Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field

Analysis of lightning-induced transient in 2.4 GHz wireless communication system

This paper analyzes the effects of transient from lightning channel to wireless communication system operating in microwave band at 2.4 GHz. Received Signal Strength Indicator (RSSI) measurement method has been used to capture 3 lightning flashes (naked eyes method) with 7 spikes recorded at r distance from the lightning channel base with fixed d Transmitter-Receiver (T-R) separation at 1 meter. The transient effects have been evaluated in terms of Electric Field Strength, Signal-to-Interference-Noise Ratio (SINR), Bit Error Rate (BER) and Packet Error Rate (PER). During the first lightning flash event, more than 50% of total transmitted packets were corrupted for at least 3 seconds duration. The same happens during the second and third lightning flash events with more than 40% and 15% packets corrupted for durations of 3 and 2 seconds respectively. Interestingly the most suffered traffic types are video and speech signal transmission while data transmission is not affected at all. © 2011 IEEE

Higher Body Mass Index (BMI) and Low-Volume Surgeons Confer Increased Operative Complications in Anterior Spinal Exposures

Background Anterior spinal exposures performed by spine or vascular surgeons have a known complication rate of 2-11%. We present the largest single institution experience of anterior spinal exposures performed by Acute Care Surgeons. The purpose of this study was to characterize the incidence of vascular and visceral injuries during spinal exposures and to identify risk factors contributing to operative complications during the exposure. We hypothesized that patients with a BMI \u3e30 as well as low-volume surgeons (\u3c20 \u3eoperations) would have an increased complication rate. Poster presented at: American Association for Surgery of Trauma (AAST) in Hawaiihttps://jdc.jefferson.edu/surgeryposters/1004/thumbnail.jp