Time-varying Relationship Research Articles

An unsupervised ocean surface waves suppression algorithm based on sub-aperture SAR images

ABSTRACT Ocean waves are the richest small-scale texture on the sea surface, from which valuable information can be inversed. In general, synthetic aperture radar (SAR) images of surface waves will inevitably have an impact on some oceanography follow-up applications due to the intricate motion of the waves. However, existing suppression methods based on image geometric properties or deep learning-based have limitations. Owing to the lack of clean images and the uncertainty of the statistical characteristics of seawater, these methods suppress ocean surface waves while harming other target information. To address this issue, we propose an unsupervised wave suppression algorithm based on sub-aperture SAR images named SAR-Noise2Noise (SAR-N2N). First, SAR-N2N exploits the geometric and time-varying relationship of the sub-aperture images to suppress the wave target selectively through sub-aperture image cancellation. Second, through the method of unsupervised learning, only noisy images are used to suppress ocean surface waves, avoiding heavy dependence on the assumption of the wave distribution. Finally, the proposed regularizer is used as an additional loss to form an end-to-end training process. We explain our approach from a theoretical perspective and further validate it through extensive experiments, including synthetic experiments with various ocean wave distributions in simulated SAR images and real-world images. Training on collocations over SAR images, we demonstrate on test data from simulated images that SAR-N2N can improve peak signal-to-noise ratio (PSNR) up to dB compared with the classical approach. Furthermore, it improves the equivalent number of looks (ENL) value of real-world SAR images by a factor of . All results and the methods are novel in terms of the accuracy achieved, combining the classical approach with deep learning techniques. We conclude that SAR-N2N has the potential to make useful contributions to several oceanographic applications as well as to the near real time (NRT) processing of multiparametric sea states.

Fund ESG performance and downside risk: Evidence from China

Whether responsible investing reduces portfolio risk remains open to discussion. We study the relationship between ESG performance and downside risk at fund level in the Chinese equity mutual fund market. We find that fund ESG performance is positively associated with fund downside risk during the period between July 2018 and March 2021, and that the positive relationship weakens during the COVID-19 pandemic. We propose three channels through which fund ESG performance could affect fund downside risk: (i) the firm channel in which the risk-mitigation effect of portfolio firms’ good ESG practices could be manifested at fund level, (ii) the diversification channel in which the portfolio concentration of high ESG-rated funds could amplify fund downside risk, and (iii) the flow channel in which funds’ better ESG performance may attract greater investor flows that could reduce fund downside risk. We show evidence that the observed time-varying relationship between fund ESG performance and downside risk is driven by the relative force of the three channels.

Dynamic relationship among climate policy uncertainty, oil price and renewable energy consumption—findings from TVP-SV-VAR approach

This paper collects monthly data for the United States from January 2005 to April 2021 and develops a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model to investigate the time-varying relationship between climate policy uncertainty, oil prices, and renewable energy consumption. In addition, this paper discusses the dynamic impact of climate policy uncertainty on five types of renewable energy consumption. Our results show that the relationship between them is time-varying. In most periods, climate policy uncertainty positively affects oil prices in the short and medium term, and positively affects total renewable energy consumption in the short and long term. In addition, the impact of climate policy uncertainty on the consumption of the five types of renewable energy is heterogeneous. The results of the robustness tests indicate that our results are robust and our study has important implications for the development of relevant policies.

Time-Varying Sequence Model

Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data problems with the use of internal memory states. However, the neuron units and weights are shared at each time step to reduce computational costs, limiting their ability to learn time-varying relationships between model inputs and outputs. In this context, this paper proposes two methods to characterize the dynamic relationships in real-world sequential data, namely, the internal time-varying sequence model (ITV model) and the external time-varying sequence model (ETV model). Our methods were designed with an automated basis expansion module to adapt internal or external parameters at each time step without requiring high computational complexity. Extensive experiments performed on synthetic and real-world data demonstrated superior prediction and classification results to conventional sequence models. Our proposed ETV model is particularly effective at handling long sequence data.

Establishment and Application of a Novel Four-Dimensional Model for Simulation of a Natural Water Flooding Reservoir—A Case Study of Nanpu No. 2 Structure in the BHW Basin

Long-term development of fluvial reservoirs causes regular changes in the microscopic pore-throat structure, physical properties and phase permeability curves of the reservoirs, and the evolution history of different oil layer varies. These effects can be explored using a four-dimensional (4D) model of the maturing field. The logging curves of 89 sand bodies of the Nanpu No. 2 structure in the Bohai Bay Basin were restored to the non-water flooded state based on the changing status of water flooding layers at different stages and levels, and an original geological model was established by combining data for the old wells. The time-varying relationship was incorporated in the dynamic model with surface flux as the variable, and the numerical simulation analysis was conducted based on the reservoir time-varying characteristics. The results showed that ① the logging data of the water-flooded layer significantly affected the simulation results of the geological model and can only be used for the establishment of the initial geological model after repositioning of time and space; ② the time variation of reservoir properties and relative permeability markedly affected the simulation results of the remaining oil. Reservoir parameters and fluid properties simulated by the 4D model were consistent with the data of water flooded layers in new wells during different periods; ③ the novel method significantly improved the fitting rate of the dynamic model, and the model was used to quantitatively describe the morphology of dominant seepage channels and reservoir variation parameters. The novel idea of ‘retreat as progress’ and ‘bring in to solve’ achieved the solution of the dynamic and static parameters of the 4D model according to each time step of space-time evolution. The results provide a guide for comprehensive adjustment and evaluation of Nanpu Oilfield for 52 well-times with an oil increase of 1.68 × 104 tons for the different stages.

Racial/ethnic differences in the relationship between wealth and health across young adulthood

BackgroundAlthough the relationship between socioeconomic position (SEP) and health has been well documented, very few prior investigations have examined the time-varying association between wealth and health across race/ethnicity. This study examined the racial/ethnic differences in the wealth–health associations during young adulthood. MethodData were drawn from the National Longitudinal Survey of Youth 1997 with three time points, when respondents were aged 20, 25, and 30. The primary dependent variable was dichotomized self-rated health (SRH). Two indices of wealth were calculated: respondents’ own reported net worth and reported parental net worth in 1997. Other SEP indicators included household income, education, employment status, and parental education. Three racial/ethnic groups were examined: nonHispanic White, non-Hispanic Black, and Hispanic. Two-level logistic regression was performed, stratified by race/ethnicity. ResultsIn the whole-sample analysis, racial/ethnic differences in SRH were not statistically significant, after controlling for wealth. For self-reported net worth during young adulthood, wealth has a incremental consistent effect on health among non-Hispanic White respondents and Hispanic respondents but not among Black respondents. Individual net worth and parental net worth were only significantly associated with health among the highest wealth quartiles among non-Hispanic Black respondents. Only individual net worth, not parental net worth, was significantly associated with SRH in the Hispanic sample. ConclusionThis study examined racial/ethnic differences in time-varying relationship between wealth and health during young adulthood. Findings indicate that there are notable racial/ethnic differences in the patterning of wealth–health association that appear to emerge in early adulthood. Implications for asset-based policy and programs are discussed.

BSS Flash Poster Session 2: The Power of Purpose and Personal Ties in Late Life

Abstract This study examined (1) the time-varying relationship between purpose in life and perceived stress reactivity, (2) the trajectory of perceived stress reactivity as it relates to both within- and between-person purpose in life, as well as (3) the predictive utility of perceived stress reactivity and its rate of change on cognitive ability and allostatic load. The sample comprised 933 participants from the Notre Dame Study of Health & Well-being, a 10-year study of annual questionnaire packets and biennial daily diary bursts. Analyses included three-level multilevel models from which random effects were extracted and used to predict allostatic load and cognitive ability. Results indicated that individuals were affectively reactive to perceived stress, and that perceived stress reactivity declined over time. Considering the effects of purpose in life on these processes, there were two cross-level interaction effects indicating (1) more purposeful individuals were less stress reactive than less purposeful individuals, and (2) more purposeful individuals declined less in negative affect over time than less purposeful individuals. There was also preliminary evidence for a within-person interaction effect between yearly purpose in life and daily affective reactivity such that when individuals felt particularly purposeful, they also tended to be less stress reactive. Finally, higher perceived stress reactivity, as well as less decline in this construct, was predictive of better cognitive ability. These findings indicate purpose in life buffers against environmental and maturational effects on negative affect, and that perceived stress reactivity may indicate a different, more adaptive process than affective reactivity to experienced stressors.

PURPOSE IN LIFE, STRESS REACTIVITY, AND COGNITIVE AGING: A LONGITUDINAL INVESTIGATION

Abstract This study examined (1) the time-varying relationship between purpose in life and perceived stress reactivity, (2) the trajectory of perceived stress reactivity as it relates to both within- and between-person purpose in life, as well as (3) the predictive utility of perceived stress reactivity and its rate of change on cognitive ability and allostatic load. The sample comprised 933 participants from the Notre Dame Study of Health & Well-being, a 10-year study of annual questionnaire packets and biennial daily diary bursts. Analyses included three-level multilevel models from which random effects were extracted and used to predict allostatic load and cognitive ability. Results indicated that individuals were affectively reactive to perceived stress, and that perceived stress reactivity declined over time. Considering the effects of purpose in life on these processes, there were two cross-level interaction effects indicating (1) more purposeful individuals were less stress reactive than less purposeful individuals, and (2) more purposeful individuals declined less in negative affect over time than less purposeful individuals. There was also preliminary evidence for a within-person interaction effect between yearly purpose in life and daily affective reactivity such that when individuals felt particularly purposeful, they also tended to be less stress reactive. Finally, higher perceived stress reactivity, as well as less decline in this construct, was predictive of better cognitive ability. These findings indicate purpose in life buffers against environmental and maturational effects on negative affect, and that perceived stress reactivity may indicate a different, more adaptive process than affective reactivity to experienced stressors.

Double-Loop Control for Torque Tracking of Dry Clutch

Torque tracking is an important control target of a dry clutch. At present, the torque tracking control method of a dry clutch generally uses the relationship between the clutch torque and release bearing position obtained via experiment to convert the torque tracking control of the clutch into the position tracking control of the release bearing. However, due to the nonlinearity and time-varying parameters of the dry clutch, it is difficult to obtain an accurate and fixed relationship between torque and position. At the same time, there are also the nonlinearity and interference problems in the position tracking control process. In order to solve the above problems, this paper takes the torque tracking control process of the dry clutch in a three-speed automatic mechanical transmission of an electric vehicle as an example and proposes a double-loop control controller including torque loop and position loop. Firstly, the drive system dynamic model of electric vehicles and clutch actuator dynamic models is established. Secondly, the torque loop controller is established with the adaptive feedforward-feedback control method. The time-varying parameters are adaptively adjusted with adaptive feedforward control to solve the problem of the time-varying relationship between torque and position. In addition, the feedback control loop is added to improve the robustness of the controller. Thirdly, the position loop controller is established with the backstepping-based active disturbance rejection control method to solve the nonlinear and disturbance problems of the position tracking control. In the end, the torque loop and position loop are cascaded to form a double-loop controller, and the torque tracking control simulations of the dry clutch are carried out. The simulation results show that the double-loop controller has good control performance, and the effectiveness of the controller has been preliminarily verified.

Time-varying relationship between geopolitical uncertainty and agricultural investment

This paper explores the relationship between Geopolitical Uncertainty (GPU) and agricultural investment. We apply wavelet-based techniques to capture the relationship at different time dimensions and explore the causal structure between the GPU and select indices from the agricultural market of the USA. We find that the GPU influences investment in agricultural spot markets in the medium run. The futures market investment is less susceptible to GPU than the spot counterparts. A clear shift in interplay is visible during the COVID-19 pandemic and the ongoing Russia-Ukraine conflict. The results can offer diversification benefits and mitigate risk in distress and volatile periods.

Data-Driven and Mechanism-Based Hybrid Model for Semiconductor Silicon Monocrystalline Quality Prediction in the Czochralski Process

The Czochralski (CZ) process is the core technology for producing semiconductor silicon monocrystalline (SMC), and it is a complex batch process. However, the crystal growth rate and crystal diameter, which are key quality indicators, are difficult to detect directly online, and the offline calculation process lags seriously, which easily causes blind crystal quality control. Therefore, this paper proposes a data-driven and mechanism-based hybrid model for semiconductor SMC quality variables prediction in the CZ process. Firstly, a data-driven model JITL-SAE-ELM based on just-in-time learning (JITL) fine-tuning strategy is proposed. This model is used to solve the nonlinear and time-varying relationship of the energy transfer process in the CZ process that cannot be accurately described by traditional mechanism models. Here, the SAE-ELM model integrates a stacked autoencoder (SAE) and an extreme learning machine (ELM), which are used to deeply capture the nonlinear and time-varying features of process data. Secondly, according to the hydrodynamics and geometric behavior, a crystal pulling dynamic mechanism model based on the crystal growth mechanism is constructed, which avoids the complicated heat transfer link. Further, considering the unmodeled dynamics caused by model parameter uncertainty during the combination of the energy transfer model and crystal pulling dynamic mechanism model, a crystal diameter compensation model SAE-ELM was developed to improve the prediction accuracy of the CZ process hybrid model. Finally, an industrial data experiment based on a CZ monocrystal furnace illustrates the proposed method.

Dynamic impact of globalization on renewable energy consumption: Non-parametric modelling evidence

Renovative energies are often presented as the primary alternative for addressing the climate challenge. This paper proposes a non-parametric method to study the relationship between globalization and aggregated (disaggregated) renewable energy consumption for a panel of 15 nations over the period 1990–2018. Specifically, we draw on the local linear dummy variable estimation (LLDVE) method that allows us to capture, at each point in time, the impact of globalization on renewable energy consumption and how the relationship changed through time. The non-parametric panel data results show a time-varying relationship between globalization and renewable energy deployment, where it is negative and statically significant between 2002 and 2011, and then turns positive and statically significant for the post-2014 period. We also find that globalization exhibits heterogeneous impacts depending on the renewable energy measure used. The time-varying effects suggest that globalization is driving renewable energy consumption through various channels, with some channels appearing to be more prominent at specific times. Therefore, there is a need for policymakers to consider the role of globalization when designing policies related to renewable energy adoption or transition, especially being proactive in policy formulation, considering what channels the process of globalization operates through.

Service Life Design for Concrete Engineering in Marine Environments of Northern China Based on a Modified Theoretical Model of Chloride Diffusion and Large Datasets of Ocean Parameters

In this study, through experimental research and an investigation on large datasets of the durability parameters in ocean engineering, the values, ranges, and types of distribution of the durability parameters employed for the durability design in ocean engineering in northern China were confirmed. Based on a modified theoretical model of chloride diffusion and the reliability theory, the service lives of concrete structures exposed to the splash, tidal, and underwater zones were calculated. Mixed concrete proportions meeting the requirement of a service life of 100 or 120 years were designed, and a cover thickness requirement was proposed. In addition, the effects of the different time-varying relationships of the boundary condition ( C s ) and diffusion coefficient ( D f ) on the service life were compared; the results showed that the time-varying relationships used in this study (i.e., C s continuously increased and then remained stable, and D f continuously decreased and then remained stable) were beneficial for the durability design of concrete structures in marine environment.

Does utilizing smart contracts induce a financial connectedness between Ethereum and non-fungible tokens?

The majority of NFTs utilize the Ethereum blockchain platform to facilitate smart contracts. In this paper, we execute various econometric analyses to determine if this technical dependence induces a financial linkage to the risk, return, and prices of assets. For robustness, we also test the same relation between Bitcoin and NFTs. Empirical analyses are conducted through SADF bubbles test, DCC-GARCH time-varying correlation analysis, Bootstrap causality tests and spillover analysis. According to the results of various price, return, and volatility analyses, we find that NFTs do not demonstrate idiosyncratic features in their price developments and thus they cannot be considered as a separate asset class. Additionally, NFTs do not possess a specific financial linkage with Ethereum from using its infrastructure. Finally, we suggest NFT investors use alternative financial instruments, rather than Ether and Bitcoin in portfolio diversification, due to the presence of significant time-varying relationships and interactions.

Regional differences in initial labour market conditions and dynamics in lifetime income trajectories.

We use longitudinal register data from Sweden to study patterns and dynamics in lifetime income trajectories. We examine divergences in these income trajectories by local economic conditions at labour market entry, in combination with other factors such as gender, education level and socio-economic background. We cannot assume that these relationships are constant over the course of individuals' working lives. Therefore, we use methods from functional data analysis, allowing for a time-varying relationship between income and the explanatory variables. Our results show a large degree of heterogeneity in how lifetime income trajectories develop for different subgroups. We find that, for men, entering the labour market in an urban area is associated with higher cumulative lifetime income, especially later in life. The exception is men with only primary education, for whom those starting their working lives in a large city have lower incomes on average. This divergence increases in size over time. Women who enter into a large urban labour market receive higher lifetime income at all education levels. This relationship is strongest for women with primary education but decreases in strength over time for these women.

Flight-to-quality or not? Evidence from China’s green bond and green equity markets during COVID-19 crisis

ABSTRACT This study investigates the time-varying interdependence relationships between green bonds and green equity returns in China before and during the COVID-19 period. The rolling-window Copula Quantile-on-Quantile regression method has been employed to capture the dynamic dependence structure of the asset returns. The empirical results are as follows: First, the green bond-green equity correlations have increased significantly during the COVID-19 pandemic era. Second, the heterogeneous dependencies across different quantiles show the time-varying information transmission mechanism between green financial markets depending on the market conditions. Specifically, the correlations have increased around median level given pandemic shocks and an opposite correlation movement can be found in extreme quantiles, supporting the ‘flight-to-quality’ effect.

The time-varying relationship between CO2 emissions, heterogeneous energy consumption, and economic growth in China

The time-varying relationship between CO2 emissions, heterogeneous energy consumption, and economic growth in China

COVID-19 effects on property markets: The pandemic decreases the implicit price of metro accessibility

The metro (or underground railways) has become a backbone in the transit systems of many cities. It has numerous externalities, such as ameliorating traffic congestion and enhancing nearby property prices. Previous studies extensively focused on the relationship between metro accessibility and property prices and obtained various interesting findings and enriched practical implications. However, this relationship in the era of the coronavirus disease 2019 (COVID-19) and other epidemic shocks has not been investigated. Based on a unique property transaction dataset (including tens of thousands of transactions stretching from 2018 to 2020) in Chengdu, China, this study develops a battery of hedonic pricing models and difference-in-differences models to decipher the time-varying relationship between metro accessibility and residential property prices. The results show that the implicit price of metro accessibility modestly decreases in COVID-19, which can be explained by the declining role of the metro. In other words, the price gap between proximate and distant properties is narrowed, and the property price gradient is flattened. Specifically, the price elasticity of distance to the metro is − 0.024 before COVID-19, but it turns to − 0.018 during the pandemic. The relative price of properties within 500 m from metro stations to those farther away (500 m − 3 km) decreases by 15.4% during the pandemic. Additionally, COVID-19 does not jeopardize property prices in Chengdu. Furthermore, the decrease in metro access premiums may be short-lived and only persisted for several months or years. The plausibility and robustness of the core findings have been confirmed through alternative treatment groups, alternative model specifications, and placebo tests.

Re-examining The Time-varying Relationship Between Health and Income Distributions

In this paper, we address the issue of the time-varying relationship between health and long-term income and show that income profile over time is more important than the permanent income of a specific period in explaining general health condition. A functional probit regression model is introduced to investigate how the income profiles of middle-aged people can affect the health condition of the last period of the given period using the Panel Study of Income Dynamics data. We also perform the probit estimation to check the robustness of the functional regression results. The empirical results of our paper clearly indicate a time-varying relationship between the entire distribution of income and health status at one point in time, which cannot be accurately explained by the current income or permanent income of the given period.

Time-varying effect of uncertainty shocks on unemployment

We examine the dynamic effects of uncertainty shocks on unemployment during recessions in the US. We estimate a Bayesian time-varying parameter structural vector autoregression. We analyze how the impact of uncertainty shocks on U.S. unemployment changes over time. We find that uncertainty shocks have both higher unit impact and higher total effect on unemployment during the Great Recession compared to those of previous recessions. Different characteristics of the Great Recession amplify the effect of uncertainty on unemployment. Two channels distinguish the Great Recession from other recessions: the federal funds rate hitting the zero lower bound and financial frictions. We find that these factors are significant determinants of the time-varying relationship between unemployment and uncertainty. Robustness analyses with alternative measures of uncertainty and alternative empirical specifications validate the results of the paper. • Uncertainty significantly affects US unemployment. • The effect of uncertainty on unemployment varies over time. • The effect is substantially higher during the Great Recession. • The zero lower bound and financial frictions are among the causes of time-varying effect.