Urban sustainability is increasingly challenged in rapidly transforming coastal regions. This study operationalizes urban green development capacity as a measurable proxy for urban sustainability and constructs a multidimensional assessment framework integrating economic development, technological innovation, green transformation performance, and green coordination capacity. Using the entropy weight-TOPSIS method, we conduct a longitudinal analysis of Qingdao (2014-2023) and a cross-sectional comparison of 24 coastal cities in eastern China in 2023. Qingdao's composite score increased steadily from 0.25 in 2014 to 0.81 in 2023, with post-2020 growth accelerating markedly following structural and policy adjustments. In contrast, inter-city disparities remain substantial: the leading cities exhibit sustainability capacity levels nearly twofold higher than those at the lower end of the distribution. Cities with stronger technological innovation intensity and institutional coordination consistently outperform others, highlighting the importance of governance-technology coupling. These results suggest that urban sustainability is associated with a coupled interaction pattern among capital, technology, performance, and institutional coordination rather than linear economic expansion. The study provides a quantitative tool for measuring and benchmarking urban sustainability capacity and offers empirical support for differentiated sustainability transition pathways in coastal and transition-economy cities.

Large language models (LLMs) are increasingly deployed in enterprise contexts to support complex problem-solving tasks. Yet individual LLMs remain bounded by model-specific capability limitations. These heterogeneous capability boundaries pose a deployment challenge, but they also create an opportunity: strategically coordinating multiple LLMs may unlock collective intelligence that exceeds the performance of any single model. Existing approaches fix how models are combined in advance, overlooking the dynamic and state-dependent role of complementarity in complex LLM problem solving. Drawing on the wisdom-of-crowds paradigm, we reconceptualize collective LLM intelligence as relay-style complementarity: a sequential coordination process in which each successor model is selected to address the specific bottleneck identified in its predecessor's output. To operationalize this relay-style complementarity, we propose WILC (Wisdom Integration of LLM Crowds), a framework grounded in two design principles. First, iterative reflectionand-refinement establishes a state-preserving workflow through which models diagnose and refine prior outputs. Second, complementarity-driven model selection governs model transitions through a dual-gate mechanism: prospective complementarity fit (PCF) identifies the worker most suited to the current bottleneck, while posterior complementarity gain (PCG) evaluates whether the selected transition improves the evolving solution. Together, these design principles support strategic model transitions that refine answers through capability complementarity. Extensive experiments across four diverse benchmarks show that WILC achieves superior performance compared to existing approaches, including single-model self-refinement, ensemble methods, and dedicated query-routing methods. Under standardized pricing assumptions, WILC achieves comparable average benchmark performance to GPT-5.2 at approximately 7× lower estimated per-query cost, while facilitating data sovereignty through self-hosted deployment. This study contributes to IS research by extending wisdom-of-crowds theory from static aggregation to sequential AI complementarity and by providing transferable design principles for multi-AI coordination.

This study examines how learning-by-exporting (LBE) might enhance innovation when a corporation adopts a configurational approach for worldwide expansion. To determine the impacts of innovation, factors such as resource-based variables and absorptive capacity are considered, along with internationalization features such as market breadth and intensity. We assert that, from the standpoint of dynamic capabilities, all three are essential for LBE. We recognize that pursuing rapid innovation, market expansion, and simultaneous export may strain resources and complicate LBE. This study analyzed the interdependencies among LBE characteristics by comparing 2,748 manufacturing enterprises over 2007-2014. This research considers both symmetric and equifinal interactions. Our study is valuable for researchers as it elucidates the importance of internationalization elements in relation to LBE. These documents also provide valuable assistance to managers aiming to enhance exporters' absorption capacity or secure funding for specific projects. Furthermore, they provide counsel to enterprises on integrating their export marketing and innovation divisions.

This article mainly focuses on the application of new media technology in non heritage tourism, while introducing technologies such as big data, artificial intelligence, and the Internet of Things to play a role in cultural digital transformation and dissemination. By analyzing tourist behavior data and cultural activity data, explore a reliable and effective identification method for a new cultural tourism data analysis model using deep learning and machine learning. Introducing the utilization of social media data and cross platform data fusion, advocating information dissemination strategies based on data, and promoting the public value and popularity of traditional cultural tourism. Finally, compare the effectiveness of several algorithms to provide a theoretical basis and reference direction for the development of intelligent tourism.

Tomatoes are annual herbaceous plants of the family Solanaceae. They have very stringent requirements for their growing environment and climate conditions. To precisely control the greenhouse environment for tomato growth, this project designed and implemented a monitoring system utilizing programmable logic controllers and a data acquisition system for monitoring. Sensors are installed at key locations in the greenhouse, such as near the ground, in the plant canopy, and under the roof, to monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration in real time. A three-layer feedforward GA-BP neural network model was established using soil temperature, soil humidity, air humidity, and illumination as predictive parameters. This technology predicts optimal environmental parameters and performs real-time monitoring and intelligent control, overcoming the traditional BP neural network’s drawbacks of slow convergence, susceptibility to disturbance, and poor generalization ability, with the current model’s average prediction error being less than 5%. It provides a theoretical basis and decision support for precision control and related environmental forecasting in agricultural projects. Based on the growth characteristics, physiological and morphological features of tomatoes, and the comprehensive impact of planting conditions, the technology enhances tomato yield and quality while reducing production costs and labor inputs. It mainly includes the establishment of tomato growth models, measurement and control of environmental parameters, and the design and optimization of intelligent decision control systems. Research outcomes in this field are significant for improving agricultural production efficiency, ensuring food security, and promoting sustainable agricultural development.

This study aims to investigate the classification of technological innovation meta-theories based on classical texts, as well as the relationships between various classifications. Both qualitative and quantitative methods are employed. From the perspective of technological innovation, using scientometric methods, 105 pieces of classic texts from the 1930s-2010s are extracted from the references of 3862 pieces of high-quality literature from the 1900s-2020s. As a result, based on a combination of qualitative data analysis and topic model analysis, we developed a typology with eight meta-theories of technological innovation, including performance-based, resource-based, knowledge-based, capability-based, network-based, technological-innovation-system, dual-innovation, and dynamic-sustainability views. Then we analyzed 1) the evolution, reification, and confusion relationships between different meta-theories; 2) the causes of technological innovation's concept jungle; and 3) an integrated framework of technological innovation meta-theories. This study analyzed the benefits of the meta-theoretical analysis on the future study of technological innovation. Additionally, the results of this study can help to measure technological innovation, construct new theories, and improve the efficiency of the connection between the practical problems of innovation and potentially useful theoretical frameworks.

At present, China is in the critical period of transforming from a manufacturing power to a manufacturing power. A large number of manufacturing enterprises are facing transformation and upgrading, and there is a great demand for talents, especially high-end core talents. However, due to various software and hardware reasons of enterprises, the phenomenon of unable to recruit and retain people is becoming more and more serious. After adopting SWOT analysis and PEST analysis on the current situation of H Company, this paper analyzes the main reasons for the brain drain of H company by using the empirical research method and puts forward the countermeasures, which is of practical significance to stabilize and enhance the human resources reserve of the enterprise and improve the talent competitiveness of the enterprise.

Research Background and Content. Intellectual property and high-quality economic development are mutually reinforcing and restrictive, China is a large but not a strong intellectual property country, and China’s high-quality economic development still has problems of inadequacy and imbalance. Based on the above research background, this paper mainly studies the relationship between the intellectual property system and the high-quality economic development system from the perspective of coupled and coordinated development. Research Objective. The objective is to measure the coupling and coordination degree between the intellectual property development system and the high-quality economic development system and to explore the spatiotemporal characteristics and differences in the coupling and coordination between the two systems. Research Method. Based on the data of 30 provinces (autonomous regions and municipalities) from 2013 to 2020, the methods used in this paper to analyse include entropy weight method, grey correlation analysis, coupling coordination degree model, and Dagum Gini coefficient. Research Results. The development of economic openness and innovation are more closely related to the development of intellectual property, and the protection, utilization, and creation of intellectual property are more closely related to high-quality economic development; the coupling coordination degree of the two systems shows the characteristics such as “high in the east and low in the west” and “coastal area is better than inland,” and the development of intellectual property lags behind the high-quality economic development; the coupling and coordination degree of the two systems shows a slight growth trend in most provinces, but the overall differences shows an expanding trend, and differences between regions are the main sources of overall regional differences. Research Innovation. Research innovation is to study the two-way relationship between intellectual property and high-quality economic development and to measure the coupling and coordination degree between the development system of intellectual property and the high-quality economic development system, and its spatiotemporal characteristics, regional differences, and sources of differences are analyzed. Research Value. Research value is to provide decision reference for the two-way empowerment of intellectual property and high-quality economic development and regional coordinated development.

This paper investigates whether the COVID-19 (coronavirus disease 2019) pandemic affects the green inventions of firms, universities, and firm–university collaborations (FUCs) differently. Our identification used provincial-level monthly data from China. Results from the difference-in-differences (DID) model showed that the COVID-19 pandemic has prompted the output of three types of green invention patents. After the parallel-trend test, placebo test, and triple-difference estimation, our conclusion has good robustness. However, the COVID-19 pandemic also influences the role of other policies, such as the SO2-emissions-trading pilot policy for universities’ green inventions. There has been a slight change in the effect of dual carbon targets on green inventions since the start of the pandemic. The positive effect of the COVID-19 pandemic has been weaker for provinces where the pandemic has been more severe than in other provinces. The results of this study are compared with the results and empirical evidence of other related studies and the theoretical logic of COVID-19 crisis-promoted green inventions are discussed.

采用组合赋权法确定序参量指标权重,改进传统复合系统协同度模型,构建具有速度特征的专利密集型产业知识产权管理能力协同度综合测度模型.基于15个专利密集型产业2011年-2016年的样本数据,分别从静态和动态视角对我国专利密集型产业知识产权管理能力协同水平及其演变趋势展开实证研究.结果表明:我国专利密集型产业知识产权管理能力协同发展水平较低,协同度在[-0.08,0.1]区间震荡.不同专利密集型产业知识产权管理能力协同发展相对不平衡,其中计算机制造业其协同水平相对较低,排名靠后;而医疗设备制造业、医药制造业、电气设备制造业及化学原料及化学制品制造业其协同水平相对稳定,排名靠前.此外,各专利密集型产业知识产权管理能力协同发展水平表现出明显的波动趋势,除了通信设备、雷达及配套设备制造业和其他电子设备制造业呈现波动下降趋势外,其他专利密集型产业呈现上升趋势,说明多数产业能均衡有序发展.研究结论以期为专利密集型产业知识产权管理能力协同度提升提供理论依据和政策建议.

为从产业区域层面深度挖掘军民融合产业知识产权管理系统协同发展水平,本文基于传统复合系统协同度模型,构建具有速度特征的复合系统协同度模型,以我国航空航天制造业2007—2016年数据为样本,实证测度全国整体及京津冀、长三角和泛珠三角区域的军民融合产业知识产权管理系统协同度水平,并从主体支撑、市场环境和产业发展3个层面剖析系统协同发展的影响因素.研究结果表明:从全国整体看,各子系统内部有序度均呈现上升趋势,系统有序发展的变化速度维持在稳定水平;从三大区域看,各区域系统的协同发展水平均不理想,系统协同度及其变化速度的综合效度呈明显空间差异性;而主体支撑和市场环境是影响系统协同发展的主要因素.研究结果以期为提高军民融合产业知识产权管理系统协同发展水平提供对策建议.

3-D printing is an additive manufacturing process, which enables products to be custom designed. Obtaining the evolution trend and identifying the potential R&D hotspots of 3-D printing technology are important strategic issues for both nations and enterprises. In this article, a combination method is proposed using the Viterbi algorithm to identify technological terms in patents, using the latent Dirichlet allocation model to capture potential technological topics in patents, and using the hidden Markov model to analyze the distribution and evolution pattern of the technological topics. Based on the results, technological topics and future R&D hotspots in 3-D printing are identified. Among the technological topics, extrusion devices and laser powder molding are gradually losing R&D; model acquisition, extrusion devices, rotary printing, and denture are the key topics of technological evolution that are difficult to overcome; laser focusing, print head, color printing, recyclable, feeding, control panel, and denture will become the focus of R&D in the near future. Finally, a comparison and experiments are given to demonstrate the plausibility of our method.

Purpose The purpose of this study is to reveal a sequential mediating process of the impact of shared leadership on team performance by studying the sequential mediating effect of team trust and team learning behavior. Design/methodology/approach This study develops and examines a sequential mediation model using the meta-analytic structural equation modeling (MASEM) method. The sample adopted consists of 347 independent effect sizes extracted from 280 empirical papers (288 independent studies, N = 21,888 groups). Findings The results indicate that team trust and team learning behavior play a sequential mediating effect in the shared leadership–team performance relationship. Practical implications The findings suggest that practitioners should share leadership functions and responsibilities among talented team members. Furthermore, practitioners should strengthen the emotional interaction among team members and give positive feedback to the team's intensive learning behaviors. Originality/value By identifying the sequential mediating effect of team trust and team learning behavior, this study not only advances the understandings of a comprehensive mediating process through which shared leadership enhances team performance, but also offers new insights into the interrelationship of different types of mediating mechanisms (i.e. team emergent state and team process) in the shared leadership–team performance relationship.

专利密集型产业国际竞争力是国家间科技力量与经济实力的重要体现.本文在整理专利密集型产业国际竞争力研究成果的基础上,构建了专利密集型产业国际竞争力分析的初选指标体系,利用群组决策特征根方法(GEM)对专利密集型产业国际竞争力初选指标的重要性和关键性进行分类筛选,最后得到专利密集型产业国际竞争力分析的指标体系.专利密集型产业国际竞争力分析指标体系能够为提升专利密集型产业国际竞争力提供方向与路径.

在我国,财务公司被归类为非银行金融机构,具有三大特性:金融性、企业性和产业性,第三个特性"产业性"是财务公司有别于其他金融机构,包括商业银行、证券公司、租赁公司、担保公司的最大区别,与商业银行相比,商业银行作为金融机构的主体,服务的对象是全社会一切可以利用的经济资源,拥有雄厚的资金实力、广泛的客户群以及完善的分支机构和结算网络,发展规划是围绕自身而建立的.

With the rapid development of Internet and information technology, networks have become an important media of information diffusion in the global. In view of the increasing scale of network data, how to ensure the completeness and accuracy of the obtainable links from networks has been an urgent problem that needs to be solved. Different from most traditional link prediction methods only focus on the missing links, a novel link prediction approach is proposed in this paper to handle both the missing links and the spurious links in networks. At first, we define the attractive force for any pair of nodes to denote the strength of the relation between them. Then, all the nodes can be divided into some communities according to their degrees and the attractive force on them. Next, we define the connection probability for each pair of unconnected nodes to measure the possibility if they are connected, the missing links can be predicted by calculating and comparing the connection probabilities of all the pairs of unconnected nodes. Moreover, we define the break probability for each pair of connected nodes to measure the possibility if they are broken, the spurious links can also be detected by calculating and comparing the break probabilities of all the pairs of connected nodes. To verify the validity of the proposed approach, we conduct experiments on some real-world networks. The results show the proposed approach can achieve higher prediction accuracy and more stable performance compared with some existing methods.