As one technique for autonomous driving, vehicular networks can achieve high efficiency with vehicle-and-infrastructure cooperation, bringing high safety and many value-added services. To achieve higher communication efficiency, much effort has been done to cope with the resource allocation issues for vehicular networks. Nevertheless, due to the strong nonconvexity and nonlinearity, the classical joint resource allocation problem in vehicular networks is typically NP-hard. The multiagent reinforcement learning (MARL) has emerged as a promising solution to tackle this challenge but its stability and scalability are not satisfactory when the amount of vehicles gets increased. In this article, we mainly investigate the issue of joint spectrum and power allocation in vehicular communication networks, and carefully consider the interactions between the vehicles and environment by incorporating the cooperative stochastic game theory with MARL, named complete-game MARL (CG-MARL), to achieve a better convergence and stability with the theoretical computational complexity$\mathcal {O}(n^{N})$with$n$denoting the dimension of action space and$N$denoting the number of V2X Vehicular. Furthermore, the mean-field game (MFG) theory is employed to further enhance the MARL for decreasing the horrible computing resource consumption caused by the CG-MARL to$\mathcal {O}(n^{2})$while maintaining an approximate performance. The simulation results demonstrate that the proposed mean-field-aided MARL (MF-MARL) for vehicular network resource allocation can achieve 95% near-optimal performance with much lower complexity, which indicates its significant potentials in the scenarios with massive and dense vehicles.

The utilization of heterogeneous end devices such as the low earth orbit (LEO) satellite, unmanned aerial vehicles (UAVs) and ground users (GUs) deployed at different altitudes, known as the space-air-ground integrated network (SAGI-Net), can be quite promising towards a bunch of advanced applications. Whereas, the energy efficiency of the SAGI-Net communication system is a key criterion needed to be improved urgently in consideration that the inappropriate communication routing will undoubtedly cause a huge communication energy cost of the system especially with a large number of communication devices inside. In this paper, we proposed a novel communication routing selection model for the SAGI-Net system and established a heterogeneous multi-agent reinforcement learning (HMF-MARL) framework to optimize the communication energy efficiency of this system, where the mean-field theory was introduced to enhance the ability of classic MARL method while still maintaining a relatively low computational complexity. The experiment results show that the capacity of the heterogeneous multi-agent system has been improved by nearly 80% using the proposed HMF-MARL method compared with the classic MARL one, which hopefully shows the potential value on the implementation of the SAGI-Net system in the future.

MOTIVATION: Due to the high complexity of metabolome, the comprehensive 2D gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) is considered as a powerful analytical platform for metabolomics study. However, the applications of GC×GC-TOF MS in metabolomics are not popular owing to the lack of bioinformatics system for data analysis. RESULTS: We developed a computational platform entitled metabolomics profiling pipeline (MetPP) for analysis of metabolomics data acquired on a GC×GC-TOF MS system. MetPP can process peak filtering and merging, retention index matching, peak list alignment, normalization, statistical significance tests and pattern recognition, using the peak lists deconvoluted from the instrument data as its input. The performance of MetPP software was tested with two sets of experimental data acquired in a spike-in experiment and a biomarker discovery experiment, respectively. MetPP not only correctly aligned the spiked-in metabolite standards from the experimental data, but also correctly recognized their concentration difference between sample groups. For analysis of the biomarker discovery data, 15 metabolites were recognized with significant concentration difference between the sample groups and these results agree with the literature results of histological analysis, demonstrating the effectiveness of applying MetPP software for disease biomarker discovery. AVAILABILITY: The source code of MetPP is available at http://metaopen.sourceforge.net CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The compound identification in gas chromatography-mass spectrometry (GC-MS) is achieved by matching the experimental mass spectrum to the mass spectra in a spectral library. It is known that the intensities with higher m/z value in the GC-MS mass spectrum are the most diagnostic. Therefore, to increase the relative significance of peak intensities of higher m/z value, the intensities and m/z values are usually transformed with a set of weight factors. A poor quality of weight factors can significantly decrease the accuracy of compound identification. With the significant enrichment of the mass spectral database and the broad application of GC-MS, it is important to re-visit the methods of discovering the optimal weight factors for high confident compound identification. RESULTS: We developed a novel approach to finding the optimal weight factors only through a reference library for high accuracy compound identification. The developed approach first calculates the ratio of skewness to kurtosis of the mass spectral similarity scores among spectra (compounds) in a reference library and then considers a weight factor with the maximum ratio as the optimal weight factor. We examined our approach by comparing the accuracy of compound identification using the mass spectral library maintained by the National Institute of Standards and Technology. The results demonstrate that the optimal weight factors for fragment ion peak intensity and m/z value found by the developed approach outperform the current weight factors for compound identification. AVAILABILITY: The results and R package are available at http://stage.louisville.edu/faculty/x0zhan17/software/ software-development.