Study on nonlinear parametric vibration of footbridge considering time-lag effect

Vegetation fluctuation is sensitive to climate change, and this response exhibits a time lag. Traditionally, scholars estimated this lag effect by considering the immediate prior lag (e.g., where vegetation in the current month is impacted by the climate in a certain prior month) or the lag accumulation (e.g., where vegetation in the current month is impacted by the last several months). The essence of these two methods is that vegetation growth is impacted by climate conditions in the prior period or several consecutive previous periods, which fails to consider the different impacts coming from each of those prior periods. Therefore, this study proposed a new approach, the weighted time-lag method, in detecting the lag effect of climate conditions coming from different prior periods. Essentially, the new method is a generalized extension of the lag-accumulation method. However, the new method detects how many prior periods need to be considered and, most importantly, the differentiated climate impact on vegetation growth in each of the determined prior periods. We tested the performance of the new method in the Loess Plateau by comparing various lag detection methods by using the linear model between the climate factors and the normalized difference vegetation index (NDVI). The case study confirmed four main findings: (1) the response of vegetation growth exhibits time lag to both precipitation and temperature; (2) there are apparent differences in the time lag effect detected by various methods, but the weighted time-lag method produced the highest determination coefficient (R2) in the linear model and provided the most specific lag pattern over the determined prior periods; (3) the vegetation growth is most sensitive to climate factors in the current month and the last month in the Loess Plateau but reflects a varied of responses to other prior months; and (4) the impact of temperature on vegetation growth is higher than that of precipitation. The new method provides a much more precise detection of the lag effect of climate change on vegetation growth and makes a smart decision about soil conservation and ecological restoration after severe climate events, such as long-lasting drought or flooding.

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Information technology investment and innovation performance: does investment paradox exist?

Journal of Asia Business Studies ◽

10.1108/jabs-07-2021-0259 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Peinan Ji ◽

Xiangbin Yan ◽

Yan Shi

Keyword(s):

Information Technology ◽

Time Lag ◽

Innovation Performance ◽

Test Model ◽

It Investment ◽

Firm Innovation ◽

Data Set ◽

Content Type ◽

Lag Effect ◽

Enterprise Innovation

Purpose The purpose of this study is to deepen the understanding of the effects of information technology (IT) investment on firm innovation performance and examining the investment paradox effect in China. Design/methodology/approach Using a sample of China’ public firms IT investment data between 2010 and 2016, the authors establish a test model of IT investment and innovation performance. Findings The result indicates that IT investment in firms have no effect on innovation performance in the investment period. However, in the full sample and manufacturing sample, the IT investment has a significant positive effect on innovation performance in the post-investment years. In addition, this study finds that large companies and low-age companies may contribute more to innovation when firm investment in IT. Research limitations/implications There are several limitations in this research. First, the authors are failed to obtain a larger sample about the IT investment information data set in China, so this study was compelled to use limited sample data from China, hence, this could lead to errors of too early generalization. Second, the authors use the number of invention patent applications to represent the performance of enterprise innovation, which may not show enterprise innovation effectively. Third, the firms in the sample are all in China Listed Companies, so this may not accurately reflect the entire environment of firm innovation performance, and could possibly. Practical implications The research confirms that there is a paradox and time lag effect in IT investment, which enterprises should pay attention to. Originality/value Existing research confirms that corporate IT investments can bring new products or services. However, the authors still do not know whether IT investment has improved the company’s ability of innovation. This study will fill this gap and the industry effect and time lag effect of the influence of IT investment on innovative performance are also examined.

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Numerical Investigations for the Effect of Slender Body on Dynamic Rolling Characteristics of a 80°/60° Double Delta Wing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.444-445.286 ◽

2013 ◽

Vol 444-445 ◽

pp. 286-292

Author(s):

Bing Han ◽

Min Xu ◽

Xi Pei ◽

Xiao Min An

Keyword(s):

Stokes Equations ◽

Delta Wing ◽

Time Lag ◽

Slender Body ◽

Vortical Flow ◽

Navier Stokes ◽

Rolling Motion ◽

Navier Stokes Equations ◽

Lag Effect ◽

Double Delta Wing

The effect of slender body on the rolling characteristics of a double delta wing is found by comparing the numerical simulation results of the double delta wing and wing-body configuration. The coupled computation system solving the Navier-Stokes equations and the rolling motion equation alternatively to obtain the unsteady vortical flow around the two configurations while rolling. The results conclusively showed the upwash effect of the slender body enhanced the energy of strake vortex and merged vortex.The aerodynamic lag of double delta wing is weak, contrarily, the time lag effect of the wing-body configuration is significant. The asymmetry vortices structure nearby the trailing edge are believed to be the main reason for the unsteady time lag effect.

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An SEIR Model for Assessment of Current COVID-19 Pandemic Situation in the UK

10.1101/2020.04.12.20062588 ◽

2020 ◽

Author(s):

Peiliang Sun ◽

Kang Li

Keyword(s):

Seasonal Influenza ◽

Time Lag ◽

Seir Model ◽

Infected People ◽

Proposed Model ◽

Lag Effect ◽

Novel Coronavirus ◽

The Uk ◽

Specific Symptoms ◽

Limited Testing

AbstractThe ongoing COVID-19 pandemic spread to the UK in early 2020 with the first few cases being identified in late January. A rapid increase in confirmed cases started in March, and the number of infected people is however unknown, largely due to the rather limited testing scale. A number of reports published so far reveal that the COVID-19 has long incubation period, high fatality ratio and non-specific symptoms, making this novel coronavirus far different from common seasonal influenza. In this note, we present a modified SEIR model which takes into account the time lag effect and probability distribution of model states. Based on the proposed model, it is estimated that the actual total number of infected people by 1 April in the UK might have already exceeded 610,000. Average fatality rates under different assumptions at the beginning of April 2020 are also estimated. Our model also reveals that the R0 value is between 7.5–9 which is much larger than most of the previously reported values. The proposed model has a potential to be used for assessing future epidemic situations under different intervention strategies.

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Non-parametric treatment time-lag effect estimation

Statistical Methods in Medical Research ◽

10.1177/09622802211032693 ◽

2021 ◽

pp. 096228022110326

Author(s):

Kristine Gierz ◽

Kayoung Park ◽

Peihua Qiu

Keyword(s):

Survival Data ◽

Treatment Time ◽

Time Lag ◽

Simulated Data ◽

Real Data ◽

Point Problem ◽

Change Point Problem ◽

Lag Effect ◽

Effect Estimation ◽

Non Parametric

In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields, and can also apply to survival data. In survival analysis, most existing methods compare two treatment groups for the entirety of the study period. Some treatments may take a length of time to show effects in subjects. This has been called the time-lag effect in the literature, and in cases where time-lag effect is considerable, such methods may not be appropriate to detect significant differences between two groups. In this paper, we propose a novel non-parametric approach for estimating the point of treatment time-lag effect by using an empirical divergence measure. Theoretical properties of the estimator are studied. The results from the simulated data and the applications to real data examples support our proposed method.

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Influence of Interfirm Social Networks on Technological Innovation and its Time Lag Effect: a Meta-Analysis

IEEE Access ◽

10.1109/access.2019.2953977 ◽

2019 ◽

Vol 7 ◽

pp. 167019-167031

Author(s):

Wei Chen ◽

Xuan Wei ◽

Ranran Liu

Keyword(s):

Social Networks ◽

Technological Innovation ◽

Meta Analysis ◽

Time Lag ◽

Lag Effect

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Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China

Remote Sensing ◽

10.3390/rs12081332 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1332 ◽

Cited By ~ 4

Author(s):

Linghui Guo ◽

Liyuan Zuo ◽

Jiangbo Gao ◽

Yuan Jiang ◽

Yongling Zhang ◽

...

Keyword(s):

Climate Change ◽

Interannual Variability ◽

Inner Mongolia ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Lag ◽

Growing Season ◽

Climate Fluctuations ◽

Temperature And Precipitation ◽

Lag Effect

An understanding of the response of interannual vegetation variations to climate change is critical for the future projection of ecosystem processes and developing effective coping strategies. In this study, the spatial pattern of interannual variability in the growing season normalized difference vegetation index (NDVI) for different biomes and its relationships with climate variables were investigated in Inner Mongolia during 1982–2015 by jointly using linear regression, geographical detector, and geographically weighted regression methodologies. The result showed that the greatest variability of the growing season NDVI occurred in typical steppe and desert steppe, with forest and desert most stable. The interannual variability of NDVI differed monthly among biomes, showing a time gradient of the largest variation from northeast to southwest. NDVI interannual variability was significantly related to that of the corresponding temperature and precipitation for each biome, characterized by an obvious spatial heterogeneity and time lag effect marked in the later period of the growing season. Additionally, the large slope of NDVI variation to temperature for desert implied that desert tended to amplify temperature variations, whereas other biomes displayed a capacity to buffer climate fluctuations. These findings highlight the relationships between vegetation variability and climate variability, which could be used to support the adaptive management of vegetation resources in the context of climate change.

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A Lag Effect of IT Investment on Firm Performance

Advances in Information Resources Management - Innovative Technologies for Information Resources Management ◽

10.4018/978-1-59904-570-2.ch015 ◽

2011 ◽

pp. 246-273

Author(s):

Sangho Lee ◽

Soung Hie Kim

Keyword(s):

Firm Performance ◽

Time Lag ◽

It Investment ◽

Performance Factors ◽

Distributed Lag ◽

Distributed Lag Model ◽

Lag Effect ◽

Lag Model ◽

Information Intensity ◽

High Information

This chapter discusses the positive effects of IT investment on firm financial performance when a distinct range of characteristics is examined. The relationship between IT investment and firm performance considering the information intensity of the industry is explored using a distributed lag model. Findings indicate both a positive effect and a positive lag effect of IT investment. The effects of IT investment in the high information-intensive industry are significantly larger than in the low information-intensive industry. Furthermore, a lagged effect of IT investment is larger than an immediate effect, regardless of the information intensity of the industry. We conclude that firms in the high information-intensive industry need to be more cognizant of performance factors when investing in IT investment than in the low information-intensive industry. Moreover, it is necessary to consider the time lag between IT investment and firm performance.

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Characterizing ecosystem phenological diversity and its macroecology with snow cover phenology

Scientific Reports ◽

10.1038/s41598-019-51602-1 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Yi Lin ◽

Juha Hyyppä

Keyword(s):

Snow Cover ◽

Climate Changes ◽

Time Lag ◽

The Arctic ◽

New Findings ◽

Lag Effect ◽

North Eurasia ◽

Phenological Dates ◽

Phenological Diversity ◽

Earth System Models

Abstract One critical challenge of exploring flora phenology is on characterizing ecosystem phenological diversity (EPD), and thus how EPD’s performance is influenced by climate changes has also been an open macro-ecological question. To fill these two gaps, we proposed an innovative method for reflecting EPD, by taking the advantage of the often-classified inverse factor of spatial resolution discrepancy between the used remote sensing datasets of vegetation phenological dates (green-up and brown-up) and snow cover phenological dates (SPDs) (onset and end) around the Arctic, and further, we examined the cross response/feedbacks of the two kinds of EPDs to the two categories of SPDs. We found that the circumpolar green-up and brown-up EPDs both were shrinking, driven more by the delaying of the onset SPDs than the advancing of the end SPDs; North America and North Eurasia performed with inconsistent EPD response/feedbacks to the related SPD anomalies; and further, the EPD-SPD response/feedbacks in some locations exhibited the time-lag effect, e.g., the green-up EPDs made the strongest response to the onset SPDs of two years earlier. Overall, the validated method and the new findings are of implications for improving the phenology modules in Earth system models, and the contributions of the present study have enlightening significance for kicking off the new EPD branch in macrosystem phenological ecology.

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Fibering of Rubber. Time Lag and Its Relation to Rubber Structure

Rubber Chemistry and Technology ◽

10.5254/1.3548020 ◽

1934 ◽

Vol 7 (3) ◽

pp. 505-515 ◽

Cited By ~ 1

Author(s):

John D. Long ◽

William E. Singer ◽

Wheeler P. Davey

Keyword(s):

Room Temperature ◽

Mechanical Energy ◽

Amorphous Material ◽

Time Lag ◽

Temperature History ◽

Time Interval ◽

X Rays ◽

Mechanical Working ◽

Typical Data ◽

Lag Effect

Abstract FOR several years it has been known that in the unstretched condition ordinary rubber acts toward x-rays like an amorphous material, but that, when it is sufficiently stretched, it acts toward the rays like a fibrous material. In 1931 Acken, Singer, and Davey (1) reported that at room temperature a time interval was required to build up the fibrous structure in cyclically stretched rubber. Investigation showed that the time-lag effect could not be accounted for in terms of a temperature change during the act of stretching. Even if all the mechanical energy of stretching were instantaneously changed into sensible heat, the temperature of the rubber sample could not have been increased momentarily by more than 5.2° C., whereas the fibering of rubber continuously stretched at 420 per cent elongation could be demonstrated up to a temperature of 47° C. Experimentally no temperature rise greater than 1.0° C. could be found in the samples used. Since the time-lag effect appeared, therefore, to be a real effect, it seemed worth while to study it in detail. It is the purpose of this paper to report: (1) typical data on the effects on the time lag, of temperature, previous temperature history, mechanical working and aging, time of relaxation, time of stretch, and rate of stretch; and (2) the relation of these results to the possible structure of the rubber fiber.

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