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Species belonging to the Pezizales are mainly saprobes in nature. They are most commonly observed in woodlands and humid environments. As a result of recent research conducted on the distribution of species in sandy areas and some National Forests Parks, five new species belonging to three genera were identified. A total of five species of disk fungi from Northeast China were identified and described based on morphological classification and molecular phylogenetics. These included Pulvinula ( Pulvinula elsenensis , Pulvinula sublaeterubra ), Microstoma ( Microstoma jilinense , Microstoma changchunense ), and Sarcoscypha ( Sarcoscypha hongshiensis ). Maximum likelihood and Bayesian analyses were performed using a combined nuc rDNA internal transcribed spacer region (ITS) and nuc 28S rDNA (nrLSU) dataset for the construction of phylogenetic trees. Morphological descriptions, line illustrations, and photographs of the ascocarps of these new species are provided, along with lists of the salient attributes exhibited by the species in the three genera under consideration.

A modified electromagnetism-like mechanism (EM) algorithm is proposed to identify structural model parameters using modal data. EM is a heuristic algorithm, which utilizes an attraction-repulsion mechanism to move the sample points towards the optimal solution. In order to improve the performance of original algorithm, a new local search strategy, new charge and force calculation formulas, new particle movement and updating rules are proposed. The test results of benchmark functions show that the modified EM algorithm has better accuracy and faster convergence rate than the original EM algorithm and the particle swarm optimization (PSO) algorithm. In order to investigate the applicability of this approach in parameter identification of structural models, one numerical truss model and one experimental shear-building model are presented as illustrative examples. The identification results show that this approach can achieve remarkable parameter identification even in the case of large noise contamination and few measurements. The modified EM algorithm can also be used to solve other optimization problems., Competing Interests: The authors declare no conflicts of interest.

A novel probabilistic approach for model updating based on approximate Bayesian computation with subset simulation (ABC-SubSim) is proposed for damage assessment of structures using modal data. The ABC-SubSim is a likelihood-free Bayesian approach in which the explicit expression of likelihood function is avoided and the posterior samples of model parameters are obtained using the technique of subset simulation. The novel contributions of this paper are on three fronts: one is the introduction of some new stopping criteria to find an appropriate tolerance level for the metric used in the ABC-SubSim; the second one is the employment of a hybrid optimization scheme to find finer optimal values for the model parameters; and the last one is the adoption of an iterative approach to determine the optimal weighting factors related to the residuals of modal frequency and mode shape in the metric. The effectiveness of this approach is demonstrated using three illustrative examples.