Constrained path planning of unmanned vehicles

The application of unmanned system performing large-scale tasks, for instance, long-term surveillance/reconnaissance, large area sensing/mapping, and long distance materials handling is a relatively new and exciting topic. However, developing a practical system is still challenging due to complex models and hardware restriction. This manuscript explores various path planning missions from a more realistic perspective, such as point-to-point obstacle avoiding, multi-targets trajectory finding, informative motion planning, and multi-Hamiltonian Path Problem (mHPP) with two types of unmanned vehicles, Unmanned Ariel Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). These problems are formulated as classical optimization problems with constraints representing the environment and kinematic limitations, and then solved by proposed numerical or heuristic optimization approaches. The selected methods are used to handle nonlinear, discontinuous, and multi-objective formulations of the constrained mission planning problems. The feasibility and effectiveness of the proposed algorithms are inspected by the performance and comparison with other proposed methods in literature. The resulting simulations and experimental tests obtained from all the methods are demonstrated and discussed

A new integrated framework for the identification of potential virus–drug associations

IntroductionWith the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases.MethodsIn this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses.ResultsThe results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes