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The unresolved phylogenetic framework within the Selaginellaceae subfamily Gymnogynoideae (ca. 130 species) has hindered our comprehension of the diversification and evolution of Selaginellaceae, one of the most important lineages in land plant evolution. Here, based on plastid and nuclear data extracted from genomic sequencing of more than 90% species of all genera except two in Gymnogynoideae, a phylogenomic study focusing on the contentious relationships among the genera in Gymnogynoideae was conducted. Our major results included the following: (1) Only single-copy region (named NR) and only one ribosomal operon was firstly found in Afroselaginella among vascular plants, the plastome structure of Gymnogynoideae is diverse among the six genera, and the direct repeats (DR) type is inferred as the ancestral state in the subfamily; (2) The first strong evidence was found to support Afroselaginella as a sister to Megaloselaginella . Alternative placements of Ericetorum and Gymnogynum were detected, and their relationships were investigated by analyzing the variation of phylogenetic signals; and (3) The most likely genus-level relationships in Gymnogynoideae might be: (( Bryodesma , Lepidoselaginella ), ((( Megaloselaginella , Afroselaginella ), Ericetorum ), Gymnogynum )), which was supported by maximum likelihood phylogeny based on plastid datasets, maximum likelihood, and Bayesian inference based on SCG dataset and concatenated nuclear and plastid datasets and the highest proportion of phylogenetic signals of plastid genes.

The family Onocleaceae represents a small family of terrestrial ferns, with four genera and around five species. It has a circumboreal to north temperate distribution, and exhibits a disjunct distribution between Eurasia and North America, including Mexico. Historically, the taxonomy and classification of this family has been subject to debate and contention among scholars, leading to contradictory classifications and disagreements on the number of genera and species within the family. Furthermore, due to this disjunct intercontinental distribution and the lack of detailed study across its wide range, this family merits further study to clarify its distributional pattern. Maximum likelihood and Bayesian phylogenetic reconstructions were based on a concatenated sequence dataset for 17 plastid loci and one nuclear locus, which were generated from 106 ingroup and six outgroup taxa from three families. Phylogenetic analyses support that Onocleaceae is composed of four main clades, and Pentarhizidium was recovered as the first branching lineages in Onocleaceae. Molecular dating and ancestral area reconstruction analyses suggest that the stem group of Onocleaceae originated in Late Cretaceous, with subsequent diversification and establishment of the genera Matteuccia, Onoclea, Onocleopsis, and Pentarhizidium during the Paleogene and Neogene. The ancestors of Matteuccia, Onoclea, and Onocleopsis could have migrated to North America via the Beringian land bridge or North Atlantic land bridge which suggests that the diversification of Matteuccia + Onoclea + Onocleopsis closely aligns with the Paleocene-Eocene Thermal Maximum (PETM). In addition, these results suggest that Onocleaceae species diversity peaks during the late Neogene to Quaternary. Studies such as this enhance our understanding of the mechanisms and climatic conditions shaping disjunct distribution in ferns and lycophytes of eastern Asia, North America, and Mexico and contribute to a growing body of evidence from other taxa, to advance our understanding of the origins and migration of plants across continents.

This paper provides a novel risk priority approach for failure mode and effect analysis (FMEA), which can overcome some inherent drawbacks of the traditional risk priority number (RPN) method in imprecise risk evaluation, risk factor weighting and questionable RPN computation. Considering FMEA team members’ vagueness and uncertainty in their evaluations on failure modes, two-dimensional uncertain linguistic variables are advised to describe the risk evaluation result of a failure mode and the reliability of the evaluation result. The grey relation analysis–technique for order preference by similarity to ideal solution (GRA–TOPSIS) is applied for determining the risk ranking of the identified failure modes. In particular, a maximizing deviation method is employed for calculating the optimal weights of risk factors in an objective way. Via a practical healthcare risk analysis case, the new FMEA is proved to be appropriate and effective in coping with the risk evaluation problems with uncertain linguistic information. Furthermore, by comparing with existing methods, it is shown that the proposed integrated approach excels in the risk evaluation and prioritization of failure modes in FMEA.

This paper evaluates the performance of the FW-test for testing part of p-regression coefficients in linear panel data model when p is divergent. The asymptotic power of the FW-statistic is obtained under some regular conditions. The theoretical development are challenging since the number of covariates increases as the sample size increases. It is worth noting that the inference approach does not require any specification of the error distribution. Some simulation comparisons are conducted and show that the simulated power coincide with theoretical power well. The method is also illustrated using a renal cancer data example.

In this puper, we consider the problem of variabie selection and model detection in varying coefficient models with longitudinM data. We propose a combined penalization procedure to select the significant variables, detect the true structure of the model and estimate the unknown regression coefficients simultaneously. With appropriate selection of the tuning parameters, we show that the proposed procedure is consistent in both variable selection and the separation of varying and constant coefficients, and the penalized estimators have the oracle property. Finite sample performances of the proposed method are illustrated by some simulation studies and the real data analysis.

Based on the Woschni correlation, a three dimensional in-cylinder heat transfer model is proposed, which develops Woschni correlation from zero dimension to three dimension. Characteristic parameters are proposed as transient flow and heat transfer parameters from in-cylinder CFD simulation, with further consideration of the influence of thermal conductivity, viscosity and Prandtl number. According to test data, the new correlation can be regressed. The new model costs little more calculation time, and it can satisfy the engineering demand.

A failure analysis has been conducted on a diesel engine crankshaft used in a commercial vehicle, which is made from 42CrMo forging steel, and the crankshaft was after induction hardening process. The fracture occurred between the 4th journal and the 4th crankpin, fracture section indicates that fatigue is the dominant mechanism of failure for this crankshaft. In order to find the failure causes, the dimension of journal boss fillet, material chemical compositions, surface hardness and depth of hardness layer were evaluated. Chemical compositions of crankshaft material, surface hardness and the depth of hardness layer are within the specified range, however the dimension of journal boss fillet is less than specified, and the transition zone of hardness layer was found on the journal boss fillet. Heavy segregation and many non-metallic inclusions aggregate zones were found from metallographic, these would lead to the reduction of endurance bending strength. FEA analysis results suggest that the dimension of journal boss fillet have an important effect on the stress and stress amplitude of the crankshaft. So the disqualification machining and material property are the main causes for the crankshaft fatigue fracture.

Video classification, as an essential task in computer vision, aims to identify and label video content using computer technology automatically. However, the current mainstream video classification models face two significant challenges in practical applications: first, the classification accuracy is not high, which is mainly attributed to the complexity and diversity of video data, including factors such as subtle differences between different categories, background interference, and illumination variations; and second, the number of model training parameters is too high resulting in longer training time and increased energy consumption. To solve these problems, we propose the OM-Video Swin Transformer (OM-VST) model. This model adds a multi-scale feature fusion module with an optimized downsampling module based on a Video Swin Transformer (VST) to improve the model’s ability to perceive and characterize feature information. To verify the performance of the OM-VST model, we conducted comparison experiments between it and mainstream video classification models, such as VST, SlowFast, and TSM, on a public dataset. The results show that the accuracy of the OM-VST model is improved by 2.81 % while the number of parameters is reduced by 54.7 % . This improvement significantly enhances the model’s accuracy in video classification tasks and effectively reduces the number of parameters during model training.

Spermatogenesis in mammals is characterized by two waves of piRNA expression： one corresponds to classic piR- NAs responsible for silencing retrotransponsons and the second wave is predominantly derived from nontransposon intergenic regions in pachytene spermatocytes, but the function of these pachytene piRNAs is largely unknown. Here, we report the involvement of pachytene piRNAs in instructing massive mRNA elimination in mouse elongating spermatids （ES）. We demonstrate that a piRNA-induced silencing complex （pi-RISC） containing murine PIWI （MIWI） and deadenylase CAF1 is selectively assembled in ES, which is responsible for inducing mRNA deadenylation and decay via a mechanism that resembles the action of miRNAs in somatic cells. Such a highly orchestrated program appears to take full advantage of the enormous repertoire of diversified targeting capacity of pachytene piRNAs de rived from nontransposon intergenic regions. These findings suggest that pachytene piRNAs are responsible for inactivating vast cellular programs in preparation for sperm production from ES.

Background： The reports on massive transfusions （MTs） in obstetrics have recently been an increasing trend. We aimed to define the clinical features, risk factors, main causes, and outcomes of MTs due to severe postpartum hemorrhage （PPH） and the frequency trends over the past 10 years. Methods： We retrospectively analyzed the data of 3552 PPH patients who were at ≥28 weeks of gestation in the Obstetric Department of Peking University First Hospital from January 2006 to February 2015. The clinical records of patients receiving MT with ≥5 units （approximately 1000 ml） of red blood cells within 24 h of giving birth were included. The Pearson＇s Chi-square and Fisher＇s exact tests were used to compare the frequency distributions among the categorical variables of the clinical features. Results： One-hundred six women were identified with MT over the 10-year period. The MT percentage was stable between the first 5-year group （2006-2010） and the second 5-year group （2011-2015） （2.5‰ vs. 2.7‰, χ^2 = 154.85, P = 0.25）. Although uterine atony remained the main cause of MT, there was a rising trend for placental abnormalities （especially placenta accreta） in the second 5-year group compared with the first 5-year group （34% vs. 23%, χ^2 = 188.26, P = 0.03）. Twenty-four （23%） women underwent hysterectomy, and among all the causes of PPH, placenta accreta had the highest hysterectomy rate of 70% （17/24）. No maternal death was observed. Conclusions： There was a rising trend for placental abnormalities underlying the stable incidence of MT in the PPH cases. Placenta accreta accounted for the highest risk of hysterectomy. It is reasonable to have appropriate blood transfusion backup for high-risk patients, especially those with placenta accreta.