On the applicability of numerical tools for simulating wave-ports close to the cutoff frequency

This paper focuses on a common drawback in electromagnetic numerical computer aided design computer aided design (CAD) tools: high frequency structure simulator (HFSS), computer simulation technology (CST) and FEKO, where the excitation by using a wave-port below and close to the cutoff frequency has unreliable values for the reflection coefficient. An example for such problem is presented in the design of a dual horn antenna fed by two different waveguide sections. To overcome this numerical error in the results of these CAD tools, a tapered waveguide section is used in the simulation as an excitation mechanism to the feeding waveguide. The cross section of the input port at this tapered waveguide section is designed to have a cutoff frequency smaller than the lowest frequency under investigation for the original problem. Then, by extracting the effect of the tapered section from the obtained reflection coefficient, it would be possible to obtain the reflection coefficient of the original problem.

CFD-CAA Method for Prediction of Pseudosound and Emitted Noise in Quadcopter Propeller

Subsonic flow air blade machines like UAV propellers generate intensive noise thus the prediction of acoustic impact, optimization of these machines in order to reduce the level of emitted noise is an urgent engineering task. Currently, the development of calculation methods for determining the amplitudes of pressure pulsations and noise characteristics by CFD-CAA methods is a necessary requirement for the development of computer-aided design methods for blade machines, where the determining factors are the accuracy and speed of calculations. The main objective is to provide industrial computer-aided design systems with a highly efficient domestic software to create optimal designs of UAV blade machines that provide a given level of pressure pulsations in the flow part and radiated noise. It comprises: 1) creation of a method for the numerical simulation of sound generation using the correct decomposition of the initial equations of hydrodynamics of a compressible medium and the selection of the source of sound waves in a three-dimensional definition, taking into account the rotation of blades and their interaction with the stator part of the UAV; 2) decomposition of the boundary conditions accounting pseudo-sound disturbances and the complex acoustic impedance at the boundaries of the computational domain 3) development of an effective SLAE solver for solving the acoustic-vortex equation in complex arithmetic (taking into account the boundary conditions in the form of complex acoustic impedance); 4) testing of a new method at all stages of development using experimental data on the generation of pressure pulsations and aerodynamic noise, including a propeller noise measurements.

The Future of Precision Prevention for Advanced Melanoma

Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.

In Silico Research of New Therapeutics Rotenoids Derivatives against Leishmania amazonensis Infection

Yearly, 1,500,000 cases of leishmaniasis are diagnosed, causing thousands of deaths. To advance in its therapy, we present an interdisciplinary protocol that unifies ethnobotanical knowledge of natural compounds and the latest bioinformatics advances to respond to an orphan disease such as leishmaniasis and specifically the one caused by Leishmania amazonensis. The use of ethnobotanical information serves as a basis for the development of new drugs, a field in which computer-aided drug design (CADD) has been a revolution. Taking this information from Amazonian communities, located in the area with a high prevalence of this disease, a protocol has been designed to verify new leads. Moreover, a method has been developed that allows the evaluation of lead molecules, and the improvement of their affinity and specificity against therapeutic targets. Through this approach, deguelin has been identified as a good lead to treat the infection due to its potential as an ornithine decarboxylase (ODC) inhibitor, a key enzyme in Leishmania development. Using an in silico-generated combinatorial library followed by docking approaches, we have found deguelin derivatives with better affinity and specificity against ODC than the original compound, suggesting that this approach could be adapted for developing new drugs against leishmaniasis.