Campanophyllummicrosporum (Agaricales, Agaricomycetes), Caloceramultiramosa, and Dacrymycesnaematelioides (Dacrymycetales, Dacrymycetes), three new species from Yunnan Province, southwestern China.

Three new species belonging to Basidiomycota from southwestern China are described based on morphological and molecular data. Campanophyllummicrosporum is morphologically characterized by dorsally pseudostipitate, pale orange to brownish orange pileus, excentric to lateral pseudostipe, crowded lamellae, cylindrical-ellipsoid basidiospores 3.0-4.2 × 1.7-2.2 µm, narrowly clavate to clavate basidia 14.5-23.0 × 3.0-4.2 µm, and cylindrical to clavate cheilocystidia 22.0-55.0 × 5.0-10.8 µm. Caloceramultiramosa is morphologically characterized by stipitate, yellowish to orange, dendroid, and dichotomously branched basidiomata, cylindrical to clavate basidia 36.5-52.5 × 3.8-6.1 µm, navicular or reniform, 1-5-septate mature basidiospores 10.4-16.7 × 5.2-7.4 µm. Dacrymycesnaematelioides is morphologically characterized by stipitate and cerebriform, orange to light brown basidiomata, cylindrical to clavate, smooth or roughened basidia 38.5-79.5 × 6.5-10.6 µm, broadly and elliptic-fusiform, 7-septate mature basidiospores 18.5-28.6 × 8.9-13.8 µm. These three new species are supported by the phylogenetic analyses using maximum likelihood (ML) and Bayesian inference (BI) analyses with combined nuclear ribosomal DNA (rDNA) internal transcribed spacer (ITS) and large ribosomal subunit (LSU) sequences. Full descriptions and photographs of these new species are provided.

Quantum computing for several AGV scheduling models.

Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.

Machine Learning Method for Fatigue Strength Prediction of Nickel-Based Superalloy with Various Influencing Factors.

The accurate prediction of fatigue performance is of great engineering significance for the safe and reliable service of components. However, due to the complexity of influencing factors on fatigue behavior and the incomplete understanding of the fatigue failure mechanism, it is difficult to correlate well the influence of various factors on fatigue performance. Machine learning could be used to deal with the association or influence of complex factors due to its good nonlinear approximation and multi-variable learning ability. In this paper, the gradient boosting regression tree model, the long short-term memory model and the polynomial regression model with ridge regularization in machine learning are used to predict the fatigue strength of a nickel-based superalloy GH4169 under different temperatures, stress ratios and fatigue life in the literature. By dividing different training and testing sets, the influence of the composition of data in the training set on the predictive ability of the machine learning method is investigated. The results indicate that the machine learning method shows great potential in the fatigue strength prediction through learning and training limited data, which could provide a new means for the prediction of fatigue performance incorporating complex influencing factors. However, the predicted results are closely related to the data in the training set. More abundant data in the training set is necessary to achieve a better predictive capability of the machine learning model. For example, it is hard to give good predictions for the anomalous data if the anomalous data are absent in the training set.

A novel index of functional connectivity: phase lag based on Wilcoxon signed rank test.

Phase synchronization has been an effective measurement of functional connectivity, detecting similar dynamics over time among distinct brain regions. However, traditional phase synchronization-based functional connectivity indices have been proved to have some drawbacks. For example, the phase locking value (PLV) index is sensitive to volume conduction, while the phase lag index (PLI) and the weighted phase lag index (wPLI) are easily affected by noise perturbations. In addition, thresholds need to be applied to these indices to obtain the binary adjacency matrix that determines the connections. However, the selection of the thresholds is generally arbitrary. To address these issues, in this paper we propose a novel index of functional connectivity, named the phase lag based on the Wilcoxon signed-rank test (PLWT). Specifically, it characterizes the functional connectivity based on the phase lag with a weighting procedure to reduce the influence of volume conduction and noise. Besides, it automatically identifies the important connections without relying on thresholds, by taking advantage of the framework of the Wilcoxon signed-rank test. The performance of the proposed PLWT index is evaluated on simulated electroencephalograph (EEG) datasets, as well as on two resting-state EEG datasets. The experimental results on the simulated EEG data show that the PLWT index is robust to volume conduction and noise. Furthermore, the brain functional networks derived by PLWT on the real EEG data exhibit a reasonable scale-free characteristic and high test-retest (TRT) reliability of graph measures. We believe that the proposed PLWT index provides a useful and reliable tool to identify the underlying neural interactions, while effectively diminishing the influence of volume conduction and noise.

Directed Connectivity Analysis of the Brain Network in Mathematically Gifted Adolescents.

The neurocognitive characteristics of mathematically gifted adolescents are characterized by highly developed functional interactions between the right hemisphere and excellent cognitive control of the prefrontal cortex, enhanced frontoparietal cortex, and posterior parietal cortex. However, it is still unclear when and how these cortical interactions occur. In this paper, we used directional coherence analysis based on Granger causality to study the interactions between the frontal brain area and the posterior brain area in the mathematical frontoparietal network system during deductive reasoning tasks. Specifically, the scalp electroencephalography (EEG) signal was first converted into a cortical dipole source signal to construct a Granger causality network over the θ-band and γ-band ranges. We constructed the binary Granger causality network at the 40 pairs of cortical nodes in the frontal lobe and parietal lobe across the θ-band and the γ-band, which were selected as regions of interest (ROI). We then used graph theory to analyze the network differences. It was found that, in the process of reasoning tasks, the frontoparietal regions of the mathematically gifted show stronger working memory information processing at the θ-band. Additionally, in the middle and late stages of the conclusion period, the mathematically talented individuals have less information flow in the anterior and posterior parietal regions of the brain than the normal subjects. We draw the conclusion that the mathematically gifted brain frontoparietal network appears to have more "automated" information processing during reasoning tasks.