Micro-Class teaching of the tourism English course: the smart education in China vocational colleges / Liao Danlu & Fong Pang Chew

Smart education becomes the inevitable trend of the global education. With the rapid international tourism development, it becomes a new issue in China to explore how to cultivate qualified tourism graduates with proficient English skills from vocational colleges in terms of smart education. Therefore, this paper aims at discussing China’s current teaching problems of tourism majors’ core course-Tourism English. There are four characteristics of micro-class, namely short-time, fine and concise, interesting, and small-volume resource. The researcher finds raised issues in the teaching classroom include monotonous teaching methods, lack of practice field, inappropriate teaching materials and short of professional teaching staff. In conclusion to solve these problems, the researcher gives three recommendations on Tourism-English micro-class teaching as follows: (1) Realize multiple and open teaching mode, and three dimensions openness of time, space and structure; (2) Increase financial and construction support from the college; (3) Establish the evaluation system of the tourism students' learning achievements in micro-class teaching. As highly information-based the smart education has just started, we should make full use of micro-lesson to improve English Teaching level in vocational colleges, to cultivate students' autonomous learning ability, to cultivate innovative and creative professional tourism talents

Effects of bisphosphonates in preventing periprosthetic bone loss following total hip arthroplasty: a systematic review and meta-analysis

Abstract Background Periprosthetic bone loss following total hip arthroplasty (THA) was a well-known phenomenon. This systematic review was to assess the effectiveness of bisphosphonates (BPs) for decreasing periprosthetic bone resorption. Methods The MEDLINE, EMBASE, and Cochrane Library databases were searched up to March 2018. Randomized controlled trials compared the effects between administrating BPs and placebo or no medication were eligible; the target participants were patients who underwent THA. Mean differences (MD) and 95% confidence interval (95% CI) were calculated by using the random-effects models. Statistical analyses were performed by RevMan 5.3 software. Results Fourteen trials involving 620 patients underwent THA were retrieved. BPs significantly prevented the loss of periprosthetic bone mineral density at 1 year (MD, 0.06 [95% CI, 0.03 to 0.08], p < 0.001), between 2 and 4 years (MD, 0.04 [95% CI, 0.01 to 0.07], p = 0.02), and more than 5 years after THA (MD, 0.08 [95% CI, 0.06 to 0.11], p < 0.001). Both serum bone alkaline phosphatase (MD, − 7.28 [95% CI, − 9.81 to − 4.75], p < 0.001) and urinary N-telopeptide of type I collagen (MD, − 24.37 [95% CI, − 36.37 to − 12.37], p < 0.001) in BP group were significantly lower. Subgroup analyses showed that the third-generation BPs were more effective in decreasing periprosthetic bone loss than the first and second generation within 1 year after THA (p = 0.001). Conclusion BPs were beneficial to decreasing periprosthetic bone loss. The third-generation BPs showed significantly efficacy for patients in short-term observation

Supervised versus Semi-Supervised Urban Functional Area Prediction: Uncertainty, Robustness and Sensitivity

To characterize a community-scale urban functional area using geo-tagged data and available land-use information, several supervised and semi-supervised models are presented and evaluated in Hong Kong for comparing their uncertainty, robustness and sensitivity. The following results are noted: (i) As the training set size grows, models’ accuracies are improved, particularly for multi-layer perceptron (MLP) or random forest (RF). The graph convolutional network (GCN) (MLP or RF) model reveals top accuracy when the proportion of training samples is less (greater) than 10% of the total number of functional areas; (ii) With a large amount of training samples, MLP shows the highest prediction accuracy and good performances in cross-validation, but less stability on same training sets; (iii) With a small amount of training samples, GCN provides viable results, by incorporating the auxiliary information provided by the proposed semantic linkages, which is meaningful in real-world predictions; (iv) When the training samples are less than 10%, one should be cautious using MLP to test the optimal epoch for obtaining the best accuracy, due to its model overfitting problem. The above insights could support efficient and scalable urban functional area mapping, even with insufficient land-use information (e.g., covering only ~20% of Beijing in the case study)