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.

The social network analysis(SNA) method was used to study the stakeholder relationship network in infrastructure investment projects, revealing the key roles and functions of project companies, construction units, supervision units, and survey and design units in the network. Research has found that project companies play a central role in the flow of information and resources, while construction and supervision units play important roles in connecting parties and bridging information. Although the current network density is low, strengthening stakeholder cooperation, optimizing network structure, and enhancing resource sharing can significantly improve project management efficiency and collaboration effectiveness. Specific measures and suggestions are proposed to enhance the overall management level of infrastructure investment projects and the synergistic effects among stakeholders. Future research can expand the scope of stakeholders, conduct dynamic network analysis, and explore the network characteristics of different types of infrastructure projects, provide more refined management strategies.

Under the “dual carbon” strategic goals, green credit has become an important driver for high-quality economic development and the transformation of enterprises. Based on operational data from A-share listed companies in China from 2004 to 2022, the implementation of the 2012 “Green Credit Guidelines” was used as a quasi-natural experiment. Employing double machine learning approach to construct an empirical model, the findings indicate that after the implementation of the Guidelines, the reduction of financing constraints, increased R&D investment, and promotion of joint ownership between banks and enterprises effectively drive continued green innovation in environmental protection enterprises.

The booming live streaming e-commerce has drawn a large number of consumers and businesses to participate. However, research on consumers’ impulse purchase willingness in live streaming is still in its infancy. Based on the SOR theory, from the perspective of consumers’ loss aversion psychology, a structural equation model was developed to analyze and validate the mechanism through which the live e-commerce atmosphere cues affected consumers’ inclination to make impulsive purchases. It is found that the live e-commerce atmosphere cues significantly affect consumers’ perceived risk and anticipated inaction regret. The mediating effect of perceived risk and anticipated regret of inaction is significant when it comes to the influence of live e-commerce atmosphere cues on willingness to make impulse purchases. In the context of live e-commerce, companies can adopt measures such as increasing product discounts, enhancing the visual appeal of the live broadcast room, and activating the atmosphere of the live broadcast room to strengthen the atmosphere cues in the context of live e-commerce, and make full use of consumers’ loss aversion to achieve promotional goals.

Hidden danger investigation is a crucial method for ensuring the safety of air traffic control, and the graded assessment of hidden dangers serves as an important basis for their management. Focusing on the hidden dangers associated with air traffic control, a grading model was established based on cloud theory grounded in expert evaluations. In response to the relatively coarse categorization provided by existing regulations regarding hidden dangers, a more precise evaluation framework was developed to enhance the accuracy of assessments. To improve the reliability of these evaluations, a group decision-making approach involving experts was employed for assessing hidden danger levels. Furthermore, to enhance the objectivity of the evaluation process, a cloud model was introduced for quantitative assessment alongside qualitative analyses. Finally, an illustrative example was presented to demonstrate the evaluation procedure. The research findings indicate that the cloud model-based approach exhibits strong feasibility and applicability.

The maintenance base, as the “backstage” of highway maintenance projects, lacks unified green environmental construction standards and scientific management methods in the process of transformation and upgrading towards greening and intelligence. Therefore, it is unable to qualitatively evaluate the technical level of intelligence and greening of maintenance bases. combining the situation of green maintenance bases in Jiangsu Province and suggestions from relevant experts on the green evaluation system of maintenance bases, based on meeting the pollutant emission standards, the Analytic Hierarchy Process(AHP) and fuzzy comprehensive evaluation method were used to conduct star rating evaluations of green environmental maintenance bases from four aspects, such as green production, energy conservation and emission reduction technology, intelligent management and control, and operational management. A good green demonstration is served for the construction of new types of maintenance bases in the future.

As global environmental issues become more prominent, wind power, a low-pollution renewable energy source, has garnered attention. However, the variability and intermittency of wind resources pose challenges for predicting wind farm output, affecting power system scheduling and operation. To improve the accuracy of wind power forecasting, weather characteristics influencing power output must be fully considered. By modeling and predicting actual wind farm data, the effectiveness of different deep learning models for ultra-short-term forecasting was compared. The results show that a multivariate time prediction method based on a long short-term memory(LSTM) network effectively predicts wind power, achieving higher accuracy and stability than other deep learning models.

In order to make enterprises more scientific and comprehensive when choosing photovoltaic investment projects, a comprehensive investment evaluation index system was established from the environment, economy and risk and a combination of empowerment-TOPSIS model investment decision-making method for photovoltaic projects was proposed. Game theory is used to combine the subjective and objective weights calculated by the order relationship analysis method and entropy weighting method, which take into account the subjective judgment of experts and also integrates the results of objective data analysis to make the decision more comprehensive and scientific. In the investment decision-making stage, TOPSIS is adopted to calculate the overall degree of superiority, and the alternative projects are ranked in order of superiority and inferiority, so as to select the optimal investment object. Finally, the applicability and feasibility of the model are verified with examples, a theoretical basis is provided for the sustainable development of the photovoltaic industry.

With the rapid development of urban infrastructure, shield tunneling has emerged as a key method in modern rail transit construction, inevitably affecting adjacent buildings and structures. Employing FLAC3D numerical simulation method, the entire construction process of shield tunnels passing beneath drainage culverts were simulated. The main focus of this study lied in analyzing the deformation pattern changes at monitoring points during the shield construction and investigating the specific impact of tunnel construction on drainage culvert structures. The findings reveal that the deformation stability of drainage culvert structures remains within a safe range during shield construction, thus providing theoretical support and reference data for similar projects in the future.

Coastal ocean circulation models have improved dramatically in recent decades, but it is still difficult to obtain sufficient data to constrain and validate simulations. National Oceanic and Atmospheric Administration, hereinafter referred to as NOAA, in cooperation with local fishing vessels, provides a low-cost data observing system that has produced a large number of hourly bottom temperature datasets over a wide area of the US Northeast Continental Shelf over a wide area, generating a large number of hourly bottom temperature datasets. In order to provide a better validation of the coastal ocean circulation model and to help fishermen have a better understanding of the fishing conditions, three existing ocean models were combined and the hourly real-time bottom temperature data was used as a reference to visualize the data from each ocean model and the data from multiple ocean models after Bayesian model averaging, which revealed that the performance of the Bayesian combined model was better. The Bayesian combination model is found to perform better.