Earticle

본 연구는 뉴스 빅데이터 분석을 통해 한국 해상풍력 산업생태계의 형성과 진화 과정을 행위자, 담론, 공간의 세 차원에서 규명하였다. 기존 표준산업분류 체계가 신산업을 적시에 포착하지 못하는 한계를 극복하기 위해, 2010년부터 2025년까지 해상풍력 관련 뉴스 5,093건을 사용하여 개체명 인식(NER), 동시출현 네트워크 분석, LDA 토픽모델링 분석을 실시하였다. 분석 결과, 행위자 네트워크는 두산중공업 중심의 단일 허브 구조에서 오스테드, 에퀴노르 등 글로벌 개발사가 참여하는 다극 구조로 전환되고 있으며, 담론은 정책에서 기술개발로, 다시 주민수용성 등 시장 현안으로 이동하는 단계적 진화를 보인다. 공간적으로는 전남의 정책·인프라 중심, 울산의 부유식 기술개발 중심, 인천의 외국 개발사 주도 등 지역별로 차별화된 산업생태계가 형성되고 있다. 본 연구는 진화경제지리학의 경로 개발 개념을 한국 에너지 전환 맥락에서 실증적으로 검증하였으며, 비정형 빅데이터가 공식 통계의 한계를 보완하는 신산업 연구의 대안적 방법론임을 제시하였다.

This study examines the formation and evolution of South Korea’s offshore wind industrial ecosystem through news big data analysis, focusing on three dimensions: actors, discourses, and space. To overcome the limitations of the conventional Standard Industrial Classification system in capturing emerging industries in a timely manner, Named Entity Recognition, co-occurrence network analysis, and LDA topic modeling were applied to 5,093 offshore wind-related news articles published between 2010 and 2025. The findings reveal that the actor network has shifted from a single-hub structure centered on Doosan Heavy Industries to a multi-polar structure involving global developers such as Ørsted and Equinor. The discourse has evolved through distinct stages: from policy-focused themes to technology development, and subsequently to market issues including community acceptance. Spatially, differentiated industrial ecosystems are emerging across regions: Jeonnam characterized by policy and infrastructure development, Ulsan by floating offshore wind technology development, and Incheon by foreign developer-led initiatives. This study empirically validates the path development concept of evolutionary economic geography within the Korean energy transition context and demonstrates the methodological potential of unstructured big data as an alternative approach to complement official statistics in emerging industry research.

China’s traditional industry clusters meet challenges and opportunities as the global economy goes ups and downs. Following the trend of industry upgradation is a highlight in both theoretical and practical domain. This paper focuses on the principles of dynamic evolution of China’s industry clusters in order to pursuit and establish its enduring growing drive and competitive advantages. This paper gives research into dynamic evolution of cluster network, centering on its inherent relationship with the core enterprise transformation and the restructure flexibility of cluster members and defines “the transformation and up gradation of the core enterprises in cluster network”, finds out the dynamic and persistent effect of transformation and upgradation of the core enterprises on network resources and puts forward the dilemma of the dynamic evolution and upgradation of China’s cluster network.

This paper proposes a novel four-dimensional autonomous system which has complex chaotic dynamics behaviors and gives analysis of novel system. More importantly, the novel system can generate three-layer chaotic attractor, four-layer chaotic attractor, five-layer chaotic attractor, multilayer chaotic attractor by choosing different parameters and initial condition. We analyze the new system by means of phase portraits, Lyapunov exponent spectrum, fractional dimension and bifurcation diagram of the system. The four-dimensional autonomous system is totally different from the well-known systems in previous work. Linear feedback control methods are used to suppress chaos to unstable equilibrium. Numerical simulations are presented to show these results.

To solve the slow convergence speed, low precision in later period and tedious parameter setting of differential evolution when applied to complex optimization functions, an improved differential evolution algorithm (dn-DADE) based on dynamic adaptive strategy is proposed. Firstly, the elite solutions of current population are utilized in the new mutation strategy (DE/current-to-dnbest/1) to guide the search direction, and then these optional elite solutions tend to the global optimal solution in the late stage of evolution to balance the diversity of population and convergence speed. Secondly, the adaptive update strategies of scaling factor and crossover factor are designed for control parameter values self-adapting at different search stages, thus improve the stability and robustness of the algorithm. A set of 14 benchmark functions is adopted to test the performance of the proposed algorithm. The results show that dn-DADE algorithm has the advantages of remarkable optimizing ability, higher search precision, faster convergence speed and outperforms several state-of-the-art improved differential evolution algorithms in terms of the main performance indexes.

Portfolio is the core issue of financial investment. This article discusses the dynamic behavioral portfolio in an evolutionary perspective. This paper first give the market selection equation as a measure of the investor market evolution level to replace the mental account of investors return, considering the impacts of heterogeneous investors to the asset return, this paper establish an adaptation GARCH model to express the dynamic evolution of asset return and risk. And considering the non-linear relationship among assets, this paper chose Clayton Copula function to put the risk of each asset into a single unified security mental account, and establish the VaR model to control investment risk and obtained the optimal weights of behavioral portfolio in each investing period. Last this paper implemented the model of the dynamic behavioral portfolio based on the market evolution in the historical data, and gave the comparison results of the dynamic behavioral portfolio based on the market evolution, to the mean-variance model and the minimum variance model. Empirical result shows that the dynamic behavioral portfolio based on the Market Evolution has a very good investment effects.

This article analyzed the principle of theory and dynamic mechanism on distribution and manufacturing sector co-evolution. Empirical study was based on collaborative degree model and co-evolution mode, applying the data of year 1986-2010 distribution and manufacturing sector of China. The empirical study demonstrated that, there existed interaction relations between distribution and manufacturing sector. The combined system of distribution and manufacturing sector was cooperative system. An equilibrium stable solution was existed in system evolution. Study also found out, the synergy degree of combined system for distribution and manufacturing sector was relatively low, but in collaborative development status overall. Order degree of manufacturing sector was higher than distribution sector, but both parties showed significant consistency.

In this paper we propose a novel differential evolution algorithm based on self-learning, in order to improve the environment adaptive ability of the population in dynamic optimization. The proposed algorithm can monitor the environment changes using re-evaluation of individuals. We direct the population evolution based on the current best individual and another two random individuals, so that the convergence speed is faster and the diversity of the population is maintained. In this way we may reduce the influence from the frequent environment changes. Testing on six dynamic functions, we study the influences caused by period and dimensions. We also compared the proposed algorithm with existing algorithms, the experimental results show that our algorithm has a better environment adaptive ability and achieves better optimization result.

The extensive application of artificial intelligence-generated content (AIGC) has significantly improved the efficiency of content marketing but intensified the complexity and dynamics of consumer behavior alienation. Based on the technical acceptance model and information overload theory, this study observes the behavior log data of e-commerce, short video, and social platforms, integrates relevant literature research results, and constructs a dynamic evolution mechanism model of consumer alienation behavior . The study finds that: (1) perceived usefulness and perceived ease of use indirectly aggravate the level of information overload by improving the acceptance of personalized recommendations; (2) information overload is the core intermediary path driving the alienation of consumer behavior; and (3) the degree of content homogenization significantly strengthens the positive effect of recommendation acceptance on information overload. This study expands beyond the traditional static analysis framework, reveals the non-linear evolution law of “technology acceptance-cognitive overload-behavioral resistance,” and proposes the hierarchical dual-path intervention strategy of dynamic threshold early warning and algorithm transparency, thereby providing a theoretical and practical basis for the balance between AIGC technology efficiency and consumer subjectivity.

본 논문은 환경 변화에 대한 기업의 동태적 적응 메카니즘을 설명하는 대조적인 두 이론을 고찰하고, 두 이론 간에 보완적 연계의 필요성을 밝히는 것이 그 목적이다. 1980년대 까지 재구조화론은 기업의 성공과 적응을 설명하는 지배적인 접근이었다. 그러나 이 접근은 환경 변화에 대해 어떻게 기업이 적응하는지, 기업들 간의 적응의 성패가 왜 상이하게 나타나는지에 대해 설명하지 않는다. 즉 재구조화론은 기업의 학습과 적응에 대한 맥락 특수적인 해석 시각을 제공하지 못한다. 한편 1990년대 이후에 기업의 학습과 진화에 대한 학문적 관심은 기업의 동태적 경쟁력을 이끄는 기업 학습의 본질을 고찰하는 것에 초점이 두어져 왔다. 조직학습론은 조직 진화에 있어 지식, 학습 그리고 역량의 중요성을 강조한다. 하지만 이 접근 또한 급진적인 환경 변화에 대한 기업의 적응 메커니즘에 대해서는 효과적으로 설명하지 못하고 있다. 본 연구는 이러한 맥락에서 기업의 동태적 적응과 진화는 재구조화 과정과 점진적 및 급진적 학습 과정의 길함을 통해 나타나는 사실을 설명하고, 이를 토대로 기업의 진화와 적응을 효과적으로 설명하기 위해서는 두 이론을 보완적으로 연계한 통합적 이론 체계가 구축될 필요가 있음을 제안한다.

This paper debates on two different theoretical positions in explaining corporate adaptation. Until the 1980s, a restructuring perspective had dominated in explaining corporate success and adaptation. However, this perspective pays little attention to how firms adapt to environmental change and why some firms adapt successfully, while some others fail to adapt. Thus a restructuring perspective does not give insights into a context-specific explanation of corporate learning and adaptation. More recently, especially since the 1990s, academic focus on corporate adaptation and evolution has shifted towards exploring the nature of learning that leads to the dynamic competitiveness. A learning perspective emphasizes the influence of knowledge, learning and competence on corporate evolution. However, it reveals that this view is also less appropriate for explaining corporate adaptation in radical shifts in environment. In this context, the evolutionary theories of the firm need to seek to maintain a balance between two theoretical positions in order to understand more effectively the dynamic evolution and adaptation of the firm. This paper shows that the dynamics of corporate adaptation and evolution are an outcome of the mixture of perpetual processes of restructuring and learning, both continuous and discontinuous.

The cell ecology and spatial niche implicated in the dynamic and sequential process of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and subsequent invasive adenocarcinoma (IAC) have not yet been elucidated. Here, we performed an integrative analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to characterize the cell atlas of the invasion trajectory of LUAD. We found that the UBE2C?+?cancer cell subpopulation constantly increased during the invasive process of LUAD with remarkable elevation in IAC, and its spatial distribution was in the peripheral cancer region of the IAC, representing a more malignant phenotype. Furthermore, analysis of the TME cell type subpopulation showed a constant decrease in mast cells, monocytes, and lymphatic endothelial cells, which were implicated in the whole process of invasive LUAD, accompanied by an increase in NK cells and MALT B cells from AIS to MIA and an increase in Tregs and secretory B cells from MIA to IAC. Notably, for AIS, cancer cells, NK cells, and mast cells were colocalized in the cancer region; however, for IAC, Tregs colocalized with cancer cells. Finally, communication and interaction between cancer cells and TME cell-induced constitutive activation of TGF-β signaling were involved in the invasion of IAC. Therefore, our results reveal the specific cellular information and spatial architecture of cancer cells and TME subpopulations, as well as the cellular interaction between them, which will facilitate the identification and development of precision medicine in the invasive process of LUAD from AIS to IAC.

This paper presents the application of Differential Evolution (DE) algorithm to obtain a solution for Bid Based Dynamic Economic Dispatch (BBDED) problem including the transmission losses and to maximize the social profit in a deregulated power system. The IEEE-30 bus test system with six generators, two customers and two trading periods are considered under various bidding strategies in a day-ahead electricity market. By matching the bids received from supplying and distributing entities, the Independent System Operator (ISO) maximize the social profit, (with the choices available). The simulation results of DE are compared with the results of Particle swarm optimization (PSO). The results demonstrate the potential of DE algorithm and show its effectiveness to solve BBDED.

New media interactive installation art forms a living art system by integrating such elements as audience, artists, media, and environment through multi-layered interactive relationships. Specifically, this paper focuses on new media interactive installation art, exploring its background, principles of form composition, and how it expands and challenges traditional art. Research methods include extensive literature collection and analysis, categorized into theoretical research, application research, and historical development research. Additionally, specific case studies delve into new media artworks under different dimensions of interaction. A comparative analysis of immersive new media artworks further highlights the distinctive characteristics and appeal of immersive experiences. First and foremost, the first chapter outlines the historical background and formation process of new media interactive installation art. This chapter probes into the concepts of interaction in the technological machine era and the electronic information era, and their expression in the art field. Special attention is given to how early "dynamic" art appeared during these periods, introducing representative interactive art works such as Marcel Duchamp's "Rotary Glass Plates" and John Cage's "4'33''". In addition, it also explores and explains the "interactivity" of new media art. When it comes to the second chapter, it investigates the form expression of new media interactive installation art, introducing specific works in various interaction modes, and exploring the aesthetic value and method changes brought about by these works. This chapter analyzes the bidirectional composition between audience and works, the inter-audience fusion, and the spatial interaction between audience and environment, which reveals the importance of audience participation and interaction in new media interactive installation art. Examples include Nam June Paik's "TV Buddha" series, Paul Simon's "TV Dream" and "TV Vision", which illustrate how technology enables interaction between art and audience. New media interactive installation art transforms the aesthetic experience of the subject through multidimensional interaction models, offers important inspiration for artistic practice in everyday life, and demonstrates innovative potential in the fusion of art and technology.

비보강 조적조는 비균일 재료로 이루어진 합성재료에 가까우므로 그 거동이 하중종류와 구조물의 손상 정도에 따라서 매우 달라지게 된다. 본 연구에서는 여러 차례의 모의 지진을 받는 구조물의 손상거동이 어떻게 전개되어 가는지 살펴보는 간단한 방법을 제시한다. 특히, 시간영역 자료를 여러 구간으로 나누어서 주파수가 어떻게 변화해 가는지 살펴보는 부분 FFT방법을 이용하였다. 또한, 주파수와 강도와의 관계식을 이용하여 단자유도계의 이선형모델도 유도하였다. 본 연구에서 제시하는 이러한 방법들을 이용하여 비보강 조적조의 비탄성 성질들을 합리적으로 구할 수 있었다.

Unlike homogeneous material structure, the behavior of masonry structure is not perfectly elastic even in the range of small deformations because it is a non-homogeneous and anisotropic composite structural material, consisting of masonry units, mortar, and grout. This paper proposes a simplified way of investigating the evolution of the deformation and damage of the structure subjected to a series of successive ground motions with varying shaking. Especially, the most simple but useful algorithm of Fast Fourier Transformation (FFT) has been adopted to investigate the evolution of the deformation and damage of the structure tested on the shaking table. Moreover, the development of a hi-linear curve for an equivalent SDOF system which is obtained by exploiting the frequency and stiffness relationship was discussed. Finally, some important findings related to inelastic properties of the URM are summarized.

We consider organizational evolution in an endogenously changing selection environment. Following the majority of research on evolution in the biological realm, organizational scholars predominantly modeled selection environment as being exogenously endowed. We introduce a selection environment that can be shaped and modified by organizations themselves. In this endogenously dynamic environment, we examine the efficacy of two most representative evolutionary operators - mutation and imitation - in both exogenously and endogenously changing selection environment. We find that imitation fares quite differently depending on the type of selection environment while mutation is robust regardless of the environment. We further show that there is a complementary effect between mutation and imitation under the exogenously changing environment while there is no such effect under the endogenous setting. Overall, our study suggests that understanding the nature of evolutionary strategies for different selection regime is critical for organizational evolution.

Optimal Power dispatch is the short-term decision of the optimal output of a number of power generation facilities, to meet the system demand, with the objective of Power dispatching at the lowest possible cost, subject to transmission lines power loss and operational constraints. The operational constraint includes power balance constraint, generator limit constraint, and emission dispatch constraint and valve point effects. In this paper, Opposition based Differential Evolution Algorithm (ODEA) has been proposed to handle the objective function and the operational constraints simultaneously. Furthermore, the valve point loading effects and transmission lines power loss are also considered for the efficient and effective Power dispatch. The ODEA has unique features such as self tuning of its control parameters, self acceleration and migration for searching. As a result, it requires very minimum executions compared with other searching strategies. The effectiveness of the algorithm has been validated through four standard test cases and compared with previous studies. The proposed method out performs the previous methods.