Enhanced TOA Estimation Using OFDM over Wide-Band Transmission Based on a Simulated Model

YesThis paper presents the advantages of using a wideband spectrum adopting multi-carrier to improve targets localization within a simulated indoor environment using the Time of Arrival (TOA) technique. The study investigates the effect of using various spectrum bandwidths and a different number of carriers on localization accuracy. Also, the paper considers the influence of the transmitters’ positions in line-of-sight (LOS) and non-LOS propagation scenarios. It was found that the accuracy of the proposed method depends on the number of sub-carriers, the allocated bandwidth (BW), and the number of access points (AP). In the case of using large BW with a large number of subcarriers, the algorithm was effective to reduce localization errors compared to the conventional TOA technique. The performance degrades and becomes similar to the conventional TOA technique while using a small BW and a low number of subcarriers

An indoor path loss prediction model using wall correction factors for wireless local area network and 5G indoor networks

A modified indoor path loss prediction model is presented, namely, effective wall loss model. The modified model is compared to other indoor path loss prediction models using simulation data and real-time measurements. Different operating frequencies and antenna polarizations are considered to verify the observations. In the simulation part, effective wall loss model shows the best performance among other models as it outperforms 2 times the dual-slope model, which is the second best performance. Similar observations were recorded from the experimental results. Linear attenuation and one-slope models have similar behavior, the two models parameters show dependency on operating frequency and antenna polarization

COVID-19-Associated cardiac pathology at the postmortem evaluation: a collaborative systematic review.

Many postmortem studies address the cardiovascular effects of COVID-19 and provide valuable information, but are limited by their small sample size. The aim of this systematic review is to better understand the various aspects of the cardiovascular complications of COVID-19 by pooling data from a large number of autopsy studies. We searched the online databases Ovid EBM Reviews, Ovid Embase, Ovid Medline, Scopus, and Web of Science for concepts of autopsy or histopathology combined with COVID-19, published between database inception and February 2021. We also searched for unpublished manuscripts using the medRxiv services operated by Cold Spring Harbor Laboratory. Articles were considered eligible for inclusion if they reported human postmortem cardiovascular findings among individuals with a confirmed SARS coronavirus type 2 (CoV-2) infection. Confirmed COVID-19 patients with post-mortem cardiovascular findings. None. Studies were individually assessed for risk of selection, detection, and reporting biases. The median prevalence of different autopsy findings with associated interquartile ranges (IQRs). This review cohort contained 50 studies including 548 hearts. The median age of the deceased was 69 years. The most prevalent acute cardiovascular findings were myocardial necrosis (median: 100.0%; IQR, 20%-100%; number of studies = 9; number of patients = 64) and myocardial oedema (median: 55.5%; IQR, 19.5%-92.5%; number of studies = 4; number of patients = 46). The median reported prevalence of extensive, focal active, and multifocal myocarditis were all 0.0%. The most prevalent chronic changes were myocyte hypertrophy (median: 69.0%; IQR, 46.8%-92.1%) and fibrosis (median: 35.0%; IQR, 35.0%-90.5%). SARS-CoV-2 was detected in the myocardium with median prevalence of 60.8% (IQR 40.4-95.6%). Our systematic review confirmed the high prevalence of acute and chronic cardiac pathologies in COVID-19 and SARS-CoV-2 cardiac tropism, as well as the low prevalence of myocarditis in COVID-19

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press