Bossenalvarado8513

Effective communication during a health crisis can ease public concerns and promote the adoption of important risk-mitigating behaviors. Public health agencies and leaders have served as the primary communicators of information related to COVID-19, and a key part of their public outreach has taken place on social media platforms.

This study examined the content and engagement of COVID-19 tweets authored by Canadian public health agencies and decision makers. We propose ways for public health accounts to adjust their tweeting practices during public health crises to improve risk communication and maximize engagement.

We retrieved data from tweets by Canadian public health agencies and decision makers from January 1, 2020, to June 30, 2020. The Twitter accounts were categorized as belonging to either a public health agency, regional or local health department, provincial health authority, medical health officer, or minister of health. We analyzed trends in COVID-19 tweet engagement and conducted a contentes in their tweets may be missing an important opportunity to engage with users about the mitigation of health risks related to COVID-19.

Public health agencies and decision makers should examine what messaging best meets the needs of their Twitter audiences to maximize sharing of their communications. Public health accounts that do not currently employ risk communication strategies in their tweets may be missing an important opportunity to engage with users about the mitigation of health risks related to COVID-19.Advances of implantable medical devices (IMD) are transforming the tradition method of providing medical treatment, especially to patients under the most challenging condition. Accordingly, the IMD-enabled artificial pancreas system (APS) has now reached global market. It is helping many patients suffering from chronic disease, called diabetes mellitus, in monitoring and maintaining blood glucose level conveniently. However, this advancement is accompanied by various security threats that place the life of patients at risk. Hence, protective measures, especially against yet unknown threats, are of paramount importance. This paper proposes a specification-based misbehavior detection as an alternative solution to effectively mitigate security threats. Moreover, an outlier detection algorithm is also introduced to validate integrity of unprotected data transmitted by the different components. The monitor agent applies a smoothened-trust-based method to assess the trustworthiness of the APS. To demonstrate effectiveness of the proposed method, we first extend the UVA/Padova simulator for glucose-insulin data collection and subsequently simulate scenario with well-behave and malicious APS in MATLAB. The results show that there exists an optimal trust value that can achieve high specificity and sensitivity rate. Moreover, the proposed technique was also compared to contemporary anomaly-based detection methods including decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN). It is shown that our approach can dominate detection performance, especially to malicious behavior that manifests habitually (hidden mode).

This study aims to develop and validate a novel framework,

Phantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. Rapamycin concentration The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients.

Given a volume of patient CT images,

Phantom segments selected anchor organs and structures (e.g., liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs which are challenging to segment (e.g., intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting digital-twin phantoms are used to assess organ doses during routine CT exams.

Phantom was validated on both with a set of XCAT digital phantoms (n=50) and an independent clinical dataset (n=10) with similar accuracy.

Phantom precisely predicted all organ locations yielding Dice Similarity Coefficients (DSC) 0.6 - 1 for anchor organs and DSC of 0.3-0.9 for all other organs.

Phantom showed <10% errors in estimated radiation dose for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors).

Phantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry.

The new framework brings the creation and application of CHPs (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research.

The new framework brings the creation and application of CHPs (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research.Balancing the supply and demand for ride-sourcing companies is a challenging issue, especially with real-time requests and stochastic traffic conditions of large-scale congested road networks. To tackle this challenge, this article proposes a robust and scalable approach that integrates reinforcement learning (RL) and a centralized programming (CP) structure to promote real-time taxi operations. Both real-time order matching decisions and vehicle relocation decisions at the microscopic network scale are integrated within a Markov decision process framework. The RL component learns the decomposed state-value function, which represents the taxi drivers' experience, the off-line historical demand pattern, and the traffic network congestion. The CP component plans nonmyopic decisions for drivers collectively under the prescribed system constraints to explicitly realize cooperation. Furthermore, to circumvent sparse reward and sample imbalance problems over the microscopic road network, this article proposed a temporal-difference learning algorithm with prioritized gradient descent and adaptive exploration techniques.