Sias University

Robert Schumann was a 19th-century German composer and music critic. "Pattern Op.18 "is a piano solo piece created by Schumann in 1839, which is closely related to Schumann's emotional life. 1839 was the year before Schumanns marriage to Clara Wick, and at this stage, and he was in a period of high creative enthusiasm, with the piano becoming his primary medium for emotional expression. This article discusses the study of Schumann's "Pattern Op.18" in three parts: firstly, an introduction to Schumann and the creative background of "Pattern Op.18"; secondly, an analysis of the melodic features of the work; and thirdly, a specific manifestation of the work's fantasy. By reviewing relevant academic literature and analyzing musical examples from both domestic and international sources, Schumann's expression of light and lively sweet longing, gentle and deep emotional expression, strong emotional tension, and hazy and distant beautiful imagination in his performance, as well as the melancholic and contemplative tone, imaginative and poetic flow, gentle and warm moments, and the unity of technique and emotion in his creation, are obtained. It is hoped that this can inspire performers to find suitable performance methods.

Prospective single-center observational cohort study. To assess whether prior arthroscopic experience is associated with a shorter learning curve in uniportal interlaminar endoscopic lumbar discectomy (IELD), primarily in terms of operative efficiency, and to descriptively evaluate perioperative complications and patient-reported outcomes. In accordance with STROBE guidelines, 240 consecutive patients with single-level lumbar disc herniation (MSU A/B, non-calcified, symptom duration ≤ 3 months) underwent IELD between 2021 and 2023 at a single academic orthopedic center. Procedures were performed by three spine surgeons without prior endoscopic experience; one surgeon had performed more than 300 shoulder arthroscopies. Operative time was analyzed using cumulative sum (CUSUM) methodology and linear regression. Missing outcome data were handled using last observation carried forward. Complications were recorded descriptively and stratified by learning phase and surgeon. Oswestry Disability Index (ODI) and Visual Analog Scale (VAS) scores for back and leg pain were assessed preoperatively and at 3 and 12 months. All surgeons demonstrated a three-phase learning curve consisting of learning, improvement, and stabilization phases. Operative efficiency stabilized after approximately 50 cases across surgeons. The surgeon with prior arthroscopic experience reached the CUSUM inflection point earlier (case 12) compared with the other surgeons (cases 24-26). The overall major complication rate was 9.2% and was highest during the initial learning phase. ODI and VAS scores improved significantly at 3 and 12 months (all p < 0.001), with no statistically significant between-surgeon differences at final follow-up. Prior arthroscopic experience was associated with earlier adaptation during the initial learning phase of IELD, as measured by operative time. Following procedural stabilization, no statistically significant differences were detected in operative efficiency, major complication rates, or patient-reported outcomes between surgeons. These findings suggest that arthroscopic experience may facilitate early adaptation to endoscopic visualization and workflow but does not independently determine long-term clinical outcomes. III.

With the rapid development of Massive Open Online Courses (MOOCs), high dropout rates have become a major challenge, limiting the quality of online education and the effectiveness of targeted interventions. Although existing MOOC dropout prediction methods have incorporated deep learning and attention mechanisms to improve predictive performance to some extent, they still face limitations in modeling differences in course difficulty and learning engagement, capturing multi-scale temporal learning behaviors, and controlling model complexity. To address these issues, this paper proposes a MOOC dropout prediction model that integrates multi-scale convolution with a symmetric dual-attention mechanism, termed SDA-Net. In the feature modeling stage, the model constructs a time allocation ratio matrix (MRatio), a resource utilization ratio matrix (SRatio), and a relative group-level ranking matrix (Rank) to characterize learners’ behavioral differences in terms of time investment, resource usage structure, and relative performance, thereby mitigating the impact of course difficulty and individual effort disparities on prediction outcomes. Structurally, SDA-Net extracts learning behavior features at different temporal scales through multi-scale convolution and incorporates a symmetric dual-attention mechanism composed of spatial and channel attention to adaptively focus on information highly correlated with dropout risk, enhancing feature representation while maintaining a relatively lightweight architecture. Experimental results on the KDD Cup 2015 and XuetangX public datasets demonstrate that SDA-Net achieves more competitive performance than traditional machine learning methods, mainstream deep learning models, and attention-based approaches on major evaluation metrics; in particular, it attains an accuracy of 93.7% on the KDD Cup 2015 dataset and achieves an absolute improvement of 0.2 percentage points in Accuracy and 0.4 percentage points in F1-Score on the XuetangX dataset, confirming that the proposed model effectively balances predictive performance and model complexity.

Abstract Eliminating lattice disorder in quantum dots (QDs) is critical for achieving high‐performance quantum dot light‐emitting diodes (QLEDs), as such disorder directly disrupts the uniformity of elemental distribution and degrades their optical properties. Here, a tertiary amine‐mediated synthesis strategy is reported that utilizes nucleophilic reagents to regulate the coordination kinetics of cationic precursors during the growth of ZnCdSeS/ZnS QDs. This strategy leverages nucleophilic reagents bearing uncoordinated lone‐pair electrons to stabilize the cationic precursors and modulate the QDs surface energy of highly reactive crystal planes, thereby promoting atomic‐scale uniform growth of the QDs, minimizing lattice mismatch, preventing stacking faults, and thus enabling the synthesis of strain‐graded QDs ( sg ‐QDs). Consequently, by achieving precise control over both elemental distribution and lattice ordering in multicomponent alloy QDs, sg ‐QDs are obtained that exhibit a photoluminescence quantum yield of 98% in solution and 95% in the solid film. The sg ‐QD films further demonstrate monoexponential decay kinetics and reduced defect density, confirming effective trap‐state suppression. The resultant green QLEDs achieve a record external quantum efficiency (EQE) of 25.2%, an operational lifetime of 1 925 900 h, and sustained EQE over 20% across a luminance range of 10 2 –10 5 cd m −2 . This nucleophile‐coordination paradigm redefines the synthesis of alloy nanocrystals, providing a dual‐advantage platform for ultrastable optoelectronics and scalable QLEDs manufacturing.

ABSTRACT The purpose of this study is to explore how integrating artificial intelligence tools into practical course instruction can enhance metacognitive skills and environmental awareness among art and design students. Using an action research methodology, this study focuses on a practical course that teaches design as part of an art and design curriculum at a university in Henan Province, China. Log records, interviews, and classroom observations were used to gather data. The research investigates the teaching paradigm of employing artificial intelligence technologies to improve students’ environmental awareness. It does this by following the four processes of action research: preparation, action, observation, and reflection. According to the study, integrating AI tools into all facets of instruction is a dynamic, continuous process. Through the three phases of project-based learning, students gradually increase their understanding of the environment, develop their environmental skills, and foster the formation of environmental attitudes. The results imply that artificial intelligence enhanced instructional designs can support students in more effectively integrating a range of components, expressing themselves creatively, and using digital technologies. It can also help students gain a deeper understanding of environmental issues and the significance of sustainable development, which raises their cognitive level of environmental awareness.

The objective of extracting relations between document components lies in identifying the Within a single document, the focus often lies on understanding the connections between different entities. This type of analysis goes beyond individual sentences, as it demands an understanding of how information from multiple sentences interacts to form these connections. Over recent years, the importance of exploring relationships involving several entities simultaneously has grown significantly. To advance the field of studying such connections across entire documents, a novel collection of data points, known as DocRED, has been introduced. Currently, the standard approach for this task involves using BiLSTM networks to process the entire document as a whole. However, this method struggles to effectively capture the intricate relationships that exist among various entities. To overcome this limitation, a new model designed for document-level relationship identification has been developed, which leverages Graph Convolutional Networks (GCN). GCNs are particularly useful here because they can gather information from surrounding entities, allowing for a more detailed modeling of their interactions. The proposed approach starts by identifying coreferential links to gather features that represent the relationships between pairs of entities. These features are then analyzed using GCN to construct a graph structure that represents the entire document, ultimately revealing the complex interactions between different entities. Testing this model on the large-scale DocRED dataset from Tsinghua University has demonstrated its strong performance in this challenging task.

Introduction Cultural values may impact how well a learner uses self-regulated learning strategies in Confucian contexts, yet research remains limited. Methods To address the gap in past research, surveys were given to 281 Chinese university students concerning preferences for Confucian values, self-regulated learning, and self-regulated use of technology. Confucian values were then statistically compared to preferences for self-regulated learning and use of technology using regression and Pearson correlations. Results Results suggest that traditional cultural values that promote collectivism and power distance significantly impact perspectives on self-regulated learning. However, these same cultural values do not appear to significantly influence perspectives on self-regulated use of technology. Whereas conceptions about learning have been clearly defined by long-standing Confucian traditions, perspectives on new educational technology have not, explaining the findings. Conclusion New techniques may be developed to enhance self-regulated learning in Confucian heritage countries. Educational techniques that mirror collective cultural beliefs and respect norms for power distance may enhance performance in the classroom.

The prediction of enterprise human resource demand is the core link in optimizing human resource allocation and supporting strategic implementation. Currently, traditional prediction methods have obvious limitations in dealing with small sample and high fluctuation human resource data: although the grey GM (1,1) model is suitable for poor information scenarios, it is difficult to capture the nonlinear relationship between demand and multiple factors; Although a single BP neural network can fit complex relationships, it is sensitive to the quality of raw data and susceptible to random interference. To address this pain point, this article constructs a series grey BP neural network model, selects key influencing indicators through grey correlation analysis, weakens data fluctuations and predicts future values of indicators based on the GM (1,1) model, and then trains the BP neural network with the predicted values of indicators as input, forming a collaborative mechanism of “indicator screening grey preprocessing BP fitting”. The experimental results show that the average absolute error, root mean square error, and average relative error of the model are significantly lower than those of the grey GM (1,1) model and a single BP neural network, and the error fluctuation amplitude is less than 5% under the scenarios of sample size changes and parameter adjustments, highlighting the advantages of accuracy and stability. At the practical level, the data required for the model comes from multiple internal systems of the enterprise, with low deployment costs and strong operability, which can provide quantitative basis for enterprises to formulate recruitment plans and optimize human resources structure; On the theoretical level, it enriches the combination model system for predicting human resource demand in small sample scenarios.