scholarly journals Urban Turbulence in Space and in Time

2012 ◽  
Vol 51 (2) ◽  
pp. 205-218 ◽  
Author(s):  
Bruce B. Hicks ◽  
William J. Callahan ◽  
William R. Pendergrass ◽  
Ronald J. Dobosy ◽  
Elena Novakovskaia
Keyword(s):  
New York ◽  
Velocity Component ◽  
Additional Data ◽  
Wind Component ◽  
Near Surface ◽  
Urban Turbulence ◽  
Wind Velocities ◽  
Standard Deviations ◽  
Using Data ◽  
The Individual

AbstractThe utility of aggregating data from near-surface meteorological networks for initiating dispersion models is examined by using data from the “WeatherBug” network that is operated by Earth Networks, Inc. WeatherBug instruments are typically mounted 2–3 m above the eaves of buildings and thus are more representative of the immediate surroundings than of conditions over the broader area. This study focuses on subnetworks of WeatherBug sites that are within circles of varying radius about selected stations of the DCNet program. DCNet is a Washington, D.C., research program of the NOAA Air Resources Laboratory. The aggregation of data within varying-sized circles of 3–10-km radius yields average velocities and velocity-component standard deviations that are largely independent of the number of stations reporting—provided that number exceeds about 10. Given this finding, variances of wind components are aggregated from arrays of WeatherBug stations within a 5-km radius of selected central DCNet locations, with on average 11 WeatherBug stations per array. The total variance of wind components from the surface (WeatherBug) subnetworks is taken to be the sum of two parts: the temporal variance is the average of the conventional wind-component variances at each site and the spatial variance is based on the velocity-component averages of the individual sites. These two variances (and the standard deviations derived from them) are found to be similar. Moreover, the total wind-component variance is comparable to that observed at the DCNet reference stations. The near-surface rooftop wind velocities are about 35% of the magnitudes of the DCNet measurements. Limited additional data indicate that these results can be extended to New York City.

Use of Deep Learning to Predict Daily Usage of Bike Sharing Systems

2018 ◽  
Vol 2672 (36) ◽  
pp. 92-102 ◽  
Author(s):  
Hong Yang ◽  
Kun Xie ◽  
Kaan Ozbay ◽  
Yifang Ma ◽  
Zhenyu Wang
Keyword(s):  
New York ◽  
Deep Learning ◽  
Additional Data ◽  
Numerical Study ◽  
Predictive Performance ◽  
Relevant Information ◽  
Modeling Process ◽  
Bike Sharing ◽  
Using Data ◽  
Citi Bike

The use of bikes among stations is often spatiotemporally imbalanced, causing many problems in daily operations. Predictively knowing how the system demand evolves in advance helps improve the preparedness of operational schemes. This paper aims to present a predictive modeling approach to analyze the use of bicycles in bike sharing systems. Specifically, a deep learning (DL) approach using the convolutional neural networks (CNNs) was proposed to predict the daily bicycle pickups at both city and station levels. A numerical study using data from the Citi Bike system in New York City (NYC) was performed to assess the performance of the proposed approach. Other than the historical records, relevant information like weather was also incorporated in the modeling process. The modeling results show that the proposed approach can achieve improved predictive performance in both city- and station-level analyses, confirming the merits of the proposed method against other baseline approaches. In addition, including information from neighboring stations into the models can help improve the performance of station-level prediction. The predictive performance of the CNN was also found to be related to parameters such as temporal window, number of neighboring stations, learning ratio, patch size, and the inclusion of additional data such as drop-offs. Thus, the implementation of the proposed models requires necessary calibration to determine appropriate parameters for a given bike sharing system.

Accent Policy and Accent Modification Enterprises as Potential Indicators of Intercultural Power Relations: A Call for an Updated Research Agenda

2013 ◽  
Vol 3 (1) ◽  
pp. 22-33 ◽  
Author(s):  
Brynne D. Ovalle ◽  
Rahul Chakraborty
Keyword(s):  
International Relations ◽  
Identity Construction ◽  
Social Theory ◽  
Power Relations ◽  
Linguistic Diversity ◽  
Cultural Organization ◽  
Using Data ◽  
The Individual ◽  
The Impact ◽  
The Relationship

This article has two purposes: (a) to examine the relationship between intercultural power relations and the widespread practice of accent discrimination and (b) to underscore the ramifications of accent discrimination both for the individual and for global society as a whole. First, authors review social theory regarding language and group identity construction, and then go on to integrate more current studies linking accent bias to sociocultural variables. Authors discuss three examples of intercultural accent discrimination in order to illustrate how this link manifests itself in the broader context of international relations (i.e., how accent discrimination is generated in situations of unequal power) and, using a review of current research, assess the consequences of accent discrimination for the individual. Finally, the article highlights the impact that linguistic discrimination is having on linguistic diversity globally, partially using data from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and partially by offering a potential context for interpreting the emergence of practices that seek to reduce or modify speaker accents.

Calculation of Predictive Odds for Possible Carriers of Haemophilia

1975 ◽  
Vol 34 (03) ◽  
pp. 740-747 ◽  
Author(s):  
C. R. M Prentice ◽  
C. D Forbes ◽  
Sandra Morrice ◽  
A. D McLaren
Keyword(s):  
Biological Activity ◽  
Factor Viii ◽  
Factor Viii Related Antigen ◽  
Using Data ◽  
The Individual ◽  
Betting Odds

SummaryBetting odds for possible carriers of haemophilia have been calculated using data derived from normal and known carrier populations. For each possible carrier the concentration of factor VIII-related antigen and factor VIII biological activity was measured and used to determine the probability of the individual being a carrier. The calculations indicated that, of the 32 possible carriers, 11 were likely to be normal (odds of more than 5:1) while 11 were likely to be haemophilia carriers (again odds of more than 5:1).

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

scholarly journals Lockdown, slow down: impact of the COVID-19 pandemic on physical activity—an observational study

Open Heart ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. e001600
Author(s):  
Joanne Kathryn Taylor ◽  
Haarith Ndiaye ◽  
Matthew Daniels ◽  
Fozia Ahmed
Keyword(s):  
Physical Activity ◽  
New York ◽  
Heart Association ◽  
Evaluation Study ◽  
Activity Data ◽  
Cardiac Devices ◽  
Significant Difference ◽  
New York Heart Association ◽  
Using Data ◽  
The Uk

AimsIn response to the COVID-19 pandemic, the UK was placed under strict lockdown measures on 23 March 2020. The aim of this study was to quantify the effects on physical activity (PA) levels using data from the prospective Triage-HF Plus Evaluation study.MethodsThis study represents a cohort of adult patients with implanted cardiac devices capable of measuring activity by embedded accelerometery via a remote monitoring platform. Activity data were available for the 4 weeks pre-implementation and post implementation of ‘stay at home’ lockdown measures in the form of ‘minutes active per day’ (min/day).ResultsData were analysed for 311 patients (77.2% men, mean age 68.8, frailty 55.9%. 92.2% established heart failure (HF) diagnosis, of these 51.2% New York Heart Association II), with comorbidities representative of a real-world cohort.Post-lockdown, a significant reduction in median PA equating to 20.8 active min/day was seen. The reduction was uniform with a slightly more pronounced drop in PA for women, but no statistically significant difference with respect to age, body mass index, frailty or device type. Activity dropped in the immediate 2-week period post-lockdown, but steadily returned thereafter. Median activity week 4 weeks post-lockdown remained significantly lower than 4 weeks pre-lockdown (p≤0.001).ConclusionsIn a population of predominantly HF patients with cardiac devices, activity reduced by approximately 20 min active per day in the immediate aftermath of strict COVID-19 lockdown measures.Trial registration numberNCT04177199.

scholarly journals Measuring Historical Urban Neighborhood Sustainability: America’s Grand Avenues

2021 ◽  
Vol 13 (3) ◽  
pp. 1358
Author(s):  
Michael R. Greenberg
Keyword(s):  
New York ◽  
New York City ◽  
Kansas City ◽  
White Supremacy ◽  
Urban Neighborhood ◽  
Industrial Cities ◽  
The Us ◽  
Education Medical ◽  
Using Data ◽  
Us Cities

From 1850 through approximately 1920, wealthy entrepreneurs and elected officials created “grand avenues” lined by mansions in New York City, Chicago, Detroit, and other developing US cities. This paper examines the birthplaces of grand avenues to determine whether they have remained sustainable as magnets for healthy and wealthy people. Using data from the US EPA’s EJSCREEN system and the CDC’s 500 cities study across 11 cities, the research finds that almost every place where a grand avenue began has healthier and wealthier people than their host cities. Ward Parkway in Kansas City and New York’s Fifth Avenue have continued to be grand. Massachusetts Avenue in Washington, D.C., Richmond’s Monument Avenue, St. Charles Avenue in New Orleans, and Los Angeles’s Wilshire Boulevard are national and regional symbols of political power, culture and entertainment, leading to sustainable urban grand avenues, albeit several are challenged by their identification with white supremacy. Among Midwest industrial cities, Chicago’s Prairie Avenue birthplace has been the most successful, whereas the grand avenues of St. Louis, Cleveland, Detroit, and Buffalo have struggled, trying to use higher education, medical care, and entertainment to try to rebirth their once pre-eminent roles in their cities.

Absence of the weekend effect and industry-style portfolios

2019 ◽  
Vol 12 (4) ◽  
pp. 463-475
Author(s):  
Selma Izadi ◽  
Abdullah Noman
Keyword(s):  
New York ◽  
Stock Market ◽  
Structural Breaks ◽  
Stock Exchange ◽  
Structural Break ◽  
Weekend Effect ◽  
Content Type ◽  
Market Portfolio ◽  
The Us ◽  
Using Data

Purpose The existence of the weekend effect has been reported from the 1950s to 1970s in the US stock markets. Recently, Robins and Smith (2016, Critical Finance Review, 5: 417-424) have argued that the weekend effect has disappeared after 1975. Using data on the market portfolio, they document existence of structural break before 1975 and absence of any weekend effects after that date. The purpose of this study is to contribute some new empirical evidences on the weekend effect for the industry-style portfolios in the US stock market using data over 90 years. Design/methodology/approach The authors re-examine persistence or reversal of the weekend effect in the industry portfolios consisting of The New York Stock Exchange (NYSE), The American Stock Exchange (AMEX) and The National Association of Securities Dealers Automated Quotations exchange (NASDAQ) stocks using daily returns from 1926 to 2017. Our results confirm varying dates for structural breaks across industrial portfolios. Findings As for the existence of weekend effects, the authors get mixed results for different portfolios. However, the overall findings provide broad support for the absence of weekend effects in most of the industrial portfolios as reported in Robins and Smith (2016). In addition, structural breaks for other weekdays and days of the week effects for other days have also been documented in the paper. Originality/value As far as the authors are aware, this paper is the first research that analyzes weekend effect for the industry-style portfolios in the US stock market using data over 90 years.

The Impact of Customer Loyalty and Restaurant Sanitation Grades on Revisit Intention and the Importance of Narrative Information: The Case of New York Restaurant Sanitation Grading System

2017 ◽  
Vol 59 (3) ◽  
pp. 275-284 ◽  
Author(s):  
Min Gyung Kim ◽  
Hyunjoo Yang ◽  
Anna S. Mattila
Keyword(s):  
New York ◽  
New York City ◽  
Customer Loyalty ◽  
York City ◽  
Letter Grades ◽  
The Difference ◽  
Revisit Intentions ◽  
Using Data ◽  
The Impact ◽  
Loyal Customers

New York City launched a restaurant sanitation letter grade system in 2010. We evaluate the impact of customer loyalty on restaurant revisit intentions after exposure to a sanitation grade alone, and after exposure to a sanitation grade plus narrative information about sanitation violations (e.g., presence of rats). We use a 2 (loyalty: high or low) × 4 (sanitation grade: A, B, C, or pending) between-subjects full factorial design to test the hypotheses using data from 547 participants recruited from Amazon MTurk who reside in the New York City area. Our study yields three findings. First, loyal customers exhibit higher intentions to revisit restaurants than non-loyal customers, regardless of sanitation letter grades. Second, the difference in revisit intentions between loyal and non-loyal customers is higher when sanitation grades are poorer. Finally, loyal customers are less sensitive to narrative information about sanitation violations.

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