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Determining how mountain communities in conservation priority areas can benefit from rich local biodiversity through sustainable development has been a challenge to governments throughout the world. The village of Hanlong in western Yunnan China has been developing bird photography tourism to capitalize on the extremely rich avifaunal diversity of the area. In this study, we analyzed the impact of the emerging tourism on local communities' livelihoods and identified the key areas of improvements for wider dissemination of the experiences. Bird photography tourism in Hanlong has contributed significantly to the sustainable livelihoods of local communities by providing a major new source of income, creating local employment opportunities, and reducing local people's dependence on the extractive use of forest resources. In the process, local communities have acquired new knowledge, skills, and social networks. They have also developed new institutions and governance mechanisms, which enable them to better adapt to the changing socioeconomic environment. Bird photography tourism offers new opportunities and hope for sustaining local livelihoods and biodiversity in conservation priority areas. Experiences in Hanlong offer an excellent heuristic example for sustainable community development, adaptation, and transformational change in conservation priority areas. They also contribute to the realization of the Sustainable Development Goals and Aichi targets and thus have value for wider upscaling. Geographical location, local leadership, and external support are all important for the success of bird photography in Hanlong. We recommend that a landscape (including transboundary landscape) approach be adopted to integrate farming practices, other economic activities, and photography tourism. Farming practices and land uses that sustain bird diversity should be encouraged.

Cognitive engagement involves mental and physical involvement, with observable behaviors as indicators. Automatically measuring cognitive engagement can offer valuable insights for instructors. However, object occlusion, inter-class similarity, and intra-class variance make designing an effective detection method challenging. To deal with these problems, we propose the Object-Enhanced–You Only Look Once version 8 nano (OE-YOLOv8n) model. This model employs the YOLOv8n framework with an improved Inner Minimum Point Distance Intersection over Union (IMPDIoU) Loss to detect cognitive engagement. To evaluate the proposed methodology, we construct a real-world Students' Cognitive Engagement (SCE) dataset. Extensive experiments on the self-built dataset show the superior performance of the proposed model, which improves the detection performance of the five distinct classes with a precision of 92.5%. [ABSTRACT FROM AUTHOR]

Background: Video conferencing learning offers a more accessible and flexible learning mode for students who cannot attend face‐to‐face class in person. However, research about the video conferencing learning is still lacked, especially for students' engagement in this setting. Objectives: This study adopts the self‐system model of motivational development to uncover effects of the video conferencing learning environment (VCLE) to students' engagement. Methods: Two hundred and ninety‐eight primary school students involved in the VCLE were surveyed. The partial least square structural equation modelling was employed to explore the link between the VCLE and students' engagement with a mediating role of basic psychological needs satisfaction (BPNS). Results and Conclusions: Results showed that the BPNS played a mediating role in the processes from the VCLE to engagement. Both pedagogical and social affordance had a significant effect on the BPNS. However, their influences were different. Specifically, pedagogical affordance yielded a stronger effect on the BPNS for autonomy than social affordance. The predicting power of social affordance on BPNS for relatedness and competence was stronger than pedagogical affordance. Generally, social affordance yielded more contributions to behavioral and emotional engagement than pedagogical affordance. Moreover, BPNS for competence was the only significant mediator between the VCLE and behavioral engagement, and it owned the strongest mediating power on the relationship between the VCLE and emotional engagement, followed by autonomy, and the smallest of relatedness. This study suggests that practitioners and researchers should give more consideration to improve social affordance of the VCLE and to meet students' BPNS especially for competence in order to design and implement an engaged video conferencing course. Lay Description: What is already known about this topic: Video conferencing learning provides an accessible and flexible learning mode.Mechanism from learning environment to engagement is helpful to design an engaged video conferencing course.Less is known about how video conferencing learning affects students' engagement. What the paper adds: This study uncovered the functioning process of video conferencing learning to students' engagement.PLS‐SEM was employed to verify the mediating role of BPNS in this process.BPNS for autonomy, competence, and relatedness played mediating roles and their powers were different. Implications for practice and/or policy: More attention is needed to improve to social affordance to enhance students' emotional engagement.BPNS especially for competence deserves special consideration to improve students' behavioral and emotional engagement. [ABSTRACT FROM AUTHOR]

Detecting infrared (IR) small targets effectively and robustly is crucial for the tasks such as infrared searching and guarding. While methods based on the human vision system (HVS) have achieved great success in this field, detecting dim targets in complex backgrounds remains a challenge due to the multi-scale framework and over-simplified disparity calculations. In this paper, infrared small targets are detected with a novel local contrast measurement named double-layer patch-based contrast (DLPC). Firstly, we crafted an elaborated double-layer local contrast measure, to suppress the background, which can accurately measure the gray difference between the target and its surrounding complex background. Secondly, we calculated the absolute value of the grayscale difference between the target and the background in the diagonal directions as a weighting factor to further enhance the target. Then, an adaptive threshold on the DLPC was employed to extract the target from the IR image. The proposed method can detect small targets effectively with a fixed-scaled mask template while being computationally efficient. Experimental results in terms of background suppression factor (BSF), signal-to-clutter ratio gain (SCRG) and receiver operating characteristic (ROC) curve on five IR image datasets demonstrated that the proposed method has better detection performance compared to six state-of-the-art methods and is more robust in addressing complex backgrounds. [ABSTRACT FROM AUTHOR]

Learning through making enhances the learning experience and effectively develops learners' creativity. In this regard, maker education has been extensively incorporated into school education as a vital learning environment. However, less is known about how making advances students' creativity in maker education. Therefore, this study aimed to explore relationships among making, tinkering, and creating. A total of 372 middle school students enrolled in maker courses through the 2021–2022 school year were surveyed. They completed a questionnaire concerning learning engagement, individual creativity, collective creativity, and learning motivation. Structural equation modeling (SEM) was employed to test the hypotheses proposed in this study. The results revealed significant relationships among learning engagement, individual creativity, and collective creativity. Specifically, behavioral engagement positively predicted emotional and cognitive engagement. Moreover, both emotional and cognitive engagement positively related to collective creativity, while only the effect of cognitive engagement on individual creativity was observed. The direct effects of behavioral engagement on individual and collective creativity were not significant. Furthermore, emotional and cognitive engagement mediated the association between behavioral engagement and collective creativity, while only cognitive engagement played a mediating role between behavioral engagement and individual creativity. In addition, the moderating analysis showed that learning motivation moderated the association between behavioral engagement and emotional engagement in school-maker education. Some implications for integrating maker education into school education and cultivating students' creativity were discussed. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. However, it is challenging to fully use spatial–spectral information for HSI classification due to the high dimensionality of the data, high intraclass variability, and the limited availability of training samples. To deal with these issues, we propose a novel feature extraction method called nonlocal correntropy matrix (NLCM) representation in this letter. NLCM can characterize the spectral correlation and effectively extract discriminative features for HSI classification. We verify the effectiveness of the proposed method on two widely used datasets. The results show that NLCM performs better than the state-of-the-art methods, especially when the training set size is small. Furthermore, the experimental results also demonstrate that the proposed method outperforms compared methods significantly when the land covers are complex and with irregular distributions. [ABSTRACT FROM AUTHOR]

Due to the impact of the COVID-19 pandemic, many students are unable to attend face-to-face courses, Therefore, in this case, distance education should be promoted to replace face-to-face education. However, because of the imbalance of education in different regions, such as the imbalance of education resources between rural and urban areas, the quality of distance education may not be guaranteed. Therefore, in China and some regions, there have been efforts made to carry out blended synchronous classroom attempts. In hybrid synchronous classroom situations, teachers' workloads have increased, and it is difficult to fully understand students' learning efficiency and class participation. We use deep learning to identify the behaviors of teachers and students in a blended synchronous classroom-based situation, aiming to automate the analysis of classroom videos, which can help teachers in classroom reflection and summary in a blended synchronous classroom or face-to-face classroom. In the behavior recognition of students and teachers, we combine the head, hand, and body posture information of teachers and students and add the feature pyramid (FPN) and convolutional block attention module (CBAM) for comparative experiments. Finally, S–T (student–teacher) analysis and engagement analysis were carried out on the identification results. [ABSTRACT FROM AUTHOR]

Detecting small targets from infrared remote sensing images is still a challenging task. In this research, we propose a multidirectional local difference measure weighted by entropy (MDLDE) to detect small targets from infrared images with messy backgrounds. First, a new multidirectional local difference measure is proposed to suppress the clutter background. Then, the entropy, which captures the overall heterogeneity between the target and the background, is utilized to enhance the target. Lastly, an adaptive threshold was adopted to segment the target region from the background. The designed MDLDE could effectively enhance the target and simultaneously suppress the background clutter. Experimental results on six datasets indicate that the proposed method outperformed other state-of-the-art methods in terms of the signal-to-clutter ratio gain (SCRG), background suppression factor (BSF), and receiver operating characteristic (ROC) curves. [ABSTRACT FROM AUTHOR]

Background: Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID-19 pandemic. However, online learning has seen students lose interest and become anxious, which affects learning performance and leads to dropout. Thus, measuring students' engagement in online learning has become imperative. It is challenging to recognize online learning engagement due to the lack of effective recognition methods and publicly accessible datasets. Methods: This study gathered a large number of online learning videos of students at a normal university. Engagement cues were used to annotate the dataset, which was constructed with three levels of engagement: low engagement, engagement, and high engagement. Then, we introduced a bi-directional long-term recurrent convolutional network (BiLRCN) for online learning engagement recognition in video. Result: An online learning engagement dataset has been constructed. We evaluated six methods using precision and recall, where BiLRCN obtained the best performance. Conclusions: Both category balance and category similarity of the data affect the performance of the results; it is more appropriate to consider learning engagement as a process-based evaluation; learning engagement can provide intervention strategies for teachers from a variety of perspectives and is associated with learning performance. Dataset construction and deep learning methods need to be improved, and learning data management also deserves attention. [ABSTRACT FROM AUTHOR]

(1) Background: To evaluate the efficacy and safety of combined surgery (limited pars plana vitrectomy, anterior-chamber stabilized phacoemulsification, IOL implantation and posterior capsulotomy, LPPV + ACSP + IOL + PC) in complex nanophthalmos. (2) Methods: Patients with complex nanophthalmos were recruited to undergo LPPV + ACSP + IOL + PC from January 2017 to February 2021. Preoperative and post-operative intraocular pressure (IOP), best corrected visual acuity (BCVA), anterior chamber depth (ACD), and number of glaucoma medications were compared using the paired t-test or Wilcoxon signed rank sum tests. Surgical success rate was evaluated. Surgery-associated complications were documented. (3) Results: Forty-five eyes of 37 patients with complex nanophthalmos were enrolled. The mean follow-up period was 21.7 ± 10.6 months after surgery. Mean IOP decreased from 32.7 ± 8.7 mmHg before surgery to 16.9 ± 4.5 mmHg (p < 0.001) at the final follow-up visit, mean logMAR BCVA improved from 1.28 ± 0.64 to 0.96 ± 0.44 (p < 0.001), mean ACD significantly increased from 1.14 ± 0.51 mm to 3.07 ± 0.66 mm (p < 0.001), and the median number of glaucoma medications dropped from 3 (1, 4) to 2 (0, 4) (p < 0.001). The success rate was 88.9% (40 eyes) at the final follow-up visit. Two eyes had localized choroidal detachments which resolved with medical treatment. (4) Conclusions: LPPV + ACSP + IOL + PC is a safe and effective surgical procedure, which can decrease IOP, improve BCVA, deepen the anterior chamber, and reduce the number of glaucoma medications in patients with complex nanophthalmos. It can be considered as one of the first treatment in nanophthalmic eyes with complex conditions. [ABSTRACT FROM AUTHOR]

Engagement plays an essential role in the learning process. Recognition of learning engagement in the classroom helps us understand the student's learning state and optimize the teaching and study processes. Traditional recognition methods such as self-report and teacher observation are time-consuming and obtrusive to satisfy the needs of large-scale classrooms. With the development of big data analysis and artificial intelligence, applying intelligent methods such as deep learning to recognize learning engagement has become the research hotspot in education. In this paper, based on non-invasive classroom videos, first, a multi-cues classroom learning engagement database was constructed. Then, we introduced the power IoU loss function to You Only Look Once version 5 (YOLOv5) to detect the students and obtained a precision of 95.4%. Finally, we designed a bimodal learning engagement recognition method based on ResNet50 and CoAtNet. Our proposed bimodal learning engagement method obtained an accuracy of 93.94% using the KNN classifier. The experimental results confirmed that the proposed method outperforms most state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

With the development of the hyperspectral imaging technique, hyperspectral image (HSI) classification is receiving more and more attention. However, due to high dimensionality, limited or unbalanced training samples, spectral variability, and mixing pixels, it is challenging to achieve satisfactory performance for HSI classification. In order to overcome these challenges, this paper proposes a feature extraction method called spatial perception correntropy matrix (SPCM), which makes use of spatial and spectral correlation simultaneously to improve the classification accuracy and robustness. Specifically, the dimension reduction is carried out firstly. Then, the spatial perception method is designed to select the local neighbour pixels. Thus, local spectral-spatial correlation is characterized by the correntropy matrix constructed using the selected neighbourhoods. Finally, SPCM representations are fed into the support vector machine for classification. The extensive experiments carried out on three widely used data sets have revealed that the proposed SPCM performs better than several state-of-the-art methods, especially when the training set is small. [ABSTRACT FROM AUTHOR]

Purpose: To identify biomarkers associated with CD8+ T cells in coronary artery disease (CAD) and initially explore their potential role in the tumor immune microenvironment. Materials and Methods: CAD-related datasets GSE12288, GSE34198, and GSE66360, were downloaded from the GEO database. First, GSVA was performed based on the GSE12288 dataset. Then WGCNA analysis was performed to identify the most relevant module and candidate hub gene for CD8+ T cells, followed by GO and KEGG analysis of this module. Secondly, the relationship between candidate hub genes and CD8+ T cells was verified using GSE34198 and GSE66360, which led to the identification of hub genes. The relationship of hub genes with CD8+ T cells in cancer was analyzed using the TIMER database. Methylation analysis of hub genes was performed using the DiseaseMeth database. CAD, pan-cancer, pan-cell lines, and pan-normal tissues, correlations between hub genes. In addition, potential drugs and TFs associated with hub genes were predicted, and the ceRNA network was constructed. Finally, GSEA was performed separately for hub genes. Results: CAD was shown to be associated with immune response by GSVA analysis. WGCNA identified the blue module as most related to CD8+ T cells and identified nine candidate hub genes. The relevance of CAD to immunity was further confirmed by GO and KEGG analysis of the module. Two additional datasets validated and identified three hub genes (FBXO7, RAD23A, and MKRN1) that significantly correlated with CD8+ T cells. In addition, we found that hub genes were positively associated with CD8+ T cells in TGCT, THCA, and KICH cancers by our analysis. Moreover, the hub gene was differentially methylated. We also analyzed the correlation between hub genes in CAD, different cancers, different cell lines, and different normal tissues. The results of all the analyses showed a positive correlation between them. Finally, we successfully constructed hub gene-associated TF-gene and ceRNA networks and predicted 11 drugs associated with hub genes. GSEA suggests that hub genes are related to multiple immune response processes. Conclusion: FBXO7, RAD23A, and MKRN1 are significantly associated with CD8+ T cells in CAD and multiple cancers and may act through immune responses in CAD and cancer. [ABSTRACT FROM AUTHOR]

The hyperspectral images (HSIs) classification technique has received widespread attention in the field of remote sensing. However, how to achieve satisfactory classification performance in the presence of a large amount of noise is still a problem worthy of consideration. In this article, a local correntropy matrix (LCEM)-based spatial–spectral feature representation method is proposed for HSI classification. Motivated by the successful application of information-theoretic learning (ITL), we propose to adopt correntropy matrix to represent the spatial–spectral features of HSI. Specifically, the dimension reduction is first performed on the original hyperspectral data. Then, for each pixel, we select its local neighbors within a sliding window using cosine distance for the construction of the LCEM. In this way, each pixel can be characterized as an LCEM. Finally, all the correntropy matrices are fed into a support vector machine (SVM) for final classification. In addition, we also propose a novel way to determine the size of the local window based on standard deviation. Because the LCEM as the feature descriptor can characterize discriminative spatial–spectral features, the proposed method has shown great interclass separability and intraclass compactness. Compared with other advanced approaches, the proposed LCEM method has achieved competitive performance in both evaluation indexes and visual effects, especially when the training size is very small. [ABSTRACT FROM AUTHOR]

A new spectral‐spatial hyperspectral image (HSI) classification method called hierarchical broad learning system (HBLS) has been proposed in this paper. Specifically, it combines wavelet, broad learning system (BLS) and Gabor filters into a hierarchical structure. First of all, wavelet is used to reduce the observation noise of HSIs. Then BLS is adopted to acquire a set of pixelwise probability maps from the input data, and Gabor filters are used to explore spatial information by refining these probability maps. These two operations (BLS and Gabor filtering) are alternated to form a hierarchical architecture. And the discriminative spectral‐spatial features can be extracted at each layer of the hierarchical architecture. Finally, the spectral‐spatial features are fed into the standard BLS for classification. Experimental results on three widely used HSIs reveal that HBLS outperforms some state‐of‐the‐art methods in terms of classification accuracy and sample complexity. [ABSTRACT FROM AUTHOR]

Considering that multiple features can improve the classification performance as they contain diversity information of images, a multiple-feature latent space learning-based method is proposed for hyperspectral image (HSI) classification in this letter. In the proposed method, a latent space that contains diversity information of multiple features and transformation matrices between the latent space and features are both learned. Moreover, spatial information is used for labeling unlabeled samples in the classification. Experimental results on the Indian Pines and University of Pavia data sets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method for hyperspectral images (HSI) classification, aiming to take advantage of both manifold learning-based feature extraction and neural networks by stacking layers applying locality sensitive discriminant analysis (LSDA) to broad learning system (BLS). BLS has been proven to be a successful model for various machine learning tasks due to its high feature representative capacity introduced by numerous randomly mapped features. However, it also produces redundancy, which is indiscriminate and finally lowers its performance and causes heavy computing demand, especially in cases of the input data bearing high dimensionality. In our work, a manifold learning method is integrated into the BLS by inserting two LSDA layers before the input layer and output layer separate, so the spectral-spatial HSI features are fully utilized to acquire the state-of-the-art classification accuracy. The extensive experiments have shown our method's superiority. [ABSTRACT FROM AUTHOR]

Uveal melanoma (UM) is the most frequent primary ocular tumour among adults. Here, we aimed to establish the immune cell-based signature to predict the overall survival (OS) of UM patients. The mRNA profile and matched clinical records of 80 UM patients were downloaded from The Cancer Genome Atlas (TCGA) database. CIBERSORT was used to verify the immune cell types of individuals. The univariate analysis found the CD8+ T cell, monocyte, CD4+ memory T cell (resting) and mast cell (resting) were significantly associated with the OS of UM patients. Subsequently, the LASSO Cox regression test was applied to establish the signature, by which the patients were separated into high- and low-risk subgroups. The Kaplan–Meier analyses found for these patients in the high-risk group had a poor survival rate than those in the low-risk group. The predictive value and stability were confirmed by the receiver operative characteristics curves. Pathway analyses found that the differentially expressed genes between the high- and low-risk subgroups were mainly centralised on immune response-related pathways. Further, the comparison of our signature with clinicopathological records confirmed its superiority and independence. In summary, we established an immune cell-based prognosis-predicting signature for UM patients, which will benefit the individual's treatment. [ABSTRACT FROM AUTHOR]

Teaching reflection based on videos is the main method in teacher education and professional development. However, it takes a long time to analyse videos, and teachers are easy to fall into the state of information overload. With the development of "AI + education," automatic recognition of teacher behavior to support teaching reflection has become an important research topic. In this paper, taking online open classroom teaching video as the data source, we collected and constructed a teacher behavior dataset. Using this dataset, we explored the behavior recognition methods based on RGB video and skeleton information, and the information fusion between them is carried out to improve the recognition accuracy. The experimental results show that the fusion of RGB information and skeleton information can improve the recognition accuracy, and the early-fusion effect is better than the late-fusion effect. This study helps to solve the problems of time-consumption and information overload in teaching reflection and then helps teachers to optimize the teaching strategies and improve the teaching efficiency. [ABSTRACT FROM AUTHOR]

This article proposes a functional data discriminant analysis (FDDA) method for hyperspectral image (HSI) classification. This method analyzes and processes the HSI data from a functional point of view, which is a novel perspective in HSI processing. The classical methods achieve dimensionality reduction by directly eliminating the redundancy of the HSI data. However, the proposed method extracts the functional features by utilizing the redundancy of the HSI data. Functional features can effectively reveal inherent characteristics of the HSI data with the change in the wavelengths. Based on this, a regularized weighted fitting model is first built for converting a spectral vector into a spectral curve. Second, an FDDA method defined in the function field is presented for extracting the functional features of the spectral curves. Finally, a novel spectral–spatial framework is designed for classification tasks of HSI data sets. Experimental results in three commonly used HSI data sets indicate that the proposed method is effective and leads to promising classification results compared with some benchmarking methods. More importantly, the work tries to diversify and develop the existing theory and methods of HSI classification from discrete (vector) data learning methods to continuous (functional) data learning methods. [ABSTRACT FROM AUTHOR]

Human activity recognition using depth videos remains a challenging problem while in some applications the available training samples is limited. In this article, we propose a new method for human activity recognition by crafting an integrated descriptor called multi-level fused features for depth sequences and devising a fast broad learning system based on matrix decomposition for classification. First, the surface normals are computed from original depth maps; the histogram of the surface normal orientations is obtained as a low-level feature by accumulating the contributions from normals, then a high-level feature is acquired by sparse coding and pooling on the aggregation of polynormals. After that, the principal component analysis is applied to the conjunction of the two-level features in order to obtain a low-dimensional and discriminative fused feature. At last, fast broad learning system based on matrix decomposition is proposed to accelerate the training process and enhance the classification results. The recognition results on three benchmark data sets show that our method outperforms the state-of-the-art methods in term of accuracy, especially when the number of training samples is small. [ABSTRACT FROM AUTHOR]

Purpose: Dickkopf 1 (DKK1) functions as a natural antagonist of the canonical Wnt/β-catenin pathway. The purpose of this study was to examine the expression of DKK1 in vitreous samples of patients with pathological myopia, in order to search for possible correlations between DKK1 and axial length. Materials and Methods: The expression of DKK1 and other cytokines in vitreous samples of 44 non-myopic eyes, 42 eyes with low-to-moderate myopia, and 51 eyes with pathological myopia were examined using multiplex cytokine detection technology. Ophthalmologic characteristics, including axial length and subfoveal choroidal thickness, were clinically measured for further analysis. Results: The intravitreous levels of DKK1 (P <.0001) were markedly higher in the pathological myopia group than in the control group. There were no differences of DKK1 levels in different vitreoretinal conditions. Additionally, we found that the DKK1 levels were positively correlated with HGF (β = 0.268, P =.032), and TIMP-3 (β = 0.209, P =.047) levels, as well as with axial length (β = 0.714, P <.0001) in the pathological myopia group. Conclusions: Elevated levels of DKK1 were found in the eyes with elongated axial length. [ABSTRACT FROM AUTHOR]

The present study assessed changes in macular capillary vessel density and thickness associated with ocular silicone oil tamponade (SOT) following complex vitreoretinal surgery. A total of 23 patients who underwent pars plana vitrectomy combined with intravitreal SOT were retrospectively enrolled, and a single eye was assessed in each participant. Furthermore, 20 patients who required silicone oil removal (SOR) were included. The macular capillary vessel density and the foveal avascular zone (FAZ) area were analyzed using optical coherence tomography angiography (OCTA) and retinal thickness was assessed by OCT. The results demonstrated that the macular capillary vessel density of the superficial capillary plexus (SCP) and deep capillary plexus (DCP), the FAZ area and full retinal thickness were retained at a stable level following SOT (P>0.05), and also following SOR (P>0.05). Furthermore, the parafoveal (P=0.008), superior-hemi (P=0.007), temporal (P=0.015), superior (P=0.028) and nasal (P=0.002) inner retinal thickness decreased in the SOT group, whereas the inner retinal thickness was unaltered after SOR vs. baseline (P>0.05). In conclusion, silicone oil has no significant effect on macular capillary vessel density within a 6-month period but may compress and reduce the thickness of the inner retina. [ABSTRACT FROM AUTHOR]

Observing student body gesture has been widely used to assess teaching effectiveness over the past few decades. However, manual observation is not suitable for the automatic data analysis in the field of learning analytics. Consequently, a student body gesture recognition method based on Fisher Broad Learning System (FBLS) and Local Log-Euclidean Multivariate Gaussian (L2EMG) is proposed in this paper. FBLS is designed by introducing the discriminative information into the hidden layer of Broad Learning System (BLS) and reducing the dimensionality of hidden-layer representations. FBLS has superiorities in accuracy and speed. In addition, L2EMG, which is a highly distinctive descriptor, characterizes the local image with a multivariate Gaussian distribution. So L2EMG features are fed into the FBLS for recognition in this paper. Extensive experimental results on self-built dataset show that the proposed student body gesture recognition method obtains better results than other benchmarking methods. [ABSTRACT FROM AUTHOR]

Statement: The current article has not been published elsewhere and has not been submitted simultaneously for publication elsewhere. Purpose: To investigate the preliminary use of three-dimensional (3D) heads-up display (HUD) viewing system for vitreoretinal surgery under various status. Materials and Methods: Nonrandomized case-control study. Consecutive cases to have vitreoretinal surgery under various status were prospectively recruited. Twenty-five-gauge vitrectomy platform and 3D viewing system were used. Main outcomes included: luminous emittance (lux) of endoillumination pipe, surgical duration, the surgeon and residents' preference and ergonomics. Consecutive patients to have vitreoretinal surgery with the conventional viewing system were recruited as control group following the same inclusion and exclusion criteria and underwent surgeries by the same surgeon with the same microscope and vitrectomy platform. Results: Thirty-one patients (31 eyes; Group Study) and twenty-eight patients (28 eyes; Group Control) were included; without significantly statistical difference in terms of age, gender, main diagnosis, surgical duration, and difficulty rating between both groups (all P > 0.05). Lower endoillumination intensity was needed in Group Study than that in Group Control (10% vs. 35%; 598.7 ± 5.4 vs. 1913.0 ± 12.9 lux, P < 0.001). The surgeon and residents expressed overwhelming preference with the 3D system in both groups. Improved ergonomic was rated in Group Study (4.4 ± 0.8 vs. 3.2 ± 1.0, P < 0.001). Some intraoperative difficulties and discomforts appeared to the surgeon and assistants when using the 3D viewing system. Conclusion: Vitreoretinal surgery under various status can be well finished with the HUD platform by novice at the system. Main benefits included lower endoillumination intensity, enhanced users' preference, and improved ergonomics. Some further refinements of the system are expected. [ABSTRACT FROM AUTHOR]

Introduction: Rhegmatogenous retinal detachment (RRD) with extensive choroidal detachment (CD), suprachoroidal fluid (SCF), and lower intraocular pressure (IOP) markedly increase the difficulty of pars plana vitrectomy (PPV) because it makes the insertion and placement of the trocar cannulas for PPV time-consuming and technically challenging. In this study, we developed a systemic approach that avoids the risks of sclerotomy or using a longer trocar to help in the safe placement of the trocar cannulas in order to prevent inadvertent fluid/air perfusion during PPV in RRD eyes with extensive CD, SCF, and hypotony.Methods: This is a prospective case series study. Consecutive patients diagnosed with RRD with extensive CD, SCF, and hypotony were recruited. The main steps included injecting transconjunctival intravitreal fluid to increase IOP and enable the passive egress of SCF into the subconjunctival space, creating and broadening the inner opening for the trocar cannulas, transillumination with an endoillumination light pipe, and indentation of the infusion cannula to create maximum visual exposure of the inner segment and help peripheral shaving around the infusion tip.Main Outcomes: the success rate of the infusion placement into the vitreous cavity, surgical duration, intra- and postoperative complications, and functional and anatomical outcomes.Results: Thirty-eight patients (38 eyes) were included. The first-time success rate of cannula placement into the vitreous cavity was 100%, with a mean surgical duration of 8.5 ± 1.8 min. No intra- or postoperative complications related to the approach were observed. Significant vision improvement was noted in 68.4% (26/38) of the patients at the final follow-up. The primary and final retinal reattachment rates were 94.7% (36/38) and 100% (38/38), respectively.Conclusion: This systemic approach is a feasible option to ensure the safe placement of trocar cannulas and prevent inadvertent perfusion during small-gauge PPV in RRD eyes with extensive CD, SCF, and hypotony, without the need for a draining sclerotomy, a longer trocar, or a sharp clearing of the cannula tip. [ABSTRACT FROM AUTHOR]