Objective: Gamma-ray detection using a nuclear radiation locator is critical for monitoring, locating, and processing radioactive sources. In recent years, gamma cameras based on coded aperture imaging techniques have been extensively utilized to identify and monitor radioactive sources. However, these detectors have limitations in terms of the imaging field. To accurately determine the specific location of radioactive sources, constant adjustment of the detection angle is required, which is often time-consuming. To expand the detection field, multiple coded aperture cameras can be used simultaneously, but this approach increases cost and equipment complexity. Some researchers have attempted to combine Compton and coded aperture imaging techniques. While the Compton camera can extend the field-of-view (FOV) to 4p, this method is complicated, costly, and limited to detecting high-energy rays. As a result, the combination of these two techniques proves inadequate when searching for low-energy sources. In this work, we proposed a system and method for locating radioactive sources with a large FOV based on combining a coded aperture with pinholes. This method addresses the limited FOV issue encountered in the aforementioned system. Methods: The coded aperture component of the system uses a modified uniformly redundant array as the uniform redundant array mask. The base mode class is 11, with a unit size of 3.3 mm?.3 mm, leading to a total size of 69.3 mm?9.3 mm. The mask thickness is 9 mm, and tungsten is used as the material. The detector section includes a 26?6 NaI (Tl) array, where each crystal pixel has dimensions of 1.45 mm?.45 mm?.00 mm. A crystal gap of 0.2 mm exists between each pixel, and the distance between the center of the coded aperture and the position-sensitive sensor is 77.5 mm.For the pinhole part of the system, a tapered pinhole with a center size of 4 mm is used. The pinhole is embedded in a shield with equally large pinholes on all four sides. For performance assessment of the system, Monte Carlo simulation experiments were performed with GATE software. A large FOV radioactive source location system is constructed, and simulation data are produced. MATLAB is employed to process the simulation data, compute the system transmission matrix using the Sidden algorithm, and conduct reconstruction using the maximum likelihood expectation maximization method. The projection and reconstruction results of the point sources at various positions are compared and analyzed. Thus, this work shows a comprehensive analysis and assessment of the developed system for locating radioactive sources with a large FOV using a combination of coded aperture and pinhole imaging techniques. Results: The results indicate that the full coding and semipseudo-film FOV of the coded aperture camera are 19.33?and 70.80? respectively, and the added pinhole extends the FOV of the system to 123.40? The developed system attains an angular resolution of 2.95?within the coded aperture FOV and 6.30?within the extended pinhole FOV, effectively imaging a 10 mCi radioactive source at a distance of 3 m. Conclusions: The developed wide FOV radiation source location system and method effectively address the limited imaging field of the coded aperture camera.

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

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

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

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.

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

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

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

Objective: 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: 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.

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

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

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: Typhoons, characterized by sudden onset, extensive geographic impact, and considerable destructive power, pose recurring threats to the stability and safety of power grid systems, particularly in China's coastal regions. As extreme weather events become more frequent due to climate change, conventional emergency management approaches are inadequate. These methods often suffer from fragmented knowledge sources, inefficient information extraction, and limited support for intelligent decision-making. Hence, this article proposes an integrated technical framework that combines knowledge graphs with large language models (LLMs). This study aimed to improve risk perception, enhance decision-making accuracy, and bolster emergency response effectiveness in typhoon-triggered power grid incidents. Zhejiang Province, a coastal area frequently impacted by typhoons, was selected as the demonstration case for the framework. Methods: The framework of knowledge graph construction included the design of two graph types: one derived from accident reports and the other derived from emergency plans. The accident-based knowledge graph was structured according to the Triangular Framework for Public Security Science and Technology at the schema layer. It organized knowledge into three primary dimensions: emergency events, affected infrastructure, and corresponding emergency management strategies. Meanwhile, the emergency-plan-based knowledge graph was structured based on the electric power production life cycle, covering key stages such as power transmission, power transformation, power distribution, power utilization, and energy storage. Both graphs worked together to support emergency planning. The system employed a hybrid approach at the data processing layer that integrated BERT with a bidirectional long short-term memory network. This hybrid model performed named entity recognition and relationship extraction. The extracted entities and relationships were visualized to improve model interpretability, enabling domain experts to validate and understand the underlying information. An enhanced discriminative similarity algorithm was introduced in the knowledge fusion process. Initially, cosine similarity and Pearson correlation filtered out low-relevance entity pairs. High-similarity entities were then semantically validated using the LLM, ensuring accurate fusion and reducing erroneous entity alignments. Experimental results showed a 10.11% improvement in accuracy compared to conventional methods. The final knowledge graphs were stored in the Neo4j graph database, which supported interactive visualization and real-time query functionalities. The system enabled intelligent reasoning for handling real-world disaster scenarios in the application stage. Using the Cypher query language, this study conducted a fuzzy query based on disaster descriptions. Relevant information was retrieved from the knowledge graph as a structured knowledge base. The ECO-STAR prompting template was used to guide the model in generating targeted risk analyses and emergency recommendations. Results: A case study was conducted in Zhejiang Province to validate the proposed framework. The results showed that integrating knowledge graphs and LLMs improved semantic precision. The integration also enhanced the relevance of decision support outputs. In addition, it reduced hallucination phenomena that often occurred when general-purpose LLMs were applied in specialized domains. Conclusions: This study highlights the value of leveraging LLMs in constructing an emergency knowledge graph for power grids during typhoons. The proposed method offers a scalable, intelligent solution for managing power grid emergencies during typhoons and serves as a valuable reference for enhancing the disaster resilience of energy systems.

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

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

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

Objective: In the past decades, the lifestyle of the Chinese population has changed because of rapid economic development. The incidence and prevalence of diabetes are increasing in China. Diabetes and its complications can cause substantial personal and social burdens. Recent experimental studies have shown that cold exposure may enhance insulin sensitivity and prevent the development of obesity-related insulin resistance and hyperglycemia. However, there is insufficient evidence of the correlation between cold exposure and diabetes in the real environment of daily life. In contrast, although people spend most of their life indoors, existing evidence usually focuses on the relationship between the outdoor thermal environment and diabetes without considering the effect of the indoor thermal environment. This study preliminarily examines the association between the real indoor thermal environment during the winter and the risk of diabetes. Methods: The study was analyzed from provincial and individual perspectives. At the provincial level, the analysis included 17 provinces. The diabetes prevalence data were obtained from the cross-sectional study on diabetes prevalence in China through Thyroid Disorders, Iodine Status, and Diabetes Epidemiological Survey (TIDE). The level of cold exposure was calculated based on the prevalence of central heating, according to the statistical yearbook data of the country and each region. Spearman correlation analyses were conducted to reveal the correlation between cold exposure and diabetes. The data were obtained from the China Health and Retirement Longitudinal Study (CHARLS) database at the individual level. Subjects' age, gender, body mass index (BMI), smoking, drinking, socioeconomic status, and whether they experienced indoor cold exposure in the winter were considered. Subjects were divided into two categories based on whether they experienced indoor cold exposure in the winter from 2011 to 2015. Chi-square and logistic regression analyses were applied to explore the correlation between indoor cold exposure and diabetes at the individual level. Results: At the provincial level, the results indicated a significant negative correlation between indoor cold exposure in the winter and diabetes prevalence (Spearman's correlation coefficient=0.505, P=0.039). At the individual level, 1) 8 403 subjects were eligible for analysis, and 1 138 (13.5%) had diabetes. 2) A total of 7 879 subjects had experienced indoor cold exposure in the winter. A significant difference was discovered in the prevalence rate between the group with and without cold exposure (P<0.001). 3) After age, gender, BMI, and smoking factors were adjusted, the group with indoor cold exposure in the winter was significantly associated with a 23% reduction in the risk of diabetes compared with the group without indoor cold exposure (OR=0.77, 95% CI: 0.61~0.97). Conclusions: At the provincial and individual levels, indoor cold exposure in the winter is significantly associated with diabetes. This study provides evidence for the correlation between the indoor thermal environment and diabetes prevalence on a long-time scale. This supports the conclusions of laboratory studies on cold exposure and diabetes and guides indoor thermal environment design.

Objective: Vortex-induced vibration (VIV) is a well-known fluid-structure interaction phenomenon that holds promising potential for harnessing energy from fluid flows. The pursuit of optimizing energy conversion efficiency from VIV has garnered significant interest. However, considerable challenges, such as the costs of experiments and computational fluid dynamics (CFD) simulations, pose significant hurdles in conducting comprehensive global optimization studies on efficiency. Methods: In response to these challenges, this study employs a reduced-order model (ROM) based on a wake oscillator. The ROM is used to compute and optimize the energy harvesting efficiency from the VIV of a circular cylinder. The rapid computational capabilities of the ROM allow the creation of high-resolution efficiency maps across various mass ratios and Reynolds numbers. These maps encompass a broad spectrum of incoming velocities and damping ratios. They not only provide valuable insights into achieving maximum efficiency but also detailed information on the optimal damping ratio and velocity. To validate the predictions of the ROM, comparisons are drawn against experiments and CFD simulations. For cases with high Reynolds numbers (high-Re), the ROM is validated using published experimental data. Conversely, for low-Re cases where experimental data is sparse, a computational fluid-structure interaction solver, named CFD-FSI, is utilized. This tool relies on direct simulations to verify the ROM results. Despite some differences observed between the ROM, experimental outcomes, and CFD data, this study demonstrates that the maximum efficiency and its occurrence conditions predicted by the ROM are acceptable. With its cost-effectiveness, the ROM emerges as a valuable tool for investigating optimal energy harvesting efficiency and providing insights into related engineering aspects. Results: Overall, the main findings of this study can be summarized as follows: 1) This study contributes high-resolution global optimization maps for energy harvesting efficiency from VIV, focusing on the optimization parameters of reduced velocities and damping ratios. Additionally, it offers a cost-effective approach and solution through the use of ROM. This method seeks to achieve a high efficiency from VIV across a large number of tested cases. 2) The maximum efficiency point is found to be influenced by the incoming velocity and the product of the mass ratio and damping ratio. This implies that if fluid flow conditions, such as the Reynolds number, remain constant, the global maximum efficiency remains consistent across different VIV energy converters despite having various structural configurations. Additionally, for different VIV energy converters, the product of the mass and the optimal damping ratio, where the global maximum efficiency occurs, tends to be similar. 3) Efficiency at a high Reynolds number is shown to surpass that at Re=150 in laminar flows. This is primarily attributed to differences in lift coefficients and Strouhal numbers between high- and low-Reynolds flows. From the perspective of the ROM, a high lift coefficient might contribute to a higher converted efficiency. Conclusions: Considering the definition of the mass ratio, the study suggests that the energy harvesting efficiency of a lighter system is more robust than that of a system with a high mass ratio. This indicates potential advantages for ocean VIV energy converters over wind VIV energy converters. Furthermore, the present work provides an effective tool to predict, analyze and optimize the energy harvesting efficiency from VIV, which would be helpful for related engineering design and future studies on this topic.

Objective: Small nuclear power plants, which find applications in various fields, are associated with such characteristics as compact size, low investment cost, and high flexibility. Analyzing the off-design performance of the secondary circuit in small modular reactor (SMR) power plants is crucial in terms of optimizing the operation and enhancing the overall performance of nuclear power units. Moreover, doing so serves as a valuable reference point for investigating prospective thermal utilization processes, including nuclear hydrogen production and urban heating. Methods: This study examines the secondary circuit system of an SMR power plant, establishing a comprehensive and validated system model using the software EBSILON. The simulation is based on the principles of mass, momentum, and energy conservation, employing the Gauss-Seidel iteration method. The primary and secondary circuits of the power plant employ helium and water as coolants, respectively, with the main steam pressure and temperature set at 13.24 MPa and 566℃, respectively. The main steam pressure remains constant under off-design conditions. The system comprises two steam generators, a steam turbine, a condenser, a shaft seal heater, two high-pressure regenerative heaters, three low-pressure regenerative heaters, a deaerator, and pumps. The analysis focuses on exploring the effects of power level variations, turbine back pressure changes, and different regenerative heater removal methods on the system's performance. The power level ranges from 30% turbine heat acceptance (THA) to 100% THA, while the steam turbine back pressure varies from 2.5 kPa to 4.5 kPa. Four different heater removal methods are considered. Notably, the fourth method involves the normal operation of all heaters, serving as a control group for comparison purposes. Results: The simulation results showed that: (1) Quantitative analysis revealed that power level changes considerably impacted various parameters, such as power generation efficiency, heat consumption rate, regenerative extraction coefficient, and high-/low-pressure cylinder output distribution. As the power level decreased from 100% THA to 30% THA, the power generation efficiency decreased by 5.427%, while the heat consumption rate increased by 1 210.487 kJ·(kW·h)^-1. (2) Under the 100% THA operating conditions, reducing the turbine back pressure by 2.0 kPa boosted the power generation efficiency by 1.444%, decreased the heat consumption rate by 272.338 kJ·(kW·h)^-1, and increased the power output by 7.120 MW. (3) Taking the main feedwater temperature as an example, partial or total removal of high-pressure heaters caused a decrease of more than 30℃ in the main feedwater temperature; however, the removal of only the low-pressure heater L₁ slightly affected the main feedwater temperature. (4) Among the three variables, the power level had the most prominent impact on the thermal performance of the system. Conclusions: The system's thermal performance is optimized under 100% THA conditions. It is conducive to improving the performance of the units by appropriately reducing the back pressure of the steam turbine. Additionally, cutting off the regenerative heaters can affect the system's thermal performance, but the impact varies with different cutting-off methods. Total removal of high-pressure heaters has a more prominent impact on the system's thermal performance than partial removal of high- or low-pressure heaters and causes a decrease in the feedwater temperature. This study can serve as a valuable reference for the operation and future expansion of SMR power plants.

Objective: The effect of negative public opinion events on social networks is underestimated. To address the issue of sentiment-based methods not being able to directly achieve early warning of negative online public opinion, this study proposes a sentiment classification and topic extraction-based approach to public opinion topic modeling. Using negative emotional topics as an entry point, this study shifts from investigating negative public opinion events to examining negative public opinion topics, thus facilitating statistical and quantifiable analysis of such events. Additionally, to address the persistent shortcomings of methods for negative public opinion early warning, we construct a novel early warning evaluation metric, which is known as the public opinion topic arithmetic index (POI). This index comprehensively assesses the developmental trends of public opinion topics across three dimensions: explosion index (EI), sentiment index (SI), and dissemination index (DI). Methods: This study employs the ERNIE 3.0 large-scale language model for sentiment classification. The annotated sentiment dataset is further trained and fine-tuned to obtain the required sentiment classifier. It performs sentiment classification on a COVID-19 Weibo emotional dataset, computing various post sentiments. The topic extraction module uses the TF-IDF algorithm to extract topics. Each noun tag is considered a potential topic, whereas each Weibo post is treated as a document. The TF-IDF method captures frequently occurring words by calculating their frequencies and avoiding less important terms that appear in each document. The TF-IDF topic extraction algorithm extracts topics from negative emotional Weibo posts and identifies relevant topics associated with negative public opinion events. Finally, POI is employed for further analysis based on the extracted public opinion topics. Consequently, early warning is achieved by analyzing negative public opinion topics instead of events. Furthermore, POI comprehensively calculates the effect of negative public opinion topics by combining EI, SI, and DI. EI reflects the growth rate of the current number of textual instances related to negative emotional topics compared to the average number in a previous period; SI mainly reflects the public's emotions and sentiments triggered by public opinion topics; and DI mainly represents the scope and speed of dissemination of public opinion topics. Finally, a comprehensive negative emotional topic public opinion index is derived by calculating the EI, SI, and DI of emotional topics and postdata information, and the topics that exceeded the warning threshold are warned. Results: The experimental results reveal that the proposed early warning model effectively predicts social media public opinion events. Among the top ten negatively perceived topics ranked based on weight, the earliest warning time exceeds the average outbreak day by 161.01 hours, with an average of 2.1 early warnings. Additionally, the earliest warning time exceeds the average peak day by 261.81 hours, with an average of 5.8 early warnings. Conclusions: We establish a threshold for triggering the arithmetic index of public opinion topics by modeling and calculating the arithmetic index of negative public opinion topics in this study. This enables us to exclude negative topics and corresponding public opinion events that surpass the threshold, thereby achieving early warning for topic-related negative public opinion events. The proposed negative public opinion warning model accomplishes its intended objective by employing sentiment analysis methods for the early detection of online public opinions.

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: 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.