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Group formation is a critical factor which influences collaborative processes and performances in computer-supported collaborative learning (CSCL). Automatic grouping has been widely used to generate groups with heterogeneous attributes and to maximize the diversity of students' characteristics within a group. But there are two dominant challenges that automatic grouping methods need to address, namely the barriers of uneven group size problem, and the inaccessibility of student characteristics. This research proposes an optimized, genetic algorithm-based grouping method that includes a conceptual model and an algorithm module to address these challenges. Through a quasi-experiment research, we compare collaborative groups' performance, processes, and perceptions in China's higher education. The results indicate that the experimental groups outperform the traditional grouping methods (i.e., random groups and student-formed groups) in terms of final performances, collaborative processes, and student perceptions. Based on the results, we propose implications for implementation of automatic grouping methods, and the use of collaborative analytics methods in CSCL.

Group formation is a critical factor which influences collaborative processes and performances in computer-supported collaborative learning (CSCL). Automatic grouping has been widely used to generate groups with heterogeneous attributes and to maximize the diversity of students' characteristics within a group. But there are two dominant challenges that automatic grouping methods need to address, namely the barriers of uneven group size problem, and the inaccessibility of student characteristics. This research proposes an optimized, genetic algorithm-based grouping method that includes a conceptual model and an algorithm module to address these challenges. Through a quasi-experiment research, we compare collaborative groups' performance, processes, and perceptions in China's higher education. The results indicate that the experimental groups outperform the traditional grouping methods (i.e., random groups and student-formed groups) in terms of final performances, collaborative processes, and student perceptions. Based on the results, we propose implications for implementation of automatic grouping methods, and the use of collaborative analytics methods in CSCL. [ABSTRACT FROM AUTHOR]

This article discusses the use of digital heritage in preserving and presenting natural and cultural heritage by preservation specialists and computer scientists. The author describes how animations, computer games, and virtual reality (VR) were applied to China's Jing-Hang Grand Canal (Da Yunhe in Chinese). The techniques in building the virtual canal are presented as well as some screenshots of the virtual grand canal itself.

Objective: To demonstrate the applicability and adaptability of uterine fibroid symptoms and quality of life (UFS-QoL) in assessing the efficacy of treatment in Chinese populations., Methods: This is a secondary analysis of a prospective cohort study involving 20 Chinese hospitals and 2,411 Chinese women with fibroids. Patients completed UFS-QoL and short form-36 (SF-36) at pre-surgery, 6-month and 12-month post-treatments. Internal consistency of the quality of life assessed by the UFS-QoL questionnaire using Cronbach's α coefficient (α). Principal axis factor analysis with orthogonal rotation was established to investigate relationships between items and subscales. Concurrent validity refers to the Spearman's correlation estimate of the correlation between UFS-QoL and SF-36. Using effect size and standardized response mean, the ability to detect change was evaluated by comparing pre- and post-6-month and post-12-month treatment scores., Results: Exploratory factor analysis yielded six subscales (concern, activities, energy/mood, control, self-consciousness, and sexual function) with eigenvalues > 1 in UFS-QoL. A 63.61% total variance was explained by the test items. Ceiling effects of self-consciousness and sexual functioning subscales from UFS-QoL were > 15%. UFS-QoL showed a positive and moderate correlation with SF-36 to establish good concurrent validity. And showed good consistency reliability (Cronbach α > 0.7 in all subscales), ability to detect change after treatment. This excluded self-consciousness (α = 0.56), which demonstrated the lowest effect size (0.38) and standardized response means (0.38) 6- and 12-months post-treatment., Conclusions: Symptom severity, activity, and mood subscales of the Chinese UFS-QoL were valid and reliable. However, the self-consciousness domain needs further investigation on cultural adaptation, such as cognitive debriefing for how Chinese interpret these questions., (© 2022. The Author(s).)

Uterine sarcomas constitute a rare heterogeneous group of gynecological malignancies with aggressive characteristics and poor prognosis. They have similar clinical features to benign leiomyomata making them difficult to reliably identify prior to hysterectomy. The preoperative prediction of uterine sarcoma remains a clinical dilemma. The current study conducted a multicentre, retrospective study to examine the accuracy of preoperative diagnosis, the consequent influence on therapy, and survival factors in patients with uterine sarcoma in Western China. Four affiliated hospitals of the medical college in Western China over a six-year period. One hundred and fourteen patients diagnosed with low-grade endometrial stromal sarcoma (LG-ESS), high-grade endometrial stromal sarcoma (HG-ESS), undifferentiated uterine sarcoma (UUS), leiomyosarcoma (LMS), or adenosarcoma (AS) were analyzed. The median age at diagnosis was 47 years. Eighty (70.2%) patients were premenopausal and 34 (29.8%) post-menopausal. The most common pathological type was LG-ESS (43.9%). The diagnostic sensitivity of ultrasound for uterine malignant tumors was 11.0%, much lower than MRI (35.3%) and CT (63.0%). Unlike MRI, most of the patients who underwent CT (88.2%) examination were at the advanced stage. Forty-seven (41.2%) patients with uterine sarcoma were diagnosed with uterine malignant tumor before operation. Thirty-two (47.8%) patients who were misdiagnosed before operation needed reoperation and five patients (4.6%) diagnosed after radical surgery developed distant metastasis simultaneously. The recommended treatment of 87.0% of the patients with uterine sarcoma was total hysterectomy and bilateral salpingooophorectomy, and 53.7% of patients received adjuvant chemotherapy after operation. Pelvic lymph node status were clarified in 47 patients (43.5%), which were higher in HG-ESS and UUS groups, and lower in LMS group ( P = 0.013). In univariate analysis, we found a significant association between tumour histological types, tumour stage, menstrual status, elevated preoperative neutrophil/lymphocyte ratio and overall survival. In multivariate analysis, we only observed a significant association between tumour histological types and tumour stage and overall survival., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

This study aimed to explore the therapeutic effect of focused ultrasound (FU) therapy in patients with symptomatic cervical ectopy. 4677 patients with symptomatic cervical ectopy were enrolled and treated with FU. Three months after treatment, we analyzed the therapeutic effects and the adverse reactions in 4014 cases with complete records. All influential factors were also analyzed with logistic regression analysis. For the treatment of symptomatic cervical ectopy, the effective rate of FU was 99.8%, and the cure rate was 72.52%. Bleeding that either reached or exceeded normal menstrual volume occurred in 12 cases. Logistic regression analysis showed that the degree of illness, vaginal cleanliness, economic level, occupation, age and artificial abortion frequency were all influential factors. Focused ultrasound is a promising new therapeutic option for the treatment of symptomatic cervical ectopy. Additionally, it can be widely used for a variety of patients., (Copyright © 2013 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)