Taishan University

The process of educational reform and the improvement of teaching practices indeed encompasses all aspects related to learning, assessment, and instruction, as well as the implementation of smart classrooms. Putting learning, assessment, and instruction at the center of this process actually means breaking down the existing barriers that are between these three key areas. At the same time, the benefits that are of a smart classroom environment do foster organic connections that are between learning, assessment, and guidance. The current smart classroom model makes use of data that is on the learning context as a central connecting element and, by smoothly integrating the assessment process into the whole learning process, makes it possible for the optimized design and effective implementation that are of educational goals. This smart classroom model not only meets the requirements that are of modern teaching but also addresses the urgent need that is for individualized student support and improved teaching quality. Moreover, the smart classroom model provides concrete examples that are of application and serves as a source that is of inspiration for new and innovative pedagogical practices that are in primary, secondary, and higher education.

Abstract. To enhance the dynamic performance and stability of high-speed railway vehicles, this study investigates the vibration attenuation mechanism of the transverse secondary inertial suspension System (TSISS). Based on vehicle–track coupled dynamics, models were established for a conventional suspension (Condition S1: parallel spring–damper) and four TSISS configurations (Condition S2: parallel inerter–spring–damper); an inerter in series with a damper, parallel to the air spring (Condition S3); an inerter in series with a spring, parallel to the air spring–damper (Condition S4); and a parallel spring–damper unit in series with an inerter, all parallel to the air spring (Condition S5). The analysis focuses on the response distribution of sensitive wavelengths under track irregularity excitations and their impact on ride comfort. Results indicate distinct frequency domain characteristics: Conditions S2 and S4 show sensitivity to short wavelengths (peaking around 20 m), improving ride quality indices by 11 % and 8 % over S1, respectively. Conversely, Conditions S3 and S5 target the longwave range, yielding improvements of 14 % and 28 %. Validated by coherence function analysis, Condition S5 significantly suppresses longwave irregularities (40–150 m), with a response peak near 105 m. Notably, with inertance set between 500 and 1000 kg, Condition S5 achieves a ride quality index of approximately 2.75 and substantially reduces lateral acceleration, demonstrating superior ride comfort.

Automated Writing Evaluation (AWE) systems have been increasingly integrated into second-language writing instruction; however, empirical evidence regarding the effectiveness of localized AWE tools in EFL contexts remains limited. This study investigated the impact of the iWrite Automated Writing Evaluation system on university EFL students’ writing performance and writing anxiety. Employing a quasi-experimental mixed-methods design, 60 Chinese university students were assigned to an experimental group using iWrite and a control group receiving traditional teacher feedback over a 12-week instructional period. Writing performance was assessed using the complexity, accuracy, and fluency (CAF) framework, while writing anxiety was measured through a validated questionnaire. Quantitative results revealed that the experimental group demonstrated significantly greater improvements in writing accuracy, fluency, and lexical complexity, as well as significantly lower levels of writing anxiety, compared with the control group. No significant difference was found in syntactic complexity. Qualitative findings further indicated that immediate, non-judgmental feedback and opportunities for repeated revision contributed to increased learner confidence and reduced anxiety. The findings suggest that localized AWE systems such as iWrite can effectively support both the cognitive and affective dimensions of EFL writing when integrated within a human–AI collaborative instructional framework.

Hyperspectral unmixing is a critical task in remote sensing, challenged by complex mixing mechanisms and long-range spatial dependency modeling. To address the limitations of existing methods in capturing spatial structures and frequency representations, we propose SFMamba, a novel spatial-frequency collaborative unmixing framework. The framework includes two key modules: (1) a multi-view spatial mamba module based on state space model, which captures directional spatial dependencies to enhance contextual representation; and (2) a hyperspectral fourier mixer, which integrates fourier transform-based frequency features to strengthen the spectral modeling of endmembers. Experiments on the Urban and Jasper Ridge datasets demonstrate that the proposed method achieves superior performance in endmember extraction, with competitive results in abundance estimation, thus showing strong potential in hyperspectral unmixing tasks. This work provides a novel and effective solution for hyperspectral unmixing with spatial-frequency collaboration.

Due to their large scale, long duration, complex geological conditions, and multiple stakeholders, water conservancy engineering projects are subject to diverse, interrelated, and uncertain risk factors that affect the construction timeline. Traditional critical chain buffer calculation methods, such as the cut-and-paste method and the root variance method, typically assume the independence of risks, which limits their effectiveness in addressing schedule delays caused by correlated risk events. To overcome this limitation, this paper proposes a novel critical chain buffer calculation approach that explicitly incorporates risk correlation analysis. A fuzzy DEMATEL-ISM-BN model is employed to systematically identify the interrelationships and influence pathways among schedule risk factors. Bayesian network inference is then used to quantify the overall occurrence probability while accounting for risk correlations. By integrating critical chain management theory, risk impact coefficients are introduced to improve the traditional root variance method, resulting in a buffer calculation model that captures interdependencies among schedule risks. The effectiveness of the proposed model is validated through a case study of the X Pumped Storage Power Station. The results indicate that, compared with conventional methods, the proposed approach significantly enhances the robustness of project schedule planning under correlated risk conditions while appropriately increasing buffer sizes. Consequently, the adaptability and reliability of schedule control are improved. This study provides novel theoretical tools and practical insights for schedule risk management in complex engineering projects.

ABSTRACT Achieving dynamic modulation of upconversion luminescence (UCL) in a monolithic rare‐earth‐ion‐doped glass is challenging. In this study, novel high YbF 3 ‐content fluorosilicate glasses (SiO 2 ‐KF‐YbF 3 ‐ErF 3 ) doped with Er 3+ were fabricated, which exhibit noticeable variation of UCL color with excitation power density. The green‐to‐red UCL intensity ratio (G/R) of the glass demonstrates a wide tuning range from 0.24 to 2.48. This significant variation in the G/R ratio was visually manifested as the color and CIE chromaticity coordinate transitions from red to yellow and to green. A visual method of distance sensing was established with the sensing precision of approximately 80 µm. The glass was also endowed with temperature‐sensing ability based on both Er 3+ thermally coupled levels ( 2 H 11/2 and 4 S 3/2 ) and non‐thermally coupled levels ( 2 H 11 / 2 and 4 F 9/2 ), achieving maximum relative sensitivities of 0.83 and 0.65% K −1 at 323 K, respectively. These findings underscore a promising material platform for developing non‐contact robust multifunctional optical sensors.