通讯机构：
[Liu, GH ] G;Guangxi Normal Univ, Coll Comp Sci & Engn, Guilin 541004, Peoples R China.
关键词：
Image retrieval;Deep contrast feature;Contrast-based layer;Generalized mean aggregation;Multi-orientational PCA whitening
摘要：
BackgroundIn the field of content-based image retrieval (CBIR), fused feature-based methods have demonstrated their advanced performance on the popular benchmark datasets. However, it is inevitable increase the vector dimensionality because the fused features have diversity. Therefore, achieving both a low-dimensional representation and high retrieval performance remains challenging.MethodsTo address this problem, an image retrieval method based on the deep contrast-based layer is proposed, namely the deep contrast feature histogram (DCFH), to image retrieval. There are three highlights as follows: (1) texture features based on the edge orientation are calculated to build contrast-based layer; it can enhance the discriminative power of deep features; (2) a generalized mean aggregation method is introduced to effectively aggregate the representative information in the deep feature maps of convolutional neural network (CNN); (3) a multi-orientational PCA whitening method is proposed to provide a compact yet discriminative representation.ResultsComparative experiments demonstrated that our method can provide outstandingly competitive retrieval performance on popular benchmark datasets.ConclusionsThis work captures visual information from both global and local perspectives, presenting an approach in line with human visual cognitive. Experiments demonstrated that our method can efficiently combine the strengths of various features to provide the robust representation, thereby improving the retrieval performance. Moreover, our method is easily to be implemented without requiring to retrain the CNN models and not the use of additional supervision.
作者机构：
[Deng, Yuhua] Hunan Inst Technol, Sch Int Studies, Hengyang 421001, Peoples R China.;[Liu, Huying] Cent South Univ Forestry & Technol, Swan Coll, Changsha 410211, Peoples R China.;[Liu, Huying] Stamford Int Univ, Grad Sch, Bangkok 10250, Thailand.
通讯机构：
[Liu, HY ] C;Cent South Univ Forestry & Technol, Swan Coll, Changsha 410211, Peoples R China.;Stamford Int Univ, Grad Sch, Bangkok 10250, Thailand.
关键词：
Test anxiety;Techno competence;Teacher support;Self-efficacy;Autonomy
摘要：
This study explores the interplay between techno competence, teacher support, self-efficacy, autonomy, and test anxiety in the context of online assessments among 30 learners from a university in China. Employing a narrative inquiry approach, the study delves into the reflective narratives of participants, capturing their perceptions, experiences, and coping strategies related to test anxiety in online assessment settings. Findings reveal nuanced relationships between techno competence, teacher support, self-efficacy, autonomy, and test anxiety, highlighting the pivotal role of these factors in shaping students’ experiences and outcomes. Learners with high techno competence demonstrate greater confidence in navigating online assessment platforms and employ adaptive coping strategies to manage test anxiety effectively. Conversely, those with low techno competence may experience heightened anxiety due to technical challenges and a lack of confidence in their digital skills. Perceived teacher support emerges as a crucial determinant in mitigating test anxiety, with supportive and nurturing relationships fostering a sense of security and confidence among learners. Additionally, self-efficacy and autonomy play significant mediating roles, influencing students’ perceptions of their capabilities and their ability to regulate their learning processes. These findings underscore the importance of fostering techno competence, providing teacher support, and promoting self-efficacy and autonomy to alleviate test anxiety and enhance academic success in online assessment environments.
摘要：
Using a sample of A-share listed enterprises in the Shanghai and Shenzhen Stock Exchanges, this study shows that environmental investors can effectively stimulate green technological innovation measured by green patents. The stimulating effect is substantially heterogeneous across different types of enterprises and innovations. The mechanism analysis reveals that environmental investors promote green innovation mainly by increasing internal environmental investment rather than by obtaining more government subsidies. This study provides rare micro-evidence of the important role of independent environmental investors in green innovation.
作者机构：
[Feng, Xiuxia] Shanghai Lida Univ, Sch Gen Educ & Foreign languages, Shanghai 201608, Peoples R China.;[Liu, Huying] Cent South Univ Forestry & Technol, SWAN Coll, Changsha 410211, Peoples R China.;[Liu, Huying] Stamford Int Univ, Grad Sch, Bangkok 10250, Thailand.
通讯机构：
[Liu, HY ] C;Cent South Univ Forestry & Technol, SWAN Coll, Changsha 410211, Peoples R China.;Stamford Int Univ, Grad Sch, Bangkok 10250, Thailand.
摘要：
This phenomenological study explored the experiences of language learners in the digital age, specifically investigating the intersection of digital literacy, technostress, online engagement, autonomy, and academic success. Twenty participants, selected through purposive sampling, shared Chinese as their native language and were between 18 and 20 years old, with five participants being female. Employing interviews and document analysis, the study aimed to understand the subjective meanings, emotions, and perceptions associated with these phenomena. The findings revealed the multifaceted nature of technostress, the crucial role of digital literacy in shaping online engagement and autonomy, and the nuanced impact on academic success. These qualitative insights contribute to a deeper understanding of the complex relationships in the digital language learning landscape. The study has implications for educators, materials developers, syllabus designers, and policy-makers, providing practical insights to enhance language learning experiences in the digital era. Future research may further explore specific dimensions uncovered in this study to adapt educational practices to the evolving digital terrain.
期刊：
World Electric Vehicle Journal,2024年15(9):403- ISSN：2032-6653
通讯作者：
Chao He
作者机构：
[Qiankun Zhang] China Information Technology Designing and Consulting Institute Co., Ltd., Beijing 100048, China;[Jian Wei] College of Information and Engineering, Swan College, Central South University of Forestry and Technology, Hunan 410211, China;[Junting Li; Wenhui Jiang] School of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing 404130, China;Author to whom correspondence should be addressed.;[Jiang Guo] Chengdu Tangyuan Electric Co., Ltd., Sichuan 610046, China
通讯机构：
[Chao He] S;School of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing 404130, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
intelligent vehicle;quintic polynomial;internet of vehicles;trajectory planning;real-time
摘要：
The autonomous lane change maneuver is a critical component in the advancement of intelligent transportation systems (ITS). To enhance safety and efficiency in dynamic traffic environments, this study introduces a novel autonomous lane change strategy leveraging a quintic polynomial function. To optimize the trajectory, we formulate an objective function that balances the time required for lane changes with the peak acceleration experienced during the maneuver. The proposed method addresses key challenges such as driver discomfort and prolonged lane change durations by considering the entire lane change process rather than just the initiation point. Utilizing a fifth-order polynomial for trajectory planning, the strategy ensures smooth and continuous vehicle movement, reducing the risk of collisions. The effectiveness of the method is validated through comprehensive simulations and real-world vehicle tests, demonstrating significant improvements in lane change performance. Despite its advantages, the model requires further refinement to address limitations in mixed traffic conditions. This research provides a foundation for developing intelligent vehicle systems that prioritize safety and adaptability.
