[Significance] Deep face manipulation technology involves the generation and manipulation of human imagery by different strategies, such as identity swapping or face reenactment between the source face and the target face. On the one hand, the rise of deep face manipulation has inspired a series of applications, including video making and advertising marketing. On the other hand, because face manipulation technology is usually open source or packaged as APPs for free distribution, it makes the threshold of tampering technology lower, resulting in the proliferation of fake videos. Moreover, when face manipulation technology is maliciously used by criminals to produce fake news, especially for important military and political officials, it will guide and intervene in public opinion, posing a great threat to national security and social stability. Therefore, the research on deep face forgery detection technology is particularly important. Hence, it is necessary to summarize the existing research to rationally guide deep face manipulation and detection technology.[Progress] Nowadays, deep face manipulation technology can be roughly divided into four types, namely, identity swapping, face reenactment, face editing, and face synthesis. Deepfakes bring real-world identity swapping to a new level of fidelity. The region-aware face-swapping network provides the identity information of source characters from local and global perspectives, making the generated faces more natural. In the field of facial reenactment, Wav2lip uses pretrained lip synchro models as expert models, encouraging the model to generate natural and accurate lip movements. In the field of face editing, FENeRF, a three-dimensional perception generator based on a neural radiation field, aligns semantic, geometric, and texture information in spatial domain and improves the consistency of the generated image between different perspectives while ensuring that the face can be edited. In the field of face synthesis, Anyface proposes a cross-modal distillation module for the alignment of language and visual representation, realizing the use of text information to generate more diversified face images. Deep face forgery detection technology can be roughly divided into image-level forgery detection and video-level forgery detection methods. In the image-level methods, SBI proposes a self-blended technique to generate realistic fake face images with data augmentation, effectively improving the generalization ability of the model. M2TR proposes a multimodal and multi-scale Transformer model to detect local artifacts at different levels of the image in spatial. Frequency domain features are also added as auxiliary information to ensure the forgery detection ability of the model for highly compressed images. In the video-level methods, RealForensics learns the natural correspondence between the face and audio in a real video in a self-supervised way, enhancing the generalization and robustness of the model.[Conclusions and Prospects] Presently, deep face manipulation and detection technologies are rapidly developing, and various corresponding technologies are in the process of continuous update and iteration. First, this survey reviews the deep face manipulation and detection methods and discusses their strengths and weaknesses. Second, the common datasets and the evaluation results of different manipulation and detection methods are summarized. Finally, the main challenges of face manipulation and fake detection are discussed, and the possible research direction in the future is pointed out.

[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.

[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 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］ 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.

[Objective] Authorship identification is a study for inferring authorship of an unknown text by analyzing its stylometry or writing style. The traditional research on authorship identification is generally based on the empirical knowledge of literature or linguistics, whereas modern research mostly relies on mathematical methods to quantify the author's writing style. Currently, researchers have proposed various feature combinations and neural network models. Some feature combinations can achieve better results with traditional machine learning classifiers, while some neural network models can autonomously learn the relationship between the input text and corresponding author to extract text features implicitly. However, the current research mostly focuses on character and lexicon features. Furthermore, the exploration of syntactic features is limited. How to use the dependency relationship between different words in a sentence and combine syntactic features with neural networks still remains unclear. This paper proposes an authorship identification method based on the syntax tree node embedding, which introduces syntactic features into a deep learning model. [Methods] We believe that an author's writing style is mainly reflected in the way he chooses words and constructs sentences. Therefore, this paper mainly develops the authorship identification model from the perspectives of words and sentences. The attention mechanism is used to construct sentence-level features. First, an embedding representation of the syntax tree node is proposed, and the syntax tree node is expressed as a sum of embeddings corresponding to all its dependency arcs. Thus, the information on sentence structure and the association between words are introduced into the neural network model. Then, a syntactic attention network using different embedding methods to vectorize text features, such as dependencies, part-of-speech tags, and words, is constructed, and a syntax-aware vector is obtained through this network. Furthermore, the sentence attention network is used to extract the features from the syntax-aware vector to distinguish between different authors, thereby generating the sentence representation. Finally, the result is obtained by the classifier and the correct rate is used to evaluate the result. [Results] Experiments on CCAT10, CCAT50, IMDb62, and the Chinese novel data sets show that an increase in the number of authors causes a downward trend in the accuracy rate of the model proposed in the paper. In some data points, an increase in the number of authors resulted in an increase instead of a decrease in the correct rate. This shows that the ability of the model proposed in this study to capture the writing style of different authors is considerably different. Furthermore, when we change the number of authors on the IMDb dataset, the correct rate of the model in the paper is found to be slightly lower than the BertAA model in the case of 5 authors; however, the rate is higher than the BertAA model in the case of 10, 25, and 50 authors. Additionally, when the experimental results of the model are compared to other models on the CCAT10, CCAT50, and IMDb62 data sets, the performance of this model is observed to be ranked as second or third. [Conclusions] The attention mechanism demonstrated its efficiency in text feature mining, which can fully capture an author's style that is reflected in different parts of the document. The integration of lexical and syntactic features based on the attention mechanism enhances the overall performance of the model. Our model performs well on different Chinese and English datasets. Notably, the introduction of dependency syntactic combination provides more space for the interpretation of the model, which can explain the text styles of different authors at the word selection and sentence construction levels.

[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] In recent years, the number of publicly disclosed vulnerabilities has increased, and software security personnel and vulnerability enthusiasts have experienced increasing difficulty in finding the vulnerability information they are interested in. A recommendation algorithm can provide personalized vulnerability suggestions to help users obtain valuable vulnerability information efficiently. However, recommendation systems related to vulnerabilities generally have problems such as one-sided analysis, complex implementation methods, strong professionalism, and data privacy, and research on directly recommending vulnerabilities as recommendation items is scarce.[Methods] This paper selects the vulnerability itself as the recommendation item, collects data from public datasets, and adopts a simple and efficient recommendation algorithm for personalized recommendations of vulnerabilities. As a classical recommendation model, the collaborative filtering recommendation algorithm is widely used and computationally efficient. However, the user–vulnerability interaction matrix is sparser than the interaction matrix analyzed by the classical recommendation model, which seriously affects the use effect of the collaborative filtering recommendation algorithm. To solve this problem, this paper introduces a vulnerability similarity research algorithm, comprehensively considers 13 features, such as vulnerability type, severity, and vulnerability description text, and integrates them into content-based recommendation algorithms, emphasizing the universal connection between vulnerabilities in recommendation algorithms. By calculating the similar vulnerabilities of each vulnerability the target user has interacted with, the algorithm summarizes the list of vulnerabilities with the highest recommended value and recommends it to the user. Simultaneously, the algorithm fully considers the characteristics of personal users and product users and combines the labeling mechanism to finally form a multi-user vulnerability recommendation algorithm based on similarity, effectively improving the sparsity and cold start of the recommendation algorithm.[Results] The experiments on public datasets show that 1) the content recommendation algorithm based on similarity can achieve better accuracy than the traditional collaborative filtering algorithm on all types of users. Particularly, the precision, recall, and F1 score of the recommendation algorithm results for product users increase by 58.86%, 58.53%, and 0.586 1, respectively. 2) The recommendation list of the content recommendation algorithm based on similarity is more effective and more consistent with the user's vulnerability preferences. For product users, the the normalized discounted cumulative gain score of the recommendation list increases by 0.596 5. 3) The result coverage of the content recommendation algorithm based on similarity is much higher than that of the collaborative filtering algorithm. Among human users, the result coverage of the content recommendation algorithm based on similarity is 7.6 times that of original interest data, which shows that the recommendation algorithm successfully mobilizes more vulnerabilities to recommend that users have not previously interacted with.[Conclusions] This paper takes vulnerabilities as a recommendation item to recommend vulnerabilities for multiple types of users and proposes a multi-user vulnerability recommendation algorithm based on similarity. The algorithm mainly introduces the vulnerability similarity calculation method and integrates it into the content-based recommendation algorithm. The algorithm proposed in this paper solves the problems of the high sparsity of a user–vulnerability interaction matrix and cold-start problems of user-based collaborative filtering algorithms and effectively improves the accuracy and effectiveness of recommendations.

[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] Deep learning models for text sentiment analysis, such as recurrent neural networks, often require many parameters and a large amount of high-quality labeled training data to effectively train and optimize recurrent neural networks. However, obtaining domain-specific high-quality sentiment-labeled data is a challenging task in practical applications. This study proposes a cross-domain text sentiment classification method based on syntactic structure transfer and domain fusion (SSTDF) to address the domain-invariant learning and distribution distance difference metric problems. This method can effectively alleviate the dependence on domain-specific annotated data due to the difference in the data distribution among different domains. [Methods] A method combining SSTDF was proposed in this study to solve the problem of cross-domain sentiment classification. Dependent syntactic features are introduced into the recurrent neural network for syntactic structure transfer for designing a migratable dependent syntactic recurrent neural network model. Furthermore, a parameter transfer strategy is employed to transfer syntactic structure information across domains efficiently for supporting sentiment transfer. The conditional maximum mean discrepancy distance metric is used in domain fusion to quantify the distribution differences between the source and target domains and further refine the cross-domain same-category distance metric information. By constraining the distributions of source and target domains, domain variable features are effectively extracted to maximize the sharing of sentiment information between source and target domains. In this paper, we used a joint optimization and training approach to address cross-domain sentiment classification. Specifically, the sentiment classification loss of source and target domains is minimized, and their fusion losses are fully considered in the joint optimization process. Hence, the generalization performance of the model and classification accuracy of the cross-domain sentiment classification task are considerably improved. [Results] The dataset used in this study is the sentiment classification dataset of Amazon English online reviews, which has been widely used in cross-domain sentiment classification studies; furthermore, it contains four domains—B (Books), D (DVD), E (Electronic), and K (Kitchen)—each with 1 000 positive and negative reviews. The experimental results show that the accuracy of the SSTDF method is higher than the baseline method, achieving 0.844, 0.830, and 0.837 for average accuracy, recall, and F1 values, respectively. Fine-tuning allows the fast convergence of the network, thereby improving its transfer efficiency. [Conclusions] Finally, we used deep transfer learning methods to solve the task of cross-domain text sentiment classification from the perspective of cross-domain syntactic structure consistency learning. A recurrent neural network model that integrates syntactic structure information is used; additionally, a domain minimum distance constraint is added to the syntactic structure transfer process to ensure that the distance between the source and target domains is as similar as possible during the learning process. The effectiveness of the proposed method is finally verified using experimental results. The next step is to increase the number of experimental and neutral samples to validate the proposed method on a larger dataset. Furthermore, a more fine-grained aspect-level cross-domain sentiment analysis will be attempted in the future.

[Objective] Collaborative filtering algorithms are widely used in various recommendation systems and can be used to recommend information of interest to users similar to the user based on historical data. Recently, collaborative filtering algorithms based on graph neural networks have become one of the hot research topics. A collaborative filtering model based on a graph structure usually encodes the interaction between users and information items as a two-part diagram, and high-order connectivity modeling of the bipartite graph can be used to capture the hidden relationship between the user and the item. However, this bipartite graph model does not explicitly obtain the similarity relationship between users and between items. Additionally, the bipartite graph sparsity causes high-order connectivity dependence problems in the model. [Methods] Herein, a collaborative filtering model is proposed based on a heterogeneous graph convolutional neural network that explicitly encodes the similarities between users and that between items into the graph structure so that the interaction relationships between users and between items are modeled as a heterogeneous graph. The heterogeneous graph structure allows the similarities between users and between items to be directly captured, reducing the need for high-order connectivity and alleviating the bipartite graph sparsity problem. [Results] We conducted experiments on four typical datasets and compared the results using four typical methods. The results showed that our model achieved better experimental results than the traditional collaborative filtering models and existing graph neural network models. Moreover, based on the different types of edges, different similarity methods, and different similarity thresholds, our model obtained better experimental results. [Conclusions] Our model explicitly encodes the similarities between users and between items into the heterogeneous graph structure as edges so that the model can directly learn these similarities during training to get the embedded information of users and items. The proposed model alleviates the sparsity and high-order connectivity modeling problems of bipartite graphs. The collaborative filtering model based on heterogeneous graph neural networks can also fully capture the interaction relationships between users and items through low-order connectivity in the graph.

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.

[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.

[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.

[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] 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.

［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.

[Objective] Traditional social concepts still hold certain negative views and stereotypes about bankruptcy. After the implementation of the Enterprise Bankruptcy Law in China in 2007, the positive role of the bankruptcy system and the impact of creditor protection are still not fairly understood. Until now, no literature has systematically analyzed and studied the effect of the Enterprise Bankruptcy Law on the innovative behavior of enterprises in financial distress. In fact, only a few quantitative analyses of the economic effects of implementing the Enterprise Bankruptcy Law have been found. This study examines the impact of the Enterprise Bankruptcy Law implementation on firm innovation and finds an increase in patent activity for firms in financial distress following the implementation of the law, thereby filling a gap in the literature on law and finance. [Methods] The 2007 Enterprise Bankruptcy Law is used as a quasi-natural experiment to construct a difference-in-difference model, as the model is intended to net out the common trend between the treatment and control firm groups. This study takes Merton's option pricing theory as the theoretical foundation of the credit monitor model and gauges the degree of financial distress faced by enterprises by calculating their distance to default. Based on the financial distress level faced by sample firms in 2006, the year before implementing the Bankruptcy Law, the firms are grouped by the annual median level of financial distress. However, firms with high financial distress typically differ from those with low financial distress in terms of other company characteristics, which could lead to concerns about missing variables. This paper introduces a propensity score matching method based on the difference model and selects four firms in the control group that belong to the same industry and have the closest propensity scores to those in the treatment group to effectively eliminate this concern. The estimation of the trend-matching scoring is performed using logical regression, and the sample set has passed the balance test. Furthermore, multistage dynamic regressions are employed to account for the interference of causal issues with the results. In addition, a volatility indicator based on the three-factor model is used to assess a firm's risk bearing to study the channel through which the Enterprise Bankruptcy Law influences firm innovation. [Results] After the implementation of the Enterprise Bankruptcy Law, the results revealed the following: 1) The number of patent applications of the experiment group increased by 18.77%, and the number of invention patent applications increased by 25.86% compared with the control group. 2) The implementation of the Enterprise Bankruptcy Law has strengthened the protection of creditors and thus improved the level of firm risk bearing, which has significantly increased innovation output and innovation quality of financially-distressed firms. 3)The above phenomena are more pronounced in firms with good corporate governance in regions with strict rules of law and strong intellectual property protection. [Conclusions] This study confirms the effect of creditor protection on business innovation. The introduction of the Enterprise Bankruptcy Law has enhanced the legitimate rights and interests of creditors in China, provided a strong guarantee for creditors to obtain potential repayment, enhanced the risk tolerance of firms, and encouraged firms to innovate. This study reveals the far-reaching effects of the legal system on the real economy and expands our knowledge of how the legal system environment influences enterprise innovation.

[Objective] Spatio-temporal correlation mining is a key technology in intelligent transportation systems and is usually applied to spatio-temporal data prediction problems such as traffic flow prediction. Accurately predicting traffic flows in urban management is extremely important for alleviating urban traffic congestion, improving traffic efficiency, and reducing traffic accident occurrences. However, it is extremely challenging to accurately predict traffic flows in large-scale traffic networks due to the high nonlinearity and complexity of the massive traffic flow data. Most existing methods usually conduct two separate components to capture the spatial and temporal correlations. A static spatial graph is constructed for each time step in the spatial dimension; furthermore, the same nodes on different time steps are connected to build a spatio-temporal graph in the temporal dimension. However, the potential correlations between the traffic flow data of different nodes at different time steps are ignored and the complex spatio-temporal correlations in the traffic flow data cannot be effectively modeled. [Methods] In this paper, we proposed a spatio-temporal combinational graph convolutional network (STCGCN) to address the issue of traffic flow prediction. STCGCN consisted of three modules: the spatio-temporal combinational graphs (STCG) construction module, the spatio-temporal combinational graph convolution (STCGC) module, and the prediction module. The STCG construction module constructed an adaptive STCG adjacency matrix across temporal slices based on spatio-temporal embedding vectors, which could automatically learn parameters during training, accommodate complex spatio-temporal correlations between nodes, and solve the problem that existing prediction methods hardly captured the potential spatio-temporal correlation between nodes. The STCGC module designed adaptive STCGC operators and adaptive STCGC layers to extract spatio-temporal features from historical traffic data of nodes and the constructed adaptive STCG. Finally, the prediction module aggregated the hidden layer representation of all historical time steps obtained using the STCGC module and outputed the prediction result via fully connected layer mapping. We evaluated STCGCN on PeMSD4 and PeMSD8, two public datasets from Caltrans performance measurement system (PeMS), by comparing it with 11 baseline methods: vector autoregressive (VAR), support vector regression (SVR), fully connected long-short term memory (FC-LSTM) neural network, diffusion convolutional recurrent neural network (DCRNN), spatio-temporal graph convolutional networks (STGCN), attention based spatial-temporal graph convolutional networks (ASTGCN), Graph WaveNet, spatial-temporal synchronous graph convolutional networks (STSGCN), adaptive graph convolutional recurrent network (AGCRN), graph multi-attention network (GMAN), and time zigzags at graph convolutional networks (Z-GCNETs). We adopted two widely used metrics for evaluation: mean absolute error and root mean squared error. [Results] The experimental results revealed that using a unified component, the proposed STCGCN model effectively modeled the dynamic temporal correlation, spatial correlation, and cross-spatio-temporal correlation in the traffic flow data. Furthermore, the model achieved the best prediction results at each moment, and its error growth was slower than other baseline methods as the prediction time increased. We also explored the effect of three hyperparameter settings in STCGCN on model performance, and the experiments demonstrated differential model performance under different hyperparameter settings. The number of parameters and training times of all models, including STCGCN and 11 baseline methods, were compared at the end of the experiment. The results showed that the STCGCN achieved the best model performance with the least number of model parameters and training time, and the algorithm efficiency was close to the best. [Conclusions] Experiments on the public datasets show that the STCGCN model outperforms 11 baseline methods in prediction accuracy.

［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: With increased globalization, multiple countries are involved in supply chains, forming complex supply networks. Frequent occurrences of natural disasters, geopolitical instability, and global health crises pose unprecedented challenges to traditional supply chain management methods. Local disruptions in the supply chain can spread internally, causing a series of chain reactions. Enhancing supply chain risk resilience and robustness has become a research focus for many scholars. The widespread use of the Internet has led to rapid information exchange between enterprises; an increasing number of scholars have recognized the importance of early warning information in preventing supply chain disruptions. Therefore, understanding how information affects the propagation of risks within the supply chain and maximizing the early warning function of information have significant practical implications. Moreover, the heterogeneity in the responses of enterprises to early warning information also needs attention. Methods: To capture the propagation of early warning information and disruption risks, a two-layer propagation model that couples risk and information is constructed. In this model, the upper layer represents the information layer and the lower layer represents the risk layer. The information of a disruption in a lower-layer enterprise is transmitted to upstream and downstream enterprises with a certain probability. After receiving the early warning information, an enterprise transitions into a conscious node and this transition is reflected in the upper layer network. In this model, there are five possible states for the nodes in the network. A microscopic Markov chain (MMC) method is used to analyze the state transition process between nodes and calculate the risk propagation threshold of the system. Furthermore, the key factors influencing the propagation of disruption risk are analyzed. An agent-based approach is used for case simulation to validate the model's effectiveness. Numerical analysis of the model reveals that the network structure, network size, extent of risk information propagation in the information layer, and the probability of disruption risk propagation are the key factors influencing the propagation of the risk. Financial data from Tesla's supply chain in China are also collected. In case simulation, an agent-based method is used to study the effects of the information layer network structure, information propagation rate, and risk propagation rate on the supply chain resilience. Results: The results show that for a low information propagation rate, the scale-free network structure accelerates information dissemination, allowing more enterprises to quickly obtain early warning information, thereby helping the supply chain resist risks and improve resilience. When the information propagation rate exceeds 0.4, the small-world network structures can propagate risks more efficiently because of their shorter average paths. Additionally, three disruption schemes are used to analyze system resilience, revealing that prioritizing the disruption of nodes with higher degrees has the greatest impact on the network, while deliberately attacking nodes with smaller degrees allows the supply chain to maintain higher operational efficiency. This finding suggests that maintaining the robustness of the key nodes in the supply chain is critical for enhancing the overall network resilience. Conclusions: Adjusting the supply chain network structure can help improve the risk resilience and robustness of the system. Enhancing risk awareness of enterprises and their response strategies can effectively improve supply chain resilience and suppress risk diffusion. Deliberate attacks on hub nodes with high degrees cause the greatest damage to the network system. Thus, this study provides theoretical support for supply chain management and can serve as a basis for decision-making to improve supply chain risk resilience and optimize management strategies.

Objective: The frequency and intensity of global flood events are increasing, which is deeply driven by climate change and human activities. The key risk factors of hazard, exposure, and vulnerability are interconnected and collectively influence the occurrence and progression of flood disasters. Therefore, the relationships between these three factors need to be understood, and a comprehensive indicator system for the integrated assessment of flood risk needs to be developed. This study aims to analyze the spatiotemporal trends of global flood events from 1965 to 2023. Moreover, based on the key risk factors of hazard, exposure, and vulnerability, the spatiotemporal characteristics of the flood risk were revealed, which could provide a scientific basis for flood prevention and disaster mitigation decision-making. Methods: This study uses the Emergency Events Database, which includes global flood data, to analyze flood events from 1965 to 2023. This study conducts a trend analysis of the global flood occurrence, affected population, and mortality per unit area from 1965 to 2023. Based on the affected population and mortality per unit area, floods in six continents are classified into light, moderate, and severe categories using the percentage method. Spatial analysis of the flood occurrence, affected population, and mortality per unit area was performed for each country. The results showed the spatial distribution and impact intensity of flood disasters in different regions. In addition, key risk indicators, such as geographic elevation, precipitation, population density, and urbanization rate, are selected to analyze the characteristics of flood risk. Elevation and precipitation represent hazards. Population density indicates exposure, and urbanization rate reflects vulnerability. Trend analysis of these indicators was performed for three distinct periods, i.e., 1965－1984, 1985－2004, and 2005－2023. To examine the spatial trends of these indicators across countries over the entire study period, the Theil-Sen slope estimation method was employed. The entropy weight method was applied to calculate the weight of each risk indicator, and the flood risk values of six continents from 1965 to 2023 were calculated. Results: The main results are as follows: (1) From 1965 to 2023, global flood events show a fluctuating upward trend, although the affected population and number of deaths have shown a downward trend since the 1990s. At the continental level, floods occur most frequently in Asia, Africa, and South America, with a total of 2 322, 1 266, and 1 084 events, respectively. At the national level, Haiti experiences the highest frequency of flood events per unit area, with 23 events per 10⁴ km². Bangladesh has the highest total number of flood-affected people per unit area, with 27.1 million people per 10⁴ km², and the highest record of cumulative deaths, with 3 313 deaths per 10⁴ km². (2) Flood hazard, exposure, and vulnerability vary significantly across six continents. Among the indicators, population density and precipitation show the greatest influence on flood risk, with weights of 0.33 and 0.30, respectively. From 1965 to 2023, an obvious regional variation in flood risk across six continents is detected. The flood risk in Asia is significantly higher than that in other continents, with the flood risk values of both Asia and Africa showing a significant increase. By contrast, the flood risk value of South America decreased after 2010. Europe and North America show relatively low and stable flood risk values. Oceania exhibits the lowest flood risk values with significant fluctuations. Conclusions: This study conducts not only a systematic analysis of global flood events over a long time series but also an analysis of the changes in risk indicators, such as precipitation, geographic elevation, population density, and urbanization rate, from 1965 to 2023. Moreover, the relative impact of different indicators is quantified, which clarifies their respective contributions to flood risk. The results further revealed the comprehensively changing characteristics of flood risk. The findings provide guidance and evidence to inform flood prevention planning and disaster response strategies. In the future, exposure change based on population mobility and integrated adaptive capacity should be considered to reveal the dynamic characteristics of flood risk.

[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] Anthropogenic heat is a crucial feature of urbanization and human activities. Large amounts of anthropogenic heat emissions can affect the regional land-atmosphere process and exacerbate the local climate change in cities. In winter, the anthropogenic heat emissions caused by central heating increase significantly in the North China, thereby enhancing the urban heat effect. The urban heat effect may have a substantial impact on winter snowfall. Therefore, studying the impact of anthropogenic heat on urban winter precipitation can help improve our understanding of the impact of urbanization and human activities on urban precipitation.[Methods] Here, the weather research and forecasting (WRF) model was used to analyze the impact of anthropogenic heat on urban winter precipitation by simulating a winter precipitation event in Beijing. To study the impact mechanism of anthropogenic heat on the urban water and thermal environment in winter and the possible threshold, sensitivity experiments were conducted using the default anthropogenic heat built in the WRF, which is an ideal experiment to explore the possible threshold of anthropogenic heat magnitude. By setting built-in anthropogenic heat values of different intensities, the impact mechanism and intensity threshold of different anthropogenic heat intensities on urban water and thermal environment in winter were compared and analyzed. Based on the conclusion of the sensitivity experiment, anthropogenic heat with high spatiotemporal resolution calculated using the large-scale urban consumption of energy (LUCY) model in Beijing was used as the input data for WRF. The WRF-LUCY coupling model was applied to investigate the impact of anthropogenic heat on winter precipitation in Beijing. By setting up simulated scenarios with and without anthropogenic heat, the impact of anthropogenic heat on winter precipitation was compared and analyzed. Furthermore, the results of the WRF-LUCY coupling model were compared with the existing effects of anthropogenic heat on summer precipitation, and the differences between summer and winter precipitation are summarized.[Results] The following results were obtained:1) Anthropogenic heat primarily affects the phase state of mixed precipitation and has little effect on single-phase precipitation. With the increase in anthropogenic heat, snowfall and rainfall gradually decrease and increase, respectively. 2) No evident threshold exists for the impact of anthropogenic heat increase on air temperature; however, a threshold for the impact height, with a maximum impact height of about 1.3 km, is noted. 3) The increase in anthropogenic heat intensifies the vertical wind shear and water vapor convergence, thereby increasing the water vapor content in the boundary layer and providing favorable developmental conditions for the convective system. 4) For summer precipitation, the effect of anthropogenic heat demonstrates large scope and magnitude, and the impact mechanism is relatively complex; in contrast, the impact on winter precipitation is primarily concentrated in the urban interior, mainly affecting the precipitation phase.[Conclusions] A threshold for the impact height of anthropogenic heat is found, while no evident threshold for the anthropogenic heat magnitude is observed. Compared with the complex mechanism of summer precipitation, the impact of anthropogenic heat on winter precipitation is more concentrated. This study analyzes the impact of anthropogenic heat on urban winter precipitation, which has a crucial scientific application for exploring the urban land-atmosphere process and improving the urban environment.

[Objective］ With the increasing prevalence of electric vehicles (EVs) in urban transportation systems, charging guidance service has become an effective means to solve the charging problem in the context of insufficient charging infrastructure. However, for optimizing the charging station selection decision-making plan of users, most existing research studies aim to minimize travel costs, which rarely considers the charging experience of users during travel and ignores the interaction of charging station selection decision-making between multiple users. To enhance the charging experience of users, based on the analysis of charging satisfaction of EV users, an EV charging guidance optimization model that integrates user satisfaction with detour distance, queuing time, and charging cost is proposed in this study. The model aims to maximize the average comprehensive satisfaction of multiple users. ［Methods］ To quantify the comprehensive satisfaction of users with charging stations during charging processes, evaluation indicators of detour distance, queuing time, and charging cost are constructed. To accurately deduce the queuing time of users at charging stations, this study fully considers the interaction influence of charging station selection decision-making between multiple EV users. Prediction models of the charging station operation state are established by considering several charging scenarios based on the arrival patterns of two successive users. According to the characteristics of the proposed model, an immune algorithm and the Floyd shortest path algorithm are applied to optimize the decision-making plan of charging station selections and the travel paths of multiple users, respectively. A numerical example with multiple charging requests is designed to confirm the feasibility and effectiveness of the proposed model and the algorithms. ［Results］ The experimental results indicated that optimal charging station selections and driving paths of multiple EVs to maximize average comprehensive satisfaction could be obtained by solving the optimization model. Compared with models with single optimization objectives, namely, minimum detour distance, shortest queuing time, and least charging cost, the average comprehensive satisfaction of EV users was increased by 15.0%, 17.8%, and 11.4%, respectively. The results also showed that average driving speed was a critical factor affecting optimal charging station selection and average comprehensive satisfaction of EV users. By analyzing the arrival patterns of two successive users at the same charging station under different charging scenarios, their queuing time after arriving at the charging station could be accurately obtained. Subsequently, optimal charging station selections by multiple users could be determined by considering the interaction that influences their selections. ［Conclusions］ The proposed optimization model provides multiple EV users with decision-making support for selecting charging stations by considering their interaction influences. Provided that the threshold values of each satisfaction indicator remain unchanged, the comprehensive average satisfaction obtained by the proposed model is considerably higher than that obtained by models with single objectives: minimum detour distance, shortest queuing time, and least charging cost. Thus, the proposed method can enhance the charging experience of EV users during travel.

[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] 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] 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] 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 ridership characteristics of urban rail transit stations are closely related to the surrounding built environment and socio-economic factors, and the influence of different influencing factors on ridership characteristics also has temporal and spatial heterogeneity. Considering the complexity of influencing factors on station ridership, this paper uses the multiscale geographical weighted regression (MGWR) model to analyze the influencing factors of ridership at rail transit stations in different temporal scales.[Methods] This paper selects the station ridership on weekdays as the dependent variable, which is divided into five categories, including the average daily ridership, inbound ridership of morning peak hours, outbound ridership of morning peak hours, inbound ridership of evening peak hours, and outbound ridership of evening peak hours. A total of 23 independent variables are selected from three aspects: station attributes, connectivity, and the built environment. The variance inflation factor and Moran index are utilized to test the linear correlation and spatial autocorrelation between independent variables, respectively. The MGWR model is applied to construct the analysis model of ridership characteristics, and three indicators, including the residual sum of squares (RSS), adjusted R², and the corrected Akaike information criterion (AICc), are employed to compare the performance of the ordinary least squares (OLS), geographically weighted regression (GWR), and MGWR models. The influencing factors and their interaction with rail transit station ridership in different temporal scales are developed. Finally, this method is applied to analyze the influence degree of ridership characteristics at Nanjing rail transit station.[Results] The following results are presented. 1) The MGWR model is more reliable than the OLS and GWR models. 2) The average daily ridership analysis model, which ignores the impact of morning and evening peak hour ridership, has the most significant independent variables. 3) The distance to the city center has a significant negative impact on station ridership, indicating that the agglomeration of station ridership is evident when the station is close to the city center. 4) The stations with a high proportion of residential and living facilities have a strong attraction to the morning peak inbound and evening peak outbound ridership, whereas those with a low proportion of residential and livings facilities have a strong attraction to the morning peak outbound and evening peak inbound ridership. Three significant local variables, namely tourism facility POI density, enterprise and office POI density, and the ratio of floor area on commercial lands to the total floor area, are available, and these local variables have different impacts on rail transit ridership at different temporal scales. Tourism facility POI density has negative spatially varying impacts on the average daily ridership, inbound ridership of morning peak hours, and outbound ridership of evening peak hours. Enterprise and office POI density has a negative spatially varying impact on inbound ridership of morning peak hours but has a positive spatially varying impact on outbound ridership of morning peak hours. The ratio of floor area on commercial lands to the total floor area has positive and negative spatially varying impacts on inbound ridership during evening peak hours. This finding implies that not all the commercial buildings around the rail transit stations are attractive to the inbound ridership during evening peak hours.[Conclusions] The MGWR model considering spatial autocorrelation can capture numerous influence scales of different variables and reduce the deviation of results. The developed method in this paper achieves the expected goal and depicts the interdependence between ridership and influencing factors from the station level.