Time-Lag Effects in Urban Environment Adaptation as a Warning System for Urban Growth Control: An Application to Flanders

1970 ◽  
Vol 2 (3) ◽  
pp. 303-322 ◽  
Author(s):  
M. van Naelten
Keyword(s):  
Factor Model ◽  
Time Lag ◽  
Growth Control ◽  
Warning System ◽  
Basic Point ◽  
Lag Effects ◽  
Environment Adaptation ◽  
Subsequent Measurement ◽  
Urban Growth Control ◽  
Urban Characteristics

In this paper we wish to discuss some aspects of a particular system approach in urban planning. An attempt has been made to explain the meaning of the first principal factor (Hotelling, 1933) in the verification of a set of supposed urban characteristics. The same factor model has been used in the subsequent measurement of the degree of urbanity in each municipal territory in Flanders. In mapping the results we have also attempted to verify some growth and communication theories for the Flanders case. Finally, the basic point of the paper is the detection of time-lag effects which create gaps between the slower development of more rigid environment elements, with which the planner is concerned, and the more quickly adapting elements—a time lag which could indicate urgent planning areas.

An Analysis of Time-Lag Effects of R&D Investment and Patents - The Difference by the Level of Industry Technology -

2018 ◽  
Vol 32 (2) ◽  
pp. 217-241
Author(s):  
Ji-Hyun Song ◽  
Keyword(s):  
Time Lag ◽  
Lag Effects ◽  
The Difference ◽  
D Investment

scholarly journals The haze extreme co-movements in Beijing–Tianjin–Hebei region and its extreme dependence pattern recognitions

2020 ◽  
Vol 103 (2) ◽  
pp. 003685042091631 ◽  
Author(s):  
Lu Deng ◽  
Zhengjun Zhang
Keyword(s):  
Correlation Coefficient ◽  
Early Warning System ◽  
Warning System ◽  
Time Difference ◽  
Lag Effects ◽  
Extreme Dependence ◽  
Level Data ◽  
Temporal And Spatial ◽  
And Control ◽  
Meteorological Models

Extreme haze was often observed at many locations in Beijing–Tianjin–Hebei region within several hours when they occurred, which is referred to as extreme co-movements and extreme dependence in statistics. This article applies tail quotient correlation coefficient to explore the temporal and spatial extreme dependence patterns of haze in this region. Hourly PM2.5 station-level data during 2014–2018 are used, and the results show that the tail quotient correlation coefficient between stations increases with month. Specifically, the simultaneous extreme dependence was strong in the fourth season, while the haze was severe. In the first season, while the haze was also severe, the extreme hazes only show strong co-movements with a time difference. These observations lead to the study of two special scenarios, that is, the concurrence/extreme dependence of the worst extreme haze and its lag effects. City clusters suffering simultaneous extreme haze or with certain time difference as well as the most frequently co-movement cities are identified. The extreme co-movements of these cities and the reasons for their occurrences have strong implications for improving the PM2.5 joint prevention and control in the Beijing–Tianjin–Hebei region. The importance of lag effects is also reflected in the precedence order of the extreme haze’s appearance. It is especially useful when setting the mechanism of the early warning system which can be triggered by the first appearance of extreme haze. The precedence orders also avail in investigating the transmission path of the haze, based on which more precise meteorological models can be made to benefit the haze forecasting of the region.

scholarly journals Wastewater-based Epidemiology – Possible Approach for the Prevalence Prediction of SARS-CoV-2 in the Population

2021 ◽  
Author(s):  
Nada Krivonakova ◽  
Andrea Soltysova ◽  
Michal Tamas ◽  
Zdenko Takac ◽  
Ján Krahulec ◽  
...  
Keyword(s):  
Early Warning ◽  
Wastewater Treatment Plants ◽  
Time Lag ◽  
Warning System ◽  
Capital City ◽  
Statistical Dependence ◽  
Local Site ◽  
Viral Particles ◽  
The City ◽  
Death Cases

Abstract COVID-19 pandemic caused by β-coronavirus SARS-CoV-2 emerges to intensive scientific research and monitoring of wastewaters because of their possible important role in identifying and early warning of a spread of the virus in the community. In our study, we investigated the prevalence of the COVID-19 disease in the population of the capital city of Slovakia, Bratislava, based on wastewater monitoring from September 2020 until March 2021. Samples were analyzed from two major wastewater treatment plants of the city with reaching nearly 0.6 million monitored inhabitants. Obtained results from the wastewater analysis suggest significant statistical dependence. High correlations between the number of viral particles in wastewater and the number of reported positive nasopharyngeal RT-qPCR tests of infected individuals with a time lag of 2 weeks / 12 days (R2 = 83.78% / R2 = 52.65%) as well as with a reported number of death cases with a time lag of 4 weeks / 27 days (R2 = 83.21% / R2 = 61.89%) was observed. The obtained results and subsequent mathematical modeling will serve in the future as an early warning system for the occurrence of a local site of infection and, at the same time, predict the load on the health system up to two weeks in advance.

Measuring dynamic efficiency with variable time lag effects

Omega ◽  
2021 ◽  
pp. 102578
Author(s):  
Dong-Joon Lim ◽  
Moon-Su Kim
Keyword(s):  
Time Lag ◽  
Dynamic Efficiency ◽  
Lag Effects ◽  
Variable Time

A Multi-Period Input DEA Model with Consistent Time Lag Effects

2019 ◽  
Vol 42 (3) ◽  
pp. 8-14
Author(s):  
Byungho Jeong ◽  
◽  
Yanshuang Zhang ◽  
Taehan Lee
Keyword(s):  
Time Lag ◽  
Dea Model ◽  
Lag Effects

scholarly journals New Psychoactive Substances: A Matter of Time

2019 ◽  
Vol 17 (9) ◽  
pp. 818-822 ◽  
Author(s):  
Flaminia Pantano ◽  
Silvia Graziano ◽  
Roberta Pacifici ◽  
Francesco Paolo Busardò ◽  
Simona Pichini
Keyword(s):  
Mechanism Of Action ◽  
Early Warning ◽  
Early Warning System ◽  
Time Lag ◽  
Warning System ◽  
Screening Tests ◽  
New Psychoactive Substances ◽  
Psychoactive Substances ◽  
Poor Knowledge ◽  
Wide Range

In the last few years, a wide range of new psychoactive substances (NPS) have been produced and marketed to elude the controlled substance lists. These molecules enter the traditional illegal and web market with poor knowledge about their toxicity, mechanism of action, metabolism, abuse potential so that they are directly tested by the consumers. This perspective highlights the main issues connected with NPS: the celerity they enter and leave the market once included in the banning laws to be substituted by new legal analogues; the unavailability of analytical screening tests and certified standards to perform toxicological analyses; the time lag between NPS identification and inclusion in the controlled substances lists. Finally, the authors take a snapshot of the commitment of the Italian Early Warning System in highlighting the recent seizures of NPS as well as the distribution of NPS related intoxication and deaths as an example of what is happening in the European countries and internationally.

scholarly journals Exploring the Initial Impact of COVID-19 Sentiment on US Stock Market Using Big Data

2020 ◽  
Vol 12 (16) ◽  
pp. 6648
Author(s):  
Hee Soo Lee
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Factor Model ◽  
Time Lag ◽  
Google Trends ◽  
Investment Planning ◽  
Excess Returns ◽  
Time Series Regression ◽  
Strategic Investment ◽  
Daily News

This study explores the initial impact of COVID-19 sentiment on US stock market using big data. Using the Daily News Sentiment Index (DNSI) and Google Trends data on coronavirus-related searches, this study investigates the correlation between COVID-19 sentiment and 11 select sector indices of the Unites States (US) stock market over the period from 21st of January 2020 to 20th of May 2020. While extensive research on sentiment analysis for predicting stock market movement use tweeter data, not much has used DNSI or Google Trends data. In addition, this study examines whether changes in DNSI predict US industry returns differently by estimating the time series regression model with excess returns of industry as the dependent variable. The excess returns are obtained from the Fama-French three factor model. The results of this study offer a comprehensive view of the initial impact of COVID-19 sentiment on the US stock market by industry and furthermore suggests the strategic investment planning considering the time lag perspectives by visualizing changes in the correlation level by time lag differences.

scholarly journals Nonuniform Time-Lag Effects of Asymmetric Warming on Net Primary Productivity across Global Terrestrial Biomes

2018 ◽  
Vol 22 (8) ◽  
pp. 1-26 ◽  
Author(s):  
Youyue Wen ◽  
Xiaoping Liu ◽  
Guoming Du
Keyword(s):  
Partial Correlation ◽  
Net Primary Productivity ◽  
Net Primary Production ◽  
Time Lag ◽  
Correlation Coefficients ◽  
Terrestrial Ecosystem ◽  
Terrestrial Vegetation ◽  
Vegetation Growth ◽  
Lag Effects ◽  
Terrestrial Biomes

Abstract Climate warming exhibits asymmetric patterns over a diel time, with the trend of nighttime warming exceeding that of daytime warming, a phenomenon commonly known as asymmetric warming. Recently, increasing studies have documented the significant instantaneous impacts of asymmetric warming on terrestrial vegetation growth, but the indirect effects of asymmetric warming carrying over vegetation growth (referred to here as time-lag effects) remain unknown. Here, we quantitatively studied the time-lag effects (within 1 year) of asymmetric warming on global plant biomes by using terrestrial vegetation net primary production (NPP) derived by the Carnegie–Ames–Stanford Approach (CASA) model and accumulated daytime and nighttime temperature (ATmax and ATmin) from 1982 to 2013. Partial correlation and time-lag analyses were conducted at a monthly scale to obtain the partial correlation coefficients between NPP and ATmax/ATmin and the lagged durations of NPP responses to ATmax/ATmin. The results showed that (i) asymmetric warming has nonuniform time-lag effects on single plant biomes, and distinguishing correlations exist in different vegetation biomes’ associations to asymmetric warming; (ii) terrestrial biomes respond to ATmax (4.63 ± 3.92 months) with a shorter protracted duration than to ATmin (6.06 ± 4.27 months); (iii) forest biomes exhibit longer prolonged duration in responding to asymmetric warming than nonforest biomes do; (iv) mosses and lichens (Mosses), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), and mixed forests (MF) tend to positively correlate with ATmax, whereas the other biomes associate with ATmax with near-equal splits of positive and negative correlation; and (v) ATmin has a predominantly positive influence on terrestrial biomes, except for Mosses and DNF. This study provides a new perspective on terrestrial ecosystem responses to asymmetric warming and highlights the importance of including such nonuniform time-lag effects into currently used terrestrial ecosystem models during future investigations of vegetation–climate interactions.

scholarly journals An Early Warning System to Detect At-Risk Students in Online Higher Education

2020 ◽  
Vol 10 (13) ◽  
pp. 4427 ◽  
Author(s):  
David Bañeres ◽  
M. Elena Rodríguez ◽  
Ana Elena Guerrero-Roldán ◽  
Abdulkadir Karadeniz
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Predictive Model ◽  
Early Warning ◽  
Early Warning System ◽  
Time Lag ◽  
At Risk Students ◽  
Warning System ◽  
Online Higher Education ◽  
Depth Analysis

Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

Sign in / Sign up

Export Citation Format

Share Document