Multidimensional Evolution Research Articles

Research on the presentation of the national image of China in the German magazine APuZ

<p class="MsoNormal" style="text-align: justify;">This study examines the portrayal of China in the German Federal Centre for Political Education magazine APuZ (<span style="font-family: 宋体;">“</span>Politics and Current Affairs<span style="font-family: 宋体;">”</span><span style="font-family: Times New Roman;">in German</span>) from 2020 to 2023. The objective is to observe and describe the manner in which the magazine portrays the image of China. A targeted literature analysis reveals the multi-dimensional evolution trend and dynamic distribution of China's image in the magazine. The study revealed that the magazine presents a comprehensive portrayal of China's political, economic and cultural images, incorporating descriptions of China's policies and international positions from a traditional Western perspective. The content of the magazine reveals changes in the image of China from the perspective of German official media, as well as the diversity of Chinese perceptions among academics and intellectuals in the German-speaking world. In summary, China is depicted as a politically traditional emerging communist power, an economically globally connected and rapidly developing country, and a culturally rich and profound country. The study aims to provide new insights into the formation of China's image in Sino-German cross-cultural communication and to contribute to the existing academic literature on the subject.</p>

Multidimensional evolutionary analysis of China’s BIM technology policy based on quantitative mapping

ABSTRACT In the wave of the digital era, Building Information Modelling (BIM) has emerged as the primary tool for intelligent construction and digital transformation in the Architecture, Engineering, and Construction (AEC) industry. China's focus on policy guidance for the dissemination and application of BIM technology has promoted high-quality development and technological innovation in the AEC industry. However, few studies have described the multi-dimensional evolution picture of BIM technology policy in China, and lack of effective grasp and prediction of policy evolution law and future direction. An improved policy quantitative analysis framework is built using natural language processing (NLP) based on deep learning in this paper. The dynamic characteristics of 242 Chinese BIM technology policy documents from 1998 to 2022 in the PKULAW database are quantitatively analyzed by the framework, and the four-stage integrated evolution of Chinese BIM technology policy is quantitatively mapped in a visual form through network analysis and dynamic topic model (DTM). Furthermore, this research conducts a comprehensive discussion from three dimensions, providing valuable insights for policy makers and industry participants in China. The findings contribute to the deep integration of policy optimization and the construction industry, while also aiming to share China's experience in BIM technology governance with other countries and regions. Ultimately, this study seeks to support the global AEC industry in its digital transformation and development by offering valuable knowledge and perspectives.

A multi-dimensional evolution modeling method for digital twin process model

Digital twin, visually monitor the dynamic evolution process of part cutting in a digital way, which has attracted widespread attention in smart manufacturing. Under this background, creating a high fidelity and evolvable virtual model has become the key to putting digital twin into practice in the manufacturing field. However, the 3D process model pays scant attention on the dynamically machining process, multi-dimensional evolutionary machining feature, and not even the coupling machining semantics. How to realize the evolution process model creation from the perspective of semantics dimension, time dimension, and feature dimension, an evolutionary concurrent modeling method is proposed based on the process knowledge and machining feature. Firstly, the geometric evolution process of typical features is analyzed, and the association mechanism between processing semantics and evolution features is constructed. Secondly, the mathematical model of the evolution feature is constructed based on the part processing information, and the creation method of processing semantics-driven evolutionary feature is proposed. Thirdly, the association mechanism of "machining semantics-process parameters-geometric features" is built, and the dynamic evolution process model which achieve synchronous evolution with the processing is created based on machining semantics. Finally, a case study is carried out with typical processing feature. The results show that the multi-dimensional evolution modeling of the digital twin process model can realize the synchronous visual monitoring and simulation of product processing. The correlation based on semantic features can improve the efficiency and quality of process model construction and provide solutions for dynamic analysis and decision-making of smart process design.

Towards sustainability: The impact of the multidimensional morphological evolution of urban land on carbon emissions

The examination of explicit and implicit urban land forms and their impact on carbon emissions (CEs) can effectively balance economic development with environmental sustainability. Prior studies have only considered individual factors, such as the scale and structure of urban land use (ULU), without accounting for its multidimensional evolution. To address this limitation, we analyzed data from 44 cities around the Bohai Sea between 2000 and 2020, developing a land-use index system that includes urban land quality. Our results present a nuanced view of the urban land use patterns (ULUPs) in the Bohai Rim region over the 20-year period. We found that all indices of ULUPs demonstrated a trend of improvement. Specifically, the structural pattern index (TS) was typically high, with values ranging between 0.65 and 0.80. The quality pattern index (TQ) witnessed the most significant growth. In contrast, the scale pattern index (TA) and the layout pattern index (TP) registered a relatively lower and slower-paced growth. Simultaneously, we observed an intriguing pattern in the CEs in the Bohai Rim region - an initial surge was followed by a considerable slowdown, as the growth rate plummeted from 88% to a mere 3%. There were noticeable regional variances, with the Beijing-Tianjin-Hebei region registering a lesser increase compared to the Liaodong Peninsula. As our study progressed, we uncovered distinctive and evolving influences of the land use morphology factors on the Bohai Rim region's CEs. A progressively rising number of factors emerged as significantly influential, with their suppressive impact on CEs becoming increasingly potent. Notably, land allocated to public service facilities, green spaces, and squares was found to be associated with reduced CEs. Moreover, our study reveals a clear link between quality optimization of ULU and effective restraint on CEs, whereas larger scales of urban land result in increased CEs. Our findings also show that a continuous and stable optimization of the multidimensional pattern of urban land, accompanied by a relatively stable optimization path, can favorably suppress CEs. This confirms the critical role of strategic land use management in controlling CEs. Our paper provides policy guidance for creating low-carbon, sustainable cities through efficient leveraging of land resources.

Transforming Commerce: A Bibliometric Exploration of E-Commerce Trends and Innovations in the Digital Age

The digital era has ushered in a transformative shift in commerce, culminating in the rise of e-commerce as a dominant force in the global market. This study employs a comprehensive bibliometric analysis, utilizing VOSviewer, to delve into the intricate fabric of e-commerce trends and innovations within the context of the digital age. Through the exploration of publication trends, co-authorship networks, keyword clustering, citation patterns, and emerging technologies, this research maps the intellectual terrain of e-commerce research. The findings illuminate the multidimensional evolution of e-commerce, from personalized mobile experiences to the integration of cutting-edge technologies. The analysis highlights pivotal contributions, collaborative dynamics, and emergent themes, providing a holistic understanding of how commerce has been reshaped in the digital age.

Multidimensional Evolution and Driving Factors of Securities Firms’ Collaborative Bond Joint Underwriting Networks in China: A Comprehensive Analysis from 2011 to 2020

This study utilizes the joint bond joint underwriting data of China’s securities firms from 2011 to 2020 to systematically explore the evolutionary characteristics of China’s collaborative bond joint underwriting networks from temporal, topological, and spatial dimensions. By employing social network analysis, Ucinet, and ArcGIS, we construct a longitudinal network panel data to quantitatively analyze the driving factors and their underlying mechanisms. The findings reveal that, in terms of topological structure, China’s bond joint underwriting networks exhibit increasingly mature, active, balanced, and accessible features, with domestic securities firms such as China Securities Co., Ltd. emerging as the backbone and foreign-backed firms gradually fading. In the spatial dimension, urban collaboration presents a transformation from triangular to butterfly-shaped, quadrilateral, and ultimately multicore networks. At the regional scale, inter-regional collaboration is most extensive between the eastern regions, followed by eastern–central regions, with eastern–western and central–western regions relatively less engaged. At the urban scale, the central positions of Beijing, Shenzhen, and Shanghai are gradually strengthening, and their external radiation scope is expanding annually. The underlying mechanism driving this evolution is the increasing opportunities for securities firms to establish and adjust their cooperative relationships due to the maturing and active bond joint underwriting networks in China. To compensate for the opportunity cost of bond joint underwriting and to maximize collaboration benefits, securities firms need to select potential partners with close geographical proximity, similar business domains, larger underwriting scales, “friends of friends,” and “network star” status, thereby promoting the continuous evolution of China’s bond joint underwriting syndicates.

Schools of thought and industrial revolutions: the multidimensional evolution of value creation

Schools of thought and industrial revolutions: the multidimensional evolution of value creation

Spatiotemporal heterogeneity and driving mechanisms of water resources carrying capacity for sustainable development of Guangdong Province in China

Sustainable water resources utilization is of great significance for sustainable socioeconomic development. Water resources carrying capacity (WRCC), as an important measurement, plays a vital role in guiding regional water resources management and sustainable utilization. Existing studies on WRCC are abundant, but in-depth studies on the multidimensional evolution of WRCC and its driving mechanism are still lacking. Therefore, taking Guangdong (GD) Province as a case, this study is aimed to propose a systematical WRCC research framework and attempts to explore the spatiotemporal heterogeneity of regional WRCC and reveal its evolutionary driving mechanisms. First, a simplified and effective WRCC index system is constructed through the correlation and contribution rate analysis to remove redundant information. Then, a coupled model consisting of an improved technique for order preference by similarity to ideal solution (TOPSIS) and grey relational analysis model is proposed to accurately evaluate WRCC. Last, the geographically and temporally weighted regression (GTWR) and geographical detector models are introduced to perform the driving analysis of WRCC. Results show that (1) from 2009 to 2020, the WRCCs of GD Province and its 21 cities all show an upward trend with the order of the WRCCs from high to low being northern GD, Pearl River Delta (PRD), eastern GD and western GD. (2) The main driving forces of WRCC in GD gradually shift from social and economic development to economic and ecological development. (3) Municipal sewage treatment rate, proportion of the tertiary industry and GDP per capita are the key driving factors of the GD WRCC. This study provides a new perspective for regional WRCC improvement and is greatly helpful to make differentiated management measures.

Structural analysis and evolutionary exploration based on the research topic network of a field: a case in high-frequency trading

This study aims to systematically analyze the distribution dynamics of research topics and uncover the development state of the research in the specific field, which will provide a practical reference for developing professional subject knowledge services in the era of big data. The research topic network is constructed and analyzed using methods and tools of scientometrics. Basic statistics on network characteristics are performed to reveal the research status. Community detection, node ordering, and other steps are conducted to generate the evolutionary alluvial diagram. Then, relevant results are analyzed to explore the knowledge structure of the specific field and evolutionary context of research topics. Visualization analysis on the network structure of the latest period is executed to distinguish related concepts and predict the research trends. Taking high-frequency trading (HFT) as a case, this study achieves diversified scientometrics analysis of the research topic network and multi-dimensional evolution exploration of the relevant research topics in the specific field, which obtaining some knowledge insights. (1) Six major topics in HFT: liquidity & market microstructure, market efficiency, financial market, incomplete market, cointegration & price discovery, and event study. (2) The research focus about markets gradually transferred from international to emerging, meanwhile continuous attention to volatility/risk related issues. (3) The emphasis will change from theory to practice, technologies (big data, etc.) and theories (behavioral finance, etc.) will have more interaction with HFT. An effective research idea is proposed to reveal the knowledge structure of field and analyze the evolutionary context of research topics, which demonstrating the knowledge insights.

Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome‐scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence‐level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.

Dynamic changes and multi-dimensional evolution of portfolio optimization

Although there has been an increasing number of studies investigate portfolio optimization from different perspectives, few attempts could be found that focus on the development trend and hotspots of this research area. Therefore, it motivates us to comprehensively investigate the development of portfolio optimization research and give some deep insights into this knowledge domain. In this paper, some bibliometric methods are utilized to analyse the status quo and emerging trends of portfolio optimization research on various aspects such as authors, countries and journals. Besides, ‘theories’, ‘models’ and ‘algorithms’, especially heuristic algorithms are identified as the hotspots in the given periods. Furthermore, the evolutionary analysis tends to presents the dynamic changes of the cutting-edge concepts of this research area in the time dimension. It is found that more portfolio optimization studies were at an exploration stage from mean-variance analysis to consideration of multiple constraints. However, heuristic algorithms have become the driving force of portfolio optimization research in recent years. Multi-disciplinary analyses and applications are also the main trends of portfolio optimization research. By analysing the dynamic changes and multi-dimensional evolution in recent decades, we contribute to presenting some deep insights of the portfolio optimization research directly, which assists researchers especially beginners to comprehensively learn this research field.

Influence of multi-dimensional characteristics and evolution of industrial structure on carbon emissionsat provincial scale in China

Influence of multi-dimensional characteristics and evolution of industrial structure on carbon emissionsat provincial scale in China

Poisson reduced-rank models with an application to political text data

Summary We discuss Poisson reduced-rank models for low-dimensional summaries of high-dimensional Poisson vectors that allow inference on the location of individuals in a low-dimensional space. We show that under weak dependence conditions, which allow for certain correlations between the Poisson random variables, the locations can be consistently estimated using Poisson maximum likelihood estimation. Moreover, we develop consistent rules for determining the dimension of the location from the discrete data. Our main motivation for studying Poisson reduced-rank models arises from applications to political text data, where word counts in a political document are modelled by Poisson random variables. We apply our method to party manifesto data taken from German political parties across seven federal elections following German reunification, to make statistical inferences on the multi-dimensional evolution of party positions.

Multivariate bias corrections of climate simulations: which benefits for which losses?

Abstract. Climate models are the major tools to study the climate system and its evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last 2 decades, in order to adjust simulations according to historical records and obtain climate projections with appropriate statistical attributes. Most of the existing and popular BC methods are univariate, i.e., correcting one physical variable and one location at a time and, thus, can fail to reconstruct inter-variable, spatial or temporal dependencies of the observations. These remaining biases in the correction can then affect the subsequent analyses. This has led to further research on multivariate aspects for statistical postprocessing BC methods. Recently, some multivariate bias correction (MBC) methods have been proposed, with different approaches to restore multidimensional dependencies. However, these methods are not yet fully apprehended by researchers and practitioners due to differences in their applicability and assumptions, therefore leading potentially to different results. This study is intended to intercompare four existing MBCs to provide end users with aid in choosing such methods for their applications. For evaluation and illustration purposes, these methods are applied to correct simulation outputs from one climate model through a cross-validation method, which allows for the assessment of inter-variable, spatial and temporal criteria. Then, a second cross-validation method is performed for assessing the ability of the MBC methods to account for the multidimensional evolutions of the climate model. Additionally, two reference datasets are used to assess the influence of their spatial resolution on (M)BC results. Most of the methods reasonably correct inter-variable and intersite correlations. However, none of them adjust correctly the temporal structure as they generate bias-corrected data with usually weak temporal dependencies compared to observations. Major differences are found concerning the applicability and stability of the methods in high-dimensional contexts and in their capability to reproduce the multidimensional changes in the model. Based on these conclusions, perspectives for MBC developments are suggested, such as methods to adjust not only multivariate correlations but also temporal structures and allowing multidimensional evolutions of the model to be accounted for in the correction.

Approach to Numerical Implementation of the Drift-Diffusion Model of Field Effects Induced by a Moving Source

An approach to numerical implementation of the diffusion-drift model is described with application to problem of estimating field effects induced in an object by a concentrated moving source. The mathematical model is formalized using initial boundary-value problem for multidimensional evolution equation of convection-reaction-diffusion type. The computational algorithm is based on modification of the predictor-corrector scheme intended for solving the diffusion problem and approximation of the drift component by the Roberts–Weiss scheme. The mathematical model has been implemented in the Matlab software package. The results of computing experiments attendant to variations of the control model parameters are presented.

Experimental investigation on the evolution of plasma properties in the discharge channel of a pulsed plasma thruster

Pulsed plasma thrusters (PPTs) are an attractive form of micro-thrusters due to advantages such as their compactness and lightweight design compared to other electric propulsion systems. Experimental investigations on their plasma properties are beneficial in clarifying the complex process of plasma evolution during the micro-second pulse discharge of a PPT. In this work, the multi-dimensional evolutions of the light intensity of the PPT plasma with wavelength, time, and position were identified. The plasma pressure was obtained using an iterative process with composition calculations. The results show that significant ion recombination occurred in the discharge channel since the line intensities of CII, CIII, CIV, and FII decreased and those of CI and FI increased as the plasma moved downstream. At the center of the discharge channel, the electron temperature and electron density were in the order of 10 000 K and 1017 cm−3, respectively. These had maximum values of 13 750 K and 2.3 × 1017 cm−3 and the maximum temperature occurred during the first half-cycle while the maximum number density was measured during the second half-cycle. The estimated plasma pressure was in the order of 105 Pa and exhibited a maximum value of 2.69 × 105 Pa.

Neumann inhomogeneous initial-boundary value problem for the 2D nonlinear Schrödinger equation

This paper is the first attempt to give a rigorous mathematical study of Neumann initial boundary value problems for the multidimensional dispersive evolution equations considering as example famous nonlinear Schrodinger equation. We consider the inhomogeneous initial-boundary value problem for the nonlinear Schrodinger equation, formulated on upper right-quarter plane with initial data $$u({\mathbf {x}},t)\left| _{t=0}\right. =u_{0}({\mathbf {x}})$$ and Neumann boundary data $$u_{x_{1}}\left| _{\partial _{1}{\mathbf {D}}}\right. =h_{1}(x_{2},t),u_{x_{2}}\left| _{\partial _{2}{\mathbf {D}}}\right. =h_{2}(x_{1},t)$$ given in a suitable weighted Lebesgue spaces. We are interested in the study of the influence of the Neumann boundary data on the asymptotic behavior of solutions for large time. We show that problem admits global solutions whose long-time behavior essentially depends on the boundary data. To get a nonlinear theory for the multidimensional model. we propose general method based on Riemann–Hilbert approach and theory Cauchy type integral equations. The advantage of this method is that it can also be applied to non-integrable equations with general inhomogeneous boundary data.

A formally second order BDF ADI difference scheme for the three-dimensional time-fractional heat equation

ABSTRACTAlternating direction implicit (ADI) method is widely utilized and computationally efficient for numerical approximation of the multidimensional evolution equations. This paper mainly formulates and analyzes a formally second order backward differentiation formula (BDF) ADI difference scheme for the three-dimensional time-fractional heat equation. The time derivative is approximated by second order BDF scheme and the integral term is treated by means of second order convolution quadrature. A fully discrete scheme is constructed with space discretization by finite difference method. Two new inner products and corresponding norms are defined to analyze the scheme. The stability and convergence are derived by the energy method. Numerical experiment in total agreement with our analysis is reported. Comparisons between the present schemes and the existing ones are included, showing that the present algorithms exhibit better performances than the existing ones.

Multivariate stochastic bias corrections with optimal transport

Abstract. Bias correction methods are used to calibrate climate model outputs with respect to observational records. The goal is to ensure that statistical features (such as means and variances) of climate simulations are coherent with observations. In this article, a multivariate stochastic bias correction method is developed based on optimal transport. Bias correction methods are usually defined as transfer functions between random variables. We show that such transfer functions induce a joint probability distribution between the biased random variable and its correction. The optimal transport theory allows us to construct a joint distribution that minimizes an energy spent in bias correction. This extends the classical univariate quantile mapping techniques in the multivariate case. We also propose a definition of non-stationary bias correction as a transfer of the model to the observational world, and we extend our method in this context. Those methodologies are first tested on an idealized chaotic system with three variables. In those controlled experiments, the correlations between variables appear almost perfectly corrected by our method, as opposed to a univariate correction. Our methodology is also tested on daily precipitation and temperatures over 12 locations in southern France. The correction of the inter-variable and inter-site structures of temperatures and precipitation appears in agreement with the multi-dimensional evolution of the model, hence satisfying our suggested definition of non-stationarity.

Financial strategy optimization of Municipal solid waste clean incineration power generation based on multi-agent evolutionary game model

Waste incineration has gradually become the mainstream way of municipal solid waste treatment. Government in China has brought out various financial policies to provide guide and support, but there are still some enterprises that do not incinerate waste according to the cleaner standard. In view of the insufficient guidance of the current financial strategy, the paper firstly constructs a multi-dimensional evolution game model of government, enterprise and residents, secondly analyzes the stable solution and convergence interval of enterprise cleaner incineration behavior strategy. Finally, the optimization proposal of financial strategy has been put forward. The result shows that the government should choose financial strategy according to the development level of cleaner incineration behaviour taken by enterprises, to improve the overall performance of social garbage disposal.