Trend Of Convergence Research Articles

A Market Convergence Prediction Framework Based on a Supply Chain Knowledge Graph

Market convergence challenges socially sustainable supply chain management (SSSCM) due to the increasing competition. Identifying market convergence trends allows companies to respond quickly to market changes and improve supply chain resilience (SCR). Conventional approaches are one-sided and biased and cannot predict market convergence trends comprehensively and accurately. To address this issue, we propose a framework based on info2vec that solves the problem of matching multidimensional data by using the technology layer as the focal layer and the supply chain as the supporting layer. The framework enriches the supply chain dimension with the technology dimension. A knowledge graph is constructed to facilitate cross-domain information connectivity by integrating different data sources. The nodes in the knowledge graph were characterized using a representation learning algorithm, which enhanced feature mining during supply chain and market convergence. Changes in market demand were predicted based on link prediction experiments. Market convergence has an impact on firm cooperation and, thus, on SCR. The framework recommends potential technological and innovative cooperation opportunities for firms. In this way, it has been demonstrated to improve SSSCM through network resilience experiments. This method predicts market convergence efficiently based on the supply chain knowledge graph, which provides decision support for enterprise development.

Tailings Behavior Assessment Using Piezocone Penetration Test

Intensive economic development is associated with an increasing demand for raw materials, including minerals. An illustrative example of this issue is the development of the copper industry. A significant problem arising from the scale of copper production is the management of an ever-growing amount of post-flotation tailings. This necessitates the need to ensure the continuity of safe storage. This study presents the results of studies on the behavior of deposits in the Żelazny Most Tailings Storage Facility (Poland). The primary objective of this study was to estimate the settlements of tailings under variable deposition conditions. The results were assessed using two methods: indirect and direct; this was based on cone penetration test (CPTU) results. The results were verified using Modified Cam Clay (MCC) modeling. Depending on the type of test, settlements ranged from several dozen centimeters to over three meters. Despite the observed differences, the results of both CPTU methods indicate a convergent trend in tailings behavior. Conversely, the results estimated using the direct method and numerical modeling demonstrate a high level of agreement.

GREEN TOTAL FACTOR PRODUCTIVITY MISMEASUREMENT WITHOUT CONSIDERING INTANGIBLES: EVIDENCE FROM CHINA

This paper aims to re-estimate green total factor productivity (GTFP) with the consideration of intangible capital in Chinese provinces between 2003 and 2017. Intangible capital is broadly defined and categorized into computerized information, innovative property and economic competency property. The finding suggests that the conventional GTFP is underestimated especially during the post-financial crisis period in China. It is also found that technical efficiency, which shows the trend of deterioration without capitalizing intangibles, improves steadily and jointly with technological progress contributing to the improvement of productivity. The mismeasurement of GTFP is more severe in developed regions in China where intangible investment is more emphasized. Furthermore, regional GTFP shows the trend of convergence after intangible capital is incorporated and the rate of convergence turns to be faster in coastal regions than that in the interior.

Regional differences, dynamic evolution, and spatial-temporal convergence of green finance development level in China.

Green finance has great potential for supporting environmental improvement, combating climate change, and the economical and efficient use of resources. In this study, based on the panel data of 30 provinces in China from 2010 to 2020, we used the weighted TOPSIS model to measure the green finance development level (GFDL) in China and its three major regions. The Dagum's Gini coefficient, kernel density estimation, Markov chain, and the convergence model are used to analyze the regional differences, dynamic evolution, and spatial-temporal convergence of GFDL in China. The results show that, in general, the GFDL shows an upward trend, but the GFDL in various regions is unbalanced, which is characterized by the spatial distribution of "high in the southeast and low in the northwest" and "high in the coast and low in the inland". The overall difference of GFDL is showing an expanding trend, which is mainly caused by inter-regional difference. The absolute differences of GFDL between the overall country, the eastern region, and the western region are on a widening trend, while that in the central region is on a narrowing trend. In addition,the GFDLs between the overall country, the eastern region, and the western region have no significant σ convergence, while there is an obvious σ convergence trend in the central region. Further, the GFDLs in China and its three major regions have obvious absolute β convergence trends and conditional β convergence trends.

A New Fast and Efficient MPPT Algorithm for Partially Shaded PV Systems Using a Hyperbolic Slime Mould Algorithm

The design of new efficient maximum power point tracking (MPPT) techniques has become extremely important due to the rapid expansion of photovoltaic (PV) systems. Because under shading conditions the characteristics of PV devices become multimodal having several power peaks, traditional MPPT techniques provide crappy performance. In turn, metaheuristic algorithms have become massively employed as a typical substitute in maximum power point tracking. In this work, a new optimizer, which was named the hyperbolic slime mould algorithm (HSMA), is designed to be employed as an efficient MPPT algorithm. The hyperbolic tangent function is incorporated into the optimizer framework equations to scale down large perturbations in the tracking stage and boost its convergence trend. Moreover, to provide a strong exploration capability, a new mechanism has been developed in such a way the search process is carried out inside the best two power peak regions along the initial iterations. This region inspection mechanism is the prime hallmark of the designed optimizer in avoiding local power peaks and excessive global search operations. The developed algorithm was examined through diverse complicated partial shading conditions to challenge its global and local search abilities. A comparative analysis was carried out against the well-regarded PSO, GWO, and the standard slime mould algorithm. In overall, the designed optimizer defeated its contenders in all aspects offering higher efficiency, superior robustness, faster convergence, and fewer fluctuations to the operating point. An experimental setup that consists of the DSpace microcontroller and a PV emulator was employed to validate the algorithm overall performance. The recorded outcomes outline that the developed optimizer can achieve a tracking time of 0.6 seconds and 0.86 seconds on average, with 99.85% average efficiency under complex partial shading conditions.

Spatial Differentiation and Convergence Trend of High-quality Development Level of China’s Tourism Economy

Spatial Differentiation and Convergence Trend of High-quality Development Level of China’s Tourism Economy

Estimation of the Smallest Acceptable Sample Size in Bilateral Approaches to Coefficient Estimation and Accuracy Prediction

This study is about smallest acceptable sample size determination in experimental design studies involving a driving simulator. The smallest acceptable sample size should be specified so researchers can make accurate inferences about their studied populations. However, the number of samples typically collected is largely subject to the expense of data collection. Working out the methodology of estimating the required number of subjects based on an initially small number is a better way for researchers to determine the smallest acceptable sample size in the experiment. Predictor estimate precision and prediction accuracy are major factors for conducting experiments. Accordingly, this study estimates the smallest acceptable sample size, with emphasis on coefficient estimation and prediction accuracy for selected significant variables. The smallest acceptable sample size is chosen to be the maximum value returned by both coefficient estimation calculation and accuracy prediction calculation approaches. This methodology is flexible and scalable, and can be tailored to other experimental situations. To validate the appropriateness of this procedure, a more than sufficient sample of 50 drivers was recruited. The smallest acceptable sample size was determined backwardly, based on the variable coefficient convergence trends of the mean squared error (MSE) curves of the significant variables. Both the clear converging trends of the MSE curves and the proposed method indicated that 30 was an acceptable sample size.

Convergence of Inequality Dimensions in China: Income, Consumption, and Wealth from 1988 to 2018

AbstractUsing household data from 1988 to 2018, we confirm that the increase in income inequality in China has come to a halt in recent years but show that inequality in wealth and consumption continues to increase. We report a clear convergence of inequality across the different dimensions of income, consumption, and wealth. Households at the top of one dimension are increasingly located at the top of the other two dimensions. The mechanisms for such convergence across dimensions are analyzed. The savings rate links income and consumption, while capital accumulation is the central mechanism linking income and wealth. In addition, the wealth and income structure contribute to intensifying the convergence trend. We argue that the relatively high level of inequality in China may continue since income, consumption and wealth are increasingly reinforcing each other.

The Dominant Role of the Summer Hemisphere in Subtropical Lower Stratospheric Wave Drag Trends

AbstractIt is well established that the shallow branch of the Brewer‐Dobson circulation accelerates in a warming climate due to enhanced wave drag in the subtropical lower stratosphere. This has been linked to the strengthening of the upper flanks of the subtropical jets. However, the seasonality of the zonal wind trends, peaking in the winter hemisphere, is opposite to that of the Eliassen‐Palm flux convergence trends, peaking in summer. We investigate the seasonality in the wave drag trends and find a different behavior for each hemisphere. The Shepherd and McLandress (2011, https://doi.org/10.1175/2010jas3608.1) mechanism, involving transient wave dissipation at higher levels following the rise of the critical lines, is found to maximize in austral summer. On the other hand, in the Northern Hemisphere the wave drag increase peaks in summer primarily due to the changes in the stationary planetary waves (monsoonal circulations) associated with enhanced deep convection.

Catch-Up Process of Carbon Decoupling in APEC: Convergence Trends and Driving Effects

Catch-Up Process of Carbon Decoupling in APEC: Convergence Trends and Driving Effects

Academic Environment from Homogenous to Heterogonous: The effect of Covid-19 on Students’ Performance

Covid-19 forced students to leave their educational institutions toward their original living place, and they moved from an undifferentiated environment to a differentiated one. The environmental change due to Covid-19 brought many concerns about learning loss and vulnerable students in distance learning. The study investigates students’ GPA data before and during Covid-19 to see how this environmental change has affected the GPA distribution among college students during Covid-19. The data consists of 537 students from the School of Law at Sultan Qaboos University SQU who attended the Spring 2019 and Spring 2020 semesters before and during the Covid-19 outbreaks. The study reveals that the students’ environmental change during Covid-19 has affected their performance, and several students’ subgroups are more vulnerable than others. The students’ performance differences in distance learning decreased compared to face-to-face learning, revealing a convergence trend in performance. Male students seem to be less vulnerable in distance learning than their female students’ counterparts. Most of the changes in students’ performance during Covid-19 are led by male students, especially in poor and excellent performance categories. Specifying the most susceptible students benefits policymakers in the education field, which precisely aims at helping the most vulnerable students in distance learning.

Сучасний вимір соціальної політики країн Європейського Союзу – орієнтир реформ в Україні

The article presents modern approaches to measuring and possibly conducting a comparative analysis of the effectiveness of social policy in individual countries with the help of global and regional special indicator systems and global indices of well-being and social progress. It is emphasized that in the modern world, the scenarios of increasing social polarization, inequality and poverty of human development both within and between countries are currently being realized, as well as those associated with the contraction of public welfare, violation of social, labour and environmental standards, and reduction of the quality of social rights. The author identifies and briefly characterizes the main indicator systems for measurement and comparative analysis of the effectiveness of social policies in the EU member states, particularly the EU statistics on incomes and living conditions (EU-SILC) and the European pillar of social rights. The effectiveness of the social policy of the EU member states is analyzed by the indicators of “Median equalized net income per household member” and “Risk of poverty and social exclusion”, which make it possible to determine convergent and divergent trends in their growth. It is confirmed that two main trends are observed in the convergence processes: actual convergence (EU member states are becoming more similar); and an upward trajectory of living and working conditions, which improves the overall EU value. Recommendations are formulated regarding the need to reform Ukraine’s social policy and its statistical support as a candidate country for joining the European Union under the current EU regulatory environment. The assessment of the dynamics of the welfare indicators for EU and Ukraine populations according to the components of the Legatum Prosperity Index, which relates to the social sphere, proves the need for reforms and the development of measures to improve the living conditions and health of Ukraine’s population.

Comparison of U.S. Generally Accepted Accounting Principles with International Financial Reporting Standards

Each country has its own mutually accepted accounting standards, which are the rules that companies in these countries must follow when preparing accounting entries and financial statements. Accounting standards ensure the standardization of economic activities recorded by enterprises, and also provide a common index for fair evaluation of the financial status and operating results of enterprises. Nowadays, the accounting standards widely used in the world are generally divided into two categories: U.S. Generally Accepted Accounting Principles and International Financial Reporting Standards. This paper discusses the measurement differences under diverse accounting standards by taking the division of R&D expenditures, inventory management and the preparation of cash flow statements as examples through theoretical analysis and overview. This paper finds that despite the many differences between GAAP and IFRS, GAAP also shows a trend of convergence with IFRS in recent years under the background of the continuous development of the international financial system.

Analysis of the change path of news dissemination under the trend of short-video convergence - Based on multi-priority dynamic thresholding algorithm

Abstract This paper proposes a multi-priority dynamic threshold algorithm for short video news transmission. The average queue threshold is dynamically adjusted according to the packet priority and unused cache space, and the data frames of short video news nodes are processed into multiple pulse data segments after the encoding process. The success rate and confidentiality of data transmission are improved by scrambling each segment of pulse data. Finally, after updating the thresholds at all levels, the overall thresholds of short video news are kept full to meet the performance requirements of the highest priority queue of short video news. The results show that 95.03% of the short video news with a high dissemination degree has a file size of less than 5 MB, and its video length is most popular among users in the range of 8~15 seconds. Compared to the traditional news dissemination mode, short video news is not limited by time and space, which greatly improves the efficiency of information dissemination.

The convergence mechanism and spatial spillover effects of urban industry-university-research collaborative innovation performance in China

ABSTRACT This study deconstructs the triple helix structure of urban industry-university-research collaborative innovation into a two-stage chain structure of knowledge innovation and technological innovation. Based on data of 285 China prefecture-level cities from 2004 to 2018, this study measures the urban industry-university-research collaborative innovation performance using the network data envelopment analysis model. Additionally, it clarifies its spatial convergence mechanism and spatial spillover effects using the spatial convergence test and spatial Durbin model. The results suggest that: (1) the performance of China's urban industry-university-research collaborative innovation at different stages shows a spatial pattern with obvious regional differences and diversified development modes. (2) The overall performance shows a trend of gradual σ convergence with a slightly fluctuating rate, and the gap is constantly decreasing. Additionally, it shows an absolute β convergence trend at different stages, with an obvious geospatial club convergence trend in the eastern, central, and western regions. (3) The spatial spillover effects of ‘research-to-production’ performance and overall performance are stronger than that of ‘university-to-research’ performance. Finally, the collaborative innovation performance of various cities is dominated by the polarisation effect, and the its role as the driving force of regional innovation needs to be strengthened.

Quantitative Topic Analysis of Materials Science Literature Using Natural Language Processing.

Materials science research has garnered extensive attention from industry, society, policy, and academia. However, understanding the research landscape and extracting strategic insights are challenging due to the increasing diversity and volume of publications. This study proposes a natural language processing-based protocol for extracting text-encoded topics from a large volume of scientific literature, uncovering research interests of scientific communities, as well as convergence trends. We report a topic map, representing the materials science research landscape with text-mined 257 topics regarding biocompatible materials, structural materials, electrochemistry, or photonics. We analyze the topic map in terms of national research interests in materials science, revealing competitive positions and strategies of active nations. For example, it is found that the increasing trend of research interest in machine learning topic was captured in the United States earlier than other nations. Similarly, our journal-level analyses serve as reference information for journal recommendations and trend guidance, showing that the main topics and research interests of materials science journals slightly changed over time. Moreover, we build the topic association network which can highlight the status and future potential of interdisciplinary research, revealing research fields with high centrality in the network such as machine learning-enabled composite modeling, energy policy, or wearable electronics. This study offers insightful results on current and near-future materials science research landscapes, facilitating the understanding of stakeholders, amidst the fast-evolving and diverse knowledge of materials science.

The continuing decline in cohort fertility and mixed evidence of narrowing educational differences

Background Typically, women with higher levels of education have had fewer children and were more likely to be childless compared to those with lower levels of education. However, in recent years, there has been a trend of fertility convergence between education levels in several high-income countries, including Australia. New data are needed to determine whether this trend has continued and fertility remains educationally stratified among Australian women.
 Aims Using the latest available data from the 2021 Australian Census, the aim of this study is to compare the average completed fertility and number of children ever born of women born between 1952 and 1981, with a specific focus on how these metrics vary by education level.
 Data and methods Data on the number of children ever born are sourced from the 2021 Australian Census to compute population statistics (completed cohort fertility, proportion of women by parity, and parity progression ratios) for six educational categories (postgraduate, graduate, bachelor, diploma, year 12, year 11 and below) and six cohort groups (1952-56, 1957-61, 1962-66, 1967-71, 1972-76, 1977-81). These statistics are used to describe trends in fertility patterns over time and by education.
 Results Completed cohort fertility has continued to decline slowly, from 2.22 for the1952-56 birth cohorts to 1.89 for the 1977-81 birth cohorts, mostly due to an increase in childlessness and a decrease in larger families with more than two children. There has been more divergence than convergence by education level, with those with bachelor's or diploma qualifications experiencing greater declines in fertility than any other education group. Although childlessness rates by education level have converged, women with lower education have also become increasingly likely to have larger families.
 Conclusions While there is evidence of convergence in childlessness rates across different levels of education, the gap in fertility rates between education groups continue to increase in Australia.

Ensemble Multifactorial Evolution With Biased Skill-Factor Inheritance for Many-Task Optimization

Current years have witnessed an increment in the number of research activities on improving the efficacy of multitasking algorithms for tackling challenging optimization problems. However, current approaches often present two potential problems. Firstly, although tasks may have different characteristics, existing literature usually utilizes only one search operator for all of them. Secondly, while multitasking environments comprise tasks of varying difficulty, previous proposals treat them equally. This paper proposes an algorithm named Ensemble Multifactorial Evolution with Biased Skill Factor Inheritance (EME-BI) for optimizing a large number of tasks simultaneously. In EME-BI, an effective parameter adaptation based on the knowledge transfer quality with Biased Skill-Factor Inheritance mechanism is designed to minimize negative transfer and allocate generated offspring to tasks that need resources. Besides, instead of using only one fixed search operator, EME-BI can automatically select the most appropriate one for each task at each evolutionary stage. Finally, the proposed algorithm is armed with a dynamically adjusted population size to promote exploitation. Empirical studies on various many-task benchmark problems and a real-world problem are conducted to verify the efficiency of EME-BI. The results portrayed that EME-BI achieves highly competitive performance compared to several state-of-the-art algorithms regarding the solution quality, convergence trend, and computation time. This proposal also won first prize at the CEC2021 Competition on Evolutionary Multi-task Optimization, Multi-task single-objective optimization.

Material resource decoupling dilemma: Convergence and traps of in-use stock productivity in national economy development

Various studies have suggested decoupling material stock from economic output as an important measure for promoting sustainable development. Here, we develop three theoretical hypotheses to describe the evolution features and economic effects of material stock intensity, and predict in theory that (1) Countries with higher material stock intensity are more likely to decouple economic growth from material stock. (2) Material stock intensity follows convergence trends. (3) Higher material stock intensity leads to higher long-run economic growth rates. To examine the adaptability of these hypotheses, we choose steel in-use stock as the proxy for the material capital stock and use panel data in 85 countries from 1950 to 2018 to conduct empirical analysis. Our empirical results in most countries support the theoretical predictions of the hypotheses. In particular, a 0.1t/k$ increase in steel stock intensity leads to a 2.12% increase in the probability of decoupling between steel stock and economic output next year and a 0.34% increase in the long-run GDP per capita growth rate annually. Moreover, steel stock intensity converges to approximately 0.25t/k$ to 0.35t/k$ at mature development stages. We predict that, except China, which is expected to follow decoupling trends, other large developing economies will couple economic output with steel stock. However, the shape of intensity curves is still uncertain for highly developed countries in the future.

The Psychosomatic Traits of “People with the Five Elements in Traditional Chinese Medicine”: A Qualitative Study

ObjectiveTo identify the representative attributes of the five elements of a person with a qualitative methodology and provide the basis for the clinical diagnosis and treatment of “people with the five elements in traditional Chinese medicine (TCM).” MethodsData collected from the literature review, two sessions of brainstorming of experts with related experience in “people with the five elements in TCM” from October 2020 to December 2020, and six rounds of in-depth interviews with 30 participants who had various attributes of the five elements from March 2021 to October 2021 were analyzed. Triangulation was used in this study, and theming and synthesizing were used to analyze the data. ResultsA total of 31 experts and 30 interviewees participated in this study. The median age of the experts and interviewees were 48.0 and 38.5 years, respectively; 51.66% and 54.8% of experts and interviewees, respectively, were men. The descriptors of facial diagrams of “people with the five elements in TCM” were complexion, shape, distribution state of facial bones, convergence trend of facial muscles, and facial expression. A theoretical model of “people with the five elements in TCM” was shaped based on these findings. ConclusionsThe study suggests a possibility for bridging the gap between personality and bodily state, identifying an avenue for personality research from the perspective of TCM.