COVID-19 affected various aspects of our life. Many college students were forced to take courses remotely. It was not clear how they adapted to this new environment and how their mental health was affected. The objective of this study is to understand college students’ learning experience one year after the outbreak of COVID-19. An online survey was developed to investigate students’ overall learning process, mental health, perception of the learning community and student support. Sixty-two college students in the U.S. were recruited through an online survey platform. Findings of this study revealed: (1) improved mental health of college students compared to the beginning of the pandemic; (2) an overall positive learning experience and perceived belongingness to their learning community, as well as high satisfaction with the student support; (3) the major hindrance in the online learning environment was the lack of interactions with teachers and classmates; (4) a relationship between family income and perception of the learning community was discovered, and the students from low-income families were found to feel more belonging to the learning community; (5) hybrid was the optimum learning mode during COVID-19; (6) on-campus students perceived more student support than off-campus students. These findings provided a guideline for future research to further explore, and improve, the online learning environment.

The minute ventilation/carbon dioxide production (VE/VCO2) slope has been widely demonstrated to have strong prognostic value in patients with chronic heart failure (CHF), and the risk of mortality is believed to increase when the VE/VCO2 slope is >32.8; however, there is little evidence concerning the prognostic value of the VE/VCO2 slope in Chinese patients. In the present study, the prognostic value of the VE/VCO2 slope was investigated in patients with CHF. A total of 258 subjects underwent symptom-limited cardiopulmonary exercise testing (CPET) and were divided into CHF (113 males and 16 females; LVEF <0.49) and control (106 males and 23 females) groups. The cardiac-related events over a median 33.7-month follow-up period subsequent to the CPET were evaluated using receiver operating characteristic curve analysis. The VE/VCO2 slope was significantly different between the CHF and control groups (P<0.001). The area under the curve (AUC) for the VE/VCO2 slope in predicting cardiac-related mortalities in the patients with CHF was 0.670 (P<0.05), and the sensitivity and specificity of the VE/VCO2 slope were 0.667 and 0.620, respectively. The optimal threshold of the VE/VCO2 slope for predicting cardiac-related mortalities in patients with CHF was ≥39.3. The AUC for the VE/VCO2 slope in predicting cardiac-related hospitalizations in patients with CHF was 0.682 (P<0.05), and the sensitivity and specificity of the VE/VCO2 slope were 0.631 and 0.778, respectively. The optimal threshold of the VE/VCO2 slope for predicting cardiac-related hospitalizations in patients with CHF was ≥32.9. In conclusion, ventilatory efficiency decreases in patients with CHF. The VE/VCO2 slope is a strong predictor of cardiac-related mortalities in the patients with CHF analyzed.

In the era of Big Data, knowledge engineering has to face fundamental challenges by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, knowledge acquisition, and knowledge inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, need to be updated to cope with both fragmented knowledge from multiple sources in the Big Data revolution, and in-depth expertise from domain experts. This article presents BigKE, a 3phase "online learning -knowledge fusion -knowledge service" knowledge engineering framework with Big Data, with three fundamental research problems: (1) fragmented knowledge modeling and online learning from multiple information sources, (2) non-linear fusion on fragmented knowledge, and (3) automated demand-driven knowledge navigation. The knowledge graph representation is advocated in BigKE. We also compare BigKE with existing models for Big Data, such as the 4P medical model, the IBM 4V model, 5 R's, and the HACE theorem.

Deconvolution methods can be used to improve the azimuth resolution in airborne radar imaging. Due to the sparsity of targets in airborne radar imaging, an L 1 regularization problem usually needs to be solved. Recently, the Split Bregman algorithm (SBA) has been widely used to solve L 1 regularization problems. However, due to the high computational complexity of matrix inversion, the efficiency of the traditional SBA is low, which seriously restricts its real-time performance in airborne radar imaging. To overcome this disadvantage, a fast split Bregman algorithm (FSBA) is proposed in this paper to achieve real-time imaging with an airborne radar. Firstly, under the regularization framework, the problem of azimuth resolution improvement can be converted into an L 1 regularization problem. Then, the L 1 regularization problem can be solved with the proposed FSBA. By utilizing the low displacement rank features of Toeplitz matrix, the proposed FSBA is able to realize fast matrix inversion by using a Gohberg–Semencul (GS) representation. Through simulated and real data processing experiments, we prove that the proposed FSBA significantly improves the resolution, compared with the Wiener filtering (WF), truncated singular value decomposition (TSVD), Tikhonov regularization (REGU), Richardson–Lucy (RL), iterative adaptive approach (IAA) algorithms. The computational advantage of FSBA increases with the increase of echo dimension. Its computational efficiency is 51 times and 77 times of the traditional SBA, respectively, for echoes with dimensions of 218 × 400 and 400 × 400 , optimizing both the image quality and computing time. In addition, for a specific hardware platform, the proposed FSBA can process echo of greater dimensions than traditional SBA. Furthermore, the proposed FSBA causes little performance degradation, when compared with the traditional SBA.