Nonlinear System Identification of Swarm of UAVs Using Deep Learning Methods

This study designs and evaluates multiple nonlinear system identification techniques for modeling the UAV swarm system in planar space. learning methods such as RNNs, CNNs, and Neural ODE are explored and compared. The objective is to forecast future swarm trajectories by accurately approximating the nonlinear dynamics of the swarm model. The modeling process is performed using both transient and steady-state data from swarm simulations. Results show that the combination of Neural ODE with a well-trained model using transient data is robust for varying initial conditions and outperforms other learning methods in accurately predicting swarm stability

Association between a genetic variant in scavenger receptor class B type 1 and its role on codon usage bias with increased risk of developing coronary artery disease

Objective: Coronary artery disease (CAD) as an important cause of morbidity and mortality globally. The scavenger receptor class B type 1 (SCARB1) plays an essential role in the reverse cholesterol transport. We have explored the association between a genetic variant, rs5888, in the SCARB1 gene with CAD and serum HDL-C levels. Methods: Patients were categorized into two groups' angiogram positive (>50% coronary stenosis) and angiogram negative (<50% coronary stenosis). Genotyping was carried out using polymerase chain reaction amplification refractory mutation system. The association between the SNP rs5888 and serum HDL-C was analyzed using a logistic regression model. Results: The results showed that the subjects carrying a T allele was associated with a decreased serum HDL-C levels compared to the C allele in total population (p < 0.001). The risk of angiogram positivity in subjects carrying a T allele was 3.1-fold higher than for the control group (p < 0.001). Conclusion: CVD patients carrying the T allele of rs5888 variant in the SCARB1 gene was associated with decreased serum level of HDL