Recommended Guidelines for Nighttime Overhead Sign Visibility

Author(s):  
Paul J. Carlson ◽  
Brad Brimley ◽  
Susan T. Chrysler ◽  
Ron Gibbons ◽  
Travis Terry

Guidelines for the provision of effective nighttime performance of overhead signs were developed. Relevant policies and guidelines with regard to sign lighting currently provide little useful information to determine when sign lighting is needed, and the reference material available to practitioners is out of date. Two complementary nighttime visibility studies were conducted, which were designed to produce results useful in developing updated guidelines for overhead sign visibility. The first was conducted on a closed course and investigated the legibility distances of three sign legend and background configurations under various sign lighting treatments. The second was conducted on the open road. It investigated the effects of sign luminance and visual complexity on the distance at which a driver can read overhead signs during a recognition task. The combined findings were used to develop revised guidelines designed to provide adequate nighttime visibility of overhead signs. The proposed guidelines are based on the needs of nighttime motorists and have been formatted specifically for AASHTO’s Roadway Lighting Design Guide, which is being updated. The revised chapter on roadway sign lighting was provided to the AASHTO task force responsible for revisions. The guidelines include a list of recommended retroreflective sheeting materials that can be used to meet nighttime driver needs for specific complexity levels.

2020 ◽  
Author(s):  
Paul Lutkevich ◽  
Ronald Gibbons ◽  
Rajaram Bhagavathula ◽  
Don McLean ◽  
◽  
...  

2020 ◽  
Author(s):  
Paul Lutkevich ◽  
Ronald Gibbons ◽  
Rajaram Bhagavathula ◽  
Don McLean ◽  
◽  
...  

EDIS ◽  
2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Sarah Thomas Hensley ◽  
Geralyn Sachs ◽  
Laura Cash

The 4-H Cloverbud Project Summary is intended for youth ages 5–7 to complete at the completion of a 4-H educational project. This 7-page revision was compiled by the Florida 4-H Awards and Recognition Task Force; Geralyn Sachs and Laura Cash, chairs.https://edis.ifas.ufl.edu/4h032


EDIS ◽  
2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Sarah Thomas Hensley ◽  
Geralyn Sachs ◽  
Laura Cash

This report form is to be completed by senior (age 14–18) 4-H members as a record of their 4-H project completion. Project Reports should be turned in annually at the end of the 4-H year to their county 4-H agent. This 10-page major revision was compiled by the Florida 4-H Awards and Recognition Task Force; Geralyn Sachs and Laura Cash, chairs.https://edis.ifas.ufl.edu/4h035


EDIS ◽  
2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Sarah Thomas Hensley ◽  
Geralyn Sachs ◽  
Laura Cash

This report form is to be completed by intermediate (age 11–13) 4-H members as a record of their 4-H project completion. Project Reports should be turned in annually at the end of the 4-H year to their county 4-H agent. This 10-page major revision was compiled by the Florida 4-H Awards and Recognition Task Force; Geralyn Sachs and Laura Cash, chairs.https://edis.ifas.ufl.edu/4h034


PEDIATRICS ◽  
1995 ◽  
Vol 96 (4) ◽  
pp. 765-779 ◽  
Author(s):  
Arno Zaritsky ◽  
Vinay Nadkarni ◽  
Mary Fran Hazinski ◽  
George Foltin ◽  
Linda Quan ◽  
...  

This consensus document is an attempt to provide an organized method of reporting pediatric ALS data in out-of-hospital, emergency department, and in-hospital settings. For this methodology to gain wide acceptance, the task force encourages development of a common data set for both adult and pediatric ALS interventions. In addition, every effort should be made to ensure that consistent definitions are used in all age groups. As health care changes, we will all be challenged to document the effectiveness of what we currently do and show how new interventions or methods of treatment improve outcome and/or reduce cost. Only through collaborative research will we obtain the necessary data. For these reasons, and to improve the quality of care and patient outcomes, it is the hope of the task force that clinical researchers will follow the recommendations in this document. It is recognized that further refinements of this statement will be needed; these recommendations will improve only when researchers, clinicians, and EMS personnel use them, work with them, and modify them. Suggestions, emendations, and other comments aimed at improving the reporting of pediatric resuscitation should be sent to Arno Zaritsky, MD, Eastern virginia Medical School, Children's Hospital of The King's Daughter, Division of Critical Care Medicine, 601 Children's Lane, Norfolk, VA 23507.


2020 ◽  
Vol 7 (5) ◽  
pp. 191487
Author(s):  
Fintan Nagle ◽  
Nilli Lavie

Perceptual load is a well-established determinant of attentional engagement in a task. So far, perceptual load has typically been manipulated by increasing either the number of task-relevant items or the perceptual processing demand (e.g. conjunction versus feature tasks). The tasks used often involved rather simple visual displays (e.g. letters or single objects). How can perceptual load be operationalized for richer, real-world images? A promising proxy is the visual complexity of an image. However, current predictive models for visual complexity have limited applicability to diverse real-world images. Here we modelled visual complexity using a deep convolutional neural network (CNN) trained to learn perceived ratings of visual complexity. We presented 53 observers with 4000 images from the PASCAL VOC dataset, obtaining 75 020 2-alternative forced choice paired comparisons across observers. Image visual complexity scores were obtained using the TrueSkill algorithm. A CNN with weights pre-trained on an object recognition task predicted complexity ratings with r = 0.83. By contrast, feature-based models used in the literature, working on image statistics such as entropy, edge density and JPEG compression ratio, only achieved r = 0.70. Thus, our model offers a promising method to quantify the perceptual load of real-world scenes through visual complexity.


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