Based on expert interviews, relevant literature research and practical experience, combined with the characteristics of the whole-process engineering consulting model, corresponding hypotheses were put forward and a model of the mechanism of constraints on the application of the whole-process engineering consulting model was constructed. Based on the PLS-SEM method, the mechanism model was verified. The verification results show that policy guarantee, market cultivation, service ability and social awareness all directly affect the application of the whole-process engineering consulting model, and the direct effects are policy guarantee> service ability> social awareness >and market cultivation. Market cultivation and service capabilities play a partly intermediary role between policy guarantee and the application of the whole-process engineering consulting model.

To realize the production-education integration, these two problems, whom to cooperate with and where to start from, should be firstly solved. Therefore, the method of selection of cooperation partners and starting points based on optimal single index principle was proposed in this paper. The global selection models of cooperation partners and starting points were built. The evaluation index systems for enterprises and universities were established respectively. The Analytic Hierarchy Process was applied to determine the weight of each index, the Questionnaire Research was used to score the qualitative index, the Standard Score theory was applied to standardize the original datas of quantitative index. The implementation procedure to select the cooperation partners and starting points based on optimal single index principle was carried out finally by an example. The research results can open a new way and provide a wide research space for selection of cooperation partners and starting points in the production-education integration.

The data of technology-based small and medium-sized enterprises listed on the A-share GEM and SME board from 2015 to 2022 were selected for empirical testing. It is necessary to test whether the development of science and technology finance can positively promote the improvement of the financing efficiency of science and technology SMEs. It is tested whether the financing efficiency of technology-based SMEs can be promoted by improving the information transparency of enterprises and the ability of scientific and technological innovation. The research is carried out from three aspects: the region, growth and property rights of enterprises, and the difference in the impact of science and technology finance on the financing efficiency of science and technology SMEs is further tested. The results show that the development of science and technology finance can significantly promote the improvement of the financing efficiency of science and technology SMEs.

Addressing the difficulties in collecting key data during battery operation and the limited amount of electrochemical impedance spectroscopy (EIS) data, is able to optimize battery performance evaluation and health monitoring, as well as optimize battery usage and charging strategies. The research method involves using data augmentation techniques to increase the sample size while ensuring data quality. The denoising diffusion probability model (DDPM), as an emerging generative model, is applied to enhance battery data. For low dimensional battery data such as current, voltage, temperature, and capacity, the DDPM model is directly applied for data augmentation. For high-dimensional EIS data, the autoencoder (AE) model is first used for dimensionality reduction, followed by data augmentation in low dimensional space, and the enhanced data is restored to the original space. The research results confirm that the proposed data augmentation method can generate high-quality data on NASA(National Aeronautics and Space Administration) and EIS public datasets and effectively reduce computational complexity. The conclusion indicates that this study provides an effective data augmentation strategy for battery performance evaluation and health management, and has certain reference and application value.

Realizing the coordinated development of the Guangdong Hong Kong Macao Greater Bay Area is one of the important goals of the national “14th Five Year Plan” period. Promoting regional collaborative innovation is an inevitable requirement for the construction of a global scientific and technological innovation center in the Greater Bay Area, which is of great significance for the high-quality development of the scientific and technological industry in the Greater Bay Area.The progress and development status of collaborative innovation in the Guangdong Hong Kong Macao Greater Bay Area was reviewed. In response to issues such as incomplete coordination mechanisms for collaborative innovation, unclear efficiency of collaborative innovation services, and significant differences in industrial development levels, it is recommended to enhance the collaborative innovation effect of the Guangdong Hong Kong Macao Greater Bay Area by improving policy and institutional design, enhancing innovation service efficiency, and strengthening the coordinated development of science and technology industries, Continuously promote the high-quality development of the technology industry in the Guangdong Hong Kong Macao Greater Bay Area.

Taking Qianguan Bridge in Dalian as the project background, the structural characteristics and stress conditions of the lower-supported steel pipe concrete tied arch bridge were analyzed. A health monitoring system scheme for the bridge was designed. The architecture design of the system and the types of sensors used for its main monitoring content were introduced. Finite element analysis of the bridge was conducted based on Midas Civil, and the measurement point layout of the bridge monitoring system was determined in accordance with relevant standards. Considering the strong subjectivity and poor overall nature of traditional manual inspections, which consume a lot of manpower, material resources, and financial resources on-site and even affect traffic operations, an intelligent bridge health monitoring platform was developed in the cloud based on BIM(building information modeling). This platform integrates real-time on-site data collection, stable and efficient transmission, rapid analysis of massive data, human-computer interaction, and visualization of models and data. It automatically triggers alerts and notifies relevant personnel through various means, achieving early detection, early warning, and early handling, providing strong support for the ultimate realization of smart bridge management and maintenance.

The failure of AI(artificial intelligence)assistant services is essentially an algorithmic failure, and the “black box” nature of the algorithmic decision-making process exacerbates consumers’ negative reactions to service failures. A between-subjects experimental approach was adopted to explore the impact mechanism of enhancing algorithmic explainability from the perspective of perceived control through explainable artificial intelligence methods(such as post hoc explanations) on consumer behavior in the context of service failure, as well as the boundary conditions. It is found that when algorithmic explainability is enhanced (compared to the control), consumers’ continued intention to use despite service failure is improved, and consumers’ perceived control plays a partial mediating role in this process. However, the above effects are not significant when the anthropomorphism level of the AI assistant is low (compared to high).

With the rise of digital economy, the integration of real economy and digital economy has become the key to drive economic growth. Taking Dalian’s retail industry as an example, the current state of development of Dalian’s retail industry under the background of digital economy was discussed. The difficulties encountered in the integration of retail industry and digital economy was analyzed from the perspectives of government and enterprises. Finally specific strategies are put forward to promote the in-depth integration of Dalian’s retail industry and digital economy, in order to promote the transformation and upgrading of Dalian’s retail industry and achieve high-quality economic development.

Ground penetrating radar(GPR) is widely used for inspecting the quality of tunnel linings. However, the raw GPR data often cannot be directly interpreted and requires various pre-processing such as denoising, gain adjustment, and image smoothing to observe meaningful information. Considering that GPR data processing is currently predominantly manual, with a complex workflow and subjective parameter selection, an end-to-end data processing method is proposed based on generative adversarial network(GAN) that transforms raw GPR data into images with clear signals. The GAN consists of a series of generators and discriminators at different scales, capable of intelligently recognizing both global and local features of GPR data and automatically performing comprehensive processing operations on the raw data. This method has been successfully applied to the processing of actual GPR data for initial lining quality inspection, achieving results comparable to manual processing and a significantly higher data processing efficiency.

Accelerating digital transformation and promoting the deep integration of digital technology with the real economy are urgent priorities for enterprises. Using listed companies from 2013 to 2023 as the sample, the impact of managerial ownership on corporate digital transformation was investigated. The results indicate that managerial ownership significantly promotes digital transformation within enterprises, and this finding remains robust even when accounting for endogeneity issues. This effect is particularly prominent in state-owned and high-tech enterprises. Mechanism analysis reveals that managerial ownership facilitates digital transformation by strengthening internal controls and curbing managerial short-sightedness.