Guangdong University of Science and Technology is a private university in Dongguan, Guangdong, China; it is on the south side of the city. It was established in 2003 by Nanbo Tech.
In the realm of mathematics, the hereditary property is a crucial characteristic. Heredity in a mathematical structure clarifies its internal architecture and drives related research forward. Suppose that D is a nontrivial design with automorphism group G. We have accomplished the classification of all hereditary (G, 3)-flag-transitive designs in this paper, for G is an almost simple group that coincides with its socle.
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.
Sketch-based 3D reconstruction aims to generate 3D models from sparse and abstract hand-drawn sketches, a task challenging due to missing depth cues and ambiguous 2D observations. We propose a hierarchical sketch encoding and text-guided 3D modeling approach, employing a deep sketch fuse encoder to capture hierarchical sketch features and a multi-modal viewpoint supervision strategy for richer guidance. Our method achieves state-of-the-art performance on both synthetic and real datasets, demonstrating its potential to simplify 3D modeling for novice users. The source code and models will be publicly available at https://github.com/QinchuanLei/HETG-3D-Modeling .
Precipitation strengthening in Cu-Ni-Si alloys is generally attributed to nanoscale continuous precipitate phases (CPPs), while discontinuous precipitate phases (DPPs) are often considered detrimental because of their coarse morphology. However, the role of DPPs in deformed Cu-Ni-Si alloys and their interaction with CPPs and deformation substructures remains insufficiently understood. In this work, a low-solute Cu-2.0Ni-0.6Si-0.8Co (wt.%) alloy was processed by vacuum-assisted die casting (VADC), followed by rolling and direct aging, to promote early-stage DPPs precipitate while retaining a high density of deformation substructures. This processing route results in a microstructure characterized by the coexistence of DPPs and CPPs across multiple length scales. The results indicate that the pinning effect of VADC-induced DPPs arising from high solute supersaturation, together with the precipitation and pinning of nanoscale CPPs, effectively suppresses recrystallization and promotes recovery-dominated softening, thereby maintaining a high dislocation density and enhancing the strengthening efficiency of CPPs. As a result, a favorable combination of hardness (316.5 f 8.4 HV), ultimate tensile strength (755 f 19 MPa), electrical conductivity (35.8 f 0.4 % IACS), and plasticity (6.0 f 0.4 % elongation) is achieved through dual-scale precipitation strengthening. This study provides a clearer understanding of the cooperative effects between DPPs, CPPs, and deformation structures, offering useful guidance for microstructural design of high-performance Cu-Ni-Si alloys.
English language learning in linguistically homogeneous contexts such as China remains a challenging process in which learners routinely face everyday academic setbacks. Drawing on the framework of Positive Psychology, this study examines academic buoyancy among 366 EFL undergraduates at a Chinese private university using an explanatory sequential mixed-methods design. Three research questions address students' perceptions of academic buoyancy, its association with English language achievement, and the external contextual factors that contribute to its development. Quantitative data were collected through a survey questionnaire; qualitative data were gathered through semi-structured interviews with five students. Results indicated that participants generally held positive perceptions of their academic buoyancy across four dimensions, with sustainability rated highest and regularity adaptation lowest. Hierarchical regression analysis, controlling for gender, age, and daily English study time, revealed that academic buoyancy was significantly and positively associated with English language achievement. Among the four dimensions, positive acceptance of academic life showed the strongest association with achievement. Thematic analysis of interview data identified two key external contributors to academic buoyancy: positive interactions with instructors and peers, and a supportive learning environment, with the dormitory emerging as a notable site of informal peer support. These findings suggest that academic buoyancy is meaningfully associated with language achievement in this context and is sustained through social and environmental conditions. The study implies that language educators should cultivate supportive learning environments and peer support systems, particularly in private university settings where informal learning networks may compensate for limited institutional resources.
