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Yuzhi Yao,Tianzhu Long,Yuhong Pan,Yin Li,Ling Wu,Benjie Fu,Hongmin Ma 한국간호과학회 2021 Asian Nursing Research Vol.15 No.3
Purpose: This study aimed to describe the clinical response to five-step systematic therapy (FSST) in the management of plugged ducts and mastitis. FSST was a comprehensive milk stasis dredging treatment, which contained five steps to make the milk out of the plugged duct. Methods: This retrospective study included 922 breastfeeding women, 714 with plugged ducts, and 208 with mastitis who received FSST from June to September 2017. The breast pain score, swelling degree, and range of breast induration were recorded pre-FSST and post-FSST. Results: After a single FSST, pain score and swelling degree were significantly improved (both p < .001) in all cases. After FSST, the mean breast pain relief score was 1.69 ± 0.70, whereas the mean swelling fade away degree was 1.61 ± 0.62. In the subgroup analysis, pain score and swelling degree were significantly improved (both p < .001) in the plugged ducts group and the mastitis group. The score of pain relief in the plugged ducts group was less than that in the mastitis group (1.63 ± 0.68 vs. 1.91 ± 0.70, t = 5.30; p < .001), whereas improvement of swelling fade away was greater in the plugged ducts group than the mastitis group (1.65 ± 0.64 vs. 1.48 ± 0.56, t = 3.49; p = .001). The composition ratio of changes in induration range between the two groups was statistically different (Pearson c2 = 137.87, p < .001), of which more obvious improvement in the plugged ducts group than the mastitis group (c2 = 25.65, p < .001). Conclusion: FSST can relieve pain, reduce breast swelling and range of induration, and for plugged ducts or mastitis varied degree differently
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THE IMPACT OF BIG DATA ON BUSINESS MODEL INNOVATION OF TRADITIONAL INDUSTRY
Shenghui Wang,Yuchen Zhang,Tianzhu Li,Jing Fei Long 글로벌지식마케팅경영학회 2016 Global Marketing Conference Vol.2016 No.7
With the rapid development of science and technology, big data has been applied in many fields and has brought commercial revolution[1]. The scientific community generally regards big data as "massive data + complex types of data". Commercial applications are more concerned about big data as an analytical (prediction) method and focus on the potential commercialization of analysis results. All walks of life will produce large amounts of data every day. The transition of data-scale brings huge commercial value, which will certainly bring the innovation of business model[2]. Particularly in the internet and other emerging industries, because they get data more convenient and fast. Like Amason, Facebook, Google etc, they use analysis of big data to innovate their business model for maximizing their profits[3], actually business model refers to "an enterprise’s profitable operation mode plus ways to make money"[4]. So the effectiveness of business model innovation of big data on emerging industries has been remarkable. But the impact of big data on traditional industries is still in the exploratory stage. Traditional industry mainly refers to the labor intensive, manufacturing oriented industries, including the traditional commerce and service industry[5]. Learning from the experience of big data on business model innovation of emerging industries, traditional industries can use big data to subvert the business model and accelerate the transformation and upgrading.
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