Input-output-based genuine value added and genuine productivity in China\u27s industrial sectors (1995-2010)

The rapid growth of China\u27s economy has brought about huge losses of natural capital in the form of natural resource depletion and damages from carbon emissions. This paper recalculates value added, capital formation, capital stock, and related multifactor productivity in China\u27s industrial sectors by further developing the genuine savings method of the World Bank. The sector-level natural capital loss was calculated using China\u27s official input–output table and their extensions for tracing final consumers. The capital output elasticity in the productivity estimation was adjusted based on these tables. The results show that although the loss of natural capital in China\u27s industrial sectors in terms of value added has slowed, the impacts on their productivity during the past decades is still quite clear

China's Role in the Rising of the South: Vision for 2030

This article examines China's major development contributions, looking at its wider impact on world development. In particular, the article examines the impact of China's development on the changing pattern between the North and South and the human development index. The factors and related regimes behind these phenomena are discussed and a conceptual model is constructed, providing a meta?analysis of the evolution of China's role, based on the structural interpretation of external impetus and barriers, as well as internal advantages and shortcomings. The authors' long?term projections show that the rise of the South, led by China, will be the most important shift in the world's landscape with respect to the development of the emerging world, perhaps leading other large developing economies to play a more prominent role in international development in the future, bringing common development, common prosperity and common progress to the world

Prediction of Carbon Dioxide Level Using Statistical Learning and Its Potential Correlation With Global Warming

The Industrial Revolution caused a huge change in the climate of our planet. Since the 19th century, a high level of atmospheric carbon dioxide has contributed to global warming and other environmental problems. We first acknowledge the substantial correlations between the CO2 levels or temperatures and the years before creating our models. In this situation, we propose that the ARIMA model, which combines the auto-regression and moving average models, is essential for issue analysis. In order to estimate CO2 concentrations and land-ocean temperatures, we create polynomial models as well as an ARIMA model with seasonality. Following these hypotheses, we discover that the CO2 concentrations and temperatures have a significant direct link. In order to forecast the future relationships between CO2 concentrations and temperatures, we also attempt to employ polynomial function. We constantly reflect on and reexamine the issues as we construct these models in order to have a greater grasp of the circumstances. Each of our models is also evaluated, and the most precise one is used to make forecasts. Based on Matlab, we can quickly calculate the data, utilize iterations to determine the ideal model parameters, and then display our findings in diagrams

Changes in aroma composition of blackberry wine during fermentation process

The study aimed at investigating the influence of fermentation (primary and secondary) on aroma composition of blackberry wine. Gas chromatography-mass spectrometry (GC-MS) was applied to quantify the compounds relevant to sparkling wine aroma. Investigation on this study revealed that a number of aroma components in raw material (55 in numbers), raw wine (54 in numbers), and aging wine (50 in numbers) were identified. In addition, 9 new aroma components such as octanoate, benzenepropanoic acid ethyl ester, ethyl benzoate, dodecyl ethyl, n-propanol, n-butanol, d-citronellol, benzaldehyde, and cedrol were detected in natural aging wine which appeared during secondary fermentation according to total peak areas of 4.69%. These findings reveal that natural aging is very important to aroma components formation of blackberry wine.Key words: Blackberry, gas chromatography, primary fermentation, secondary fermentation

Grid investment capability prediction based on path analysis and BP neural network

With the more complex investment environment of China’s power grid, the accurate prediction of the investment ability of power grid enterprises has become an important prerequisite for managers to make precise investment decisions. This paper first selects the factors affecting the investment capacity of the power grid from the internal and external environment, and establishes the index system of the factors affecting the investment capacity. Secondly, the path analysis is used to deeply explore the interaction relationship and influence degree of each index and investment capacity. Finally, the maximum investment capacity of the power network can be predicted based on the BP neural network prediction model. The results show that the BP neural network prediction model can achieve higher prediction accuracy when predicting the power grid investment capability

Robust Representation Learning for Unified Online Top-K Recommendation

In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item advertising, neglecting the significance of content advertising. This oversight results in inconsistencies within the multi-entity structure and unfair retrieval. Furthermore, the challenge of retrieving top-k advertisements from multi-entity advertisements across different domains adds to the complexity. Recent research proves that user-entity behaviors within different domains exhibit characteristics of differentiation and homogeneity. Therefore, the multi-domain matching models typically rely on the hybrid-experts framework with domain-invariant and domain-specific representations. Unfortunately, most approaches primarily focus on optimizing the combination mode of different experts, failing to address the inherent difficulty in optimizing the expert modules themselves. The existence of redundant information across different domains introduces interference and competition among experts, while the distinct learning objectives of each domain lead to varying optimization challenges among experts. To tackle these issues, we propose robust representation learning for the unified online top-k recommendation. Our approach constructs unified modeling in entity space to ensure data fairness. The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations. Moreover, the proposed method balances conflicting objectives through the homoscedastic uncertainty weights and orthogonality constraints. Various experiments validate the effectiveness and rationality of our proposed method, which has been successfully deployed online to serve real business scenarios.Comment: 14 pages, 6 figures, submitted to ICD

Expression of P450arom and Estrogen Receptor Alpha in the Oviduct of Chinese Brown Frog ( Rana dybowskii

One specific physiological phenomenon of Chinese brown frog (Rana dybowskii) is that its oviduct expands prior to hibernation instead of expanding during the breeding period. In this study, we investigated the expression of P450arom and estrogen receptors α and β (ERα and ERβ) in the oviduct of Rana dybowskii during the breeding period and prehibernation. The results of the present study showed that there were significant differences in both oviductal weight and size with values markedly higher in prehibernation than in the breeding period. P450arom was observed in stromal tissue in both the breeding period and prehibernation. ERα was expressed in stromal tissue and epithelial cells in both periods, whereas ERβ could not be detected. The mean protein and mRNA levels of P450arom and ERα were significantly higher in prehibernation as compared to the breeding period. Besides, oviductal content of 17β-estradiol was also higher in prehibernation than in the breeding period. These results suggested that estrogen may play autocrine/paracrine roles mediated by ERα in regulating the oviductal hypertrophy during prehibernation