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

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

Significance: Amid the rapid development of new productivity tools, the active thermal management system of power lithium-ion batteries is facing significant challenges, such as improving charge and discharge ratios and adapting to harsh application scenarios. To maintain stable operations of the power system in the best state, the technical bottleneck of efficient and long-term heat dissipation needs to be overcome. At the same time, in the consumer market, the cost factors of engineering products, including design, materials, space volume, cooling refrigerants, and plumbing systems, need to be carefully considered. Therefore, the active thermal management system of power lithium-ion batteries, which is widely used and has great potential, needs to be systematically summarized. Progress: This paper comprehensively reviews research progress on the active thermal management of power lithium-ion batteries in recent years. First, we summarize the research status of single-phase thermal management methods, including forced air cooling, natural air cooling, immersion liquid cooling, and microchannel liquid cooling. In the context of low charge and discharge ratios and lightweight engineering, air cooling still plays an important role. The main factors affecting battery temperature include air flow rate, air flow velocity, battery layout, and flow channel design. The air cooling system has unique engineering advantages because of its low cost. With an increase in charge and discharge ratios, the effect of microchannel and immersion liquid cooling is significantly enhanced, which is beneficial in controlling the battery's temperature and temperature uniformity. Several factors, such as liquid flow rate and channel design, have notable effects on the battery's heat dissipation; however, corresponding costs also increase. Second, we discuss advanced cooling techniques based on gas/liquid two-phase flow, such as submerged boiling cooling and spray-integrated cooling. In the context of increasing demand for batteries with high charge and discharge ratios, these technologies provide efficient, flexible, and adaptable solutions to thermal management challenges. The cooling medium, the flow rate, and the nozzle arrangement all have different effects on the temperature of the battery, along with the size of the droplets. The feasibility of the comprehensive and market recovery costs to maintain profits and long-term development of the enterprise also needs to be considered. Conclusions and Prospects: Based on the literature review, this paper forecasts the progress trend of active thermal management technology from multiple application scenarios to meet the development needs of lithium electric power in sea, land, and air. We believe that the development of the active thermal management technology of the new generation of power lithium-ion batteries should fully consider practical engineering requirements, such as charge and discharge ratios and harsh application scenarios. Future research and development should focus on improving heat transfer efficiency, system integration, and intelligent control capabilities while overcoming the challenges of reliability, cost, adaptability to extreme operating conditions, and energy consumption optimization.

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

Objective: 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: 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: 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 $ \left(1-\frac{\alpha}{2}\right)$ and $ \frac{\alpha}{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.

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

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

Objective: Overlay targets with a small range and requiring extremely high measurement accuracy are used in the lithography system's alignment, exposure, and measurement processes. They are inevitably affected by many errors when performing specialized functions, e.g., line width and design difference, line edge roughness, and target edge effect. This paper conducts a simulation study on the micro diffraction-based overlay target (μDBO) in the condition of wafer stage overlay (WSO), which is commonly used in lithography, based on the design of overlay alignment targets in the field of measurement. Litho-target design simulation software is known to be more advanced than lithographic imaging simulation software, but its license is usually rarer and more expensive. We propose a novel method for simulating lithography target metrology results using lithography imaging simulation software. The method predicts the detected light intensity distribution in the experiment qualitatively and can calculate target performance in metrology. Methods: This paper compares the similarities between metrology process imaging in the optical method and lithography imaging. In the metrology process imaging simulation, the target, lens, and imaging sensor modules have analogs in the lithography simulation, which are the mask, lens, and aerial image modules. Thus, metrology target patterns can be represented as mask patterns, and the output of an aerial image from lithography simulation software can be analyzed to obtain metrology information. We use WSO-μDBO targets, which have two different critical dimensions and suffer from a heavy marginal effect in experiments. The self-made DrM software in Matlab code with the rigorous couple wave analysis (RCWA) algorithm and the commercial program HyperLith with the finite-difference time-domain algorithm are used as tools to restore the phenomenon in the experiment. Both programs import the illumination patterns of the experimental instruments as well as the structures of WSO-μDBO targets. The Abbe imaging principle in DrM and the Hopkins approximation in HyperLith are used in the simulations. The pupil imaging simulation is applied to analyze the marginal effect on the original targets. In this simulation study, four key performance indicators (KPI) of WSO-μDBO performance are selected as evaluation indices. Results: The simulation of WSO-μDBO targets showed that: (1) The special phenomenon of bright and dark lines in the experiment can be reproduced in simulation using both tools and algorithms. (2) In the simulation, this method can provide calculated KPI of μDBO targets in simulation. (3) The aerial image generated by this method was closed between both tools but with a relative error of 1% to 5%, resulting in a discrepancy of around 1 nm between the simulated overlay values. (4) This work presented a novel design of WSO-μDBO targets (Edge, Windmill, and Hybrid) with a relative improvement in simulated stack sensitivity(SS) of 202.6% over the original design. As a result, the simulated metrology error of the target was reduced by 50.9% to 1.3%. Conclusions: The feasibility of our simulation method is verified by comparing simulated and experimental WSO-μDBO images, which can serve as a new simulation research platform for studying metrology targets. This method is also simple and easy to implement, and it has the potential to reduce the software cost investment in marking design during the production process.

Objective: The research on soil salinization is complicated, and the traditional literature review method struggles to grasp the development trend systematically due to its subjective nature. Hence, it becomes important and necessary to seek alternative methods to objectively summarize and analyze the existing research papers and reasonably guide the future development of this field. Methods: Based on the quantitative analysis of the Web of Science and China's national knowledge network databases related to soil salinization and saline-alkali land management and its utilization in the past 30 years, this paper proposes a quantitative review method for obtaining the research trend in this subject. This paper uses various software tools, including VOSviewer, Citespace, and SPSS, to analyze the number of publications, cooperation networks, and keywords within this field. Results: The results show a continuous increase in the research on soil salinization at home and abroad. The number of scholars participating in the study of soil salinization has also increased significantly. The primary stage of development was before 1999, the stage of stable development was from 2000 to 2011, and the stage of rapid development was from 2012 to the present. Despite significant progress, according to Kuhn's model, the field remains in the primary science stage, indicating ample room for development. English journals such as Agricultural Water Management and Science of the Total Environment and Chinese journals such as Soil Bulletin and Soil can be regarded as core journals in the field of soil salinization, with the highest number of publications and higher journal impact factors. China, the United States, India, Australia, and other countries (in order of the number of papers published by them) have made outstanding contributions to soil salinization research, with Chinese scholars leading in terms of the highest number of published papers and are the main force in the study of soil salinization. The cooperation network analysis shows the importance of research institutions and direct government agencies in promoting institutional cooperation. However, at present, most cooperations are limited to intra-institutional cooperation, but future efforts should focus on the positive impact of cross-context, cross-institutional, transnational, and interdisciplinary cooperations on leapfrog and diversified development of soil salinization research. Keyword burst detection shows that "biochar", "yield", "salt stress", "quality", "freeze-thaw cycle" and "water and salt transport" are the recent hot spots of concern. Additionally, keyword co-occurrence and trend analyses show the shift in research focus from irrigation and drainage and saline-alkali land improvement in the 1990s to soil-plant salt interaction mechanism, salt-tolerant plant cultivation, soil water and salt regulation mechanism, optimization control technology, research and development technology of saline-alkali soil amendment, large-scale applications, remote sensing monitoring of saline-alkali land, and salinization impact assessment. It represents the developmental trend of research on soil salinization and saline-alkali land management and its utilization from drainage improvement to comprehensive utilization. Conclusions: The research results of this paper quantitatively highlight the research hotspot and developmental trend of soil salinization and provide a reference for relevant researchers to grasp the developmental trends of the field and explore valuable new research directions.

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: Promoting urban renewal activities using digital technologies is essential to achieve high-quality economic development. This study examines how digital technology can be integrated into various stages of urban renewal, such as real estate rights, planning, construction, completion, and operational management. The objective is to connect all data in the urban renewal process and protect rights related to resources, assets, and capitals involved in these projects. Furthermore, this study drives the industrialization of digital technology, accelerates the digitalization-driven transformation of traditional industries, and explores the transition from a land-based economy to a digital economy, thereby continuously strengthening, enhancing, and expanding China's digital economy. Methods: This study first examines the existing issues in the complete process management of urban renewal in China and the research status of digital platforms for urban renewal both domestically and internationally. This study then outlines construction concepts for an urban renewal big data platform, proposes a data-driven approach to managing urban renewal, and designs a platform system comprising support environment, data resource, foundational platform, and application system layers. Finally, the issue of information nonshareability is addressed by integrating multiple-source data fusion technology based on metadata to create an urban renewal data system. In addition, data attribute-based model lightening technologies and the integration of building information modeling (BIM) and geographic information systems (GIS) are used to design and develop the urban renewal big data platform. Results: The big data platform for urban renewal developed using the methods outlined in this study has been effectively applied to urban renewal projects across Wuhan. The platform, which is customized to meet the specific needs of each area, features several fundamental functional modules, including basic information queries, demolition management, asset maintenance, renewal project management, digital delivery, and smart community operational management. This platform, which embodies an innovative examination of urban renewal strategies and methodologies supported by digital technology, facilitates digital management throughout the process of urban renewal. This platform has been deployed and used in various urban renewal projects, such as Sanyang Design City, Wuhan Station, and Wuchang South Station, to effectively manage and control the quality of urban renewal projects and their financial investments. Conclusions: Urban renewal activities are crucial for assessing the inventory of resources, assets, and capitals within urban districts. Detailed data on these capitals supporting urban renewal initiatives are essential. The big data platform constructed using advanced digital technologies ensures data interconnectivity throughout the urban renewal process and facilitates the quantitative tracking and analytical evaluation of resources, assets, and capitals. Further studies should further examine data standards, data governance, data collection, and platform promotion mechanisms to improve these processes. This study contributes significantly to digitizing the traditional urban renewal industry, supports local governments in exploring new economic development models with transition from a land-based economy to a digital economy, and promotes high-quality economic development. This study shows the transformative potential of digital platforms in urban renewal through in-depth analysis and practical applications, thereby setting a benchmark for future urban renewal developments in China's digital economy.

Significance: Marine environmental pollution has become a globally concerning environmental issue, which has attracted the great attention of governments and scientists in coastal countries. The release of large quantities of pollutants into the oceans has inevitable lasting effects and harm to marine life and can even bring about irreversible changes leading to the degradation or deterioration of the ecosystem. However, marine organisms under pollution stress do not always await their doom. Considerable evidence suggests that marine organisms could respond to pollution stress at the individual, population, and community levels, leading to molecular-level to ecosystem-scale effects and eventually reaching either adaptation/coexistence or degradation/death, with dynamic processes of interactions and changeable evolutionary outcomes between the two. Progress: In general, organisms can adapt to complex environments in three mechanisms, namely, behavioral change, phenotypic alteration, and genetic evolution. The initial response to environmental stress from pollution is generally behavioral (such as escape or migration to favorable habitats or vigilance). However, the continuous degradation of the environment makes migration more difficult, and some sedentary organisms (e.g., corals) can hardly move once immobilized, forcing the organisms to immediately react in situ. Altering the phenotype appears to be the best solution. This rapid nongenetic mechanism is based on the ability of individual genotypes to produce different phenotypes in response to the environment, which allows marine organisms to adapt and survive in the polluted marine environment. However, such phenotypic adjustments can be highly variable in the life cycle within a generation or even across generations. Phenotypic plasticity within a generation may play an important role in survival through rapid environmental change. In marine pollution, organisms may alter their macro or micromorphology and sex specificity or adjust the transcriptome characteristics and metabolic pathway expression of different organs, which play a detoxification-oriented role, within a generation for self-preservation. Although plasticity is usually shown under environmental conditions experienced by the parental generation, these conditions can also affect their offspring from one to multiple generations. That is, populations can adjust their phenotypes within several generations, which is called transgenerational phenotypic plasticity. Environmental pollution persists across generations for most species, and these organisms may evolve through transgenerational phenotypic plasticity to fight against environmental pollution. This may help in understanding the effects of pollution on marine organisms. Accordingly, supposing that plasticity works for the adaptation of organisms, its primary role is likely to buffer the cost of evolutionary mismatches and facilitate genetic evolution by "gaining time". Consequently, the phenotypes could be more adaptive to the current conditions, and these genetically based adaptations would continue to evolve until completion. Conclusions and Prospects: This review has done comprehensive literature research, collected various scenarios, and summarized the laws of the short-term response and long-term adaptation of marine organisms under pollution stress based on the viewpoint of evolutionary ecology and relevant principles in pollution ecology. From the perspective of behavior, adaptation, and evolution, this review preliminarily discussed the significance of marine pollution evolutionary ecology and attempted to summarize and explore the interactions and connections between pollution and organisms in the marine environment, providing new insights for the protection of the marine environment and the development of marine sciences.

Significance: Medical devices such as catheters, endoscopes, guidewires, artificial joints, and stents are in prolonged direct contact with human tissue. Therefore, surface treatment must be performed to enhance their lubricity and biocompatibility. Among the surface treatment techniques, lubricating coatings are widely used. The coatings reduce friction between medical devices and biological tissues, thereby minimizing tissue damage, alleviating patient discomfort due to friction, reducing the risk of rejection, infection, and inflammation, and making the treatment process smoother. With their structure similar to biological tissues and their ability to interact and retain large amounts of water, hydrogels are easily modified and less likely to cause immune rejection, making them suitable for fabricating lubricating coatings. However, the application of hydrogels as lubricating coatings faces many challenges. Initially, the physicochemical properties of hydrogels are diverse and complicated, resulting in different friction and lubrication mechanisms, and targeted modification of hydrogels for lubrication is challenging. Additionally, because of the unique formation methods and structures of hydrogels, achieving stable and strong adhesion with other substrates is difficult. Therefore, summarizing the existing research is crucial to guide further development of lubricating hydrogel coatings. Progress: In the study of the lubrication mechanisms of hydrogels, articular cartilage was an important reference, primarily involving boundary lubrication and hydrodynamic lubrication mechanisms, relying on the synergistic interaction of various charged or polar macromolecules. The lubrication theory of synthetic hydrogels was similar to that of articular cartilage. In terms of hydrogel-solid substrate friction, the repulsion-adsorption theory explained the impact of microscopic interactions between the superficial hydrogel polymers and the solid substrate on lubrication performance. The friction between hydrogel surfaces was more complex, requiring careful consideration of the surface properties of both hydrogel counterparts. Current research on hydrogel coating modification for lubrication purposes primarily focused on three aspects: modification based on the hydrodynamic lubrication mechanism, structural modification, and intelligent response modification design. The first modification could simply and effectively improve the lubricity of the hydrogel surface. Structural modification, often bioinspired from specific biological tissue structures such as articular cartilage, aimed to balance the lubrication performance and stable mechanical properties of hydrogels. The intelligent response modification endowed the hydrogels with various responsive characteristics in lubrication performance, such as pH, light, and shear stress responses. These typical enhancements greatly improved the functionality of the hydrogel coatings from multiple perspectives. Hydrogels were primarily formed on substrates via chemical interactions such as surface bridging, surface initiation, gel coating, and biological modification. The first three methods involved the polymerization and crosslinking of hydrogels, with similar principles but different procedures, whereas biological modification directly used bacteria or other microorganisms for adhesion and gel formation. These methods were adapted to different production scenarios and were suitable for various hydrogel materials and substrates. Conclusions and Prospects: Current lubricating hydrogel coatings excel in lubrication, reliability, stability, and ease of modification, yet they fall short of the comprehensive excellence of articular cartilage. Further research into the lubrication mechanism of hydrogels, the integration of lubrication properties, and other functional modifications with coating methods are anticipated to considerably improve the design of various hydrogel coatings with superior performance, enabling their biomedical application in various conditions.

Objective: Designing ship propellers is a comprehensive engineering task that synergistically considers hydrodynamics, cavitation, vibration, and noise performance. To address the limitations of current design guidance cases and existing open design software, an integrated software called OpenProp+ was developed. This application is designed to facilitate advancements in marine propeller design by incorporating theoretical formulas for noncavitation noise, empirical formulas for cavitation noise estimation, and the new Burrill diagram into the open-source software, OpenProp+. Methods: The process of blade geometry design begins with determining the number of blades based on design requirements and empirical knowledge. The diameter is determined by the maximum power density limit and the optimal speed that meets the main engine's speed constraint, while the rotational speed is established according to the relative optimal efficiency. The three essential parameters for 3D blade section optimization along the radial direction are accomplished, including determining chord length distribution, applying a highly skewed angle, and suitably increasing the maximum thickness of the blade section from 0.70R to 0.95R. Notably, the chord length distribution should differ between five-blade and seven-blade propellers. The optimal skew value for the critical blade lies between 50.0% and 70.0%, with an initial recommended value of 60.0%. Appropriately increasing the tip thickness and its rake enhances anti-cavitation performance. Following design and optimization, performance prediction involves utilizing theoretical formulas of the propeller's free sound field by National Advisory Committee for Aeronoutics (NACA) to predict the source level of the sound pressure spectrum under noncavitation conditions and to illustrate its longitudinal acoustic direction diagram at discrete line spectrums. Factors such as ship speed, propeller rotating speed, diameter, blade numbers, thrust, and torque contributions to sound pressure are integrated into these formulas. The new Burrill spectrum can subsequently be employed to ascertain the presence of cavitation, estimate its range if it does exist, and qualitatively measure its noise performance under specific operating conditions. Finally, the Brown empirical formula estimates the propeller cavitation noise spectrum, while the Fraser empirical formula and International Council for the Exploration of the Sea (ICES) standard are used to quantitatively evaluate noise performance levels. Results: The effectiveness of the integrated design software, OpenProp+, was validated through the design and performance prediction of a low-noise five-blade propeller, which yielded positive feedback. Within the full operating range, the open water performance curve of the designed blade almost coincided with the measured values of the original blade. Even on the off-design operating condition farthest from the designed advance ratio, the deviation between the thrust coefficient and torque coefficient compared to their measured value was only 4.65%. Considering the true ship wake flow distribution, the design point efficiency decreased by about 4% and the anti-cavitation margin decreased by about 12%. This indicated that the design program could effectively design the blades and reasonably predict their hydrodynamic and cavitation performance. Conclusions: OpenProp+ not only reliably predicts the open-water performance of existing propellers but also designs new propellers and accurately forecasts their open-water performance. It can determine the presence of cavitation, quantify its range if present, predict the noncavitation noise source level, and estimate the cavitation noise spectrum source level. Thus, OpenProp+ and the complete design chart incorporated in the software can directly aid in the engineering application of ship propeller design.

Objective: The legislative process for the European Union (EU)'s carbon border adjustment mechanism (CBAM) has been completed, and it officially became EU law in May 2023. The EU's CBAM imposes charges on imported products from selected industries based on their carbon emissions and the prevailing carbon prices in the EU emissions trading system through the issuance of "CBAM certificates." Considering the intensified global competition in low-carbon development and the profound changes in the geopolitical energy landscape, the implementation of the EU's CBAM as a unilateral trade measure will have an impact on the global economy, trade, and industry, making it a focal point in the competition among major powers in the long run. Existing studies focus more on the impact of the EU's CBAM on exports and pay less attention to the changes in the international industrial landscape caused by the industrial output changes due to the implementation of the EU's CBAM. Furthermore, most existing studies primarily performed static analysis for the benchmark year and failed to capture the impact of cumulative changes over time and the evolution of CBAM rules on the future economy, trade, and industry. Limited analysis exists on the dynamic effects of the EU's CBAM. In addition, most studies exogenously assume carbon prices, unable to simulate the endogenous trends of carbon prices and their impacts within major economies under the latest low-carbon transition policies. Methods: This research employs the China-in-global energy model (C-GEM) developed by Tsinghua University to simulate and analyze the impact of the EU's CBAM. C-GEM is a computable general equilibrium model that effectively represents the interlinkages and interactions between different sectors of the economy and the energy system, allowing the assessment of the economic impact of climate policies on major economies. C-GEM is a global multiregional model that can evaluate the effects of the EU's CBAM on the EU and other economies while analyzing the changes in the global industrial landscape from a global perspective. Furthermore, C-GEM is a recursive dynamic model that can simulate the medium- and long-term emissions reduction targets of various economies and analyze the future implications of the EU's CBAM. The EU's CBAM is depicted in the model as follows: First, the CBAM tax rates are calculated based on value-at-the-border, using the value-based carbon intensity, trade values, and endogenous carbon prices from the C-GEM. Second, these tax rates are applied to the EU's import sectors in the model. Finally, the EU's CBAM is made dynamic, and assumptions are made regarding the covered sectors, sectoral coverage ratios, emission types, and other factors at different time points. Results: The simulated results from the C-GEM revealed the following: (1) Due to the substantial exports of steel and other products to the EU, Russia was heavily affected, with projected GDP changes of -0.12% and -0.32% in 2025 and 2030, respectively, while the EU increased its GDP by producing substitute domestic products. (2) Russia experienced the largest decline in total exports, with projected changes of -0.86% and -2.48% in 2025 and 2030, respectively, while the EU considerably benefited from the exports of specific industries related to the CBAM. (3) The output of industries such as steel, nonmetallic minerals, nonferrous metals, and chemicals in economies such as Russia, Turkey, and China experienced varying degrees of decline, with the chemical and steel industries being more affected than others. (4) The international market shares of key industries in developing economies like Russia and China mostly declined, replaced by increased market share for related industries in developed economies such as the EU. The percentage increase in the EU's market share considerably exceeded the percentage decrease in other economies. However, China's nonferrous metals industry exhibited a trend of further increasing its international market share, reflecting its competitive advantage. Conclusions: Implementing the EU's CBAM showed heterogeneous impacts on various economies at different times and reshaped the existing international industrial landscape. There is a trend of global key industry output shifting from developing economies with a high dependence on carbon-intensive product exports to developed or more competitive developing economies. To respond to the EU's CBAM, China needs to strengthen multilateral cooperation and proactively address the unilateral trade measures taken by EU and the United States, optimize industrial and trade structures and promote green and low-carbon development, accelerate the development of the national carbon market and improve domestic carbon pricing mechanisms, and actively participate in the formulation of international standards and rules in the areas of climate change and trade.

Objective: Threat assessment of targets serves as a critical reference for commanders in wartime decision-making. With the rapid development of unmanned systems and smart technologies, the future of warfare is progressing toward unmanned, multi-domain, and clustered operations. However, existing studies on target threat assessment fall short of effectively satisfying these demands of future warfare, demonstrating three main issues: 1) Majority of combat scenarios focus on singular settings, such as maritime air defense, air-to-air combat, and ground-based air defense, with scant research on multi-domain operations (land, low-altitude, and electromagnetic environments). 2) Research is mainly concentrated on individual entities or cluster targets, such as fighter aircraft, unmanned aerial vehicle swarms, and unmanned surface vessels, with inadequate investigation of clustered equipment integrating manned/unmanned ground combat vehicles and low-altitude manned/unmanned aircraft. 3) Current methodologies predominantly consider the state and characteristics of Blue Force targets, ignoring the influence of dynamic changes in Red Force equipment on the weighting of threat indicators for Blue Force targets. Methods: To deal with these problems, we proposed a dynamic assessment method for threats to clustered targets in low-altitude, multi-domain battlefields based on hesitant fuzzy sets. First, we explored the laws governing low-altitude, multi-domain battlefields and the operational characteristics of clustered equipment that involves manned/unmanned air and ground elements. We analyzed five major influencing factors in the threat assessment of cluster targets, namely, operational cluster type, urgency, comprehensive strike capability, intelligent collaborative capability, and importance of the attack area, and determine an indicator system for threat assessment of clustered targets. Afterward, leveraging the Weber-Fechner law, we explored the relationship between changes in the Red Force's situation and the psychological pressure experienced by commanders and proposed a Weber-Fechner law-based weight determination method, which adjusted the weight values of the Red Force's comprehensive strike capability and the Blue Force's air power strike capability in conjunction with variations in the damage rate of the Red force's air defense capability. Finally, by combining the variable weight method under a hesitant fuzzy environment, a dynamic assessment model based on hesitant fuzzy sets for threats to cluster targets in low-altitude, multi-domain battlefields was constructed. Results: In a simulation, when the Red Force's air defense system sustains serious damage, the threat posed by the Blue Force's air power intensifies significantly. By utilizing the Weber-Fechner law-based weight adjustment method, the weight determination becomes more scientifically reasonable, effectively and promptly reflecting the psychological changes encountered by commanders when faced with the stimulation of the battlefield situation and reducing the subjectivity and arbitrariness related to weight optimization adjustments. Comparative analysis of the threat assessment results under constant and variable weights demonstrates that cluster targets with air power superiority exhibit more sensitive and timely adjustments in threat assessment results under variable weight conditions with a higher level of consistency. Conclusions: These results further confirm the accuracy and effectiveness of the model, providing commanders with feasible and reliable decision support.

Objective: Named entity recognition (NER), a central task in the information extraction realm, aims to precisely identify various named entity types in textual content, including personal names, locations, and organizational names. In Chinese NER domain, deep learning techniques are crucial for character and vocabulary representations and feature extractions, yielding remarkable research achievements. Common deep learning models for NER include sequence labeling, span-based approaches, generative methods, and table-based strategies. Nevertheless, this task suffers from the scarcity of lexical information. Hence, this challenge is perceived as a primary hindrance limiting the development of high-performance Chinese NER systems. Despite developing extensive lexical dictionaries encompassing rich vocabulary boundaries and semantic insights, effective incorporation of this lexical knowledge into Chinese NER task remains a considerable challenge. Particularly, the seamless integration of semantic information from matching vocabulary and its contextual cues into Chinese character sequence remains intricate. Moreover, ensuring the accurate delimitation of named entity boundaries is still a remarkable concern. Methods: In the realm of intelligent judicial systems, the NER task within legal documents has garnered significant attention. Nonetheless, prevailing sequence labeling models predominantly rely on character information, constraining their capacity to capture semantic and lexical contextual nuances and inadequately addressing entity boundary constraints. To resolve these challenges, this paper introduces an innovative model called semantic and boundary enhanced named entity recognition (SBENER). To enhance the semantic features of legal documents within the SBENER model, external information containing vocabulary pertinent to theft crimes is smartly integrated. Initially, word vectors for theft crime terms are acquired through pretraining. Subsequently, a vocabulary dictionary tree is constructed, enabling the potential vocabulary candidate identification for each character. Further, these candidates are amalgamated into a final external information vector via a bilinear attention mechanism. Additionally, a linear gating structure is introduced to mitigate interference from external information in the original text. To overcome the limitations of sequence labeling models for managing entity boundary constraints, this study designs a boundary pointer network within the model to confine entity boundaries. This involves embedding the character sequence into hidden layer representations via bidirectional long short-term memory followed by decoding to introduce probability constraints for each entity span. Ultimately, contextual and boundary information is inputted into a conditional random field for obtaining the ultimate entity classification outcomes. This design adroitly tackles the issues of vocabulary loss and boundary constraint scarcity within sequence labeling models. Results: Experimental results on the CAILIE 1.0 and LegalCorpus datasets corroborated the effectiveness of the proposed method, yielding F₁ scores of 88.70% and 87.67%, respectively, surpassing other baseline models. Additionally, the study conducted ablation experiments to validate the effectiveness of each component. The experimental results showed that integrating external semantic information related to theft, enhancing entity boundary constraints through pointer networks, and incorporating gating mechanisms to restrict irrelevant information fusion were all effective approaches for achieving higher F₁ scores for the model. Furthermore, this paper applied dimensionality reduction to external semantic word vector information and conducted experimental analysis on different fusion layers. Single-layer fusion outperformed multilayer fusion, while fusion at intermediate levels yielded better results. This underscored the marked enhancement in judicial NER facilitated by the proposed approach. Conclusions: The SBENER model effectively enhances the proficiency of recognizing named entities in legal documents through the fusion of external information and reinforcement of boundary constraints. This pioneering method substantially contributes to advancements within the intelligent judicial systems.

Objective: Ontology structure has been proved to be particularly important in the construction and organization of a knowledge graph (KG). A comprehensive method for the modeling, updating, and application of KG with the guidance of domain ontology needs to be explored. In view of the common knowledge gap in engineering safety management, this paper aims to propose an ontology-based framework to achieve domain knowledge modeling and updating. Using the highway engineering field as an example, this paper demonstrates how safety knowledge can be automatically extracted from industry-standard text data to facilitate the construction of a domain KG. Subsequently, the safety management scenarios are developed based on the building information model (BIM), and the auxiliary role of intelligent knowledge in safety management is demonstrated to verify the effectiveness of the engineering application of the developed KG. Methods: This paper used the ontology-guided domain knowledge extraction method to construct the domain KG and proposed a knowledge network-guided method to update the ontology. Specifically, a layered knowledge system with multiple dimensions was summarized as the ontology layer based on the management approach and the established standard specifications within highway engineering. Following the guidance of the ontology layer, a structured knowledge network acting as the data layer was extracted from massive text materials by developing a series of knowledge extraction procedures. Consequently, a knowledge-flowing method from the data layer to the ontology layer was proposed. Three categories of methods based on the essence and composition of entities and the clustering of the entity's core words were summarized to realize the automatic updating of the ontology layer. Finally, combined with the developed highway safety information retrieval and application system, this paper demonstrated the organization and application of the constructed domain KG, thus verifying the effect of introducing ontology in the organization and deployment of knowledge. Results: The developed ontology of highway engineering safety knowledge was featured as a layered knowledge system with seven levels and 390 nodes connected with ~300 000 valid entity nodes in the data layer, facilitating the creation and integration of the KG's logical structures. The proposed method for updating the domain ontology aided by over 1 000 technical terms was demonstrated to be effective, with an increment of 51.5% in the expansion of the nodes to the ontology. The designed method of linking the ontology-guided domain KG with the BIM was validated for its feasibility through practical implementation within a real highway engineering safety management system, displaying the positive impact of ontology's guidance in the organization and expansion of knowledge. Conclusions: This paper concentrates on the domain of highway engineering safety and presents a comprehensive paradigm for constructing, updating, and applying a domain KG to demonstrate methodological innovation in ontology updating. The results extend the application scope and technical approaches of KG technology, thereby enhancing information technology level in engineering safety management. Moreover, the findings of this research can be used for BIM evaluation and safety guidance in highway engineering construction projects, thereby advancing the level of information technology in construction safety management.

Objective: The structural integrity of bridges is a critical concern as infrastructure ages, necessitating the development of reliable methods for detecting potential failures. Among these, the identification of small target cracks is particularly important, as these cracks often grow undetected until they result in severe damage. Traditional inspection methods, such as manual visual inspections, are hindered by their labor-intensive nature and susceptibility to human error, often resulting in the oversight of small but significant defects. Recent advancements in computer vision and deep learning technologies offer new opportunities to improve the accuracy and efficiency of bridge inspections. This study introduces an innovative approach for detecting small target cracks in bridge structures by employing an enhanced version of the You Only Look Once (YOLOv8) object detection model, a widely recognized algorithm known for its rapid processing capabilities and high detection accuracy. The enhanced YOLOv8 model is tailored to detect small-scale cracks on bridge surfaces that may not be easily identifiable by traditional inspection methods or earlier versions of computer vision models. Methods: The proposed algorithm modifies the standard YOLOv8 model to address the specific challenges associated with detecting small cracks on bridge surfaces. A key modification is the integration of efficient vision transformer (EfficientViT) into the backbone of the YOLOv8 model. EfficientViT is an advanced transformer-based architecture that reduces redundant parameters and optimizes the extraction of local features from high-resolution images, enabling more precise detection of subtle crack features. This enhancement is crucial, as small cracks often exhibit low contrast against their background and may be easily overlooked by less sophisticated models. In addition to EfficientViT, the proposed algorithm also incorporates large selective kernel network (LSKNet) within the C2f module of YOLOv8. LSKNet employs a dynamic kernel selection mechanism that allows the model to adaptively adjust the size of the convolutional kernels based on the input features, making it highly suitable for detecting cracks of varying sizes, orientations, and morphological characteristics. This adaptability ensures that the model can detect small cracks, regardless of their form. Furthermore, the model uses bidirectional feature pyramid network (BiFPN) to merge feature maps at different scales. Traditional models struggle with detecting small targets due to the loss of critical information during downsampling operations. BiFPN mitigates this issue by preserving high-resolution feature maps across multiple layers, enhancing the model's ability to detect small cracks that would otherwise be missed. The combined effect of these modifications improves the accuracy of small target crack detection while maintaining computational efficiency. Results: The effectiveness of the proposed model was validated using a dataset of crack images from a specific bridge, captured by unmanned aerial vehicles (UAVs). UAVs provided detailed images from areas that were often difficult or dangerous to access using traditional inspection methods. The experimental results demonstrated that the enhanced YOLOv8 model significantly outperformed the original version in terms of key performance metrics. Specifically, the modified model achieved improvements of 3.7%, 3.5%, 3.5%, 3.9%, and 7.4% in terms of the detection precision, recall, F₁ score, mAP50, and mAP50-95, respectively. These results indicated a substantial improvement in the model's ability to detect small cracks that often had low contrast and irregular shapes, which were typical characteristics of cracks on bridge surfaces. Furthermore, compared to conventional methods, the proposed model was able to detect cracks with higher precision and fewer false positives, making it a promising tool for improving the efficiency of bridge inspections. Conclusions: In conclusion, the improved YOLOv8 algorithm introduced in this study represents a significant advancement in the detection of small target cracks in bridge structures. The modifications made to the original YOLOv8 model, including the integration of EfficientViT, LSKNet, and BiFPN, result in a more accurate and computationally efficient model for crack detection. This approach offers a practical and scalable solution for the widespread application of bridge health monitoring, particularly in areas that are difficult to inspect using traditional methods. By leveraging advanced surface data processing techniques, this research contributes to the development of modern methods for assessing the health of bridge structures, ultimately helping to ensure the safety and longevity of infrastructure systems.

Objective: Weaving areas are bottlenecks of freeways, and lane-changing behavior is one of the main reasons for the capacity decline and traffic congestion in weaving areas. Frequent merging behaviors may lead to traffic flow disturbance upstream from the weaving area, affect the normal running of surrounding vehicles, and in severe cases may even lead to multi-vehicle accidents. An in-depth understanding of merging decision behavior in the weaving area is essential to reduce the vehicle collision risk and improve the traffic safety level. A newly developed nonparametric regression model—multiple adaptive regression splines (MARS)—is adopted to model the gap selection decision during merging to study the merging behavior in the freeway weaving area. Methods: This study investigates complex interactions between merging and surrounding vehicles during merging. Trajectory data are extracted from the US-101 dataset provided by the dataset of next generation simulation program, and the symmetric exponential moving average filter method is used to smooth the data. Merging vehicles are influenced by surrounding vehicles in the auxiliary and adjacent main lanes. Thus, explanatory variables such as speeds, speed differences, gaps, and locations are calculated. Longitudinal and lateral collision risk indicators and time-to-collision are also considered to study the influence of collision risk on merging behaviors. Finally, 925 observations are obtained and randomly divided into two subdatasets to train and test the model. The MARS model is compared with four state-of-the-art machine learning techniques: classification and regression tree, gradient boosting decision tree (GBDT), random forest, and logistic regression models. Results: The speed difference between the merging vehicle and vehicles in the adjacent main lane played the most important role in gap selection. Interactions of influencing variables were observed. In particular, the best interaction level was 4 in the final model. The comparison showed that GBDT and MARS had the lowest rates of prediction error at 0.138 and 0.141, respectively. However, MARS could provide explicit expression functions that reflect the interaction between the influencing variables, which was beneficial to engineering applications. Conclusions: By using the optimal variable transformation and potential variable interaction in the regression modeling scheme, MARS could easily handle complex nonlinear relationships in merging behaviors. This model could accurately predict the gap selection behavior and provide explicit expression functions, thus simplifying its understanding and application to driver assistance systems and autonomous driving systems.