Link prediction in author collaboration network based on BP neural network

Recently, more and more authors have been encouraged for collaboration because it often produces good results. However, the author collaboration network contains experts in various research directions within various fields, and it is difficult for individual authors to decide which authors are best suited to their expertise. This paper uses the relationships among authors to predict new relationships that may arise, recommending each author with the collaborators they may be interested in. The data source comes from 4-year data in DBLP from 2001 to 2004. After data cleaning, the training set and test set are constructed and then used BP neural network to build model. At the same time, this article compares the performance with Logistic Regression, SVM and Random Forest. The experiment shows that the BP neural network can get better result, and it is feasible to predict links in the author collaboration network

The Development Trends of Fuel Cell Technologies Based on Patent Analysis

Fuel cells are made from fuel and oxygen. Because of its low pollution, high energy conversion efficiency and high reliability, fuel cell has become the future direction of new energy application, the technological development path in the field of fuel cell research has great significance to the development of technological and energy innovation. Using the patent analysis method, this paper analyses the patent data from Derwent Innovation Index quantitively to study the state of application for patents, core technologies, highly cited patents and the main patentees. It shows that auxiliary device and related methods were a research hotspot in recent years; as the biggest patent holder of fuel cell technologies, Toyota, Honda motor Co. and Nissan motor Co. have an advantage. This paper has discovered some potential problems behind the phenomena and some suggestions are put forward finally

Link prediction in author collaboration network based on BP neural network

Recently, more and more authors have been encouraged for collaboration because it often produces good results. However, the author collaboration network contains experts in various research directions within various fields, and it is difficult for individual authors to decide which authors are best suited to their expertise. This paper uses the relationships among authors to predict new relationships that may arise, recommending each author with the collaborators they may be interested in. The data source comes from 4-year data in DBLP from 2001 to 2004. After data cleaning, the training set and test set are constructed and then used BP neural network to build model. At the same time, this article compares the performance with Logistic Regression, SVM and Random Forest. The experiment shows that the BP neural network can get better result, and it is feasible to predict links in the author collaboration network

Patients with endometriosis may experience worse clinical manifestations and therapeutic outcomes during COVID-19 in western China- a case series comparative analysis

Abstract Background Endometriosis is a crippling, ongoing, chronic inflammatory condition. The management of these patients has been impacted by the current COVID-19 pandemic, which is still controversial. This study compared the clinical therapy outcomes and psychological scores between before and during- the epidemic. Method The data of patients who were diagnosed with endometriosis in the Department of Gynecology, Chongqing Traditional Chinese Medicine Hospital from January 2018 to December 2022 were collected. The patients were divided into pre- and intra-COVID groups. The treatment results and psychological status of the two groups were compared. Results A total of 1022 patients with endometriosis were enrolled, with a mean age of 33.16 ± 9.81 years and a BMI of 23.90 ± 3.04 kg/m2, of which 434 cases (434/1022, 42.5%) were in the pre-COVID group and 588 cases (588/1022, 57.5%) in the intra-COVID group. Both groups were well balanced for age, BMI, history of abdominopelvic surgery, family relationships, education level, and duration between initial diagnosis and admission. Compared to the Pre-COVID group, the intra-COVID group had a higher proportion of patients with chronic pelvic pain (297/434, 68.4% vs. 447/588, 76.0%, p = 0.007) and dysmenorrhea (249/434, 62.8% vs. 402/588, 70.0%, p < 0.001), more patients requiring surgery (93/434, 21.4% vs. 178/588, 30.3%, p = 0.002) and longer hospital stays (5.82 ± 2.24 days vs. 7.71 ± 2.15 days, p < 0.001). A total of 830 questionnaires were completed. In the Intra-COVID group, PHQ-2 (2 (2, 3) vs. 3 (2,4), p < 0.001), GAD-2 (2 (1, 2) vs. 3 (2, 3), p < 0.001), PHQ-4 (4 (3, 5) vs. 5 (4, 7), EHP-5 (20.26 ± 6.05 vs. 28.08 ± 7.95, p < 0.001) scores were higher than that in the pre-COVID group, while BRS (3.0 (2.2, 4.0) vs. 2.4 (1.8, 3.8), p = 0.470) were not significantly different. Conclusion During the COVID-19 epidemic, patients with endometriosis may have reduced visits to the hospital, more severe related symptoms, longer length of hospital stays, and worse quality of life, with the possible cause being a disturbance in hormone levels through increased anxiety and depression. This provides a valid clinical basis for optimizing the management of patients with endometriosis and for early psychological intervention during the epidemic

The global disruption index (GDI): an incorporation of citation cascades in the disruptive index

&lt;p&gt;The disruption index (DI) proposed by Funk and Owen Smith (2017), and improved by Wu et al. (2019) has been widely studied and applied in bibliometrics recently. This paper examines the relationship between disruptiveness and influence and finds that highly influential publications tend to be more disruptive. However, the original formula of the disruption index (DI) shows inaccurate results in our sample data. We identify three limitations of DI and propose the global disruption index (GDI), which considers multiple generations of cited or citing documents in citation cascades, to address these limitations. Our empirical study, based on a dataset of 134,003 papers published in the field of Information Science Library Science from 1900 to 2021, and all articles published in Scientometrics, shows that GDI has a significant correlation with the citation count and the number of disruptive citations. We argue that GDI is a more appropriate metric for measuring the disruptiveness of scientific publications and has great significance for academic evaluation and scientific development in the future. Furthermore, our study suggests that the number of consolidated citations also facilitates publications&rsquo; disruptiveness. These findings have important implications for understanding the nature of scientific impact and for developing more accurate and comprehensive bibliometric measures.&lt;/p&gt