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This study proposes BAGWO, a novel hybrid optimization algorithm that integrates the Beetle Antennae Search algorithm (BAS) and the Grey Wolf Optimizer (GWO) to leverage their complementary strengths while enhancing their original strategies. BAGWO introduces three key improvements: the charisma concept and its update strategy based on the sigmoid function, the local exploitation frequency update strategy driven by the cosine function, and the switching strategy for the antennae length decay rate. These improvements are rigorously validated through ablation experiments. Comprehensive evaluations on 24 benchmark functions from CEC 2005 and CEC 2017, along with eight real-world engineering problems, demonstrate that BAGWO achieves stable convergence and superior optimization performance. Extensive testing and quantitative statistical analyses confirm that BAGWO significantly outperforms competing algorithms in terms of solution accuracy and stability, highlighting its strong competitiveness and potential for practical applications in global optimization.

Pigs are essential agricultural animals, and among the various breeds, the Meishan pig, a native breed of China, is renowned for its high reproductive performance. This breed has been introduced to many countries to enhance local pig breeding programs. However, there have been limited genomic and population genetics studies focusing on Meishan pigs. We created a chromosomal-level genomic assembly using high-depth PacBio sequencing and Illumina sequencing data collected from a Meishan pig. Additionally, we analyzed whole-genome sequencing (WGS) data from Chinese boars and Meishan pigs to identify domestication selection signals within the Meishan breed. The assembled genome of the Meishan pig (MSjxau) was found to be 2.45 Gb in size, with a scaffold length of 139.17 Mb. The quality value was 37.06, and the BUSCO score was 96.2%, indicating good completeness, continuity, and accuracy. We annotated transposable elements, segmental duplication, and genes in the MSjxau genome. By combining these data with 28 publicly available genomes, we provide a high-quality structural variants resource for pigs. Furthermore, we identified 716 selective sweep intervals between Chinese wild pigs and Meishan pigs, where the selected gene PGR may be linked to the high fertility observed in Meishan pigs. Our study offers valuable genomic and variation resources for pig breeding and identifies several genes associated with the domestication of the Meishan pig. This lays the groundwork for further investigation into the genetic mechanisms behind complex traits in pigs.

The energy management strategy (EMS) is a core technology for improving the fuel economy of hybrid electric vehicles (HEVs). However, the coexistence of both discrete and continuous control variables, along with complex physical constraints in HEV powertrains, presents significant challenges for the design of efficient EMSs based on deep reinforcement learning (DRL). To further enhance fuel efficiency and coordinated powertrain control under complex driving conditions, this study proposes a hierarchical DRL-based EMS. The proposed strategy adopts a layered control architecture: the upper layer utilizes the soft actor–critic (SAC) algorithm for continuous control of engine torque, while the lower layer employs a deep Q-network (DQN) for discrete gear selection optimization. Through offline training and online simulation, experimental results demonstrate that the proposed strategy achieves fuel economy performance comparable to dynamic programming (DP), with only a 3.06% difference in fuel consumption. Moreover, it significantly improves computational efficiency, thereby enhancing the feasibility of real-time deployment. This study validates the optimization potential and real-time applicability of hierarchical reinforcement learning for hybrid control in HEV energy management. Furthermore, its adaptability is demonstrated through sustained and stable performance under long-duration, complex urban bus driving conditions.

Understanding the community structure of the lower respiratory tract microbiome is crucial for elucidating its roles in respiratory tract diseases. However, there are few studies about this topic due to the difficulty in obtaining microbial samples from both healthy and disease individuals. Here, using 744 high‐depth metagenomic sequencing data of lower respiratory tract microbial samples from 675 well‐phenotyped pigs, we constructed a lung microbial gene catalog containing the largest scale of 10,031,593 nonredundant genes to date, 44.8% of which are novel. We obtained 356 metagenome‐assembled genomes (MAGs) which were further clustered into 256 species‐level genome bins with 41.8% being first reported in the current databases. Based on these data sets and through integrated analysis of the isolation of the related bacterial strains, in vitro infection, and RNA sequencing, we identified and confirmed that Mesomycoplasma hyopneumoniae (M. hyopneumoniae) MAG_47 and its adhesion‐related virulence factors (VFs) were associated with lung lesions in pigs. Differential expression levels of adhesion‐ and immunomodulation‐related VFs likely determined the heterogenicity of adhesion and pathogenicity among M. hyopneumoniae strains. M. hyopneumoniae adhesion activated several pathways, including nuclear factor kappa‐light‐chain‐enhancer of activated B, mitogen‐activated protein kinase, cell apoptosis, T helper 1 and T helper 2 cell differentiation, tumor necrosis factor signaling, interleukin‐6/janus kinase 2/signal transducer and activator of transcription signaling, and response to reactive oxygen species, leading to cilium loss, epithelial cell‒cell barrier disruption, and lung tissue lesions. Finally, we observed the similar phylogenetic compositions of the lung microbiome between humans with Mycoplasma pneumoniae and pigs infected with M. hyopneumoniae. The results provided important insights into pig lower respiratory tract microbiome and its relationship with lung health. In this study, we utilized 744 metagenomic sequencing data from lower respiratory tract microbial samples of 675 well‐phenotyped pigs across five cohorts. From these, we constructed a microbial gene catalog containing 10,031,593 nonredundant genes and 356 metagenome‐assembled genomes (MAGs), which were further clustered into 256 species‐level genome bins. Based on these data sets, we observed the significant association between Mesomycoplasma hyopneumoniae (M. hyopneumoniae) and lung lesions at the strain level. This association was confirmed by in vitro infection of porcine bronchial epithelial cells (BECs) with two M. hyopneumoniae strains. By employing dual RNA sequencing of both M. hyopneumoniae and infected BECs, alongside RNA‐sequencing data from two pure‐cultured M. hyopneumoniae strains, we elucidated the mechanism of M. hyopneumoniae causing pig lung lesions and explained the observed heterogeneity in pathogenicity among M. hyopneumoniae strains. Highlights We constructed a comprehensive microbial gene catalog of lower respiratory tract microbiome, containing 10,031,593 nonredundant genes and obtained 356 nonredundant metagenome‐assembled genomes using five representative pig populations. Significant associations of Mesomycoplasma hyopneumoniae (M. hyopneumoniae) strains and their adhesion‐related virulence factors with lung lesions were observed and confirmed in two different pig populations, as well as through in vitro infection of porcine bronchial epithelial cells. M. hyopneumoniae adhesion activated several pathways, such as nuclear factor kappa‐light‐chain‐enhancer of activated B, mitogen‐activated protein kinase, cell apoptosis and response to reactive oxygen species, which resulted in cilium loss, epithelial cell‒cell barrier disruption, and lung tissue lesions.

Only using single feature information as input feature cannot fully reflect the transformer fault classification and improve the accuracy of transformer fault diagnosis. To address the above problem, the convolution neural networks’ model is applied for transformer fault assessment designed to implement an end-to-end “different space feature extraction + transformer state diagnosis classification” to enable information from possibly heterogeneous sources to be integrated. This method integrates various feature information of the power transformer operation state to form the isomeric feature, and the model can be used to automatically extract different feature spaces’ information from isomeric feature quantity using its unique one-dimensional convolution and pooling operations. The performance of the proposed approach is compared with that of other models, such as a support vector machine (SVM), backpropagation neural network (BPNN), deep belief network (DBNs), and others. The experimental results show that the proposed one-dimensional convolution neural networks based on an isomeric feature (IF-1DCNN) can accurately classify the fault state of transformer and reduce the adverse interaction between different feature space information in the mixed feature, which has a good engineering application prospect.

It is simulated that the photoacoustic effect in three-dimensional model to compare photoacoustic effect with attenuated different Gaussian radii distribution light sources by the finite element method (FEM). We motivate the enclosed photoacoustic cell at a range of frequencies and compare the results of the amplitude of acoustic pressure frequency curve, the distribution of acoustic pressure and the variation of temperature within the gas layer.

Learning robust speaker representations under noisy conditions presents significant challenges, which requires careful handling of both discriminative and noise-invariant properties. In this work, we proposed an anchor-based stage-wise learning strategy for robust speaker representation learning. Specifically, our approach begins by training a base model to establish discriminative speaker boundaries, and then extract anchor embeddings from this model as stable references. Finally, a copy of the base model is fine-tuned on noisy inputs, regularized by enforcing proximity to their corresponding fixed anchor embeddings to preserve speaker identity under distortion. Experimental results suggest that this strategy offers advantages over conventional joint optimization, particularly in maintaining discrimination while improving noise robustness. The proposed method demonstrates consistent improvements across various noise conditions, potentially due to its ability to handle boundary stabilization and variation suppression separately.

Understanding the community structure and functional capacity of the lower respiratory tract microbiome is crucial for elucidating its roles in respiratory tract diseases. However, there are few studies about it owing to the difficulty in obtaining microbial samples from the lower respiratory tract. Here we collected 745 microbial samples from the porcine lower respiratory tract by harvesting 675 pigs, and constructed a gene catalog containing 10,337,194 nonredundant genes, of which only 30% could be annotated taxonomically. We obtained 397 metagenome-assembled genomes (MAGs) including 111 MAGs with high-quality. These 397 MAGs were further clustered into 292 species-level genome bins (SGBs), among which 56% SGBs are unknown with current databases. Combining with the lung lesion phenotype, we found that Mycoplasma hyopneumoniae strains and the adhesion-related virulence factors harboring in their genomes were significantly associated with porcine lung lesions, implying the role of adhesion and overgrowth of pathogenic M. hyopneumoniae in host lung diseases. This study provided important resources for the study of porcine lower respiratory tract microbiome and lung health.

How to compose existing web services automatically and to guarantee the correctness of the design (e.g. freeness of deadlock and unspecified reception, and temporal constraints) is an important and challenging problem in web services. Most existing approaches require a detailed specification of the desired behaviors of a composite service beforehand and then perform certain formal verification to guarantee the correctness of the design, which makes the composition process both complex and time-consuming. In this paper, we propose a novel approach, referred to as AutoSyn to compose web services, where the correctness is guaranteed in the synthesis process. For a given set of services, a composite service is automatically constructed based on L* algorithm, which guarantees that the composite service is the most general way of coordinating services so that the correctness is ensured. We show the soundness and completeness of our solution and give a set of optimization techniques for reducing the time consumption. We have implemented a prototype system of AutoSyn and evaluated the effectiveness and efficiency of AutoSyn through an experimental study.