Market integration in China

Over the last three decades, China's product, labor, and capital markets have become gradually more integrated within its borders, although integration has been significantly slower for capital markets. There remains a significant urban-rural divide, and Chinese cities tend to be under-sized by international standards. China has also integrated globally, initially through the Special Economic Zones on the coast as launching grounds to connect with world markets, and subsequently through the accession to the World Trade Organization. For future policy considerations, this paper argues that its economic production needs to be spatially concentrated, and its social services need to be spread out to the interior to ensure harmonious development and domestic integration (through inclusive rural-urban transformations and effective territorial development).Economic Theory&Research,Banks&Banking Reform,Debt Markets,Emerging Markets,Access to Finance

Using Machine Learning to Predict Microvascular Complications in Patients With Type 1 Diabetes

Background: Diabetic microvascular complications can lead to long-term morbidity and mortality, significantly drive healthcare costs, and impair quality of life of patients with type 1 diabetes (T1D). Early prediction and prevention of microvascular complications, including nephropathy, retinopathy, and neuropathy in T1D patients can support informed clinical decision making and potentially delay the progression to long-term adverse outcomes. Although machine learning (ML) methods have been applied for disease prediction in healthcare, there is very limited research using advanced ML methods (e.g., neural networks) for the prediction of microvascular complications in T1D patients. Moreover, there is no study that has explicitly compared the performance of different predictive models. In addition, none of the predictive models in previous studies incorporated A1C variability as a predictor, specifically in ML models. Objectives: The first objective of this study was to develop and compare predictive models, namely, ML and conventional statistical models for 3 microvascular complications (diabetic nephropathy, retinopathy, and neuropathy) in T1D patients. The second objective of this study was to develop and compare predictive models, namely, ML and conventional statistical models and evaluate whether A1C variability can help better predict each of the 3 microvascular complications (diabetic nephropathy, retinopathy and neuropathy) in T1D patients. Methods: This was a factorial experimental study using retrospective real-world registry data. Adult T1D patients participating in the T1D Exchange Clinic Registry and met the eligibility criteria were included for the analysis. Baseline characteristics of eligible T1D patients that were measured between 2010 and 2012 were used to predict three microvascular complications that were measured till 2017. Two ML methods, i.e., support vector machine (SVM) and neural network (NN) and one conventional statistical method, i.e., logistic regression (LR) were used to develop predictive models. The three microvascular complications, i.e., diabetic nephropathy, retinopathy and neuropathy were operationalized as binary variables (yes/no). Predictors for each microvascular complication were selected. Specifically, A1C variability was manipulated into the following 5 levels: a) single A1C, b) mean A1C, c) combination single, d) combination mean, and e) multiple. Models were first developed through 10-fold cross-validation on the train set. Then the model was fit on the entire train set and evaluated on the test set. Hence, for each microvascular complication, 11 (10+1) predictive models were developed using each modeling method with each predictor set. A total of 495 models (11 x 5 predictor sets x 3 modeling method x 3 microvascular complications) were developed, 165 models for each microvascular complication. Performance measure was operationalized as F1 score. Factorial analysis of variance (ANOVA) was used to test research hypotheses. Post hoc Tukey-Kramer test was performed to evaluate which levels within a factor were significantly different. An alpha level of <0.05 was used to determine statistical significance of an association. Data preparation process, summary statistics, correlation analysis and LR were performed using SAS 9.4 (SAS Institute, Inc. Cary, NC). Predictive modelling by SVM and ANN were performed through Scikit-learn 0.22.1 and the Keras application programming interface (API) of TensorFlowTM online version 1.0.0. Results: A total of 4476, 3595, and 4072 patients met the eligibility criteria and included in the cohort of nephropathy, retinopathy, and retinopathy, respectively. Within each cohort, 510 (11%), 659 (18%) and 579 (14%) developed nephropathy, retinopathy, and neuropathy, respectively during the follow-up period. Patients of the three cohorts were on average 38-40 (±14.5-15.4) years and had been diagnosed with T1D for an average (±SD) of 19-21 (±11.3-12.5) years. Slightly more than half (53-55%) of patients were women. For the first objective, the mean (±SD) F1 score of 33 LR models were 0.19±0.10, lower than that of 33 SVM models (0.38±0.03) and 33 NN models (0.38±0.03). Two-way ANOVA indicated a significant interaction between the effects of modeling method and microvascular complication on performance measure (F1 scores, p<.0001). ML models performed significantly better than LR models within each study cohort. Post hoc Tukey-Cramer test indicated there was no statistical difference between F1 scores of SVM and NN models. For objective 2, three-way ANOVA indicated significant interactions between modeling method, microvascular complication and A1C variability. Hence, two-way ANOVA was performed within each cohort. F test indicates that A1C variability had significant effect on F1 score of the nephropathy cohort when the modeling method was NN (F=6.78, p<.0001). Post hoc Tukey-Kramer test indicates that mean F1 scores of the nephropathy cohort from NN models using d) combination mean or e) multiple were significantly higher than using b) mean A1C or c) combination single. In the cohort of retinopathy, there is no effect of A1C variability on performance measure. Lastly, in the cohort of neuropathy, F test indicates the A1C variability had significant effect on performance measure when the modeling method was LR (F=8.19, p<.0001). Post hoc Tukey-Kramer test indicates that mean F1 score of the neuropathy cohort from LR models using e) multiple was significantly lower than using other A1C variability measures. Across all three cohorts, ML models performed significantly better than LR models. Conclusion: The study indicates that ML models compared to LR models produced significantly higher F1 scores for predicting all three types of microvascular complications irrespective of which A1C variability measure was used. The study indicates that it is better to use A1C variability combination mean or multiple for evaluating A1C variability when predicting diabetic nephropathy in T1D patients using NN machine learning models. Future research is needed to develop decision support systems that can advise clinicians based on the results from predictive models

The Relationship between Serum Osteocalcin Concentration and Glucose Metabolism in Patients with Type 2 Diabetes Mellitus

To study the correlations between serum osteocalcin and glucose metabolism in patients with type 2 diabetes, 66 cases were collected to determine total osteocalcin, undercarboxylated osteocalcin, fasting blood glucose, fasting insulin, and HbA1c. Osteocalcin concentrations were compared between groups of different levels of HbA1c, and parameters of glucose metabolism were compared between groups of different levels of total osteocalcin and undercarboxylated osteocalcin. The relationship between osteocalcin and parameters of glucose metabolism was also analyzed. We found that the total osteocalcin concentration of high-HbA1c group was significantly lower than that of low-HbA1c group. The fasting blood glucose of low-total-osteocalcin group was significantly higher than that of high-total-osteocalcin group in male participants, while the fasting blood glucose of low-undercarboxylated-osteocalcin group was significantly higher than that of high-undercarboxylated-osteocalcin group in all participants and in male participants. Total osteocalcin was inversely correlated with HbA1c, and undercarboxylated osteocalcin was inversely correlated with fasting blood glucose. However, no significant correlation was found between osteocalcin and HOMA-IR. Total osteocalcin was an independent related factor of HbA1c level. In summary, decreased serum total osteocalcin and undercarboxylated osteocalcin are closely related to the exacerbation of glucose metabolism disorder but have no relations with insulin resistance

Auditory Synaesthesia and Near Synonyms: A Corpus-Based Analysis of sheng1 and yin1 in Mandarin Chinese

This paper explores the nature of linguistic synaesthesia in the auditory domain through a corpus-based lexical semantic study of near synonyms. It has been established that the near synonyms 聲 sheng “sound ” and 音 yin “sound ” in Mandarin Chinese have different semantic functions in representing auditory production and auditory perception respec-tively. Thus, our study is devoted to test-ing whether linguistic synaesthesia is sensi-tive to this semantic dichotomy of cognition in particular, and to examining the relation-ship between linguistic synaesthesia and cog-nitive modelling in general. Based on the cor-pus, we find that the near synonyms exhibit both similarities and differences on synaesthe-sia. The similarities lie in that both 聲 and音 are productive recipients of synaesthetic trans-fers, and vision acts as the source domain most frequently. Besides, the differences exist in se-lective constraints for 聲 and 音 with synaes-thetic modifiers as well as syntactic functions of the whole combinations. We propose that the similarities can be explained by the cogni-tive characteristics of the sound, while the dif-ferences are determined by the influence of the semantic dichotomy of production/perception on synaesthesia. Therefore, linguistic synaes-thesia is not a random association, but can be motivated and predicted by cognition.

Study on the Contribution of CO2 Emission Reducing of Yellow River Upstream LONGyangxia and LIUjiaxia Cascade Reservoirs

AbstractIn order to analyze the contribution of energy saving and emission reducing of Yellow River upstream LONGyangxia and LIUjiaxia cascade reservoirs, the compensation regulation simulation model of the Yellow River mainstream cascade reservoirs is adopted to study the dynamic characteristics of emission reducing contribution of Longyangxia and Liujiaxia reservoirs in three level years of 2005, 2015 and 2020. The result indicates that the emission reducing contribution of Longyangxia and Liujiaxia reservoirs are 15.8077 million ton, 16.1808 million ton, and 13.3092 million ton in 2005, 2015, and 2020 level years. The change laws of the Long-Liu compensation system emission reducing contribution is the same as the one of Longyangxia reservoir emission reducing contribution, which descends after ascending. The change laws of the Liujiaxia reservoir emission reducing ontribution is descending along with time