Experimental recognition of vortex beams in oceanic turbulence combining the Gerchberg-Saxton algorithm and convolutional neural network.

In underwater wireless optical communication (UWOC), vortex beams carrying orbital angular momentum (OAM) can improve channel capacity but are vulnerable to oceanic turbulence (OT), leading to recognition errors. To mitigate this issue, we propose what we believe to be a novel method that combines the Gerchberg-Saxton (GS) algorithm-based recovery with convolutional neural network (CNN)-based recognition (GS-CNN). Our experimental results demonstrate that superposed Laguerre-Gaussian (LG) beams with small topological charge are ideal information carriers, and the GS-CNN remains effective even when OT strength 2 is high up to 10 .

Health Effects of Household Solid Fuel Use: Findings from 11 Countries within the Prospective Urban and Rural Epidemiology Study.

Background: Household air pollution (HAP) from solid fuel use for cooking affects 2.5 billion individuals globally and may contribute substantially to disease burden. However, few prospective studies have assessed the impact of HAP on mortality and cardiorespiratory disease.

Data-driven approach of CUSUM algorithm in temporal aberrant event detection using interactive web applications.

Objective: In 2014/2015, Public Health Ontario developed disease-specific, cumulative sum (CUSUM)-based statistical algorithms for detecting aberrant increases in reportable infectious disease incidence in Ontario. The objective of this study was to determine whether the prospective application of these CUSUM algorithms, based on historical patterns, have improved specificity and sensitivity compared to the currently used Early Aberration Reporting System (EARS) algorithm, developed by the US Centers for Disease Control and Prevention.

Method: A total of seven algorithms were developed for the following diseases: cyclosporiasis, giardiasis, influenza (one each for type A and type B), mumps, pertussis, invasive pneumococcal disease.