VLOG-BASED EFL TEACHING MODEL FOR UNIVERSITY STUDENTS: IMPACT ON SPEAKING SKILLS AND ENGAGEMENT

This study aims to investigate the efficacy of a vlog-based teaching model in enhancing oral speaking skills and engagement among university students in an English as a Foreign Language (EFL) setting. With the digital technologies reshaping educational methodologies, this research integrates vlogs (video blog) into EFL teaching to build a more engaging learning path. The study was conducted over a semester with 30 university EFL learners in China, employing a mixed-methods approach to evaluate the impact of vlog-based learning. Pre-and-post oral speaking tests were administered to assess improvements in students' language proficiency, while questionnaires measured levels of engagement and motivation. Initial findings suggest that the vlog-based teaching model significantly improved students' speaking skills and heightened their engagement in learning English, indicating a promising avenue for digital media integration in language education. This paper contributes to the evolving field of digital media in EFL teaching, offering insights into the potential of vlogs to enrich language learning experiences and outcomes

VLOG-BASED EFL TEACHING MODEL FOR UNIVERSITY STUDENTS: IMPACT ON SPEAKING SKILLS AND ENGAGEMENT

This study aims to investigate the efficacy of a vlog-based teaching model in enhancing oral speaking skills and engagement among university students in an English as a Foreign Language (EFL) setting. With the digital technologies reshaping educational methodologies, this research integrates vlogs (video blog) into EFL teaching to build a more engaging learning path. The study was conducted over a semester with 30 university EFL learners in China, employing a mixed-methods approach to evaluate the impact of vlog-based learning. Pre-and-post oral speaking tests were administered to assess improvements in students' language proficiency, while questionnaires measured levels of engagement and motivation. Initial findings suggest that the vlog-based teaching model significantly improved students' speaking skills and heightened their engagement in learning English, indicating a promising avenue for digital media integration in language education. This paper contributes to the evolving field of digital media in EFL teaching, offering insights into the potential of vlogs to enrich language learning experiences and outcomes

Augmentation of Nab-Paclitaxel Chemotherapy Response by Mechanistically Diverse Antiangiogenic Agents in Preclinical Gastric Cancer Models

Gastric adenocarcinoma (GAC) remains the third most common cause of cancer-related deaths worldwide. Systemic chemotherapy is commonly recommended as a fundamental treatment for metastatic GAC; however, standard treatment has not been established yet. Angiogenesis plays a crucial role in the progression and metastasis of GAC. We evaluated therapeutic benefits of mechanistically diverse antiangiogenic agents in combination with nab-paclitaxel, a next-generation taxane, in preclinical models of GAC. Murine survival studies were performed in peritoneal dissemination models, whereas tumor growth studies were performed in subcutaneous GAC cell-derived or patient-derived xenografts. The mechanistic evaluation involved IHC and Immunoblot analysis in tumor samples. Nab-paclitaxel increased animal survival that was further improved by the addition of antiangiogenic agents ramucirumab (or its murine version DC101), cabozantinib and nintedanib. Nab-paclitaxel combination with nintedanib was most effective in improving animal survival, always greater than 300% over control. In cell-derived subcutaneous xenografts, nab-paclitaxel reduced tumor growth while all three antiangiogenic agents enhanced this effect, with nintedanib demonstrating the greatest inhibition. Furthermore, in GAC patient-derived xenografts the combination of nab-paclitaxel and nintedanib reduced tumor growth over single agents alone. Tumor tissue analysis revealed that ramucirumab and cabozantinib only reduced tumor vasculature, whereas nintedanib in addition significantly reduced tumor cell proliferation and increased apoptosis. Effects of nab-paclitaxel, a promising chemotherapeutic agent for GAC, can be enhanced by new-generation antiangiogenic agents, especially nintedanib. The data suggest that nab-paclitaxel combinations with multitargeted antiangiogenic agents carry promising potential for improving clinical GAC therapy

Data Tracking Analysis of the Geomagnetic Fixed-Station Network in China

Data tracking analysis is an important mechanism for increasing data analysis capacity and eliminating interference from observational data. In this study, the technique was applied to the geomagnetic fixed-station network to improve the efficiency and accuracy of analysis to extract useful information. This paper introduces the scope, workflow, analysis platform, abnormal variation status, and results of the geomagnetic data tracking analysis. We present some typical examples of abnormal variations in addition to our proposals for future work

Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way

Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification algorithms for light curves. Among them, periodic variable stars are frequently studied. Different categories of periodic variable stars have a high degree of class imbalance and pose a challenge to algorithms including deep learning methods. We design two kinds of architectures of neural networks for the classification of periodic variable stars in the Catalina Survey's Data Release 2: a multi-input recurrent neural network (RNN) and a compound network combing the RNN and the convolutional neural network (CNN). To deal with class imbalance, we apply Gaussian Process to generate synthetic light curves with artificial uncertainties for data augmentation. For better performance, we organize the augmentation and training process in a "bagging-like" ensemble learning scheme. The experimental results show that the better approach is the compound network combing RNN and CNN, which reaches the best result of 86.2% on the overall balanced accuracy and 0.75 on the macro F1 score. We develop the ensemble augmentation method to solve the data imbalance when classifying variable stars and prove the effectiveness of combining different representations of light curves in a single model. The proposed methods would help build better classification algorithms of periodic time series data for future sky surveys (e.g., LSST).Comment: 10 pages, 8 figures, accepte

Backpropagation Path Search On Adversarial Transferability

Deep neural networks are vulnerable to adversarial examples, dictating the imperativeness to test the model's robustness before deployment. Transfer-based attackers craft adversarial examples against surrogate models and transfer them to victim models deployed in the black-box situation. To enhance the adversarial transferability, structure-based attackers adjust the backpropagation path to avoid the attack from overfitting the surrogate model. However, existing structure-based attackers fail to explore the convolution module in CNNs and modify the backpropagation graph heuristically, leading to limited effectiveness. In this paper, we propose backPropagation pAth Search (PAS), solving the aforementioned two problems. We first propose SkipConv to adjust the backpropagation path of convolution by structural reparameterization. To overcome the drawback of heuristically designed backpropagation paths, we further construct a DAG-based search space, utilize one-step approximation for path evaluation and employ Bayesian Optimization to search for the optimal path. We conduct comprehensive experiments in a wide range of transfer settings, showing that PAS improves the attack success rate by a huge margin for both normally trained and defense models.Comment: Accepted by ICCV202