Do opioid prescriptions lead to fatal car crashes?

2021 ◽  
Author(s):  
Michael R. Betz ◽  
Lauren E. Jones
Keyword(s):  
Car Crashes

Uncovering the Wealth of Physics Examples from Videos on the Internet for Use in Physics Lessons

2020 ◽  
Vol 02 (01) ◽  
pp. 2050005
Author(s):  
Shen Yong Ho
Keyword(s):  
Everyday Life ◽  
Life Events ◽  
Teaching And Learning ◽  
The Internet ◽  
Physics Teaching ◽  
Traditional Lecture ◽  
Lightning Strikes ◽  
Online Videos ◽  
Physics Concepts ◽  
Car Crashes

It is well known among educators that carefully planned Physics demonstrations incorporated into lessons can enhance the teaching and learning of Physics. However, there are also everyday life events, such as car crashes and lightning strikes that also aptly demonstrate concepts in Physics but cannot be easily recreated in class. Today, many of these events are captured on video and are easily available on the internet. To facilitate teachers to find what they need, we classify online videos useful for Physics teaching into six broad categories. Some of these videos can be more useful than traditional lecture demonstrations in providing relevant contexts for introducing Physics concepts. We will also discuss some principles for designing class activities to help students make sense of the underlying Physics in the videos.

Risk of disability due to car crashes: a review of the literature and methodological issues

Injury ◽  
2004 ◽  
Vol 35 (11) ◽  
pp. 1116-1127 ◽  
Author(s):  
Shanthi N. Ameratunga ◽  
Robyn N. Norton ◽  
Derrick A. Bennett ◽  
Rod T. Jackson
Keyword(s):  
Review Of The Literature ◽  
Methodological Issues ◽  
Car Crashes

Non-Head Impact Cervical Spine Injuries in Frontal Car Crashes to Lap-Shoulder Belted Occupants

1992 ◽  
Author(s):  
Donald F. Huelke ◽  
G. Murray Mackay ◽  
Andrew Morris ◽  
Maureen Bradford
Keyword(s):  
Cervical Spine ◽  
Head Impact ◽  
Cervical Spine Injuries ◽  
Spine Injuries ◽  
Car Crashes

Differences between older and younger drivers; characteristics of fatal car crashes and driver injuries

1996 ◽  
Vol 23 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Harmeet Sjögren ◽  
Ulf Björnstig ◽  
Anders Eriksson ◽  
Mats Öström
Keyword(s):  
Car Crashes ◽  
Driver Injuries

Soft computing based audio signal analysis for accident prediction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hima Bindu Valiveti ◽  
Anil Kumar B. ◽  
Lakshmi Chaitanya Duggineni ◽  
Swetha Namburu ◽  
Swaraja Kuraparthi
Keyword(s):  
Feature Extraction ◽  
Audio Signal ◽  
Support Vector ◽  
Classification Algorithms ◽  
Spectral Features ◽  
Road Accidents ◽  
Content Type ◽  
Detection Techniques ◽  
Car Crashes ◽  
High Level

Purpose Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence. Design/methodology/approach The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques. Findings Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested. Practical implications Denoising of the audio samples for perfect feature extraction was a tedious chore. Originality/value The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

Seducing-Machines

2016 ◽  
Author(s):  
Sean McQueen
Keyword(s):  
Middle Class ◽  
Film Adaptation ◽  
The Novel ◽  
Theoretical Approaches ◽  
Car Crashes ◽  
Primary Focus

This chapter turns to the 1973 J.G. Ballard novel Crash as well as its 1996 film adaptation by Cronenberg. It aims to make careful distinctions between Deleuze and Baudrillard and show why they gravitate to Crash. The primary focus in the novel is a cult of bored, middle-class professionals who feel alive only after modifying their bodies via staged car crashes. From here, the chapter reveals that Crash is notably quite flexible and can be subjected to many theoretical approaches, at times producing contradictory readings as a result. While Crash the novel might be a distinctly Baudrillardian creature, for example, Crash the Cronenberg film appears to lean more toward Deleuze.

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