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The release of silicon and calcium elements contained in silicon-based materials promotes the formation of bone. For bioactive glass prepared by the sol-gel method, water-soluble binders are usually added when preparing 3D printed scaffolds. However, the obtained scaffolds are prone to collapse when exposed to water and have low strength. At the same time, the binder needs to be removed for clinical applications, so the 3D printed scaffolds need to be sintered. Under high temperature, bioactive glass scaffolds will be transformed into composite scaffolds composed of bioglass, CaSiO 3 and hydroxyapatite, while different sintering temperatures will form different crystal types of CaSiO 3. In this study, SrBG-βCS-HA and BG-βCS-HA were obtained at a heating rate of 5 °C/min to 1100 °C and at the same rate to room temperature. SrBG-αCS-HA and BG-αCS-HA were obtained at a heating rate of 2 °C/min to 1200 °C and at the same rate to room temperature. In vitro and in vivo experiments verified that the presence of strontium in the obtained scaffolds after sintering further enhanced the osteogenic properties of the scaffolds. SrBG-αCaSiO 3 -HA and SrBG-βCaSiO 3 -HA were found to have relatively better osteogenic properties. The results show that SrBG-CaSiO 3 -HA 3D printing scaffolds have excellent clinical application potential. [ABSTRACT FROM AUTHOR]

To explore the intricate connections between Chinese English as a Foreign Language (EFL) learners' classroom emotions (anxiety, boredom and enjoyment) and their engagement within the context of English as a medium of instruction (EMI) in technology-based learning environments, four modified and validated questionnaires focusing on assessing emotions, engagement levels, and comfort with technology integration were administered to university EFL learners on the Chinese mainland. A structural equation modeling (SEM) analysis was employed to discern the direct and indirect influences of EFL emotions on engagement, considering the mediating effects of EMI and technology comfort. The results indicate that the indirect impact of emotions on learners' academic engagement through technology and EMI as mediators was significant at 95% confidence level. It means that 69% of changes in learners' academic engagement can be explained by interaction among learners' emotions and their technology and EMI perceptions. These results imply that both technology-enhanced learning and EMI serve as significant mediators in the relationship between emotions and academic engagement. The positive estimates suggest that higher levels of positive emotions may lead to increased academic engagement through these mediated pathways. Our findings may have implications for pedagogical practices, curriculum development, and the design of technology-enhanced language instruction. [ABSTRACT FROM AUTHOR]

The widespread use of Artificial Intelligence (AI) in language education contexts has motivated several scholars around the world to uncover the advantages and disadvantages of AI and AI-powered instruments in different language classrooms. Yet, as the review of earlier investigations revealed, few inquiries have been carried out to divulge the pros and cons of leveraging AI in EFL classes. To narrow this gap, using the phenomenological approach, this inquiry investigated the opportunities and challenges of implementing AI in EFL classes from the perspective of Chinese EFL students. To do so, through the criterion sampling technique, a total of 45 EFL students was recruited from different educational institutions in China. To collect the dataset, participants were asked to complete an open-ended questionnaire. For the sake of triangulation, among the 45 participants, 15 were randomly selected to engage in a follow-up interview session. With the aid of MAXQDA software (version 2023), participants' perceptions of AI opportunities and challenges were carefully analyzed. Overall, the analysis findings uncovered that leveraging AI in EFL classes can bring numerous opportunities for EFL students, including individualized learning, timely and immediate feedback, rich educational resources, and an interactive learning atmosphere. However, as demonstrated by the analysis outcomes, implementing AI in EFL courses may also face students with a range of challenges and problems. The research outcomes would be of great help to teachers and educational leaders in mitigating the challenges of leveraging AI in language classrooms. • Investigated AI opportunities and challenges in EFL classes from Chinese students' perspective. • Found that AI can bring numerous opportunities for EFL students. • Revealed that AI implementation in EFL courses poses serious challenges for students. [ABSTRACT FROM AUTHOR]

Visual localization, a fundamental component of several computer vision tasks, has been predominantly realized by scene coordinate regression (SCoRe) techniques. These methods leverage neural networks for scene coordinates prediction, followed by a PnP algorithm to recover the 6-DOF camera pose. However, similar image patches are prevalent in indoor scenes, which results in the extraction of comparable features for the regression of different scene coordinates. As a result, the localization accuracy is severely declined. In this work, we develop ALNet, a novel SCoRe method that incorporates a local discrepancy perception module (LDPM) and an adaptive channel attention module (ACAM) to address this challenge. For LDPM, our key insight lies in that scene attributes around different similar image patches are inconsistent. Technically, for each image patch, LDPM identifies a certain number of the most dissimilar patches around it and computes difference vectors to enrich its own features, thereby enabling the differentiation of similar image patches. Considering geometric attributes are beneficial for distinguishing similar patches while semantic context is conducive to encoding regression issues, integrating multi-level features is an effective approach to elevate the localization accuracy. Therefore, ACAM concatenates multi-level features together and leverages both average pooling and max pooling to generate reliable channel-wise weighting coefficient, thereby modeling the correlation among channels to integrate multi-level features effectively. Comprehensive experiments are conducted on mainstream indoor localization benchmarks and an actual environment, showing that ALNet achieves impressive performance. Source code and the experimental results video are available at https://github.com/DAMMONGAO/alnet. • We propose a SCoRe-based method that achieves excellent localization accuracy. • We propose a module to distinguish similar image patches to improve accuracy. • We propose an attention module to integrate multi-level features. [ABSTRACT FROM AUTHOR]

Local feature matching involves establishing accurate pixel-wise correspondences between an image pair, which is a critical component in several visual applications (e.g., visual localization). Recently, detector-free techniques have realized excellent performance in this task. However, existing methods tend to focus on the entire image without prioritizing overlapping regions, resulting in undesirable interference from non-overlapping areas during the descriptors enhancement process. Moreover, these approaches neglect unreliable ground-truth matching labels triggered by measurement noise in datasets, leading to sub-optimal network optimization. In this study, we develop a novel overlapping areas-based network OAMatcher to resolve these issues. For the first issue, OAMatcher employs an overlapping regions perception block (ORPB) that captures the overlapping areas of image pairs to filter out plentiful mismatches and circumvent interference from non-overlapping regions during descriptors enhancement process. Specifically, the ORPB first enhances the descriptors of all keypoints to mimic the human behaviour of scrutinizing entire images back and forth at the start of feature matching. Subsequently, the ORPB introduces an overlapping regions extraction block (OREB) that captures the keypoints within overlapping zones to mimic the humans behaviour of shifting the focus from the whole images to co-visible areas. After OREB, ORPB performs descriptors enhancement exclusively among the keypoints within these co-visible regions, ensuring minimal disturbances from non-overlapping areas. In addition, the ORPB confines the predicted matches strictly to co-visible regions, thus efficiently filtering out a significant number of mismatches in non-overlapping zones. For the second issue, OAMatcher proposes a labels weighting algorithm (LWA) that predicts the label credibility for ground-truth matching labels. LWA assigns low credibility to unreliable labels and utilizes the credibility to weight loss, effectively diminishing the influence of unreliable labels. Extensive experiments show that OAMatcher delivers excellent results for homography estimation, pose estimation, and visual localization tasks. • OAMatcher extracts accurate matches by shifting the focus regions of network. • OAMatcher detects accurate co-visible areas to realize clean message passing. • OAMatcher reduces the impact of the measurement noise through weighting the loss. • OAMatcher exhibits excellent performance on several tasks. [ABSTRACT FROM AUTHOR]

Local feature matching constitutes the cornerstone of multiple computer vision applications (e.g., 3D reconstruction and long-term visual localization), and has been successfully resolved by detector-free methods. To further improve the matching performance, more recent research has focused on designing sophisticated architectures but endures additional computational overhead. In this study, with a different perspective from previous studies, we aim to develop a deep and compact matching network to improve performance while reducing computing cost. The key insight is that a local feature matcher with deep layers can capture more human-intuitive and simpler-to-match features. To this end, we propose DeepMatcher, a deep transformer-based network that tackles the inherent obstacles of not being able to build a deep local feature matcher with current methods. DeepMatcher consists of: (1) a local feature extractor (LFE), (2) a feature-transition module (FTM), (3) a slimming transformer (SlimFormer), (4) a coarse matches module (CMM), and (5) a fine matches module (FMM). The LFE is utilized to generate dense keypoints with enriched features from the images. We then introduce the FTM to ensure a smooth transition of feature scopes from LFE to the subsequent SlimFormer because of their different receptive fields. Subsequently, we develop SlimFormer dedicated to DeepMatcher, which leverages vector-based attention to model the relevance among all keypoints, enabling the network to construct a deep Transformer architecture with less computational cost. Relative position encoding is applied to each SlimFormer to explicitly disclose the relative distance information, thereby improving the representation of the keypoints. A layer-scale strategy is also employed in each SlimFormer to enable the network to adaptively assimilate message exchange, thus endowing it to simulate human behavior, in which humans can acquire different matching cues each time they scan an image pair. By interleaving the self- and cross-SlimFormers multiple times, DeepMatcher can easily establish pixel-wise dense matches at the coarse level using the CMM. Finally, we consider match refinement as a combination of classification and regression problems and design an FMM to predict confidence and offset concurrently, thus generating robust and accurate matches. Compared with our baseline LoFTR in indoor/outdoor pose estimation, DeepMatcher surpasses it by 3.32%/2.91% in AUC@5 ∘. Besides, DeepMatcher and DeepMatcher-L significantly reduce computational cost and only consume 77.89% and 92.46% GFLOPs of LoFTR. Large DeepMatcher considerably outperforms state-of-the-art methods on several benchmarks, including outdoor pose estimation (MegaDepth dataset), indoor pose estimation (ScanNet dataset), homography estimation (HPatches dataset), and image matching (HPatches dataset), demonstrating the superior matching capability of a deep local feature matcher. • We propose DeepMatcher, a deep Transformer-based network for local feature matching. • We propose SlimFormer to enable DeepMatcher to be extended into dozen layers. • We propose FTM to ensure a smooth transition from CNN to SlimFormer. • We propose FMM to optimize coarse matches, deriving robust and accurate matches. [ABSTRACT FROM AUTHOR]

Local feature matching involves the task of establishing the pixel-wise correspondences between a pair of images. As an integral component of plentiful computer vision applications (e.g., visual localization), this task has been successfully performed using Transformer-based methods. However, these methods typically extract numerous keypoints from sparse texture regions to construct a densely-connected graph neural network (GNN) for long-range feature aggregation, which inevitably triggers redundant message exchange and hampers the learning process. Furthermore, they employ transformer encoder layers that consider images as 1D sequences, leaving them incapable of extracting multiscale local structural information from the images, which is critical for establishing correspondence in image pairs with significantly scales shifts. In this study, we develop FMAP, an innovative detector-free approach that enables accurate local feature matching. For the first issue, FMAP employs an anchor points feature aggregation module (APAM) that captures representative keypoints and discards the extraneous keypoints to build a sparsified GNN for compact yet clean message exchange, with the key insight that the keypoints containing abundant visual information are distinguishable from their neighbors. For the second issue, FMAP proposes a global–local multiscale perception module (GMPM), which incorporates abundant multiscale local context information into global feature representation by employing multiple depth-wise convolutions with varying kernel sizes, thereby generating discriminative features that are robust to scale shifts. In addition, the depth-wise convolutions are utilized in the feed-forward network of the GMPM to further fuse the global context information and local feature representation. Extensive experiments on several standard benchmarks demonstrate that the proposed FMAP method significantly outperforms state-of-the-art methods. Compared to the cutting-edge methods MatchFormer, QuadTree, and TopicFM in relative pose estimation task, FMAP surpasses them by 2.27%, 0.58%, and 1.08% in terms of AUC@5°. Besides, FMAP noticeably outperforms the baseline LoFTR by (2. 38 % , 1. 89 % , 1. 45 %) in terms of AUC@(5°, 10°, 20°). Moreover, we integrate FMAP into an official visual localization framework and conduct a visual localization experiment, with the results showing that FMAP exceeds LoFTR by 2.3% in terms of AP. • We propose a Transformer-based method that achieves excellent matching accuracy. • We constructs sparsified GNN to alleviate redundant message exchange. • We integrate global context and multi-scale local features to boost robustness. [ABSTRACT FROM AUTHOR]