玉林师范学院

Multi-source interval-incomplete data are widely encountered in real-world applications, such as medical testing, climate monitoring, remote sensing, and economic analysis. However, some of these data sources may have relatively low importance, or even no practical value. Consequently, how to effectively perform information fusion and attribute reduction on multi-source data remains a critical challenge. This paper proposes an adaptive swarm intelligence attribute selection method for a multi-source incomplete interval-value data based on conditional information amount and mutual information. First, the metric formulas on single-source incomplete interval-valued data are established, and the neighborhood granularity structure with respect to an adjustable parameter is constructed accordingly to the defined metric. Then, a fusion method based on the minimizing of conditional information amount is presented to fuse a multi-source incomplete interval-value data into a single-source incomplete interval-valued data. This method is able to select important and reliable information sources. To identify the most effective subset of features, two adaptive strategies are incorporated into the standard whale optimization algorithm (WOA) to improve its parameter selection process. Without modifying the original search operators, an adaptive WOA-based attribute selection method is developed by leveraging mutual information. The proposed method focuses on improving the robustness and effectiveness of the attribute selection process. Finally, comprehensive experiments are conducted on 12 benchmark datasets to evaluate the effectiveness of the proposed method. The results show that the proposed information fusion method has certain advantages in terms of approximate classification accuracy and quality, while the designed attribute selection algorithm surpasses several state-of-the-art methods in classification accuracy, with statistical analyses further confirming its advantage.

Drought and phosphorus deficiency frequently co-occur in the red soil region of southern China and severely limit maize growth and productivity; however, the interactive effects of these two stresses and the genotypic differences in their combined tolerance remain poorly understood. This study evaluated the individual and combined effects of drought and phosphorus supply on the growth of two maize inbred lines (Line1 and Line129) with contrasting stress tolerance. A pot experiment was conducted using a randomized complete block design with three factors: genotype, phosphorus level (low and high phosphorus), and water regime (well-watered and drought). Biomass accumulation, leaf development, root morphology, and plant phosphorus content were measured at the seedling stage. Drought, phosphorus, interaction of drought and phosphorus significantly affcted shoot, root, leaf and sheath dry weight, total root length, distribution and phosphorus content of two lines. Drought caused significantly reduced shoot, leaf and root dry weight, final total leaf area by 36 %, 30 %, 34 %, 30 % in Line 129 under low phosphorus supply, whereas no significant reductions were observed in Line1. Low phosphorus supply increased the root:shoot ratio by 20 % in Line1 and 35 % in Line129 across water regimes, Line1 consistently maintained a higher root:shoot ratio than Line129. We conclude that Line1 was more drought and low phosphorus tolerant than Line129, and our findings are critical for breeding of drought and phosphorus stress tolerant maize in red soil region of southern China.

In practical applications, due to the high cost of data labeling, some real-valued data are often only partially labeled, and such data can be processed using semi-supervised learning algorithms. For this scenario, this paper focuses on the fuzzy β -covering based partially labeled real-valued decision information system (F β -Cp-RVIS), investigates its uncertainty measurement problem and explores semi-supervised attribute reduction algorithms for real-valued data. Firstly, F β -Cp-RVIS is decomposed into two decision information systems: the fuzzy β -covering based labeled real-valued decision information system (F β -Cl-RVIS) and the fuzzy β -covering based unlabeled real-valued decision information system (F β -Cu-RVIS). Secondly, the importance of attribute subsets in F β -Cp-RVIS is defined based on the indiscernibility relation and conditional information entropy. This importance, obtained by weighted summation of F β -Cl-RVIS and F β -Cu-RVIS according to the missing rate, serves as the uncertainty measurement for F β -Cp-RVIS. Thirdly, experimental analyses and statistical tests on 12 datasets verify the effectiveness of the proposed uncertainty measurement. Based on this, an adaptive algorithm for F β -Cp-RVIS is proposed, which can automatically adapt to different missing rates. Finally, experimental and statistical results on 12 datasets show that the proposed algorithm significantly outperforms existing ones in classification accuracy.

This study explores the willingness of English teachers to engage in elderly education within the context of an aging population and declining birthrate. Using K-means clustering and decision tree analysis, teachers were categorized based on professional identity, attitudes toward seniors, and willingness to teach elderly learners. Results showed most teachers had low willingness to teach seniors, due to unfamiliar career paths and lack of aging training. K-means clustering identified two groups: high professional identity/positive attitudes/willingness vs. low identity/negative attitudes. Decision tree analysis confirmed professional identity and gerontology knowledge as key influencers. The study concludes that policy interventions, focused preparation programs, and institutional support are needed to build a capable teaching workforce for elderly education. Future research should explore additional factors and validate findings across different regions and subjects.

We consider a nonlinear Robin problem driven by a differential operator with unbalanced growth and a reaction which exhibits the competing effects of a parametric concave (sublinear) term and of a convex (superlinear) term. Using the Nehari method, we show that for all small values of the parameter, the problem has two bounded, ground state (least energy) solutions. In the process of the proof, we establish some auxiliary results which are of independent interest.