Stages Of Integration Research Articles

Spatiotemporal integration of contextual and sensory information within the cortical hierarchy in human pain experience.

Pain is not a mere reflection of noxious input. Rather, it is constructed through the dynamic integration of current predictions with incoming sensory input. However, the temporal dynamics of the behavioral and neural processes underpinning this integration remain elusive. In the current study involving 59 human participants, we identified a series of brain mediators that integrated cue-induced expectations with noxious inputs into ongoing pain predictions using a semicircular scale designed to capture rating trajectories. Temporal mediation analysis revealed that during the early-to-mid stages of integration, the frontoparietal and dorsal attention network regions, such as the lateral prefrontal, premotor, and parietal cortex, mediated the cue effects. Conversely, during the mid-to-late stages of integration, the somatomotor network regions mediated the effects of stimulus intensity, suggesting that the integration occurs along the cortical hierarchy from the association to sensorimotor brain systems. Our findings advance the understanding of how the brain integrates contextual and sensory information into pain experience over time.

THE PHENOMENON OF EURASIAN ECONOMIC UNION LAW

The purpose of the article is to comprehensively analyze the definition “The Law of the Eurasian Economic Union” and determine its place in the international law system. Research methods: historical-legal, axiological, functional and contextual approaches of comparative legal method, formal-legal method. The authors consider the genesis of Eurasianism as a historical phenomenon and philosophical direction, analyze the law-forming social factors that form the basis for the Eurasian law development. They conclude that Eurasian integration has traditionally been free and legal and the system of Eurasian law is based on the postulates of both Western and Eastern influences. These are the rule of law, collectivism and the priority of natural relations within the group. The law-forming factors that influenced the creation of the EAEU and its law are peculiar and fundamentally different from the factors and prerequisites characteristic of other integration economic formations. The EAEU was created by the states that had previously been part of a single state; its statutory documents, along with taking into account the peculiarities of European law, also took into account the confessional and civilizational identity of Eurasianism. Formally, at this stage of Eurasian integration, the EAEU law is a sub-branch of international economic law and represents a system of principles and norms in the sphere of international communication of Eurasian states, created to regulate public relations on the creation (liquidation) of regional associations, cooperation of a regional association with other international organizations, its participants and structures in the spheres established by EAEU statutes.

Consumption‐Based Emissions of African Countries: An Analysis of Decoupling Dynamics and Drivers

AbstractFormulating equitable climate policies should not overlook the challenges faced by less developed regions. African countries are at a crucial stage of economic development and deeper integration into global trade. Therefore, understanding their carbon footprints (i.e., consumption‐based CO2 emissions) is essential for crafting a sustainable development pathway for Africa and developing comprehensive and fair climate policies. Here, we investigate consumption‐based CO2 emissions in 55 African economics using a new Multi‐Regional Input‐Output model called “EMERGING” for 2015–2019; we also analyze the impacts of global trade participation on emissions, the decoupling status of emissions and economic, and hidden influencing factors. Results show that 65% of African countries experienced rapid growth in consumption‐based emissions, with an average annual growth rate of 6.4%. Significantly, 87% of African countries are net emissions importers, predominantly attributed to their trade relations with other developing countries (i.e., South‐South trade), a condition characterizing 68% of all trade interactions; The embodied carbon in imports is primarily concentrated in the transportation, petroleum refining, metal products, and machinery sectors. The decoupling analysis indicates that 15 countries strongly decoupled from production‐based carbon emissions, and 14 from consumption‐based; however, only 9 have concurrently achieved decoupling in both domains of emissions. Optimizing the carbon emission efficiency of final demand, particularly within the tertiary sector, is a key for successful decoupling and emissions reduction. The findings provide essential insights from consumption‐based emissions that could guide more effective, targeted climate policies contributing to the mitigation of climate impacts and fostering sustainable development in African nations.

Evidence-Informed Provision of Doxycycline Post-Exposure Prophylaxis for Prevention of Bacterial Sexually Transmitted Infections.

Doxycycline post-exposure prophylaxis (doxy-PEP) reduces the risk of bacterial sexually transmitted infections (STIs) among men who have sex with men and transgender women. In the United States, doxy-PEP is in an early stage of integration into clinical practice, and national guidelines for its use were recently released. The goal of this manuscript is to provide practical guidance for clinicians who are considering or currently prescribing doxy-PEP. We address five clinical questions using post hoc analyses of data from the DoxyPEP randomized controlled trial and discuss the potential implications and limitations of each question with the goal of informing clinical practice and implementation of doxy-PEP programs. The questions address patient eligibility criteria for doxy-PEP, the expected benefit and associated doxy-PEP doses for the average patient, the initial number of doses prescribed, and laboratory monitoring of persons taking doxy-PEP.

Challenges to implementing AI in health care

Abstract Germany, renowned for its robust healthcare system, finds itself at the early stages of AI integration. This is a reality for many European countries, emphasizing that, despite general technological prowess, AI adoption remains in its absolute infancy. However, in Germany there are areas where AI shows promise, notably in fields like radiology. Beyond such niche applications though, Germany grapples with a significant challenge - the scarcity of robust data essential for meaningful AI utilization, particularly in the realm of public health. While these insights are specific to the German context, understanding the intricate hurdles impeding widespread AI implementation is a meaningful takeaway for countries around the world. In order to effectively leverage new technologies, there is an acute need for advancements in data infrastructure and governance to unlock the full potential of AI in healthcare. Overcoming barriers and charting a path towards a future where AI serves as a transformative force in German healthcare, could one day lead to enhanced system efficiencies and patient experiences, but for now we have to be thoughtful about the realities of implementing AI within current existing health system infrastructure.

Does the integration of digital and real economies promote urban green total factor productivity? Evidence from China

Enhancing urban green total factor productivity (UGTFP) is of great significance in driving the green transformation of cities. The integration of the digital economy and real economy (IDR) offers a novel approach to improving UGTFP. This paper constructs a theoretical analytical framework to examine the impact of IDR on UGTFP. Utilizing a range of econometric models and Chinese urban data, this paper empirically analyzes the influence, mechanism, and spatial spillover effect of IDR on UGTFP. The findings reveal that China's IDR is currently at a stage of low integration, exhibiting significant regional disparities, with a spatial distribution pattern declining from east to west. IDR can significantly enhance UGTFP, and the conclusion is supported by robustness tests. Heterogeneity analysis indicates that the positive effect of IDR on UGTFP is more pronounced in the eastern and western regions, as well as in areas with a large Internet scale. The mechanism test suggests that economic agglomeration can significantly amplify the promotional effect of IDR on UGTFP. IDR can boost UGTFP by elevating innovation levels and optimizing labor resource allocation. Further exploration reveals a significant positive spatial spillover effect of IDR on UGTFP. These insights not only offer a practical pathway for green transformation but also provide empirical evidence for policymakers to formulate scientific policies, enhance coordination, and improve data governance, thereby facilitating high-quality and green economic development.

Individual refresh ability modulates irrelevant speech effect in reading: Evidence from eye movements

This study randomly recruited 245 college students aged between 18 and 21 years old who had never studied Spanish and selected 60 subjects (30 with high refresh ability and 30 with low refresh ability) based on their scores on a refresh ability test. The mixed experimental design of 2 (refresh ability: high, low) ×3 (background sound: meaningful, meaningless, silent) was adopted. The Eyelink1000 eye tracker was used to record the eye movement data of the subjects when reading the sentences of scientific papers. At the same time, meaningful (news broadcasting) or meaningless (Spanish) background sounds are played. Based on this, the moderating effect of individual refresh ability with the impact of irrelevant speech was investigated. According to the results, the interaction between refresh ability and the meaningful background sound condition was significant, and it had an impact on the totalreading time, gaze count, regression count, total fixation time, and regression-path duration. However, the interaction with the meaningless background sound condition was not significant. The results showed that: (1) refresh ability significantly regulates the irrelevant speech effect in reading, compared with individuals with high refresh ability. The meaningful background sounds have a greater negative impact on the reading of individuals with low refresh ability; (2) the regulating effect of refresh ability occurs in the late stage of vocabulary processing and semantic integration. The results supported the attentional capture theory and found that the irrelevant speech effect in Chinese reading is modulated by individual refresh ability, which mainly acts on semantic components in late reading processing.

Historical and legal aspects of corporate governance in LLCs according to the legislation of Poland and Ukraine

The article provides a detailed examination of the evolution of corporate governance in Ukraine and Poland, focusing on the development of legislation in both countries. The author analyzes the historical development of corporate structures, tracing their evolution from antiquity and the Middle Ages to the present day, highlighting key stages and changes in legal norms regulating corporate governance in Poland and Ukraine. The article begins with a review of early forms of corporate structures in ancient civilizations and tracks their development through significant historical periods in Europe. The stages of corporate governance development in Poland and Ukraine during the 19th and 20th centuries are separately examined. In Ukraine, the formation of corporate governance was constrained by the legal norms of the Russian Empire, and only with the transition to a market economy in the 1990s a significant development of corporate forms was noticed, particularly it concerned limited liability companies (LLCs). In contrast, Poland’s development of corporate law was marked by intense changes, especially after the fall of the communist regime and accession to the European Union, which facilitated the eligibility of Polish corporate law according to European standards. The author provides a detailed analysis of the key stages of legal changes in both countries, including the codification of corporate law, adaptation to European norms, and the challenges encountered at each stage. The article also explores the impact of economic and political changes on the development of corporate governance in both countries, emphasizing that although the initial stages of corporate governance development in Ukraine and Poland were somehow similar due to geographical and historical closeness, their further development differed significantly. Poland went through stages of intensive reforming and EU integration, which substantially influenced the modernization of its corporate law. Meanwhile, significant changes in corporate governance in Ukraine began only in the 1990s and continue to modern days, facing certain challenges and obstacles on the way to full integration into European legal standards. The article reveals both common and different features of the evolution of corporate law in these countries. The findings provide a deeper understanding of the impact of historical and political factors on the formation of modern corporate practices and norms in both countries. The author concludes that despite the fact that both countries started moving into corporate governance from similar foundations, their subsequent development was significantly different because of the influence of both internal and external reasons. Poland has managed to successfully adapt its legislation according to European standards, while Ukraine is still in the process of transformation. This highlights the necessity for continued reforms in Ukraine, particularly for integration into the European legal space, which is critical for ensuring the stability and transparency of corporate governance in the country.

Analysis and Research on Intelligent Logistics Data under Internet of Things and Blockchain

ABSTRACT To explore the security performance of logistics data transmission in an Intelligent Logistics System (ILS) under the Internet of Things (IoT) and Blockchain (BC), IoT is introduced to optimize the logistics transportation process. First, BC is adopted to improve the ILS under IoT, and ILS under IoT combined with BC is established. Next, an analysis of the relationship between the information service provider and the transportation provider in the logistics transaction process is conducted to perform data analysis and to establish an incentive mechanism for logistics transportation under BC. Finally, the simulation analysis of the constructed model is implemented. The results reveal that the constructed model is highly efficient. It can effectively solve the traceability problem of logistics transaction information, and the calculation cost is stable within 1,000 ms. In the comparative analysis of system transmission performance, the model exhibits outstanding results in transmission latency, consistently maintaining an average delay of approximately 350 ms. This stability is primarily attributed to the seamless integration of BC technology, which optimizes data packet transmission paths and mitigates waiting time. Furthermore, the system efficiently manages high data volumes while upholding robust data processing capabilities, owing to the optimization of smart contracts and decentralized storage mechanisms. As for energy consumption, the model notably reduces overall energy usage within the logistics network by curtailing unnecessary data transmission and computation, particularly in mobile nodes and wireless communication channels. The proposed algorithm excels across various transmission metrics, including transmission delay, data successful acceptance rate, network throughput, and energy consumption are all shown to be the best transmission performance of the proposed algorithm, in which the data successful acceptance rate is close to 1. As a result, the developed ILS model not only guarantees low latency performance but also demonstrates high data transmission security, facilitating more efficient and precise real-time information transmission. In this study, within the ILS framework, the relationship between information service providers and transportation service providers is established through BC, and an incentive mechanism is constructed. However, some shortcomings in this incentive mechanism are recognized. For instance, during the initial stages of BC integration, there are issues of information asymmetry concerning factors such as credit, transportation capacity, and effort level. Moreover, the open access policy of couriers in the model raises concerns, as the access policy itself constitutes sensitive information, which may result in privacy breaches. Hence, as part of future work, the introduction of attribute encryption schemes is planned to obfuscate access policies, thereby safeguarding the privacy of logistics personnel’s access policies.

Unique Features of the EAEU Interregional and Crossborder Cooperation

The article is devoted to the analysis of the main distinctive features of interregional and cross-border cooperation at the current stage of integration in the EAEU. It is shown that today the development of interregional and cross-border cooperation in the association is considered to be one of the priorities that contribute to deepening and ensuring the dynamism of integration processes. The intensification of initiatives at the intergovernmental level in this field dates back to the 2020s. However, up to the present, despite quite a deep degree of economic integration of the EAEU members, the sphere of interregional and cross-border cooperation remains insufficiently institutionalized and is of recommendatory nature. The association lacks multilateral agreements and cooperation mechanisms aimed at developing interaction specifically at the regional level. The main emphasis in the analysis is made on the fact that the current state of interregional and cross-border cooperation is asymmetrical, which is revealed in the following aspects – unequal degree of importance of cross-border cooperation for the participating countries, heterogeneity of the scale and nature of interregional cooperation, unequal level of autonomy of regions in the member states, divergence in applied formats of interaction, significant regional divergence in socio-economic development. It is concluded that in order to overcome such asymmetry and fragmentation, it is essential to have flexible multilateral mechanisms as well as financial and information institutions, the activities of which will contribute to the development of horizontal ties and economic connectivity at the regional level

Evaluation of a Task-Specific Self-Supervised Learning Framework in Digital Pathology Relative to Transfer Learning Approaches and Existing Foundation Models

An integral stage in typical digital pathology workflows involves deriving specific features from tiles extracted from a tessellated whole-slide image. Notably, various computer vision neural network architectures, particularly the ImageNet pretrained, have been extensively used in this domain. This study critically analyzes multiple strategies for encoding tiles to understand the extent of transfer learning and identify the most effective approach. The study categorizes neural network performance into 3 weight initialization methods: random, ImageNet-based, and self-supervised learning. Additionally, we propose a framework based on task-specific self-supervised learning, which introduces a shallow feature extraction method, employing a spatial-channel attention block to glean distinctive features optimized for histopathology intricacies. Across 2 different downstream classification tasks (patch classification and weakly supervised whole-slide image classification) with diverse classification data sets, including colorectal cancer histology, Patch Camelyon, prostate cancer detection, The Cancer Genome Atlas, and CIFAR-10, our task-specific self-supervised encoding approach consistently outperforms other convolutional neural network–based encoders. The better performances highlight the potential of task-specific attention-based self-supervised training in tailoring feature extraction for histopathology, indicating a shift from using pretrained models originating outside the histopathology domain. Our study supports the idea that task-specific self-supervised learning allows domain-specific feature extraction, encouraging a more focused analysis.

Hydroxyapatite nano-pillars on TI-6Al-4V: Enhancements in cell spreading and proliferation during cell/surface integration.

Despite the attractive combinations of cell/surface interactions, biocompatibility, and good mechanical properties of Ti-6Al-4V, there is still a need to enhance the early stages of cell/surface integration that are associated with the implantation of biomedical devices into the human body. This paper presents a novel, easy and reproducible method of nanoscale and nanostructured hydroxyapatite (HA) coatings on Ti-6Al-4V. The resulting nanoscale coatings/nanostructures are characterized using a combination of Raman spectroscopy, scanning electron microscopy equipped with energy dispersive x-ray spectroscopy. The nanostructured/nanoscale coatings are shown to enhance the early stages of cell spreading and integration of bone cells (hFOB cells) on Ti-6Al-4V surfaces. The improvements include the acceleration of extra-cellular matrix, cell spreading and proliferation by nanoscale HA structures on the coated surfaces. The implications of the results are discussed for the development of HA nanostructures for the improved osseointegration of Ti-6Al-4V in orthopedic and dental applications.

Challenges and future prospects of EU Citizenship

Abstract This paper explores the evolution, rights, challenges, and future prospects of European Union (EU) citizenship. Tracing its origins from the early stages of European integration to the present day, the paper examines the development of EU citizenship rights and their impact on individuals’ lives within the EU. It delves into the rights and benefits conferred by EU citizenship, including free movement, social entitlements, and political participation. Furthermore, the paper discusses the challenges and controversies surrounding EU citizenship, such as citizenship acquisition, integration policies, and the implications of Brexit. Through case studies and comparative analysis, it offers insights into member state practices and legal precedents shaping EU citizenship. Looking ahead, the paper explores potential reforms, challenges, and opportunities for strengthening EU citizenship in the context of evolving European integration. Additionally, it underscores the importance of citizenship education and awareness in fostering civic engagement and promoting a sense of belonging among EU citizens. By providing a comprehensive analysis of EU citizenship, this paper contributes to the ongoing discourse on European identity and integration, with implications for policymakers, scholars, and citizens alike.

Development of a CT-assessed adiposity nomogram for predicting outcome in localized ccRCC.

This study aimed to develop and validate a computed tomography-based nomogram assessing visceral and subcutaneous adiposity for predicting outcomes in localized clear cell renal cell carcinoma (ccRCC). A cohort of 364 patients with pathologically confirmed ccRCC participated in this retrospective study, with 254 patients assigned to the training set and 110 to the validation set (a 7:3 distribution ratio). The adipose score (AS) was generated using the least absolute shrinkage and selection operator Cox regression. Subsequently, a nomogram was constructed by integrating the clinical independent predictor with the AS to predict disease-free survival (DFS) in localized ccRCC after surgery. The performance of the nomogram was compared with the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade, and Necrosis (SSIGN) score. In both the training and validation cohorts, the nomogram exhibited superior discrimination compared to SSIGN and UISS (C-index: 0.897 vs. 0.781 vs. 0.776 in the training cohort, and 0.752 vs. 0.596 vs. 0.686 in the validation cohort; 5year AUC: 0.907 vs. 0.805 vs. 0.820 in the training cohort, and 0.832 vs. 0.577 vs. 0.726 in the validation cohort). Decision curve analysis (DCA) revealed a superior net benefit across a wider range of threshold probabilities for predicting 5year DFS compared to UISS and SSIGN scores. The developed prognostic nomogram demonstrated high accuracy and overall superior performance compared to existing prognostic models.

Consistency of Youth Identity Construction in Media Texts: Development and Adaptation of Actor-Network Theory for Empirical Media Research (Сase Study)

The formation of youth identity in communication is on the agenda of many areas of humanitarian knowledge. We analyzed communication within the framework of an educational media project, which resulted in the construction of the expected identity. In the course of the analysis, we used a conceptual model of communication that combines the stages of identity construction in interaction with the environment and the actions of actors from the standpoint of the methodology of actor-network theory. A three-dimensional model of communication is presented for the first time, where actors and actants change the communicative process by their affordances and resistance. The obtained results allow to delineate the necessary actions of human and non-human actors at each stage of communication and identity integration, giving the opportunity to justify a new theoretical direction in the study of the problems of identity construction through media communication. The methodology requires further testing, but there are already positive results. The tendency of actualization of urban identity of young people depending on the focus on interaction with the heritage of the city and according to the stages of communication, according to the conceptual model, has been established. The article is intended for specialists in mass communication and journalism, and will also be useful for those interested in the ontological turn in humanitarian knowledge, actor-network theory, discourse and media text analysis.

ТЕОРИЯ НЕЧЕТКИХ МНОЖЕСТВ В ИНТЕЛЛЕКТУАЛЬНОМ АНАЛИЗЕ ДАННЫХ

The article is devoted to the application of data mining (KDD) and fuzzy sets to work with large amounts of data. The digital revolution has led to a significant increase in the volume and speed of data, which makes traditional analysis methods ineffective. Data mining covers the stages of purification, integration, transformation and extraction of knowledge from data. Fuzzy logic, which extends Boolean logic, allows for uncertainty and approximate reasoning, which makes it especially useful for processing complex and noisy data. The article discusses in detail the applications of fuzzy sets in tasks such as clustering, associative rule detection, data generalization, time series analysis, and web applications. Fuzzy logic helps solve problems related to incomplete or inaccurate data by providing more flexible and understandable solutions. The author also notes that the use of fuzzy sets improves the adaptation of systems to specific data requirements and features, which contributes to more accurate analysis and decision-making in areas such as healthcare, finance and telecommunications. These advantages make fuzzy sets an important tool for improving the efficiency of data mining and improving user interaction.

Applications of Machine Learning: Classifying Wheat Seeds through Computer Neural Networks

In the past few decades, the incompetent processes of traditional mechanical seed sorting have given rise to new, innovative methods, along with the integration of smart technology in agriculture. The present study created a multi-classification machine learning model that classifies wheat seeds based on given characteristics by giving highly accurate predictions for future samples based on given data. Due to various uses of different types of seeds, effective sorting is an integral preparatory stage for food processing. The dataset used in the study included seven features (directly measured characteristics of seeds), three labels (types of seeds), and around two hundred examples. The seven features include area, perimeter, compactness, kernel length, kernel width, asymmetry coefficient, and kernel groove. The first model was created through neural networks trained on all seven characteristics, reaching an overall accuracy of 0.94. Implementing a decision tree method, feature selection was applied to the data, which distinguished kernel groove, kernel width, and asymmetry coefficient as the three most important features. A new model was constructed with only the three selected features, which reduced the risk of overfitting, eliminated noise, and yielded a higher accuracy (0.96) than the previous model. Both multi-classification models used the softmax activation function and results were compared; feature selection noticeably reduced any inaccuracies, including categorical cross entropy loss. The main incentive of the study was to develop an effective model to sort wheat seeds (to replace traditional mechanical sorting) and provide reasonable recommendations for practical use through an analysis of feature selection.

DEEP LEARNING MODEL FOR DETECTING TERROR FINANCING PATTERNS IN FINANCIAL TRANSACTIONS

Terror financing remains a critical threat to global security, with illicit actors continually adapting their methods to evade detection. Traditional financial monitoring systems often struggle to identify the complex and covert patterns associated with terror-related transactions due to their reliance on predefined rules and statistical thresholds. This study introduces advanced deep learning models designed to detect terror financing patterns within vast datasets of financial transactions. We developed and evaluated several neural network architectures, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), to capture both spatial and temporal transaction features. The models were trained and tested on a dataset comprising anonymized financial transactions labeled for suspicious activities related to terror financing. Our deep learning models demonstrated superior performance over conventional machine learning approaches, achieving higher accuracy, precision, and recall in identifying suspicious transactions. Notably, the LSTM-based model excelled in detecting sequential transaction patterns indicative of layering and integration stages commonly used in terror financing. The results underscore the potential of deep learning techniques in enhancing the capabilities of financial institutions and regulatory bodies to combat terror financing. Implementing such models can lead to more proactive and effective monitoring systems that adapt to evolving illicit financing strategies.

Attention redirection transformer with semantic oriented learning for unbiased scene graph generation

Scene Graph Generation (SGG) plays an important role in scene understanding because all of the objects and relations in an image can be abstracted into a concise topological graph. Due to the complexity of visual scenes, including mutual occlusion between objects and semantic ambiguity, SGG is still a challenging task. Most of the existing models only focused on the context of a single object while contexts provided by paired objects are ignored. In this paper, we propose an Attention Redirection Transformer (ART) to extract pair-level contexts specifically, which is divided into an attention distraction stage and an attention integration stage. In this way, the attention of the model is forced to be redirected, which explores the implicit information in the background. In addition, to incorporate the semantic information of predicates, a Semantic Oriented Learning Module (SOL) is designed, which may assist in getting better textual semantics and also prompts cross-modal information fusion. At last, a self-diversity driven Dual Translation Embedding Module (DTM) is designed, which refines representations of subject and object and makes them distinct. Experimental results on the Visual Genome dataset demonstrate the effectiveness of our proposed method. Moreover, our method outperforms state-of-the-art methods on the mR@K metric. The source codes are released on Github: https://github.com/Nora-Zhang98/ART-SOL.

Research on Convergence Media Ecological Model Based on Blockchain

Currently, the media industry is in the rapid development stage of media integration, which has brought about great changes in content production mode, presentation form, communication mechanism, operation and maintenance management, etc. At the same time, it is also faced with problems such as difficult information traceability, declining industry credibility, low data circulation quality and efficiency, difficult data security and user privacy protection, etc. Utilizing blockchain’s characteristics can solve these problems that the media industry is currently facing. This paper designs a convergence media ecology model based on blockchain (CMEM-BC), focusing on the basic elements of the model, node operation and maintenance system, node management mechanism, value circulation mechanism, and storage mechanism, trying to establish a decentralized, traceable, and immutable convergence media ecosystem. On this basis, this paper summarizes the ecological framework and ecological model of CMEM-BC. Finally, the paper describes the verification of the effectiveness of CMEM-BC in key links through simulation experiments, verifying that CMEM-BC has high originality and is more suitable for the application of convergence media ecology through model analysis and comparison.