The Importance of Twitter to Destination Marketing Organizations

Social media has experienced quick and expansive growth since its beginnings. While the initial users were individuals, social media has become a melting pot of businesses, brands, and celebrities all vying for engagement from followers in the hopes of increasing likes, engagement levels and sales. Destination marketing organizations (DMO’s) have been slow to adopt social media platforms and integrate them into their marketing strategies. The goal of this study is to analyze what kinds of social media content produces the highest levels of engagement in order to make specific social media strategy recommendations to the Columbia DMO, Experience Columbia. An empirical analysis of data from DMO Twitter accounts was done to determine what types of content achieved the highest engagement levels, while an observational analysis was performed to determine characteristics of successful accounts. The findings showed that tweets with media had the highest engagement levels, while tweets solely containing URL links decreased engagement levels. These results showed that engagement levels are affected by the types of content posted as well as several other subjective factors

Modelling the tsunami free oscillations in the Marquesas (French Polynesia)

The tsunami resonance inside basins (closed or semi-enclosed) depends on the period of the incident waves, reflection and energy dissipation, characteristics of the boundary and the geometry of the basin.When waves continuously enter the basin, they caus

The 2006 July 17 Java (Indonesia) tsunami from satellite imagery and numerical modelling: A single or complex source?

The Mw 7.8 2006 July 17 earthquake off the southern coast of Java, Indonesia, has been responsible for a very large tsunami causing more than 700 casualties. The tsunami has been observed on at least 200 km of coastline in the region of Pangandaran (Wes

Three-dimensional numerical modeling of tsunami-related internal gravity waves in the Hawaiian atmosphere

The tremendous tsunami following the 2011 Tohoku Earthquake produced internal gravity waves (IGWs) in the neutral atmosphere and large disturbances in the. overlying ionospheric plasma while propagating through the Pacific ocean. To corroborate the tsunamigenic hypothesis of these perturbations, we use a 3D numerical modeling of the ocean-atmosphere coupling, to reproduce the tsunami signature observed in the airglow by the imager located in Hawaii and clearly showing the shape of the modeled IGW. The agreement between data and synthetics not only supports the interpretation of the tsunami-related-IGW behavior, but strongly shows that atmospheric and ionospheric remote sensing can provide new tools for oceanic monitoring and tsunami detection

Single-Molecule Localization Microscopy Reconstruction Using Noise2Noise for Super-Resolution Imaging of Actin Filaments

Single-molecule localization microscopy (SMLM) is a super-resolution imaging technique developed to image structures smaller than the diffraction limit. This modality results in sparse and non-uniform sets of localized blinks that need to be reconstructed to obtain a super-resolution representation of a tissue. In this paper, we explore the use of the Noise2Noise (N2N) paradigm to reconstruct the SMLM images. Noise2Noise is an image denoising technique where a neural network is trained with only pairs of noisy realizations of the data instead of using pairs of noisy/clean images, as performed with Noise2Clean (N2C). Here we have adapted Noise2Noise to the 2D SMLM reconstruction problem, exploring different pair creation strategies (fixed and dynamic). The approach was applied to synthetic data and to real 2D SMLM data of actin filaments. This revealed that N2N can achieve reconstruction performances close to the Noise2Clean training strategy, without having access to the super-resolution images. This could open the way to further improvement in SMLM acquisition speed and reconstruction performance

Extracting Axial Depth and Trajectory Trend Using Astigmatism, Gaussian Fitting, and CNNs for Protein Tracking

Accurate analysis of vesicle trafficking in live cells is challenging for a number of reasons: varying appearance, complex protein movement patterns, and imaging conditions. To allow fast image acquisition, we study how employing an astigmatism can be utilized for obtaining additional information that could make tracking more robust. We present two approaches for measuring the z position of individual vesicles. Firstly, Gaussian curve fitting with CNN-based denoising is applied to infer the absolute depth around the focal plane of each localized protein. We demonstrate that adding denoising yields more accurate estimation of depth while preserving the overall structure of the localized proteins. Secondly, we investigate if we can predict using a custom CNN architecture the axial trajectory trend. We demonstrate that this method performs well on calibration beads data without the need for denoising. By incorporating the obtained depth information into a trajectory analysis, we demonstrate the potential improvement in vesicle tracking