1,462 research outputs found

    A study of public relations strategies employed by healthcare organizations in Missouri to combat COVID-19 vaccine misinformation

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    The development of the COVID-19 vaccine marked a significant milestone in the global effort to combat the COVID-19 pandemic. However, along with the invention of the COVID-19 vaccine came the challenge of misinformation, which threatened vaccination efforts and public health. Misinformation surrounding the safety and efficacy of the COVID-19 vaccines spread rapidly across various platforms, particularly on social media. Misinformation about the COVID-19 vaccine posed a significant challenge to vaccine acceptance, affecting public health efforts to achieve herd immunity and put the COVID-19 pandemic under control. The importance of effective public relations strategies to address COVID-19 misinformation cannot be overstated. This study explored strategies implemented by healthcare professionals working in Missouri to address COVID-19-related vaccine misinformation. I performed a mixed approach by using a survey with 153 responses from healthcare professionals working in Missouri and having semi-structured in-depth interviews with 4 local health department administrators. The survey data was examined in SPSS to find correlations between variables such as professional role, years of experience, and preferred sources of COVID-19 vaccine information. Additionally, thematic analysis was conducted on the qualitative data gathered from the in-depth interviews to identify key themes and insights regarding public relations strategies employed by healthcare organizations. The results show that peer-reviewed research, healthcare experts, and government agencies emerged as primary sources of reliable information about the COVID-19 vaccine among healthcare professionals. In-depth interview participants emphasized the importance of building trust with communities to provide accurate information and combat misinformation effectively. People's awareness of the echo chamber effect was positively correlated with confidence in distinguishing between accurate information and misinformation about COVID-19 vaccines. This suggests that awareness of how information circulates within social media platforms may enhance individuals' ability to distinguish credible information from misinformation. Healthcare professionals and organizations need to continue prioritizing communication strategies that build trust and engage communities. Additionally, raising awareness of how social media algorithms amplify sensational or controversial content within like-minded communities is crucial for empowering individuals to evaluate information and navigate digital environments effectively and critically. Through implementing proper public relations strategies, healthcare professionals can build trust with communities, empower individuals to make informed decisions about their health, and effectively combat the spread of misinformation.Includes bibliographical references

    Geometric Cover with Outliers Removal

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    We study the problem of partial geometric cover, which asks to find the minimum number of geometric objects (unit squares and unit disks in this work) that cover at least (n-t) of n given planar points, where 0 ? t ? n/2. When t = 0, the problem is the classical geometric cover problem, for which many existing works adopt a general framework called the shifting strategy. The shifting strategy is a divide and conquer paradigm which partitions the plane into equal-width strips, applies a local algorithm on each strip and then merges the local solutions with only a small loss on the overall approximation ratio. A challenge to extend the shifting strategy to the case of outliers is to determine the number of outliers in each strip. We develop a shifting strategy incorporating the outlier distribution, which runs in O(tn log n) time. We also develop local algorithms on strips for the outliers case, improving the running time over previous algorithms, and consequently obtain approximation algorithms to the partial geometric cover

    Electromagnetically induced transparency of interacting Rydberg atoms with two-body dephasing

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    We study electromagnetically induced transparency in a three-level ladder type configuration in ultracold atomic gases, where the upper level is an electronically highly excited Rydberg state. An effective distance dependent two-body dephasing can be induced in a regime where dipole-dipoles interaction couple nearly degenerate Rydberg pair states. We show that strong two-body dephasing can enhance the excitation blockade of neighboring Rydberg atoms. Due to the dissipative blockade, transmission of the probe light is reduced drastically by the two-body dephasing in the transparent window. The reduction of transmission is accompanied by a strong photon-photon anti-bunching. Around the Autler-Townes doublets, the photon bunching is amplified by the two-body dephasing, while transmission is largely unaffected. Besides relevant to the ongoing Rydberg atom studies, our study moreover provides a setting to explore and understand two-body dephasing dynamics in many-body systems

    A new flywheel energy storage device for converting potential energy into kinetic energy

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    Generally, vehicles with axle structure do not use the gravitational potential energy of people and objects reasonably during transportation, but use the extra energy to make the vehicles operate. The purpose of this project is to study a flywheel energy storage device that converts potential energy into kinetic energy, so as to store gravitational potential energy and convert it into kinetic energy for output on demand, which is widely used in industry, civil transportation, medical rescue and other fields

    Relaxed Attention for Transformer Models

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    The powerful modeling capabilities of all-attention-based transformer architectures often cause overfitting and - for natural language processing tasks - lead to an implicitly learned internal language model in the autoregressive transformer decoder complicating the integration of external language models. In this paper, we explore relaxed attention, a simple and easy-to-implement smoothing of the attention weights, yielding a two-fold improvement to the general transformer architecture: First, relaxed attention provides regularization when applied to the self-attention layers in the encoder. Second, we show that it naturally supports the integration of an external language model as it suppresses the implicitly learned internal language model by relaxing the cross attention in the decoder. We demonstrate the benefit of relaxed attention across several tasks with clear improvement in combination with recent benchmark approaches. Specifically, we exceed the former state-of-the-art performance of 26.90% word error rate on the largest public lip-reading LRS3 benchmark with a word error rate of 26.31%, as well as we achieve a top-performing BLEU score of 37.67 on the IWSLT14 (DE\rightarrowEN) machine translation task without external language models and virtually no additional model parameters. Code and models will be made publicly available
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