Trend Of Convergence Research Articles

Node-depth based Genetic Algorithm to solve Inter-Domain path computation problem

Inter-Domain Path Computation problem under Node-defined Domain Uniqueness Constraint (IDPC-NDU) is an NP-Hard problem that applies to packet routing in multi-domain networks, particularly Hierarchical Path Computation Element (h-PCE) architecture. Its objective is to determine the shortest route between two specified nodes that visit each domain no more than once. In recent years, many proposals have been made to tackle this problem and improve existing algorithms. However, most of these strategies retain a potential weakness that lies in the complex encoding and decoding methods, which consume a lot of computer resources and computation time, especially for large-scale problems. For this reason, this paper introduces an effective Node-depth based Genetic Algorithm (NDEGA) for IDPC-NDU. We propose a novel solution encoding for the problem by using tree representation that helps to satisfy the domain constraint and makes the corresponding solution construction more practical. Instead of conventional encoding techniques, NDEGA is equipped with an improved Node-depth encoding, which is famous for its low time complexity and efficiency for tree problems. Besides, a corresponding decoding method, as well as node-depth search operators, are also well-developed for the problem. To examine the efficiency of NDEGA, we carefully conduct extensive experiments and compare our approach to many competitive baselines on a wide range of synthetic datasets. The empirical results demonstrate that our proposed algorithm significantly outperforms all compared algorithms regarding solution quality, convergence trend, and computation time and obtains the state-of-the-art performance for IDPC-NDU. Notably, our NDEGA achieves 65.17−85.66% solution quality improvement on average compared to the previous approaches while running faster, approximately 50−70 times.

Convergence Trends in Euro Economies: Financial Crisis Recovery and the COVID-19 Pandemic

The configurative comparative method, Dynamic Pattern Synthesis (DPS) is used to replicate previous research into the impact of the euro on economic convergence. The DPS method ensures a forensic examination of the diverse variable patterns that influence cluster memberships. As with previous research conclusions, there are multiple patterns of convergence and divergence. Consistent clusters across the time periods compared are Germany, the Netherlands, Luxembourg, and Ireland; Slovakia and Estonia; Italy, Spain, and Slovenia; and Portugal and Greece. The variable patterns most likely to influence cluster definitions are differences in GDP per capita, productivity, and investment, although there are other differing variable patterns that influence specific smaller cluster memberships and the consistency of memberships over time. Externalities undermine nominal convergence. An example is the divergence of the experience of consumer inflation between 2016 and 2022. Nevertheless, some convergence in long-term interest rates is achieved. There is also divergence in the real convergence target of GDP per capita. As regards structural changes, productivity differences widen, and investment as a percentage of GDP converges during COVID-19. The theoretical implications are that the complex dynamics between collaboration, competitive markets, and global instabilities makes convergence unlikely. Real convergence, such as reducing the distribution differences of GDP per capita, is only likely to be possible over many decades, and needs considerable government interventions. Complex systems theory informs us that limits to convergence are inevitable in dynamic systems where events bring unplanned divergences.

Spatial convergence and differentiation characteristics of ecological efficiency of forestry carbon sink: Evidence from China

Calculating and analyzing ecological efficiency of forestry carbon sink is key to develop China's forestry carbon sink effectively. This paper utilized super-efficiency stochastic frontier model (SBM) to measure ecological efficiency of forestry carbon sink of 30 provinces (municipalities and autonomous regions) in China from 2000 to 2017. In addition, this study calculated and analyzed convergence trend by constructing β convergence model under consideration of spatial conditions. Study results were revealed as follows. Firstly, ecological efficiency of China's forestry carbon sink presented a "double peak" distribution characteristic of a steady rise with fluctuations. However, overall level was not high, and large room still existed for improvement. Secondly, a significant spatial convergence existed in ecological efficiency of forestry carbon sink in China. Spatial factors had an accelerating effect on convergence trend, making spatial differences in ecological efficiency of forestry carbon sink among regions further narrowed over time. Thirdly, five major forest regions possessed significant spatial convergence, and convergence rates were higher than national average level. The southwest forest region had the highest convergence rate, followed by the northeast, south, northwest and the North China forest regions. Spatial convergence speeds of two periods from 2000 to 2010 and 2011 to 2017 were higher than classical convergence speed, and shown a decreasing change process. Due to existing of spatial convergence, different regions should explore value realization mechanism of forestry carbon sink, clarify regional positioning, and formulate measures based on local conditions to develop forestry carbon sink.

A Study on the Application of New Media in Broadcast Newscasting and Writing under the Perspective of Media Integration

The development trend of media convergence is related to user demand, market competition and other factors, based on this environmental background, the broadcasting news industry makes full use of the advantages of new media to innovate the development path, which is the right choice to comply with the changes and needs of social development. The rapid development of new media has brought a variety of information dissemination forms and broadened information dissemination channels, which is a new opportunity and a new challenge for the broadcast news industry. In this regard, this paper starts from the application of new media in broadcast news broadcasting and writing, focusing on the broadcast news industry to play the advantages of new media to promote their own transformation and upgrading. For example, broadcast news industry practitioners can start from the characteristics of the new media industry, revolutionize the concept of work and work thinking, change the writing ideas and innovative programs, improve the interactivity of broadcast news, so as to enhance the audience’s sense of experience and improve the competitiveness of broadcast news.

A comparative scientometric analysis of investor sentiment and trading behaviour research

The purpose of this study is to conduct a comparative analysis of investor sentiment and trading behavior in the field of behavioral finance. This study analyzes and compares the research evolution within these two domains using various scientometric analysis methods. The analysis reveals that the number of publications in the field of investor sentiment has grown exponentially while research activity in trading behavior has slowed down in recent years. Investor sentiment research has attracted more attention and generated more publications despite a relatively late start. The USA and China are the major countries conducting research in these two fields and the research output is primarily derived from economically developed regions. Investor sentiment and trading behavior research shows a convergence trend with common research hotspots including COVID-19, Bitcoin and machine learning. Both fields are concentrated in financial market research and have emerging research frontiers such as financial crises, social media and cryptocurrencies. Highly cited articles indicate a degree of overlap between the two fields wherein the domain of trading behavior is predominantly linked to investor sentiment. This study provides valuable insights into the research progress on investor sentiment and trading behavior.

Semantic Web and blockchain technologies: Convergence, challenges and research trends

In recent years, on the one hand, we have witnessed the rise of blockchain technology, which has led to better transparency, traceability, and therefore, trustworthy exchange of digital assets among different actors. On the other hand, achieving trustworthy content exchange has been one of the primary objectives of the Semantic Web, part of the World Wide Web Consortium. Semantic Web and blockchain technologies are the fundamental building blocks of Web3 (the third version of the Internet), which aims to link data through a decentralized approach. Blockchain provides a decentralized and secure framework for users to safeguard their data and take control over their data and Web3 experiences. However, developing trustworthy decentralized applications (Dapps) is a challenge because many blockchain-based functionalities must be developed from scratch, and combined with data semantics to open new innovative opportunities. In this survey paper, we explore the cross-cutting domain of the Semantic Web and blockchain and identify the critical building blocks required to achieve trust in the Next-Generation Internet. The application domains that could benefit from these technologies are also investigated. We developed a deep analysis of the published literature between 2015 and 2023. We performed our analysis in different digital libraries (e.g., Elsevier, IEEE, ACM), and as a result of our research, we retrieved 137 papers, of which 97 were retrieved as relevant to include in the paper. Furthermore, we studied several aspects (e.g., network type, transactions per second) of existing blockchain platforms. Semantic Web and blockchain technologies can be used to realize a verification and certification process for data quality. Examples of mechanisms to achieve this are the Decentralized Identities of the Semantic Web or the various blockchain consensus protocols that help achieve decentralization and realize democratic principles. Therefore, Semantic Web and blockchain technologies should be combined to achieve trust in the highly decentralized, semantically complex, and dynamic environments needed to build smart applications of the future.

Sensitivity-Based Geometric Parametrization and Automatic Scaling for Aerodynamic Shape Optimization

Aerodynamic shape optimization has become well established, with designers routinely performing wing and full aircraft optimizations with hundreds of geometric design variables. However, increased geometric design freedom increases optimization difficulty. These optimizations converge slowly, often taking hundreds of design iterations. In addition, designers have to manually scale design variables through trial and error to achieve a well-behaved optimization problem, which is tedious and time-consuming. In this work, we propose a sensitivity-based geometric parametrization approach that maps the design space onto one better suited for gradient-based optimization while keeping the same optimization problem. At the same time, the process can automatically determine design variable scaling so that the new optimization problem can be solved more effectively. We demonstrate the approach on two aerodynamic shape optimizations and show improved terminal convergence trends compared to the traditional approach, without requiring manual adjustments to the design variable scaling.

Analysis of Measurement, Regional Differences, Convergence and Dynamic Evolutionary Trends of the Green Production Level in Chinese Agriculture

The development of green agriculture is conducive to accelerating the agricultural modernization process, making a significance for the sustainable development of agriculture and the environment in China. This paper constructs a comprehensive evaluation model by selecting 17 representative indicators from the perspective of sustainability. Then, this paper uses the entropy value method to measure the level of green agricultural production in 31 provinces from 2011 to 2021. We use Dagum’s Gini coefficient and decomposition method, the kernel density estimation method and spatial Markov chain analysis method to explore the main sources of spatial differences and dynamic evolution trends. The results show that: (1) The overall level of green production in Chinese agriculture is low, and the level in the central region is higher than that in eastern and western regions; (2) There are significant regional differences in the level of green production in China’s agriculture, and this is likely to widen. The interaction of inter- and intra-regional differences is the main reason for overall differences; (3) The trend of polarization in the level of green agricultural production is more obvious in the eastern part of China, while it is weaker in central and western regions; (4) There is a spatial spillover effect in the dynamic evolution of China’s agricultural green production level. And the longer the overall time, the more obvious the spillover effect is. This paper analyzes the past development of green agriculture in China, makes predictions and provides constructive suggestions for the improvement and development of green agricultural production in China in the future.

The global distribution of gains from globalization

Global interpersonal inequality is increasingly driven by inequalities within countries while the role of inequality between countries diminishes. Is this due to globalization? To answer this question, we use comprehensive global panel data at the country-decile-group level for the past half century and exploit the geographic diffusion of liberalization policies to identify the effect of globalization. Across countries, we find that income gains are substantial for countries at early stages of the globalization process, but the ‘marginal returns to globalization’ diminish as globalization rises, eventually becoming insignificant for the most globalized countries. Within countries, gains from globalization are largest for the richest ten percent of national income distributions, resulting in substantial increases in national income inequalities. A simple quantitative model is consistent with these empirical results. Over the past half century, globalization has promoted a dual trend of income convergence across countries and income divergence within countries.

Correlation between carbon emissions and water consumption in different industries in China: Spatial and temporal distribution characteristics and driving factors

In order to quantitatively analyze the correlation between carbon emissions and water consumption and identify the main driving factors. Energy consumption and water consumption data of different industries in different provinces of China were collected to calculate the water consumption per unit of carbon dioxide emissions (WCCD) and analyze the characteristics of its spatial and temporal distribution, as well as spatial correlation. The driving factors of WCCD change were quantitatively analyzed by correlation analysis and principal component analysis methods. The results showed that: (1) From 2000 to 2020, WCCD values in each province gradually decreased and approached a plateau. The overall spatial distribution showed more concentration in the south and less in the north, with the North Coastal Economic Zone showing lower values. (2) The spatial correlation of WCCD values is positively correlated, that is, WCCD values have agglomeration characteristics. The WCCD values are mainly ‘high-high aggregation’ and ‘low-low aggregation, which suggests a significant convergence trend and serious polarization. (3) Per capita GDP, per capita energy consumption, and urbanization rate showed an extremely strong correlation with WCCD, while the proportion of the tertiary industry in GDP showed a strong correlation with WCCD. Per capita water resources showed a weak correlation with WCCD. The principal component analysis revealed an increasing impact of economic development on WCCD indicators. This study provides a new perspective for carbon-water relationship research and serves as a fundamental support for water-saving and emission reduction measures.

Analysis of the Temporal and Spatial Pattern and Convergence Characteristics of High-Quality Sustainable Economic Development of Urban Agglomeration

Based on the sample data of 149 cities in ten major urban agglomerations from 2004 to 2019, the entropy method, Dagum Gini coefficient, and three-dimensional kernel density estimation method are used to calculate and describe the spatial pattern of the high-quality, sustainable economic development of these ten major urban agglomerations. We then use the spatial econometric model to estimate the β convergence trend within the urban agglomerations and among the urban agglomerations at different levels. Our main findings include the following: First, the urban agglomeration tends to develop a high-quality economy, but the gap between the urban agglomerations can be large. Second, although the gap within the overall group of urban agglomerations is expanding, the gap between high-quality individuals and the average is constantly shrinking; the gap between groups is still the leading cause of the spatial gap, with a contribution rate of 70.51%. Third, all urban agglomerations have an absolute and conditional β convergence trend, and the convergence speed presents the characteristics of “high level slow, low level fast”. Government intervention, financial development, urbanization, and human capital contribute to the high-quality, sustainable economic development of each urban agglomeration. There is a heterogeneous influence; there is also absolute and conditional β convergence among urban agglomerations at all levels, and the convergence rate presents a gradient characteristic of “third level > second level > first level”, and by balancing the financial relationships between city groups within each level, development differences can promote the dynamic coordination of high-quality, sustainable economic development rates.

An Effective Obstacle Avoidance and Motion Planning Design for Underwater Telescopic Arm Robots Based on a Tent Chaotic Dung Beetle Algorithm

As the underwater environment is complex, the existence of obstacles will produce a certain collision interference to underwater robot operations, which causes the overall path planning and time costs to increase. In this paper, we propose a Tent chaotic mapping and dung beetle hybrid algorithm (MDBO) application for trajectory optimal planning and effective obstacle avoidance for an underwater telescopic arm robot. The method invokes the unique obstacle avoidance habit and foraging optimization idea of the dung beetle algorithm. Introducing it into the chaotic Tent mapping idea prevents the dung beetle algorithm (DBO) from falling into local optimality and increases the coverage of a global search. Simulation results show that the MDBO algorithm exhibits strong optimization ability and stability when multiple algorithms are verified using eight test functions. The MATLAB test reflects the performance indexes of the six joints of the underwater telescopic arm, and compared with various algorithms, the MDBO algorithm has an obvious convergence trend and strong global search ability. The algorithm is applied to real underwater experiments to verify that the improved dung beetle algorithm has better obstacle avoidance ability and reduces trajectory planning time by 30%, which helps the underwater robot to complete motion planning.

Fertility transition of Han and ethnic minorities in China: A tale of convergence and variation

China's large and diverse population exhibits interethnic variations in fertility, socioeconomic status, and culture. This study comprehensively analyzes data from multiple population censuses and qualitative interviews to identify fertility transition dynamics among ethnic groups in China. Taken together, the quantitative and qualitative results reveal a rapid decline in total fertility rates among both Han and ethnic minorities and a trend of convergence across different ethnic groups over the past few decades; in addition, the findings also show significant heterogeneity within ethnic minority populations. Our analysis sheds further light on the crucial role of social context and macro-level factors in shaping individuals’ reproductive behaviors. The results offer insights into how policies may address low fertility rates in China.

Convergence and Disparities in Higher Education Fiscal Expenditures in China: A Regional Perspective

This research investigates the disparities and convergence in higher education fiscal expenditures across different regions in China. The study utilises Gini coefficient analysis and σ-convergence/β-convergence tests to quantify the extent of disparities and explore convergence trends over a twelve-year investigation period (2007–2018). The results shed light on the imbalances in resource allocation and provide valuable insights into the efforts required to achieve a more equitable distribution of fiscal resources for higher education. The findings reveal significant disparities in higher education fiscal expenditures between the Eastern, Central, Western, and Northeastern regions, with the Eastern region exhibiting the largest gap compared to others. Remarkably, the disparity between the Eastern and Central regions is even greater than that between the Eastern and Western regions, emphasising the need for targeted interventions to address regional imbalances. Over the study period, the gap between the Eastern and Central regions remained consistently higher than other regional disparities. Moreover, the research shows a general trend towards narrowing regional fiscal expenditure disparities, with the most pronounced convergence observed between the Central and Northeastern regions. The Western region exhibits slightly larger disparities than the Central and Northeastern regions, possibly attributed to greater fiscal policy support and lower student enrollments. Nevertheless, the fiscal expenditure gap between the Western and Central regions has shown a trend towards reduction. The study also explores absolute and conditional β-convergence, revealing notable convergence patterns in the Eastern and Central regions. However, the Western and Northeastern regions exhibit varying degrees of convergence, indicating the necessity for region-specific convergence mechanisms. To achieve a balanced allocation of financial resources for higher education across regions, the study recommends targeted fiscal policies, additional funding, and improved transparency and accountability. Policymakers should focus on enhancing convergence mechanisms to ensure a more equitable distribution of resources and foster the sustainable development of higher education throughout the country. While this research provides valuable insights, it is essential to consider other potential factors influencing fiscal expenditure disparities, such as policy orientation, economic disparities, and demographic structures, for a more comprehensive understanding. Future research may benefit from qualitative investigations to further explore the complexities of higher education fiscal expenditure imbalances and identify effective policy interventions.

Multidisciplinary Research Mapping in Automation and Artificial Intelligence: A Bibliometric Analysis to Identify Science Convergence

The field of Automation and Artificial Intelligence (AI) has witnessed rapid evolution, marked by interdisciplinary collaborations and groundbreaking advancements. This bibliometric analysis delves into the multidisciplinary research landscape within Automation and AI, aiming to identify science convergence and key trends. Utilizing a comprehensive dataset, we employed co-authorship analysis, citation analysis, keyword analysis, temporal analysis, and VOSviewer visualizations to map the dynamic landscape of Automation and AI research. Our analysis revealed extensive interdisciplinary collaboration among researchers from diverse domains, highlighting the role of cross-disciplinary innovation in advancing the field. Influential authors and highly cited papers were identified, emphasizing the impact of key contributions. Dominant research themes, such as machine learning, ethics in AI, and AI applications in healthcare, emerged from keyword analysis, reflecting the field's evolving priorities. VOSviewer visualizations provided clear representations of science convergence, showcasing the interconnectedness of disciplines like computer science, engineering, ethics, and economics. Interdisciplinary hubs and bridges were identified, underscoring the importance of cross-disciplinary research in shaping the future of Automation and AI. The findings of this analysis offer valuable insights for researchers, policymakers, and practitioners, providing a foundation for enhanced collaboration, ethical considerations, innovation in healthcare, and tailored education and training programs to meet evolving demands.

Annual wages in the Kingdom of the Two Sicilies from 1800 to 1860 and the beginning of the Italian regional divide

Abstract This study presents new urban and rural annual wage series for the South of Italy before the unification. The use of annual wages overcomes the intrinsic biases of daily wages and mirrors the effective living standards of unskilled workers making regional comparisons more reliable. The comparisons with existing analogous series for the centre and the north of the country show a divide at the time of Italy’s unification in line with the majoritarian view expressed in earlier studies. However, unexpected convergence trends emerge at the urban level, mostly before 1840. Instead, rural areas consistently displayed divergence that has been worsening since 1850.

The Interaction between the Investment Behavior of Transnational Corporations and the International Political and Economic System

With the development of economic globalization and world modularization, more and more economic activities transcend national boundaries, making the connection between the organic economic whole formed through foreign trade, capital flow, technology transfer and other activities become closer. Transnational Corporations (TNCs) come into being in this context, making the global convergence trend of the economies of the world in production, distribution, exchange and consumption links more obvious. Because TNCs usually conduct economic activities in two or more countries, their strong economic power will inevitably affect the economic, political, cultural and social life of the country. On the one hand, the investment and business behavior of TNCs will have a series of effects on the local economy of the host country, and in some economic fields, TNCs can even interfere with the political order of the host country. On the other hand, in the process of globalization, transnational corporations themselves will also be affected by the international political and economic situation.

Transatlantic strategic convergence on China and the scramble for critical minerals

Purpose The purpose of this paper is to investigate the trend of transatlantic strategic convergence and policy coordination in response to Chinese trade practices and technological competition, specifically in the critical minerals sector. Design/methodology/approach The research draws on primary and secondary source material to identify evidence of and examine the drivers, manifestations and prospects for effective US-European efforts to advance the shared aim of reducing vulnerabilities in critical minerals supply chains. Findings The interests of the USA and Europe would be best served by prioritizing their own security, diversification and resilience strategies while seeking areas of common ground and constructive engagement with China. Research limitations/implications The research offers a fresh perspective on the growing alignment and persistent gaps in US and European perspectives on China’s rising influence and assertive behavior. Originality/value The research highlights the vital role of critical minerals in national security, economic competitiveness, technological advancement and sustainable resource management. It underscores the shared recognition on both sides of the Atlantic that securing a stable supply of critical minerals – essential for maintaining strategic capabilities, driving innovation and ensuring long-term economic prosperity – necessitates tighter transatlantic coordination as well as constructive engagement with China.

Convergent trends and spatiotemporal patterns of Aedes-borne arboviruses in Mexico and Central America.

Aedes-borne arboviruses cause both seasonal epidemics and emerging outbreaks with a significant impact on global health. These viruses share mosquito vector species, often infecting the same host population within overlapping geographic regions. Thus, comparative analyses of the virus evolutionary and epidemiological dynamics across spatial and temporal scales could reveal convergent trends. Focusing on Mexico as a case study, we generated novel chikungunya and dengue (CHIKV, DENV-1 and DENV-2) virus genomes from an epidemiological surveillance-derived historical sample collection, and analysed them together with longitudinally-collected genome and epidemiological data from the Americas. Aedes-borne arboviruses endemically circulating within the country were found to be introduced multiple times from lineages predominantly sampled from the Caribbean and Central America. For CHIKV, at least thirteen introductions were inferred over a year, with six of these leading to persistent transmission chains. For both DENV-1 and DENV-2, at least seven introductions were inferred over a decade. Our results suggest that CHIKV, DENV-1 and DENV-2 in Mexico share evolutionary and epidemiological trajectories. The southwest region of the country was determined to be the most likely location for viral introductions from abroad, with a subsequent spread into the Pacific coast towards the north of Mexico. Virus diffusion patterns observed across the country are likely driven by multiple factors, including mobility linked to human migration from Central towards North America. Considering Mexico's geographic positioning displaying a high human mobility across borders, our results prompt the need to better understand the role of anthropogenic factors in the transmission dynamics of Aedes-borne arboviruses, particularly linked to land-based human migration.

A Trend of AI Conference Convergence in Similarity: An Empirical Study Through Trans-Temporal Heterogeneous Graph

Publishing the research works on academic publications is an important part of the scientific process. Since the development of computer science research is very fast, researchers tend to publish the research works in a fast way, such as conferences whose review processes are faster than the journals. In the past decades, one conference usually focuses on a specific research field and the topic or method overlap between conferences is low. We have noticed that, in recent years, some topics or methods which were once studied in a small number of specific research fields have become popular in many other fields. Naturally, we come up with two research questions: (1) Do the conferences indeed become similar? and (2) How do conferences become similar? In this paper, we first use a trans-temporal heterogeneous graph network to model academic conferences in recent 20 years. Due to the large number of conferences, we categorize these conferences into 6 research fields for brevity. Then, we first quantitatively and qualitatively assess “Do the research fields become similar?” and then focus on exploring “How do research fields become similar?”. From the result, we find the reason for the research fields in computer science become similar is that AI becomes pervasive and researchers tend to apply the machine learning methods to different application fields. Since the methods become universal between different research fields, researchers should pay more attention to advanced information in other fields to motivate more interdisciplinary works. To assist the researchers to explore related interdisciplinary advanced information, it is crucial to measure the cross-field impact of papers using the citation information and recommend the paper which has a high cross-field impact on the related researchers. As for the newly published papers which do not have any citations, we also propose a cross-field impact prediction model to recommend the cutting-edge research works to related researchers accurately. Experiments conducted on real-world datasets verify the effectiveness of the proposed method.