapy such as Nitric Oxide (iNO) during mechanical ventilation may depend on airway patency. We hypothesized that airway closure and lung collapse, countered by positive end-expiratory pressure (PEEP), influence iNO efficacy. This could support the role of an adequate PEEP titration for inhalation therapy. The main aim of this study was to assess the effect of iNO with PEEP set above or below the airway opening pressure (AOP) generated by airway closure, on hemodynamics and gas exchange in swine models of acute respiratory distress syndrome. Fourteen pigs randomly underwent either bilateral or asymmetrical two-hit model of lung injury. Airway closure and lung collapse were measured with electrical impedance tomography as well as ventilation/perfusion ratio (V/Q). After AOP detection, the effect of iNO (10ppm) was studied with PEEP set randomly above or below regional AOP. Respiratory mechanics, hemodynamics, and gas-exchange were recorded.ResultsAll pigs presented airway closure (AOP > 0.5cmH2O) after injury. In bilateral injury, iNO was associated with an improved mean pulmonary pressure from 49 ± 8 to 42 ± 7mmHg; (p = 0.003), and ventilation/perfusion matching, caused by a reduction in pixels with low V/Q and shunt from 16%[IQR:13–19] to 9%[IQR:4–12] (p = 0.03) only at PEEP set above AOP. iNO had no effect on hemodynamics or gas exchange for PEEP below AOP (low V/Q 25%[IQR:16–30] to 23%[IQR:14–27]; p = 0.68). In asymmetrical injury, iNO improved pulmonary hemodynamics and ventilation/perfusion matching independently from the PEEP set. iNO was associated with improved oxygenation in all cases.ConclusionsIn an animal model of bilateral lung injury, PEEP level relative to AOP markedly influences iNO efficacy on pulmonary hemodynamics and ventilation/perfusion match, independently of oxygenation.

Thermodynamic excess properties of the methyl tert-butyl ether + benzene + cyclohexane ternary system and its binary subsystems at temperature T = (293.15, 298.15, 303.15, and 313.15) K and ambient pressure

ABSTRACT This work reports for the first time, the experimental densities and sound speeds for the ternary system {methyl tert-butyl ether + benzene + cyclohexane} within the temperature range (293.15 to 313.15) K and under ambient pressure conditions. The related binary subsystems have also been reported. Experimental data were used to derive excess molar volumes and excess isentropic compressibilities. Redlich-Kister and Cibulka’s equations correlated the binary and ternary excess properties, respectively, with standard deviations falling below the estimated uncertainties of the corresponding properties. The results have been interpreted in terms of molecular interactions between mixtures’ components and structural effects. Additionally, two symmetric (Kohler and Muggianu) and two asymmetric (Hillert and Toop) binary contribution models have been tested for their ability to predict the excess properties of the ternary excess properties.

Urban system equilibrium model in an asymmetric polycentric city and the influence of rail transit investment

Taking bus and subway into account, an asymmetric polycentric urban system equilibrium model is constructed to explore the impacts of rail transit on the distribution of residents and labor force in a linear polycentric urban system. By comparing the urban system equilibria before and after the introduction of rail transit, the influence of rail transit on urban performance is analyzed. The results show that the introduction of rail transit makes the advantaged center more attractive for the labor force, and further widens the economic level gap between the two business centers. Specifically, larger distance between the two commercial centers and the higher subway travel cost would strengthen the polarization effect caused by rail transit investment. Since the improvement of transportation makes it easier for residents to accept long-distance commuting and they are more willing to choose the jobs in the advanced center.

The asymmetric power model 20 years on

Abstract It is over 20 years since the publication of the Asymmetric Power Model (APM). In the ensuing period, Britain has faced multiple challenges: the increasing fragmentation of public services; austerity politics; devolution; and, most recently, coronavirus disease and Brexit. It is this backdrop which provides the context to this article. First, it addresses how the literature has subsequently evolved. It then examines what has changed over the last two decades while also emphasizing what remains the same. Here, we highlight issues regarding both increased inequality and instability, focusing particularly on tension emerging from a de-centralizing-recentralizing approach to governance. The article concludes by offering a revised APM as a means of understanding British politics moving forward.

Energy price and returns: a threshold heteroscedasticity model approach

ABSTRACT The energy market is characterized by its intricate dynamics, influenced by various factors that result in erratic behavior, making it challenging to model energy price returns and volatility. In energy market, the changes in Brent and West Texas Intermediate (WTI) crude oil price yields and volatility are the focus of great interest and attention. Due to the influence of different periods and some economic factors, the fluctuation of crude oil price shows asymmetric characteristics. For exploring the asymmetric characteristics of energy market, we introduce the application of the threshold double autoregressive (TDAR) model to handle the complexities of energy price returns and volatility. The TDAR model is able to capture nonlinearities and regime shifts in the oil market, making it particularly suitable for understanding the inherent complexities in crude oil price movements. The empirical results implied that TDAR model demonstrates superior performance in capturing the intricate dynamics of crude oil prices compared to traditional asymmetric GARCH-type models, making it a valuable tool for market practitioners and policymakers. Our findings reveal the subtle movements in Brent and WTI prices and offer valuable insights for risk management and investment strategies, which provide an alternative for market practitioners and policymakers.

Image of the solid-state rotary motion encoded in the dielectric response

The future development of advanced molecular systems with controlled rotation requires the development of an effective methodology for assessing the rotational performance of artificial machine components. We identified two patterns of the dielectric behavior for polar rotators in a static non-polar framework of sizable crystal showing relations between the spectral and molecular-level features of solid-state rotary motion. Various functionalization of phenylene rotors with a fluorine atom(s) changed rotational performance from high to low with rotational barriers ranging from 6.06 to 11.84 kcal mol-1. The meta-F-substitution favored rotator-rotator contacts allowing for the implementation of fast rotary motion. Contrary, the presence of rotator-stator contacts inhibited independent rotator dynamics leading to opposite spectral behavior in terms of temperature evolution of loss peak amplitude. Our observations, supported by an analysis based on an asymmetric double well-potential model, show that easily noticeable spectral differences encoded some molecular-level information important for the implementation of rotary motion.

Stock Market Development and Environmental Sustainability in Saudi Arabia: Asymmetry Analysis

Stock Market Development (SMD) can have positive or negative environmental effects by providing the capital to clean or dirty production processes, respectively. The present study investigates the effect of SMD on CO2 emissions in Saudi Arabia from 1985 to 2022 by conducting symmetrical and asymmetrical analyses. The results validate the environmental Kuznets curve in both symmetrical and asymmetrical analyses. The turning point of the curve is found at 61500 SR and 49944 of income per capita in symmetrical and asymmetrical analyses, respectively, in the long run. The turning points are found at 65895 SR and 54979 SR in the symmetrical and asymmetrical models, respectively, in the short run. All points are within the range of sample period’s income. Thus, Saudi economic growth has no environmental problems. Moreover, SMD and increasing SMD help reduce CO2 emissions in the long run with elasticity parameters 0.8548 and 0.9854 in symmetrical and asymmetrical models, respectively. Thus, SMD should further be focused to have pleasant environmental effects in Saudi Arabia.

CIRA: Class imbalance resilient adaptive Gaussian process classifier

The problem of class imbalance is pervasive across various real-world applications, resulting in machine learning classifiers exhibiting bias towards majority classes. Algorithm-level balancing approaches adapt the machine learning algorithms to learn from imbalanced datasets while preserving the data’s original distribution. The Gaussian process classifier is a powerful machine learning classification algorithm, however, as with other standard classifiers, its classification performance could be exacerbated by class imbalance. In this work, we propose the Class Imbalance Resilient Adaptive Gaussian process classifier (CIRA), an algorithm-level adaptation of the binary Gaussian process classifier to alleviate the class imbalance. To the best of our knowledge, the proposed algorithm (CIRA) is the first adaptive method for the Gaussian process classifier to handle unbalanced data. The proposed CIRA algorithm consists of two balancing modifications to the original classifier. The first modification balances the posterior mean approximation to learn a more balanced decision boundary between the majority and minority classes. The second modification adopts an asymmetric conditional prediction model to give more emphasis to the minority points during the training process. We conduct extensive experiments and statistical significance tests on forty-two real-world unbalanced datasets. Through the experiments, our proposed CIRA algorithm surpasses six popular data sampling methods with an average of 2.29%, 3.25%, 3.67%, and 1.81% in terms of the Geometric mean, F1-measure, Matthew correlation coefficient, and Area under the receiver operating characteristics curve performance metrics respectively.