Innovation Clusters Research Articles

The Impact of Global Value Chain Position on Emerging Industrial Cluster Innovation Efficiency in China

The Impact of Global Value Chain Position on Emerging Industrial Cluster Innovation Efficiency in China

Quantifying the Emergence of Basic Research Capabilities in Cluster Enterprises: An Analytical Framework Based on Information Entropy

This study looks at how basic research capabilities develop within enterprise clusters, focusing on the complex and adaptive nature of these systems. It builds a conceptual model using systems theory and applies information entropy to measure how much these capabilities have emerged. This study introduces an innovative application of information entropy to model and quantify the emergence of research capabilities within enterprise clusters, offering a novel framework for assessing research development. To dive deeper, China Pingmei Shenma Group (Henan, China) was used as a case study. A case study approach was used to gather empirical data. This case—focused on a state-owned enterprise cluster in China’s coal-based energy and chemical industries—highlights the key factors that influence research capability growth. These factors include support from external systems, how internal resources are used, and their renewal over time. From 2017 to 2022, the study tracked how the organization of research capabilities evolved over time by tracking changes in entropy, revealing the process of research development driven by both internal and external forces. The methodology involves measuring system entropy to evaluate the degree of orderliness and innovation performance, incorporating entropy generation and exchange metrics, which allows for a more precise understanding of system emergence and complexity. The interactions within the system, such as knowledge exchange, research collaboration, and external input from government subsidies or tax incentives, are modeled to track how they influence the system’s overall entropy. This study finds that the ability of an enterprise cluster to bring in external resources and reduce internal inefficiencies is critical for enhancing research capabilities. This model can help policymakers and enterprises in strategic decision-making, particularly in industries undergoing technological transformation. This framework also provides practical insights for improving research collaboration and innovation in enterprise clusters, especially in rapidly evolving industries like energy and chemicals.

Mining of primary raw materials as the critical foundation of 'sustainable' metals: a wicked problem for technology innovation clusters.

A transition to a more sustainable human-nature system is inextricably linked to raw materials production, if economic growth is to be maintained or increased by the emergence of new, energy- and metal-hungry technology innovation clusters. The dependence on mined raw materials is a wicked problem for societies vulnerable to negative ecological impacts and for global power bases wanting to secure access to an increasing array of feedstocks. We interrogate the issue of what constitutes a sustainable metal from a triple perspective: (i) the characteristics of ore deposits and the primary extractive operations that supply critical raw materials; (ii) the impediments for complex and interacting supply chains to maintain critical (and other) metals in use; and (iii) the lack of transparency in supply chains that makes it challenging for customers to avoid resources that have been produced by unsustainable and poor practices. We examine existing and emerging structures for resource management to explain the limits to the circular economy and what constitutes a meaningful systemic structure for primary production by responsible mining. We call for the inclusion of a standardized statement of the 'natural capital' embodied in R&D for technological materials as a means to create transparency about what constitutes a sustainable metal.This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

Towards transformative innovation ecosystems: a systemic approach to responsible innovation

ABSTRACT To address societal challenges, innovators committed to responsibility need to find ways to break away from unsustainable, or otherwise undesirable, path-dependencies in sociotechnical regimes. Such path-breaking innovation should not come at the expense of socioethical desirability. The emerging literature on responsible innovation ecosystems has focused on socioethical desirability but has neglected sociotechnical viability beyond a protected niche. Drawing on theoretical insights and concepts from the literature on Responsible Innovation, innovation ecosystems, and transition studies, we distinguish four types of ecosystems along the axes of desirability and viability and discuss examples of these types. We introduce the concept of transformative innovation ecosystems to refer to a type of ecosystem that combines desirability with viability. The concept is developed by bringing theoretical perspectives into conversation with empirical insights from the high-tech research and innovation cluster NeuroSys, which aims to create an ecosystem around brain-inspired computing.

Theoretical Thinking on the Development of New Quality Productive Forces According to Local Conditions in Heilongjiang Province

The new quality productive forces reflects the systematic remodeling and revolutionary transition of the old productive forces, which is the quality of productivity in line with the requirements of high-quality development. The formation of new quality productive forces can effectively energize the revitalization and development of Heilongjiang Province. From the perspective of the revitalization of the old industrial base in Northeast China, opportunities, advantages and challenges coexist in the development of new quality productive forces in Heilongjiang Province. Based on the current situation, the practical path for the development of new quality productive forces according to local conditions in Heilongjiang Province is proposed: Cultivate innovative thinking leading the development of new quality productivity, form the industrial base for the development and growth of new quality productivity, shape the innovation economic system for the development of new quality productivity, and build innovation clusters of new quality productivity.

Research on the Multilingual Talent Cultivation System Empowered by AI under the New Liberal Arts Context

This paper explores the AI (artificial intelligence) empowerment in foreign language education within the context of “New Liberal Arts” and its impact on a diversified foreign language talent cultivation system. Though challenges brought by AI, AI empowerment could facilitate the foreign language discipline development. Drawing upon this basis, the paper further explores the breakthroughs in foreign language talent cultivation. That suggests a shift from information tools to character education, from language proficiency to core competency strengthening, from single-discipline to interdisciplinary talent cultivation, and from imparting language knowledge to advancing general education understanding. This paper proposes paths for diversified foreign language talent cultivation in the AI era, including AI empowerment character education cultivation approaches, the construction of a diversified curriculum system, the innovation of interdisciplinary curriculum clusters, AI empowerment reshaping of digital teaching practice settings, and AI empowerment reconstruction of foreign language digital teaching resources. With those approaches, the adjusting focus and methods of foreign language talents cultivation, an effective Multilingual Talent Cultivation System will be established with international perspectives, cross-cultural communication skills, and innovation capabilities to meet the development needs of the AI era.

Effect of Printing Orientation on the Mechanical Properties of Low-Force Stereolithography-Manufactured Durable Resin

This study presents the results of fracture toughness tests conducted on specimens obtained by additive manufacturing techniques, specifically using low-force stereolithography. The samples were manufactured from a transparent 3D printing material for biocompatible applications, the so-called BioMed Durable Resin, which is a Formlabs-patented polymer material that simulates the strength and rigidity of polyethylene. The selected toughness tests in this context were performed following the ASTM D5045-99 guidelines. All tests were conducted under controlled laboratory conditions at 23 °C and 50% relative humidity, ensuring adherence to the standard and the replicability of the experimental results. To investigate the influence of printing plane orientation, specimens were produced at three printing orientation angles (0, 45, and 90 degrees). These angles were selected to provide a comprehensive evaluation of the anisotropy effects in the material. They cover both extreme orientations (0° and 90°) and include an intermediate value (45°), allowing us to assess variations in mechanical behavior across a representative range of printing orientations, consistent with prior research in the field. The experimental tests yielded data on the crack resistance and energy release rate for each angle of orientation. There are various implications of the findings, beyond materials engineering, for applications in biomedicine. Indeed, this same approach opens the door to new research methods for manufacturing certified biocompatible materials from such durable resins. Finally, complementary issues such as related medical applications have been slightly addressed for future work, since biomedicine innovation clusters can contribute to accelerating growth in this crucial field for productive sector activity and the local business environment.

Geography of research networks in the Big South of Russia

The geography of research and innovation activity is a traditional topic of research in human geography. Classical studies of the clustering and diffusion of innovation in the 1960s and 70s received a new impetus in the 21st century with the understanding of the openness of innovation and the importance of interregional collaborative networks. Digitalization has played an important role, providing access to new sources of large geocoded data on the movement of key elements of the knowledge economy, including publications as a formalized outcome of scientific activity. The purpose of the article is to identify the center-periphery research ties of one of the largest macroregions of Russia – the Big South of Russia. The aim was to assess the territorial patterns of interregional research networks with the identification of gravity poles and interconnections at the local and national levels. The study uses the method of spatial scientometrics. Geocoded data on publications from the Scopus bibliographic database were used. In the course of the study the hypotheses of “Moscow-centricity” and “St. Petersburg-centricity” of the knowledge domain of the macroregion are tested, the influence of the factor of geographical proximity and the role of the diversity of interregional ties are evaluated, and the “heights-lowlands” of the landscape are revealed. The results of the research have shown a high degree of connectivity between the regions of the Big South of Russia in the research area. The hypothesis about the presence of several centers of gravity of scientific activity in the macroregion was confirmed. First of all, the peaks of the “scientific ridge” of the Big South form Rostov oblast with the largest urban agglomeration in the south of the country, the Rostov urban agglomeration, as well as the Krasnodar krai and Volgograd oblast. The hypothesis of the existence of several centers of scientific activity in the macroregion was confirmed. The influence of the factor of territorial proximity in the formation of research ties is not high and is manifested mainly in the southern Russian regions with relatively low indicators of research output (primarily, it is typical for national republics). Interregional research cooperation between advanced regions is not limited to immediate geographical proximity but is due to a combination of non-territorial factors. The “Moscow-centricity” of the scientific agenda of the regions of the Big South of Russia is true for most of the southern regions of the Russian Federation, which is particularly pronounced in regions with low internal scientific potential and in new regions undergoing a period of transformation of their scientific systems.

Multi-objective scheduling for an energy-efficient flexible job shop problem with peak power constraint

Controlling electricity costs, which are closely related to the peak power reached, while simultaneously reducing completion time and energy consumption, is crucial for achieving sustainability in the manufacturing industry. However, most existing research on energy-efficient flexible job shop scheduling problems primarily focuses on optimizing completion time and energy consumption, often neglecting the critical aspect of controlling electricity costs. With decreasing resources and increasing energy prices, it is necessary to control electricity costs by limiting the peak power reached during production. To address this gap, this paper investigates an energy-efficient flexible job shop scheduling problem with peak power constraint considering setup and transportation time (EEFJSSP-PPST). We establish a mathematical model for EEFJSSP-PPST and propose an improved non-dominated sorting genetic algorithm II (INSGA-II) incorporating several key improvements. We first introduce three scheduling rules in decoding to optimize objectives and employ heuristic rules to generate a high-quality initial population. Then, we propose an innovative cluster crossover method to accelerate convergence and design an adaptive-based local optimization strategy to further enhance performance. Finally, our experiments explore the impacts of different degrees of the peak power constraint on objectives and evaluate the effectiveness of the three scheduling rules. Additionally, the performance of INSGA-II is tested using 135 benchmark instances. The results indicate that INSGA-II outperformed its variations without improvements, achieving the best hypervolume (HV) indicator in 80% of the instances and the best inverted generational distance (IGD) indicator in 68.89% of the instances. Compared to other state-of-the-art algorithms, INSGA-II achieves the best HV and IGD indicators in 82.2% and 81.5% of the instances, respectively, and demonstrates better convergence and a superior Pareto front. Therefore, we conclude that the improvements in INSGA-II are effective and enhance the algorithm’s performance, making it outstanding in solving EEFJSSP-PPST.

Clustering uncertain overlapping symptoms of multiple diseases in clinical diagnosis

In various fields, including medical science, datasets characterized by uncertainty are generated. Conventional clustering algorithms, designed for deterministic data, often prove inadequate when applied to uncertain data, posing significant challenges. Recent advancements have introduced clustering algorithms based on a possible world model, specifically designed to handle uncertainty, showing promising outcomes. However, these algorithms face two primary issues. First, they treat all possible worlds equally, neglecting the relative importance of each world. Second, they employ time-consuming and inefficient post-processing techniques for world selection. This research aims to create clusters of observed symptoms in patients, enabling the exploration of intricate relationships between symptoms. However, the symptoms dataset presents unique challenges, as it entails uncertainty and exhibits overlapping symptoms across multiple diseases, rendering the formation of mutually exclusive clusters impractical. Conventional similarity measures, assuming mutually exclusive clusters, fail to address these challenges effectively. Furthermore, the categorical nature of the symptoms dataset further complicates the analysis, as most similarity measures are optimized for numerical datasets. To overcome these scientific obstacles, this research proposes an innovative clustering algorithm that considers the precise weight of each symptom in every disease, facilitating the generation of overlapping clusters that accurately depict the associations between symptoms in the context of various diseases.

Deep Contrast Clustering Analysis to Distinguish Diabetic Complications in Elderly Chinese Patients.

To establish an innovative clustering method for predicting variable categories of diabetic complications in Chinese ≥65 with diabetes. We selected and extracted data from elderly patients with diabetes (n=4980) from a medical examination group of 51,400 people followed up annually from 2014 to date in Kunshan, China. A deep contrast clustering approach was used to cluster and predict diabetic complications. The clustering approach was further validated using data from elderly patients with diabetes (n=397) from one medical examination cohort of 20,000 people followed up yearly from 2014 to date in Beijing Jiuhua Hospital. The patients were clustered into 6 categories by analysing 20 indicators. Cluster 1-Heavy smoking and a high cardiovascular disease (CVD) risk; Cluster 2-High alcohol consumption, high aminotransferase levels, the highest risk of stroke complications, and a high fatty liver disease (FLD) risk; Cluster 3-High blood lipid levels and a risk of FLD and stroke complications; Cluster 4-Good health indicators and a low risk of FLD, stroke, and CVD complications; Cluster 5-Older age, higher uric acid concentration and creatinine level, and the highest risk of CVD complications; Cluster 6-Large waist circumference, high BMI, high blood pressure, and the highest risk of FLD complications. The gene for nonalcoholic fatty liver disease in cluster 2 had the highest risk coefficient. This was consistent with cluster 2, which had a higher FLD prevalence. A new clustering method was developed from two large Chinese cohorts of older patients with diabetes, which may effectively predict complications by clustering into different categories.

Exploring other clustering methods and the role of Shannon Entropy in an unsupervised setting

In the ever-expanding landscape of digital technologies, the exponential growth of data in information science and health informatics presents both challenges and opportunities, demanding innovative approaches to data curation. This study focuses on evaluating various feasible clustering methods within the Data Washing Machine (DWM), a novel tool designed to streamline unsupervised data curation processes. The DWM integrates Shannon Entropy into its clustering process, allowing for adaptive refinement of clustering strategies based on entropy levels observed within data clusters. Rigorous testing of the DWM prototype on various annotated test samples revealed promising outcomes, particularly in scenarios with high-quality data. However, challenges arose when dealing with poor data quality, emphasizing the importance of data quality assessment and improvement for successful data curation. To enhance the DWM’s clustering capabilities, this study explored alternative unsupervised clustering methods, including spectral clustering, autoencoders, and density-based clustering like DBSCAN. The integration of these alternative methods aimed to augment the DWM’s ability to handle diverse data scenarios effectively. The findings demonstrated the practicability of constructing an unsupervised entity resolution engine with the DWM, highlighting the critical role of Shannon Entropy in enhancing unsupervised clustering methods for effective data curation. This study underscores the necessity of innovative clustering strategies and robust data quality assessments in navigating the complexities of modern data landscapes. This content is structured by the following sections: Introduction, Methodology, Results, Discussion, and Conclusion.

Strategic Thinking Rules in Innovation Clusters Strategizing Methodology

Strategic Thinking Rules in Innovation Clusters Strategizing Methodology

THE ROLE OF UNIVERSITIES IN ENSURING TECHNOLOGICAL INDEPENDENCE IN INNOVATIVE DEVELOPMENT

The presented scientific article analyzes the role of universities in ensuring technological independence in the context of innovative development. The current trends and key mechanisms by which universities influence the achievement of technological independence of the state are considered, as well as the prospects for the development of this area are analyzed. Special attention is paid to the importance of cooperation between universities and the business community, including active partnerships with industrial enterprises and innovative start-ups. The study also examines educational programs and technological transfers, which are valuable tools for the formation of innovations that contribute to improving the competitiveness of the national economy. The analysis reflects the active role of universities in developing innovative solutions, converting knowledge into successful commercial products and encouraging technological development of the country. Also, within the framework of this study, the importance of developing innovation clusters and technology parks is considered, which contribute to the convergence of science, business and production, and the creation of a favorable ecosystem for innovation. The role of state support for innovative projects and scientific research carried out by universities, financing mechanisms for innovative research and development are considered. The research is an analysis of the current state and prospects for the development of interaction between the academic sphere, business and the state in order to increase technological independence, strengthen innovation and stimulate sustainable economic growth. The results of this study provide material for the development of possible partnership strategies between universities, the business community and the state aimed at ensuring technological independence and sustainable development of the national innovation system, including in order to ensure technological independence and sustainable development of the national innovation system.

Spatiotemporal trajectory of energy efficiency in the Guangdong-Hong Kong-Macao Greater Bay Area and implications on the route of economic transformation.

The Guangdong-Hong Kong-Macao Greater Bay Area has attracted attention for its extraordinary pace of economic development and is considered to be leading the way in China's transformation from a manufacturing to an innovation cluster. However, due to rapid economic expansion and rapid urbanization, the Great Bay Area still struggles with low energy efficiency and environmental degradation, which has slowed down the pace of development. Therefore, in order to alleviate energy pressure, promote the country's sustainable development and gain a competitive advantage in the global market, researching energy efficiency and improving energy utilization efficiency is crucial. In this study, macro-level energy efficiency indicators are constructed using energy consumption data from various cities in the Greater Bay Area for the period from 2000 to 2020, and the spatio-temporal evolution of energy efficiency is analysed. The results show that all cities in the Greater Bay Area experienced an increasing trend in energy efficiency from 2000 to 2019, with significant variation in growth rates and magnitudes between cities. Compared to the nine cities in Guangdong province, Hong Kong and Macao exhibited significantly superior energy efficiency, with Foshan recording the highest growth rate of 14%. In 2020, most cities experienced a decline in energy efficiency due to the COVID-19 pandemic, with Macao experiencing the greatest decrease at 57%. Hong Kong and Macao are both in the "low consumption and high efficiency" target region, while Guangzhou, Shenzhen and Zhuhai are consistently in the "both high" region. Changes in the industrial upgrading index correspond significantly with changes in energy efficiency trajectories, with the transition from primary to secondary and tertiary industries playing a more substantial role. There is no significant association found between the strength of environmental regulation and changes in energy efficiency. The study's findings indicate that the most effective way to achieve economic transformation in the majority of China's regions is to combine adequate environmental legislation with industrial structural adjustment.

The politics of knowledge in black carbon mitigation: Policy entrepreneurship of Finnish actors and the Climate and Clean Air Coalition

Mitigation of black carbon (BC) is a potent short-term measure for climate and environmental policy due to the short lifetime of BC particles in the atmosphere. Fewer BC emissions would decelerate warming especially in the Arctic and bring significant co-benefits particularly in populous countries owing to BC’s negative public health, food security and socio-economic effects. However, scientific knowledge on BC remains uncertain regarding the measurement, monitoring and precise effects of BC while the respective global policy framework is fragmented. To address this situation, we scrutinize the science/policy nexus in BC mitigation on the regional level where some promising openings exist. For this end, our new analytical framework focuses on policy entrepreneurs, their interests and cognitive frames, and the structural environment. Utilising content analysis of expert interviews and document data, three cases are covered with significant policy entrepreneurship on BC. First, we examine Finland ‘s high-profile BC initiative, finding it to frame BC pollution as an Arctic climate threat. Second, Finland’s research, development and innovation cluster, which has substantial expertise on air pollution, frames BC as a public health hazard, and scopes prospects for policy frameworks helping to open markets for mitigation technologies and solutions. Third, the Climate and Clean Air Coalition approaches BC with a wide multipollutant frame, driven by broad socio-economic and developmental interests. The Finnish initiative’s potential is found to suffer from insufficient coordination with the RDI cluster. Both Finland’s BC specific and the CCAC’s wider multipollutant frame can in different ways contribute to abatement.

Сетевая инновационная кластерная педагогическая лаборатория как инструмент повышения качества функционирования муниципальных образовательных комплексов и организаций

The article introduces a model developed in the Republic of Bashkortostan aimed at improving the functioning of educational organizations at the municipal level – the Network Innovative Cluster Pedagogical Laboratory, validated with direct involvement from the author. The necessity and effectiveness of the proposed management solution are substantiated. On one hand, numerous vectors of reform in Russian education converge on the common goal of implementing an individualized approach in modern schools. This approach aims to help each student develop their abilities and acquire skills for independent problem-solving in academic and life contexts. On the other hand, any changes in social institutions today occur within the contextual framework of an information society, altering the content and necessary set of knowledge, skills, and competencies. The structure of the Network Innovative Cluster Pedagogical Laboratory is examined, emphasizing the need for creating an information-educational environment, transitioning general education schools into a mode of innovative development, and focusing management efforts on positive social outcomes. This innovative educational complex contributes to the holistic development of student’s personality – intellectual, communicative, and emotional-moral, but requires ongoing monitoring of all elements from both objective and subjective perspectives.

The spatial effects of innovation agglomeration on air quality in China: from the perspective of industrial development

ABSTRACT This study aims to explore the impact of innovation agglomeration on air pollution and industrial transformation within the context of China’s green development transition. We developed a general equilibrium model to theoretically explore these effects and employed an econometric analysis using panel data from 284 prefecture-level cities in China between 2005 and 2019 to empirically validate our findings. The main results are as follows: (1) Innovation agglomeration can significantly improve air quality, with a strong spatial spillover effect. (2) Innovation agglomeration can significantly promote transformation and upgrading of industrial structure, and it may also lead to the convergence of some industrial structures. Moreover, industrial structure transformation and upgrading partially mediates the relationship between innovation agglomeration and air pollution. (3) There is a nonlinear relationship between innovation agglomeration and air pollution. As industrial complementarity is developed, the effect of innovation agglomeration on air pollution enhances. These findings suggest that fostering innovation clusters and encouraging industrial diversification are key policy strategies for achieving sustainable urban development and improving environmental outcomes.

Attributes and types of innovation clusters in the context of digital transformation

Attributes and types of innovation clusters in the context of digital transformation

Making hospitals innovative: Macro-level policy to sustain micro-innovations in healthcare.

Successful innovation clusters are notoriously difficult to establish, and many attempts fail. How can we go about designing such systems reliably? We describe how ecosystems can be strengthened through grassroots bottom-up efforts that empower user and community innovation, as opposed to economic policies that dictate innovation. Specifically focusing on the healthcare industry, we advocate that community hospitals which constitute 90% of all hospitals in Canada are the ideal setting for such community innovation efforts. We investigated the distribution of innovation output from hospitals over the past 13years and found a decrease in predominance of major teaching hospitals, supporting the potential role for community hospitals in this space. We categorize different types of innovations and recommend institutional policies that can sustain bottom-up, micro-level efforts. Such policies could improve and enhance the development of micro-innovations and the creation of health innovation clusters.