A New Compact Dual-band Antenna Based on Sierpinski Curve Slotted Ground Plane and Current Distribution Analysis

A new design approach has been used to achieve a dual band response from a multi-band resonance. The design approach is wholly depending on current distribution analysis on the surface of a multi-band antenna. The proposed multi-band antenna consists of a slotted ground plane with a simple 50-ohm microstrip feed line on the other side of an FR4 substrate having 4.4 relative dielectric constant and 1.6 mm thickness. The geometry of the first iteration Sierpinski curve fractal has been employed to the slotted ground plane antenna structure. Two small squares have been inserted to both internal upper corners of the slotted ground plane as a technique to control the path of electrical current on the surface of the multi-band antenna. With this technique, the resulted antenna can offer two resonating bands with respect to -10 dB S11. The first band (2.28-2.6) GHz, while the second band (5-5.58) GHz. The proposed antennas have been modeled and simulated using two softwares (CST Microwave Studio and High-Frequency Structure Simulation HFSS) to verify the results of both antennas and the other antenna parameters have been studied by using CST only

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Parameter identification of chaotic systems using improved differential evolution algorithm

[[abstract]]In this paper, an improved differential evolution algorithm, named the Taguchi-sliding-based differential evolution algorithm (TSBDEA), is proposed to solve the problem of parameter identification for Chen, L? and Rossler chaotic systems. The TSBDEA, a powerful global numerical optimization method, combines the differential evolution algorithm (DEA) with the Taguchi-sliding-level method (TSLM). The TSLM is used as the crossover operation of the DEA. Then, the systematic reasoning ability of the TSLM is provided to select the better offspring to achieve the crossover, and consequently enhance the DEA. Therefore, the TSBDEA can be more robust, statistically sound, and quickly convergent. Three illustrative examples of parameter identification for Chen, L? and Rossler chaotic systems are given to demonstrate the applicability of the proposed TSBDEA, and the computational experimental results show that the proposed TSBDEA not only can find optimal or close-to-optimal solutions but also can obtain both better and more robust results than the DEA