In this paper, we present a new fractional epidemiological model on a heterogeneous network to investigate Middle East respiratory syndrome (MERS-CoV), which is caused by a virus in the coronavirus family.

From an evolutionary perspective, it is inferred that termites evolved to build tunneling patterns in a way that optimizes search and transport efficiency. So far, there have been many studies on search efficiency, but few studies on transport efficiency due to the difficulty of direct observation under the field condition. To overcome the difficulty, we developed an individual-based model to simulate the transport process.

A binary time series can be transformed into a Branch Length Similarity (BLS) entropy prole by being mapped to a circumference called a time-circle. In this study, we explored how peaks and cliffs are formed and how they relate to time series.

The early prediction of significant coronary artery lesion, including coronary vasospasm, have yet to be studied. It is essential to discern the disorders with significant coronary lesions (SCDs) requiring coronary angiography from mimicking disease. We aimed to determine which of all clinical variables were more important using conventional logistic regression (cLR) and machine learning (ML).

We report a deep learning-based emotion recognition method using EEG data collected while applying cosmetic creams. Four creams with different textures were randomly applied, and they were divided into two classes, “like (positive)” and “dislike (negative)”, according to the preference score given by the subject. We extracted frequency features using wellknown frequency bands, i.e., alpha, beta and low and high gamma bands, and then we created a matrix including frequency and spatial information of the EEG data.

We present a directed search for continuous gravitational wave (CW) signals emitted by spinning neutron stars located in the inner parsecs of the Galactic Center (GC). Compelling evidence for the presence of a numerous population of neutron stars has been reported in the literature, turning this region into a very interesting place to look for CWs.

We report on a search for compact binary coalescences where at least one binary component has a mass between 0.2 M⊙ and 1.0 M⊙ in Advanced LIGO and Advanced Virgo data collected between 1 April 2019 1500 UTC and 1 October 2019 1500 UTC.We extend our previous analyses in two main ways: we include data from the Virgo detector and we allow for more unequal mass systems, with mass ratio q ≥ 0.1. We do not report any gravitational-wave candidates.

In this study, we developed an innovativeMARmethod to achieve optimal restoration of anatomical details. Approach. The proposedMARapproach is based on a two-stage deep learning-based method.

Dental cone-beam computed tomography (CBCT) has been increasingly used for dental and maxillofacial imaging. However, the presence of metallic inserts, such as implants, crowns, and dental braces, violates the CT model assumption, which leads to severe metal artifacts in the reconstructed CBCT image, resulting in the degradation of diagnostic performance. In this study,we used deep learning to reduce metal artifacts.

This survey is dedicated to providing guidelines for adapting the most suitable post-quantum candidates to the requirements of various devices and suggesting efficient and physically secure implementations that can be built into existing embedded applications as easily as traditional PKC.