The Core Values of Principals in School Management under Chinese Education Reform

The values of principals in school management play a pivotal role in shaping school leadership, teacher behaviours, and student performance. However, research studies focusing on principals’ values are relatively abundant in Western countries, yet still limited in the Chinese context. To fill this gap, this paper adopts a qualitative research approach to investigate the fundamental values of Chinese principals in leading and managing primary schools within the current education reform landscape. The findings reveal that the principals in the study emphasised nine core values: equity, fairness, openness, respect, empowerment, encouragement, recognition, trust, and democracy. These values were found to contribute to a positive school climate that promoted the growth of teachers, students, and the school. The results have significant implications for policy makers and principals in China, suggesting the necessity to foster ethical and relational skills among principals and to acknowledge the invaluable contributions of teacher leaders and teachers in school development. Keywords: principals, values, school management, education reform DOI: 10.7176/JEP/14-24-09 Publication date:August 31st 202

Quaternion-Based Graph Convolution Network for Recommendation

Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings. However, GCN is vulnerable to noisy and incomplete graphs, which are common in real world, due to its recursive message propagation mechanism. In the literature, some work propose to remove the feature transformation during message propagation, but making it unable to effectively capture the graph structural features. Moreover, they model users and items in the Euclidean space, which has been demonstrated to have high distortion when modeling complex graphs, further degrading the capability to capture the graph structural features and leading to sub-optimal performance. To this end, in this paper, we propose a simple yet effective Quaternion-based Graph Convolution Network (QGCN) recommendation model. In the proposed model, we utilize the hyper-complex Quaternion space to learn user and item representations and feature transformation to improve both performance and robustness. Specifically, we first embed all users and items into the Quaternion space. Then, we introduce the quaternion embedding propagation layers with quaternion feature transformation to perform message propagation. Finally, we combine the embeddings generated at each layer with the mean pooling strategy to obtain the final embeddings for recommendation. Extensive experiments on three public benchmark datasets demonstrate that our proposed QGCN model outperforms baseline methods by a large margin.Comment: 13 pages, 7 figures, 6 tables. Submitted to ICDE 202

Bifunctional Electrocatalysts for Oxygen Reduction and Borohydride Oxidation Reactions Using Ag3Sn Nanointermetallic for the Ensemble Effect

2017-2018 > Academic research: refereed > Publication in refereed journal201805 bcrcAccepted ManuscriptOthersNational Natural Science Foundation of China; the Research Fund of State Key Laboratory of Solidification Processing in China; the Aeronautic Science Foundation Program of China; the Science and Technology Innovation Fund of Western Metal Materials; the Doctoral Fund of Ministry of Education of ChinaPublishe

3D-Aware Multi-Class Image-to-Image Translation with NeRFs

Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multi-class image-to-image (3D-aware I2I) translation. Naively using 2D-I2I translation methods suffers from unrealistic shape/identity change. To perform 3D-aware multi-class I2I translation, we decouple this learning process into a multi-class 3D-aware GAN step and a 3D-aware I2I translation step. In the first step, we propose two novel techniques: a new conditional architecture and an effective training strategy. In the second step, based on the well-trained multi-class 3D-aware GAN architecture, that preserves view-consistency, we construct a 3D-aware I2I translation system. To further reduce the view-consistency problems, we propose several new techniques, including a U-net-like adaptor network design, a hierarchical representation constrain and a relative regularization loss. In extensive experiments on two datasets, quantitative and qualitative results demonstrate that we successfully perform 3D-aware I2I translation with multi-view consistency.Comment: Accepted by CVPR202

Demethyleneberberine alleviated the inflammatory response by targeting MD-2 to inhibit the TLR4 signaling

IntroductionThe colitis induced by trinitrobenzenesulfonic acid (TNBS) is a chronic and systemic inflammatory disease that leads to intestinal barrier dysfunction and autoimmunedisorders. However, the existing treatments of colitis are associated with poor outcomes, and the current strategies remain deep and long-time remission and the prevention of complications. Recently, demethyleneberberine (DMB) has been reported to be a potential candidate for the treatment of inflammatory response that relied on multiple pharmacological activities, including anti-oxidation and antiinflammation. However, the target and potential mechanism of DMB in inflammatory response have not been fully elucidated.MethodsThis study employed a TNBS-induced colitis model and acute sepsis mice to screen and identify the potential targets and molecular mechanisms of DMB in vitro and in vivo. The purity and structure of DMB were quantitatively analyzed by high-performance liquid chromatography (HPLC), mass spectrometry (MS), Hydrogen nuclear magnetic resonance spectroscopy (1H-NMR), and infrared spectroscopy (IR), respectively. The rats were induced by a rubber hose inserted approximately 8 cm through their anus to be injected with TNBS. Acute sepsis was induced by injection with LPS via the tail vein for 60 h. These animals with inflammation were orally administrated with DMB, berberine (BBR), or curcumin (Curc), respectively. The eukaryotic and prokaryotic expression system of myeloid differentiation protein-2 (MD-2) and its mutants were used to evaluate the target of DMB in inflammatory response.ReslutsDMB had two free phenolic hydroxyl groups, and the purity exceeded 99% in HPLC. DMB alleviated colitis and suppressed the activation of TLR4 signaling in TNBS-induced colitis rats and LPS-induced RAW264.7 cells. DMB significantly blocked TLR4 signaling in both an MyD88-dependent and an MyD88-independent manner by embedding into the hydrophobic pocket of the MD-2 protein with non-covalent bonding to phenylalanine at position 76 in a pi–pi T-shaped interaction. DMB rescued mice from sepsis shock induced by LPS through targeting the TLR4–MD-2 complex.ConclusionTaken together, DMB is a promising inhibitor of the MD-2 protein to suppress the hyperactivated TLR4 signaling in inflammatory response

Neuro-Inspired Hierarchical Multimodal Learning

Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Distinct from most traditional fusion models that aim to incorporate all modalities as input, our model designates the prime modality as input, while the remaining modalities act as detectors in the information pathway. Our proposed perception model focuses on constructing an effective and compact information flow by achieving a balance between the minimization of mutual information between the latent state and the input modal state, and the maximization of mutual information between the latent states and the remaining modal states. This approach leads to compact latent state representations that retain relevant information while minimizing redundancy, thereby substantially enhancing the performance of downstream tasks. Experimental evaluations on both the MUStARD and CMU-MOSI datasets demonstrate that our model consistently distills crucial information in multimodal learning scenarios, outperforming state-of-the-art benchmarks