Search Results (104)

Improving the green innovation quality (GIQ) of center cities is crucial to achieve sustainable urban agglomeration development. Utilizing data on green patent citations and economic indicators across cities in the Yangtze River Delta urban agglomeration (YRD) from 2003 to 2022, this research examines the influence of center cities’ GIQ on the economic performance of peripheral municipalities. The results show the following: (1) Center cities’ GIQ exerts a significant suppressive effect on peripheral cities’ economic growth overall. Heterogeneity analysis uncovers a distance-dependent duality. GIQ stimulates growth in proximate cities (within 300 km) but suppresses it beyond this threshold. This spatial siphoning effect is notably amplified in national-level center cities. (2) Mechanisms suggest that GIQ accelerates the outflow of skilled labor in peripheral cities through factor agglomeration and industry transfer mechanisms. Concurrently, it impedes the gradient diffusion of urban services, collectively hindering peripheral development. (3) Increased government green attention (GGA) and industry–university–research cooperation (IURC) in centers can mitigate these negative impacts. This paper contributes to the theoretical discourse on center cities’ spatial externalities within agglomerations and offers empirical support and policy insights for the exertion of spillover effects of high-quality green innovation from center cities and the sustainable development of urban agglomeration. Full article

The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. The single weighting method can only determine the weight of the flood season division indicators from a certain perspective and cannot comprehensively reflect the time-series attributes of the indicators. This study proposes a Flood Season Division Model based on the Goose Optimization Algorithm and Minimum Deviation Combined Weighting (FSDGOAMDCW). The model uses the Goose Optimization Algorithm (GOA) to solve the Minimum Deviation Combination model, integrating weights from two subjective methods (Expert Scoring and G1) and three objective methods (Entropy Weight, CV, and CRITIC). Combined with the Set Pair Analysis Method (SPAM), it realizes comprehensive flood season division. Based on daily precipitation data of the Nandujiang River (1961–2022), the study determines its flood season from 1 May to 30 October. Comparisons show that: ① GOA converges faster than the Genetic Algorithm, stabilizing at T = 5 and achieving full convergence at T = 24; and ② The model’s division results have the smallest Intra-Class Differences, avoiding indistinguishability between flood and non-flood seasons under special conditions. This research aims to support flood season division studies in tropical islands. Full article

Enhancing innovation capability can effectively promote the development of traditional industries. Based on Lewin’s behavioral model theory, this study investigated the relationship between industrial support policies and innovation behavior within traditional industries. Utilizing survey data collected from 152 traditional industrial enterprises in 2024 and employing structural equation modeling, the main findings are as follows: Industrial support policies can effectively alleviate the “innovation inertia” of traditional industries, with all policies being significant at the 1% confidence level. Among them, policies related to industry–university–research cooperation platforms have the most significant impact, with a standardized coefficient of 0.941, followed by fiscal and taxation policies (standardized coefficient: 0.846) and financial policies (standardized coefficient: 0.729). Innovation motivation acts as a mediating mechanism between industrial policies and innovation behavior. Industrial support policies accelerate the conversion of reserve-oriented patent portfolios into practical applications, helping to break through patent barriers and effectively alleviate innovation inertia. Consequently, the government should prioritize improving public services, and policy formulation needs to be oriented towards enhancing innovation efficiency. While ensuring industrial security, it is advisable to moderately increase competition to guide traditional industry market players towards thriving in competitive environments. Full article

China has made a major strategic decision to build world-class universities and first-class disciplines (abbreviation: Double First-Class), aimed at enhancing the global competitiveness of Chinese higher education. Industry-specific universities have a special historical evolution and distinctive characteristics. China’s industry-specific universities have always played an important role in the higher education system and made significant contributions to the development of the country. However, the “Double First-Class” initiative presents both opportunities and challenges for industry-specific universities. This paper employs the SWOT analysis method to conduct a qualitative analysis of industry-specific universities and proposes a strategic matrix for decision-making. At the same time, from a niche perspective, this paper explores the sustainable development strategies of these institutions within the initiative through the calculation of niche breadth, niche overlap, and their relationship analysis. The research results indicate that the “Double First-Class” initiative has played a positive role in promoting the expansion of universities’ ecological niches. However, it has also led to excessive niche overlap and intense competition. Industry-specific universities face opportunities and challenges in terms of structure, strategy, and policy for their sustainable development. Key findings highlight the importance of strategic alignment with national demand, industry cooperation, and policy orientation for sustainable growth. This paper proposes recommendations for the construction of a sustainable development framework, implementation of strategic initiatives, and policy guidance for universities with industrial characteristics from three perspectives: government, industry, and universities. Full article

In the new media era, the green technology alliance with multi-participation has emerged as a powerful contributor to achieving the strategic goal of a green economy. Therefore, this paper constructs a stochastic evolutionary game model of green technology innovation led by the government under an uncertain environment and jointly promoted by enterprises, universities, and research institutes. Then, this study firstly explores the influence of different factors on evolutionary equilibrium and secondly discusses the role of main factors on the behavior strategies of each game subject. Furthermore, numerical simulation analysis using Matlab R2019a 9.6 will be used to prove the model’s validity. The research has shown (1) that media monitoring positively impacts the stability of the alliance and that product greenness can further accelerate alliance evolution when media monitoring is in place. When this factor is small, it will lead to the transformation of Industry-University-Research’s (IUR) optimal strategy into non-cooperation in the early stage. (2) The green degree of products positively affects the decision-making choice of the IUR, but it is not the case for the government. And the role of media supervision will further coordinate its influence and accelerate the evolution of alliances. (3) The enhancement of media monitoring capacity can encourage game subjects to evolve in a more beneficial way. In addition, the implementation of media supervision will help reduce the cost of government supervision and provide reputation benefits. The research fully accounts for the complexity and variability of the environment, and the results provide theoretical support and practical advice for the high-quality development of the green technology innovation alliance. Full article

New quality productive forces (NQPFs) are a key driver for sustainable and high-quality development, where digital innovation (DI) plays a crucial role in promoting the evolution of NQPFs. Based on this, this paper takes 2740 A-share listed companies from 2011 to 2022 as research samples and utilizes double machine learning to explore the impact and transmission mechanisms of DI on NQPFs. The study finds that DI significantly empowers the development of NQPF; mechanism-wise, DI achieves this through industry–university–research cooperation (IURC), increasing market concentration (MC) and enhancing government innovation subsidies (GISs); heterogeneity analysis reveals that the empowering effect of DI on NQPFs is stronger in large cities, small cities, the region northwest of the Hu Line, and the old industrial bases, whereas in megacity behemoths, megacities, regions along the Hu Line and the southeast region, and non-old industrial base enterprises, the effects are relatively smaller. This study provides both theoretical and empirical insights into how DI drives the development of NQPFs and supports sustainable economic growth, offering valuable guidance for future development strategies. Full article

At present, the high complexity of the environment, the uncertainty of income, and the choice of strategies have attracted extensive attention from all walks of life who are committed to studying the game of collaborative innovation between government and industry–university–research. Based on this, first of all, with the help of stochastic evolutionary game theory and fuzzy theory, this paper constructs a multi-party stochastic evolutionary game model of green technology innovation about the government guidelines and the joint promotion of industry, universities, and research institutes. Secondly, it discusses the evolution law of behavior strategies of each game subject and the main factors to maintain the alliance’s stability under fuzzy income. The numerical simulation results show the following: (1) Reputation gains have a significant positive correlation with the evolution stability of alliance behavior, and the incorporation of reputation gains or losses will effectively maintain the cooperation stability of the alliance. (2) Under the influence of product greenness, government subsidies, and long-term benefits, it will promote the pace consistency of cooperative decision-making between industry, universities, and research institutes, and accelerate the evolution of alliances. (3) The enterprise’s ability and the research party’s ability will restrict each other. When one party’s ability is low, its willingness to choose a cooperation strategy may be slightly low due to technology spillover and other reasons. When the two parties’ abilities match, their behavior strategies will increase their willingness to cooperate with their abilities. Compared with the traditional evolutionary game, this study fully considers the uncertainty of the environment and provides theoretical support and practical guidance for the high-quality development strategy of the industry–university–research green technology innovation alliance. Full article

Condition surveys are an important part of the whole scientific research of torso buildings, which we understand to be buildings with usually missing roofs, wooden ceilings, doors, windows, and other envelope constructions along with damaged internal and external infrastructure and surfaces. The aim of the processed condition surveys is to gather basic data on the technical state of the buildings. Torso buildings are, in our environment, especially the ruins of castles and manor houses, but also some churches, the remains of industrial enterprises, or even others. The proposed condition surveys can be used at any of them, not only in our country, but also in any other country of the world. The processed system of condition surveys allows us to anticipate further damage to architectural heritage buildings caused by their aging. It can contribute in a major way to the identification of eventual construction risks and to anticipate them. Condition surveys are designed as a quick, cheap, and easy to handle way to gather the basic overview needed for undertaking a basic stabilization of the most endangered parts of the ruins. In the next step, further scientific research using instruments i.e., in laboratories, can be undertaken. It is also important to know which parts of the torso buildings are dangerous and can endanger not only visitors, but also scientists realizing research on site. The first goal of the project focused on bettering of the stability of the torso buildings, especially ruins of castles, which was granted by the Slovak Ministry of Culture to elaborate and prepare a system for the identification of the most endangered parts of the ruins, which are in danger of dilapidation soon. The second goal was focused on preparing source material for further scientific research of the torso architecture. Experts from the Faculty of Civil Engineering of the Slovak University of Technology, with the cooperation of experts from praxis and from the Architectural Heritage Protection Office prepared a system for judging the construction details of torso buildings from the point of view of their construction–technical state. The aim of this judging lies in identifying the parts of their constructions that are most endangered by decay. Based on the condition survey results, conservation activities can be organized to save valuable details of the torso buildings before destruction and to protect the visitors of such localities before injuries, maybe even tragic injuries. Full article

This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The system leverages advanced computer vision algorithms and an extended Kalman filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive maneuver computation. A novel hardware-in-the-loop (HIL) simulation environment was developed and used for validation, enabling the evaluation of closed-loop real-time interaction between the “Sense” and “Avoid” subsystems. Extensive numerical simulations and a flight test campaign demonstrate the system’s effectiveness in real-time detection and the avoidance of non-cooperative obstacles, ensuring compliance with UAV aero mechanical and safety constraints in terms of minimum separation requirements. The novelty of this research lies in (1) the design of an innovative and efficient visual processing pipeline tailored for SWaP-constrained mini-UAVs, (2) the formulation an EKF-based data fusion strategy integrating optical data with a custom-built Doppler radar, and (3) the development of a unique HIL simulation environment with realistic scenery generation for comprehensive system evaluation. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, significantly contributing to the safety of flight in non-segregated airspaces Full article

The rapid development of highways greatly affects the flow of people, finance, goods, and information between cities, and monitoring the OD flow of travel has become a very important task for intelligent transportation systems (ITS). The temporal dynamics and complex spatial correlations of OD traffic distribution, as well as the sparsity and incompleteness of data caused by uneven traffic distribution, make OD traffic prediction complex and challenging. This paper proposes a multi-task prediction model for OD traffic between highway stations. The model adopts a hard parameter shared multi-task learning network structure, which is divided into sub-task learning inflow trend modules, sub-task learning outflow trend modules, and main task learning modules for OD traffic. At the same time, the attraction intensity matrix between stations is constructed using the population density data as the external feature of the sub-task module for outlet outflow flow, and stronger constraints between tasks are introduced to achieve better fitting results. Finally, an OD flow prediction case experiment was conducted between stations on highways in Sichuan Province. The experimental results showed that the proposed model not only had higher accuracy in predicting results than other baseline models, but also had better effectiveness and robustness. Full article

Megaproject construction endeavors and technological innovation activities, led by industry–university–research (IUR) collaboration, demonstrate marked disparities in value orientations, implementing entities, and constituent components. These discrepancies lead to a mismatch between innovation demands and actual activities, as well as insufficient innovation motivation among construction entities, subsequently impacting innovation effectiveness and the commercialization of outcomes and failing to adequately support engineering construction needs. In response to this predicament, the academic community widely acknowledges IUR collaborative innovation as a solution. This research integrates fundamental theoretical analysis with a multi-case study approach and systematically dissects the distinctive features at the micro, meso, and macro levels, grounded in the collaborative innovation practices of IUR in three iconic railway engineering projects in China. Subsequently, it unravels the inherent operational mechanics of the IUR collaborative innovation system within large-scale projects. Specifically, at the micro level, the profound engagement of governments and project owners fosters a solid supportive environment and collaborative platform for IUR collaboration, while past successful cooperation experiences among key innovation entities enhance their technological and knowledge interactions. At the meso level, shared industry cognitions and values, hierarchical organizational structures, flexible institutional designs, and resource allocation strategies based on balancing risks and benefits collectively constitute the supporting system for megaproject collaborative innovation. At the macro level, the tight integration of the innovation chain and industrial chain promotes the formation of an open cooperation ecosystem, ensuring the continuity and systematic nature of innovation activities and accelerating the rapid commercialization and efficient utilization of innovation outcomes. This study not only enriches the theoretical connotations of IUR collaborative innovation in the context of major engineering projects but also provides theoretical foundations for strategy formulation and management practices for major project managers, holding significant value in guiding the innovation management of future major engineering projects. Full article

The focus of this study is on presenting the concept of the Mini Company initiative launched in 2023 in the ZalaZONE automotive science park and proposing its development through international good examples. The aim of the project, based on the learning factory model, is to enable dual university students to experience the real market environment in a realistic, educational environment. The search for international good examples can help to further develop the concept, especially regarding the future role of automotive stakeholders. The presented results and good examples can be used in new research projects, develop teaching methods, and strengthen cooperation between industry and academia. Full article

As the “carbon neutrality” strategy is implemented, green technology R&D, a core competitive strength for sustainable enterprise development, is an essential pathway for China’s transformation and green growth. Green technology, a breakthrough over traditional production technologies, involves lengthy and costly R&D processes with high risks typically beyond the reach of a single enterprise. It requires the heterogeneous functions of enterprises, universities, and research institutions to complement each other’s advantages and establish an “industry–university–research” collaborative innovation alliance for green technologies. This paper constructs differential game models for non-cooperative and cooperative green technology R&D involving a green manufacturer and a research institution. We solve and compare the profits for both parties under these scenarios, apply a time-consistent payment distribution mechanism to allocate cooperative profits, and ensure that neither party deviates from the optimal cooperative trajectory over a prolonged period, achieving Pareto improvement and enhancing social welfare. Full article

Anthracnose of the tea plant (Camellia sinensis), caused by Colletotrichum spp., poses a significant threat to both the yield and quality of tea production. To address this challenge, researchers have looked to the application of endophytic bacteria as a natural alternative to the use chemical pesticides, offering potential for enhancing disease resistance and abiotic stress tolerance in tea plants. This study focused on identifying effective microbial agents to combat tea anthracnose caused by Colletotrichum fructicola. A total of 38 Bacillus-like strains were isolated from the tea rhizosphere, with 8 isolates showing substantial inhibitory effects against the mycelial growth of C. fructicola, achieving an average inhibition rate of 60.68%. Among these, strain T3 was particularly effective, with a 69.86% inhibition rate. Through morphological, physiological, and biochemical characterization, along with 16S rRNA gene phylogenetics analysis, these strains were identified as B. inaquosorum (T1 and T2), B. tequilensis (T3, T5, T7, T8, and T19), and B. spizizenii (T6). Biological and molecular assays confirmed that these strains could induce the expression of genes associated with antimicrobial compounds like iturin, fengycin, subtilosin, and alkaline protease, which effectively reduced the disease index of tea anthracnose and enhanced tea plant growth. In conclusion, this study demonstrates that B. inaquosorum, B. tequilensis, and B. spizizenii strains are promising biocontrol agents for managing tea anthracnose. Full article

This review paper explores Urban Digital Twins (UDTs) and their crucial role in developing smarter cities, focusing on making urban areas more sustainable and well-planned. The methodology adopted an extensive literature review across multiple academic databases related to UDTs in smart cities, sustainability, and urban environments, conducted by a bibliometric analysis using VOSviewer to identify key research trends and qualitative analysis through thematic categorization. This paper shows how UDTs can significantly change how cities are managed and planned by examining examples from cities like Singapore and Dubai. This study points out the main hurdles like gathering data, connecting systems, handling vast amounts of information, and making different technologies work together. It also sheds light on what is missing in current research, such as the need for solid rules for using UDTs effectively, better cooperation between various city systems, and a deeper look into how UDTs affect society. To address research gaps, this study highlights the necessity of interdisciplinary collaboration. It also calls for establishing comprehensive models, universal standards, and comparative studies among traditional and UDT methods. Finally, it encourages industry, policymakers, and academics to join forces in realizing sustainable, smart cities. Full article