[Objective] Gamma-ray detection using a nuclear radiation locator is critical for monitoring, locating, and processing radioactive sources. In recent years, gamma cameras based on coded aperture imaging techniques have been extensively utilized to identify and monitor radioactive sources. However, these detectors have limitations in terms of the imaging field. To accurately determine the specific location of radioactive sources, constant adjustment of the detection angle is required, which is often time-consuming. To expand the detection field, multiple coded aperture cameras can be used simultaneously, but this approach increases cost and equipment complexity. Some researchers have attempted to combine Compton and coded aperture imaging techniques. While the Compton camera can extend the field-of-view (FOV) to 4π, this method is complicated, costly, and limited to detecting high-energy rays. As a result, the combination of these two techniques proves inadequate when searching for low-energy sources. In this work, we proposed a system and method for locating radioactive sources with a large FOV based on combining a coded aperture with pinholes. This method addresses the limited FOV issue encountered in the aforementioned system. [Methods] The coded aperture component of the system uses a modified uniformly redundant array as the uniform redundant array mask. The base mode class is 11, with a unit size of 3.3 mm×3.3 mm, leading to a total size of 69.3 mm×69.3 mm. The mask thickness is 9 mm, and tungsten is used as the material. The detector section includes a 26×26 NaI (Tl) array, where each crystal pixel has dimensions of 1.45 mm×1.45 mm×6.00 mm. A crystal gap of 0.2 mm exists between each pixel, and the distance between the center of the coded aperture and the position-sensitive sensor is 77.5 mm.For the pinhole part of the system, a tapered pinhole with a center size of 4 mm is used. The pinhole is embedded in a shield with equally large pinholes on all four sides. For performance assessment of the system, Monte Carlo simulation experiments were performed with GATE software. A large FOV radioactive source location system is constructed, and simulation data are produced. MATLAB is employed to process the simulation data, compute the system transmission matrix using the Sidden algorithm, and conduct reconstruction using the maximum likelihood expectation maximization method. The projection and reconstruction results of the point sources at various positions are compared and analyzed. Thus, this work shows a comprehensive analysis and assessment of the developed system for locating radioactive sources with a large FOV using a combination of coded aperture and pinhole imaging techniques. [Results] The results indicate that the full coding and semipseudo-film FOV of the coded aperture camera are 19.33° and 70.80°, respectively, and the added pinhole extends the FOV of the system to 123.40°. The developed system attains an angular resolution of 2.95° within the coded aperture FOV and 6.30° within the extended pinhole FOV, effectively imaging a 10 mCi radioactive source at a distance of 3 m. [Conclusions] The developed wide FOV radiation source location system and method effectively address the limited imaging field of the coded aperture camera.

[Objective] Saline-alkali soil is an important reserve resource of cultivated land and potential granary in China, and its management and utilization are related to national food security. Therefore, innovative techniques and amendments should be developed to address these challenges in saline-alkali regions. Among these, calcium supplementation is recognized as one of the most effective methods for ameliorating saline-alkali soil. In the past two decades, gypsum from the desulfurization of flue gas (FGDG) in coal-fired power plants has become a preferred calcium source for ameliorating saline-alkali soil because of its high calcium content and economic feasibility. Given that FGDG has developed into a soil amendment and has been widely used, a profound understanding of the progress of its patents can provide technical guidance for the large-scale amelioration of saline-alkali soil. [Methods] Based on the incoPat global patent database, a bibliometric analysis was conducted on 520 invention patents in the field of using FGDG to ameliorate saline-alkali soil from 2003 to 2022. The application and authorization trends, high-yield mechanisms, operational status, substance composition, and their correlation with patents in this field were systematically analyzed. In addition, a comparative analysis was conducted on the effectiveness of 52 patents with application cases. [Results] The results showed that the annual number of patent applications for using FGDG amendments to ameliorate saline-alkali soil has a trend of first increasing and then decreasing, with a peak period of 115 patents in 2016. Most patents take 20-30 months from publication to authorization. However, the overall proportion of authorization has shown a decreasing trend. The number of patents granted by universities and research institutes is higher than that granted by enterprises, whereas the number of patents jointly granted by universities and enterprises accounts for 15.6% of the total. A total of 37 patents were converted, 7 of which were pledged, accounting for 33.3% of the total number of grants, all of which were transferred by universities to enterprises and pledged by enterprises for financing. More than 70% of patents comprised three or more substances, primarily including organic and inorganic minerals, microbial agents, and nutrient supplements. Organic materials can directly provide nutrients for the soil to make up for the shortage of FGDG in terms of nutrients, with the frequency of application as high as 95.7%, followed by inorganic minerals, which account for 44.5%; microbial agents, which account for 41.3%; and nutrient supplements, which account for 21.3%. Compared with soils with or without other types of amendments, the application of FGDG amendments significantly decreased soil pH, exchangeable sodium percentage, and salt ions that are toxic to crop growth and increased soil Ca²⁺, SO₄^2-, and total/available nitrogen and phosphorus contents, which provided a better soil environment, thereby increasing crop yield. [Conclusions] Generally, research and development on FGDG amendments for saline-alkali soil amelioration have matured, and some innovative achievements have been transformed into real productivity; thus, the value of related patents has been increasingly highlighted. However, problems such as the relatively simple composition of current patents, unclear technical requirements for the amount of application and method, and serious homogeneity of patents have been encountered. In the future, we should strengthen the cooperation among schools, enterprises, universities, and research institutes, intensify research on the FGDG formula used in saline-alkali soil, and enhance the application benefits of FGDG amendments.

[Significance] Since the turn of the 21st century, artificial intelligence (AI) has advanced considerably in many domains, including government affairs. Furthermore, the emergence of deep learning has taken the development of many AI fields, including natural language processing (NLP), to a new level. Language models (LMs) are key research directions of NLP. Referred to as statistical models, LMs were initially used to calculate the probability of a sentence; however, in recent years, there have been substantial developments in large language models (LLMs). Notably, LLM products, such as the generative pretrained transformer (GPT) series, have driven the rapid revolution of large language research. Domestic enterprises have also researched LLMs, for example, Huawei’s Pangu and Baidu's enhanced language representation with informative entities (ERNIE) bot. These models have been widely used in language translation, abstract construction, named-entity recognition, text classification, and relationship extraction, among other applications, and in government affairs, finance, biomedicine, and other domains. [Progress] In this study, we observe that improving the efficiency of governance has become one of the core tasks of the government in the era of big data. With the continuous accumulation of government data, traditional statistical models relying on expert experience and local features gradually suffer limitations during application. However, LLMs, which offer the advantages of high flexibility, strong representation ability, and effective results, can rapidly enhance the intelligence level of government services. First, we review the research progress on early LMs, such as statistical LMs and neural network LMs. Subsequently, we focus on the research progress on LLMs, namely the Transformers series, GPT series, and bidirectional encoder representations from transformers (BERT) series. Finally, we introduce the application of LLMs in government affairs, including government text classification, relationship extraction, public opinion risk identification, named-entity recognition, and government question answering. Moreover, we propose that research on LLMs for government affairs must focus on multimodality, correctly benefit from the trend of “model as a service,” focus on high data security, and clarify government responsibility boundaries. Additionally, a technical path for studying LLMs for government affairs has been proposed. [Conclusions and Prospects] The application of LLMs in government affairs mainly focuses on small-scale models, lacking examples of application in large-scale models. Compared with smaller models, large models offer many advantages, including high efficiency, broader application scenarios, and more convenience. These advantages can be understood as follows. In terms of efficiency, large models are usually trained on a large amount of heterogeneous data, thus delivering better performance. In terms of application scenarios, large models gradually support multimodal data, resulting in more diverse application scenarios. In terms of convenience, we emphasize the “pretraining + fine-tuning” mode and the invocation method of interfaces, making LLMs more convenient for research and practical applications. This study also analyzes the issues suffered by LLMs, specifically from the technological and ethical perspectives, which have resulted in a panic to a certain extent. For example, ChatGPT has generated many controversies, including whether the generated files are novel, whether using ChatGPT will lead to plagiarism and ambiguity as to who are property rights owners for the generated files. Overall, it can be said that LLMs are in the stage of vigorous development. As the country promotes research on AI and its application in government affairs, LLMs will play an increasingly crucial role in the field.

Objective: The β coefficient is a critical indicator for stock sector investment, and its stability is essential for making informed future investment decisions based on historical data. The real estate sector, known for its high investment risks and stock fluctuations, plays a crucial role in many investors' portfolios. Although there is a growing body of literature on the β coefficient of the real estate sector, research on its systematic calculation and stability remains limited. This paper analyzes the changes and stability of the β coefficient in the real estate sector, providing valuable insights for investors. Methods: Through method screening, this paper uses the single index equation to calculate the monthly and annual β coefficients of the Chinese A-share real estate sector from 2013 to 2022. After confirming data stationarity, daily data are processed through least squares regression analysis to obtain accurate and reliable monthly and annual β coefficients. The stability of the β coefficient is assessed using the Chow test for adjacent calendar months and years, and statistical analysis is conducted on the results. Ultimately, the study includes a comparative analysis between the real estate, financial, and construction sectors to provide a comprehensive understanding of the β coefficient characteristics. Results: The research results reveal the followings: (1) The monthly and annual mean β coefficients of the real estate sector are close to but less than 1. Monthly β coefficients show significant variability, while the annual β coefficient initially increases and then decreases. (2) The monthly β coefficient demonstrates stronger stability compared to the annual β coefficient. (3) The trajectories of the β coefficient in both the real estate and construction sectors are highly similar, with the stability of the β coefficient in the real estate sector being lower than that of the construction sector but higher than that of the financial sector. Conclusions: There are clear differences in the stability characteristics of the monthly and annual β coefficients in the real estate sector, and these differences vary across different sectors. This paper suggests that the followings: (1) Short-term investors should monitor changes in monthly β coefficients to predict market volatility. (2) For long-term investment decisions based on the real estate sector's β coefficients, timely adjustments should be made according to macroeconomic factors and other variables. (3) When investing across different stock sectors, investors should focus on the volatility relationship among the construction, financial, and the real estate sectors, and adopt appropriate risk hedging strategies to reasonably diversify investment risks.

[Significance］ The issue of climate change is extremely complex and encompasses multiple factors such as the environment, economy, society, and related aspects. With the ongoing maturation of complex system modeling technology, low-carbon transportation research using the computable general equilibrium (CGE) model presents a new approach to policy evaluation. The CGE model has three primary advantages for analyzing the economic challenges of transitioning to low-carbon transportation. First, the approach has a solid microeconomic foundation that can directly reflect the mechanism and influence of economic subjects' behavior under the assumption of a rational economic player. Second, CGE models are capable of fully simulating the connections of different economic sectors, which can uncover the transmission effect of transportation policy impact among various sectors, as well as the response of various sectors to the policy impact. Third, the model has two major types, static and dynamic CGE models, which can analyze the short- and long-term impact of different policies, respectively. As an essential prediction tool for policy impact and trend analysis, CGE models can comprehensively reveal the interaction characteristics between the transportation industry and the whole national economy, enabling the prediction of the economic and social impact of low-carbon transportation policies. ［Progress］ This study investigates contemporary research on transportation policies based on the CGE model. A total of 78 relevant empirical studies are collected from the Web of Science, Science Direct, and China National Knowledge Infrastructure, of which more than 50% focus on predicting the impact of low-carbon transportation policies, indicating that the investigation of traffic-related carbon emissions has gradually become a popular topic of empirical analysis using CGE models. The research topics include: (1) The influence of low-carbon transportation economic incentives, such as carbon tax, emission trading scheme, and transportation subsidies. (2) The application effect of low-carbon technologies, such as electric vehicles and carbon capture and storage. (3) The effect of low-carbon transportation urban planning, including land use, vehicle speed limits, walking-oriented urban design, and bicycle-oriented urban space development. (4) Predicting the economic and social impact of the implementation of nationally determined contributions and fuel economy standards. Previous research establishes a solid foundation for prediction and policy analysis in low-carbon transportation research; however, in the context of China's 2030 carbon peak and 2060 carbon neutrality goals, some issues remain that require further exploration and investigation. ［Conclusions and Prospects］ First, regarding emissions reduction policies, differing transportation needs, transportation structure, energy structure, technical level, and macropolicies will affect transportation carbon emissions. The carbon emissions reduction potential of various policies requires further study, and it is essential to propose structured solutions referencing the prediction and design of composite system transportation emissions reduction policies. Based on China's 1+N policy system for advancing the dual carbon goals, this study constructs a low-carbon transportation policy matrix based on the “avoid/shift/improve-planning/regulatory/economic/information/technological (ASI-PREIT)” structure, producing a proposed “policy basket” for low-carbon transportation CGE modeling. This policy matrix will comprehensively reveal the correlation between policy tools for low-carbon transportation CGE modeling and help put forward structured low-carbon solutions. Second, in terms of model construction, accessibility is the most intuitive factor for transportation. As with other sectors, treating the transportation sector simply as a product production sector risks neglecting network and external benefits; therefore, this study proposes the inclusion of transportation accessibility factors in low-carbon transportation CGE models as spatial computable general equilibrium model to identify regional economic correlations and regional product flow. Third, in terms of synergies, carbon emissions reduction in transportation is crucial to achieving China's dual carbon goals and can advance innovation and economic growth, leveraging a wide range of synergies, including sustainable development, improving public health, and enhancing the overall quality of life. Currently, increasingly severe ecological and environmental challenges are forcing global economies to reassess the GDP-centered development model, seeking balanced and sustainable development strategies that include environment, economy, and society. This study proposes the development of a comprehensive low-carbon transportation CGE model to compare and analyze the optimal solutions for balancing the co-benefits of environment-economy-society from a global perspective and design low-carbon transportation policy combinations to advance sustainable development. In summary, this study endeavors to systematically review the empirical research applying CGE models in the field of low-carbon transportation, provide a reference for expanding the research on low-carbon transportation, and help policymakers and the transportation sector achieve China's dual carbon goals.

[Objective］ The determination of bearing capacity of foundation soil is one of the classical research fields of soil mechanics. For large area artificial fill projects, the current specifications of China do not consider the correction factor of foundation width in the calculation of bearing capacity. This paper discusses this problem, and investigates the definition and measurement methods of the foundation bearing capacity, related theoretical principles, and the method of determining allowable bearing capacity. We also discuss and speculate the possible basis for the selection of correction factors of the bearing capacity of foundation soil made of artificial fill and soft underlying stratum. It may provides theoretical guidance for width and depth correction of bearing capacity of foundation soil in large area artificial fill under current technical conditions. ［Methods］ In this paper, two main approaches for calculating bearing capacity of foundation soil are studied. One is to determine the bearing capacity of the foundation soil according to the extension range of the plastic zone; the other is to use the ultimate load as the ultimate bearing capacity, and then divide it by the safety factor to obtain the bearing capacity. The width and depth correction of bearing capacity is also based on these theories. We reviews the sources of the calculation method of bearing capacity together with the width and depth correction factors in the current specifications of China. On the basis of deriving the calculation formula and analyzing the principle of bearing capacity of foundation soil, two cases are investigated in this paper: one is a homogeneous foundation soil, and the other is that the shear strength of the soil outside the foundation boundaries is lower than the foundation soil. The possible essence for the correction factors of the bearing capacity is then discussed in order to guide the determination of the width and depth correction factors for large area artificial fill. ［Results］ For large area artificial fill projects, the correction factor of width is not considered in current specifications of China. The main reasons may be as follows: 1) The compaction quality of the foundation soil is not be able to guarantee easily. 2) The quality control standard of the soil under the foundation is stricter than that outside the foundation boundaries. 3) Post-construction settlement may occur for artificial fill. Since that the existing construction technology and quality control level has made great progress compared to the past, it is theoretically feasible to increase the correction factors of width and depth for large area artificial fill such as in island and reef. However, the following items are needed: 1) The degree of compaction should meet the requirement in the fill site. 2) The engineering quality of the fill outside the foundation boundaries should also be guaranteed not to be lower than the foundation soil. 3) Settlement calculation of the buildings are necessary. ［Conclusions］ Based on the theories of determining the bearing capacity of the foundation soil, this paper investigates the method of selecting the values of correction factors of width and depth. It is thought that the correction factors of width and depth can be appropriately increased under certain conditions for large area artificial fill. This will have good economic benefits, especially for offshore islands and reefs.

[Objective] The land market plays a key role in achieving a stable and healthy market for real estate development and has a significant impact on macroeconomic conditions, government finances, and overall financial stability. However, many participants in the Chinese land market engage in blind expansion and irrational land acquisition, which interferes with achieving policy objectives such as price stability for land and housing, as well as the healthy development of the land market. This study analyzes market feedback of land auction participants and the factors influencing them. We use auction theory to examine the existence of the winner's curse phenomenon in the Beijing land market and provide policy recommendations for the government to regulate the land market. [Methods] This study is based on micro-level auction data from land sales in Beijing between 2013 and 2018 and from the Wind enterprise database. We first construct models to calculate cumulative abnormal returns for the auction participants' stock prices in the periods following land auctions. We then use an event study to explore the effects of participant, land, and auction characteristics on stock price changes. Particular attention is given to market feedback on the auction winners. The uniqueness of this study lies in the vast amount of data used. We consider factors that are crucial elements of market feedback but have been relatively unexplored in previous studies, such as participants' bidding premiums, past experiences in land auctions, and joint bidding. [Results] We find that:(1) The higher the final bidding premium of land auction participants, the more negative the market's reaction. Previous experience with repeated bidding and joint bidding enables participants to access more market information, helping mitigate irrational bidding. Variables such as land value, bidding intensity, and the frequency of winning bids on a single day that reflect a bidder's economic strength lead to more positive market evaluations. (2) Evidence of the winner's curse phenomenon is observed in the Beijing land market. Although cumulative abnormal returns do not show significant inter-group differences between winners and losers, results of controlling for the final bidding premium reveal that higher bidding premiums result in more negative market evaluations for the winners. Joint bidding helps winners to make rational bids, but the effect of repeated participation in the short term is not significant. (3) The market holds a significantly negative view of bidders who are active over an extended period, and this effect is more pronounced for the winners, providing additional evidence for the existence of the winner's curse phenomenon. [Conclusions] Based on these findings, we recommend the government to enact policies to encourage market participants to make rational bids. This could be achieved by promoting complementary advantages and sharing market information through joint bidding to some extent. The government should also enhance information disclosure through various means to alleviate information asymmetry in the market and strengthen supervision of active market participants' funds and the development and construction processes to reduce irrational bidding behavior.

[Significance] Climate change is the primary challenge that intensely affects sustainable human development. The transport sector has been one of the major sources of carbon emissions and is considerably affected by climate change. Because of the growth of China's economy and total transport demand, transport-related carbon emissions are also gradually increasing. Moreover, frequent complex and extreme climate events with clear regional differences have negatively affected the construction, maintenance, and operation of the transport infrastructure. Therefore, China's transport sector needs to reduce carbon emissions for green and low-carbon developments and improve its adaptability and resistance to various adverse climatic conditions. However, China's transport sector still faces many challenges in mitigating and adapting to climate change, and its policy tools, measures, and basic capacity to cope with climate change need to be enhanced. Therefore, transport sector-related strategies and routes to adapt to climate change need to be explored. [Progress] First, the policies and measures implemented in different countries to address climate change were introduced from the perspectives of mitigation and adaptation. Second, the advancements made by China's transport sector in mitigating climate change were summarized from the perspectives of the construction of green and low-carbon transport infrastructure, optimization of the transport structures, and promotions and applications of new and clean energy. The measures implemented to adapt to climate change in China's transport sector were summarized from the perspectives of improving the adaptability of the transport infrastructure, strengthening the monitoring and warning systems of climate change, and managing risk. Third, the interactions between each subfield and sublink of the transport system and climate change, as well as the main measures implemented to mitigate and adapt to climate change in the transport sector, were analyzed. Finally, key areas, strategies, and methods to mitigate and adapt to climate change were proposed. [Conclusions and Prospects] Analysis results are provided and discussed. First, the current plan for China's transport response to climate change needs improvement. The capacity to respond to climate change has not been planned at the subfield and sublink level of the transport system. For mitigating climate change, carbon emissions reduction measures, such as the promotion of new energy vehicles and ships, as well as the optimization of the transport structure, are inadequate. Furthermore, the assessment of the effects of the transport infrastructure on climate change is still in its infancy. Second, the direction of the transport system's development should be combined with the strategic requirements of mitigation and adaptation to climate change. Third, in the transport field, the infrastructure, equipment, and transport structure should be improved; moreover, the infrastructure should be adapted to climate change, and emergency support of transport equipment and transportation organization in extreme weather should be optimized to enhance the capability to adapt to climate change. Finally, the following measures are proposed: Mitigation and adaptation to climate change should be jointly and appropriately implemented to comprehensively address climate change in the transport sector. Greenhouse gases and air pollutants should be jointly controlled to realize the goal of “double carbon”. Adaptation to climate change should be applied in conjunction with ecological protection and restoration to strengthen the capacity of the transport sector to adapt to climate change.

[Objective] The arc fault of the low-voltage distribution system is one of the primary causes of residential fires. Due to the diverse load types and complex connection methods in residential areas, arcs fault exhibit many similar and concealed characteristics, making them difficult to detect. This frequently leads to issues with arc fault protection devices, such as false alarms and missed detections. The conventional detection method, which is based on manually extracting arc fault feature vectors, is incomplete and heavily relies on expert-designed features. Consequently, this impedes the development of highly generalizable models. In addressing these challenges of multiload systems, this paper proposes a method for serial arc fault diagnosis and load recognition based on a one-dimensional dilated convolutional neural network (1D-DCNN). [Methods] First, a series of experiments on multiload arcs fault are conducted using a custom-designed experimental platform. This platform supports both single-load and dual-branch load conditions during testing. Normal and faulty current data on the main bus are sampled under various operating conditions at a rate of 500 kHz. During the data processing phase, the continuous time series data are discretized and normalized based on the half-cycle length. Subsequently, a 1D-DCNN is used to extract features from the high-sampling-rate arc fault current data. Furthermore, the scaled exponential linear unit activation functions and residual connections are introduced to address the challenges of gradient vanishing and network degradation. Moreover, the cyclic padding method is adopted to alleviate boundary effects and enhance the model's robustness to dataset shift. The arc fault detection model is developed by integrating average ensemble learning with a Softmax multiclassifier. Precision, recall, and specificity are used to assess the efficiency of the model. Finally, the accuracy of the proposed model in load classification, load state recognition, and overall accuracy is compared with that of other classical models, providing a comprehensive assessment of its efficacy. [Results] The findings of this method were as follows: (1) The accuracy of arc detection using a recurrent neural network was considerably low, primarily due to gradient vanishing and exploding gradients, making it difficult to effectively train the model. (2) Under the condition of equal parameter count between a 1D-DCNN and 1D-CNN, the dilated convolutional operation expanded the receptive field, resulting in greater accuracy than the 1D-CNN model. (3) The proposed method achieved a remarkable accuracy of 99.67% in detecting arc faults for both single-load and mixed-load scenarios, with an accuracy of 99.95% and an overall accuracy of 99.62%. [Conclusions] This research presents a unique model capable of autonomously learning features from high-sampling-rate current data without requiring manual feature extraction. It efficiently detects arcs fault while identifying the type of faulty load simultaneously. The model outperforms typical convolutional neural networks on validation of the test set, thereby meeting the requirements for arc fault identification. This advancement has major implications for serial arc fault detection and load recognition applications.

[Objective］ In the airport ground-transport system, it is operationally important to match the evacuation requirement of passengers, and the capacity of multimodal transport vehicles is crucial. Numerous studies have investigated single-mode transport capacity allocation; however, research on multimode allocation is scarce. ［Methods］ To mitigate the difficulty in realizing an exact match between the evacuation demand of passengers and the capacity of multimodal transport, a bi-level programming model for multimodal transport resource allocation is proposed according to the analysis of the interaction between capacity allocation and passenger travel choice. A utility function of multiple travel modes, including airport buses, metro, taxis, and private cars, is formulated with the following four features: travel time, travel cost, punctuality, and comfort. The upper-level objective is to minimize the total enterprise-operation cost, passenger-waiting cost, and carbon emission cost for optimizing the headway of public transit and the taxi arrival rate, which is subject to the capacity of each transit mode, the range of each public-transit headway decided by fixed equipment, and the range of taxi arrival rate decided by the capacity of boarding location. Based on the output of the transport capacity allocation scheme used by the upper level, the low-level objective is to assign the passenger flow toward multiple travel modes according to a stochastic user equilibrium-logit model with a utility function. Furthermore, an improved genetic algorithm combined with method of successive algorithm (MSA) is designed to solve the proposed bi-level programming model. To improve the solving efficiency of the algorithm, a pre-search mechanism is proposed, in which the infeasible solution is filtered out using low-precision MSA to reduce the computational cost of repeatedly calling the low-level model. ［Results］ The Beijing Daxing International Airport was considered as a case study to illustrate the efficiency and effectiveness of the proposed bi-level programming model in optimizing transport capacity allocation in airport ground-transport centers. The transport capacity allocation scheme obtained via the proposed model reduced the average passenger-waiting time and the total carbon emission of the system by 14.08% and 6.21%, respectively, while increasing the operation cost by only 1.32%. Moreover, the optimized capacity allocation scheme resulted in the switching of 6.7% of passengers who availed taxis and private cars to buses and metro, which were more environmentally friendly. The proposed solution algorithm could efficiently solve the bi-level model. Under the pre-search mechanism, the generation time of the scheme was 217.6 s, which could meet the production demand within the acceptable time. ［Conclusions］ Results show that the optimized scheme obtained from the bi-level model and algorithms is considerably better than before. The proposed scheme reduces passenger-waiting time and the carbon emissions of the multimodal transport system at a negligible cost. Using the optimized scheme, the organizers of airport ground-transport centers can coordinate the capacities of landside multiple transport modes and guide passengers reasonably. This will reduce operation costs, improve airport landside traffic structure, and encourage green and low-carbon travel.

[Significance] The increasing demand for diversified space missions necessitates addressing the extreme heat transfer challenges in space nuclear power systems, such as spatial constraints, long-distance unpowered cycles, and zero-gravity environments. Thus, the stable transfer of the substantial heat generated by the reactor core to the energy conversion device is crucial. High-temperature heat pipes, characterized by high heat flux, high operating temperatures, minimal temperature differences in heat transfer, and strong adaptability, are ideal for the key components of nuclear thermal energy conversion in space power systems. Traditional nuclear reactor power systems using coolants are less competitive in space because of their complexity, leakage risk, and stringent material strength requirements. Conversely, space heat-pipe-cooled reactors do not require auxiliary equipment such as high-temperature pumps. The phase change of the working fluid and the diffusive transport of vapor in a high-temperature heat pipe form a natural cycle that transfers heat from the core to the thermoelectric converter, thereby reducing system complexity, enhancing safety and reliability, and thus providing an effective solution for efficient heat transfer and energy conversion in space nuclear power systems. [Progress] The research and development of high-temperature heat pipe technology are pivotal for improving energy conversion efficiency and heat transfer performance in space nuclear power applications. Research on high-temperature heat pipes encompasses working fluid flow and heat transfer mechanisms, model establishment, experimental verification of frozen startup and heat transfer limits, steady-state heat transfer experimental analysis, and failure mechanisms, yielding promising results. As this technology advances, its applications and research in space nuclear thermal energy conversion systems have become more extensive and in-depth. Studies have focused on the startup and safety performance of space nuclear power systems, the coupling performance of high-temperature heat pipes in nuclear thermal energy conversion systems, and the research and design of high-temperature heat pipes in the radiators of space reactors. However, high-temperature heat pipes face challenges in achieving efficient thermal energy transfer and management, structural design of heat pipes and wicks, and adaptability to extreme space environments when applied to space nuclear power systems for thermal energy conversion. In response to these challenges, new research and attempts have been conducted. Studies on heat pipe performance under microgravity conditions have demonstrated their feasibility. In addition, research on shaped high-temperature heat pipes designs more flexible and efficient structures to meet complex heat transfer requirements. Furthermore, studies on additive manufacturing, aerogel insulation, and advanced testing techniques provide theoretical support and a technical foundation for the space application of high-temperature heat pipes. The current research on the space applications of high-temperature heat pipes still has some limitations. Ground-based tests of high-temperature heat pipes have advanced but cannot match the demands of real space scenarios. This mismatch prevents us from knowing their true performance in space reactors. Moreover, studies on the performance of high-temperature heat pipes coupled with nuclear reactors and thermoelectric converters are scarce. Thus, the overall coupling performance is largely unknown. [Conclusions and Prospects] High-temperature heat pipe technology shows promise, but still faces challenges in space applications. Currently, most space heat-pipe-cooled reactors are in the design and feasibility exploration stages. Future research will focus on optimizing high-temperature heat pipe models and their applicability, exploring the heat and mass transfer mechanisms of working fluids in space environments, conducting theoretical research and complex wick design and manufacturing studies for shaped heat pipes, and ensuring reliable coupling of high-temperature heat pipes with other components in space nuclear power systems.

Objective: Tunnel fires pose remarkable challenges for evacuation and fire rescue operations due to inadequate ventilation and associated hazards, such as smoke accumulation, elevated temperatures, rapid heat release rates (HRRs), and severely reduced visibility. While various monitoring techniques, such as thermocouples, fibers, and CCTV cameras, have been proposed to monitor fire development trends and assist in firefighting and evacuation efforts, obtaining critical tunnel fire information, specifically real-time fire HRR and fire source locations, remains challenging. These difficulties arise mainly because conventional detection methods are often disrupted by high temperatures or obstructed by dense smoke, hindering effective information transmission. Hence, an improved method to predict tunnel fires is urgently needed. Methods: In this study, external smoke images, i.e., the smoke structure observed from outside the tunnel gate, and CNN-based deep-learning algorithms are used to predict real-time fire HRR and location within the tunnel. A 100-m full-scale tunnel is selected as the target, and its behavior is simulated using the Fire Dynamics Simulator to form an image database. During simulation, different fire parameters, such as maximum HRR, soot yield rate, and location, are varied based on typical vehicle types found in real tunnels, resulting in approximately 900 different tunnel models that generate diverse external smoke morphologies. The simulated smoke images are captured at 1 s intervals from four observation angles: front and side views from the left and right tunnel gates. As a result, approximately 388, 800 smoke images are collected in the database. For the deep-learning algorithm, the VGG16 model, proposed by the Oxford CNN team, is employed as the target AI model for tunnel prediction. During model training, the VGG16 model continuously refines its internal parameters to minimize the error between AI predictions and the FDS simulation. Results: Results show that the proposed method can effectively predict real-time variations in fire HRR variation and location. The model trained using front-view images from both tunnel gates achieved the highest prediction accuracy, with an HRR error of less than 25% and a location error of less than 10 m. Additional tunnel simulations were conducted to further validate the robustness of the proposed method. In these simulations, the fire source is not stable but continuously moving within the tunnel at velocities ranging from 0 to 2 m/s, simulating a scenario where a vehicle catches fire but does not stop immediately. The results show that, although trained on stable fire cases, the AI model still maintains high accuracy in predicting the moving fire source, with small HRR and location errors, thus confirming the effectiveness of the smoke image-based detection method. Conclusions: Notably, further efforts are still necessary for the application of this method in real tunnels because the current work does not consider the complex background interference in actual smoke images, nor does it consider the impacts of environmental factors such as wind, sprinklers, and exhaust systems on the external smoke structure. However, this study represents an important first step toward predicting tunnel fires based on external smoke, which could play a valuable role in future smart fire prediction and firefighting applications.

[Objective] Optimising travel structure, improving travel efficiency, and reducing transport carbon emissions are essential paths to green and low-carbon transport development. Research into fine-grained carbon management has received much attention in recent years. However, the implementation is complex, and setting a price on carbon estimation tends to elicit negative feelings from travellers. [Methods] In the concept of mobility as a service (MaaS), the service can provide an end-to-end travel service by the combination of multi-transport modes, including roads and public transport, as well as many new forms of transportation. Thus, the service provider can realise flexible price adjustments for multi-transport modes and sections in a single trip. Consequently, this paper proposes a low-carbon-oriented pricing strategy for the service provider. From the different perspectives of the MaaS servicer, travellers and the environment, we propose a multi-objective optimisation model. The object includes maximising service providers' revenue and minimising network travel time and transportation network carbon emissions. The model is a two-layer planning model. The upper layer of the model is the process of finding decision variables to calculate the objective function. The lower layer is the joint traffic mode and route choice process, as well as traffic equilibrium allocation in a multi-modal transportation network. In this model, the joint choice of mode and route of travellers depends on the upper-layer decision variables. Then, to solve the above optimisation problem, the reference point based non-dominated sorting genetic algorithm (NSGA-Ⅲ) and the method of successive algorithm (MSA) are introduced. [Results] The case study was conducted on an example network with 1 origin-destination pair, 16 sections in 3 traffic modes (travel by car, bus, and metro), and 6 nodes. Three representative strategies of Pareto solutions were selected, including optimise service provider benefits (OP-S), optimise network travel time (OP-T), and optimise transportation carbon emissions (OP-C). Furthermore, the original (OR) state was also presented as the background. The result showed that the travel price significantly increased in OP-S, which was unfriendly to travellers. In contrast, OP-T and OP-C were respectively metro-friendly and public transport-friendly strategies. Compared with the OR state, service benefits and carbon emissions were optimised, which means that the service provider could achieve emission reductions in multi-modal transport networks while ensuring their own profitability through rationalised regulation of service pricing. The traffic volume analysis also proved that the service provider could optimise the network travel mode structure, thereby reducing road congestion and increasing the share of public transport. By comparing the results of the optimisation strategies under different demands, we found that with the travel demand increased, the service provider benefits continued to grow (especially in OP-S). Although traffic carbon emissions increased, the optimisations could always reduce the traffic carbon emissions of the system. [Conclusions] This paper validates the feasibility of travel service pricing strategies in multi-modal network traffic optimisation and low-carbon transport development. Service providers should not only seek to maximise their own revenue but also take into account the cost of travel and its impact on the transport environment and take responsibility for the coordination and reduction of transport system emissions. This paper identifies the profitability and responsibilities of travel service providers in the green and low-carbon development of transport and provides a basis for service pricing strategies.

[Objective] Many recent studies have indicated that graph neural networks exhibit a lack of robustness when facing adversarial attacks involving perturbations in both graph structures and node features, and the subsequent predictions of these networks may become unreliable under such circumstances. This issue affects graph contrastive learning methods similarly. However, the existing evaluation of robustness methods is often entangled with attack algorithms, data labels, and downstream tasks, which are best avoided, especially within the self-supervised setup of graph contrastive learning. Therefore, this paper introduces a robustness verification algorithm for graph contrastive learning to assess the robustness of graph convolutional networks against node feature adversarial attacks.[Methods] To begin with, considering the nature of positive and negative pairs found in graph contrastive learning models, this paper defines the robustness verification problem of graph contrastive learning as a similarity comparison between adversarial samples and the target node along with its negative samples. This problem is then expressed as a dynamic programming problem, which avoids dependency on attack algorithms, data labels, and downstream tasks. To address this dynamic programming problem, a series of novel and effective methods are proposed in this paper. For the binary attributes commonly used in graph data, corresponding perturbation spaces are therefore constructed here. Considering the challenge posed by a large negative sample space in graph contrastive learning, a negative sample sampling strategy is designed to improve the efficiency of problem-solving. In cases where binary discrete attributes and nonlinear activation functions render the dynamic programming problem difficult to address, this paper employs relaxation techniques and uses dual problem optimization methods to further improve the solution's efficiency.[Results] To assess the effectiveness of the proposed graph contrastive learning robustness verification algorithm, we conducted experiments using the classic GRACE model on the Cora and CiteSeer datasets. Employing the robustness verification algorithm introduced for graph contrastive learning, we evaluated its robustness. As the perturbation intensity increased, the proportion of nodes that were verified as robust decreased rapidly, and the proportion of nodes that were verified as non-robust increased significantly. Simultaneously, the proportion of unverifiable nodes remained at a lower level. These observations show the effectiveness of the proposed framework for verifying the robustness of graph contrastive learning. Additionally, the experiments revealed that the robustness of the contrastive learning model ARIEL, designed against specific attack algorithms, lacks generalizability and exhibits poor verifiable robustness performance, suggesting its vulnerability to other attack algorithms. Besides, ablation experiments identified the adversarial attack components of ARIEL as the main reason for its diminished verifiable robustness. Lastly, parameter experiments demonstrated the reasonability of the proposed negative sample sampling strategy. The results showed that sampling 20 negative samples is sufficient to achieve a favorable performance of our robustness verification algorithm with high efficiency.[Conclusions] Through the analysis of our methods and experimental results, the graph contrastive learning robustness verification algorithm proposed in this study not only eliminates dependency on attack algorithms, data labels, and downstream tasks but also presents a more comprehensive measurement compared to traditional robustness metrics. It can verify robustness in multiple directions, thereby boosting the development of comprehensively robust graph contrastive learning algorithms.

[Objective] Oblong holes are commonly used across various industries to improve fault tolerance and adjustment capabilities. However, their complex geometric characteristics pose significant challenges for vision detection and location algorithms in industrial applications, impacting their utilization in automatic assembly processes. [Methods] This research investigates a high-precision and robust vision segmentation and location algorithm tailored for oblong holes. First, the geometric features of oblong holes, which are symmetric but lack a simple analytical description, are analyzed. This complexity renders traditional imaging methods ineffective for accurate localization. The detection and segmentation of oblong hole features are conducted using a novel vision location algorithm that integrates deep learning with conventional image processing techniques. Specifically, the algorithm employs a sequential connection framework of YOLO and fully convolutional networks to achieve accurate localization. This framework first identifies the region of interest and then performs semantic segmentation. YOLO networks rapidly detect the region of interest, prioritizing areas where the oblong hole is prominently featured. Semantic segmentation is subsequently performed using fully convolutional networks. Afterward, a skeleton feature extraction method based on medial axis transformation is applied to precisely locate the oblong hole. This method effectively reduces the impact of shape errors from semantic segmentation, achieving subpixel accuracy. However, medial axis transformation may produce redundant lines owing to the presence of image artifacts, potentially leading to inaccuracies. To address this issue, principal component analysis is employed to approximate the center of the oblong hole, thereby minimizing errors. For further precision, a Hough transformation ellipse detection method is utilized to identify the central skeleton of the oblong hole, which is interpreted both as a line segment and a special ellipse. The center of this skeleton represents the center of the oblong hole. [Results] Experimental validation conducted in a specific robotics automatic assembly system confirms the effectiveness of the proposed algorithm. The robustness of the algorithm is further demonstrated through image sampling using camera hardware distinct from that used in the training dataset. Additionally, the impact of surface features and oblong hole shapes on the detection performance is analyzed. The experimental outcomes indicate the optimal performance of the algorithm on objects with nonreflective surfaces, with minimal effect from the shape of the oblong hole on accuracy. Despite potential deformations in segmentation output due to hardware variations, the oblong hole region degenerating location algorithm, based on medial axis transformation, accurately locates the center. The final location error is recorded at 1.05 pixels, which surpasses the accuracy achieved through the direct calculation of the center of gravity of the segmented region. These results underscore the substantial benefits of the algorithm in scenarios with varying hardware and object conditions, demonstrating its high accuracy and exceptional robustness. [Conclusions] By merging deep learning techniques with traditional image processing methods, the location tasks for diverse objects are effectively resolved. The extraction of highly nonlinear features through deep learning, followed by processing with traditional image methods incorporating prior geometric knowledge, enhances the robustness and accuracy of the algorithm, making it suitable for practical production applications.

[Significance] Aerospace vehicles have undergone significant modifications in terms of aerodynamic shape, flight speed, flight environment, and flight duration compared with conventional flight vehicles. They must withstand harsh aerodynamic thermal environments for long durations and maintain a sharp leading-edge shape with a high lift-to-drag ratio, imposing extremely stringent requirements on the temperature resistance, durability, structural efficiency, and reliability of the thermal protection system. Traditional thermal protection depends largely on passive methods such as heat insulation, heat sink, and radiation heat dissipation. Although the thermal protection performance of related technologies has improved, which is restricted by several constraints, such as ensuring that the prototype is safe under harsh conditions of extremely high heat flux and ultrahigh temperature along with structural stability, long-term operation, light-weight nature, and repeatability. Thus, a new active thermal protection technology is necessary. In this context, transpiration cooling technology offers the advantage of high thermal efficiency without requiring any changes in the prototype of a vehicle. It has been widely considered a potential active thermal protection technology. However, when transpiration cooling is used for thermal protection of a flight vehicle, some challenges related to the complexity of the system, a mismatch between coolant supply and demand, unstable control of the operation, and development of a high-precision prediction model etc., arise. [Progress] Research on transpiration cooling primarily focused on quick evaluation of performance, numerical simulation of flow and heat transfer, evaluation of cooling mechanism performance, development of optimal control algorithm for efficiency, and optimization of structure form and yielded beneficial results. However, several fundamental scientific issues needed to be urgently addressed to fully realize the engineering application of this technology in aerospace vehicles. In the context of numerical simulation, the accuracy and adaptability of the heat and mass transfer model should be improved. Most existing studies had mathematically described and solved the physical process of heat and mass transfer in porous media at the macroscale. But some parameters related to specific phase change heat and mass transfer (such as evaporation/condensation coefficient and fluid-solid convection heat transfer coefficient) that affect the model's accuracy must be modified through experiments, and the adaptation was partially successful. Most existing models assumed that the temperature of porous media, liquid phases, and gas phases were equal. Although a few models explored the nonequilibrium effect between porous media and fluids, they did not consider the nonequilibrium effect between gas and liquid phases. There were few flight experiments in the research and a large gap between the ground experimental test and practical use conditions. Furthermore, extreme effects related to high-temperature, real, and rarefied gases and shock wave/boundary layer interference during high-speed flight could not be effectively reproduced on the ground. Moreover, there was a lack of experimental data that could be used to verify the accuracy of the heat and mass transfer model. The experimental test method was relatively simple, and the flow and heat transfer process of the liquid in the porous medium could not be obtained. It was challenging to effectively obtain the boundary layer flow law of the liquid when it entered the high-speed mainstream flow from the porous medium. In terms of control strategy, the present research on transpiration cooling control systems lacked a transient simplified mathematical model that could be quickly established, particularly for liquid phase change transpiration cooling with the multiphase flow and phase change process. Simultaneously, there were few transpiration cooling control systems with practical engineering values based on modern control theory, which made it difficult to achieve optimal performance in practical engineering applications. Some adaptive and self-driven transpiration cooling systems had been proposed as new forms of transpiration cooling structures; however, they were still at the mechanism verification stage, and the engineering application effect needed to be verified. [Conclusions and Prospects] Follow-up research will focus on the micro/mesoscale fine numerical calculation model, advanced visual experimental testing methods, rapid response-precise control strategies, self-driven and adaptive structural engineering systems, and combined active and passive thermal protection.

Objective: Due to the high flammability of nonflame-retardant pure acrylonitrile-butadiene-styrene (ABS), a material often used for passenger luggage, it is easily ignited by open flames, posing risks to aviation operations. Therefore, in-depth research on the pyrolytic combustion characteristics of ABS at high temperatures and high radiation intensities is crucial for the safe operation of aircraft. Methods: This study evaluated the thermal stability and combustion characteristics of ABS under different heating rates and radiation intensity conditions using thermogravimetric analysis and cone calorimeter systems. This study also analyzed the variations in the characteristic parameters of ABS. Results: The results show that the pyrolysis process of ABS can be divided into an initial volatilization stage, a rapid decomposition stage, a residual combustion stage, and a pyrolysis termination stage. In the rapid decomposition stage, when ABS reaches temperatures of approximately 310 ℃ to 343 ℃, the main polymer chains of ABS undergo cleavage, breaking down into different components, such as acrylonitrile and polyethylene monomers, leading to the decomposition of polymer molecules. When heated, the main chain of ABS ruptures. The molecular structure of ABS contains different components, such as styrene and butadiene, which are prone to decomposition and cross-linking reactions upon heating, resulting in the occurrence of the pyrolysis process. An increase in heating rate significantly shortens the pyrolysis time and enhances the maximum thermal decomposition rate. As the radiation intensity increases, the combustion process of ABS accelerates, with the heat release rate increasing and the peak heat release rate increasing by 53%. The combustion and ignition times decrease by 32% and 78%, respectively, because of the increase in material temperature and the exacerbation of heat conduction and convection phenomena leading to an increase in heat release rate. Under low radiation intensities, ABS cannot rapidly absorb energy to reach combustion conditions. However, as the radiation intensity increases, ABS can rapidly absorb sufficient energy for faster decomposition, thus shortening the combustion time. The generation time of carbon monoxide (CO) and carbon dioxide (CO₂) is enhanced, and the maximum generation amounts of CO₂ and CO increase by 49% and 74%, respectively. The oxygen consumption increases and the oxygen consumption rate accelerates due to the intensified molecular motion caused by thermal radiation, leading to a faster reaction with oxygen in the air. The mass loss time is enhanced, the remaining sample mass decreases, and the maximum mass loss rate increases by 53.8%. Based on the thermal penetration model, 2 mm thick ABS material is classified as a thermally thin material, and verification is conducted. Based on the ignition time model, a critical radiative heat flux formula is established, and the critical radiative heat flux is calculated to be 16.255 kW/m². Finally, according to the fire performance indicators, as the radiation intensity increases, the material combustion rate increases, releasing higher amounts of heat, leading to faster fire growth and development, thereby increasing fire risk. The fire risk of ABS is positively correlated with the radiation intensity. Conclusions: This study concludes that ABS exhibits a high fire risk. This research provides crucial data and practical references on the fire risks associated with ABS material for safe aviation operations.

［Objective］ The feedforward controller is crucial to achieving nano-level motion accuracy for the lithography wafer stage under high acceleration and deceleration conditions. Traditional 4-order feedforward is widely used to control precision motion systems because of its intuitive physical meaning and simple parameter tuning. However, its capacity to fit the inverse model is inadequate, and it is difficult to eliminate the repetitive error caused by the input trajectory. Therefore, a feedforward control architecture using the 4-order feedforward and an extra rational fraction compensator is proposed. ［Methods］ In this study,the input signal of the compensator is the higher-order derivative of the reference trajectory, and the numerator and denominator of the compensator use the delay unit as the basis function. Therefore, obtaining the unknown parameters of the basis function is crucial to the design. This paper proposes a data-driven iterative parameter tuning strategy for the compensation controller. The difficulty is that the tuning problem is a nonconvex optimization problem, making global parameter optimization challenging. This paper uses the relevant rules of system identification to address the issue at hand. The purpose of adding compensatory feedforward is to eliminate the residual error after using the 4-order feedforward, which is equivalent to achieving a zero-generalized error. Since the generalized error has a linear connection with the compensator parameters, the original nonconvex optimization problem is successfully transformed into a convex problem by minimizing the 2-norm of the generalized error. Through the above transformation, the global optimal point is obtained by the Gauss—Newton method, and the step size condition for ensuring iterative convergence is provided. In addition, the gradient and Hessian matrix of the objective function need to be incorporated into the parameter updating law, even though their exact values are difficult to obtain. This paper derives their unbiased estimates using two impulse response experiments and 2 trajectory tracking experiments. ［Results］ The proposed method was applied to the wafer stage of the lithography machine, and the experiment showed the following results: (1) Using the proposed method to tune three compensation controllers with different orders, their error 2-norm almost converged after five iterations. (2) After adding compensation feedforward, the acceleration and deceleration phase errors were reduced from ±35 nm to ±10 nm; the constant velocity phase error was almost equal to the positioning error, and its trajectory tracking effect was very close to that of iterative learning control (ILC) compensation. (3) Compared with the existing compensation controller parameter tuning method, the maximum moving average and moving standard deviation at velocity phase of the proposed method were smaller, and the lower the compensator order, the more obvious the advantage. (4) After changing trajectory, the proposed compensator could still achieve a better control effect than ILC compensation. ［Conclusions］ The above experiments verify the convergence performance of the proposed parameter tuning algorithm. It is shown that the proposed feedforward compensation architecture can effectively eliminate the residual repetition error of the 4-order feedforward; simultaneously, it can adapt to variable trajectories. In addition, compared to the current compensator tuning result, this method can achieve a superior trajectory tracking control effect while using a low-order compensation controller.

[Significance] The supercritical carbon dioxide (S-CO₂) Brayton cycle is a power cycle at intermediate temperature and high pressure. This cycle is considered an important solution to improving the efficiency of traffic power systems such as gas turbines and internal combustion engines by recycling exhaust energy at high temperatures. The compressor is considered one of the most important components of this cycle. Its efficient and stable operation plays an important role in cycle performance. [Progress] In this paper, the research progress on S-CO₂ centrifugal compressor flow characteristics was reviewed from four aspects: experiment, one-dimensional flow analysis, three-dimensional flow characteristics, and flow control. Researchers learned from the experimental studies of the S-CO₂ centrifugal compressor that the special thermophysical properties of S-CO₂, particularly their dramatic change near the critical point, brought great challenges to the design and stable operation of this compressor. Therefore, the problems of compressor flow caused by the drastic physical properties change near the critical point of the working medium, and the related research contents were emphatically expounded. The current research on one-dimensional flow analysis of the S-CO₂ centrifugal compressor is mainly conducted by the one-dimensional mean streamline method considering the special thermophysical properties of S-CO₂ fluid. The preliminary aerodynamic design of the S-CO₂ centrifugal compressor was conducted using one-dimensional flow analysis. This method is limited by its prediction accuracy under off-design conditions. In addition, the flow details inside the compressor could not be obtained by this method. To reveal the flow mechanism of the S-CO₂ centrifugal compressor, its three-dimensional flow characteristics must be deeply understood, and its internal flow field information must be obtained. The research on the three-dimensional flow characteristics of the S-CO₂ centrifugal compressor was mostly conducted by the computational fluid dynamics (CFD) numerical simulation method, which can be used to obtain the flow field of the centrifugal compressor and present the relevant flow phenomenon. Because of the drastic variations in the thermophysical properties of S-CO₂ fluid near the critical point, special consideration was taken in the process of the CFD simulation of the flow inside the centrifugal compressor. By applying CFD to S-CO₂ centrifugal compressor three-dimensional flow characteristics, researchers found that this special thermal physical property also brought special flow phenomena inside the flow domain of the S-CO₂ centrifugal compressor. The research on S-CO₂ centrifugal compressor three-dimensional flow characteristics mainly focused on its steady flow and needs to further reveal deeply and comprehensively the flow mechanism of the S-CO₂ centrifugal compressor under various unsteady working conditions. The flow control of the S-CO₂ centrifugal compressor was mainly by the passive flow control method using the relevant control method of the air compressor for reference, and its effect was remarkable. As for the active flow control method, few studies have heeded its effect on the S-CO₂ centrifugal compressor. [Conclusions and Prospects] In this paper, the flow characteristics of the S-CO₂ centrifugal compressor are summarized, and their research prospects are proposed. These flow characteristics are considerably different from those of centrifugal compressors with conventional fluids, mainly because of the special physical properties of S-CO₂ fluid. In the future, more advanced research methods are expected to be used, such as visual flow experiments, one-dimensional flow analysis incorporating machine learning algorithms, and active flow control, to conduct more in-depth and comprehensive studies of S-CO₂ compressor flow characteristics.

［Objective］ The rapid proliferation of electric vehicles (EVs) and the large-scale deployment of charging facilities have considerably increased the electrification of transportation road networks. However, road networks exhibit vulnerability to failure at several critical sections, which in turn may trigger a cascade of failures, ultimately leading to widespread road network disruptions. In the context of mixed electric and nonelectric vehicular flows, such adverse impacts may further spread and cascade due to EV-specific characteristics, such as limited EV range and required charging time. Protective measures for vulnerable road sections of electrified road networks against hazards could mitigate the risk of cascading failures and the further spread of disruptive events. Therefore, assessing the vulnerability of electrified transportation road networks and identifying critical road sections have become paramount. Given that the vulnerability of electrified transportation road networks has been scarcely explored in existing literature, this paper proposes a two-layered attacker-defender model to study the vulnerability of electrified transportation road networks. ［Methods］ The outer layer model aims to minimize system performance by targeting roads within the system for disruption, i.e., maximizing the total system travel time. The inner layer model serves as a defender, minimizing the total system travel time by dynamically and optimally distributing traffic flows containing both electric and nonelectric vehicles. The inner layer model is formulated based on an enhanced link transmission model, taking into consideration the critical characteristics of the electrified transportation road networks. This two-layered model can describe the temporal and spatial evolution of the mixed electric and nonelectric vehicular flows. Additionally, this paper provides a detailed solution method and theoretical analysis of this model. A mixed-integer quadratic programming problem is obtained by considering the dual of the inner problem and combining the inner problem with the outer problem. This problem is subsequently converted into a mixed-integer linear programming problem using the big M method. ［Results］ The proposed model is applied to a segment of the highway network in North Carolina, U.S. The experimental results reveal that (1) critical road sections as determined with and without EVs differ considerably. Therefore, it is necessary to incorporate EVs when analyzing the vulnerability of an electrified transportation road network. (2) The set of critical road sections varies depending on the level of attack resources. In particular, the set of critical road sections in the low attack resource level scenarios is not necessarily a subset of the critical road sections in the high attack resource level scenarios. (3) The experimental results confirm the existence of a critical point in the attack resource level. When this critical point is reached, the system performance displays a phase change phenomenon, marked by a notable decline. ［Conclusions］ The results verify that the proposed model can identify the set of critical road sections in the system and provide theoretical support to improve the vulnerability of the electrified transportation road networks.

[Objective] Rivers carry water and material transport within certain boundary forms. Due to the difficulties of in-site measurement, limited hydrological observation stations, and the precision constraints of digital elevation model (DEM), there is a significant scarcity of information on small river cross-sections distributed in river source areas, mountainous regions, and remote areas, which hinders research on river hydrology and hydraulic processes. Since the beginning of this century, the International Association of Hydrological Sciences (IAHS) has been advocating for solutions to the challenges of hydrological prediction in ungauged basins (PUB). Despite large-scale remote sensing technology is increasingly applied to the extraction of river hydraulic parameters, the spatial resolution of satellite altimetry data is too low for small rivers (river width less that 150 m), which account for a high proportion of river networks. The National Aeronautics and Space Administration (NASA) launched the ICESat-2 (Ice, Cloud and land Elevation Satellite-2) satellite in 2018. This satellite was equipped with the Advanced Topographic Laser Altimeter System (ATLAS), a photon-counting LiDAR system for the first time. The light spot (footprint) it projects onto the Earth's surface has a diameter of about 17 m, with a center-to-center distance between spots of only 0.7 m. This allows for the acquisition of photon point cloud data with smaller spots and higher density along the track. The high-density photon point cloud provided by ICESat-2 offers the possibility of extracting hydrological parameters of narrow rivers with high precision. This study focuses on the small rivers with a river width of less than 10 m in the Huangfu River basin, a first-order tributary of the middle reaches of the Yellow River, which is a data-scarce region. [Methods] A method for extracting the cross-sectional morphology of small rivers using ICESat-2 ATL03 data has been proposed. First, photons with medium to high confidence levels are selected to eliminate most of the noise. Then, a smoothing filter is applied for precise de-noising. Finally, the point cloud that has been precisely de-noised is manually edited to generate a DEM. The cross-sectional morphology of three different locations of small rivers were extracted based on DEM and compared with unmanned aerial vehicle (UAV) in-situ measurement results. [Results] The results show that: (1) the method proposed in this study, which combines the selection of medium to high confidence point clouds with filtering denoising, can effectively remove the noise from photon point clouds, with a denoising rate above 63%; (2) the completeness and richness of ground points extracted based on ATL03 data are superior to those obtained by reclassifying ATL03 using ATL08 product; (3) the results of river cross-sections extracted based on ATL03 data are basically consistent with the UAV in-situ measurement results (R²>0.96, RMSE=0.69 m). [Conclusions] The research results preliminarily demonstrate the feasibility of using ICESat-2 altimetry data for extracting cross-sections of small rivers in data-scarce areas, partially supplying the three-dimensional spatiotemporal of small rivers in data-deficient areas, providing technical support for construction of three-dimensional river network across entire basins and the simulation of hydrological and hydraulic processes. This also indicates that ICESat-2 altimetry data have research prospects in obtaining hydrological parameters of rivers in data-scarce areas.

[Objective] Formations of fixed-wing unmanned aerial vehicles (UAVs), which are commonly used in military, rescue, and other missions, often do not have the ability to hover and have a large turning radius. Thus, when operating in an unknown environment, it is easy for the formations to collide in the presence of obstacles, which will gravely affect flight safety if not guarded against. It is difficult to avoid unknown environmental obstacles using traditional modeling methods. However, artificial potential field methods can address deadlock problems such as target infeasibility and cluster congestion. [Methods] To achieve the cooperation of UAV formations without collision, a deep deterministic policy gradient (DDPG)-based centralized UAV formation control method is proposed in this study, which is designed by combining the centralized communication architecture, reinforcement learning, and artificial potential field method. First, a greedy-DDPG flight control method is studied for leader UAVs, which improves collision avoidance effectiveness. Considering maneuver constraints, reward functions, action spaces, and state spaces are improved. Additionally, to shorten the training duration, the exploration strategy of DDPG is improved using the greedy scheme. This improvement mainly uses the critic network to evaluate the value of random action groups and improves greedy selection to make actions more inclined, thus achieving rapid updates regarding the critic network and accelerating the update of the overall network. Based on this, incorporated with the artificial potential field method and leader-follower consensus, a collision-free control method is designed for followers, which can ensure collision-free following cooperation. [Results] The numerical simulation experimental results show that the improved DDPG algorithm has a 5.9% shorter training time than the original algorithm. In the same scenario, the method that we proposed perceives the same number of obstacles as the artificial potential field method. The artificial potential field method has significant fluctuations in heading angle, while the proposed method has relatively small fluctuations. The DDPG algorithm has a smoother heading angle due to a smaller number of perceived obstacles; however, the minimum distance from the obstacles is only 9.1 m. The method that we proposed here is above 17 m from the obstacles. Furthermore, Monte Carlo experimental data under different scenarios of the long aircraft show that the ability of obstacle avoidance generalization of the proposed method is improved. Moreover, experiments were applied to the proposed formation control method. Under the same scenario and control parameters, the UAV formation control method based on the proposed architecture has lower formation errors during flight, with a maximum error of no more than 10 m. However, the artificial potential field-based formation control method has a maximum formation error of over 25 m. When encountering narrow gaps, our proposed method can quickly pass through without congestion, while the artificial potential field-based formation control method appears to hover in front of obstacles, which is not conducive to flight safety. During the entire flight, this method has a greater distance from obstacles and higher safety. [Conclusions] Compared with the original DDPG algorithm, the improved DDPG algorithm has faster training speed and better training effect. The formation control method can realize the formation flight of unmanned aerial vehicles under unknown obstacles. Compared with the formation control method based on artificial potential field, the formation control method avoids the hovering in place before obstacles, which is of great significance to the formation flight safety of unmanned aerial vehicles.

[Objective] Full-model flutter test is crucial for the aeroelastic design and verification of aircraft. One of the key challenges of the test is ensuring that the model's suspension design meets the natural frequency and motion adjustment range requirements. This study proposes a cable suspension system with four cables/three springs for the full-model flutter wind tunnel test under transonic conditions to address the current research gap in verifying suspension systems other than the existing two-cable or three-cable suspension mechanisms. The designed four-cable suspension method is expected to offer distinct advantages for transonic wind tunnel tests, such as suitability for the static unstable aircraft models and their intelligent controls. [Method] The stability and kinematic characteristics of the proposed four-cable suspension system are analyzed and validated through a series of methods. First, the stiffness expression of the mechanism is established based on the differential kinematics and used for deriving the stability criterion in light of the principle of virtual work by considering the system dynamic equations and the aerodynamic model of the aircraft. Subsequently, the eigenvalues of the stiffness and aerodynamic derivatives matrix are determined, and the pose variations of the aircraft model subjected to aerodynamic forces are numerically investigated to demonstrate the suitability of the suspension system for static unstable aircraft models. Additionally, the system impact response and the factors influencing its frequency are studied, proving that the four-cable suspension system meets the natural frequency requirements of the full-model flutter wind tunnel test. Numerical calculations and Adams software simulations are performed to verify that the four-cable suspension system can achieve effective adjustment of the aircraft model pose by controlling the cable length and manipulating the aileron and rudder surfaces. Finally, a simple prototype is built for modal frequency experiments to verify the feasibility of the proposed theoretical method. [Results] The simulation and numerical calculation results demonstrated that the proposed four-cable suspension method was a viable solution for the full-model flutter wind tunnel test under transonic conditions, providing five degrees of freedom to the model. The high-speed incoming flow dynamic response results revealed that the four-cable suspension system exhibited outstanding stability, with the largest magnitude observed in the centroid displacement along the sideslip direction of the aircraft model, which was less than 0.04 m, while the rotational angle amplitudes did not exceed 15.0°. The initial pre-tension force could be adjusted to ensure that the cable continuously remained in tension. Furthermore, the natural frequencies of the mechanism in the three rotation directions were approximately 0.8~1.0 Hz, and the natural frequencies in the sideslip and heave directions were within 3.0 Hz. The study also examined the influence of different traction positions and spring numbers on the natural frequency and revealed that the attitude angle adjustment range of the four-cable suspension system with three springs could meet the requirements of the test through cable length adjustment and rudder surface control. The simple prototype frequency experiment demonstrated that the roll, pitch, and yaw direction modal frequencies were less than 3.0 Hz. [Conclusion] This study demonstrates the feasibility of using the proposed four-cable suspension system for transonic full-model flutter wind tunnel testing through numerical calculations, software simulations, and prototype experiments, providing a approach for the model suspension technology in transonic full-model flutter test.

[Objective] Risk quantification is crucial in risk assessment of accidents or disasters. This study aims to investigate the risk quantification method utilized in over-temperature faults in electrical circuits. The existing technologies of the abovementioned method are summarized in electrical and fire signals of disaster early warning. Thus, a new method based on the fire big data is proposed. [Methods] Different frequency electrical parameters are collected by the detector arranged at the front end and transmitted in real time to the fire big data to mine the influencing factors and changing patterns of electrical circuit temperature based on the deep-learning method. Subsequently, the probability distribution of temperature is determined statically, and risk is described by comparison of prediction temperature in different cumulative probabilities with actual temperature. To predict the electrical circuit temperature, a recurrent neural network (RNN) is utilized to model temperature prediction. The input parameters are voltage, current, temperature, and residual current. Among the parameters, there are two data sources for the model: one is real electrical fire data, 6 min^-1, used to learn the periodic law of temperature increase in electrical circuits of RNN for low-frequency data (LF-RNN), and the other is experimental data based on simulated fault of the temperature increase in electrical circuits. This experiment is implemented in three-phase resistive electrical circuits. Exceeding rated current is utilized to produce temperature rising. Meanwhile, electrical parameters are collected to study the law of temperature oscillation of RNN for high-frequency data (HF-RNN). Among these electrical parameters, the sampling frequency of voltage, current, and residual current is 50 kHz, but 1 Hz for temperature exceptionally. The optimization method, hyperparameter traversal, aims to minimize the loss function and root mean square error; thus, temperature prediction in electrical circuits is preliminarily applied. To increase the accuracy of the prediction model and elucidate the relationship between fire risk and prediction result, a temperature probability prediction model is established based on its second-order residual normal distribution. The error and its reducing methods are analyzed, and the relationship between prediction error and temperature mutation is determined. [Results] The results demonstrated that temperature mutation within three window lengths had a remarkable linear correction effect with temperature prediction error; moreover, the second-order residual approximately followed a normal distribution. The upper and lower limited of temperature prediction confidence intervals with different significant levels (α=0.02, 0.04, 0.06,…, 0.98) can be computed by interval estimation, which had a one-to-one correspondence with temperature prediction accumulate probability (1-α/2) and (α/2). The results revealed that the cumulative distribution probability 1% prediction curve and 99% prediction curve appeared to have a fine coverage effect on the actual temperature. With the aim of measuring temperature prediction probability distribution with electrical fire risk, the concept of “early-warning quantile” similar to cumulative distribution probability, was proposed. The ability to predict temperature was established using different “early-warning quantile curves” and confirmed through 2 943 sets of real electrical fire scene data. The results demonstrated that early-warning quantiles in the range of 10%-30% could overlap the majority of the actual temperature data, and the higher the quantile of the curve was, the higher the frequency of overestimating the temperature was. [Conclusions] To summarize, when the temperature in electrical circuits suddenly increases, there is a substantial upward trend in the early-warning quantile of the actual temperature. Thus, the use of LF-RNN and HF-RNN can timely and accurately predict the temperature probability distribution to characterize fire risks in electrical circuits so that early dynamic perception of fire risk is realized.

Significance: Empowering machines to understand human emotions remains one of the primary challenges in developing artificial intelligence (AI). The affective brain-computer interface (BCI), which decodes emotional states based on brain signals, is an emerging field combining psychology, neuroscience, and AI. Brain signals are inherently uncontrollable, contain rich emotion-specific information, and provide a promising physiological basis for developing computing systems that support continual emotion monitoring. Since its inception, affective BCI research has required close collaboration across various disciplines: it depends on the use of information science for feature engineering and algorithm development, psychology for theoretical frameworks of emotion, and neuroscience for revealing the neural mechanisms underlying emotional processes. Such a demand for multidisciplinary co-operation forms the core focus of this review. Specifically, this paper focuses on the methods by which psychology-and neuroscience-based insights can inspire and advance affective BCI research. Progress: We summarize the current progress at three levels: theoretical, technical, and applied. In the first one, recent advances in affective science offer new perspectives for shaping affective BCI paradigms. The traditional discrete and dimensional frameworks have laid the groundwork for emotion decoding but often overlook positive emotions and the dynamic intensity of affective experiences. Recent emotion theories emphasizing refined positive emotions, mixed emotions, and context-dependent emotions provide valuable directions for improving emotion representation. Affective computing should align with these developments, integrating them into computational models to enhance ecological validity. In turn, affective BCI research may also contribute to psychology by offering evidence to test and refine emotion theories, fostering reciprocal progress across disciplines. At the technical level, neuroscience provides crucial insights for building more robust affective BCIs. Findings on emotional valence lateralization and distributed emotion-associated brain representations can inform the design of models that better capture emotional processing complexity. Moreover, inter-subject brain synchronization research has revealed mechanisms that enhance model generalizability across users, suggesting that incorporating neuroscientific findings can substantially improve the performance and reliability of affective BCIs. At the application level, affective BCIs are expanding beyond emotion recognition toward understanding emotion-related individual differences. Variability between individuals—often treated as noise—may instead offer meaningful information about personality traits or mental health conditions. In the long term, the goal of affective BCI systems may evolve from accurately identifying emotions to comprehensively understanding each individual's psychological tendencies and dynamic affective patterns across multimodal neural and behavioral data. We advocate for stronger integration between affective BCI technologies and practical domains. Such integration allows practical demands to drive technological development, ensuring that affective BCI remains human-centered. Conclusions and Prospects: Finally, we discuss the technical challenges of affective BCI, including extending algorithms from controlled laboratory settings to real-world scenarios, advancing sensor technology for more convenient and reliable brain-signal acquisition, and leveraging large models to enhance performance for affective BCI. Specifically, we emphasize the vital role of ethical considerations: as affective BCIs move from passive emotion detection toward active emotional support or intervention, the responsibility of humans as rational moral agents in a future era of man-computer symbiosis must be considered, ensuring the autonomy of human emotions.

[Objective] Firefighting suits worn by firefighters in high-temperature radiation environments, coupled with moderate physical exertion, can lead to body heat accumulation and increased microenvironment temperature inside the suits, which triggers adverse physiological reactions due to severe heat stress, resulting in decreased work efficiency, heat cramps, and other harms. Hence, cooling vests are widely used by firefighters to effectively alleviate heat stress in firefighting scenarios. However, most contemporary cooling vests encounter issues such as severe cold stress, short cooling duration, and poor comfort. Therefore, it is necessary to conduct research on the cooling performance of these vests. [Methods] The cooling effectiveness of three types of firefighter cooling vests was assessed using a thermal manikin system in an environmental chamber under the following conditions: temperatures of 35 ℃, 40 ℃, and 45 ℃ and directed thermal radiation of 1.5 and 2.5 kW/m², with low, moderate, and high levels of physical exertion. D1922L temperature sensors were deployed on the thermal manikin system to measure the temperatures of the outer, insulation, comfort, and inner layers of the cooling vest as well as the temperatures of the inner and outer layers of the firefighting suit. [Results] The experiments reveal that the cooling vests effectively reduce the microenvironmental temperature inside the firefighting suit, with a temperature difference of 6.6 ℃ between the inner and outer layers of the firefighting suit after 200 min of exposure. The cooling effect is most pronounced in the abdominal area with direct radiation. Among the three types of cooling vests assessed, Cooling Vest 1 exhibits the best performance, achieving a cooling power of 4.097 W and a cooling duration exceeding 2 h. Meanwhile, Cooling Vest 3 has a relatively poor cooling performance, with a cooling power of 0.753 W and a cooling duration of 90 min. As the physical exertion levels increase, the cooling effect of the vests is less pronounced. The linear fitting slopes of the temperature variation curves for the manikin's abdominal areas at 65, 110, and 165 W/m² are 0.106 8, 0.273 4, and 0.508 6, respectively. Furthermore, the cooling performance of the vests diminishes with increasing environmental temperatures. At an environmental temperature of 45 ℃, the temperature gradients in the manikin's chest, shoulders, abdomen, and back areas increase by 50.0%, 40.6%, 60.0%, and 50.0%, respectively. Moreover, the directed thermal radiation has a significant impact on the cooling performance of the vests. The temperature of the manikin's chest area reaches 35.69 ℃ within only 7 min with a directed thermal radiation of 2.5 kW/m². [Conclusions] These research findings are anticipated to serve as the preliminary data to enable the determination of rational work durations and intensity. In real fire rescue scenarios, the distribution of phase change materials for heat storage over the chest area and the duration of high-intensity work should be carefully considered. Additionally, these findings provide technical support for the development, testing, and evaluation of personal thermal protective equipment.

[Significance] Welding has reached a very important position in large structural workpieces. The welding of large structural members also has been covered many fields, including high-end manufacturing fields such as shipbuilding, oil and gas chemical industry, nuclear power engineering, energy and power, building steel structure, and rail transit. The problems of traditional manual welding are instability and low efficiency. Even semi-automatic welding such as gantry welding and rail robot welding, which has not yet to meet the requirements of more efficient and high-quality automation industry. The welding development trend of large structural parts is more intelligent and automatic welding. In this case, the welding robot that has no track and can crawl in all positions has become the representative of intelligent welding equipment, and gradually has promote and solve the automatic intelligent welding of large structural parts. [Progress] In this paper, the research status of intelligent robots for welding large structural parts at home and abroad was systematically introduced, and the welding robots were classified according to the welding robots with and without rails. The application scenarios, advantages and disadvantages of these two types of welding robots were compared. Through these comparisons, it was concluded that the welding robot without rail was more suitable for welding large structural parts because of its better flexibility, adaptability and convenience. There were many problems in the welding process of large structural parts, especially when multi-layer and multi pass thick plates were welded. At the same time, these problems caused low welding process accuracy and low welding quality. Problems included low machining accuracy, inaccurate assembly, metal thermal deformation, many weld passes, weld beads stacked together and many other reasons. In the welding of large structural parts, there were still many difficulties that need to be solved urgently. Automatic backing welding technology and multi-layer and multi-channel automatic routing technology were two very critical and difficult problems that hindered the progress of the welding industry. Until now, the backing welding of large structural parts has mainly depends on manual work, because backing welding was the first weld connecting two welding workpieces, which was very critical in the whole welding process. In the process of automatic backing welding, there were strict requirements on the assembly clearance, unfitness, blunt edge, curling and welding heat input. If these factors were not well controlled, a large number of welding defects such as missing welding and incomplete welding would occur in the process of backing welding. Multi layer and multi pass welding also depended on manual arrangement to cause unstable welding quality. Therefore, it was more and more important to automatically plan the number of welding passes and layers, arrange the welding sequence, determine the position coordinates of each weld pass and adjust the welding parameters. [Conclusions and Prospects] Based on this situation, this paper summarizes the exploration of related technologies from the aspects of automatic backing welding technology, seam tracking technology, multi-layer and multi-channel automatic lane arrangement technology, etc. Multi-modal deep learning and multi-sensor fusion technology are widely used in many fields, and have become a concern in military, industrial and high-tech development. Thus, these two technologies will be the key to developing welding robots and provide guidance for the intelligent welding of large structural parts. In the future, artificial intelligence technology will lead the welding robot to achieve better welding quality.

[Objective] Intelligent manufacturing requires the agile development of industrial software, and microservices architecture for industrial software application is an important research direction at present. To improve developmental efficiency of industrial software, functional components in the form of microservices must be published on the internet, and a microservices library needs to be built for management, enabling users to choose according to their needs. To meet the demands of the increasingly complex business needs of users, the microservices library must efficiently combine numerous contained microservices to expand achievable functions. Based on the binding mechanism between the registry and microservices library, the quality of the two links of retrieval and selection in the microservices combination significantly impacts user experience.[Methods] In the process of service retrieval, a service clustering algorithm based on Sentence BERT and SOM (SBERT-SOM-k) is proposed to efficiently find a group of microservices that meet specific functional requirements of the microservices library. This method converts microservices into a text vector through Sentence BERT, mapping them to the output layer nodes of SOM. The iterated weight vector represents these output layer nodes, which can effectively improve the disadvantage of the k-means algorithm that is sensitive to noise and outliers. Because of the absence of a special microservices set related to industrial software at present, the experiment uses the open web service test dataset OWLS-TC4 to compare SBERT-SOM-k with three clustering algorithms:SBERT-k, LDA-k, and TFIDF-k and uses three indicators of accuracy, recall, and F-measure to evaluate the performance of the algorithm. In the service selection phase, to further select the optimal combination of microservices, nonfunctional requirements, such as availability, reliability, and response time, are considered as quality of service (QoS) indicators, which transformed into a multiconstraint single-objective optimization problem based on QoS. Furthermore, an improved genetic algorithm is proposed, which is redesigned in terms of encoding, crossover, mutation, selection, and constraint punishment. The improved genetic algorithm is used to achieve stable solutions for multiconstraint single-objective optimization problems. The experiment used QWS2.0, a real web service dataset that exists on the internet, to compare the number of iterations and fitness values of the improved genetic algorithm (IGA), cross unimproved but constrained genetic algorithm (NICGA), and cross unconstrained improved genetic algorithm (NICCGA) for obtaining the optimal solution under different task node numbers.[Results] The results of service clustering experiments show that the average accuracy of different clustering algorithms is the same. However, SBERT-SOM-k still has significant advantages over SBERT-k, LDA-k, and TF-IDF-k, with the average recall rates increased by 29.56%, 19.36%, and 31.70%, and the corresponding average F-measure increased by 22.41%, 13.77%, and 25.33%, respectively. Additionally, IGA can improve the optimization speed and quality of the service composition, effectively preventing premature convergence.[Conclusions] The proposed clustering and selection method has achieved good results. It can rapidly select microservices from the microservices library, meeting the needs of users. Furthermore, integrating these microservices into an industrial software system registry based on the microservices architecture will enhance user satisfaction and help to realize the efficient use of microservices.

[Objective] High-quality development and exploration of a new development model for the real estate industry has recently attracted considerable attention. Scale expansion and relevant land-buying are essential components of the development model. Much of the literature investigating land-buying behaviors of real estate firms has focused on the number of lands bought rather than the number of cities entered, which is also an important aspect. Besides buying lands, listed real estate firms must concern the market value management related to financing and investors' valuation. Therefore, this paper investigates the relationship between the land-buying behaviors of listed real estate firms and their capital market performance. Investors can build more efficient stock portfolios and real estate firms can improve land-buying strategies based on the results. [Methods] We employed regression analysis to estimate the influence of land-buying behaviors on the capital market performance of A-listed real estate firms. The data was primarily obtained from the CREIS and CSMAR databases, spanning from 2009 to 2021 and involving 87 firms. The model included the number of lands bought, cities entered, and other aspects of land-buying behaviors. Capital market performance was divided into two aspects:firm investment value and firm risk, which are represented using Tobin's Q and Beta coefficients, respectively. The model also controlled firm and manager characteristics, financial ratios, and market environment and employed the two-way fixed effect ordinary least squares (OLS) method. The heterogeneity effect on ownership, firm scale, and year of listing was explored using a regression on the subsample, while robustness testing was conducted by handling extreme values. Finally, we discussed a potential channel through which land-buying behaviors affected the capital market performance. [Results] The results show an inverted U-shaped relation between the firm investment value and number of cities entered; furthermore, the investment value increases when more cities are entered within a certain range throughout the study period. In the market downturn period, i.e., since 2017, the lifting effect due to entering more cities on the firm investment value has decreased, while that on firm risk has increased. Comparatively, the effect of the number of lands bought on capital market performance is not as important as that of the number of cities entered. The capital market performance of non-state-owned, smaller, or shorter-listed real estate firms is more affected by their land-buying behaviors. The impact of land-buying behaviors on capital market performance passes through the land-buying expense and sale revenue ratio channel. [Conclusions] The findings indicate a change in the reaction of the capital market to the regional expansion of land-buying. We recommend that real estate firms respond to feedback from the capital market and adjust their land-buying strategies in time, especially when selecting and entering more cities. Furthermore, investors should pay attention to region-focused real estate firms. The findings also suggest that the government should be cautious about structural changes in the real estate market, and a shift from focusing on the investment demand of real estate firms to focusing on the end-use demand of space is required to coordinate and guide the further improvement of the development model.

[Objective] The increasing maturity of large language model technology has facilitated its widespread application in downstream tasks across various vertical fields. Large language models have exhibited beneficial performance in text summarization tasks in general fields, such as news and art. However, the highly specific language style in the judicial field and the unique complexity of judicial documents in terms of structure and logic make it difficult for large language models to generate judicial document summaries. This study aims to combine prompt learning with large language models to explore their performance in summarizing judicial documents. Prompt templates containing structural information and judicial documents are used as inputs for fine-tuning large language models. As a result, large language models can generate judicial document summaries that adhere to judicial language styles and the structural and logical complexities of judicial documents. [Methods] This study proposes a judicial document summary method that combines prompt learning and the Qwen large language model. Judicial document data are used as the input for fine-tuning a large language model using supervised fine-tuning technology to enhance its applicability in the judicial field. Simultaneously, prompt templates that incorporate structural information and role instructions are designed to optimize summary generation to more accurately reflect the structural characteristics and logical relationships of documents. According to the characteristics of the pretraining data format of the large language model, the fine-tuning data were constructed in the form of question-answer pairs. [Results] The experimental results show that the proposed method improves the F1 of the baseline model by 21.44%, 28.50%, and 28.97% in ROUGE-1, ROUGE-2, and ROUGE-L, respectively, and exceeds all of the comparison models. The ablation experiment demonstrated that the summary generation method using prompt learning was superior to the method without prompt learning for all indicators, and the performance of summarization generated by the large language model utilizing prompt learning was significantly enhanced. The case demonstration reveals that after prompt learning is used to enhance the perception of structural information in the judgment document by the large language model, the judgment document summary generated by this model can better capture and retain key information in the judgment document. Moreover, the language style of this model is closer to that of a real judgment document summary, which further illustrates the effectiveness of the proposed method. [Conclusions] This study integrates the structural information of a judgment document into the task of generating a judgment document summary using a large language model in the form of prompt templates. Prompt templates containing structural information are used to assist the large language model in summarization generation. Therefore, the model can focus on the key information in the judgment document and capture deeper semantic logical relationships. The results demonstrate that after fine-tuning the large language model with judicial document data and introducing structural information, the model demonstrated excellent performance and great application potential in the judicial document summary task. The proposed method can effectively enhance the capability of a large language model in the field of judicial document summaries.

[Objective] With the continuous advancement in urbanization in recent years, the urban impervious rate has increased; hence, urban areas have to primarily rely on urban pipe networks for drainage. However, most of these pipe networks constructed in the early stages of cities cannot manage the extreme rainstorms caused by global warming, resulting in severe urban waterlogging. Low impact development (LID) facilities can effectively reduce urban waterlogging by increasing infiltration surface. However, the LID allocation method using the traditional full-range equal proportion (FREP) method, which is based on different land-use types, is usually adopted for determining the layout of LID facilities. The layout location of these facilities is determined by the distribution of land-use types. There will be no LID facilities in areas with severe waterlogging using FREP method, whereas more LID facilities will be available in areas with less overflow, thereby wasting LID facility resources. LID facilities can be fully utilized to better resolve waterlogging control effects if the layout of the waterlogging source is considered. Therefore, to address the above issue, this paper proposes the LID allocation method using the overflow point upstream tracking (OPUT) method. [Methods] OPUT method used storm water management model (SWMM) to build a drainage model, simulate pipe network overflow in different return periods, lock the overflow point, track the nodes of pipelines upstream of the overflow point layer by layer, and determine the corresponding catchment area level. LID facilities were laid on the catchment area depending on the land-use type. FREP method and OPUT method were compared from three aspects: runoff, waterlogging, and economy. [Results] The results obtained using the OPUT method show that in terms of relieving waterlogging, the reduction percentage of the overflow volume is 12.82% to 1.73% under design rainfall of 180 minutes (short-duration rainfall) with increasing return period and is 8.16% to 1.12% under design rainfall of 1,440 minutes (long-duration rainfall) for a unit LID area. Meanwhile, the FREP method yields reduction percentages of 1.87%—1.22% and 1.87%—0.83% under short- and long-duration rainfall, respectively. From the runoff reduction perspective, the reductions in runoff volume and peak runoff obtained using the two methods are different for LID per unit layout area under different return periods. The reduction obtained for peak runoff using the FREP method is always better than that of the OPUT method; however, as the return period increases, the reduction exhibited by the OPUT method is closer to that of the FREP method. For the reduction in runoff volume, the FREP method exhibits better results when the return period is small, whereas that of the OPUT method is better for a large return period. When the two methods have approximately the same reduction effect on overflow, under short- and long-duration rainfall, the cost for the layout requirement obtained using the OPUT method is 61.5—325 and 73.7—333 million yuan, respectively, as the return period increases. Meanwhile, with an increase in the return period, the short- and long-duration the costs for the layout requirement obtained using the FREP method are 66.1—423 and 137—423 million yuan, respectively. [Conclusions] The LID layout requirements obtained using the OPUT method exhibit a better reduction effect and economy for relieving waterlogging.