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This study addresses the limitations of traditional bankruptcy prediction models, which often struggle with nonlinear financial data due to their tendency to fall into local optima, low efficiency in parameter optimization, and insufficient prediction accuracy. To overcome these issues, we propose a novel bankruptcy prediction model, EEOOA-KELM, which integrates the Elite-Elimination Osprey Optimization Algorithm (EEOOA) with Kernel Extreme Learning Machine (KELM). First, the standard Osprey Optimization Algorithm is enhanced by incorporating three core mechanisms: an elite-guided Lévy mutation strategy, a precise elimination and generation mechanism, and a global-best-guided boundary control. These improvements effectively balance the algorithm’s global exploration and local exploitation capabilities. Benchmark evaluations on the CEC2020 and CEC2022 test suites demonstrate that EEOOA achieves superior convergence speed and solution accuracy on unimodal, multimodal, and hybrid test functions in both 10- and 20-dimensional settings, significantly outperforming seven state-of-the-art algorithms, including the Grey Wolf Optimizer and Whale Optimization Algorithm. Building upon this, EEOOA is employed to optimize the kernel parameters and regularization coefficient of KELM, using the classification error rate from 10-fold cross-validation as the fitness function, thus constructing the EEOOA-KELM bankruptcy prediction model. Experiments on the Wieslaw enterprise bankruptcy dataset reveal that the proposed model achieves higher performance across multiple metrics—accuracy (76.6677%), precision (74.6096%), recall (77.6439%), and F1-score (75.5041%)—compared with competing models, while also demonstrating better stability and generalization ability. Further analyses of the fitness iteration curves and boxplots confirm that EEOOA provides faster convergence and lower error rates when optimizing KELM parameters, with minimal performance fluctuations across multiple cross-validation runs. Overall, this research introduces an efficient and reliable method for financial risk early warning, offering both significant theoretical contributions and practical value.

To present the global, regional, and national burden of knee osteoarthritis (KOA) and its causative factors, categorized by age, gender, and sociodemographic indices from 1990 to 2021, and to project future trends to 2035. A comprehensive analysis of KOA epidemiology was conducted using data from the 2021 Global Burden of Disease Study (GBD). The study examined change trends in KOA burden between 1990 and 2021, including prevalence, incidence, disability-adjusted life years (DALYs), and associated risk factors. Health inequality analyses were performed using slope index of inequality (SII) and concentration index (CI). Decomposition analysis was conducted to understand the contributions of population growth, aging, and epidemiological changes to the increasing burden. Future projections were made for global, Chinese, and Indian trends to 2035. In 2021, the global prevalence of KOA was 374.7 million cases, with an annual incidence of 3.0846 million cases, totaling 12.01 million DALYs. Age-standardized rates for prevalence, incidence, and DALYs increased by 8.3%, 7.1%, and 8.2% respectively since 1990. Health inequality analyses revealed widening disparities across SDI levels, with SII for crude incidence rates increasing from 251 to 400 per 100,000 between 1990 and 2021. Decomposition analysis showed population growth as the primary driver of increased burden globally (75.07% for DALYs), with variations across SDI regions. Projections to 2035 indicate substantial increases in global burden, with incidence expected to rise by 33.6%, prevalence by 43.8%, and DALYs by 41.4%. China and India show differing patterns in projected burden increases. KOA remains a significant public health concern with increasing burden and widening health inequalities. The projected increases highlight the need for targeted interventions, especially in rapidly growing populations. Preventive measures should focus on reducing high BMI, implementing gender-specific treatments, and addressing regional disparities to mitigate the future burden of KOA.

The complexity and challenging underwater environment leading to degradation in underwater image. Measuring the quality of underwater image is a significant step for the subsequent image processing step. Existing Image Quality Assessment (IQA) methods do not fully consider the characteristics of degradation in underwater images, which limits their performance in underwater image assessment. To address this problem, an Underwater IQA (UIQA) method based on color space multi-feature fusion is proposed to focus on underwater image. The proposed method converts underwater images from RGB color space to CIELab color space, which has a higher correlation to human subjective perception of underwater visual quality. The proposed method extract histogram features, morphological features, and moment statistics from luminance and color components and concatenate the features to obtain fusion features to better quantify the degradation in underwater image quality. After features extraction, support vector regression(SVR) is employed to learn the relationship between fusion features and image quality scores, and gain the quality prediction model. Experimental results on the SAUD dataset and UIED dataset show that our proposed method can perform well in underwater image quality assessment. The performance comparisons on LIVE dataset, TID2013 dataset,LIVEMD dataset,LIVEC dataset and SIQAD dataset demonstrate the applicability of the proposed method.

Background Paenibacillus polymyxa WLY78 is a Gram-positive, endospore-forming and N 2 -fixing bacterium. Our previous study has demonstrated that GlnR acts as both an activator and a repressor to regulate the transcription of the nif ( ni trogen f ixation) operon ( nifBHDKENXhesAnifV ) according to nitrogen availability, which is achieved by binding to the two GlnR-binding sites located in the nif promoter region. However, further study on the GlnR-mediated global regulation in this bacterium is still needed. Results In this study, global identification of the genes directly under GlnR control is determined by using chromatin immunoprecipitation-quantitative PCR (ChIP-qPCR) and electrophoretic mobility shift assays (EMSA). Our results reveal that GlnR directly regulates the transcription of 17 genes/operons, including a nif operon, 14 nitrogen metabolism genes/operons ( glnRA , amtBglnK , glnA1 , glnK1 , glnQHMP , nasA , nasD1 , nasD2EF , gcvH , ansZ , pucR , oppABC , appABCDF and dppABC) and 2 carbon metabolism genes ( ldh3 and maeA1 ). Except for the glnRA and nif operon, the other 15 genes/operons are newly identified targets of GlnR. Furthermore, genome-wide transcription analyses reveal that GlnR not only directly regulates the expression of these 17 genes/operons, but also indirectly controls the expression of some other genes/operons involved in nitrogen fixation and the metabolisms of nitrogen and carbon. Conclusion This study provides a GlnR-mediated regulation network of nitrogen fixation and the metabolisms of nitrogen and carbon.

Paenibacillus polymyxa has widely been studied as a model of plant-growth promoting rhizobacteria (PGPR). Here, the genome sequences of 9 P. polymyxa strains, together with 26 other sequenced Paenibacillus spp., were comparatively studied. Phylogenetic analysis of the concatenated 244 single-copy core genes suggests that the 9 P. polymyxa strains and 5 other Paenibacillus spp., isolated from diverse geographic regions and ecological niches, formed a closely related clade (here it is called Poly-clade). Analysis of single nucleotide polymorphisms (SNPs) reveals local diversification of the 14 Poly-clade genomes. SNPs were not evenly distributed throughout the 14 genomes and the regions with high SNP density contain the genes related to secondary metabolism, including genes coding for polyketide. Recombination played an important role in the genetic diversity of this clade, although the rate of recombination was clearly lower than mutation. Some genes relevant to plant-growth promoting traits, i.e. phosphate solubilization and IAA production, are well conserved, while some genes relevant to nitrogen fixation and antibiotics synthesis are evolved with diversity in this Poly-clade. This study reveals that both P. polymyxa and its closely related species have plant growth promoting traits and they have great potential uses in agriculture and horticulture as PGPR.

We experimentally demonstrate an 80 Gbit/s Nyquist-dense wavelength division multiplexed (Nyquist-DWDM) transmission system operating in a simulated atmospheric turbulence channel. The system utilizes eight wavelength-tunable lasers with 100 GHz spacing, modulated by cascaded Mach–Zehnder modulators, to generate phase-locked Nyquist pulse sequences with a 10 GHz repetition rate and a temporal width of 66.7 ps. Each channel is synchronously modulated with a 10 Gbit/s pseudo-random bit sequence (PRBS) and transmitted through controlled weak turbulence conditions generated by a temperature-gradient convection chamber. Experimental measurements reveal that, as the turbulence intensity increases from Cn2=1.01×10−16 to 5.71×10−16 m−2/3, the signal-to-noise ratio (SNR) of the edge channel (C29) and central channel (C33) decreases by approximately 6.5 dB while maintaining stable Nyquist waveform profiles and inter-channel orthogonality. At a forward-error-correction (FEC) threshold of 3.8×10−3, the minimum receiver sensitivity is −17.66 dBm, corresponding to power penalties below 5 dB relative to the back-to-back condition. The consistent SNR difference (<2 dB) between adjacent channels confirms uniform power distribution and low inter-channel crosstalk under turbulence. These findings verify that Nyquist pulse shaping substantially mitigates phase distortion and scintillation effects, demonstrating the feasibility of high-capacity DWDM free-space optical (FSO) systems with enhanced spectral efficiency and turbulence resilience. The proposed configuration provides a scalable foundation for future multi-wavelength FSO links and hybrid fiber-wireless optical networks.

Citri Reticulatae Pericarpium (CRP), the dried peel of citrus fruits, holds notable dietary and medicinal value. Its quality and price largely depend on origin and aging. Lower-grade CRP is often adulterated to imitate premium products, making accurate authentication of region and vintage essential for quality assurance and fair market valuation. Existing methods for vintage classification are limited due to complex equipment and high operational costs, restricting their scalability in practical applications. To address these issues, a convenient method for the accurate identification of Citri Reticulatae Pericarpium using image and multi-stream is proposed. The method comprises three main stages. Firstly, an object detection network with bounding box refinement localizes exocarp and albedo regions from whole CRP images. Secondly, a three-stream feature extractor processes the whole images along with exocarp and albedo patches to capture complementary visual details. A channel-level feature interaction module further enhances robustness through cross-region feature integration. Thirdly, a meta-learning module enables rapid adaptation to images captured under varying conditions by different consumer-grade devices. Experimental results demonstrate that the proposed method achieves an accuracy of 95.5% on iPhone-captured images. In addition, for images captured by different devices, the proposed method achieves a relative accuracy improvement of more than 34% over the direct transfer method, mainly owing to the meta-learning adaptation to different devices.

Chrysanthemum is a traditional Chinese medicinal herb that contains significant medicinal and economic value. However, the medicinal and economic value of chrysanthemum differs depending on its regions and types. Therefore, it is valuable to classify chrysanthemum accurately. Traditional classification methods are costly, time-consuming, and mainly rely on manual processes, chemical testing, or genetic analysis. In light of these challenges, this paper proposes a Chrysanthemum Classification via Color Space Fusion Transformer, which is both cost-effective and capable of real-time processing. First, the chrysanthemum images in the RGB color space are converted to the LAB color space. Second, a multi-path network is designed to independently extract color space features from both the RGB and LAB color spaces, followed by their integration through an inter-path fusion module. Finally, the Transformer module further analyzes the semantic characteristics of these extracted color space features. Experimental results indicate that the proposed method achieves superior accuracy and stability compared to existing classification methods, with a classification accuracy of 96.16%. This method provides an efficient and practical solution for chrysanthemum origin traceability.

The dual‐hop free‐space optical (FSO) communication link for polarization‐division multiplexing quadrature phase‐shift keying (PDM‐QPSK) signals is proposed and experimentally demonstrated in an atmospheric chamber with the optical phase conjugation (OPC) compensating the turbulence‐induced signal distortions. The phase fluctuations can be effectively reduced by the OPC, as the error vector magnitudes are 10.03% and 8.28% decreased for x‐ and y‐polarization. At the bit error rate of 1 × 10–3, the power penalty of the dual‐hop link with OPC is 2.85 or 2.34 dB lower than that of each polarization without OPC.

In automatic driving target detection problem, the neural network is applied to two methods, vision, and LiDAR. These two methods have some relatively mature models based on neural networks. Combining the two to complement each other has become a hot topic. At present, most autonomous driving sensor fusion methods focus on fusion strategy and feature alignment, and there are few studies on the weight ratio of the two sensors after fusion in different environments. In this paper, a fusion target detection model of camera and LiDAR is proposed based on the weighted weight allocation method. The weighted fusion method is adopted, image feature points are extracted by Fast RCNN, and then LiDAR point cloud data is fused into the model by the weighted method, environment variables are introduced, and different weight allocation methods are output under different environments through full connection layer preprocessing. The results on the Nuscenes dataset show that compared with the results without weight assignment, the model can effectively achieve targeted weight assignment in different situations, and the performance is due to the single-sensor method.