Temporal Lag Effect Research Articles

The varying effects of residential built environment on travel behavior of internal migrants and locals

The built environment impacts multiple dimensions of travel behavior of urban residents. However, limited research has explicitly unveiled how these impacts vary between diverse socioeconomic groups, especially internal migrants versus locals, notwithstanding hundreds of millions of internal migrants are living in cities in developing countries. Hence, employing a large-scale travel behavior survey dataset collected in Xiamen (China) in 2015 and applying the propensity score matching method, this study comparatively examines the differentiations in responsiveness of such two groups to the built environment. The results demonstrate that (1) the effects of the built environment on travel behavior differ between internal migrants and locals in some dimensions (e.g., active travel frequency and duration); (2) the built environment has larger effects on all the travel dimensions of locals than on those of internal migrants, implying the existence of “transportation assimilation” and a temporal lag effect; (3) the internal migrants are less impacted by residential self-selection effects. These findings can enlighten decision-makers about more nuanced, targeted, and socially inclusive intervention strategies, thus effectively accommodating the travel demands of both locals and internal migrants.

Does information infrastructure and technological infrastructure reduce carbon dioxide emissions in the context of sustainable development? Examining spatial spillover effect

AbstractTo cope with global climate change and realize sustainable development, China has invested heavily in the construction of information infrastructure and technological infrastructure (IT) in recent years. In this research, we first find out the effect of IT on CO2 emissions and how IT impacts CO2 emissions, considering an expanded endogenous growth model. Then, using the dynamic spatial lag model and the system generalized method of moments (SGMM), this study investigates the impact of IT on CO2 emissions and their mechanisms in 30 provinces of China from 2005 to 2019. The results are: (1) CO2 emissions have a spatial lag effect, a temporal lag effect, and a spatio‐temporal lag effect. (2) IT reduces local CO2 emissions. (3) IT promotes the decrease of CO2 emissions in local areas over the short and long terms. In the long term, IT promotes the decrease of CO2 emissions in the surrounding regions. Both in the short and long terms, the direct effect is greater than the indirect effect. (4) IT reduces CO2 emissions through optimizing the energy consumption structure (ENS) and enhancing the level of green technology innovation (GI). This study provides a theoretical basis for realizing the dual carbon goals of emissions peak and carbon neutrality.

The heterogeneous drivers of CO2 emissions in China's two major economic belts: new evidence from spatio-temporal analysis.

CO2 emissions have become increasingly prominent in China, and the primary emitters are economic belts that are spread throughout China. Two major economic belts, i.e., the Yangtze River Economic Belt (YTREB) and the Yellow River Economic Belt (YREB). Combined with stochastic impacts by regression on population, affluence and technology model, the spatial Durbin model under the space-and-time fixed effect and the Geographical and Time-Weighted Regression are employed to explore the spatio-temporal distribution characteristics and heterogeneous drivers of CO2 emissions in the two economic belts. The results are as follows. First, CO2 emissions exhibit obvious spatial correlation features in the YREB, but no such obvious spatial correlation is found in the YRETB. Second, in the YREB, the magnitude of the total influencing factors on CO2 emissions follows an order where affluence (A) is the biggest driver, followed by energy intensity (EI), technology (TEC) and openness (OP), while the biggest driver in the YRETB is industrial structure supererogation (ISS), followed by population (P), energy intensity (EI), and affluence (A). Both direct and spatial spillover effects of the drivers are observed in the two economic belts. Third, the CO2 emissions show a notable temporal lag effect in the YREB, but not in the YRETB. Fourth, the effects of the CO2 emission drivers illustrate significant spatio-temporal heterogeneity in the two economic belts.

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

Spatio-temporal land-use change (LUC) modeling provides vital information about land development dynamics. However, accounting for such dynamics faces methodological challenges. This research introduces a Dynamic Spatial Panel Data (DSPD) modeling framework for LUC, incorporating spatial and temporal dependencies. A continuous response variable is introduced to take advantage of traditional spatial regression models. The DSPD model is applied to balanced spatial panel data at the block-group level covering Florida between 2010 and 2019 and incorporating both new and previously used proxy variables. The urban growth impacts of site-specific, proximity, neighborhood, socio-economic, and transportation factors are investigated. This study contributes to the literature by providing extensive insights into spatial autocorrelation, spillover, heterogeneity, and temporal lag effects in urban growth. Also, the study reveals the importance of mobility and mortgage financing in land development. The proposed modeling framework achieves high accuracy. The dynamic structure of this model provides an opportunity to predict future urban growth without the need for a land development scenario. Such predictions provide insights about future land development to practitioners and policymakers.

Characterization of spatio-temporal patterns of grassland utilization intensity in the Selinco watershed of the Qinghai-Tibetan Plateau from 2001 to 2019 based on multisource remote sensing and artificial intelligence algorithms

ABSTRACT Due to the limitations of spatial quantification methods, the spatio-temporal patterns of grassland utilization intensity (GUI) in the Selinco watershed (SLCW), the core region of ecological security on the Qinghai-Tibetan Plateau, is unclear under multiple utilization modes. This paper quantified GUI by constructing the association between the potential and actual Enhanced Vegetation Index (EVI) of grasslands in terms of interannual variability. To obtain an accurate spatio-temporal dataset of potential EVI, the following two components were considered on. Firstly, the temporal lag effects of each raw climate factor were investigated to determine the optimal climate variables affecting vegetation productivity. Secondly, four machine learning (ML) algorithms, including an artificial neural network, random forest, support vector machine, and gradient boosting regression tree combined with the Bayesian model average, were used to construct grassland potential EVI models involving EVI, grassland type, and environmental factors (topography, soil, raw climate, and bioclimatic). Meanwhile, to maximize the performance of ML models, variable selection, variable transformation, and hyperparameter optimization were systematically implemented, where the hyperparameter optimization algorithms employ the grid search algorithm, Bayesian optimization, genetic algorithm, and particle swarm optimization. Then, the spatio-temporal dataset of GUI in the SLCW from 2001 to 2019 was established by using the above quantification method based on multisource remote sensing and artificial intelligence algorithms. The analysis of spatio-temporal variation in GUI showed that the implementation of ecological restoration projects leads to a significant and rapid decline in the overall GUI of the SLCW after 2010 (declining by 4.8%), which is more obvious in the non-nature reserve (declining by 9.3%). In the Qiangtang Nature Reserve within the SLCW, although the GUI shows a declining trend after 2010 because of the implementation of ecological restoration projects, it shows an insignificant increase from 2001 to 2019 due to the recovery increase of wildlife populations in recent decades. Besides, by exploring the effects of elevation and slope on the GUI, it is found that grasslands on higher slopes at lower elevations are at a greater risk of degradation due to more intensive grassland utilization.

A zero-inflated spatiotemporal analysis for snowpack variations and influence of environmental factors in the Northern Hemisphere

Snowpack will vary with climate change and is of vital importance for water supply, the energy balance, and heat exchange. Environmental factors such as thermal, atmospheric, terrestrial, and hydrological factors influence variations in snow mass. This study aimed to investigate associations with environmental factors and variations in snow mass changes based on satellite-retrieved products in the Northern Hemisphere (NH). A zero-inflated space–time autoregressive (ZI-STAR) model is proposed to identify the environmental factors associated with the SWE changes during the snow accumulation and ablation periods in North America and Eurasia because of the spatial patterns, temporal lag effect, and excessive zero observations captured with the satellite-based snow water equivalent (SWE) product. The proposed ZI-STAR model, which considers spatial, time lag, and zero-inflation effects together, offered the best overall performance for SWE simulations with high precision (average R2 of 0.90 while the others range from 0.69 to 0.84) and low spatially random distributed residuals (RMSE 23%–33% less than other model), compared to time-lag, and spatial autoregressive models. Based on the results, air temperature and snow precipitation dominate the variations in SWE across the period of interest. The influence of water vapor varies and the wind speed and topography have little effect due to the missing SWE observations in mountainous areas. The variations in the environmental factors for SWE furnish a foundation for further research about snow changes in the NH. The proposed ZI-STAR model also gives a feasible approach for spatio-temporal continuous variables modeling with excessive zero observations in other fields.

Longitudinal modelling of housing prices with machine learning and temporal regression

Purpose The purpose of this paper is to model housing price temporal variations and to predict price trends within the context of land use–transportation interactions using machine learning methods based on longitudinal observation of housing transaction prices. Design/methodology/approach This paper examines three machine learning algorithms (linear regression machine learning (ML), random forest and decision trees) applied to housing price trends from 2001 to 2016 in the Greater Toronto and Hamilton Area, with particular interests in the role of accessibility in modelling housing price. It compares the performance of the ML algorithms with traditional temporal lagged regression models. Findings The empirical results show that the ML algorithms achieve good accuracy (R2 of 0.873 after cross-validation), and the temporal regression produces competitive results (R2 of 0.876). Temporal lag effects are found to play a key role in housing price modelling, along with physical conditions and socio-economic factors. Differences in accessibility effects on housing prices differ by mode and activity type. Originality/value Housing prices have been extensively modelled through hedonic-based spatio-temporal regression and ML approaches. However, the mutually dependent relationship between transportation and land use makes price determination a complex process, and the comparison of different longitudinal analysis methods is rarely considered. The finding presents the longitudinal dynamics of housing market variation to housing planners.

Spatio-Temporal Heterogeneity of Carbon Emissions and Its Key Influencing Factors in the Yellow River Economic Belt of China from 2006 to 2019.

The Yellow River Economic Belt (YREB) performs an essential function in the low-carbon development of China as an important ecological protection barrier, and it is of great importance to identify its spatio-temporal heterogeneity and key influencing factors. In this study, we propose a comprehensively empirical framework to conduct this issue. The STIRPAT model was applied to determine the influencing factors of carbon emissions in the YREB from 2006 to 2019. The results show that the carbon emissions in the YREB had significant clustering characteristics in the spatial auto-correlation analysis. In addition, the estimation results of the spatial panel analysis demonstrate that the carbon emissions showed a distinct spatial lag effect and temporal lag effect. Moreover, the three traditional factors including population, affluence, technology are identified as the key influencing factors of carbon emissions in the YREB of China. Furthermore, the spatio-temporal heterogeneity is illustrated vividly by employing the GTWR-STIRPAT model. Finally, policy implications are provided to respond to the demand for low-carbon development.

How Air Quality Affect Health Industry Stock Returns: New Evidence From the Quantile-on-Quantile Regression.

This paper discusses the asymmetric effect of air quality (AQ) on stock returns (SR) in China's health industry through the quantile-on-quantile (QQ) regression method. Compared to prior literature, our study provides the following contributions. Government intervention, especially industrial policy, is considered a fresh and essential component of analyzing frameworks in addition to investors' physiology and psychology. Next, because of the heterogeneous responses from different industries to AQ, industrial heterogeneity is thus considered in this paper. In addition, the QQ method examines the effect of specific quantiles between variables and does not consider structural break and temporal lag effects. We obtain the following empirical results. First, the coefficients between AQ and SR in the health service and health technology industries change from positive to negative as AQ deteriorates. Second, AQ always positively influences the health business industry, but the values of the coefficients are larger in good air. In addition, different from other industries, the coefficients in the health equipment industry are negative, but the values of the coefficients change with AQ. The conclusions provide important references for investors and other market participants to avoid biased decisions due to poor AQ and pay attention to government industrial policies.

Research on the Impacts of Heterogeneous Environmental Regulations on Green Productivity in China: The Moderating Roles of Technical Change and Efficiency Change.

Due to the publicly owned resource attributes of the ecological environment, the treatment and governance of the environment should be guided by governments, which are mainly represented as environmental regulations. However, whether environmental regulations affect green productivity and what effects heterogeneous environmental regulations have on green productivity are still implicit. In addition, the moderating roles of technical change and efficiency change are ignored. To examine these issues, this study investigated the impacts of heterogeneous environmental regulations on green productivity and the moderating roles of technical change and efficiency change using the dynamic spatial Durbin model based on the panel data of 30 Chinese provinces from 2000 to 2018. The results show the following: compared with efficiency change, technical change has a stronger promotion effect on green productivity in China; considering the spatial spillover effects and the temporal lag effects of green productivity simultaneously, the negative path-dependent feature is not supported any longer, while the spatial spillover effect is still the power source for promoting green productivity in China; the moderating roles of technical change and efficiency change for the nexus between heterogeneous environmental regulations and green productivity in China are partly and conditionally supported at national and regional levels; the direct and indirect effects of heterogeneous environmental regulations on green productivity at the regional level have a feature of spatial heterogeneity. This study provides both theoretical and practical implications, in particular for China, to promote green productivity from the dual perspectives of space and time.

Temporal effects of climate on vegetation trigger the response biases of vegetation to human activities

Quantitatively distinguishing the effects of human activities and climate change on vegetation change is crucial for planning scientific vegetation management measures. Previous studies have employed the residual trend method (RESTREND) and remote sensing data to address this issue. However, the mathematical relationships between climate and remote sensing vegetation index under RESTREND are variable and not comprehensively described due to temporal effects of climate on vegetation. Using Tibetan Plateau (TP), this study investigated how the temporal effects of climate on vegetation trigger response biases of vegetation to human activities under four temporal effects (no temporal effect [No], temporal lag effect [Lag], temporal accumulation effect [Acc], and both temporal lag and temporal accumulation effects [Lagacc]). The results revealed the following. (1) The mean explanation degrees of climatic factors to vegetation growth over entire TP under No, Lag, Acc, and Lagacc effects were 0.41 ± 0.02, 0.42 ± 0.02, 0.44 ± 0.02, and 0.47 ± 0.02, respectively. Therefore, the Lagacc effect is the best temporal effect for establishing the vegetation-climate relationships. (2) Under the Lagacc effect, the temperature exhibited 1.60 ± 0.13 months lag and 4.56 ± 0.28 months accumulation on vegetation growth over entire the TP, whereas the precipitation exhibited 1.57 ± 0.16 months lag and 4.99 ± 0.31 months accumulation. Furthermore, the grids affected by the combined temporal lag and temporal accumulation effects of climatic factors accounted for 43.42–45.78% of the total grids. These indicated the temporal effects of climate on vegetation, and vegetation growth was largely affected by the combined temporal effects of climatic factors. (3) Under the Lagacc effect, 11.6% of the region experienced significant residual increase, and the slope of the residual was 22.2 × 10−4, which was primarily distributed in the north and southeast of the TP. However, the area and slope of significant residual decrease were 1.3% and 24.0 × 10−4, respectively, which were primarily distributed in the center of the TP. Furthermore, the average contributions of human activities on vegetation growth in the regions of residual significant increase and decrease were 54.5% and 55.5%, respectively. Therefore, the Lagacc effect exhibited the smallest influence area and the lowest influence degree for the contribution of human activities on vegetation growth. We thus suggest that the Lagacc effect should be fully considered when establishing the mathematical relationship between climatic factors and the vegetation index.

Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China.

By collecting the panel data of 29 regions in China from 2008 to 2017, this study used the spatial Durbin model (SDM) to explore the spatial effect of PM2.5 exposure on the health burden of residents. The most obvious findings to emerge from this study are that: health burden and PM2.5 exposure are not randomly distributed over different regions in China, but have obvious spatial correlation and spatial clustering characteristics. The maximum PM2.5 concentrations have a significant positive effect on outpatient expense and outpatient visits of residents in the current period, and the impact of PM2.5 pollution has a significant temporal lag effect on residents’ health burden. PM2.5 exposure has a spatial spillover effect on the health burden of residents, and the PM2.5 concentrations in the surrounding regions or geographically close regions have a positive influence on the health burden in the particular region. The impact of PM2.5 exposure is divided into the direct effect and the indirect effect (the spatial spillover effect), and the spatial spillover effect is greater than that of the direct effect. Therefore, we conclude that PM2.5 exposure has a spatial spillover effect and temporal lag effect on the health burden of residents, and strict regulatory policies are needed to mitigate the health burden caused by air pollution.

Patterns of genetic differentiation in Colorado potato beetle correlate with contemporary, not historic, potato land cover.

Changing landscape heterogeneity can influence connectivity and alter genetic variation in local populations, but there can be a lag between ecological change and evolutionary responses. Temporal lag effects might be acute in agroecosystems, where land cover has changed substantially in the last two centuries. Here, we evaluate how patterns of an insect pest’s genetic differentiation are related to past and present agricultural land cover change over a 150‐year period. We quantified change in the amount of potato, Solanum tuberosum L., land cover since 1850 using county‐level agricultural census reports, obtained allele frequency data from 7,408 single‐nucleotide polymorphism loci, and compared effects of historic and contemporary landscape connectivity on genetic differentiation of Colorado potato beetle, Leptinotarsa decemlineata Say, in two agricultural landscapes in the United States. We found that potato land cover peaked in Wisconsin in the early 1900s, followed by rapid decline and spatial concentration, whereas it increased in amount and extent in the Columbia Basin of Oregon and Washington beginning in the 1960s. In both landscapes, we found small effect sizes of landscape resistance on genetic differentiation, but a 20× to 1,000× larger effect of contemporary relative to historic landscape resistances. Demographic analyses suggest population size trajectories were largely consistent among regions and therefore are not likely to have differentially impacted the observed patterns of population structure in each region. Weak landscape genetic associations might instead be related to the coarse resolution of our historical land cover data. Despite rapid changes in agricultural landscapes over the last two centuries, genetic differentiation among L. decemlineata populations appears to reflect ongoing landscape change. The historical landscape genetic framework employed in this study is broadly applicable to other agricultural pests and might reveal general responses of pests to agricultural land‐use change.

Rescaling social dynamics in climate change: The implications of cumulative exposure, climate justice, and community resilience

In this study, we explore cumulative exposure, climate justice, and flood risk with specific reference to community resilience, vulnerability, and social justice characteristics at the county-level within the U.S. Mississippi River basin from 1990 to 2009. Using a basic conceptual model of spatial resilience to climate risks, temporal lag effect of community capacity, urban and rural spatial classification, integrative cumulative exposure, and spatial clustering of risk, we examine spatial climate risk outcomes and the role of community resilience in reducing such risks. Our approach accounted for local social, economic, environmental, regulatory policy, and planning mitigation contexts. Results suggest that community social and ecological characteristics were influenced by flood losses and that social capital and climate justice characteristics combined with local proactive planning and policy measures lead to lower disaster losses and enhanced community resilience.

Causal relationship in the interaction between land cover change and underlying surface climate in the grassland ecosystems in China

Land-climate interactions are driven by causal relations that are difficult to ascertain given the complexity and high dimensionality of the systems. Many methods of statistical and mechanistic models exist to identify and quantify the causality in such highly-interacting systems. Recent advances in remote sensing development allowed people to investigate the land-climate interaction with spatially and temporally continuous data. In this study, we present a new approach to measure how climatic factors interact with each other under land cover change. The quantification method is based on the correlation analysis of the different order derivatives, with the canonical mathematical definitions developed from the theories of system dynamics and practices of the macroscopic observations. We examined the causal relationship between the interacting variables on both spatial and temporal dimensions based on macroscopic observations of land cover change and surface climatic factors through a comparative study in the different grassland ecosystems of China. The results suggested that the interaction of land-climate could be used to explain the temporal lag effect in the comparison of the three grassland ecosystems. Significant spatial correlations between the vegetation and the climatic factors confirmed feedback mechanisms described in the theories of eco-climatology, while the uncertain temporal synchronicity reflects the causality among the key indicators. This has been rarely addressed before. Our research show that spatial correlations and the temporal synchronicity among key indicators of the land surface and climatic factors can be explained by a novel method of causality quantification using derivative analysis.

A review of contaminant losses to water from pastoral hill lands and mitigation options

Pastoral hill lands deliver a range of contaminants to receiving environments that are of concern to the wider sector stakeholder community: principally sediment, phosphorus, nitrogen and faecal microorganisms. Thermal energy may also be considered a contaminant. Pastoral waterways generally have higher concentrations of suspended sediments, nutrients, faecal micro-organisms, and water temperature relative to forested waterways. These effects can be quantitatively linked to animal stocking rates and management. The large variation in the micro-climates, parent materials, soil types and vegetation resources inherent in hill country is the major driver of spatial and temporal dynamics of contaminant losses. This variation is modified by animal behaviour and physiology. Stores of contaminants in surface or sub-surface flow paths create important temporal lags resulting from land use and management change. The concept of critical source areas has become a key focus for the development of mitigation options. A wide range of biophysical options are now available, covering multiple scales and levels of cost-benefit. The use of farm planning tools is critical in balancing the implementation of mitigations with farm system objectives to improve whole-system sustainability. More research is needed on long-term impacts, given spatial and temporal variation in drivers and known spatial and temporal lag effects. There will be ongoing demand for mitigations that have been developed through co-innovation processes. Keywords: environmental mitigations, erosion, hill country, nutrient loss, pastoral, sediment export

Experimental investigations of graded sediment transport under unsteady flow hydrographs

Natural fluvial channels can experience significant variations in sediment transport rates under unsteady flow conditions, especially during flood hydrograph events. At present, however, there is a distinct lack of understanding of the interaction between unsteady hydrograph flow properties and temporal variability in graded sediment transport rates. In the current study, a series of parametric experiments were conducted to investigate the response of two-graded sediment beds to a range of different unsteady hydrograph flow conditions. Investigations of the total and fractional bed-load sediment transport rates revealed strong temporal variations in transport over the hydrographs, with size-dependent temporal lag effects observed between peak flow conditions and peak bed-load transport rates. Specifically, coarse gravels had increased mobility during the rising limb of the hydrographs, attaining their peak bed-load transport rate either prior to, or near, peak flow conditions. By contrast, the finer grades tended to have enhanced mobility during the receding limb of the hydrographs, with peak transport rates measured after peak flow conditions had passed. Grain size distributions measured from the collected bed-load samples also indicated material coarsening over the rising limb and fining during the receding limb, while corresponding image analysis measurements of bed surface composition showed only marginal variation over the hydrographs. Computation of total and fractional sediment yields revealed that the bimodal sediment mixture tested was transported at significantly higher rates than the uni-modal mixture over all hydrograph conditions tested. This finding indicated that the uni-modal sediment bed was inherently more stable than the bimodal bed due to the increased abundance of medium-sized gravels present in the uni-modal sediment grade. The parametric dependences established in the study have clear implications for improved understanding of fractional inter-granular effects at the bed surface and their influence on graded sediment transport processes within natural fluvial channels under flood flow hydrographs.

Floodplain storage of sediment contaminated by mercury and copper from historic gold mining at Gold Hill, North Carolina, USA

Previous research on the environmental consequences of mining has shown that metal contaminants can have long-lasting impacts on water quality and aquatic ecosystems because of the remobilization of sediment-associated contaminants that have been stored in floodplains. We examined the magnitude and distribution of mercury (Hg) and copper (Cu) contamination of floodplain deposits associated with nineteenth century gold (Au) mining activities in the Gold Hill mining district, North Carolina. A comparison of post-mining metal concentrations in overbank deposits with sediment quality guidelines indicates that overall about 21% are contaminated above the probable effect concentration (PEC; above which adverse effects are expected to occur more often than not) for both Cu and Hg. The highest contamination occurs upstream near the mine source where 51% of the samples exceed the PEC for Hg and 57% exceed the PEC for Cu. Of the three different methods used to estimate metal mass storage, the most reliable estimate suggests that about 6.8Mg of Hg and 619Mg of Cu currently reside in floodplain deposits within this watershed. Although overbank sediment storage increases downstream and with valley width, about 75% of the Hg and Cu mass are stored in the upstream portion of the watershed. Hg mass storage displays a strong negative relationship with cross-sectional stream power, but the relationship between Cu mass storage and stream power is insignificant. We used vertical changes in overbank metal concentrations and the mining history to estimate a mean sedimentation rate of 2.7cmy−1 during the most intensive period of Au mining at Gold Hill (1842–1856) that is three times the long-term (1842–2007) rate of 0.9cmy−1. Long-term average rates at Gold Hill are comparable to those reported elsewhere in the eastern Piedmont. The downstream increase in long-term rates may indicate a spatial and temporal lag effect where the locus of deposition shifts downstream with time.

Effects of number of items and interval length on the acquisition of temporal order discrimination in radial maze in rats

Temporal order discrimination in rats was analyzed using a radial maze. The task consisted of a study phase in which two to five items (arms) were presented sequentially and, after a delay, a test phase in which two of these were simultaneously presented and the rat had to choose the arm presented earlier in the study phase. Acquisition of the task was better as the number of items in the study phase increased when the total study phase length was constant (4 min). Interval length between items in the study phase did not affect the acquisition regardless of the number of items. Additionally, discrimination performance was better when two items with more intervening items in the study phase (temporal lag) were used for the test. However, this tendency was obtained only when the last item of the study phase was included in the test pair. Results suggest that the number of items presented in the sequence, but not interval length between items, is an important factor in temporal order memory, and that a larger number of intervening items, as well as containing the last item in the study phase, contributes to the occurrence of the temporal lag effect.

A Regularized Contact Model with Asymmetric Damping and Dwell-Time Dependent Friction

A general regularized contact model, including normal compliance, energydissipation, and tangential friction, is described in this paper. Thenormal damping coefficient is formulated as a function of the coefficientof restitution e and the impact velocity only; the results areenergy-consistent, with continuous force progression at the beginningand end of the impact, for both small and large values of e.The introduced seven parameter friction model based on an explicitformulation of the friction forces is suitable for real-timeapplications. The friction forces are split into its sliding andsticking contribution and a temporal lag effect, the dwell-time, isincluded using a novel dwell-time dependent stick state variable. Several examples are presented to demonstrate the features of thisgeneral contact model. The simulation results for a double pendulumhitting a plane are obtained, and a comparison with a benchmark problem shows the model behavior is in good agreement with published results.